system

Abstract

An object of the system according to the embodiment is to allow even a person who is not good at presentation to effectively practice and improve his / her skill.SOLUTION: A system includes a virtual presentation experience part, a material creation part, and a presentation skill instruction part. The virtual presentation experience unit utilizes AR and VR technologies to provide a virtual presentation experience. The material creation part inputs the presentation conditions to the AI and creates an appropriate material. In the presentation skill instructor, a AI instructs a presentation skill.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 technologies do not adequately provide a means for people who are not good at presentations to effectively practice and improve their skills, and there is room for improvement.

[0005] The system according to the embodiment aims to enable even those who are not good at presentations to practice effectively and improve their skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a virtual presentation experience unit, a material creation unit, and a presentation skills training unit. The virtual presentation experience unit uses AR and VR technologies to provide a virtual presentation experience. The material creation unit inputs presentation conditions into AI and creates appropriate materials. The presentation skills training unit uses AI to provide training in presentation skills. [Effects of the Invention]

[0007] The system according to the embodiment allows even those who are not good at presentations to practice effectively and improve their skills. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A presentation support system according to an embodiment of the present invention uses AI to train even those who are not good at presentations to become professional presenters. This system utilizes AR and VR technologies to provide a virtual presentation experience, input presentation conditions into the AI, create appropriate materials, and have the AI ​​provide training in presentation skills. This allows the presentation support system to train even those who are not good at presentations to become professional presenters.

[0029] A presentation support system according to an embodiment includes a virtual presentation experience unit, a document creation unit, and a presentation skill training unit. The virtual presentation experience unit utilizes AR and VR technologies to provide a virtual presentation experience. For example, a user can wear a VR headset and experience a simulation of giving a presentation in a virtual conference room or auditorium. Furthermore, AR technology can be used to recreate a situation similar to a real presentation environment, providing realistic feedback. The document creation unit inputs presentation conditions into an AI and creates appropriate documents. For example, by inputting information such as the presentation topic, target audience, and time allocation, the generation AI creates optimal slides and documents based on that information. It also provides advice on presentation structure and delivery style. The presentation skill training unit uses AI to provide presentation skill training. For example, when a user gives a presentation in a virtual environment, the generation AI provides real-time feedback and points out areas for improvement. It also provides advice on the user's delivery style and gestures to improve the quality of the presentation. This allows the presentation support system according to an embodiment to train even those who are not good at presentations to become professional presenters.

[0030] The virtual presentation experience unit simulates audience reactions within a virtual environment, allowing users to practice responding appropriately to those reactions. In the virtual presentation experience unit, for example, a generation AI simulates audience reactions within the virtual environment. For example, it recreates reactions when the audience is interested or bored, allowing users to practice responding appropriately to each situation. The generation AI also adds a function to the audience simulation that allows users to randomly ask questions, allowing users to practice responding appropriately to unexpected questions. Furthermore, the audience reactions within the virtual environment can be changed in real time. For example, the audience's facial expressions and attitudes can change depending on the content and speaking style of the user's presentation, allowing users to practice responding to those changes. This allows users to practice responding appropriately to audience reactions, helping them prepare for actual presentations.

[0031] The virtual presentation experience can be made multilingual to accommodate audiences of different cultures and languages. To make the virtual presentation experience multilingual, for example, the generation AI adds a function to translate the presentation content in real time. For example, it can support multiple languages, such as English, Japanese, and Chinese. Furthermore, the generation AI provides advice that takes cultural backgrounds and customs into account, so that appropriate presentations can be made to audiences of different cultures. For example, it can point out differences in gestures and expressions. Furthermore, to realize a multilingual virtual presentation experience, a function can be added to automatically switch the text and audio in the virtual environment according to the language selected by the user. This will enable global presentations by making it possible to accommodate audiences of different cultures and languages.

[0032] The virtual presentation experience unit can add a function that records presentations in a virtual environment and allows the generation AI to provide detailed feedback later. The virtual presentation experience unit, for example, adds a function that records presentations in a virtual environment and allows users to play back and review the recording later. The generation AI provides detailed feedback based on the recorded content. The generation AI also analyzes the recorded presentation content and points out specific areas for improvement. For example, it provides advice on how to speak or use slides. Furthermore, a function is added that allows the recorded presentation content to be shared with other users and receive feedback. This allows opinions from other perspectives to be incorporated. This allows presentations in a virtual environment to be recorded and detailed feedback to be provided later, improving the quality of presentations.

[0033] When creating presentation materials, the material creation unit allows the generation AI to refer to past success stories and propose the optimal material structure. For example, when creating presentation materials, the material creation unit allows the generation AI to search a database for past success stories and propose the optimal material structure. For example, presentation materials on the same topic are used as reference. The generation AI also analyzes past success stories and extracts their commonalities and characteristics. Based on this, it proposes the optimal material structure to the user. Furthermore, during the presentation material creation process, the generation AI presents past success stories in real time, allowing the user to create materials while referring to them. This allows effective presentation materials to be created by referring to past success stories and proposing the optimal material structure.

