System

Abstract

An object of a system according to an embodiment is to ease the tension of a presenter and to perform an effective presentation.SOLUTION: A system includes a recognition unit, a conversion unit, a reaction recognition unit, and a feedback unit. The recognition unit recognizes a video. The conversion unit analyzes the video image recognized by the recognition unit and converts the video image into a video image that relieves tension of the user. The reaction recognition unit recognizes a reaction of an audience. The feedback unit provides feedback based on the reaction recognized by the reaction recognition 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 techniques do not provide sufficient means to ease the presenter's nervousness, which may reduce the effectiveness of the presentation.

[0005] The system according to the embodiment aims to alleviate the presenter's nervousness and enable the presenter to give an effective presentation. [Means for solving the problem]

[0006] The system according to the embodiment includes a recognition unit, a conversion unit, a reaction recognition unit, and a feedback unit. The recognition unit recognizes an image. The conversion unit analyzes the image recognized by the recognition unit and converts it into an image that relieves tension for the user. The reaction recognition unit recognizes reactions of the audience. The feedback unit provides feedback based on the reactions recognized by the reaction recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the presenter to relax and give an effective presentation. [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 system according to an embodiment of the present invention is a system that relieves presenters' tension and provides interactive feedback based on audience reactions. This system recognizes images, converts them into images that relieve the user's tension, recognizes the audience's reactions, and provides feedback. For example, dedicated goggles recognize images within the user's field of vision, and AI analyzes the images and converts them into images that relieve the user's tension. Next, the AI ​​in the goggles recognizes the audience's reactions and provides feedback in real time. For example, it generates and displays "like" marks, or prompts for further explanation if the audience's understanding is insufficient. It is also possible to rearrange the order of presentations based on the audience's reactions. This allows the system to relieve presenters' tension and provide interactive feedback based on the audience's reactions. For example, presenters can deliver effective presentations without feeling nervous.

[0029] A presentation support system according to an embodiment includes a recognition unit, a conversion unit, a reaction recognition unit, and a feedback unit. The recognition unit recognizes video. The recognition unit acquires video using, for example, a camera or a sensor in the goggles. The recognition unit can also estimate a user's emotions and adjust the accuracy of video recognition based on the estimated user's emotions. For example, if the user is nervous, the recognition unit improves the recognition accuracy of the video and accurately recognizes details. The conversion unit analyzes the video recognized by the recognition unit and converts it into video that relieves the user's tension. The conversion unit converts the video into, for example, a relaxing scene or a cheering message. The conversion unit can also estimate a user's emotions and adjust the content of the converted video based on the estimated user's emotions. For example, if the user is nervous, a relaxing scene or a cheering message is displayed. The reaction recognition unit recognizes audience reactions. The reaction recognition unit analyzes, for example, the facial expressions and movements of the audience. The reaction recognition unit can also estimate the audience's emotions and adjust the accuracy of reaction recognition based on the estimated audience's emotions. For example, if the audience is excited, the reaction recognition unit improves the accuracy of reaction recognition. The feedback unit provides feedback based on the reactions recognized by the reaction recognition unit. The feedback unit, for example, generates and displays a "like" mark. The feedback unit can also display a message prompting a re-explanation if the audience's understanding is insufficient. For example, if the audience looks confused, the feedback unit displays a message prompting a re-explanation of that part. In this way, the presentation support system according to the embodiment can ease the presenter's tension and provide interactive feedback according to the audience's reactions.

[0030] The recognition unit can acquire images using a camera or sensor in the goggles. Examples of cameras or sensors include, but are not limited to, high-resolution cameras, infrared sensors, and depth sensors. The recognition unit can acquire images using, for example, a high-resolution camera. The recognition unit can also acquire images in dark places using an infrared sensor. The recognition unit can also acquire three-dimensional images using a depth sensor. This improves the accuracy of image acquisition by using the camera or sensor in the goggles. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input image data acquired by a camera or sensor to a generation AI and have the generation AI analyze the image data.

[0031] The conversion unit can convert the image into a video of a relaxing landscape or a cheering message. Examples of relaxing landscapes include, but are not limited to, natural landscapes, images of the ocean, and images of forests. The conversion unit, for example, displays a natural landscape. The conversion unit can also display an image of the ocean. The conversion unit can also display an image of a forest. Examples of cheering messages include, but are not limited to, text messages, audio messages, and video messages. The conversion unit, for example, displays a text message. The conversion unit can also play an audio message. The conversion unit can also play a video message. This conversion into a relaxing image relieves tension for the user. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the image data acquired by the recognition unit into a generation AI and cause the generation AI to convert the image into a relaxing image.

[0032] The reaction recognition unit can analyze the facial expressions and movements of the audience. Examples of the audience's facial expressions include, but are not limited to, smiling, confusion, surprise, etc. The reaction recognition unit, for example, analyzes smiling. The reaction recognition unit can also analyze a confused expression. The reaction recognition unit can also analyze a surprised expression. Examples of the audience's movements include, but are not limited to, clapping, nodding, and raising a hand. The reaction recognition unit, for example, analyzes clapping. The reaction recognition unit can also analyze nodding. The reaction recognition unit can also analyze raising a hand. This allows for more accurate reaction recognition by analyzing the audience's facial expressions and movements. Some or all of the above-described processing in the reaction recognition unit may be performed using, or without, AI. For example, the reaction recognition unit can input data on the audience's facial expressions and movements into the generation AI and cause the generation AI to analyze the facial expressions and movements.

[0033] The feedback unit can generate and display a "like" mark. Examples of "like" marks include, but are not limited to, a thumbs-up icon, a heart mark, a smiley face icon, and the like. The feedback unit can, for example, display a thumbs-up icon. The feedback unit can also display a heart mark. The feedback unit can also display a smiley face icon. In this way, generating and displaying a "like" mark gives the presenter a sense of security. Some or all of the above-described processing in the feedback unit may be performed using, or without using, AI, for example. For example, the feedback unit can input audience reaction data into the generation AI and cause the generation AI to generate a "like" mark.

[0034] The feedback unit can display a message prompting a further explanation if the audience's level of understanding is low. Examples of low understanding include, but are not limited to, a confused facial expression, frequent questions, and a lack of response. The feedback unit, for example, recognizes a confused facial expression and displays a message prompting a further explanation. The feedback unit can also display a message prompting a further explanation if frequent questions are asked. The feedback unit can also display a message prompting a further explanation if there is no response. This improves the effectiveness of the presentation by prompting a further explanation if the audience's level of understanding is insufficient. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input audience reaction data into the generation AI and cause the generation AI to generate a message prompting a further explanation.

[0035] The feedback unit can change the order of the presentation depending on the audience's reaction. Examples of changing the order of the presentation include, but are not limited to, changing the order of slides, switching topics, and re-presenting important points. For example, the feedback unit can change the order of slides. The feedback unit can also switch topics. The feedback unit can also re-present important points. This allows for a more interactive presentation by changing the order of the presentation depending on the audience's reaction. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input audience reaction data into the generation AI and cause the generation AI to change the order of the presentation.

[0036] When acquiring video, the recognition unit can prioritize recognition of important portions using the user's eye tracking data. Examples of eye tracking data include, but are not limited to, gaze position, eye movement, and gaze fixation time. For example, when the user directs their gaze to a specific slide, the recognition unit prioritizes recognition of the content of that slide. Furthermore, when the user directs their gaze to the audience's reaction, the recognition unit can prioritize recognition of that reaction. Furthermore, when the user directs their gaze to a specific portion of the document, the recognition unit can recognize that portion in detail. Thus, by using the user's eye tracking data, important portions can be prioritized. Some or all of the above-described processing in the recognition unit may be performed, for example, using AI, or may be performed without AI. For example, the recognition unit can input the eye tracking data to a generation AI and have the generation AI recognize important portions.

