System

Abstract

An object of a system according to an embodiment is to analyze a presentation practice video and provide effective advice.SOLUTION: A system includes a moving image reading unit, an analysis unit, an advice generation unit, and an advice providing unit. The video reading unit reads a presentation practice video. The analysis unit analyzes the moving image read by the moving image reading unit. The advice generation unit generates advice based on the analysis result obtained by the analysis unit. The advice providing unit provides the user with the advice generated by the advice generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to obtain feedback to maximize the effectiveness of presentation practice.

[0005] The system according to the embodiment aims to analyze a presentation practice video and provide effective advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a video reading unit, an analysis unit, an advice generation unit, and an advice providing unit. The video reading unit reads a presentation practice video. The analysis unit analyzes the video read by the video reading unit. The advice generating unit generates advice based on the analysis results obtained by the analysis unit. The advice providing unit provides the advice generated by the advice generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a presentation practice video and provide effective advice. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 analysis system according to an embodiment of the present invention uses a generation AI to analyze a practice video of a presentation recorded by a user and provides appropriate advice. This allows the presentation analysis system to provide specific advice to improve the user's presentation skills.

[0029] The presentation analysis system according to the embodiment includes a video loading unit, an analysis unit, an advice generation unit, and an advice provision unit. The video loading unit loads a practice video of a presentation recorded by a user. For example, the user can upload the recorded video to the system. The video loading unit can also support video uploads from different devices (smartphones, tablets, and PCs). For example, it can provide an upload interface compatible with each device. The analysis unit analyzes the loaded video. For example, it can analyze what the user says and what slides are displayed using voice recognition technology or image analysis technology. The analysis unit can also analyze the user's facial expressions and gestures in real time to track changes in emotions. For example, it can use a camera to detect the user's smiling or surprised expressions and record these changes chronologically. The advice generation unit generates advice based on the analysis results. For example, it can evaluate the user's speaking speed, tone of voice, eye movements, slide design, etc., and suggest specific areas for improvement. The advice generation unit can also provide customized feedback tailored to the user's presentation style. For example, it can analyze the user's speaking style and gesture characteristics and provide advice based on them. The advice providing unit provides the generated advice to the user. For example, specific advice such as "You speak too fast, try speaking more slowly" or "The slide design is too simple, so it would be good to add graphs or images" may be provided. The advice providing unit may also provide the advice in video format to make it easier for the user to understand visually. For example, the advice content may be explained in video to make it easier for the user to understand visually. In this way, the presentation analysis system according to the embodiment can provide specific advice to improve the user's presentation skills. For example, the user can clearly understand areas for improvement in their presentation and use this information in their next practice.

[0030] The video loading unit can simultaneously load data from the presentation tool used by the user and evaluate the degree of match between the slide content and speaking style. For example, the video loading unit simultaneously acquires data from the presentation tool used by the user when loading a presentation video. For example, it analyzes the slide content and animation timing. The video loading unit can also evaluate the degree of match between the slide content and speaking style. For example, it checks whether the spoken content matches the slide content. This allows the consistency of the presentation to be improved by evaluating the degree of match between the slide content and speaking style.

[0031] The video loading unit supports video uploading from different devices, improving user convenience. For example, the video loading unit builds a system that supports uploading presentation videos from different devices, such as smartphones, tablets, and PCs. For example, it provides an upload interface compatible with each device. This allows for video uploading from different devices, improving user convenience.

[0032] The analysis unit can evaluate the relevance between what the user says and the content of the slides, and recommend a consistent presentation. For example, when analyzing a video, the analysis unit builds a system that compares what the user says with the content of the slides and evaluates the relevance. For example, it checks whether what is said matches the content of the slides. The analysis unit can also evaluate the relevance between what the user says and the content of the slides, and recommend a consistent presentation. For example, if what is said and the content of the slides do not match, it points out areas for improvement. In this way, by evaluating the relevance between what is said and the content of the slides and recommending a consistent presentation, the quality of the presentation can be improved.

