System

Abstract

An object of a system according to an embodiment is to effectively improve individual communication skill.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a feedback unit, a training unit, and a visualization unit. The reception unit inputs voice data. The analysis unit analyzes the voice data input by the reception unit. The feedback unit provides feedback based on the analysis result obtained by the analysis unit. The training unit provides a training plan based on the feedback provided by the feedback unit. The visualization unit visually displays the analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Current technologies leave room for improvement as they do not provide sufficient feedback and training to effectively improve individual communication skills.

[0005] The system according to the embodiment aims to effectively improve individual communication skills. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, an analysis unit, a feedback unit, a training unit, and a visualization unit. The reception unit inputs voice data. The analysis unit analyzes the voice data input by the reception unit. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The training unit provides a training plan based on the feedback provided by the feedback unit. The visualization unit visually displays the analysis results. [Effects of the Invention]

[0007] The system according to the embodiment can effectively improve individual communication skills. [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 voice data analysis system according to an embodiment of the present invention is a system for improving an individual's communication skills. This system inputs voice data, analyzes it using AI, provides feedback, and offers a training plan. For example, the system includes a reception unit for inputting voice data, an analysis unit, a feedback unit, a training unit, and a visualization unit. This allows users to objectively assess and improve their communication skills. For example, the system evaluates factors such as clarity of pronunciation, flow of speech, and emotional expression, and identifies individual strengths and weaknesses. Furthermore, the system supports the improvement of the user's communication skills through periodic analysis and feedback. For example, the system analyzes voice data on a weekly basis and provides the results as feedback. This allows users to continuously improve their skills. This makes the voice data analysis system a powerful tool for improving individual communication skills. For example, a salesperson can record conversations with customers and analyze the audio data to develop more effective sales pitches. Furthermore, students can practice presentations and receive feedback on the results to improve their presentation skills. In this way, the voice data analysis system can be widely used in business and education.

[0029] The voice data analysis system according to the embodiment includes a reception unit, an analysis unit, a feedback unit, a training unit, and a visualization unit. The reception unit receives voice data. The voice data may include, but is not limited to, an audio file format (WAV, MP3, etc.), a sampling rate, and a bit rate. The reception unit collects voice data using, for example, a microphone. The reception unit can also upload existing voice files to the system. The reception unit can also stream voice data in real time. For example, the reception unit collects a user's voice in real time using a microphone and inputs it into the system. Existing voice files are uploaded to the system in a specific format. The real-time voice data is input into the system using streaming technology. The analysis unit analyzes the voice data input by the reception unit using AI. The analysis is performed, for example, based on voice recognition technology or an acoustic feature extraction method, but is not limited to, for example. For example, the analysis unit converts voice data into text using voice recognition technology. The analysis unit can also extract acoustic features and analyze the characteristics of the voice data. The analysis unit can also analyze the content of the voice data using AI. For example, the analysis unit converts voice data into text using voice recognition technology and performs analysis based on the text data. Acoustic features are indicators that indicate the characteristics of voice data, and include, for example, pitch, formants, and spectrum. The AI ​​analyzes the content of the voice data and extracts specific patterns and features. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback may be provided in, for example, text format or audio format, but is not limited to these examples. For example, the feedback unit provides the analysis results to the user in text format. The feedback unit can also provide the analysis results to the user in audio format. The feedback unit can also provide the analysis results to the user in visual format. For example, the feedback unit provides the analysis results to the user in text format and points out specific areas for improvement. Audio format feedback conveys the analysis results to the user audibly.The visual feedback visually displays the analysis results using graphs or charts. The training unit provides a training plan based on the feedback provided by the feedback unit. The training plan may be composed of, for example, practice tasks and training steps, but is not limited to these examples. For example, the training unit provides a practice method for improving pronunciation. The training unit may also provide training for smooth speech flow. The training unit may also provide training for enriching emotional expression. For example, the training unit provides a practice method for specific phonemes to improve pronunciation. Training for smooth speech flow includes practice in logical structure and storytelling. Training for enriching emotional expression includes practice in tone of voice and intonation. The visualization unit visually displays the analysis results. The visual display may be in the form of, for example, a graph, chart, dashboard, or the like, but is not limited to these examples. For example, the visualization unit displays the analysis results in the form of a bar graph or line graph. The visualization unit may also display the analysis results in the form of a pie chart. The visualization unit may also display the analysis results in the form of a dashboard. For example, the visualization unit displays the analysis results in a bar graph to visually show the evaluation of each element. A line graph is used to show changes over time. A pie chart is used to show the proportion of each element. A dashboard displays multiple graphs and charts in an integrated manner, allowing the overall analysis results to be understood at a glance. This makes the voice data analysis system according to the embodiment a powerful tool for improving an individual's communication skills. For example, the user can objectively understand and improve their own communication skills. Furthermore, the system can support the improvement of the user's communication skills through regular analysis and feedback.

[0030] The analysis unit can evaluate elements including clarity of pronunciation, flow of speech, and emotional expression. The analysis unit, for example, evaluates clarity of pronunciation. For example, the analysis unit evaluates accuracy of phonemes. The analysis unit can also evaluate consistency of pronunciation. The analysis unit can also evaluate flow of speech. For example, the analysis unit can evaluate logical structure. The analysis unit can also evaluate coherence of speech. The analysis unit can also evaluate emotional expression. For example, the analysis unit can evaluate tone of voice. The analysis unit can also evaluate intonation. This allows the analysis unit to evaluate detailed elements, enabling more specific feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI and cause the generation AI to perform an analysis to evaluate clarity of pronunciation, flow of speech, and emotional expression.

[0031] The feedback unit can point out unclear pronunciation or unnatural speech flow and provide specific advice for improvement. For example, the feedback unit can point out unclear pronunciation. For example, the feedback unit can point out phoneme errors. The feedback unit can also point out inconsistencies in pronunciation. The feedback unit can also point out unnatural speech flow. For example, the feedback unit can point out logical leaps. The feedback unit can also point out lack of coherence in speech. The feedback unit can also provide specific advice for improvement. For example, the feedback unit can provide practice methods for improving pronunciation. The feedback unit can also provide training for smoothing speech flow. In this way, the provision of specific advice supports the improvement of the user's communication skills. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can use a generation AI to point out incorrect pronunciation or unnatural speech flow and generate specific advice for improvement.

[0032] The feedback unit can suggest ways to express emotions more effectively when emotions are not expressed sufficiently. For example, when emotions are not expressed sufficiently, the feedback unit points out this. For example, the feedback unit points out a flat tone of voice. The feedback unit can also point out a lack of emotion. Furthermore, the feedback unit suggests ways to express emotions more effectively. For example, the feedback unit suggests ways to change the tone of voice. The feedback unit can also suggest ways to improve intonation. This supports improvement of emotional expression, thereby achieving more effective communication. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can use a generation AI to point out areas where emotions are not expressed sufficiently and suggest ways to express emotions more effectively.

[0033] The training unit can provide practice methods for improving pronunciation and training for smoothing the flow of speech. The training unit, for example, provides practice methods for improving pronunciation. For example, the training unit provides a practice method for specific phonemes. The training unit can also provide repetitive pronunciation practice. The training unit can also provide training for smoothing the flow of speech. For example, the training unit provides practice in storytelling. The training unit can also provide practice in logical construction. This improves the user's communication skills through training. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can use a generation AI to provide practice methods for improving pronunciation and training for smoothing the flow of speech.

[0034] The visualization unit can visually display the analysis results using graphs or charts. The visualization unit can display the analysis results using, for example, bar graphs or line graphs. For example, the visualization unit can display the analysis results using bar graphs to visually show the evaluation of each element. The visualization unit can also display the analysis results using line graphs to show changes over time. The visualization unit can also display the analysis results using pie charts. For example, the visualization unit uses pie charts to show the proportions of each element. The visualization unit can also display the analysis results in a dashboard format. For example, the visualization unit can integrate and display multiple graphs or charts to allow the overall analysis results to be grasped at a glance. This allows the analysis results to be intuitively understood through visual display. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or can be performed without using AI. For example, the visualization unit can use generative AI to convert the analysis results into graphs or charts and visually display them.

