system

Abstract

The system according to the embodiment aims to provide an optimal presentation practice method based on an individual's character and voice quality. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an advice unit, and a practice unit. The collection unit collects audio data or video data. The analysis unit analyzes the data collected by the collection unit and identifies the user's individual character, voice quality, and voice volume. The advice unit provides advice based on the analysis results obtained by the analysis unit. The practice unit provides tools for the user to practice based on the advice provided by the advice unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide a way to practice presentations based on an individual's character or voice quality, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal presentation practice method based on an individual's character and voice quality. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an advice unit, and a practice unit. The collection unit collects audio data or video data. The analysis unit analyzes the data collected by the collection unit and identifies the user's individual character, voice quality, and voice volume. The advice unit provides advice based on the analysis results obtained by the analysis unit. The practice unit provides tools for the user to practice based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal presentation practice method based on an individual's character and voice quality. [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 presentation support system according to an embodiment of the present invention provides personalized advice and practice tools based on a user's individual character, voice quality, and voice volume. The system analyzes the user's individual character, voice quality, and voice volume, and provides advice on presentation methods, vocalization, pauses, and the order in which materials are presented that are suited to the user's character. For example, the system provides advice on various aspects, such as how to speak loudly in a low voice and how to take appropriate pauses. Users can improve their presentation skills by practicing in accordance with the AI's advice. For example, audio data and video data are collected to analyze a user's individual character, voice quality, and voice volume. For example, the user's speaking voice and presentation video are collected. This data is collected by a collection unit. The collected data is then analyzed. The analysis unit analyzes the audio data and video data collected by the collection unit to identify the user's individual character, voice quality, and voice volume. For example, the system analyzes the user's voice pitch, volume, and speaking style characteristics. Based on the analysis results, the system provides advice. The advice unit provides advice on presentation methods, vocalization, pauses, and the order in which materials are presented that are suited to the user's character. For example, it provides specific advice such as how to speak loudly in a low voice or how to take appropriate pauses. Finally, it provides tools for the user to practice. The practice section provides tools that allow the user to practice according to the advice and receive feedback. For example, the user can record their presentation, and the AI ​​can analyze the recording and provide feedback. This allows the user to improve their presentation skills. In this way, the presentation support system can improve the user's presentation skills.

[0029] A presentation support system according to an embodiment includes a collection unit, an analysis unit, an advice unit, and a practice unit. The collection unit collects audio data or video data. For example, the collection unit collects audio of a user speaking or video of a presentation. The collection unit can record audio using a microphone. The collection unit can also record video using a camera. The collection unit can also import existing audio files or video files. For example, the collection unit can import audio files in MP3 or WAV format, or video files in MP4 or AVI format. The analysis unit analyzes the audio data or video data collected by the collection unit to identify the user's individual character, voice quality, and voice volume. For example, the analysis unit analyzes the audio data to identify the pitch and volume of the user's voice. For example, the analysis unit can analyze the frequency components of the audio data to measure the pitch in hertz (Hz). The analysis unit can also analyze the amplitude of the audio data to measure the volume in decibels (dB). The analysis unit can also analyze the video data to identify the characteristics of the user's speaking style. For example, the analysis unit can analyze frames of video data and identify speaking speed, intonation, rhythm, etc. The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit provides advice, for example, on presentation methods, vocalizations, pauses, and the order in which materials are explained that suit the user's character. For example, the advice unit can advise how to speak loudly in a low voice. The advice unit can also advise how to take appropriate pauses. Furthermore, the advice unit can advise on the order in which materials should be explained. For example, the advice unit can advise on the order in which materials should be explained based on logical order or importance. The practice unit provides tools that allow the user to practice based on the advice provided by the advice unit. For example, the practice unit provides tools that allow the user to record their presentation and have an AI analyze the recording and provide feedback. For example, the practice unit allows the user to record their presentation using a smartphone or camera.Furthermore, the practice unit can use AI to analyze the recorded video and provide feedback. For example, the practice unit can use AI to analyze video data, evaluate the pitch and volume of the user's voice, characteristics of the speaking style, and provide feedback on areas for improvement. This allows the presentation support system according to the embodiment to improve the user's presentation skills.

[0030] The collection unit can collect audio of a user speaking or video of a presentation. For example, the collection unit can record the audio of a user speaking with a microphone. For example, the collection unit can record audio using a microphone when a user gives a presentation. The collection unit can also record video using a camera when a user gives a presentation. For example, the collection unit can record video using a camera when a user gives a presentation and collect the video data. The collection unit can also import existing audio files or video files. For example, the collection unit can import audio files or video files previously recorded by a user and collect the data. By collecting the user's audio and video data, basic data for providing personalized advice can be obtained. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can optimize the timing of audio and video collection using AI to automate the collection of audio and video data.

[0031] The analysis unit can analyze the collected audio data or video data to identify the pitch or volume of the user's voice and characteristics of speaking style. The analysis unit, for example, analyzes the audio data to identify the pitch of the user's voice. For example, the analysis unit analyzes the frequency components of the audio data to measure the pitch in hertz (Hz). The analysis unit can also analyze the audio data to identify the volume of the user's voice. For example, the analysis unit analyzes the amplitude of the audio data to measure the volume in decibels (dB). The analysis unit can also analyze the video data to identify characteristics of the user's speaking style. For example, the analysis unit can analyze frames of the video data to identify the speaking rate, intonation, rhythm, etc. This can identify the characteristics of the user's voice and provide more appropriate advice. 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 optimize audio and video analysis algorithms using AI to automate the analysis of audio and video data.

[0032] The advice unit can provide advice on a presentation method, voice usage, pauses, and the order in which to explain materials that are suited to the user's character. The advice unit, for example, provides advice on a presentation method that is suited to the user's character. For example, the advice unit provides advice on how to use slides and gestures based on the user's character. The advice unit can also provide advice on how to use the user's voice. For example, the advice unit can provide advice on how to speak louder with a lower voice. The advice unit can also provide advice on how to take pauses. For example, the advice unit can provide advice on the timing of speaking and how to pause. The advice unit can also provide advice on the order in which to explain materials. For example, the advice unit can provide advice on the order in which to explain materials based on logical order and importance. This allows the user to improve their presentation skills by providing advice that is suited to their character. Some or all of the above-described processing by the advice unit may be performed using, or without, AI. For example, the advice unit can optimize an advice algorithm using AI to automatically generate advice based on the user's character.

[0033] The practice unit allows a user to record their presentation, and the AI ​​analyzes the recording and provides feedback. For example, the practice unit allows a user to record a presentation using a smartphone or camera. For example, the practice unit records a video of the user giving a presentation using a smartphone or camera and collects the video data. The practice unit also allows an AI to analyze the recorded video and provide feedback. For example, the practice unit allows an AI to analyze the video data, evaluate the pitch and volume of the user's voice, speaking characteristics, etc., and provide feedback on areas for improvement. This allows a user to record their presentation and have the AI ​​provide feedback, thereby improving their presentation skills. Some or all of the above-described processing in the practice unit may be performed using, or without, an AI. For example, the practice unit may use an AI to optimize an analysis algorithm for automatically analyzing the recorded video data.

[0034] The advice unit can provide specific advice, such as how to speak loudly in a low voice or how to take appropriate pauses. The advice unit, for example, gives advice on how to speak loudly in a low voice. For example, the advice unit provides the user with a vocal training method for speaking loudly in a low voice. The advice unit can also give advice on how to take appropriate pauses. For example, the advice unit specifically instructs the user on the timing of speaking and how to pause. Furthermore, the advice unit can also provide specific advice. For example, the advice unit specifically advises the user on when to take pauses in a presentation and how to speak. This makes it easier for the user to put the advice into practice. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can automatically generate optimal advice using AI based on data on the user's voice characteristics and speaking style.