[0033] The analysis unit can analyze the user's gaze movements and evaluate the frequency of eye contact with the audience. For example, the analysis unit builds a system that tracks the user's gaze movements and evaluates the frequency of eye contact with the audience when analyzing a video. For example, the analysis unit records the user's gaze movements using gaze tracking technology. The analysis unit can also analyze the user's gaze movements and evaluate the frequency of eye contact with the audience. For example, the analysis unit evaluates the number and duration of eye contact within a certain period of time. In this way, the quality of presentations can be improved by analyzing the user's gaze movements and evaluating the frequency of eye contact with the audience.

[0034] The analysis unit can automatically translate what the user says into different languages ​​and provide advice in multiple languages. For example, the analysis unit builds a system that automatically translates what the user says into different languages ​​when analyzing a video. For example, it supports multiple languages, such as English to Japanese and French to English. The analysis unit can also automatically translate what the user says into different languages ​​and provide advice in multiple languages. For example, it provides advice corresponding to each language based on the translated content. In this way, the quality of international presentations can be improved by automatically translating what the user says into different languages ​​and providing advice in multiple languages.

[0035] The analysis unit can visualize the video analysis results to enable the user to intuitively understand. The analysis unit, for example, builds a system that visualizes the video analysis results. For example, it displays changes in speaking speed and tone of voice in graphs or charts. The analysis unit can also visualize the video analysis results to enable the user to intuitively understand. For example, by visually displaying the analysis results, the user can intuitively grasp areas for improvement. In this way, visualizing the video analysis results makes it easier for the user to intuitively understand.

[0036] The advice providing unit can compare the current video with the user's past presentation videos and evaluate the progress. For example, when providing advice, the advice providing unit compares the current video with the user's past presentation videos and builds a system to evaluate the progress. For example, it compares changes in speaking speed and tone of voice over time. The advice providing unit can also compare the current video with the user's past presentation videos and evaluate the progress. For example, it points out areas for improvement based on a comparison with the past presentation videos. In this way, by comparing the current video with the user's past presentation videos and evaluating the progress, it is possible to clarify areas for improvement in the presentation.

[0037] The advice providing unit can provide customized feedback tailored to the user's presentation style. The advice providing unit, for example, builds a system that provides customized feedback tailored to the user's presentation style when providing advice. For example, the advice providing unit analyzes the characteristics of the user's speaking style and gestures and provides advice based on the analysis. The advice providing unit can also provide customized feedback tailored to the user's presentation style. For example, the advice providing unit points out specific areas for improvement according to the user's presentation style. In this way, the quality of presentations can be improved by providing customized feedback tailored to the user's presentation style.

[0038] The advice providing unit can propose a specific improvement plan based on the goal set by the user. For example, the advice providing unit constructs a system that, when providing advice, proposes a specific improvement plan based on the goal set by the user. For example, it proposes a specific plan such as "Practice for five minutes every day to improve your speaking speed." The advice providing unit can also propose a specific improvement plan based on the goal set by the user. For example, it proposes specific action steps according to the user's goal. In this way, by proposing a specific improvement plan based on the goal set by the user, the quality of presentations can be improved.

[0039] The advice providing unit can provide advice in video format to make it easier for the user to understand visually. The advice providing unit, for example, builds a system that provides advice in video format. For example, the advice content is explained in video to make it easier for the user to understand visually. The advice providing unit can also provide advice in video format to make it easier for the user to understand visually. For example, the length of the video and the structure of the content can be devised so that the user can intuitively understand. In this way, providing advice in video format makes it easier for the user to understand visually.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The presentation analysis system can also include an audio analysis unit that analyzes background sounds during a user's presentation. For example, it can detect noise and background sounds that occur during a presentation and evaluate their impact. The audio analysis unit can also analyze the type and intensity of background sounds and suggest appropriate measures to the user. For example, if the background sounds are too loud, it can advise the user to record in a quieter environment. This can improve the audio quality of the presentation.

[0042] The presentation analysis system can also include a posture analysis unit that analyzes the user's posture during the presentation. For example, a camera can be used to analyze the user's standing or sitting posture and evaluate whether their posture is good or bad. The posture analysis unit can also suggest specific improvements based on the user's posture. For example, it can suggest straightening the back or an appropriate standing position. This can improve the user's posture during the presentation and create a more professional impression.