[0035] The reception unit can analyze the user's past voice data reception history and select the optimal reception method. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. For example, the reception unit can analyze the past voice data reception history and evaluate the frequency of voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. For example, the reception unit can analyze the past text input history and evaluate the frequency of text input. Furthermore, if the user has preferred image input in the past, the reception unit can preferentially suggest image input. For example, the reception unit can analyze the past image input history and evaluate the frequency of image input. This improves user convenience by selecting the optimal reception method based on the past history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the past voice data reception history into a generation AI and cause the generation AI to select the optimal reception method.

[0036] When receiving voice data, the reception unit can filter the voice data based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception unit can only receive voice data related to the meeting. For example, the reception unit can analyze the user's schedule and detect that the user is in a meeting. Furthermore, if the user is giving a presentation, the reception unit can also receive only voice data related to the presentation. For example, the reception unit can analyze the user's calendar and detect that the user is giving a presentation. Furthermore, if the user is relaxing, the reception unit can only receive voice data related to relaxation. For example, the reception unit can analyze the user's behavioral patterns and detect a relaxed state. This allows for more appropriate data to be collected by receiving voice data according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's schedule and calendar data into the generation AI and cause the generation AI to perform filtering based on the user's current situation and areas of interest.

[0037] When receiving voice data, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit prioritizes receiving the voice data. For example, the reception unit detects the user's input method and prioritizes processing the voice input. Furthermore, if the user selects text input, the reception unit can also prioritize receiving text data. For example, the reception unit detects the user's input method and prioritizes processing the text input. Furthermore, if the user selects image input, the reception unit can also prioritize receiving image data. For example, the reception unit detects the user's input method and prioritizes processing the image input. This improves reception efficiency by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's input method to a generation AI and cause the generation AI to select the optimal reception means.

[0038] When receiving voice data, the reception unit can prioritize receiving highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving voice data related to that area. For example, the reception unit analyzes GPS data to identify the user's current location. Furthermore, when the user is traveling, the reception unit can prioritize receiving voice data related to the user's travel destination. For example, the reception unit can identify the user's travel destination using a location information service. Furthermore, when the user is at home, the reception unit can prioritize receiving voice data related to the user's home. For example, the reception unit can identify the user's home using a location information service. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input GPS data or data from a location information service to the generation AI and cause the generation AI to prioritize receiving highly relevant data.

[0039] The reception unit may analyze the user's social media activity and receive related data when receiving the voice data. The reception unit may, for example, receive voice data related to a location where the user checked in on social media. For example, the reception unit may analyze the content of social media posts and identify the location where the user checked in. The reception unit may also analyze the content of the user's social media posts and receive related voice data. For example, the reception unit may analyze the content of social media posts and identify related topics. The reception unit may also receive related voice data by referring to the activities of the user's friends on social media. For example, the reception unit may analyze the content of social media posts and identify related topics. In this way, highly relevant data can be efficiently received by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the content of social media posts and friend activity data into the generation AI and cause the generation AI to receive related data.

[0040] When receiving voice data, the reception unit can customize the reception method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. For example, the reception unit can analyze the past feedback and evaluate the frequency of voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. For example, the reception unit can analyze the past feedback and evaluate the frequency of text input. Furthermore, if the user has preferred image input in the past, the reception unit can preferentially suggest image input. For example, the reception unit can analyze the past feedback and evaluate the frequency of image input. In this way, by reflecting the past feedback, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI and cause the generation AI to customize the optimal reception method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio data. For example, in the case of important audio data, the analysis unit performs a detailed analysis. For example, the analysis unit analyzes the content of the audio data and evaluates the importance. The analysis unit can also perform a standard analysis in the case of general audio data. For example, the analysis unit analyzes the content of the audio data and performs a standard evaluation. The analysis unit can also perform a simplified analysis in the case of audio data with low importance. For example, the analysis unit analyzes the content of the audio data and performs a simplified evaluation. This makes it possible to provide appropriate analysis results by performing a detailed analysis according to the importance of the audio data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and have the generation AI perform an analysis with a level of detail based on the importance.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the audio data. For example, in the case of conversation audio data, the analysis unit applies a conversation analysis algorithm. For example, the analysis unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the analysis unit can also apply a presentation analysis algorithm. For example, the analysis unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the analysis unit can also apply an interview analysis algorithm. For example, the analysis unit analyzes interview audio data and evaluates characteristics of the interview. In this way, by applying an analysis algorithm depending on the category of the audio data, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the past analysis results and extracts a specific pattern. The analysis unit can also extract a specific pattern from the user's past analysis results to improve the analysis accuracy. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis based on the past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission date of the voice data. The analysis unit, for example, prioritizes analysis of the most recent voice data. For example, the analysis unit analyzes the submission date and time of the voice data to identify the most recent data. The analysis unit can also postpone voice data that was submitted earlier. For example, the analysis unit analyzes the submission date and time of the voice data to identify the older data. The analysis unit can also adjust the analysis order based on the submission date and time. For example, the analysis unit analyzes the submission date and time of the voice data to determine the analysis order. In this way, by determining the analysis priority based on the submission date and time, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time of the voice data to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio data. The analysis unit, for example, prioritizes analysis of important audio data. For example, the analysis unit analyzes the content of the audio data and evaluates the importance. The analysis unit can also prioritize analysis of highly relevant audio data. For example, the analysis unit analyzes the content of the audio data and evaluates the relevance. The analysis unit can also postpone analysis of less relevant audio data. For example, the analysis unit analyzes the content of the audio data and evaluates the relevance. This enables efficient analysis by prioritizing analysis of highly relevant data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and have the generation AI execute an analysis order based on the relevance.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, the analysis unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. Furthermore, if the user has general knowledge, the analysis unit can also use less technical terms. For example, the analysis unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. Furthermore, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terms. For example, the analysis unit analyzes the user's past learning history and work experience to evaluate the user's beginner level. This allows for analysis results that are easy to understand by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate analysis results according to the user's level of expertise.

[0047] The feedback unit can adjust the level of detail of the feedback based on the importance of the audio data when providing feedback. For example, in the case of important audio data, the feedback unit provides detailed feedback. For example, the feedback unit analyzes the content of the audio data and evaluates the importance. The feedback unit can also provide standard feedback in the case of general audio data. For example, the feedback unit analyzes the content of the audio data and performs a standard evaluation. The feedback unit can also provide simplified feedback in the case of audio data with low importance. For example, the feedback unit analyzes the content of the audio data and performs a simplified evaluation. This enables appropriate feedback by providing feedback according to the importance of the audio data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input audio data to a generation AI and cause the generation AI to perform feedback detail based on the importance.

[0048] The feedback unit can apply different feedback algorithms depending on the category of the audio data when providing feedback. For example, in the case of conversation audio data, the feedback unit applies a conversation feedback algorithm. For example, the feedback unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the feedback unit can also apply a presentation feedback algorithm. For example, the feedback unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the feedback unit can also apply an interview feedback algorithm. For example, the feedback unit analyzes interview audio data and evaluates characteristics of the interview. This enables more accurate feedback by applying a feedback algorithm depending on the category of the audio data. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the audio data to a generation AI and cause the generation AI to apply a feedback algorithm depending on the category.

[0049] The feedback unit can improve the accuracy of feedback by referring to the user's past feedback results when providing feedback. The feedback unit, for example, adjusts a feedback algorithm based on the user's past feedback results. For example, the feedback unit analyzes the past feedback results and extracts a specific pattern. The feedback unit can also extract a specific pattern from the user's past feedback results to improve the feedback accuracy. For example, the feedback unit adjusts the feedback algorithm based on the past feedback results. The feedback unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the feedback unit adjusts the level of detail of the feedback based on the past feedback results. In this way, the accuracy of the feedback can be improved by referring to the past feedback results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the past feedback results to a generation AI and cause the generation AI to improve the accuracy of the feedback.

[0050] The feedback unit can determine the priority of feedback based on the submission date of the voice data when providing feedback. The feedback unit, for example, provides feedback preferentially to the most recent voice data. For example, the feedback unit analyzes the submission date and time of the voice data to identify the most recent data. The feedback unit can also provide feedback to older submitted voice data later. For example, the feedback unit analyzes the submission date and time of the voice data to identify older data. The feedback unit can also adjust the order of feedback based on the submission date and time. For example, the feedback unit analyzes the submission date and time of the voice data to determine the order of feedback. In this way, by determining the priority of feedback based on the submission date and time, feedback can be quickly provided to the most recent data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the submission date and time of the voice data to the generation AI and cause the generation AI to determine the priority of feedback.