[0035] The collection unit can analyze the user's past presentation history and select an appropriate collection method. For example, the collection unit refers to the data collection method used by the user in successful presentations in the past. For example, the collection unit can analyze the recording data and evaluation results of the user's past presentations to identify the data collection method used in successful presentations. The collection unit can also avoid the data collection method used by the user in unsuccessful presentations in the past. For example, the collection unit can analyze the recording data and evaluation results of the user's past presentations to identify the data collection method used in unsuccessful presentations and avoid it. Furthermore, the collection unit can select the most effective collection timing from the user's past presentation history. For example, the collection unit can analyze the recording data and evaluation results of the user's past presentations to identify the most effective collection timing. This enables more effective data collection by analyzing the past presentation history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can optimize an analysis algorithm using AI to automatically analyze the user's past presentation history.

[0036] When collecting audio or video data, the collection unit can perform filtering based on the user's current presentation environment or situation. For example, the collection unit minimizes noise filtering when the user is in a quiet environment. For example, the collection unit sets noise filtering to minimum when the user is giving a presentation in a quiet conference room. The collection unit can also strengthen noise filtering when the user is in a noisy environment. For example, the collection unit strengthens noise filtering when the user is giving a presentation in a noisy cafe. Furthermore, the collection unit can perform filtering to remove wind noise when the user is outdoors. For example, the collection unit sets filtering to remove wind noise when the user is giving a presentation outdoors. This allows for filtering according to the environment and situation, thereby collecting higher quality data. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can use AI to analyze the user's current presentation environment and situation in real time and optimize filtering settings.

[0037] When collecting audio data or video data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a conference room, the collection unit prioritizes collecting data related to a presentation. For example, when the user is giving a presentation in a conference room, the collection unit prioritizes collecting data related to the environment. Furthermore, when the user is at home, the collection unit can prioritize collecting data in a relaxed environment. For example, when the user is giving a presentation at home, the collection unit prioritizes collecting data related to the environment. Furthermore, when the user is out and about, the collection unit can prioritize collecting data related to the location. For example, when the user is giving a presentation while out and about, the collection unit prioritizes collecting data related to the location. In this way, more relevant data can be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to analyze the user's geographical location information in real time and prioritize collecting highly relevant data.

[0038] The collection unit may analyze the user's social media activity and collect related data when collecting audio or video data. For example, the collection unit may collect data on presentations shared by the user on social media. For example, the collection unit may collect video and audio data of presentations shared by the user on social media. The collection unit may also collect data based on feedback received by the user on social media. For example, the collection unit may analyze comments and ratings received by the user on social media and collect data based on the results. Furthermore, the collection unit may collect presentation data on experts the user follows on social media. For example, the collection unit may collect presentation video and audio data of the experts the user follows. This allows for the collection of more relevant data by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may use AI to analyze the user's social media activity in real time and collect related data.

[0039] The analysis unit can adjust the accuracy of the analysis based on the purpose of the user's presentation during analysis. For example, if the user is giving a business presentation, the analysis unit performs a detailed analysis. For example, if the analysis unit determines that the purpose of the user's presentation is business, the analysis unit performs a detailed analysis to evaluate the content and structure of the presentation. The analysis unit can also perform a specialized analysis if the user is giving an academic presentation. For example, if the analysis unit determines that the purpose of the user's presentation is academic, the analysis unit performs a specialized analysis to evaluate the logic of the presentation and the accuracy of the data. The analysis unit can also perform a simplified analysis if the user is giving a casual presentation. For example, if the analysis unit determines that the purpose of the user's presentation is casual, the analysis unit performs a simplified analysis to evaluate the flow and atmosphere of the presentation. This allows for more appropriate analysis by adjusting the level of detail of the analysis depending on the purpose of the presentation. 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 analyze the purpose of the user's presentation in real time and use AI to optimize the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of the user's presentation. For example, in the case of a business presentation, the analysis unit applies a business analysis algorithm. For example, if the analysis unit determines that the category of the user's presentation is business, it applies a business analysis algorithm to evaluate the content and structure of the presentation. Furthermore, in the case of an academic presentation, the analysis unit can also apply an academic analysis algorithm. For example, if the analysis unit determines that the category of the user's presentation is academic, it applies an academic analysis algorithm to evaluate the logic of the presentation and the accuracy of the data. Furthermore, in the case of a casual presentation, the analysis unit can also apply a casual analysis algorithm. For example, if the analysis unit determines that the category of the user's presentation is casual, it applies a casual analysis algorithm to evaluate the flow and atmosphere of the presentation. This enables more appropriate analysis by applying an analysis algorithm depending on the category of the presentation. 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 may use AI to analyze the category of the user's presentation in real time and apply an appropriate analysis algorithm.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission time of the user's presentation. For example, the analysis unit prioritizes analysis of the presentation that the user will submit most recently. For example, the analysis unit analyzes the submission deadline of the user's presentation and prioritizes analysis of the presentation that will be submitted most recently. The analysis unit can also prioritize analysis of presentations that the user has been preparing for a long time. For example, the analysis unit analyzes the submission deadline of the user's presentation and prioritizes analysis of the presentation that has been prepared for a long time. The analysis unit can also prioritize analysis of presentations that the user is urgently preparing. For example, the analysis unit analyzes the submission deadline of the user's presentation and prioritizes analysis of the presentation that is urgently prepared. This enables more effective analysis by determining the analysis priority based on the submission time. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the submission time of the user's presentation in real time and use AI to optimize the analysis priority.

[0042] During analysis, the analysis unit can determine the order of analysis based on the relevance of the user's presentation. For example, the analysis unit prioritizes analysis of presentations that are highly relevant to the user. For example, the analysis unit analyzes the relevance of the topics and audiences of the user's presentation and prioritizes analysis of the highly relevant presentations. The analysis unit can also postpone presentations that are less relevant to the user. For example, the analysis unit analyzes the relevance of the topics and audiences of the user's presentation and postpones the less relevant presentations. Furthermore, if the user is preparing multiple presentations, the analysis unit can analyze them in order of relevance. For example, the analysis unit analyzes the relevance of the topics and audiences of the user's presentation and analyzes them in order of relevance. This enables more effective analysis by adjusting the order of analysis based on relevance. 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 analyze the relevance of the user's presentation in real time and use AI to optimize the order of analysis.

[0043] When providing advice, the advice unit can adjust the accuracy of the advice based on the importance of the user's presentation. For example, if the user is giving an important presentation, the advice unit provides detailed advice. For example, the advice unit analyzes the importance of the user's presentation and provides detailed advice if it is determined to be an important presentation. The advice unit can also provide simple advice if the user is giving a casual presentation. For example, the advice unit analyzes the importance of the user's presentation and provides simple advice if it is determined to be a casual presentation. The advice unit can also provide quick advice if the user is giving an urgent presentation. For example, the advice unit analyzes the importance of the user's presentation and provides quick advice if it is determined to be an urgent presentation. This allows for more appropriate advice to be provided by adjusting the level of detail of the advice depending on the importance of the presentation. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may analyze the importance of the user's presentation in real time and use AI to optimize the level of detail of the advice.