[0043] The presentation analysis system can also include a gaze analysis unit that analyzes the user's gaze movements during a presentation. For example, a camera can be used to track the direction of the user's gaze and record the gaze movements. The gaze analysis unit can also suggest specific improvements based on the user's gaze movements. For example, if the user's gaze is too concentrated in a specific area, the system can advise the user to disperse their gaze. This can improve the user's gaze movements and increase eye contact with the audience.

[0044] The presentation analysis system may further include a hand movement analysis unit that analyzes the user's hand movements during the presentation. For example, a camera may be used to analyze the user's hand movements and record those movements. The hand movement analysis unit may also suggest specific improvements based on the user's hand movements. For example, if the user's hand movements are too few, the system may advise the user to incorporate more appropriate gestures. This may improve the user's hand movements and enhance the expressiveness of the presentation.

[0045] The presentation analysis system may further include a voice intensity analysis unit that analyzes the strength of the user's voice during the presentation. For example, the voice intensity analysis unit may use voice analysis technology to analyze the strength and weakness of the user's voice and record any changes. The voice intensity analysis unit may also suggest specific improvements based on the user's voice intensity. For example, if the user's voice is too quiet, the system may advise the user to speak louder. This may improve the strength of the user's voice and make the presentation easier to hear.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The video importer imports a practice video of the presentation recorded by the user. For example, the user can upload the recorded video to the system. The video importer can also support uploading videos from different devices (smartphones, tablets, PCs). For example, it provides an upload interface compatible with each device. Step 2: The analysis unit analyzes the loaded video. For example, it uses voice recognition and image analysis technology to analyze what the user is saying and what is displayed on the slides. The analysis unit can also analyze the user's facial expressions and gestures in real time to track changes in emotions. For example, it can use a camera to detect the user's smiling or surprised expressions and record these changes over time. Step 3: The advice generator generates advice based on the analysis results. For example, it evaluates speaking speed, tone of voice, eye movements, slide design, etc., and suggests specific areas for improvement. The advice generator can also provide customized feedback tailored to the user's presentation style. For example, it analyzes the characteristics of the user's speaking style and gestures and provides advice based on that. Step 4: The advice providing unit provides the generated advice to the user. For example, specific advice such as "You speak too fast, try speaking a little more slowly" or "The slide design is too simple, so it would be better to add graphs or images" can be provided. The advice providing unit can also provide the advice in video format to make it easier for the user to understand visually. For example, the advice content can be explained in a video to make it easier for the user to understand visually.

[0048] (Example 2) A presentation analysis system according to an embodiment of the present invention uses a generation AI to analyze a practice video of a presentation recorded by a user and provides appropriate advice. This allows the presentation analysis system to provide specific advice to improve the user's presentation skills.

[0049] The presentation analysis system according to the embodiment includes a video loading unit, an analysis unit, an advice generation unit, and an advice provision unit. The video loading unit loads a practice video of a presentation recorded by a user. For example, the user can upload the recorded video to the system. The video loading unit can also support video uploads from different devices (smartphones, tablets, and PCs). For example, it can provide an upload interface compatible with each device. The analysis unit analyzes the loaded video. For example, it can analyze what the user says and what slides are displayed using voice recognition technology or image analysis technology. The analysis unit can also analyze the user's facial expressions and gestures in real time to track changes in emotions. For example, it can use a camera to detect the user's smiling or surprised expressions and record these changes chronologically. The advice generation unit generates advice based on the analysis results. For example, it can evaluate the user's speaking speed, tone of voice, eye movements, slide design, etc., and suggest specific areas for improvement. The advice generation unit can also provide customized feedback tailored to the user's presentation style. For example, it can analyze the user's speaking style and gesture characteristics and provide advice based on them. The advice providing unit provides the generated advice to the user. For example, specific advice such as "You speak too fast, try speaking more slowly" or "The slide design is too simple, so it would be good to add graphs or images" may be provided. The advice providing unit may also provide the advice in video format to make it easier for the user to understand visually. For example, the advice content may be explained in video to make it easier for the user to understand visually. In this way, the presentation analysis system according to the embodiment can provide specific advice to improve the user's presentation skills. For example, the user can clearly understand areas for improvement in their presentation and use this information in their next practice.