[0051] The feedback unit can adjust the order of feedback based on the relevance of the audio data when providing feedback. The feedback unit, for example, provides feedback preferentially to important audio data. For example, the feedback unit analyzes the content of the audio data and evaluates the importance. The feedback unit can also provide feedback preferentially to highly relevant audio data. For example, the feedback unit analyzes the content of the audio data and evaluates the relevance. The feedback unit can also provide feedback later to less relevant audio data. For example, the feedback unit analyzes the content of the audio data and evaluates the relevance. This enables efficient feedback by providing feedback preferentially to highly relevant data. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input audio data to a generation AI and cause the generation AI to execute the feedback order based on the relevance.

[0052] When providing feedback, the feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise. For example, if the user has technical expertise, the feedback unit uses a lot of technical terms. For example, the feedback unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. Furthermore, if the user has general knowledge, the feedback unit can also use less technical terms. For example, the feedback unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. Furthermore, if the user is a beginner, the feedback unit can provide feedback that avoids technical terms. For example, the feedback unit analyzes the user's past learning history and work experience to evaluate the user's beginner level. This allows for providing feedback that is easy to understand by providing feedback that is appropriate for the user's level of expertise. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit may input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate feedback appropriate for the user's level of expertise.

[0053] When providing a training plan, the training unit can adjust the level of detail of the training plan based on the importance of the audio data. For example, in the case of important audio data, the training unit provides a detailed training plan. For example, the training unit analyzes the content of the audio data and evaluates the importance. The training unit can also provide a standard training plan for general audio data. For example, the training unit analyzes the content of the audio data and performs a standard evaluation. The training unit can also provide a simplified training plan for audio data with low importance. For example, the training unit analyzes the content of the audio data and performs a simplified evaluation. This enables appropriate training by providing a training plan according to the importance of the audio data. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input audio data to a generation AI and cause the generation AI to adjust the level of detail of the training plan based on the importance.

[0054] When providing a training plan, the training unit can apply different training algorithms depending on the category of audio data. For example, in the case of conversation audio data, the training unit applies a conversation training algorithm. For example, the training unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the training unit can also apply a presentation training algorithm. For example, the training unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the training unit can also apply an interview training algorithm. For example, the training unit analyzes interview audio data and evaluates characteristics of the interview. This enables more effective training by applying a training algorithm depending on the category of audio data. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input audio data to a generation AI and cause the generation AI to apply a training algorithm depending on the category.

[0055] When providing a training plan, the training unit can improve the accuracy of the training plan by referring to the user's past training results. The training unit, for example, adjusts the training algorithm based on the user's past training results. For example, the training unit analyzes the past training results and extracts a specific pattern. The training unit can also extract a specific pattern from the user's past training results to improve training accuracy. For example, the training unit adjusts the training algorithm based on the past training results. The training unit can also adjust the level of detail of the training plan by referring to the user's past training results. For example, the training unit adjusts the level of detail of the training plan based on the past training results. In this way, the accuracy of the training plan can be improved by referring to the past training results. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the past training results into the generation AI and cause the generation AI to improve the accuracy of the training plan.

[0056] When providing training plans, the training unit can determine the priority of the training plans based on the submission date of the audio data. For example, the training unit provides training plans preferentially to the most recent audio data. For example, the training unit analyzes the submission date and time of the audio data to identify the most recent data. The training unit can also provide training plans to audio data that was submitted earlier. For example, the training unit analyzes the submission date and time of the audio data to identify the older data. The training unit can also adjust the order of training plans based on the submission date and time. For example, the training unit analyzes the submission date and time of the audio data to determine the order of the training plans. In this way, by determining the priority of training plans based on the submission date and time, training plans can be quickly provided for the most recent data. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the submission date and time of the audio data to the generation AI and have the generation AI determine the priority of the training plans.

[0057] When providing training plans, the training unit can adjust the order of the training plans based on the relevance of the audio data. For example, the training unit provides training plans preferentially to important audio data. For example, the training unit analyzes the content of the audio data and evaluates its importance. The training unit can also provide training plans preferentially to highly relevant audio data. For example, the training unit analyzes the content of the audio data and evaluates its relevance. The training unit can also provide training plans later to less relevant audio data. For example, the training unit analyzes the content of the audio data and evaluates its relevance. This enables efficient training by providing training plans preferentially to highly relevant data. Some or all of the above-described processing in the training unit may be performed using, or without, AI. For example, the training unit can input audio data to a generation AI and have the generation AI execute the order of training plans based on the relevance.

[0058] When providing a training plan, the training unit can adjust the use of technical terms in the training plan according to the user's level of expertise. For example, if the user has technical expertise, the training unit uses a lot of technical terms. For example, the training unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. Furthermore, if the user has general knowledge, the training unit can also use less technical terms. For example, the training unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. Furthermore, if the user is a beginner, the training unit can provide a training plan that avoids technical terms. For example, the training unit analyzes the user's past learning history and work experience to evaluate the user's beginner level. This allows for easy-to-understand training by providing a training plan according to the user's level of expertise. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without AI. For example, the training unit can input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate a training plan according to the user's level of expertise.

[0059] The visualization unit can adjust the level of detail of the visualization based on the importance of the audio data during visualization. For example, in the case of important audio data, the visualization unit provides detailed visualization. For example, the visualization unit analyzes the content of the audio data and evaluates the importance. The visualization unit can also provide standard visualization in the case of general audio data. For example, the visualization unit analyzes the content of the audio data and performs a standard evaluation. The visualization unit can also provide simplified visualization in the case of audio data with low importance. For example, the visualization unit analyzes the content of the audio data and performs a simplified evaluation. This enables appropriate visualization by providing visualization according to the importance of the audio data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input audio data to a generation AI and cause the generation AI to perform visualization detail based on the importance.

[0060] The visualization unit can apply different visualization algorithms depending on the category of the audio data during visualization. For example, in the case of conversation audio data, the visualization unit applies a conversation visualization algorithm. For example, the visualization unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the visualization unit can also apply a presentation visualization algorithm. For example, the visualization unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the visualization unit can also apply an interview visualization algorithm. For example, the visualization unit analyzes interview audio data and evaluates characteristics of the interview. In this way, by applying a visualization algorithm depending on the category of the audio data, more accurate visualization is possible. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input audio data to a generation AI and cause the generation AI to apply a visualization algorithm depending on the category.

[0061] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. The visualization unit, for example, adjusts the visualization algorithm based on the user's past visualization results. For example, the visualization unit analyzes the past visualization results and extracts a specific pattern. The visualization unit can also extract a specific pattern from the user's past visualization results to improve the visualization accuracy. For example, the visualization unit adjusts the visualization algorithm based on the past visualization results. The visualization unit can also adjust the level of detail of the visualization by referring to the user's past visualization results. For example, the visualization unit adjusts the level of detail of the visualization based on the past visualization results. In this way, the accuracy of the visualization can be improved by referring to the past visualization results. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the past visualization results to the generation AI and cause the generation AI to improve the accuracy of the visualization.

[0062] During visualization, the visualization unit can determine the priority of visualization based on the submission date of the audio data. The visualization unit, for example, prioritizes visualization of the most recent audio data. For example, the visualization unit analyzes the submission date and time of the audio data to identify the most recent data. The visualization unit can also visualize older submitted audio data later. For example, the visualization unit analyzes the submission date and time of the audio data to identify older data. The visualization unit can also adjust the order of visualization based on the submission date and time. For example, the visualization unit analyzes the submission date and time of the audio data to determine the order of visualization. This allows for the most recent data to be quickly visualized by determining the priority of visualization based on the submission date and time. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the submission date and time of the audio data to the generation AI and have the generation AI determine the visualization priority.

[0063] The visualization unit can adjust the visualization order based on the relevance of the audio data during visualization. The visualization unit, for example, prioritizes visualization of important audio data. For example, the visualization unit analyzes the content of the audio data and evaluates its importance. The visualization unit can also prioritize visualization of highly relevant audio data. For example, the visualization unit analyzes the content of the audio data and evaluates its relevance. The visualization unit can also postpone visualization of less relevant audio data. For example, the visualization unit analyzes the content of the audio data and evaluates its relevance. This enables efficient visualization by providing visualization preferentially for highly relevant data. Some or all of the above-described processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input audio data to a generation AI and cause the generation AI to execute a visualization order based on the relevance.