[0037] When acquiring video, the recognition unit can analyze surrounding environmental sounds and reflect them in the recognition of the video. Examples of surrounding environmental sounds include, but are not limited to, background sounds, noise, and specific voices. For example, when the surrounding environmental sounds are quiet, the recognition unit can improve the accuracy of video recognition. Furthermore, when the surrounding environmental sounds are noisy, the recognition unit can also remove noise and recognize the video. Furthermore, when the surrounding environmental sounds have a specific pattern, the recognition unit can recognize the video by taking that pattern into consideration. In this way, by analyzing the surrounding environmental sounds, the accuracy of video recognition is improved. Some or all of the above-described processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input surrounding environmental sound data into the generation AI and cause the generation AI to execute processing to reflect the data in the video recognition.

[0038] When acquiring video, the recognition unit can improve recognition accuracy by referring to the user's past visual data. Past visual data includes, but is not limited to, past video history, visual features, and visual patterns. For example, the recognition unit improves recognition accuracy by referring to video data viewed by the user in the past. The recognition unit can also improve recognition accuracy by learning specific patterns from the user's past visual data. The recognition unit can also prioritize recognition of important parts based on the user's past visual data. This improves recognition accuracy by referring to the user's past visual data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input past visual data into a generation AI and cause the generation AI to improve recognition accuracy.

[0039] When acquiring video, the recognition unit can analyze the user's physical movements and reflect them in the recognition of the video. Examples of the user's physical movements include, but are not limited to, raising a hand, walking around, and specific gestures. For example, when the user raises their hand, the recognition unit recognizes the movement and prioritizes recognition of related video. Furthermore, when the user walks around, the recognition unit can recognize the movement and recognize video in accordance with changes in field of view. Furthermore, when the user performs a specific gesture, the recognition unit can recognize the gesture and prioritize recognition of related video. This improves the accuracy of video recognition by analyzing the user's physical movements. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or without AI. For example, the recognition unit can input the user's physical movement data into the generation AI and cause the generation AI to execute a process to reflect the data in the video recognition.

[0040] When acquiring an image, the recognition unit can adjust the recognition accuracy based on the temperature or humidity around the user. Temperature or humidity includes, but is not limited to, variations in ambient temperature and humidity, specific environmental conditions, and the like. For example, when the ambient temperature is high, the recognition unit adjusts the image recognition accuracy to reduce the effects of heat. Furthermore, when the ambient humidity is high, the recognition unit can adjust the image recognition accuracy to reduce the effects of humidity. Furthermore, when the ambient temperature or humidity fluctuates, the recognition unit can adjust the image recognition accuracy taking into account the fluctuations. This improves the image recognition accuracy by taking into account the temperature and humidity around the user. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input ambient temperature and humidity data into the generation AI and cause the generation AI to adjust the image recognition accuracy.

[0041] When acquiring video, the recognition unit can improve the recognition method by reflecting the user's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. The recognition unit, for example, customizes the recognition method based on feedback provided by the user in the past. The recognition unit can also learn specific patterns from the user's past feedback and optimize the recognition method. The recognition unit can also refer to the user's past feedback and prioritize recognition of important parts. In this way, the recognition method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input past feedback data into the generation AI and cause the generation AI to improve the recognition method.

[0042] When converting an image, the conversion unit can adjust the color tone and brightness of the image based on the user's preferences. Examples of color tone and brightness include, but are not limited to, hue, brightness, and contrast. For example, the conversion unit can adjust the color of the image based on the user's preferred color tone. The conversion unit can also adjust the brightness of the image based on the user's preferred brightness. The conversion unit can also set optimal color tone and brightness by referencing the user's past preference data. This allows for a more comfortable image display by adjusting the color tone and brightness of the image based on the user's preferences. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's preference data into a generation AI and have the generation AI adjust the color tone and brightness.

[0043] When converting video, the conversion unit can improve conversion accuracy by referring to the user's past visual data. Past visual data includes, but is not limited to, past video history, visual features, and visual patterns. For example, the conversion unit can improve conversion accuracy by referring to video data viewed by the user in the past. The conversion unit can also learn specific patterns from the user's past visual data and improve conversion accuracy. The conversion unit can also prioritize conversion of important parts based on the user's past visual data. By referring to the user's past visual data, conversion accuracy is thereby improved. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past visual data into a generation AI and cause the generation AI to improve conversion accuracy.

[0044] When converting the video, the conversion unit can adjust the video taking into account the user's current heart rate and stress level. Examples of the heart rate and stress level include, but are not limited to, a heart rate sensor, a stress analysis algorithm, and biofeedback data. For example, the conversion unit can display a relaxing video when the user's heart rate is high. Furthermore, the conversion unit can also display a message of encouragement when the user's stress level is high. Furthermore, the conversion unit can adjust the video taking into account fluctuations in the user's heart rate and stress level. This enables more appropriate video conversion by taking the user's heart rate and stress level into consideration. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input heart rate and stress level data into a generation AI and have the generation AI adjust the video.

[0045] When converting an image, the conversion unit can adjust the content of the image taking into account the user's visual fatigue level. Examples of visual fatigue levels include, but are not limited to, a visual fatigue sensor, a fatigue level analysis algorithm, and a visual fatigue index. For example, the conversion unit can display an image with eye-friendly color tones when the user is visually fatigued. Furthermore, the conversion unit can also display an image with normal color tones when the user is not visually fatigued. Furthermore, the conversion unit can adjust the content of the image taking into account fluctuations in the user's visual fatigue level. This allows for a more comfortable image display by taking into account the user's visual fatigue level. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can input visual fatigue level data into a generation AI and have the generation AI adjust the content of the image.

[0046] When converting a video, the conversion unit can add audio to the video by referring to the user's auditory data. The auditory data includes, but is not limited to, music preferences, relaxing audio, and past auditory data. For example, the conversion unit can add music that the user likes to the video. The conversion unit can also add audio that the user finds relaxing to the video. The conversion unit can also add optimal audio to the video by referring to the user's past auditory data. This allows more appropriate audio to be added to the video by referring to the user's auditory data. Some or all of the above-described processing in the conversion unit can be performed using, or without, AI. For example, the conversion unit can input the auditory data to a generation AI and have the generation AI add the audio.

[0047] When converting video, the conversion unit can customize the conversion method by reflecting the user's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. The conversion unit customizes the conversion method, for example, based on feedback provided by the user in the past. The conversion unit can also learn specific patterns from the user's past feedback and optimize the conversion method. The conversion unit can also refer to the user's past feedback and prioritize conversion of important parts. In this way, the conversion method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past feedback data into a generation AI and have the generation AI customize the conversion method.

[0048] When recognizing audience reactions, the reaction recognition unit can prioritize recognition of important reactions using audience gaze tracking data. Examples of the gaze tracking data include, but are not limited to, gaze position, gaze movement, and gaze fixation time. For example, the reaction recognition unit prioritizes recognition of an audience reaction when the audience gazes at a specific slide. The reaction recognition unit can also prioritize recognition of an audience reaction when the audience gazes at the presenter. The reaction recognition unit can also recognize detailed reactions when the audience gazes at a specific portion of the materials. Thus, by using the audience gaze tracking data, important reactions can be prioritized. Some or all of the above-described processing in the reaction recognition unit may be performed using, for example, AI, or without AI. For example, the reaction recognition unit can input the gaze tracking data into a generation AI and have the generation AI recognize important reactions.

[0049] When recognizing an audience reaction, the reaction recognition unit can analyze the audience's voice data and reflect it in the recognition of the reaction. The voice data includes, but is not limited to, for example, laughter, applause, questions, and comments. The reaction recognition unit can, for example, recognize the audience's laughter and reflect the reaction. The reaction recognition unit can also recognize the audience's applause and reflect the reaction. The reaction recognition unit can also recognize the audience's questions and comments and reflect the reaction. In this way, by analyzing the audience's voice data, the accuracy of the reaction recognition is improved. Some or all of the above-mentioned processing in the reaction recognition unit may be performed, for example, using AI or without AI. For example, the reaction recognition unit can input the voice data to the generation AI and cause the generation AI to execute processing to reflect the voice data in the recognition of the reaction.