[0050] The video loading unit can analyze the user's facial expressions and gestures in real time to track changes in emotions. For example, when loading a presentation video, the video loading unit analyzes the user's facial expressions in real time to track changes in emotions. For example, a camera is used to detect the user's smiling or surprised expressions and record these changes in time series. The video loading unit can also analyze the user's gestures in real time to track changes in emotions. For example, hand movements and body movements are analyzed and these changes are recorded. This makes it possible to track changes in the user's emotions in real time and use this information to improve the presentation.

[0051] The video loading unit can analyze the tone and pitch of a user's voice and evaluate the intensity of their emotions. For example, the video loading unit analyzes the audio data of a presentation video and tracks changes in the tone and pitch of the user's voice. For example, it records the pitch and strength of the voice in chronological order and evaluates the intensity of their emotions. The video loading unit can also analyze the tone and pitch of a user's voice and evaluate the intensity of their emotions. For example, it uses audio analysis technology to analyze changes in the tone and pitch of the voice and evaluate the intensity of their emotions. This allows the analysis of the tone and pitch of a user's voice and evaluation of the intensity of their emotions to be useful in improving presentations.

[0052] The analysis unit can estimate the user's stress and tension during a presentation and provide advice based on that data. For example, when analyzing a presentation video, the analysis unit estimates stress and tension based on the user's facial expressions and tone of voice. For example, it detects facial tension and vocal tremors and records that data. The analysis unit can also estimate the user's stress and tension during a presentation and provide advice based on that data. For example, it analyzes changes in heart rate and body movements to estimate stress and tension. This allows the user's stress and tension to be estimated and advice to be provided based on that data, which can be useful for improving presentations.

[0053] The video loading unit can simultaneously load data from the presentation tool used by the user and evaluate the degree of match between the slide content and speaking style. For example, the video loading unit simultaneously acquires data from the presentation tool used by the user when loading a presentation video. For example, it analyzes the slide content and animation timing. The video loading unit can also evaluate the degree of match between the slide content and speaking style. For example, it checks whether the spoken content matches the slide content. This allows the consistency of the presentation to be improved by evaluating the degree of match between the slide content and speaking style.

[0054] The video loading unit supports video uploading from different devices, improving user convenience. For example, the video loading unit builds a system that supports uploading presentation videos from different devices, such as smartphones, tablets, and PCs. For example, it provides an upload interface compatible with each device. This allows for video uploading from different devices, improving user convenience.

[0055] The video loading unit can analyze the user's emotions in real time when uploading a presentation video and provide positive feedback. For example, the video loading unit analyzes the user's facial expressions and tone of voice when uploading a presentation video to estimate the user's emotions in real time. For example, the video loading unit can analyze the user's emotions using a camera or microphone. The video loading unit can also analyze the user's emotions in real time when uploading a presentation video and provide positive feedback. For example, if the user is nervous, the video loading unit can provide words of encouragement. In this way, the user's emotions can be analyzed in real time when uploading a presentation video and positive feedback can be provided, thereby improving the user's motivation.

[0056] The analysis unit can evaluate the relevance between what the user says and the content of the slides, and recommend a consistent presentation. For example, when analyzing a video, the analysis unit builds a system that compares what the user says with the content of the slides and evaluates the relevance. For example, it checks whether what is said matches the content of the slides. The analysis unit can also evaluate the relevance between what the user says and the content of the slides, and recommend a consistent presentation. For example, if what is said and the content of the slides do not match, it points out areas for improvement. In this way, by evaluating the relevance between what is said and the content of the slides and recommending a consistent presentation, the quality of the presentation can be improved.

[0057] The analysis unit can analyze the user's gaze movements and evaluate the frequency of eye contact with the audience. For example, the analysis unit builds a system that tracks the user's gaze movements and evaluates the frequency of eye contact with the audience when analyzing a video. For example, the analysis unit records the user's gaze movements using gaze tracking technology. The analysis unit can also analyze the user's gaze movements and evaluate the frequency of eye contact with the audience. For example, the analysis unit evaluates the number and duration of eye contact within a certain period of time. In this way, the quality of presentations can be improved by analyzing the user's gaze movements and evaluating the frequency of eye contact with the audience.