[0064] The visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise during visualization. For example, if the user has technical expertise, the visualization unit uses a lot of technical terms. For example, the visualization unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. The visualization unit can also use less technical terms if the user has general knowledge. For example, the visualization unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. The visualization unit can also provide visualization results that avoid technical terms if the user is a beginner. For example, the visualization unit analyzes the user's past learning history and work experience to evaluate the beginner level. This allows for visualizations that are easy to understand by providing visualizations that correspond to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without AI. For example, the visualization unit can input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate visualization results according to the user's level of expertise.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analyzer can also analyze background sounds in the audio data and provide feedback tailored to the user's environment. For example, if the background sounds are noisy, the analyzer can suggest noise cancellation. Alternatively, if the recording is made in a quiet environment, the analyzer can provide feedback to enhance the clarity of the audio. Furthermore, the analyzer can detect specific background sounds (e.g., traffic sounds or natural sounds) and provide advice accordingly. This allows the analyzer to suggest specific improvements tailored to the user's recording environment.

[0067] The feedback module can also analyze the user's past feedback history to provide personalized feedback. For example, the feedback module can review previously identified pronunciation issues and evaluate whether improvements have been made. The feedback module can also provide targeted advice to address specific areas of the user's learning difficulties based on past feedback. Furthermore, the feedback module can track the user's progress and suggest long-term improvement plans, providing the user with specific guidance for continually improving their skills.

[0068] The training department can also provide training methods tailored to the user's learning style. For example, visual learners can receive training using visual aids. Auditory learners can receive training centered on audio guides. Furthermore, hands-on learners can receive training using role-playing and simulations. This allows users to effectively improve their skills in the way that best suits them.

[0069] The visualization unit can also track and visually display a user's progress over time. For example, the visualization unit can provide a graph showing the user's skill improvement. The visualization unit can also highlight areas of improvement or challenges within a specific time period. Furthermore, the visualization unit can provide a dashboard showing the user's progress toward their goals, allowing the user to see their progress at a glance and stay motivated.

[0070] The reception unit can also provide a function to protect the user's privacy when collecting the user's voice data. For example, the reception unit can encrypt and store the voice data. In addition, if the user requests to delete specific data, the reception unit can quickly delete the data. Furthermore, the reception unit can provide an interface for the user to confirm the purpose of use of the data and obtain consent. This allows the user to use the system with peace of mind.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit inputs audio data. The audio data includes, for example, the audio file format (WAV, MP3, etc.), sampling rate, bit rate, etc. The reception unit can collect audio data using a microphone or upload an existing audio file to the system. It is also possible to stream audio data in real time. Step 2: The analysis unit uses AI to analyze the voice data entered by the reception unit. The analysis is performed based on voice recognition technology and acoustic feature extraction methods. For example, voice recognition technology is used to convert the voice data into text, and acoustic features are extracted to analyze the characteristics of the voice data. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback can be provided in text, audio, or visual format. For example, the analysis results can be provided to the user in text format, and specific improvements can be suggested. Audio feedback communicates the analysis results to the user by voice. Visual feedback visually displays the analysis results using graphs and charts. Step 4: The training department provides a training plan based on the feedback provided by the feedback department. The training plan consists of exercises and training steps. For example, it provides practice methods to improve pronunciation, training to improve speech flow, and training to enrich emotional expression. Step 5: The visualization section visually displays the analysis results. Visual displays are made in the form of graphs, charts, dashboards, etc. For example, analysis results can be displayed in the form of bar graphs, line graphs, pie charts, or dashboards, visually showing the evaluation of each element, changes over time, and the proportion of each element.

[0073] (Example 2) A voice data analysis system according to an embodiment of the present invention is a system for improving an individual's communication skills. This system inputs voice data, analyzes it using AI, provides feedback, and offers a training plan. For example, the system includes a reception unit for inputting voice data, an analysis unit, a feedback unit, a training unit, and a visualization unit. This allows users to objectively assess and improve their communication skills. For example, the system evaluates factors such as clarity of pronunciation, flow of speech, and emotional expression, and identifies individual strengths and weaknesses. Furthermore, the system supports the improvement of the user's communication skills through periodic analysis and feedback. For example, the system analyzes voice data on a weekly basis and provides the results as feedback. This allows users to continuously improve their skills. This makes the voice data analysis system a powerful tool for improving individual communication skills. For example, a salesperson can record conversations with customers and analyze the audio data to develop more effective sales pitches. Furthermore, students can practice presentations and receive feedback on the results to improve their presentation skills. In this way, the voice data analysis system can be widely used in business and education.

[0074] The voice data analysis system according to the embodiment includes a reception unit, an analysis unit, a feedback unit, a training unit, and a visualization unit. The reception unit receives voice data. The voice data may include, but is not limited to, an audio file format (WAV, MP3, etc.), a sampling rate, and a bit rate. The reception unit collects voice data using, for example, a microphone. The reception unit can also upload existing voice files to the system. The reception unit can also stream voice data in real time. For example, the reception unit collects a user's voice in real time using a microphone and inputs it into the system. Existing voice files are uploaded to the system in a specific format. The real-time voice data is input into the system using streaming technology. The analysis unit analyzes the voice data input by the reception unit using AI. The analysis is performed, for example, based on voice recognition technology or an acoustic feature extraction method, but is not limited to, for example. For example, the analysis unit converts voice data into text using voice recognition technology. The analysis unit can also extract acoustic features and analyze the characteristics of the voice data. The analysis unit can also analyze the content of the voice data using AI. For example, the analysis unit converts voice data into text using voice recognition technology and performs analysis based on the text data. Acoustic features are indicators that indicate the characteristics of voice data, and include, for example, pitch, formants, and spectrum. The AI ​​analyzes the content of the voice data and extracts specific patterns and features. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback may be provided in, for example, text format or audio format, but is not limited to these examples. For example, the feedback unit provides the analysis results to the user in text format. The feedback unit can also provide the analysis results to the user in audio format. The feedback unit can also provide the analysis results to the user in visual format. For example, the feedback unit provides the analysis results to the user in text format and points out specific areas for improvement. Audio format feedback conveys the analysis results to the user audibly.The visual feedback visually displays the analysis results using graphs or charts. The training unit provides a training plan based on the feedback provided by the feedback unit. The training plan may be composed of, for example, practice tasks and training steps, but is not limited to these examples. For example, the training unit provides a practice method for improving pronunciation. The training unit may also provide training for smooth speech flow. The training unit may also provide training for enriching emotional expression. For example, the training unit provides a practice method for specific phonemes to improve pronunciation. Training for smooth speech flow includes practice in logical structure and storytelling. Training for enriching emotional expression includes practice in tone of voice and intonation. The visualization unit visually displays the analysis results. The visual display may be in the form of, for example, a graph, chart, dashboard, or the like, but is not limited to these examples. For example, the visualization unit displays the analysis results in the form of a bar graph or line graph. The visualization unit may also display the analysis results in the form of a pie chart. The visualization unit may also display the analysis results in the form of a dashboard. For example, the visualization unit displays the analysis results in a bar graph to visually show the evaluation of each element. A line graph is used to show changes over time. A pie chart is used to show the proportion of each element. A dashboard displays multiple graphs and charts in an integrated manner, allowing the overall analysis results to be understood at a glance. This makes the voice data analysis system according to the embodiment a powerful tool for improving an individual's communication skills. For example, the user can objectively understand and improve their own communication skills. Furthermore, the system can support the improvement of the user's communication skills through regular analysis and feedback.

[0075] The analysis unit can evaluate elements including clarity of pronunciation, flow of speech, and emotional expression. The analysis unit, for example, evaluates clarity of pronunciation. For example, the analysis unit evaluates accuracy of phonemes. The analysis unit can also evaluate consistency of pronunciation. The analysis unit can also evaluate flow of speech. For example, the analysis unit can evaluate logical structure. The analysis unit can also evaluate coherence of speech. The analysis unit can also evaluate emotional expression. For example, the analysis unit can evaluate tone of voice. The analysis unit can also evaluate intonation. This allows the analysis unit to evaluate detailed elements, enabling more specific feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data to a generation AI and cause the generation AI to perform an analysis to evaluate clarity of pronunciation, flow of speech, and emotional expression.