[0034] The material creation unit can simulate the audience's level of comprehension of presentation materials and provide advice for creating easy-to-understand materials. For example, the material creation unit has the generation AI analyze presentation materials and simulate the audience's level of comprehension. For example, if difficult terminology or complex diagrams are included, the unit provides advice to change them to simpler expressions and easier-to-understand diagrams. To simulate the audience's level of comprehension, the generation AI also references past presentation materials and their evaluation data. Based on this, the unit provides the user with advice on creating optimal materials. Furthermore, during the presentation material creation process, the generation AI simulates the audience's level of comprehension in real time, allowing the user to improve the materials while referring to the results. This allows the creation of effective presentation materials by simulating the audience's level of comprehension and providing advice for creating easy-to-understand materials.

[0035] The document creation unit can add a function to automatically convert presentation materials into different formats. For example, the document creation unit adds a function to automatically convert presentation materials created by the generation AI into PDF format, allowing users to easily share the materials. It also adds a function to convert presentation materials into PPT format, allowing users to give presentations in slideshow format. The generation AI also automatically sets the slide design and animation. It also adds a function to convert presentation materials into video format, allowing users to create video presentations. The generation AI also automatically adds audio narration and subtitles. This allows presentation materials to be automatically converted into different formats, allowing for flexible sharing of materials and presentation formats.

[0036] The document creation unit allows the generation AI to automatically search for the latest relevant research and data when creating presentation materials and reflect them in the materials. For example, when creating presentation materials, the document creation unit allows the generation AI to automatically search for the latest research papers and data on the Internet and reflect them in the materials. For example, the latest statistical data and research results are added to slides. The generation AI also searches relevant databases and automatically collects information necessary for the presentation materials. For example, industry reports and market research data are reflected in the materials. Furthermore, during the process of creating presentation materials, the generation AI searches for the latest research and data in real time, allowing the user to create materials while referring to them. This allows the generation AI to automatically search for the latest research and data and reflect them in the materials, making it possible to create highly reliable presentation materials.

[0037] The presentation skill coaching department can add a generation AI function that analyzes the user's voice tone and speed and suggests the optimal speaking style. For example, the generation AI analyzes the user's voice tone and speed in real time and suggests the optimal speaking style. For example, if the voice tone is monotonous, it will provide advice to add intonation. The generation AI also records the user's speaking style and analyzes the data to point out areas for improvement. For example, if the speaking speed is too fast, it will provide advice to speak more slowly. Furthermore, the generation AI analyzes the user's voice tone and speed and suggests the optimal speaking style based on the content of the presentation. For example, it will advise on how to speak to emphasize important points. In this way, the quality of presentations is improved by analyzing the user's voice tone and speed and suggesting the optimal speaking style.

[0038] The presentation skills coaching unit can track gestures and posture during a presentation and provide feedback on areas for improvement in real time. For example, the generation AI uses a camera to track the user's gestures and posture and provide real-time feedback. For example, if there are few hand movements, it will offer advice to increase gestures. The generation AI can also analyze the user's posture and provide advice on maintaining appropriate posture. For example, it may instruct the user to keep their back straight. Furthermore, based on gesture and posture data, the generation AI can point out specific areas for improvement to improve the quality of the presentation. For example, it can offer advice on eye distribution and the timing of hand movements. In this way, the quality of presentations can be improved by tracking gestures and posture during a presentation and providing real-time feedback.

[0039] The presentation skills training unit can provide presentation skills training as a customizable training program specialized for different industries or occupations. In the presentation skills training unit, for example, the generation AI provides a presentation skills training program specialized for different industries or occupations. For example, it customizes presentation training for sales positions or presentation training for technical positions. In addition, a function is added that allows users to select their own industry or occupation and receive a presentation skills training program that suits them. For example, it provides presentation training for the medical industry. Furthermore, the generation AI automatically creates training programs specialized for different industries or occupations, allowing users to select one that suits their needs. For example, it provides presentation training for the education industry. In this way, by providing customizable training programs specialized for different industries and occupations, it becomes possible to improve presentation skills according to the users' needs.

[0040] The presentation skill coaching department can gamify the improvement of presentation skills, allowing users to hone their skills while having fun. The presentation skill coaching department can, for example, gamify presentation skill training, allowing users to hone their skills while having fun. For example, a presentation mini-game can be used to practice speaking and gestures. A feature can also be added that allows users to compete for presentation scores within the game, allowing users to improve their skills while competing with other players. For example, a ranking function can be introduced. Furthermore, gamifying presentation skill training makes it easier for users to train continuously. For example, a system can be introduced in which users can set daily training goals and receive rewards for achieving them. In this way, gamifying the improvement of presentation skills allows users to hone their skills while having fun.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The presentation support system can also add a generation AI function that analyzes the user's voice tone and speed and suggests the optimal speaking style. For example, if the voice tone is monotonous, advice is given to add more inflection. The generation AI can also record the user's speech and analyze the data to point out areas for improvement. For example, if the speech speed is too fast, advice is given to speak more slowly. The generation AI can also analyze the user's voice tone and speed and suggest the optimal speaking style based on the content of the presentation. For example, advice is given on how to speak to emphasize important points. This allows the quality of presentations to be improved by analyzing the user's voice tone and speed and suggesting the optimal speaking style.