[0050] When recognizing audience reactions, the reaction recognition unit can improve recognition accuracy by referring to the audience's past reaction data. Past reaction data includes, but is not limited to, reaction history, evaluation data, and specific reaction patterns. For example, the reaction recognition unit improves recognition accuracy by referring to the audience's past reaction data. The reaction recognition unit can also improve recognition accuracy by learning specific patterns from the audience's past reaction data. The reaction recognition unit can also prioritize recognition of important reactions based on the audience's past reaction data. In this way, recognition accuracy is improved by referring to the audience's past reaction data. Some or all of the above-described processing in the reaction recognition unit may be performed, for example, using AI or without AI. For example, the reaction recognition unit can input past reaction data into the generation AI and cause the generation AI to improve recognition accuracy.

[0051] When recognizing audience reactions, the reaction recognition unit can analyze the audience's physical movements and reflect them in the recognition of the reactions. Examples of audience physical movements include, but are not limited to, raising a hand, nodding, and leaning forward. For example, when an audience member raises their hand, the reaction recognition unit recognizes that movement and prioritizes the recognition of related reactions. Furthermore, when an audience member nods, the reaction recognition unit can recognize that movement and recognize it as a positive reaction. Furthermore, when an audience member leans forward, the reaction recognition unit can recognize that movement and determine that it shows interest. Thus, analyzing the audience's physical movements improves the accuracy of the reaction recognition. Some or all of the above-described processing in the reaction recognition unit may be performed using, or without, AI. For example, the reaction recognition unit can input audience member physical movement data into the generation AI and cause the generation AI to execute a process to reflect the data in the reaction recognition.

[0052] When recognizing audience reactions, the reaction recognition unit can adjust the recognition accuracy by taking into account environmental sounds around the audience. Examples of environmental sounds around the audience include, but are not limited to, background sounds, noise, and specific voices. For example, when the environmental sounds around the audience are quiet, the reaction recognition unit can improve the reaction recognition accuracy. Furthermore, when the environmental sounds around the audience are noisy, the reaction recognition unit can also recognize reactions by removing the noise. Furthermore, when the environmental sounds around the audience have a specific pattern, the reaction recognition unit can recognize reactions by taking the pattern into account. Thus, by taking into account the environmental sounds around the audience, the reaction recognition accuracy is improved. Some or all of the above-described processing in the reaction recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the reaction recognition unit can input environmental sound data into the generation AI and cause the generation AI to adjust the reaction recognition accuracy.

[0053] When recognizing audience reactions, the reaction recognition unit can customize the recognition method by reflecting the audience's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. The reaction recognition unit customizes the recognition method, for example, based on feedback provided by the audience in the past. The reaction recognition unit can also learn specific patterns from the audience's past feedback and optimize the recognition method. The reaction recognition unit can also refer to the audience's past feedback and prioritize recognition of important reactions. In this way, the recognition method can be optimized by reflecting the audience's past feedback. Some or all of the above-described processing in the reaction recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the reaction recognition unit can input past feedback data into the generation AI and have the generation AI customize the recognition method.

[0054] When providing feedback, the feedback unit can analyze audience reaction data in real time to optimize the feedback. Examples of real-time analysis include, but are not limited to, data processing speed, analysis algorithms, and methods for acquiring real-time data. For example, the feedback unit can adjust the content of the feedback based on the audience's real-time reaction data. The feedback unit can also learn specific patterns from the audience's real-time reaction data and optimize the feedback. The feedback unit can also refer to the audience's real-time reaction data and prioritize feedback of important parts. This enables more effective feedback by optimizing the feedback based on the audience's real-time reaction data. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input real-time reaction data into a generation AI and cause the generation AI to optimize the feedback.

[0055] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to past reaction data of the audience. Past reaction data includes, but is not limited to, reaction history, evaluation data, and specific reaction patterns. For example, the feedback unit can improve the accuracy of the feedback by referring to past reaction data of the audience. The feedback unit can also learn specific patterns from the past reaction data of the audience and improve the accuracy of the feedback. The feedback unit can also prioritize feedback of important parts based on the past reaction data of the audience. In this way, the accuracy of the feedback can be improved by referring to the past reaction data of the audience. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input past reaction data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0056] When providing feedback, the feedback unit can evaluate the audience's level of understanding and adjust the content of the feedback. Examples of evaluation of the level of understanding include, but are not limited to, the frequency of questions, changes in facial expressions, and speed of responses. For example, the feedback unit can provide detailed feedback when the audience's level of understanding is high. Furthermore, the feedback unit can also provide concise feedback when the audience's level of understanding is low. Furthermore, when the audience's level of understanding fluctuates, the feedback unit can adjust the content of the feedback taking into account the fluctuations. This enables more appropriate feedback by evaluating the audience's level of understanding. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input comprehension data into a generation AI and have the generation AI adjust the content of the feedback.

[0057] When providing feedback, the feedback unit can adjust the content of the feedback taking into account the audience's visual fatigue level. Examples of visual fatigue levels include, but are not limited to, a visual fatigue sensor, a fatigue level analysis algorithm, and a visual fatigue index. For example, if the audience is visually fatigued, the feedback unit can provide feedback in a color tone that is easy on the eyes. Alternatively, if the audience is not visually fatigued, the feedback unit can provide feedback in a normal color tone. Furthermore, if the audience's visual fatigue level fluctuates, the feedback unit can adjust the content of the feedback taking into account the fluctuations. This enables more comfortable feedback by taking into account the audience's visual fatigue level. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input visual fatigue level data into a generation AI and cause the generation AI to adjust the content of the feedback.

[0058] When providing feedback, the feedback unit can add audio to the feedback by referring to the auditory data of the audience. The auditory data includes, but is not limited to, for example, music preferences, relaxing audio, and past auditory data. For example, the feedback unit can add music that the audience likes to the feedback. The feedback unit can also add audio that the audience finds relaxing to the feedback. The feedback unit can also add optimal audio to the feedback by referring to the past auditory data of the audience. In this way, by referring to the auditory data of the audience, more appropriate audio can be added to the feedback. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without using, AI. For example, the feedback unit can input the auditory data to a generation AI and cause the generation AI to add audio.

[0059] When providing feedback, the feedback unit can customize the feedback method by reflecting the audience's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. For example, the feedback unit customizes the feedback method based on feedback provided by the audience in the past. The feedback unit can also learn specific patterns from the audience's past feedback and optimize the feedback method. The feedback unit can also refer to the audience's past feedback and prioritize feedback of important parts. In this way, the feedback method can be optimized by reflecting the audience's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past feedback data into a generation AI and cause the generation AI to customize the feedback method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The presentation support system may further include a speech recognition unit. The speech recognition unit may analyze the presenter's speech in real time and evaluate the clarity and speed of pronunciation. For example, if the presenter is speaking too quickly, the speech recognition unit may provide feedback to adjust the speed. The speech recognition unit may also analyze the tone and strength of the presenter's voice and suggest appropriate adjustments based on the audience's reactions. Furthermore, if the presenter's pronunciation is unclear, the speech recognition unit may provide feedback to encourage the presenter to repeat the pronunciation. Thus, providing feedback on the presenter's speech enables more effective presentations.

[0062] The presentation support system can further include a gesture recognition unit. The gesture recognition unit can analyze the presenter's hand and body movements and suggest appropriate gestures. For example, if the presenter is speaking without using his or her hands, the gesture recognition unit can provide feedback to encourage additional hand movements. The gesture recognition unit can also suggest that the presenter reduce his or her movements if the presenter's movements are excessive. Furthermore, the gesture recognition unit can suggest more effective gestures if the presenter's movements are not effective for the audience. This allows for more effective presentations by providing feedback on the presenter's gestures.