[0044] When providing advice, the advice unit can apply an appropriate advice algorithm depending on the category of the user's presentation. For example, in the case of a business presentation, the advice unit applies a business advice algorithm. For example, if the advice unit determines that the category of the user's presentation is business, it applies a business advice algorithm to evaluate the content and structure of the presentation. Furthermore, in the case of an academic presentation, the advice unit can also apply an academic advice algorithm. For example, if the advice unit determines that the category of the user's presentation is academic, it applies an academic advice algorithm to evaluate the logic of the presentation and the accuracy of the data. Furthermore, in the case of a casual presentation, the advice unit can also apply a casual advice algorithm. For example, if the advice unit determines that the category of the user's presentation is casual, it applies a casual advice algorithm to evaluate the flow and atmosphere of the presentation. This enables more appropriate advice to be provided by applying an advice algorithm depending on the category of the presentation. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may use AI to analyze the category of the user's presentation in real time and apply an appropriate advice algorithm.

[0045] When providing advice, the advice unit can determine the order of advice based on the submission timing of the user's presentation. For example, the advice unit prioritizes advice for the presentation that the user will submit most recently. For example, the advice unit analyzes the submission deadline of the user's presentation and prioritizes advice for the presentation that will be submitted most recently. The advice unit can also prioritize advice for presentations that the user has been preparing for a long time. For example, the advice unit analyzes the submission deadline of the user's presentation and prioritizes advice for presentations that have been prepared for a long time. The advice unit can also prioritize advice for presentations that the user is urgently preparing. For example, the advice unit analyzes the submission deadline of the user's presentation and prioritizes advice for presentations that are urgently prepared. This enables more effective advice by determining the priority of advice based on the submission timing. Some or all of the above-described processing by the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can analyze the submission timing of the user's presentation in real time and use AI to optimize the order of advice.

[0046] When providing advice, the advice unit can determine the order of advice based on the relevance of the user's presentation. For example, the advice unit prioritizes advice on presentations that are highly relevant to the user. For example, the advice unit analyzes the relevance of the topics and target audience of the user's presentation and prioritizes advice on presentations that are highly relevant. The advice unit can also postpone presentations that are less relevant to the user. For example, the advice unit analyzes the relevance of the topics and target audience of the user's presentation and postpones presentations that are less relevant. Furthermore, if the user is preparing multiple presentations, the advice unit can also provide advice in order of relevance. For example, the advice unit analyzes the relevance of the topics and target audience of the user's presentation and provides advice in order of relevance. This enables more effective advice by adjusting the order of advice based on relevance. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may analyze the relevance of the user's presentation in real time and use AI to optimize the order of advice.

[0047] During practice, the practice unit can select an appropriate practice method by referring to the user's past practice history. The practice unit, for example, refers to practice methods that the user has used successfully in the past. For example, the practice unit analyzes the user's past practice history to identify successful practice methods. The practice unit can also avoid practice methods that the user has used unsuccessfully in the past. For example, the practice unit analyzes the user's past practice history to identify and avoid unsuccessful practice methods. Furthermore, the practice unit can select the most effective practice method from the user's past practice history. For example, the practice unit analyzes the user's past practice history to identify the most effective practice method. This allows a more effective practice method to be selected by referring to the past practice history. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without AI. For example, the practice unit can optimize an analysis algorithm using AI to automatically analyze the user's past practice history.

[0048] The practice unit can adjust the practice content based on the user's current presentation skills during practice. For example, if the user is a beginner, the practice unit provides basic practice content. For example, the practice unit evaluates the user's current presentation skills and determines that the user is a beginner, and provides basic practice content. Furthermore, the practice unit can also provide applied practice content if the user is an intermediate level user. For example, the practice unit evaluates the user's current presentation skills and determines that the user is an intermediate level user, and provides applied practice content. Furthermore, the practice unit can also provide specialized practice content if the user is an advanced level user. For example, the practice unit evaluates the user's current presentation skills and determines that the user is an advanced level user, and provides specialized practice content. This allows for more effective practice by customizing the practice content based on the user's current skills. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without AI. For example, the practice unit can analyze the user's current presentation skills in real time and use AI to optimize the practice content.

[0049] The practice unit can select an appropriate practice method during practice by taking into account the user's geographic location information. For example, if the user is in a conference room, the practice unit provides a practice related to a presentation. For example, if the practice unit analyzes the user's geographic location information and determines that the user is in a conference room, the practice unit provides a practice related to the presentation. The practice unit can also provide a practice in a relaxed environment if the user is at home. For example, if the practice unit analyzes the user's geographic location information and determines that the user is at home, the practice unit provides a practice in a relaxed environment. Furthermore, if the user is out and about, the practice unit can also provide a practice related to the location. For example, if the practice unit analyzes the user's geographic location information and determines that the user is out and about, the practice unit provides a practice related to the location. This allows for the selection of a more effective practice method by taking the geographic location information into account. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without AI. For example, the practice unit can analyze the user's geographic location information in real time and use AI to select an appropriate practice method.

[0050] During practice, the practice unit can analyze the user's social media activity to determine the content of the practice. The practice unit can, for example, suggest practice exercises based on the content of presentations shared by the user on social media. For example, the practice unit can analyze the user's social media activity and suggest practice exercises based on the content of the presentations shared by the user. The practice unit can also suggest practice exercises based on feedback the user has received on social media. For example, the practice unit can analyze comments and ratings the user has received on social media and suggest practice exercises based on the content. Furthermore, the practice unit can suggest practice exercises based on the content of presentations given by experts the user follows on social media. For example, the practice unit can analyze the content of presentations given by experts the user follows and suggest practice exercises based on the content. In this way, more effective practice content can be suggested by analyzing social media activity. Some or all of the above-described processing in the practice unit can be performed using, for example, AI, or without AI. For example, the practice unit can analyze the user's social media activity in real time and use AI to determine the content of the practice exercises.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can also monitor the user's gestures and posture during a presentation in real time and collect this data. For example, the collection unit can record what gestures the user uses during the presentation using a camera and send the data to the analysis unit. The collection unit can also detect the user's posture using a sensor and record changes in posture during the presentation. Furthermore, the collection unit can track the user's eye movements and collect gaze data. This allows for the collection of data including the user's non-verbal communication elements, enabling more comprehensive advice.

[0053] The analysis unit can analyze data on a user's gestures and posture during a presentation and identify how these factors affect the effectiveness of the presentation. For example, the analysis unit can analyze the frequency and type of a user's gestures and identify effective ways to use gestures. The analysis unit can also analyze the stability and changes in a user's posture and identify areas for improvement in posture during a presentation. Furthermore, the analysis unit can analyze a user's eye movement and provide advice on how to use eye gaze. This makes it possible to provide more effective advice by analyzing data including a user's non-verbal communication elements.

[0054] The advice unit can provide specific advice regarding the user's gestures and posture during a presentation. For example, the advice unit can advise the user on how to use gestures effectively. Specifically, the advice unit can provide guidance on what gestures to use during a presentation, such as hand movements and body orientation. The advice unit can also provide advice regarding the user's posture. For example, the advice unit can provide specific guidance to the user on how to maintain a stable posture and how to improve their posture. Furthermore, the advice unit can provide advice regarding how the user should use their gaze. For example, the advice unit can provide guidance to the user on how to use their gaze to effectively communicate with the audience. In this way, by providing advice including the user's non-verbal communication elements, it is possible to improve the user's presentation skills.

[0055] The practice unit may provide a tool for the user to practice gestures and posture during a presentation. For example, the practice unit may provide a tool for the user to record their gestures and play them back later for review. The practice unit may also provide a tool for monitoring the user's posture in real time and providing feedback on posture improvements. Furthermore, the practice unit may provide a tool for tracking the user's eye gaze and providing feedback on how the user uses their gaze. This allows the user to improve their presentation skills by practicing presentations that include non-verbal communication elements.