[0076] The feedback unit can point out unclear pronunciation or unnatural speech flow and provide specific advice for improvement. For example, the feedback unit can point out unclear pronunciation. For example, the feedback unit can point out phoneme errors. The feedback unit can also point out inconsistencies in pronunciation. The feedback unit can also point out unnatural speech flow. For example, the feedback unit can point out logical leaps. The feedback unit can also point out lack of coherence in speech. The feedback unit can also provide specific advice for improvement. For example, the feedback unit can provide practice methods for improving pronunciation. The feedback unit can also provide training for smoothing speech flow. In this way, the provision of specific advice supports the improvement of the user's communication skills. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can use a generation AI to point out incorrect pronunciation or unnatural speech flow and generate specific advice for improvement.

[0077] The feedback unit can suggest ways to express emotions more effectively when emotions are not expressed sufficiently. For example, when emotions are not expressed sufficiently, the feedback unit points out this. For example, the feedback unit points out a flat tone of voice. The feedback unit can also point out a lack of emotion. Furthermore, the feedback unit suggests ways to express emotions more effectively. For example, the feedback unit suggests ways to change the tone of voice. The feedback unit can also suggest ways to improve intonation. This supports improvement of emotional expression, thereby achieving more effective communication. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can use a generation AI to point out areas where emotions are not expressed sufficiently and suggest ways to express emotions more effectively.

[0078] The training unit can provide practice methods for improving pronunciation and training for smoothing the flow of speech. The training unit, for example, provides practice methods for improving pronunciation. For example, the training unit provides a practice method for specific phonemes. The training unit can also provide repetitive pronunciation practice. The training unit can also provide training for smoothing the flow of speech. For example, the training unit provides practice in storytelling. The training unit can also provide practice in logical construction. This improves the user's communication skills through training. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can use a generation AI to provide practice methods for improving pronunciation and training for smoothing the flow of speech.

[0079] The visualization unit can visually display the analysis results using graphs or charts. The visualization unit can display the analysis results using, for example, bar graphs or line graphs. For example, the visualization unit can display the analysis results using bar graphs to visually show the evaluation of each element. The visualization unit can also display the analysis results using line graphs to show changes over time. The visualization unit can also display the analysis results using pie charts. For example, the visualization unit uses pie charts to show the proportions of each element. The visualization unit can also display the analysis results in a dashboard format. For example, the visualization unit can integrate and display multiple graphs or charts to allow the overall analysis results to be grasped at a glance. This allows the analysis results to be intuitively understood through visual display. Some or all of the above-described processing in the visualization unit can be performed using, for example, AI, or can be performed without using AI. For example, the visualization unit can use generative AI to convert the analysis results into graphs or charts and visually display them.

[0080] The reception unit can estimate the user's emotions and adjust the timing of receiving voice data based on the estimated user emotions. For example, if the user is nervous, the reception unit waits until the user relaxes before receiving the voice data. For example, the reception unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can also immediately receive the voice data. For example, the reception unit analyzes the tone and speed of the user's voice to detect the user's relaxed state. Furthermore, if the user is in a hurry, the reception unit can also quickly receive the voice data. For example, the reception unit analyzes the user's behavioral patterns and schedule to detect the user's hurry. This allows the voice data to be received at an appropriate timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0081] The reception unit can analyze the user's past voice data reception history and select the optimal reception method. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. For example, the reception unit can analyze the past voice data reception history and evaluate the frequency of voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. For example, the reception unit can analyze the past text input history and evaluate the frequency of text input. Furthermore, if the user has preferred image input in the past, the reception unit can preferentially suggest image input. For example, the reception unit can analyze the past image input history and evaluate the frequency of image input. This improves user convenience by selecting the optimal reception method based on the past history. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the past voice data reception history into a generation AI and cause the generation AI to select the optimal reception method.

[0082] When receiving voice data, the reception unit can filter the voice data based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception unit can only receive voice data related to the meeting. For example, the reception unit can analyze the user's schedule and detect that the user is in a meeting. Furthermore, if the user is giving a presentation, the reception unit can also receive only voice data related to the presentation. For example, the reception unit can analyze the user's calendar and detect that the user is giving a presentation. Furthermore, if the user is relaxing, the reception unit can only receive voice data related to relaxation. For example, the reception unit can analyze the user's behavioral patterns and detect a relaxed state. This allows for more appropriate data to be collected by receiving voice data according to the user's situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's schedule and calendar data into the generation AI and cause the generation AI to perform filtering based on the user's current situation and areas of interest.

[0083] When receiving voice data, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit prioritizes receiving the voice data. For example, the reception unit detects the user's input method and prioritizes processing the voice input. Furthermore, if the user selects text input, the reception unit can also prioritize receiving text data. For example, the reception unit detects the user's input method and prioritizes processing the text input. Furthermore, if the user selects image input, the reception unit can also prioritize receiving image data. For example, the reception unit detects the user's input method and prioritizes processing the image input. This improves reception efficiency by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's input method to a generation AI and cause the generation AI to select the optimal reception means.

[0084] The reception unit can estimate the user's emotions and determine the priority of voice data to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit prioritizes receiving important voice data. For example, the reception unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can equally receive all voice data. For example, the reception unit analyzes the tone and speed of the user's voice to detect the user's relaxed state. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving voice data that requires prompt processing. For example, the reception unit analyzes the user's behavioral patterns and schedule to detect the user's hurry. Thus, by receiving voice data in a priority order according to the user's emotions, important data can be processed preferentially. Emotion estimation is realized using an emotion estimation function, such as 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 reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0085] When receiving voice data, the reception unit can prioritize receiving highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving voice data related to that area. For example, the reception unit analyzes GPS data to identify the user's current location. Furthermore, when the user is traveling, the reception unit can prioritize receiving voice data related to the user's travel destination. For example, the reception unit can identify the user's travel destination using a location information service. Furthermore, when the user is at home, the reception unit can prioritize receiving voice data related to the user's home. For example, the reception unit can identify the user's home using a location information service. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input GPS data or data from a location information service to the generation AI and cause the generation AI to prioritize receiving highly relevant data.

[0086] The reception unit may analyze the user's social media activity and receive related data when receiving the voice data. The reception unit may, for example, receive voice data related to a location where the user checked in on social media. For example, the reception unit may analyze the content of social media posts and identify the location where the user checked in. The reception unit may also analyze the content of the user's social media posts and receive related voice data. For example, the reception unit may analyze the content of social media posts and identify related topics. The reception unit may also receive related voice data by referring to the activities of the user's friends on social media. For example, the reception unit may analyze the content of social media posts and identify related topics. In this way, highly relevant data can be efficiently received by analyzing social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the content of social media posts and friend activity data into the generation AI and cause the generation AI to receive related data.

[0087] When receiving voice data, the reception unit can customize the reception method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. For example, the reception unit can analyze the past feedback and evaluate the frequency of voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. For example, the reception unit can analyze the past feedback and evaluate the frequency of text input. Furthermore, if the user has preferred image input in the past, the reception unit can preferentially suggest image input. For example, the reception unit can analyze the past feedback and evaluate the frequency of image input. In this way, by reflecting the past feedback, the optimal reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI and cause the generation AI to customize the optimal reception method.