[0063] The presentation support system may further include a background music providing unit. The background music providing unit can provide appropriate background music depending on the content and atmosphere of the presentation. For example, if a relaxed atmosphere is desired, the background music providing unit can play calm music. Alternatively, if an important part of the presentation is desired to be emphasized, the background music providing unit can play tense music. Furthermore, the background music providing unit can adjust the music selection based on the audience's reaction. In this way, the background music provided can enhance the effectiveness of the presentation.

[0064] The presentation support system may further include a visual effects provider. The visual effects provider may provide appropriate visual effects according to the content of the presentation. For example, the visual effects provider may add animations to slides to emphasize important points. The visual effects provider may also add dynamic effects to slides to attract the audience's attention. The visual effects provider may also adjust the design of the slides according to the atmosphere of the presentation. This may enhance the effectiveness of the presentation by providing visual effects.

[0065] The presentation support system can further include a question and answer support unit. The question and answer support unit can analyze questions from the audience in real time and suggest appropriate answers. For example, if the presenter cannot answer a question immediately, the question and answer support unit can provide related information. The question and answer support unit can also refer to past question data and suggest answers to similar questions. Furthermore, the question and answer support unit can suggest answer structures so that the presenter can provide appropriate answers to questions. This can improve the effectiveness of presentations by providing support for question and answering.

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

[0067] Step 1: The recognition unit recognizes the image. The recognition unit acquires the image using, for example, a camera or sensor inside the goggles. The recognition unit can also estimate the user's emotions and adjust the accuracy of the image recognition based on the estimated user emotions. For example, if the user is nervous, the recognition unit will increase the accuracy of the image recognition and accurately recognize even the smallest details. Step 2: The conversion unit analyzes the image recognized by the recognition unit and converts it into an image that relieves the user's tension. For example, the conversion unit converts it into an image of a relaxing scene or a message of encouragement. The conversion unit can also estimate the user's emotions and adjust the content of the converted image based on the estimated user emotions. For example, if the user is nervous, the conversion unit displays a relaxing scene or a message of encouragement. Step 3: The reaction recognition unit recognizes the reactions of the audience. For example, the reaction recognition unit analyzes the facial expressions and movements of the audience. The reaction recognition unit can also estimate the emotions of the audience and adjust the accuracy of reaction recognition based on the estimated emotions of the audience. For example, if the audience is excited, the reaction recognition unit increases the accuracy of reaction recognition. Step 4: The feedback unit provides feedback based on the reactions recognized by the reaction recognition unit. For example, the feedback unit generates and displays a "like" mark. The feedback unit can also display a message prompting a re-explanation if the audience's level of understanding is insufficient. For example, if the audience has a confused look on their face, the feedback unit displays a message prompting a re-explanation of that part.

[0068] (Example 2) A system according to an embodiment of the present invention is a system that relieves presenters' tension and provides interactive feedback based on audience reactions. This system recognizes images, converts them into images that relieve the user's tension, recognizes the audience's reactions, and provides feedback. For example, dedicated goggles recognize images within the user's field of vision, and AI analyzes the images and converts them into images that relieve the user's tension. Next, the AI ​​in the goggles recognizes the audience's reactions and provides feedback in real time. For example, it generates and displays "like" marks, or prompts for further explanation if the audience's understanding is insufficient. It is also possible to rearrange the order of presentations based on the audience's reactions. This allows the system to relieve presenters' tension and provide interactive feedback based on the audience's reactions. For example, presenters can deliver effective presentations without feeling nervous.

[0069] A presentation support system according to an embodiment includes a recognition unit, a conversion unit, a reaction recognition unit, and a feedback unit. The recognition unit recognizes video. The recognition unit acquires video using, for example, a camera or a sensor in the goggles. The recognition unit can also estimate a user's emotions and adjust the accuracy of video recognition based on the estimated user's emotions. For example, if the user is nervous, the recognition unit improves the recognition accuracy of the video and accurately recognizes details. The conversion unit analyzes the video recognized by the recognition unit and converts it into video that relieves the user's tension. The conversion unit converts the video into, for example, a relaxing scene or a cheering message. The conversion unit can also estimate a user's emotions and adjust the content of the converted video based on the estimated user's emotions. For example, if the user is nervous, a relaxing scene or a cheering message is displayed. The reaction recognition unit recognizes audience reactions. The reaction recognition unit analyzes, for example, the facial expressions and movements of the audience. The reaction recognition unit can also estimate the audience's emotions and adjust the accuracy of reaction recognition based on the estimated audience's emotions. For example, if the audience is excited, the reaction recognition unit improves the accuracy of reaction recognition. The feedback unit provides feedback based on the reactions recognized by the reaction recognition unit. The feedback unit, for example, generates and displays a "like" mark. The feedback unit can also display a message prompting a re-explanation if the audience's understanding is insufficient. For example, if the audience looks confused, the feedback unit displays a message prompting a re-explanation of that part. In this way, the presentation support system according to the embodiment can ease the presenter's tension and provide interactive feedback according to the audience's reactions.

[0070] The recognition unit can acquire images using a camera or sensor in the goggles. Examples of cameras or sensors include, but are not limited to, high-resolution cameras, infrared sensors, and depth sensors. The recognition unit can acquire images using, for example, a high-resolution camera. The recognition unit can also acquire images in dark places using an infrared sensor. The recognition unit can also acquire three-dimensional images using a depth sensor. This improves the accuracy of image acquisition by using the camera or sensor in the goggles. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input image data acquired by a camera or sensor to a generation AI and have the generation AI analyze the image data.

[0071] The conversion unit can convert the image into a video of a relaxing landscape or a cheering message. Examples of relaxing landscapes include, but are not limited to, natural landscapes, images of the ocean, and images of forests. The conversion unit, for example, displays a natural landscape. The conversion unit can also display an image of the ocean. The conversion unit can also display an image of a forest. Examples of cheering messages include, but are not limited to, text messages, audio messages, and video messages. The conversion unit, for example, displays a text message. The conversion unit can also play an audio message. The conversion unit can also play a video message. This conversion into a relaxing image relieves tension for the user. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the image data acquired by the recognition unit into a generation AI and cause the generation AI to convert the image into a relaxing image.

[0072] The reaction recognition unit can analyze the facial expressions and movements of the audience. Examples of the audience's facial expressions include, but are not limited to, smiling, confusion, surprise, etc. The reaction recognition unit, for example, analyzes smiling. The reaction recognition unit can also analyze a confused expression. The reaction recognition unit can also analyze a surprised expression. Examples of the audience's movements include, but are not limited to, clapping, nodding, and raising a hand. The reaction recognition unit, for example, analyzes clapping. The reaction recognition unit can also analyze nodding. The reaction recognition unit can also analyze raising a hand. This allows for more accurate reaction recognition by analyzing the audience's facial expressions and movements. Some or all of the above-described processing in the reaction recognition unit may be performed using, or without, AI. For example, the reaction recognition unit can input data on the audience's facial expressions and movements into the generation AI and cause the generation AI to analyze the facial expressions and movements.

[0073] The feedback unit can generate and display a "like" mark. Examples of "like" marks include, but are not limited to, a thumbs-up icon, a heart mark, a smiley face icon, and the like. The feedback unit can, for example, display a thumbs-up icon. The feedback unit can also display a heart mark. The feedback unit can also display a smiley face icon. In this way, generating and displaying a "like" mark gives the presenter a sense of security. Some or all of the above-described processing in the feedback unit may be performed using, or without using, AI, for example. For example, the feedback unit can input audience reaction data into the generation AI and cause the generation AI to generate a "like" mark.