[0058] The analysis unit can simulate the audience's emotional reaction to what the user is saying and provide advice based on the results. For example, when analyzing a video, the analysis unit builds a system that simulates the audience's emotional reaction to what the user is saying. For example, it predicts positive and negative reactions to what is being said. The analysis unit can also simulate the audience's emotional reaction to what the user is saying and provide advice based on the results. For example, it can use an emotion estimation function to predict the audience's emotional reaction to what the user is saying and provide advice based on the results. In this way, by simulating the audience's emotional reaction to what the user is saying and providing advice based on the results, the quality of the presentation can be improved.

[0059] The analysis unit can automatically translate what the user says into different languages ​​and provide advice in multiple languages. For example, the analysis unit builds a system that automatically translates what the user says into different languages ​​when analyzing a video. For example, it supports multiple languages, such as English to Japanese and French to English. The analysis unit can also automatically translate what the user says into different languages ​​and provide advice in multiple languages. For example, it provides advice corresponding to each language based on the translated content. In this way, the quality of international presentations can be improved by automatically translating what the user says into different languages ​​and providing advice in multiple languages.

[0060] The analysis unit can visualize the video analysis results to enable the user to intuitively understand. The analysis unit, for example, builds a system that visualizes the video analysis results. For example, it displays changes in speaking speed and tone of voice in graphs or charts. The analysis unit can also visualize the video analysis results to enable the user to intuitively understand. For example, by visually displaying the analysis results, the user can intuitively grasp areas for improvement. In this way, visualizing the video analysis results makes it easier for the user to intuitively understand.

[0061] The analysis unit can identify emotionally emphasized points in what the user is saying and provide advice to enhance those points. The analysis unit, for example, builds a system for identifying emotionally emphasized points in what the user is saying when analyzing a video. For example, it analyzes emotionally charged parts and points that the user wants to emphasize. The analysis unit can also identify emotionally emphasized points in what the user is saying and provide advice to enhance those parts. For example, it uses an emotion estimation function to identify emotionally emphasized points in what the user is saying and provide advice to enhance those parts. In this way, by identifying emotionally emphasized points in what the user is saying and providing advice to enhance those parts, the quality of the presentation can be improved.

[0062] The advice providing unit can compare the current video with the user's past presentation videos and evaluate the progress. For example, when providing advice, the advice providing unit compares the current video with the user's past presentation videos and builds a system to evaluate the progress. For example, it compares changes in speaking speed and tone of voice over time. The advice providing unit can also compare the current video with the user's past presentation videos and evaluate the progress. For example, it points out areas for improvement based on a comparison with the past presentation videos. In this way, by comparing the current video with the user's past presentation videos and evaluating the progress, it is possible to clarify areas for improvement in the presentation.

[0063] The advice providing unit can provide customized feedback tailored to the user's presentation style. The advice providing unit, for example, builds a system that provides customized feedback tailored to the user's presentation style when providing advice. For example, the advice providing unit analyzes the characteristics of the user's speaking style and gestures and provides advice based on the analysis. The advice providing unit can also provide customized feedback tailored to the user's presentation style. For example, the advice providing unit points out specific areas for improvement according to the user's presentation style. In this way, the quality of presentations can be improved by providing customized feedback tailored to the user's presentation style.

[0064] The advice providing unit can analyze the emotional reaction of the user when receiving advice and reinforce positive feedback. The advice providing unit, for example, analyzes the user's facial expressions and tone of voice when providing advice, and builds a system that analyzes the emotional reaction when receiving advice. For example, the user's emotions are analyzed using a camera or microphone. The advice providing unit can also analyze the emotional reaction of the user when receiving advice and reinforce positive feedback. For example, if the user has a positive reaction when receiving advice, the feedback is reinforced. In this way, the user's motivation can be improved by analyzing the emotional reaction of the user when receiving advice and reinforcing positive feedback.

[0065] The advice providing unit can propose a specific improvement plan based on the goal set by the user. For example, the advice providing unit constructs a system that, when providing advice, proposes a specific improvement plan based on the goal set by the user. For example, it proposes a specific plan such as "Practice for five minutes every day to improve your speaking speed." The advice providing unit can also propose a specific improvement plan based on the goal set by the user. For example, it proposes specific action steps according to the user's goal. In this way, by proposing a specific improvement plan based on the goal set by the user, the quality of presentations can be improved.