[0088] The analysis unit can estimate the user's emotions and adjust the expression method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can analyze the user's tone and speed of voice to detect the user's relaxed state. The analysis unit can also provide a concise analysis result if the user is in a hurry. For example, the analysis unit can analyze the user's behavioral patterns and schedule to detect the user's hurry. This allows the analysis result to be presented in an expression method appropriate for the user's emotions, making it easy to understand. Emotion estimation is achieved using an emotion estimation function, such as 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 can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the audio data. For example, in the case of important audio data, the analysis unit performs a detailed analysis. For example, the analysis unit analyzes the content of the audio data and evaluates the importance. The analysis unit can also perform a standard analysis in the case of general audio data. For example, the analysis unit analyzes the content of the audio data and performs a standard evaluation. The analysis unit can also perform a simplified analysis in the case of audio data with low importance. For example, the analysis unit analyzes the content of the audio data and performs a simplified evaluation. This makes it possible to provide appropriate analysis results by performing a detailed analysis according to the importance of the audio data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and have the generation AI perform an analysis with a level of detail based on the importance.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the audio data. For example, in the case of conversation audio data, the analysis unit applies a conversation analysis algorithm. For example, the analysis unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the analysis unit can also apply a presentation analysis algorithm. For example, the analysis unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the analysis unit can also apply an interview analysis algorithm. For example, the analysis unit analyzes interview audio data and evaluates characteristics of the interview. In this way, by applying an analysis algorithm depending on the category of the audio data, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the past analysis results and extracts a specific pattern. The analysis unit can also extract a specific pattern from the user's past analysis results to improve the analysis accuracy. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the level of detail of the analysis based on the past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit performs a short and concise analysis. For example, the analysis unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, the analysis unit analyzes the user's tone and speed of voice to detect the user's relaxed state. The analysis unit can also perform a quick analysis if the user is in a hurry. For example, the analysis unit analyzes the user's behavioral patterns and schedule to detect the user's hurry. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as 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 can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0093] During analysis, the analysis unit can determine the analysis priority based on the submission date of the voice data. The analysis unit, for example, prioritizes analysis of the most recent voice data. For example, the analysis unit analyzes the submission date and time of the voice data to identify the most recent data. The analysis unit can also postpone voice data that was submitted earlier. For example, the analysis unit analyzes the submission date and time of the voice data to identify the older data. The analysis unit can also adjust the analysis order based on the submission date and time. For example, the analysis unit analyzes the submission date and time of the voice data to determine the analysis order. In this way, by determining the analysis priority based on the submission date and time, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission date and time of the voice data to the generation AI and have the generation AI determine the analysis priority.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the audio data. The analysis unit, for example, prioritizes analysis of important audio data. For example, the analysis unit analyzes the content of the audio data and evaluates the importance. The analysis unit can also prioritize analysis of highly relevant audio data. For example, the analysis unit analyzes the content of the audio data and evaluates the relevance. The analysis unit can also postpone analysis of less relevant audio data. For example, the analysis unit analyzes the content of the audio data and evaluates the relevance. This enables efficient analysis by prioritizing analysis of highly relevant data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input audio data to a generation AI and have the generation AI execute an analysis order based on the relevance.

[0095] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. For example, the analysis unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. Furthermore, if the user has general knowledge, the analysis unit can also use less technical terms. For example, the analysis unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. Furthermore, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terms. For example, the analysis unit analyzes the user's past learning history and work experience to evaluate the user's beginner level. This allows for analysis results that are easy to understand by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate analysis results according to the user's level of expertise.

[0096] The feedback unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit provides feedback in gentle words. For example, the feedback unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. The feedback unit can also provide detailed feedback if the user is relaxed. For example, the feedback unit can analyze the user's tone and speed of voice to detect the user's relaxed state. The feedback unit can also provide concise feedback if the user is in a hurry. For example, the feedback unit can analyze the user's behavioral patterns and schedule to detect the user's hurry. This enables more effective feedback by providing feedback 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0097] The feedback unit can adjust the level of detail of the feedback based on the importance of the audio data when providing feedback. For example, in the case of important audio data, the feedback unit provides detailed feedback. For example, the feedback unit analyzes the content of the audio data and evaluates the importance. The feedback unit can also provide standard feedback in the case of general audio data. For example, the feedback unit analyzes the content of the audio data and performs a standard evaluation. The feedback unit can also provide simplified feedback in the case of audio data with low importance. For example, the feedback unit analyzes the content of the audio data and performs a simplified evaluation. This enables appropriate feedback by providing feedback according to the importance of the audio data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input audio data to a generation AI and cause the generation AI to perform feedback detail based on the importance.

[0098] The feedback unit can apply different feedback algorithms depending on the category of the audio data when providing feedback. For example, in the case of conversation audio data, the feedback unit applies a conversation feedback algorithm. For example, the feedback unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the feedback unit can also apply a presentation feedback algorithm. For example, the feedback unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the feedback unit can also apply an interview feedback algorithm. For example, the feedback unit analyzes interview audio data and evaluates characteristics of the interview. This enables more accurate feedback by applying a feedback algorithm depending on the category of the audio data. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the audio data to a generation AI and cause the generation AI to apply a feedback algorithm depending on the category.

[0099] The feedback unit can improve the accuracy of feedback by referring to the user's past feedback results when providing feedback. The feedback unit, for example, adjusts a feedback algorithm based on the user's past feedback results. For example, the feedback unit analyzes the past feedback results and extracts a specific pattern. The feedback unit can also extract a specific pattern from the user's past feedback results to improve the feedback accuracy. For example, the feedback unit adjusts the feedback algorithm based on the past feedback results. The feedback unit can also adjust the level of detail of the feedback by referring to the user's past feedback results. For example, the feedback unit adjusts the level of detail of the feedback based on the past feedback results. In this way, the accuracy of the feedback can be improved by referring to the past feedback results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the past feedback results to a generation AI and cause the generation AI to improve the accuracy of the feedback.

[0100] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. For example, if the user is nervous, the feedback unit provides short and to-the-point feedback. For example, the feedback unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. The feedback unit can also provide detailed feedback if the user is relaxed. For example, the feedback unit can analyze the user's tone and speed of voice to detect the user's relaxed state. The feedback unit can also provide quick feedback if the user is in a hurry. For example, the feedback unit can analyze the user's behavioral patterns and schedule to detect the user's hurry. This allows appropriate feedback to be provided by adjusting the length of the 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0101] The feedback unit can determine the priority of feedback based on the submission date of the voice data when providing feedback. The feedback unit, for example, provides feedback preferentially to the most recent voice data. For example, the feedback unit analyzes the submission date and time of the voice data to identify the most recent data. The feedback unit can also provide feedback to older submitted voice data later. For example, the feedback unit analyzes the submission date and time of the voice data to identify older data. The feedback unit can also adjust the order of feedback based on the submission date and time. For example, the feedback unit analyzes the submission date and time of the voice data to determine the order of feedback. In this way, by determining the priority of feedback based on the submission date and time, feedback can be quickly provided to the most recent data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the submission date and time of the voice data to the generation AI and cause the generation AI to determine the priority of feedback.

[0102] The feedback unit can adjust the order of feedback based on the relevance of the audio data when providing feedback. The feedback unit, for example, provides feedback preferentially to important audio data. For example, the feedback unit analyzes the content of the audio data and evaluates the importance. The feedback unit can also provide feedback preferentially to highly relevant audio data. For example, the feedback unit analyzes the content of the audio data and evaluates the relevance. The feedback unit can also provide feedback later to less relevant audio data. For example, the feedback unit analyzes the content of the audio data and evaluates the relevance. This enables efficient feedback by providing feedback preferentially to highly relevant data. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input audio data to a generation AI and cause the generation AI to execute the feedback order based on the relevance.

[0103] When providing feedback, the feedback unit can adjust the use of technical terms in the feedback depending on the user's level of expertise. For example, if the user has technical expertise, the feedback unit uses a lot of technical terms. For example, the feedback unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. Furthermore, if the user has general knowledge, the feedback unit can also use less technical terms. For example, the feedback unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. Furthermore, if the user is a beginner, the feedback unit can provide feedback that avoids technical terms. For example, the feedback unit analyzes the user's past learning history and work experience to evaluate the user's beginner level. This allows for providing feedback that is easy to understand by providing feedback that is appropriate for the user's level of expertise. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit may input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate feedback appropriate for the user's level of expertise.

[0104] The training unit can estimate the user's emotions and adjust the presentation method of the training plan based on the estimated user's emotions. For example, if the user is nervous, the training unit provides a training plan to relax. For example, the training unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. Furthermore, if the user is relaxed, the training unit can also provide a detailed training plan. For example, the training unit analyzes the user's tone and speed of voice to detect the user's relaxed state. Furthermore, if the user is in a hurry, the training unit can provide a training plan that can be quickly implemented. For example, the training unit analyzes the user's behavioral patterns and schedule to detect the user's state of hurry. This enables effective training by providing a training plan according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or without AI. For example, the training unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0105] When providing a training plan, the training unit can adjust the level of detail of the training plan based on the importance of the audio data. For example, in the case of important audio data, the training unit provides a detailed training plan. For example, the training unit analyzes the content of the audio data and evaluates the importance. The training unit can also provide a standard training plan for general audio data. For example, the training unit analyzes the content of the audio data and performs a standard evaluation. The training unit can also provide a simplified training plan for audio data with low importance. For example, the training unit analyzes the content of the audio data and performs a simplified evaluation. This enables appropriate training by providing a training plan according to the importance of the audio data. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input audio data to a generation AI and cause the generation AI to adjust the level of detail of the training plan based on the importance.