[0056] The collection unit can monitor environmental sounds during a user's presentation in real time and perform noise cancellation. For example, when a user gives a presentation, the collection unit collects ambient environmental sounds using a microphone and performs noise cancellation in real time. The collection unit can also perform filtering to remove wind noise when the user gives a presentation outdoors. Furthermore, the collection unit can also perform filtering to reduce background noise when the user gives a presentation in a noisy environment. In this way, by monitoring environmental sounds in real time and performing noise cancellation, clearer audio data can be collected.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects audio or video data. For example, it collects audio of a user speaking or video of a presentation. The collection unit can use a microphone to record audio and a camera to record video. It can also import existing audio or video files. For example, it can import audio files in MP3 or WAV format, or video files in MP4 or AVI format. Step 2: The analyzer analyzes the audio and video data collected by the collector to identify the user's individual character, voice quality, and volume. For example, the analyzer analyzes the frequency components of the audio data to measure the voice pitch in hertz (Hz), and analyzes the audio data amplitude to measure the voice volume in decibels (dB). It can also analyze frames of video data to identify speaking speed, intonation, rhythm, etc. Step 3: The advice unit provides advice based on the analysis results obtained by the analysis unit. For example, it provides advice on presentation methods, voice usage, pauses, and the order in which materials should be presented that suit the user's personality. It can provide advice on how to speak loudly in a low voice, how to take appropriate pauses, and the order in which materials should be presented based on logical order and importance. Step 4: The practice unit provides tools for the user to practice based on the advice provided by the advice unit. For example, the practice unit provides a tool that allows the user to record their own presentation and then the AI ​​analyzes the recording and provides feedback. The AI ​​analyzes the recorded video, evaluates the pitch and volume of the user's voice, characteristics of their speaking style, etc., and provides feedback on areas for improvement.

[0059] (Example 2) A presentation support system according to an embodiment of the present invention provides personalized advice and practice tools based on a user's individual character, voice quality, and voice volume. The system analyzes the user's individual character, voice quality, and voice volume, and provides advice on presentation methods, vocalization, pauses, and the order in which materials are presented that are suited to the user's character. For example, the system provides advice on various aspects, such as how to speak loudly in a low voice and how to take appropriate pauses. Users can improve their presentation skills by practicing in accordance with the AI's advice. For example, audio data and video data are collected to analyze a user's individual character, voice quality, and voice volume. For example, the user's speaking voice and presentation video are collected. This data is collected by a collection unit. The collected data is then analyzed. The analysis unit analyzes the audio data and video data collected by the collection unit to identify the user's individual character, voice quality, and voice volume. For example, the system analyzes the user's voice pitch, volume, and speaking style characteristics. Based on the analysis results, the system provides advice. The advice unit provides advice on presentation methods, vocalization, pauses, and the order in which materials are presented that are suited to the user's character. For example, it provides specific advice such as how to speak loudly in a low voice or how to take appropriate pauses. Finally, it provides tools for the user to practice. The practice section provides tools that allow the user to practice according to the advice and receive feedback. For example, the user can record their presentation, and the AI ​​can analyze the recording and provide feedback. This allows the user to improve their presentation skills. In this way, the presentation support system can improve the user's presentation skills.

[0060] A presentation support system according to an embodiment includes a collection unit, an analysis unit, an advice unit, and a practice unit. The collection unit collects audio data or video data. For example, the collection unit collects audio of a user speaking or video of a presentation. The collection unit can record audio using a microphone. The collection unit can also record video using a camera. The collection unit can also import existing audio files or video files. For example, the collection unit can import audio files in MP3 or WAV format, or video files in MP4 or AVI format. The analysis unit analyzes the audio data or video data collected by the collection unit to identify the user's individual character, voice quality, and voice volume. For example, the analysis unit analyzes the audio data to identify the pitch and volume of the user's voice. For example, the analysis unit can analyze the frequency components of the audio data to measure the pitch in hertz (Hz). The analysis unit can also analyze the amplitude of the audio data to measure the volume in decibels (dB). The analysis unit can also analyze the video data to identify the characteristics of the user's speaking style. For example, the analysis unit can analyze frames of video data and identify speaking speed, intonation, rhythm, etc. The advice unit provides advice based on the analysis results obtained by the analysis unit. The advice unit provides advice, for example, on presentation methods, vocalizations, pauses, and the order in which materials are explained that suit the user's character. For example, the advice unit can advise how to speak loudly in a low voice. The advice unit can also advise how to take appropriate pauses. Furthermore, the advice unit can advise on the order in which materials should be explained. For example, the advice unit can advise on the order in which materials should be explained based on logical order or importance. The practice unit provides tools that allow the user to practice based on the advice provided by the advice unit. For example, the practice unit provides tools that allow the user to record their presentation and have an AI analyze the recording and provide feedback. For example, the practice unit allows the user to record their presentation using a smartphone or camera.Furthermore, the practice unit can use AI to analyze the recorded video and provide feedback. For example, the practice unit can use AI to analyze video data, evaluate the pitch and volume of the user's voice, characteristics of the speaking style, and provide feedback on areas for improvement. This allows the presentation support system according to the embodiment to improve the user's presentation skills.

[0061] The collection unit can collect audio of a user speaking or video of a presentation. For example, the collection unit can record the audio of a user speaking with a microphone. For example, the collection unit can record audio using a microphone when a user gives a presentation. The collection unit can also record video using a camera when a user gives a presentation. For example, the collection unit can record video using a camera when a user gives a presentation and collect the video data. The collection unit can also import existing audio files or video files. For example, the collection unit can import audio files or video files previously recorded by a user and collect the data. By collecting the user's audio and video data, basic data for providing personalized advice can be obtained. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can optimize the timing of audio and video collection using AI to automate the collection of audio and video data.

[0062] The analysis unit can analyze the collected audio data or video data to identify the pitch or volume of the user's voice and characteristics of speaking style. The analysis unit, for example, analyzes the audio data to identify the pitch of the user's voice. For example, the analysis unit analyzes the frequency components of the audio data to measure the pitch in hertz (Hz). The analysis unit can also analyze the audio data to identify the volume of the user's voice. For example, the analysis unit analyzes the amplitude of the audio data to measure the volume in decibels (dB). The analysis unit can also analyze the video data to identify characteristics of the user's speaking style. For example, the analysis unit can analyze frames of the video data to identify the speaking rate, intonation, rhythm, etc. This can identify the characteristics of the user's voice and provide more appropriate advice. 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 optimize audio and video analysis algorithms using AI to automate the analysis of audio and video data.

[0063] The advice unit can provide advice on a presentation method, voice usage, pauses, and the order in which to explain materials that are suited to the user's character. The advice unit, for example, provides advice on a presentation method that is suited to the user's character. For example, the advice unit provides advice on how to use slides and gestures based on the user's character. The advice unit can also provide advice on how to use the user's voice. For example, the advice unit can provide advice on how to speak louder with a lower voice. The advice unit can also provide advice on how to take pauses. For example, the advice unit can provide advice on the timing of speaking and how to pause. The advice unit can also provide advice on the order in which to explain materials. For example, the advice unit can provide advice on the order in which to explain materials based on logical order and importance. This allows the user to improve their presentation skills by providing advice that is suited to their character. Some or all of the above-described processing by the advice unit may be performed using, or without, AI. For example, the advice unit can optimize an advice algorithm using AI to automatically generate advice based on the user's character.