[0066] The advice providing unit can provide advice in video format to make it easier for the user to understand visually. The advice providing unit, for example, builds a system that provides advice in video format. For example, the advice content is explained in video to make it easier for the user to understand visually. The advice providing unit can also provide advice in video format to make it easier for the user to understand visually. For example, the length of the video and the structure of the content can be devised so that the user can intuitively understand. In this way, providing advice in video format makes it easier for the user to understand visually.

[0067] The advice providing unit can analyze the user's emotions when receiving advice in real time and provide additional advice to increase motivation. The advice providing unit, for example, analyzes the user's facial expressions and tone of voice when providing advice, and builds a system that analyzes the user's emotions when receiving advice in real time. For example, the user's emotions are analyzed using a camera or microphone. The advice providing unit can also analyze the user's emotions when receiving advice in real time and provide additional advice to increase motivation. For example, if the user has a negative reaction when receiving advice, words of encouragement are provided. In this way, the user's emotions when receiving advice are analyzed in real time and additional advice to increase motivation is provided, thereby improving the user's motivation.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The presentation analysis system can also include an audio analysis unit that analyzes background sounds during a user's presentation. For example, it can detect noise and background sounds that occur during a presentation and evaluate their impact. The audio analysis unit can also analyze the type and intensity of background sounds and suggest appropriate measures to the user. For example, if the background sounds are too loud, it can advise the user to record in a quieter environment. This can improve the audio quality of the presentation.

[0070] The presentation analysis system can also include a posture analysis unit that analyzes the user's posture during the presentation. For example, a camera can be used to analyze the user's standing or sitting posture and evaluate whether their posture is good or bad. The posture analysis unit can also suggest specific improvements based on the user's posture. For example, it can suggest straightening the back or an appropriate standing position. This can improve the user's posture during the presentation and create a more professional impression.

[0071] The presentation analysis system can also include a gaze analysis unit that analyzes the user's gaze movements during a presentation. For example, a camera can be used to track the direction of the user's gaze and record the gaze movements. The gaze analysis unit can also suggest specific improvements based on the user's gaze movements. For example, if the user's gaze is too concentrated in a specific area, the system can advise the user to disperse their gaze. This can improve the user's gaze movements and increase eye contact with the audience.

[0072] The presentation analysis system may further include a hand movement analysis unit that analyzes the user's hand movements during the presentation. For example, a camera may be used to analyze the user's hand movements and record those movements. The hand movement analysis unit may also suggest specific improvements based on the user's hand movements. For example, if the user's hand movements are too few, the system may advise the user to incorporate more appropriate gestures. This may improve the user's hand movements and enhance the expressiveness of the presentation.

[0073] The presentation analysis system may further include a voice intensity analysis unit that analyzes the strength of the user's voice during the presentation. For example, the voice intensity analysis unit may use voice analysis technology to analyze the strength and weakness of the user's voice and record any changes. The voice intensity analysis unit may also suggest specific improvements based on the user's voice intensity. For example, if the user's voice is too quiet, the system may advise the user to speak louder. This may improve the strength of the user's voice and make the presentation easier to hear.

[0074] The presentation analysis system can further include an emotion analysis unit that estimates the user's emotions during the presentation and provides advice based on those emotions. For example, the emotion analysis unit can analyze the user's facial expressions and tone of voice to estimate emotions. The emotion analysis unit can also suggest specific improvements based on the user's emotions. For example, if the user is nervous, the system can advise them on how to relax. In this way, the quality of the presentation can be improved by analyzing the user's emotions and providing advice based on those emotions.

[0075] The presentation analysis system may further include an emotion feedback unit that estimates the user's emotions during the presentation and provides feedback based on those emotions. For example, the emotion feedback unit may analyze the user's facial expressions and tone of voice to estimate the user's emotions. The emotion feedback unit may also provide specific feedback based on the user's emotions. For example, if a positive emotion is expressed, the system may advise the user to emphasize that point. In this way, the quality of the presentation can be improved by analyzing the user's emotions and providing feedback based on those emotions.