[0106] When providing a training plan, the training unit can apply different training algorithms depending on the category of audio data. For example, in the case of conversation audio data, the training unit applies a conversation training algorithm. For example, the training unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the training unit can also apply a presentation training algorithm. For example, the training unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the training unit can also apply an interview training algorithm. For example, the training unit analyzes interview audio data and evaluates characteristics of the interview. This enables more effective training by applying a training algorithm depending on the category of audio data. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input audio data to a generation AI and cause the generation AI to apply a training algorithm depending on the category.

[0107] When providing a training plan, the training unit can improve the accuracy of the training plan by referring to the user's past training results. The training unit, for example, adjusts the training algorithm based on the user's past training results. For example, the training unit analyzes the past training results and extracts a specific pattern. The training unit can also extract a specific pattern from the user's past training results to improve training accuracy. For example, the training unit adjusts the training algorithm based on the past training results. The training unit can also adjust the level of detail of the training plan by referring to the user's past training results. For example, the training unit adjusts the level of detail of the training plan based on the past training results. In this way, the accuracy of the training plan can be improved by referring to the past training results. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the past training results into the generation AI and cause the generation AI to improve the accuracy of the training plan.

[0108] The training unit can estimate the user's emotions and adjust the length of the training plan based on the estimated user emotions. For example, if the user is nervous, the training unit provides a short and concise training plan. For example, the training unit captures the user's facial expression with a camera and detects the user's nervous state using an emotion estimation algorithm. Furthermore, if the user is relaxed, the training unit can provide a detailed training plan. For example, the training unit analyzes the user's tone and speed of voice to detect the user's relaxed state. Furthermore, if the user is in a hurry, the training unit can provide a training plan that can be quickly executed. For example, the training unit analyzes the user's behavioral patterns and schedule to detect the user's state of hurry. This allows the user to provide appropriate training by adjusting the length of the training plan according to the user's emotions. The emotion estimation is realized 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 training unit can be performed using, for example, AI, or without AI. For example, the training unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0109] When providing training plans, the training unit can determine the priority of the training plans based on the submission date of the audio data. For example, the training unit provides training plans preferentially to the most recent audio data. For example, the training unit analyzes the submission date and time of the audio data to identify the most recent data. The training unit can also provide training plans to audio data that was submitted earlier. For example, the training unit analyzes the submission date and time of the audio data to identify the older data. The training unit can also adjust the order of training plans based on the submission date and time. For example, the training unit analyzes the submission date and time of the audio data to determine the order of the training plans. In this way, by determining the priority of training plans based on the submission date and time, training plans can be quickly provided for the most recent data. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input the submission date and time of the audio data to the generation AI and have the generation AI determine the priority of the training plans.

[0110] When providing training plans, the training unit can adjust the order of the training plans based on the relevance of the audio data. For example, the training unit provides training plans preferentially to important audio data. For example, the training unit analyzes the content of the audio data and evaluates its importance. The training unit can also provide training plans preferentially to highly relevant audio data. For example, the training unit analyzes the content of the audio data and evaluates its relevance. The training unit can also provide training plans later to less relevant audio data. For example, the training unit analyzes the content of the audio data and evaluates its relevance. This enables efficient training by providing training plans preferentially to highly relevant data. Some or all of the above-described processing in the training unit may be performed using, or without, AI. For example, the training unit can input audio data to a generation AI and have the generation AI execute the order of training plans based on the relevance.

[0111] When providing a training plan, the training unit can adjust the use of technical terms in the training plan according to the user's level of expertise. For example, if the user has technical expertise, the training unit uses a lot of technical terms. For example, the training unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. Furthermore, if the user has general knowledge, the training unit can also use less technical terms. For example, the training unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. Furthermore, if the user is a beginner, the training unit can provide a training plan that avoids technical terms. For example, the training unit analyzes the user's past learning history and work experience to evaluate the user's beginner level. This allows for easy-to-understand training by providing a training plan according to the user's level of expertise. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without AI. For example, the training unit can input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate a training plan according to the user's level of expertise.

[0112] The visualization unit can estimate the user's emotions and adjust the visualization expression method based on the estimated user emotions. For example, if the user is nervous, the visualization unit provides a simple, highly visible visualization. For example, the visualization unit captures the user's facial expressions with a camera and detects the nervous state using an emotion estimation algorithm. The visualization unit can also provide a detailed visualization if the user is relaxed. For example, the visualization unit can analyze the tone and speed of the user's voice to detect the relaxed state. The visualization unit can also provide a visualization that focuses on the main points if the user is in a hurry. For example, the visualization unit can analyze the user's behavioral patterns and schedule to detect the state of hurry. This allows for easy-to-understand analysis results by providing visualizations that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0113] The visualization unit can adjust the level of detail of the visualization based on the importance of the audio data during visualization. For example, in the case of important audio data, the visualization unit provides detailed visualization. For example, the visualization unit analyzes the content of the audio data and evaluates the importance. The visualization unit can also provide standard visualization in the case of general audio data. For example, the visualization unit analyzes the content of the audio data and performs a standard evaluation. The visualization unit can also provide simplified visualization in the case of audio data with low importance. For example, the visualization unit analyzes the content of the audio data and performs a simplified evaluation. This enables appropriate visualization by providing visualization according to the importance of the audio data. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input audio data to a generation AI and cause the generation AI to perform visualization detail based on the importance.

[0114] The visualization unit can apply different visualization algorithms depending on the category of the audio data during visualization. For example, in the case of conversation audio data, the visualization unit applies a conversation visualization algorithm. For example, the visualization unit analyzes the conversation audio data and evaluates characteristics of the conversation. Furthermore, in the case of presentation audio data, the visualization unit can also apply a presentation visualization algorithm. For example, the visualization unit analyzes presentation audio data and evaluates characteristics of the presentation. Furthermore, in the case of interview audio data, the visualization unit can also apply an interview visualization algorithm. For example, the visualization unit analyzes interview audio data and evaluates characteristics of the interview. In this way, by applying a visualization algorithm depending on the category of the audio data, more accurate visualization is possible. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input audio data to a generation AI and cause the generation AI to apply a visualization algorithm depending on the category.

[0115] During visualization, the visualization unit can improve the accuracy of the visualization by referring to the user's past visualization results. The visualization unit, for example, adjusts the visualization algorithm based on the user's past visualization results. For example, the visualization unit analyzes the past visualization results and extracts a specific pattern. The visualization unit can also extract a specific pattern from the user's past visualization results to improve the visualization accuracy. For example, the visualization unit adjusts the visualization algorithm based on the past visualization results. The visualization unit can also adjust the level of detail of the visualization by referring to the user's past visualization results. For example, the visualization unit adjusts the level of detail of the visualization based on the past visualization results. In this way, the accuracy of the visualization can be improved by referring to the past visualization results. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the past visualization results to the generation AI and cause the generation AI to improve the accuracy of the visualization.

[0116] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated user emotions. For example, if the user is nervous, the visualization unit provides a short and concise visualization. For example, the visualization unit captures the user's facial expressions with a camera and detects the user's nervous state using an emotion estimation algorithm. The visualization unit can also provide a detailed visualization if the user is relaxed. For example, the visualization unit can analyze the user's tone and speed of voice to detect the user's relaxed state. The visualization unit can also provide a quick visualization if the user is in a hurry. For example, the visualization unit can analyze the user's behavioral patterns and schedule to detect the user's hurry. This allows the visualization to be adjusted in length according to the user's emotions, thereby providing an appropriate visualization. Emotion estimation is achieved using an emotion estimation function, such as 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 visualization unit can be performed using, for example, AI, or without AI. For example, the visualization unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0117] During visualization, the visualization unit can determine the priority of visualization based on the submission date of the audio data. The visualization unit, for example, prioritizes visualization of the most recent audio data. For example, the visualization unit analyzes the submission date and time of the audio data to identify the most recent data. The visualization unit can also visualize older submitted audio data later. For example, the visualization unit analyzes the submission date and time of the audio data to identify older data. The visualization unit can also adjust the order of visualization based on the submission date and time. For example, the visualization unit analyzes the submission date and time of the audio data to determine the order of visualization. This allows for the most recent data to be quickly visualized by determining the priority of visualization based on the submission date and time. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the visualization unit can input the submission date and time of the audio data to the generation AI and have the generation AI determine the visualization priority.