[0074] The feedback unit can display a message prompting a further explanation if the audience's level of understanding is low. Examples of low understanding include, but are not limited to, a confused facial expression, frequent questions, and a lack of response. The feedback unit, for example, recognizes a confused facial expression and displays a message prompting a further explanation. The feedback unit can also display a message prompting a further explanation if frequent questions are asked. The feedback unit can also display a message prompting a further explanation if there is no response. This improves the effectiveness of the presentation by prompting a further explanation if the audience's level of understanding is insufficient. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input audience reaction data into the generation AI and cause the generation AI to generate a message prompting a further explanation.

[0075] The feedback unit can change the order of the presentation depending on the audience's reaction. Examples of changing the order of the presentation include, but are not limited to, changing the order of slides, switching topics, and re-presenting important points. For example, the feedback unit can change the order of slides. The feedback unit can also switch topics. The feedback unit can also re-present important points. This allows for a more interactive presentation by changing the order of the presentation depending on the audience's reaction. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input audience reaction data into the generation AI and cause the generation AI to change the order of the presentation.

[0076] The recognition unit can estimate the user's emotions and adjust the accuracy of image recognition based on the estimated user's emotions. Examples of user emotions include, but are not limited to, tension, relaxation, and concentration. For example, when the user is tense, the recognition unit can increase the accuracy of image recognition and accurately recognize details. Furthermore, when the user is relaxed, the recognition unit can appropriately adjust the accuracy of image recognition to reduce the processing load. Furthermore, when the user is concentrated, the recognition unit can optimize the accuracy of image recognition and prioritize recognition of important parts. This enables more appropriate image recognition by adjusting the accuracy of image recognition based on the user's emotions. Some or all of the above-described processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data to the generation AI and cause the generation AI to adjust the accuracy of image recognition.

[0077] When acquiring video, the recognition unit can prioritize recognition of important portions using the user's eye tracking data. Examples of eye tracking data include, but are not limited to, gaze position, eye movement, and gaze fixation time. For example, when the user directs their gaze to a specific slide, the recognition unit prioritizes recognition of the content of that slide. Furthermore, when the user directs their gaze to the audience's reaction, the recognition unit can prioritize recognition of that reaction. Furthermore, when the user directs their gaze to a specific portion of the document, the recognition unit can recognize that portion in detail. Thus, by using the user's eye tracking data, important portions can be prioritized. Some or all of the above-described processing in the recognition unit may be performed, for example, using AI, or may be performed without AI. For example, the recognition unit can input the eye tracking data to a generation AI and have the generation AI recognize important portions.

[0078] When acquiring video, the recognition unit can analyze surrounding environmental sounds and reflect them in the recognition of the video. Examples of surrounding environmental sounds include, but are not limited to, background sounds, noise, and specific voices. For example, when the surrounding environmental sounds are quiet, the recognition unit can improve the accuracy of video recognition. Furthermore, when the surrounding environmental sounds are noisy, the recognition unit can also remove noise and recognize the video. Furthermore, when the surrounding environmental sounds have a specific pattern, the recognition unit can recognize the video by taking that pattern into consideration. In this way, by analyzing the surrounding environmental sounds, the accuracy of video recognition is improved. Some or all of the above-described processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input surrounding environmental sound data into the generation AI and cause the generation AI to execute processing to reflect the data in the video recognition.

[0079] When acquiring video, the recognition unit can improve recognition accuracy by referring to the user's past visual data. Past visual data includes, but is not limited to, past video history, visual features, and visual patterns. For example, the recognition unit improves recognition accuracy by referring to video data viewed by the user in the past. The recognition unit can also improve recognition accuracy by learning specific patterns from the user's past visual data. The recognition unit can also prioritize recognition of important parts based on the user's past visual data. This improves recognition accuracy by referring to the user's past visual data. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input past visual data into a generation AI and cause the generation AI to improve recognition accuracy.

[0080] The recognition unit can estimate the user's emotions and determine the priority of videos to be recognized based on the estimated user's emotions. Examples of user emotions include, but are not limited to, tension, relaxation, and concentration. For example, if the user is tense, the recognition unit can prioritize recognizing videos that are relaxing. Furthermore, if the user is relaxed, the recognition unit can prioritize recognizing videos related to a presentation. Furthermore, if the user is concentrated, the recognition unit can prioritize recognizing important slides and materials. This enables more appropriate video recognition by prioritizing videos based on the user's emotions. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input user's emotion data into a generation AI and have the generation AI determine the priority of videos.

[0081] When acquiring video, the recognition unit can analyze the user's physical movements and reflect them in the recognition of the video. Examples of the user's physical movements include, but are not limited to, raising a hand, walking around, and specific gestures. For example, when the user raises their hand, the recognition unit recognizes the movement and prioritizes recognition of related video. Furthermore, when the user walks around, the recognition unit can recognize the movement and recognize video in accordance with changes in field of view. Furthermore, when the user performs a specific gesture, the recognition unit can recognize the gesture and prioritize recognition of related video. This improves the accuracy of video recognition by analyzing the user's physical movements. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or without AI. For example, the recognition unit can input the user's physical movement data into the generation AI and cause the generation AI to execute a process to reflect the data in the video recognition.

[0082] When acquiring an image, the recognition unit can adjust the recognition accuracy based on the temperature or humidity around the user. Temperature or humidity includes, but is not limited to, variations in ambient temperature and humidity, specific environmental conditions, and the like. For example, when the ambient temperature is high, the recognition unit adjusts the image recognition accuracy to reduce the effects of heat. Furthermore, when the ambient humidity is high, the recognition unit can adjust the image recognition accuracy to reduce the effects of humidity. Furthermore, when the ambient temperature or humidity fluctuates, the recognition unit can adjust the image recognition accuracy taking into account the fluctuations. This improves the image recognition accuracy by taking into account the temperature and humidity around the user. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without AI. For example, the recognition unit can input ambient temperature and humidity data into the generation AI and cause the generation AI to adjust the image recognition accuracy.

[0083] When acquiring video, the recognition unit can improve the recognition method by reflecting the user's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. The recognition unit, for example, customizes the recognition method based on feedback provided by the user in the past. The recognition unit can also learn specific patterns from the user's past feedback and optimize the recognition method. The recognition unit can also refer to the user's past feedback and prioritize recognition of important parts. In this way, the recognition method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input past feedback data into the generation AI and cause the generation AI to improve the recognition method.

[0084] The conversion unit can estimate the user's emotions and adjust the content of the video to be converted based on the estimated user's emotions. Examples of the user's emotions include, but are not limited to, tension, relaxation, and concentration. For example, if the user is nervous, the conversion unit can display a relaxing scene or a message of encouragement. If the user is relaxed, the conversion unit can also display a video related to a presentation. If the user is concentrating, the conversion unit can also highlight important slides or materials. This enables more appropriate video conversion by adjusting the content of the video based on the user's emotions. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input the user's emotion data into a generation AI and have the generation AI adjust the content of the video.

[0085] When converting an image, the conversion unit can adjust the color tone and brightness of the image based on the user's preferences. Examples of color tone and brightness include, but are not limited to, hue, brightness, and contrast. For example, the conversion unit can adjust the color of the image based on the user's preferred color tone. The conversion unit can also adjust the brightness of the image based on the user's preferred brightness. The conversion unit can also set optimal color tone and brightness by referencing the user's past preference data. This allows for a more comfortable image display by adjusting the color tone and brightness of the image based on the user's preferences. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can input the user's preference data into a generation AI and have the generation AI adjust the color tone and brightness.

[0086] When converting video, the conversion unit can improve conversion accuracy by referring to the user's past visual data. Past visual data includes, but is not limited to, past video history, visual features, and visual patterns. For example, the conversion unit can improve conversion accuracy by referring to video data viewed by the user in the past. The conversion unit can also learn specific patterns from the user's past visual data and improve conversion accuracy. The conversion unit can also prioritize conversion of important parts based on the user's past visual data. By referring to the user's past visual data, conversion accuracy is thereby improved. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past visual data into a generation AI and cause the generation AI to improve conversion accuracy.