[0076] The presentation analysis system may further include an emotion / motivation unit that estimates the user's emotions during the presentation and provides advice to improve motivation based on those emotions. For example, the emotion / motivation unit may analyze the user's facial expressions and tone of voice to estimate the user's emotions. The emotion / motivation unit may also provide specific advice to improve motivation based on the user's emotions. For example, if negative emotions are expressed, the emotion / motivation unit may provide words of encouragement. In this way, the quality of the presentation can be improved by analyzing the user's emotions and providing advice to improve motivation based on those emotions.

[0077] The presentation analysis system may further include an emotional stress reduction unit that estimates the user's emotions during the presentation and provides advice for reducing stress based on those emotions. For example, the emotional stress reduction unit may analyze the user's facial expressions and tone of voice to estimate the user's emotions. The emotional stress reduction unit may also provide specific stress reduction advice based on the user's emotions. For example, if stress is high, the emotional stress reduction unit may provide advice on deep breathing and relaxation techniques. In this way, the quality of the presentation can be improved by analyzing the user's emotions and providing advice for reducing stress based on those emotions.

[0078] The presentation analysis system can further include an emotion content adjustment unit that estimates the user's emotions during the presentation and provides advice for adjusting the content of the presentation based on those emotions. For example, the emotion content adjustment unit can analyze the user's facial expressions and tone of voice to estimate the user's emotions. The emotion content adjustment unit can also provide specific advice for adjusting the content based on the user's emotions. For example, if the user's emotions are high, the adjustment unit can advise the user to emphasize those parts. In this way, the quality of the presentation can be improved by analyzing the user's emotions and providing advice for adjusting the content of the presentation based on those emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The video importer imports a practice video of the presentation recorded by the user. For example, the user can upload the recorded video to the system. The video importer can also support uploading videos from different devices (smartphones, tablets, PCs). For example, it provides an upload interface compatible with each device. Step 2: The analysis unit analyzes the loaded video. For example, it uses voice recognition and image analysis technology to analyze what the user is saying and what is displayed on the slides. The analysis unit can also analyze the user's facial expressions and gestures in real time to track changes in emotions. For example, it can use a camera to detect the user's smiling or surprised expressions and record these changes over time. Step 3: The advice generator generates advice based on the analysis results. For example, it evaluates speaking speed, tone of voice, eye movements, slide design, etc., and suggests specific areas for improvement. The advice generator can also provide customized feedback tailored to the user's presentation style. For example, it analyzes the characteristics of the user's speaking style and gestures and provides advice based on that. Step 4: The advice providing unit provides the generated advice to the user. For example, specific advice such as "You speak too fast, try speaking a little more slowly" or "The slide design is too simple, so it would be better to add graphs or images" can be provided. The advice providing unit can also provide the advice in video format to make it easier for the user to understand visually. For example, the advice content can be explained in a video to make it easier for the user to understand visually.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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, in order to avoid confusion and to 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.

[0147] 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. [Explanation of symbols]

[0148] 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 video loading section for loading presentation practice videos, an analysis unit that analyzes the video read by the video reading unit; an advice generation unit that generates advice based on the analysis result obtained by the analysis unit; an advice providing unit that provides the user with the advice generated by the advice generating unit; A system characterized by:

2. The video reading unit Analyze the user's facial expressions and gestures in real time to track changes in their emotions.

2. The system of claim 1.

3. The video reading unit The data of the presentation tool used by the user is simultaneously read, and the degree of agreement between the content of the slides and the speaking style is evaluated.

2. The system of claim 1.

4. The analysis unit Evaluate the relevance of the user's speech and slide content, and recommend a consistent presentation.

2. The system of claim 1.

5. The advice providing unit Compare the user's past presentation videos to evaluate their progress 2. The system of claim 1.

6. The video reading unit Analyzing the tone and pitch of the user's voice to assess the intensity of their emotion 2. The system of claim 1.

7. The video reading unit Supports video uploading from different devices, improving user convenience.

2. The system of claim 1.

8. The analysis unit Simulating the emotional reaction of an audience to what the user is saying and providing the advice based on the result 2. The system of claim 1.

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