[0064] The practice unit allows a user to record their presentation, and the AI ​​analyzes the recording and provides feedback. For example, the practice unit allows a user to record a presentation using a smartphone or camera. For example, the practice unit records a video of the user giving a presentation using a smartphone or camera and collects the video data. The practice unit also allows an AI to analyze the recorded video and provide feedback. For example, the practice unit allows an AI to analyze the video data, evaluate the pitch and volume of the user's voice, speaking characteristics, etc., and provide feedback on areas for improvement. This allows a user to record their presentation and have the AI ​​provide feedback, thereby improving their presentation skills. Some or all of the above-described processing in the practice unit may be performed using, or without, an AI. For example, the practice unit may use an AI to optimize an analysis algorithm for automatically analyzing the recorded video data.

[0065] The advice unit can provide specific advice, such as how to speak loudly in a low voice or how to take appropriate pauses. The advice unit, for example, gives advice on how to speak loudly in a low voice. For example, the advice unit provides the user with a vocal training method for speaking loudly in a low voice. The advice unit can also give advice on how to take appropriate pauses. For example, the advice unit specifically instructs the user on the timing of speaking and how to pause. Furthermore, the advice unit can also provide specific advice. For example, the advice unit specifically advises the user on when to take pauses in a presentation and how to speak. By providing specific advice, the user can easily put it into practice. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can automatically generate optimal advice using AI based on data on the user's voice characteristics and speaking style.

[0066] The collection unit can estimate the user's emotions and adjust the timing of collecting audio data or video data based on the estimated user emotions. For example, if the user is nervous, the collection unit waits until the user relaxes before collecting audio data or video data. For example, the collection unit analyzes the user's facial expressions and tone of voice and pauses collection if it determines that the user is nervous. Furthermore, if the user is relaxed, the collection unit can collect data for a short period of time to maintain that state. For example, the collection unit monitors the user's heart rate and breathing rhythm and quickly collects data if it determines that the user is relaxed. Furthermore, if the user is excited, the collection unit can wait until the user's emotions subside before collecting data. For example, the collection unit analyzes the volume of the user's voice and speaking rate and pauses collection if it determines that the user is excited. This allows for more natural data to be collected by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to analyze user emotion data in real time and optimize the collection timing.

[0067] The collection unit can analyze the user's past presentation history and select an appropriate collection method. For example, the collection unit refers to the data collection method used by the user in successful presentations in the past. For example, the collection unit can analyze the recording data and evaluation results of the user's past presentations to identify the data collection method used in successful presentations. The collection unit can also avoid the data collection method used by the user in unsuccessful presentations in the past. For example, the collection unit can analyze the recording data and evaluation results of the user's past presentations to identify the data collection method used in unsuccessful presentations and avoid it. Furthermore, the collection unit can select the most effective collection timing from the user's past presentation history. For example, the collection unit can analyze the recording data and evaluation results of the user's past presentations to identify the most effective collection timing. This enables more effective data collection by analyzing the past presentation history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can optimize an analysis algorithm using AI to automatically analyze the user's past presentation history.

[0068] When collecting audio or video data, the collection unit can perform filtering based on the user's current presentation environment or situation. For example, the collection unit minimizes noise filtering when the user is in a quiet environment. For example, the collection unit sets noise filtering to minimum when the user is giving a presentation in a quiet conference room. The collection unit can also strengthen noise filtering when the user is in a noisy environment. For example, the collection unit strengthens noise filtering when the user is giving a presentation in a noisy cafe. Furthermore, the collection unit can perform filtering to remove wind noise when the user is outdoors. For example, the collection unit sets filtering to remove wind noise when the user is giving a presentation outdoors. This allows for filtering according to the environment and situation, thereby collecting higher quality data. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can use AI to analyze the user's current presentation environment and situation in real time and optimize filtering settings.

[0069] The collection unit can estimate the user's emotions and determine the order of data to be collected based on the estimated user emotions. For example, if the user is nervous, the collection unit prioritizes collecting voice data. For example, the collection unit analyzes the user's facial expressions and voice tone and determines that the user is nervous, and prioritizes collecting voice data. The collection unit can also prioritize collecting video data if the user is relaxed. For example, the collection unit monitors the user's heart rate and breathing rhythm and prioritizes collecting video data if it determines that the user is relaxed. Furthermore, the collection unit can collect both types of data in a balanced manner if the user is excited. For example, the collection unit analyzes the volume of the user's voice and speaking rate and determines that the user is excited, and collects both voice data and video data in a balanced manner. This enables more effective data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to analyze user emotion data in real time and optimize the priority of data to be collected.

[0070] When collecting audio data or video data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a conference room, the collection unit prioritizes collecting data related to a presentation. For example, when the user is giving a presentation in a conference room, the collection unit prioritizes collecting data related to the environment. Furthermore, when the user is at home, the collection unit can prioritize collecting data in a relaxed environment. For example, when the user is giving a presentation at home, the collection unit prioritizes collecting data related to the environment. Furthermore, when the user is out and about, the collection unit can prioritize collecting data related to the location. For example, when the user is giving a presentation while out and about, the collection unit prioritizes collecting data related to the location. In this way, more relevant data can be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to analyze the user's geographical location information in real time and prioritize collecting highly relevant data.

[0071] The collection unit may analyze the user's social media activity and collect related data when collecting audio or video data. For example, the collection unit may collect data on presentations shared by the user on social media. For example, the collection unit may collect video and audio data of presentations shared by the user on social media. The collection unit may also collect data based on feedback received by the user on social media. For example, the collection unit may analyze comments and ratings received by the user on social media and collect data based on the results. Furthermore, the collection unit may collect presentation data on experts the user follows on social media. For example, the collection unit may collect presentation video and audio data of the experts the user follows. This allows for the collection of more relevant data by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may use AI to analyze the user's social media activity in real time and collect related data.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis method for the audio data or video data based on the estimated user emotions. For example, if the user is nervous, the analysis unit prioritizes analysis of data in a relaxed state. For example, the analysis unit analyzes the user's facial expressions and tone of voice and, if it determines that the user is nervous, prioritizes analysis of data in a relaxed state. Furthermore, if the user is relaxed, the analysis unit can select an analysis method to maintain that state. For example, the analysis unit monitors the user's heart rate and breathing rhythm and, if it determines that the user is relaxed, selects an analysis method to maintain that state. Furthermore, if the user is excited, the analysis unit can select an analysis method to suppress the user's emotions. For example, the analysis unit analyzes the volume of the user's voice and speaking rate and, if it determines that the user is excited, selects an analysis method to suppress the user's emotions. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may use AI to analyze user emotion data in real time and optimize the analysis method.