[0087] When converting the video, the conversion unit can adjust the video taking into account the user's current heart rate and stress level. Examples of the heart rate and stress level include, but are not limited to, a heart rate sensor, a stress analysis algorithm, and biofeedback data. For example, the conversion unit can display a relaxing video when the user's heart rate is high. Furthermore, the conversion unit can also display a message of encouragement when the user's stress level is high. Furthermore, the conversion unit can adjust the video taking into account fluctuations in the user's heart rate and stress level. This enables more appropriate video conversion by taking the user's heart rate and stress level into consideration. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input heart rate and stress level data into a generation AI and have the generation AI adjust the video.

[0088] The conversion unit can estimate the user's emotions and adjust the length of the video to be converted based on the estimated user's emotions. Examples of video lengths include, but are not limited to, short videos, long videos, and videos that focus on the main points. For example, if the user is nervous, the conversion unit can display short, to-the-point videos. Furthermore, if the user is relaxed, the conversion unit can display longer videos with detailed explanations. Furthermore, if the user is concentrating, the conversion unit can display videos that emphasize important parts. This allows for more appropriate video display by adjusting the length of the video based on the user's emotions. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI. For example, the conversion unit can input the user's emotion data into a generation AI and have the generation AI adjust the length of the video.

[0089] When converting an image, the conversion unit can adjust the content of the image taking into account the user's visual fatigue level. Examples of visual fatigue levels include, but are not limited to, a visual fatigue sensor, a fatigue level analysis algorithm, and a visual fatigue index. For example, the conversion unit can display an image with eye-friendly color tones when the user is visually fatigued. Furthermore, the conversion unit can also display an image with normal color tones when the user is not visually fatigued. Furthermore, the conversion unit can adjust the content of the image taking into account fluctuations in the user's visual fatigue level. This allows for a more comfortable image display by taking into account the user's visual fatigue level. Some or all of the above-described processing in the conversion unit can be performed using, for example, AI, or without AI. For example, the conversion unit can input visual fatigue level data into a generation AI and have the generation AI adjust the content of the image.

[0090] When converting a video, the conversion unit can add audio to the video by referring to the user's auditory data. The auditory data includes, but is not limited to, music preferences, relaxing audio, and past auditory data. For example, the conversion unit can add music that the user likes to the video. The conversion unit can also add audio that the user finds relaxing to the video. The conversion unit can also add optimal audio to the video by referring to the user's past auditory data. This allows more appropriate audio to be added to the video by referring to the user's auditory data. Some or all of the above-described processing in the conversion unit can be performed using, or without, AI. For example, the conversion unit can input the auditory data to a generation AI and have the generation AI add the audio.

[0091] When converting video, the conversion unit can customize the conversion method by reflecting the user's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. The conversion unit customizes the conversion method, for example, based on feedback provided by the user in the past. The conversion unit can also learn specific patterns from the user's past feedback and optimize the conversion method. The conversion unit can also refer to the user's past feedback and prioritize conversion of important parts. In this way, the conversion method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past feedback data into a generation AI and have the generation AI customize the conversion method.

[0092] The reaction recognition unit can estimate the emotions of the audience and adjust the reaction recognition accuracy based on the estimated emotions. Examples of audience emotions include, but are not limited to, excitement, relaxation, and concentration. For example, the reaction recognition unit can increase the reaction recognition accuracy when the audience is excited. Furthermore, the reaction recognition unit can also appropriately adjust the reaction recognition accuracy when the audience is relaxed. Furthermore, the reaction recognition unit can prioritize recognition of important reactions when the audience is concentrated. This enables more accurate reaction recognition by adjusting the reaction recognition accuracy based on the emotions of the audience. Some or all of the above-described processing in the reaction recognition unit may be performed using, or without, AI. For example, the reaction recognition unit can input audience emotion data into the generation AI and have the generation AI adjust the reaction recognition accuracy.

[0093] When recognizing audience reactions, the reaction recognition unit can prioritize recognition of important reactions using audience gaze tracking data. Examples of the gaze tracking data include, but are not limited to, gaze position, gaze movement, and gaze fixation time. For example, the reaction recognition unit prioritizes recognition of an audience reaction when the audience gazes at a specific slide. The reaction recognition unit can also prioritize recognition of an audience reaction when the audience gazes at the presenter. The reaction recognition unit can also recognize detailed reactions when the audience gazes at a specific portion of the materials. Thus, by using the audience gaze tracking data, important reactions can be prioritized. Some or all of the above-described processing in the reaction recognition unit may be performed using, for example, AI, or without AI. For example, the reaction recognition unit can input the gaze tracking data into a generation AI and have the generation AI recognize important reactions.

[0094] When recognizing an audience reaction, the reaction recognition unit can analyze the audience's voice data and reflect it in the recognition of the reaction. The voice data includes, but is not limited to, for example, laughter, applause, questions, and comments. The reaction recognition unit can, for example, recognize the audience's laughter and reflect the reaction. The reaction recognition unit can also recognize the audience's applause and reflect the reaction. The reaction recognition unit can also recognize the audience's questions and comments and reflect the reaction. In this way, by analyzing the audience's voice data, the accuracy of the reaction recognition is improved. Some or all of the above-mentioned processing in the reaction recognition unit may be performed, for example, using AI or without AI. For example, the reaction recognition unit can input the voice data to the generation AI and cause the generation AI to execute processing to reflect the voice data in the recognition of the reaction.

[0095] When recognizing audience reactions, the reaction recognition unit can improve recognition accuracy by referring to the audience's past reaction data. Past reaction data includes, but is not limited to, reaction history, evaluation data, and specific reaction patterns. For example, the reaction recognition unit improves recognition accuracy by referring to the audience's past reaction data. The reaction recognition unit can also improve recognition accuracy by learning specific patterns from the audience's past reaction data. The reaction recognition unit can also prioritize recognition of important reactions based on the audience's past reaction data. In this way, recognition accuracy is improved by referring to the audience's past reaction data. Some or all of the above-described processing in the reaction recognition unit may be performed, for example, using AI or without AI. For example, the reaction recognition unit can input past reaction data into the generation AI and cause the generation AI to improve recognition accuracy.

[0096] The reaction recognition unit can estimate the emotions of the audience and determine the priority of reactions to be recognized based on the estimated emotions of the audience. Examples of emotions of the audience include, but are not limited to, excitement, relaxation, and concentration. For example, if the audience is excited, the reaction recognition unit can prioritize positive reactions. Also, if the audience is relaxed, the reaction recognition unit can recognize the overall reactions evenly. Also, if the audience is concentrated, the reaction recognition unit can prioritize important reactions. This enables more appropriate reaction recognition by prioritizing reactions based on the emotions of the audience. Some or all of the above-described processing in the reaction recognition unit may be performed using, or without, AI. For example, the reaction recognition unit can input audience emotion data into the generation AI and have the generation AI determine the priority of reactions.

[0097] When recognizing audience reactions, the reaction recognition unit can analyze the audience's physical movements and reflect them in the recognition of the reactions. Examples of audience physical movements include, but are not limited to, raising a hand, nodding, and leaning forward. For example, when an audience member raises their hand, the reaction recognition unit recognizes that movement and prioritizes the recognition of related reactions. Furthermore, when an audience member nods, the reaction recognition unit can recognize that movement and recognize it as a positive reaction. Furthermore, when an audience member leans forward, the reaction recognition unit can recognize that movement and determine that it shows interest. Thus, analyzing the audience's physical movements improves the accuracy of the reaction recognition. Some or all of the above-described processing in the reaction recognition unit may be performed using, or without, AI. For example, the reaction recognition unit can input audience member physical movement data into the generation AI and cause the generation AI to execute a process to reflect the data in the reaction recognition.