[0118] The visualization unit can adjust the visualization order based on the relevance of the audio data during visualization. The visualization unit, for example, prioritizes visualization of important audio data. For example, the visualization unit analyzes the content of the audio data and evaluates its importance. The visualization unit can also prioritize visualization of highly relevant audio data. For example, the visualization unit analyzes the content of the audio data and evaluates its relevance. The visualization unit can also postpone visualization of less relevant audio data. For example, the visualization unit analyzes the content of the audio data and evaluates its relevance. This enables efficient visualization by providing visualization preferentially for highly relevant data. Some or all of the above-described processing in the visualization unit may be performed using, or without, AI. For example, the visualization unit can input audio data to a generation AI and cause the generation AI to execute a visualization order based on the relevance.

[0119] The visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise during visualization. For example, if the user has technical expertise, the visualization unit uses a lot of technical terms. For example, the visualization unit analyzes the user's past learning history and work experience to evaluate the user's level of expertise. The visualization unit can also use less technical terms if the user has general knowledge. For example, the visualization unit analyzes the user's past learning history and work experience to evaluate the user's level of general knowledge. The visualization unit can also provide visualization results that avoid technical terms if the user is a beginner. For example, the visualization unit analyzes the user's past learning history and work experience to evaluate the beginner level. This allows for visualizations that are easy to understand by providing visualizations that correspond to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or may be performed without AI. For example, the visualization unit can input data on the user's learning history and work experience into a generation AI and cause the generation AI to generate visualization results according to the user's level of expertise. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, feedback unit, training unit, and visualization unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit collects voice data using the microphone or camera of the smart device 14, which is then analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, converts the voice data into text and extracts acoustic features. The feedback unit, implemented, for example, by the control unit 46A of the smart device 14, provides the analysis results to the user in text or voice format. The training unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides a training plan based on the feedback. The visualization unit, for example, displays the analysis results in graphs or charts using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, feedback unit, training unit, and visualization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit collects voice data using the microphone 238 or camera 42 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12, converts the voice data into text, and extracts acoustic features. The feedback unit, for example, is realized by the control unit 46A of the smart glasses 214, and provides the analysis results to the user in text or voice format. The training unit, for example, is realized by the specific processing unit 290 of the data processing device 12, and provides a training plan based on the feedback. The visualization unit, for example, displays the analysis results in graphs or charts using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, feedback unit, training unit, and visualization unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit collects voice data using the microphone 238 or camera 42 of the headset-type terminal 314, and the collected voice data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, converts the voice data into text and extracts acoustic features. The feedback unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides the analysis results to the user in text or voice format. The training unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a training plan based on the feedback. The visualization unit, for example, displays the analysis results in graphs or charts using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, feedback unit, training unit, and visualization unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit collects voice data using the microphone 238 or camera 42 of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, converts the voice data into text and extracts acoustic features. The feedback unit, realized, for example, by the control unit 46A of the robot 414, provides the analysis results to the user in text or voice format. The training unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides a training plan based on the feedback. The visualization unit, for example, displays the analysis results in graphs or charts using the display of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analyzer can also analyze background sounds in the audio data and provide feedback tailored to the user's environment. For example, if the background sounds are noisy, the analyzer can suggest noise cancellation. Alternatively, if the recording is made in a quiet environment, the analyzer can provide feedback to enhance the clarity of the audio. Furthermore, the analyzer can detect specific background sounds (e.g., traffic sounds or natural sounds) and provide advice accordingly. This allows the analyzer to suggest specific improvements tailored to the user's recording environment.

[0122] The feedback module can also analyze the user's past feedback history to provide personalized feedback. For example, the feedback module can review previously identified pronunciation issues and evaluate whether improvements have been made. The feedback module can also provide targeted advice to address specific areas of the user's learning difficulties based on past feedback. Furthermore, the feedback module can track the user's progress and suggest long-term improvement plans, providing the user with specific guidance for continually improving their skills.

[0123] The training department can also provide training methods tailored to the user's learning style. For example, visual learners can receive training using visual aids. Auditory learners can receive training centered on audio guides. Furthermore, hands-on learners can receive training using role-playing and simulations. This allows users to effectively improve their skills in the way that best suits them.

[0124] The visualization unit can also track and visually display a user's progress over time. For example, the visualization unit can provide a graph showing the user's skill improvement. The visualization unit can also highlight areas of improvement or challenges within a specific time period. Furthermore, the visualization unit can provide a dashboard showing the user's progress toward their goals, allowing the user to see their progress at a glance and stay motivated.

[0125] The reception unit can also provide a function to protect the user's privacy when collecting the user's voice data. For example, the reception unit can encrypt and store the voice data. In addition, if the user requests to delete specific data, the reception unit can quickly delete the data. Furthermore, the reception unit can provide an interface for the user to confirm the purpose of use of the data and obtain consent. This allows the user to use the system with peace of mind.

[0126] The analysis unit can also estimate the user's emotions and adjust the way the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. In this way, it is possible to provide analysis results that are easy to understand by presenting them in a way that suits the user's emotions.

[0127] The feedback unit can also estimate the user's emotions and adjust the way feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise feedback. This allows for more effective feedback by providing feedback according to the user's emotions.

[0128] The training unit can also estimate the user's emotions and adjust the way the training plan is presented based on the estimated user's emotions. For example, if the user is tense, a training plan for relaxation can be provided. Also, if the user is relaxed, a detailed training plan can be provided. Furthermore, if the user is in a hurry, a training plan that can be quickly implemented can be provided. This allows for effective training by providing a training plan that matches the user's emotions.

[0129] The visualization unit can also estimate the user's emotions and adjust the visualization method based on the estimated user emotions. For example, if the user is nervous, a simple visualization with high visibility can be provided. If the user is relaxed, a detailed visualization can be provided. Furthermore, if the user is in a hurry, a visualization that focuses on the main points can be provided. In this way, by providing visualizations according to the user's emotions, it is possible to provide analysis results that are easy to understand.

[0130] The reception unit can also estimate the user's emotions and adjust the timing of receiving voice data based on the estimated user's emotions. For example, if the user is nervous, the reception unit can wait until the user is relaxed before receiving voice data. If the user is relaxed, the reception unit can also receive voice data immediately. Furthermore, if the user is in a hurry, the reception unit can also receive voice data quickly. This makes it possible to receive voice data at an appropriate timing according to the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit inputs audio data. The audio data includes, for example, the audio file format (WAV, MP3, etc.), sampling rate, bit rate, etc. The reception unit can collect audio data using a microphone or upload an existing audio file to the system. It is also possible to stream audio data in real time. Step 2: The analysis unit uses AI to analyze the voice data entered by the reception unit. The analysis is performed based on voice recognition technology and acoustic feature extraction methods. For example, voice recognition technology is used to convert the voice data into text, and acoustic features are extracted to analyze the characteristics of the voice data. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback can be provided in text, audio, or visual format. For example, the analysis results can be provided to the user in text format, and specific improvements can be suggested. Audio feedback communicates the analysis results to the user by voice. Visual feedback visually displays the analysis results using graphs and charts. Step 4: The training department provides a training plan based on the feedback provided by the feedback department. The training plan consists of exercises and training steps. For example, it provides practice methods to improve pronunciation, training to improve speech flow, and training to enrich emotional expression. Step 5: The visualization section visually displays the analysis results. Visual displays are made in the form of graphs, charts, dashboards, etc. For example, analysis results can be displayed in the form of bar graphs, line graphs, pie charts, or dashboards, visually showing the evaluation of each element, changes over time, and the proportion of each element.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting voice data; an analysis unit that analyzes the voice data input by the reception unit; a feedback unit that provides feedback based on the analysis result obtained by the analysis unit; a training unit that provides a training plan based on the feedback provided by the feedback unit; a visualization unit that visually displays the analysis results. A system characterized by:

2. The analysis unit Evaluate factors including clarity of pronunciation, flow of speech, and emotional expression 2. The system of claim 1.

3. The feedback unit Point out any unclear pronunciation or unnatural flow of speech and offer specific suggestions for improvement 2. The system of claim 1.

4. The feedback unit Suggest ways to express emotions more effectively when they are not fully expressed 2. The system of claim 1.

5. The training section Providing exercises to improve pronunciation and training to improve speaking flow 2. The system of claim 1.

6. The visualization unit Visually display analysis results using graphs or charts 2. The system of claim 1.

7. The reception unit The user's emotions are estimated, and the timing of receiving voice data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past voice data reception history and select the optimal reception method 2. The system of claim 1.

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