[0073] The analysis unit can adjust the accuracy of the analysis based on the purpose of the user's presentation during analysis. For example, if the user is giving a business presentation, the analysis unit performs a detailed analysis. For example, if the analysis unit determines that the purpose of the user's presentation is business, the analysis unit performs a detailed analysis to evaluate the content and structure of the presentation. The analysis unit can also perform a specialized analysis if the user is giving an academic presentation. For example, if the analysis unit determines that the purpose of the user's presentation is academic, the analysis unit performs a specialized analysis to evaluate the logic of the presentation and the accuracy of the data. The analysis unit can also perform a simplified analysis if the user is giving a casual presentation. For example, if the analysis unit determines that the purpose of the user's presentation is casual, the analysis unit performs a simplified analysis to evaluate the flow and atmosphere of the presentation. This allows for more appropriate analysis by adjusting the level of detail of the analysis depending on the purpose of the presentation. 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 analyze the purpose of the user's presentation in real time and use AI to optimize the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the category of the user's presentation. For example, in the case of a business presentation, the analysis unit applies a business analysis algorithm. For example, if the analysis unit determines that the category of the user's presentation is business, it applies a business analysis algorithm to evaluate the content and structure of the presentation. Furthermore, in the case of an academic presentation, the analysis unit can also apply an academic analysis algorithm. For example, if the analysis unit determines that the category of the user's presentation is academic, it applies an academic analysis algorithm to evaluate the logic of the presentation and the accuracy of the data. Furthermore, in the case of a casual presentation, the analysis unit can also apply a casual analysis algorithm. For example, if the analysis unit determines that the category of the user's presentation is casual, it applies a casual analysis algorithm to evaluate the flow and atmosphere of the presentation. This enables more appropriate analysis by applying an analysis algorithm depending on the category of the presentation. 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 may use AI to analyze the category of the user's presentation in real time and apply an appropriate analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit analyzes the user's facial expressions and tone of voice and selects a simple, highly visible display method if it determines that the user is nervous. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit monitors the user's heart rate and breathing rhythm and selects a display method that includes detailed information if it determines that the user is relaxed. Furthermore, the analysis unit can provide a display method that focuses on the main points if the user is in a hurry. For example, the analysis unit analyzes the volume of the user's voice and speaking speed and selects a display method that focuses on the main points if it determines that the user is in a hurry. This allows for a more visible display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may use AI to analyze user emotion data in real time and optimize the display method of the analysis results.

[0076] During analysis, the analysis unit can determine the analysis priority based on the submission time of the user's presentation. For example, the analysis unit prioritizes analysis of the presentation that the user will submit most recently. For example, the analysis unit analyzes the submission deadline of the user's presentation and prioritizes analysis of the presentation that will be submitted most recently. The analysis unit can also prioritize analysis of presentations that the user has been preparing for a long time. For example, the analysis unit analyzes the submission deadline of the user's presentation and prioritizes analysis of the presentation that has been prepared for a long time. The analysis unit can also prioritize analysis of presentations that the user is urgently preparing. For example, the analysis unit analyzes the submission deadline of the user's presentation and prioritizes analysis of the presentation that is urgently prepared. This enables more effective analysis by determining the analysis priority based on the submission time. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the submission time of the user's presentation in real time and use AI to optimize the analysis priority.

[0077] During analysis, the analysis unit can determine the order of analysis based on the relevance of the user's presentation. For example, the analysis unit prioritizes analysis of presentations that are highly relevant to the user. For example, the analysis unit analyzes the relevance of the topics and audiences of the user's presentation and prioritizes analysis of the highly relevant presentations. The analysis unit can also postpone presentations that are less relevant to the user. For example, the analysis unit analyzes the relevance of the topics and audiences of the user's presentation and postpones the less relevant presentations. Furthermore, if the user is preparing multiple presentations, the analysis unit can analyze them in order of relevance. For example, the analysis unit analyzes the relevance of the topics and audiences of the user's presentation and analyzes them in order of relevance. This enables more effective analysis by adjusting the order of analysis based on relevance. 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 analyze the relevance of the user's presentation in real time and use AI to optimize the order of analysis.

[0078] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the advice unit provides advice in gentle words. For example, the advice unit analyzes the user's facial expressions and tone of voice and determines that the user is nervous, and provides advice in gentle words. The advice unit can also provide detailed advice if the user is relaxed. For example, the advice unit monitors the user's heart rate and breathing rhythm and provides detailed advice if it determines that the user is relaxed. Furthermore, the advice unit can also provide concise and quick advice if the user is in a hurry. For example, the advice unit analyzes the volume of the user's voice and speaking speed and provides concise and quick advice if it determines that the user is in a hurry. This enables more effective advice by adjusting the way the advice is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit may use AI to analyze user emotion data in real time and optimize the way advice is presented.

[0079] When providing advice, the advice unit can adjust the accuracy of the advice based on the importance of the user's presentation. For example, if the user is giving an important presentation, the advice unit provides detailed advice. For example, the advice unit analyzes the importance of the user's presentation and provides detailed advice if it is determined to be an important presentation. The advice unit can also provide simple advice if the user is giving a casual presentation. For example, the advice unit analyzes the importance of the user's presentation and provides simple advice if it is determined to be a casual presentation. The advice unit can also provide quick advice if the user is giving an urgent presentation. For example, the advice unit analyzes the importance of the user's presentation and provides quick advice if it is determined to be an urgent presentation. This allows for more appropriate advice to be provided by adjusting the level of detail of the advice depending on the importance of the presentation. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may analyze the importance of the user's presentation in real time and use AI to optimize the level of detail of the advice.

[0080] When providing advice, the advice unit can apply an appropriate advice algorithm depending on the category of the user's presentation. For example, in the case of a business presentation, the advice unit applies a business advice algorithm. For example, if the advice unit determines that the category of the user's presentation is business, it applies a business advice algorithm to evaluate the content and structure of the presentation. Furthermore, in the case of an academic presentation, the advice unit can also apply an academic advice algorithm. For example, if the advice unit determines that the category of the user's presentation is academic, it applies an academic advice algorithm to evaluate the logic of the presentation and the accuracy of the data. Furthermore, in the case of a casual presentation, the advice unit can also apply a casual advice algorithm. For example, if the advice unit determines that the category of the user's presentation is casual, it applies a casual advice algorithm to evaluate the flow and atmosphere of the presentation. This enables more appropriate advice to be provided by applying an advice algorithm depending on the category of the presentation. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may use AI to analyze the category of the user's presentation in real time and apply an appropriate advice algorithm.

[0081] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user emotions. For example, if the user is nervous, the advice unit provides short, concise advice to the point. For example, the advice unit analyzes the user's facial expressions and tone of voice and determines that the user is nervous, and provides short, concise advice to the point. The advice unit can also provide detailed advice if the user is relaxed. For example, the advice unit monitors the user's heart rate and breathing rhythm and provides detailed advice if it determines that the user is relaxed. Furthermore, the advice unit can also provide concise, quick advice if the user is in a hurry. For example, the advice unit analyzes the volume of the user's voice and speaking speed and provides concise, quick advice if it determines that the user is in a hurry. This enables more effective advice by adjusting the content of the advice according to the user's emotions. 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 advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit may use AI to analyze the user's emotional data in real time and optimize the content of advice.

[0082] When providing advice, the advice unit can determine the order of advice based on the submission timing of the user's presentation. For example, the advice unit prioritizes advice for the presentation that the user will submit most recently. For example, the advice unit analyzes the submission deadline of the user's presentation and prioritizes advice for the presentation that will be submitted most recently. The advice unit can also prioritize advice for presentations that the user has been preparing for a long time. For example, the advice unit analyzes the submission deadline of the user's presentation and prioritizes advice for presentations that have been prepared for a long time. The advice unit can also prioritize advice for presentations that the user is urgently preparing. For example, the advice unit analyzes the submission deadline of the user's presentation and prioritizes advice for presentations that are urgently prepared. This enables more effective advice by determining the priority of advice based on the submission timing. Some or all of the above-described processing by the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can analyze the submission timing of the user's presentation in real time and use AI to optimize the order of advice.