[0098] When recognizing audience reactions, the reaction recognition unit can adjust the recognition accuracy by taking into account environmental sounds around the audience. Examples of environmental sounds around the audience include, but are not limited to, background sounds, noise, and specific voices. For example, when the environmental sounds around the audience are quiet, the reaction recognition unit can improve the reaction recognition accuracy. Furthermore, when the environmental sounds around the audience are noisy, the reaction recognition unit can also recognize reactions by removing the noise. Furthermore, when the environmental sounds around the audience have a specific pattern, the reaction recognition unit can recognize reactions by taking the pattern into account. Thus, by taking into account the environmental sounds around the audience, the reaction recognition accuracy is improved. Some or all of the above-described processing in the reaction recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the reaction recognition unit can input environmental sound data into the generation AI and cause the generation AI to adjust the reaction recognition accuracy.

[0099] When recognizing audience reactions, the reaction recognition unit can customize the recognition method by reflecting the audience's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. The reaction recognition unit customizes the recognition method, for example, based on feedback provided by the audience in the past. The reaction recognition unit can also learn specific patterns from the audience's past feedback and optimize the recognition method. The reaction recognition unit can also refer to the audience's past feedback and prioritize recognition of important reactions. In this way, the recognition method can be optimized by reflecting the audience's past feedback. Some or all of the above-described processing in the reaction recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the reaction recognition unit can input past feedback data into the generation AI and have the generation AI customize the recognition method.

[0100] The feedback unit can estimate the emotions of the audience and adjust the content of the feedback based on the estimated emotions of the audience. Examples of the emotions of the audience include, but are not limited to, excitement, relaxation, and concentration. For example, the feedback unit can provide positive feedback when the audience is excited. The feedback unit can also provide general feedback when the audience is relaxed. The feedback unit can also provide detailed feedback when the audience is concentrated. This allows for more appropriate feedback by adjusting the content of the feedback based on the emotions of the audience. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input audience emotion data into the generation AI and cause the generation AI to adjust the content of the feedback.

[0101] When providing feedback, the feedback unit can analyze audience reaction data in real time to optimize the feedback. Examples of real-time analysis include, but are not limited to, data processing speed, analysis algorithms, and methods for acquiring real-time data. For example, the feedback unit can adjust the content of the feedback based on the audience's real-time reaction data. The feedback unit can also learn specific patterns from the audience's real-time reaction data and optimize the feedback. The feedback unit can also refer to the audience's real-time reaction data and prioritize feedback of important parts. This enables more effective feedback by optimizing the feedback based on the audience's real-time reaction data. Some or all of the above-described processing in the feedback unit can be performed using, or without, AI. For example, the feedback unit can input real-time reaction data into a generation AI and cause the generation AI to optimize the feedback.

[0102] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to past reaction data of the audience. Past reaction data includes, but is not limited to, reaction history, evaluation data, and specific reaction patterns. For example, the feedback unit can improve the accuracy of the feedback by referring to past reaction data of the audience. The feedback unit can also learn specific patterns from the past reaction data of the audience and improve the accuracy of the feedback. The feedback unit can also prioritize feedback of important parts based on the past reaction data of the audience. In this way, the accuracy of the feedback can be improved by referring to the past reaction data of the audience. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input past reaction data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0103] When providing feedback, the feedback unit can evaluate the audience's level of understanding and adjust the content of the feedback. Examples of evaluation of the level of understanding include, but are not limited to, the frequency of questions, changes in facial expressions, and speed of responses. For example, the feedback unit can provide detailed feedback when the audience's level of understanding is high. Furthermore, the feedback unit can also provide concise feedback when the audience's level of understanding is low. Furthermore, when the audience's level of understanding fluctuates, the feedback unit can adjust the content of the feedback taking into account the fluctuations. This enables more appropriate feedback by evaluating the audience's level of understanding. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input comprehension data into a generation AI and have the generation AI adjust the content of the feedback.

[0104] The feedback unit can estimate the emotions of the audience and adjust the feedback display method based on the estimated emotions. Examples of the emotions of the audience include, but are not limited to, excitement, relaxation, and concentration. For example, the feedback unit can provide a visually stimulating display method when the audience is excited. The feedback unit can also provide a calm display method when the audience is relaxed. The feedback unit can also provide a highly visible display method when the audience is concentrated. This allows for more appropriate feedback display by adjusting the feedback display method based on the emotions of the audience. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input audience emotion data into the generation AI and cause the generation AI to adjust the feedback display method.

[0105] When providing feedback, the feedback unit can adjust the content of the feedback taking into account the audience's visual fatigue level. Examples of visual fatigue levels include, but are not limited to, a visual fatigue sensor, a fatigue level analysis algorithm, and a visual fatigue index. For example, if the audience is visually fatigued, the feedback unit can provide feedback in a color tone that is easy on the eyes. Alternatively, if the audience is not visually fatigued, the feedback unit can provide feedback in a normal color tone. Furthermore, if the audience's visual fatigue level fluctuates, the feedback unit can adjust the content of the feedback taking into account the fluctuations. This enables more comfortable feedback by taking into account the audience's visual fatigue level. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input visual fatigue level data into a generation AI and cause the generation AI to adjust the content of the feedback.

[0106] When providing feedback, the feedback unit can add audio to the feedback by referring to the auditory data of the audience. The auditory data includes, but is not limited to, for example, music preferences, relaxing audio, and past auditory data. For example, the feedback unit can add music that the audience likes to the feedback. The feedback unit can also add audio that the audience finds relaxing to the feedback. The feedback unit can also add optimal audio to the feedback by referring to the past auditory data of the audience. In this way, by referring to the auditory data of the audience, more appropriate audio can be added to the feedback. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without using, AI. For example, the feedback unit can input the auditory data to a generation AI and cause the generation AI to add audio.

[0107] When providing feedback, the feedback unit can customize the feedback method by reflecting the audience's past feedback. Past feedback includes, but is not limited to, feedback history, evaluation data, and specific feedback patterns. For example, the feedback unit customizes the feedback method based on feedback provided by the audience in the past. The feedback unit can also learn specific patterns from the audience's past feedback and optimize the feedback method. The feedback unit can also refer to the audience's past feedback and prioritize feedback of important parts. In this way, the feedback method can be optimized by reflecting the audience's past feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past feedback data into a generation AI and cause the generation AI to customize the feedback method. === Hard Collateral 1-1 === Each of the multiple elements, including the recognition unit, conversion unit, reaction recognition unit, and feedback unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the recognition unit can acquire video using the camera 42 or a sensor of the smart device 14 and recognize the video using the specific processing unit 290 of the data processing device 12. For example, the conversion unit can analyze the video recognized by the specific processing unit 290 of the data processing device 12 and convert it into video that relieves tension for the user. For example, the reaction recognition unit can recognize audience reactions using the camera 42 or a sensor of the smart device 14 and analyze them using the specific processing unit 290 of the data processing device 12. For example, the feedback unit can provide feedback based on the reactions recognized by the specific processing unit 290 of the data processing device 12, and display the feedback on the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned recognition unit, conversion unit, reaction recognition unit, and feedback unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit can acquire an image using the camera 42 or a sensor of the smart glasses 214 and recognize the image using the specific processing unit 290 of the data processing device 12. The conversion unit can, for example, analyze the image recognized by the specific processing unit 290 of the data processing device 12 and convert it into an image that relieves tension for the user. The reaction recognition unit can, for example, recognize the reaction of the audience using the camera 42 or a sensor of the smart glasses 214 and analyze it using the specific processing unit 290 of the data processing device 12. The feedback unit can, for example, provide feedback based on the reaction recognized by the specific processing unit 290 of the data processing device 12, and display it through the display or speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned recognition unit, conversion unit, reaction recognition unit, and feedback unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the recognition unit can acquire video using the camera 42 or a sensor of the headset type terminal 314 and recognize the video using the specific processing unit 290 of the data processing device 12. For example, the conversion unit can analyze the video recognized by the specific processing unit 290 of the data processing device 12 and convert it into video that relieves tension for the user. For example, the reaction recognition unit can recognize reactions of the audience using the camera 42 or a sensor of the headset type terminal 314 and analyze them using the specific processing unit 290 of the data processing device 12. For example, the feedback unit can provide feedback based on the reaction recognized by the specific processing unit 290 of the data processing device 12, and display it on the display or speaker of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned recognition unit, conversion unit, reaction recognition unit, and feedback unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the recognition unit can acquire video using the camera 42 or a sensor of the robot 414 and recognize the video using the specific processing unit 290 of the data processing device 12. The conversion unit can, for example, analyze the video recognized by the specific processing unit 290 of the data processing device 12 and convert it into video that relieves tension for the user. The reaction recognition unit can, for example, recognize the audience's reaction using the camera 42 or a sensor of the robot 414 and analyze it using the specific processing unit 290 of the data processing device 12. The feedback unit can, for example, provide feedback based on the reaction recognized by the specific processing unit 290 of the data processing device 12, and display it through a display or speaker of the robot 414.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The presentation support system may further include a speech recognition unit. The speech recognition unit may analyze the presenter's speech in real time and evaluate the clarity and speed of pronunciation. For example, if the presenter is speaking too quickly, the speech recognition unit may provide feedback to adjust the speed. The speech recognition unit may also analyze the tone and strength of the presenter's voice and suggest appropriate adjustments based on the audience's reactions. Furthermore, if the presenter's pronunciation is unclear, the speech recognition unit may provide feedback to encourage the presenter to repeat the pronunciation. Thus, providing feedback on the presenter's speech enables more effective presentations.