[0083] When providing advice, the advice unit can determine the order of advice based on the relevance of the user's presentation. For example, the advice unit prioritizes advice on presentations that are highly relevant to the user. For example, the advice unit analyzes the relevance of the topics and target audience of the user's presentation and prioritizes advice on presentations that are highly relevant. The advice unit can also postpone presentations that are less relevant to the user. For example, the advice unit analyzes the relevance of the topics and target audience of the user's presentation and postpones presentations that are less relevant. Furthermore, if the user is preparing multiple presentations, the advice unit can also provide advice in order of relevance. For example, the advice unit analyzes the relevance of the topics and target audience of the user's presentation and provides advice in order of relevance. This enables more effective advice by adjusting the order of advice based on relevance. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit may analyze the relevance of the user's presentation in real time and use AI to optimize the order of advice.

[0084] The practice unit can estimate the user's emotions and adjust the content of the practice tools provided based on the estimated user emotions. For example, if the user is nervous, the practice unit provides a practice tool that helps the user relax. For example, the practice unit analyzes the user's facial expressions and tone of voice and, if it determines that the user is nervous, provides a practice tool that helps the user relax. The practice unit can also provide detailed practice tools if the user is relaxed. For example, the practice unit monitors the user's heart rate and breathing rhythm and, if it determines that the user is relaxed, provides detailed practice tools. Furthermore, the practice unit can also provide concise and quick practice tools if the user is in a hurry. For example, the practice unit analyzes the volume of the user's voice and speaking speed and, if it determines that the user is in a hurry, provides a concise and quick practice tool. This allows for more effective practice by adjusting the method of providing practice tools according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit may use AI to analyze the user's emotional data in real time and optimize the content of the practice tool.

[0085] During practice, the practice unit can select an appropriate practice method by referring to the user's past practice history. The practice unit, for example, refers to practice methods that the user has used successfully in the past. For example, the practice unit analyzes the user's past practice history to identify successful practice methods. The practice unit can also avoid practice methods that the user has used unsuccessfully in the past. For example, the practice unit analyzes the user's past practice history to identify and avoid unsuccessful practice methods. Furthermore, the practice unit can select the most effective practice method from the user's past practice history. For example, the practice unit analyzes the user's past practice history to identify the most effective practice method. This allows a more effective practice method to be selected by referring to the past practice history. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without AI. For example, the practice unit can optimize an analysis algorithm using AI to automatically analyze the user's past practice history.

[0086] The practice unit can adjust the practice content based on the user's current presentation skills during practice. For example, if the user is a beginner, the practice unit provides basic practice content. For example, the practice unit evaluates the user's current presentation skills and determines that the user is a beginner, and provides basic practice content. Furthermore, the practice unit can also provide applied practice content if the user is an intermediate level user. For example, the practice unit evaluates the user's current presentation skills and determines that the user is an intermediate level user, and provides applied practice content. Furthermore, the practice unit can also provide specialized practice content if the user is an advanced level user. For example, the practice unit evaluates the user's current presentation skills and determines that the user is an advanced level user, and provides specialized practice content. This allows for more effective practice by customizing the practice content based on the user's current skills. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without AI. For example, the practice unit can analyze the user's current presentation skills in real time and use AI to optimize the practice content.

[0087] The practice unit can estimate the user's emotions and determine the order of practice exercises based on the estimated user emotions. For example, if the user is nervous, the practice unit prioritizes relaxation exercises. For example, if the practice unit analyzes the user's facial expressions and tone of voice and determines that the user is nervous, it prioritizes relaxation exercises. The practice unit can also prioritize detailed practice exercises if the user is relaxed. For example, the practice unit monitors the user's heart rate and breathing rhythm and prioritizes detailed practice exercises if it determines that the user is relaxed. Furthermore, the practice unit can prioritize concise practice exercises if the user is in a hurry. For example, the practice unit analyzes the volume of the user's voice and speaking speed and prioritizes concise practice exercises if it determines that the user is in a hurry. This enables more effective practice by determining the priority of practice exercises according to the user's emotions. 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 practice unit may be performed using, for example, AI, or may be performed without using AI. For example, the practice unit may use AI to analyze the user's emotional data in real time and optimize the order of practice.

[0088] The practice unit can select an appropriate practice method during practice by taking into account the user's geographic location information. For example, if the user is in a conference room, the practice unit provides a practice related to a presentation. For example, if the practice unit analyzes the user's geographic location information and determines that the user is in a conference room, the practice unit provides a practice related to the presentation. The practice unit can also provide a practice in a relaxed environment if the user is at home. For example, if the practice unit analyzes the user's geographic location information and determines that the user is at home, the practice unit provides a practice in a relaxed environment. Furthermore, if the user is out and about, the practice unit can also provide a practice related to the location. For example, if the practice unit analyzes the user's geographic location information and determines that the user is out and about, the practice unit provides a practice related to the location. This allows for the selection of a more effective practice method by taking the geographic location information into account. Some or all of the above-described processing in the practice unit may be performed using, for example, AI, or may be performed without AI. For example, the practice unit can analyze the user's geographic location information in real time and use AI to select an appropriate practice method.

[0089] During practice, the practice unit can analyze the user's social media activity to determine the content of the practice. The practice unit can, for example, suggest practice exercises based on the content of presentations shared by the user on social media. For example, the practice unit can analyze the user's social media activity and suggest practice exercises based on the content of the presentations shared by the user. The practice unit can also suggest practice exercises based on feedback the user has received on social media. For example, the practice unit can analyze comments and ratings the user has received on social media and suggest practice exercises based on the content. Furthermore, the practice unit can suggest practice exercises based on the content of presentations given by experts the user follows on social media. For example, the practice unit can analyze the content of presentations given by experts the user follows and suggest practice exercises based on the content. In this way, more effective practice content can be suggested by analyzing social media activity. Some or all of the above-described processing in the practice unit can be performed using, for example, AI, or without AI. For example, the practice unit can analyze the user's social media activity in real time and use AI to determine the content of the practice exercises. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, advice unit, and practice unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects audio data and video data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's individual character, voice quality, and voice volume. The advice unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and provides advice on presentation methods, voice production, etc. based on the analysis results. The practice unit is realized, for example, by the control unit 46A of the smart device 14 and provides tools for the user to practice according to the advice. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, advice unit, and practice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects audio data and video data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's individual character, voice quality, and voice volume. The advice unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and provides advice on presentation methods, voice projection, etc. based on the analysis results. The practice unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides tools for the user to practice according to the advice. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, advice unit, and practice unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects audio data and video data using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's individual character, voice quality, and voice volume. The advice unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and provides advice on presentation methods, voice production, etc. based on the analysis results. The practice unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides tools for the user to practice according to the advice. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, advice unit, and practice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects audio data and video data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the collected data to identify the user's individual character, voice quality, and voice volume. The advice unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and gives advice on presentation methods, voice production, etc. based on the analysis results. The practice unit is realized, for example, by the control unit 46A of the robot 414, and provides tools for the user to practice according to the advice.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The collection unit can also monitor the user's gestures and posture during a presentation in real time and collect this data. For example, the collection unit can record what gestures the user uses during the presentation using a camera and send the data to the analysis unit. The collection unit can also detect the user's posture using a sensor and record changes in posture during the presentation. Furthermore, the collection unit can track the user's eye movements and collect gaze data. This allows for the collection of data including the user's non-verbal communication elements, enabling more comprehensive advice.

[0092] The analysis unit can analyze data on a user's gestures and posture during a presentation and identify how these factors affect the effectiveness of the presentation. For example, the analysis unit can analyze the frequency and type of a user's gestures and identify effective ways to use gestures. The analysis unit can also analyze the stability and changes in a user's posture and identify areas for improvement in posture during a presentation. Furthermore, the analysis unit can analyze a user's eye movement and provide advice on how to use eye gaze. This makes it possible to provide more effective advice by analyzing data including a user's non-verbal communication elements.

[0093] The advice unit can provide specific advice regarding the user's gestures and posture during a presentation. For example, the advice unit can advise the user on how to use gestures effectively. Specifically, the advice unit can provide guidance on what gestures to use during a presentation, such as hand movements and body orientation. The advice unit can also provide advice regarding the user's posture. For example, the advice unit can provide specific guidance to the user on how to maintain a stable posture and how to improve their posture. Furthermore, the advice unit can provide advice regarding how the user should use their gaze. For example, the advice unit can provide guidance to the user on how to use their gaze to effectively communicate with the audience. In this way, by providing advice including the user's non-verbal communication elements, it is possible to improve the user's presentation skills.

[0094] The practice unit may provide a tool for the user to practice gestures and posture during a presentation. For example, the practice unit may provide a tool for the user to record their gestures and play them back later for review. The practice unit may also provide a tool for monitoring the user's posture in real time and providing feedback on posture improvements. Furthermore, the practice unit may provide a tool for tracking the user's eye gaze and providing feedback on how the user uses their gaze. This allows the user to improve their presentation skills by practicing presentations that include non-verbal communication elements.

[0095] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is nervous, the collection unit prioritizes collecting voice data. For example, if the collection unit analyzes the user's facial expressions and tone of voice and determines that the user is nervous, it prioritizes collecting voice data. The collection unit can also prioritize collecting video data if the user is relaxed. For example, the collection unit monitors the user's heart rate and breathing rhythm and prioritizes collecting video data if it determines that the user is relaxed. Furthermore, the collection unit can collect both types of data in a balanced manner if the user is excited. For example, if the collection unit analyzes the volume of the user's voice and speaking rate and determines that the user is excited, it collects both voice data and video data in a balanced manner. This allows for more effective data collection by adjusting the type of data according to the user's emotions.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit analyzes the user's facial expressions and tone of voice and selects a simple, highly visible display method if it determines that the user is nervous. The analysis unit can also provide a display method including detailed information if the user is relaxed. For example, the analysis unit monitors the user's heart rate and breathing rhythm and selects a display method including detailed information if it determines that the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points if the user is in a hurry. For example, the analysis unit analyzes the volume of the user's voice and speaking speed and selects a display method that focuses on the main points if it determines that the user is in a hurry. In this way, by adjusting the display method according to the user's emotions, a more highly visible display is possible.

[0097] The advice unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the advice unit provides advice in gentle words. For example, if the advice unit analyzes the user's facial expression and tone of voice and determines that the user is nervous, it provides advice in gentle words. The advice unit can also provide detailed advice if the user is relaxed. For example, the advice unit monitors the user's heart rate and breathing rhythm and provides detailed advice if it determines that the user is relaxed. Furthermore, the advice unit can also provide concise and quick advice if the user is in a hurry. For example, the advice unit analyzes the volume of the user's voice and speaking speed and provides concise and quick advice if it determines that the user is in a hurry. This makes it possible to adjust the way in which advice is expressed according to the user's emotions, thereby enabling more effective advice.

[0098] The practice unit can estimate the user's emotions and adjust the content of the practice tools provided based on the estimated user's emotions. For example, if the user is nervous, the practice unit provides a practice tool that helps the user relax. For example, the practice unit analyzes the user's facial expressions and tone of voice and, if it determines that the user is nervous, provides a practice tool that helps the user relax. The practice unit can also provide detailed practice tools if the user is relaxed. For example, the practice unit monitors the user's heart rate and breathing rhythm and, if it determines that the user is relaxed, provides detailed practice tools. Furthermore, the practice unit can also provide concise and quick practice tools if the user is in a hurry. For example, the practice unit analyzes the volume of the user's voice and speaking speed and, if it determines that the user is in a hurry, provides a concise and quick practice tool. This allows for more effective practice by adjusting the method of providing practice tools according to the user's emotions.

[0099] The practice unit can estimate the user's emotions and determine the order of practice exercises based on the estimated user's emotions. For example, if the user is nervous, the practice unit prioritizes relaxation practice exercises. For example, if the practice unit analyzes the user's facial expressions and tone of voice and determines that the user is nervous, it prioritizes relaxation practice exercises. The practice unit can also prioritize detailed practice exercises if the user is relaxed. For example, the practice unit monitors the user's heart rate and breathing rhythm and prioritizes detailed practice exercises if it determines that the user is relaxed. Furthermore, the practice unit can also prioritize concise practice exercises if the user is in a hurry. For example, the practice unit analyzes the volume of the user's voice and speaking speed and prioritizes concise practice exercises if it determines that the user is in a hurry. This allows for more effective practice by determining the priority of practice exercises according to the user's emotions.

[0100] The collection unit can monitor environmental sounds during a user's presentation in real time and perform noise cancellation. For example, when a user gives a presentation, the collection unit collects ambient environmental sounds using a microphone and performs noise cancellation in real time. The collection unit can also perform filtering to remove wind noise when the user gives a presentation outdoors. Furthermore, the collection unit can also perform filtering to reduce background noise when the user gives a presentation in a noisy environment. In this way, by monitoring environmental sounds in real time and performing noise cancellation, clearer audio data can be collected.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects audio or video data. For example, it collects audio of a user speaking or video of a presentation. The collection unit can use a microphone to record audio and a camera to record video. It can also import existing audio or video files. For example, it can import audio files in MP3 or WAV format, or video files in MP4 or AVI format. Step 2: The analyzer analyzes the audio and video data collected by the collector to identify the user's individual character, voice quality, and volume. For example, the analyzer analyzes the frequency components of the audio data to measure the voice pitch in hertz (Hz), and analyzes the audio data amplitude to measure the voice volume in decibels (dB). It can also analyze frames of video data to identify speaking speed, intonation, rhythm, etc. Step 3: The advice unit provides advice based on the analysis results obtained by the analysis unit. For example, it provides advice on presentation methods, voice usage, pauses, and the order in which materials should be presented that suit the user's personality. It can provide advice on how to speak loudly in a low voice, how to take appropriate pauses, and the order in which materials should be presented based on logical order and importance. Step 4: The practice unit provides tools for the user to practice based on the advice provided by the advice unit. For example, the practice unit provides a tool that allows the user to record their own presentation and then the AI ​​analyzes the recording and provides feedback. The AI ​​analyzes the recorded video, evaluates the pitch and volume of the user's voice, characteristics of their speaking style, etc., and provides feedback on areas for improvement.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 collection unit for collecting audio data or video data; an analysis unit that analyzes the data collected by the collection unit and identifies the user's individual character, voice quality, and voice volume; an advice unit that provides advice based on the analysis results obtained by the analysis unit; a practice unit that provides a tool for the user to practice based on the advice provided by the advice unit. A system characterized by:

2. The collecting unit Capture audio of a user speaking or video of a presentation The system of claim 1 .

3. The analysis unit Analyzing collected audio or video data to identify the pitch or volume of a user's voice and speaking style characteristics The system of claim 1 .

4. The advice unit Advise on presentation methods, voice usage, pauses, and the order in which materials should be presented that suit the user's character. The system of claim 1 .

5. The practice section: Users record their presentations, and AI analyzes the recordings and provides feedback. The system of claim 1 .

6. The advice unit Providing specific advice on how to speak loudly in a low voice or how to take appropriate pauses The system of claim 1 .

7. The collecting unit To estimate a user's emotion and adjust a timing of collecting audio data or video data based on the estimated user's emotion. The system of claim 1 .

8. The collecting unit Analyze users' past presentation history and select the appropriate collection method The system of claim 1 .

Images