[0110] The presentation support system can further include a gesture recognition unit. The gesture recognition unit can analyze the presenter's hand and body movements and suggest appropriate gestures. For example, if the presenter is speaking without using his or her hands, the gesture recognition unit can provide feedback to encourage additional hand movements. The gesture recognition unit can also suggest that the presenter reduce his or her movements if the presenter's movements are excessive. Furthermore, the gesture recognition unit can suggest more effective gestures if the presenter's movements are not effective for the audience. This allows for more effective presentations by providing feedback on the presenter's gestures.

[0111] The presentation support system may further include a background music providing unit. The background music providing unit can provide appropriate background music depending on the content and atmosphere of the presentation. For example, if a relaxed atmosphere is desired, the background music providing unit can play calm music. Alternatively, if an important part of the presentation is desired to be emphasized, the background music providing unit can play tense music. Furthermore, the background music providing unit can adjust the music selection based on the audience's reaction. In this way, the background music provided can enhance the effectiveness of the presentation.

[0112] The presentation support system may further include a visual effects provider. The visual effects provider may provide appropriate visual effects according to the content of the presentation. For example, the visual effects provider may add animations to slides to emphasize important points. The visual effects provider may also add dynamic effects to slides to attract the audience's attention. The visual effects provider may also adjust the design of the slides according to the atmosphere of the presentation. This may enhance the effectiveness of the presentation by providing visual effects.

[0113] The presentation support system can further include a question and answer support unit. The question and answer support unit can analyze questions from the audience in real time and suggest appropriate answers. For example, if the presenter cannot answer a question immediately, the question and answer support unit can provide related information. The question and answer support unit can also refer to past question data and suggest answers to similar questions. Furthermore, the question and answer support unit can suggest answer structures so that the presenter can provide appropriate answers to questions. This can improve the effectiveness of presentations by providing support for question and answering.

[0114] The presentation support system further includes an emotion estimation unit that can estimate the presenter's emotion and provide feedback based on the estimated emotion. For example, if the presenter is nervous, the emotion estimation unit can provide relaxing images or music. If the presenter is confident, the emotion estimation unit can also provide positive feedback that further increases that confidence. Furthermore, if the presenter is tired, the emotion estimation unit can also provide feedback encouraging the presenter to take a break. In this way, the effectiveness of the presentation can be improved by providing appropriate feedback based on the presenter's emotion.

[0115] The presentation support system can also estimate the emotions of the audience and adjust the content of the presentation based on the estimated emotions. For example, if the audience is excited, the tempo of the presentation can be increased. If the audience is relaxed, the tempo of the presentation can be decreased. Furthermore, if the audience is focused, the content of the presentation can be adjusted to emphasize important points. This allows for more effective presentations by adjusting the content of the presentation based on the emotions of the audience.

[0116] The presentation support system can also estimate the emotions of the audience and adjust the content of the feedback based on the estimated emotions. For example, if the audience is excited, it can provide positive feedback. If the audience is relaxed, it can provide general feedback. Furthermore, if the audience is focused, it can provide detailed feedback. This allows for more appropriate feedback by adjusting the content of the feedback based on the audience's emotions.

[0117] The presentation support system can also estimate the emotions of the audience and adjust the feedback display method based on the estimated emotions. For example, if the audience is excited, a visually stimulating display method can be provided. If the audience is relaxed, a calm display method can be provided. Furthermore, if the audience is focused, a highly visible display method can be provided. This allows for more appropriate feedback display by adjusting the feedback display method based on the audience's emotions.

[0118] The presentation support system can also estimate the presenter's emotions and adjust the progress of the presentation based on the estimated emotions. For example, if the presenter is nervous, the system can slow down the progress. On the other hand, if the presenter is confident, the system can smooth out the progress. Furthermore, if the presenter is tired, the system can adjust the progress of the presentation to include breaks. In this way, adjusting the progress of the presentation based on the presenter's emotions enables more effective presentations.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The recognition unit recognizes the image. The recognition unit acquires the image using, for example, a camera or sensor inside the goggles. The recognition unit can also estimate the user's emotions and adjust the accuracy of the image recognition based on the estimated user emotions. For example, if the user is nervous, the recognition unit will increase the accuracy of the image recognition and accurately recognize even the smallest details. Step 2: The conversion unit analyzes the image recognized by the recognition unit and converts it into an image that relieves the user's tension. For example, the conversion unit converts it into an image of a relaxing scene or a message of encouragement. The conversion unit can also estimate the user's emotions and adjust the content of the converted image based on the estimated user emotions. For example, if the user is nervous, the conversion unit displays a relaxing scene or a message of encouragement. Step 3: The reaction recognition unit recognizes the reactions of the audience. For example, the reaction recognition unit analyzes the facial expressions and movements of the audience. The reaction recognition unit can also estimate the emotions of the audience and adjust the accuracy of reaction recognition based on the estimated emotions of the audience. For example, if the audience is excited, the reaction recognition unit increases the accuracy of reaction recognition. Step 4: The feedback unit provides feedback based on the reactions recognized by the reaction recognition unit. For example, the feedback unit generates and displays a "like" mark. The feedback unit can also display a message prompting a re-explanation if the audience's level of understanding is insufficient. For example, if the audience has a confused look on their face, the feedback unit displays a message prompting a re-explanation of that part.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 recognition unit that recognizes the video; a conversion unit that analyzes the image recognized by the recognition unit and converts it into an image that relieves tension for the user; a reaction recognition unit that recognizes reactions of the audience; a feedback unit that provides feedback based on the reaction recognized by the reaction recognition unit. A system characterized by:

2. The recognition unit Capture images using a camera or sensor inside the goggles 2. The system of claim 1.

3. The conversion unit Convert it into a video of a relaxing scene or a message of encouragement 2. The system of claim 1.

4. The reaction recognition unit Analyzing the audience's facial expressions and movements 2. The system of claim 1.

5. The feedback unit If the audience's level of understanding is low, a message will be displayed prompting further explanation.

2. The system of claim 1.

6. The feedback unit Change the order of your presentation depending on audience reaction 2. The system of claim 1.

7. The recognition unit Estimate the user's emotions and adjust the accuracy of video recognition based on the estimated user emotions.

2. The system of claim 1.

Images