[0043] Presentation support systems can also be made multilingual to accommodate audiences of different cultures and languages. For example, to make the virtual presentation experience multilingual, the generation AI can add a function to translate the presentation content in real time. For example, it can support multiple languages ​​such as English, Japanese, and Chinese. Furthermore, to ensure appropriate presentations for audiences of different cultures, the generation AI can provide advice that takes cultural backgrounds and customs into account. For example, it can point out differences in gestures and expressions. Furthermore, to realize a multilingual virtual presentation experience, a function can be added to automatically switch the text and audio in the virtual environment according to the language selected by the user. This will enable global presentations by making it possible to accommodate audiences of different cultures and languages.

[0044] Furthermore, when creating presentation materials, the presentation support system's generation AI can refer to past success stories and propose the optimal material structure. For example, it can refer to presentation materials on the same topic. The generation AI can also analyze past success stories and extract commonalities and characteristics. Based on this, it can propose the optimal material structure to the user. Furthermore, during the presentation material creation process, the generation AI can present past success stories in real time, allowing the user to create their materials while referring to them. This allows for the creation of effective presentation materials by referring to past success stories and proposing the optimal material structure.

[0045] The presentation support system can also simulate the audience's level of comprehension of presentation materials and provide advice for creating easy-to-understand materials. For example, the generation AI analyzes presentation materials and simulates the audience's level of comprehension. For example, if difficult terminology or complex diagrams are included, advice is provided to change them to simpler expressions and easier-to-understand diagrams. To simulate the audience's level of comprehension, the generation AI also references past presentation materials and their evaluation data. Based on this, it provides the user with advice on creating optimal materials. Furthermore, during the presentation material creation process, the generation AI simulates the audience's level of comprehension in real time, allowing the user to improve the materials while referring to the results. This allows the creation of effective presentation materials by simulating the audience's level of comprehension and providing advice for creating easy-to-understand materials.

[0046] The presentation support system can also add a function to automatically convert presentation materials into different formats. For example, a function can be added to automatically convert presentation materials created by the generation AI into PDF format, allowing users to easily share the materials. A function can also be added to convert presentation materials into PPT format, allowing users to give presentations in slideshow format. The generation AI can also automatically set the slide design and animation. A function can also be added to convert presentation materials into video format, allowing users to create video presentations. The generation AI can also automatically add audio narration and subtitles. This allows presentation materials to be automatically converted into different formats, allowing for flexible sharing of materials and presentation formats.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The Virtual Presentation Experience Unit uses AR and VR technologies to provide a virtual presentation experience. For example, users can wear a VR headset and experience a simulation of giving a presentation in a virtual conference room or auditorium. AR technology can also be used to recreate a situation close to a real presentation environment, allowing users to receive realistic feedback. Step 2: The material creation department inputs the presentation conditions into the AI ​​and creates appropriate materials. For example, by inputting information such as the presentation theme, target audience, and time allocation, the generation AI will create optimal slides and materials based on that information. It will also provide advice on the presentation structure and delivery style. Step 3: The Presentation Skills Coaching Unit uses AI to teach presentation skills. For example, when a user gives a presentation in a virtual environment, the AI ​​provides real-time feedback and points out areas for improvement. It also provides advice on the user's speaking style and gestures to improve the quality of the presentation.

[0049] (Example 2) A presentation support system according to an embodiment of the present invention uses AI to train even those who are not good at presentations to become professional presenters. This system utilizes AR and VR technologies to provide a virtual presentation experience, input presentation conditions into the AI, create appropriate materials, and have the AI ​​provide training in presentation skills. This allows the presentation support system to train even those who are not good at presentations to become professional presenters.

[0050] A presentation support system according to an embodiment includes a virtual presentation experience unit, a document creation unit, and a presentation skill training unit. The virtual presentation experience unit utilizes AR and VR technologies to provide a virtual presentation experience. For example, a user can wear a VR headset and experience a simulation of giving a presentation in a virtual conference room or auditorium. Furthermore, AR technology can be used to recreate a situation similar to a real presentation environment, providing realistic feedback. The document creation unit inputs presentation conditions into an AI and creates appropriate documents. For example, by inputting information such as the presentation topic, target audience, and time allocation, the generation AI creates optimal slides and documents based on that information. It also provides advice on presentation structure and delivery style. The presentation skill training unit uses AI to provide presentation skill training. For example, when a user gives a presentation in a virtual environment, the generation AI provides real-time feedback and points out areas for improvement. It also provides advice on the user's delivery style and gestures to improve the quality of the presentation. This allows the presentation support system according to an embodiment to train even those who are not good at presentations to become professional presenters.

[0051] The virtual presentation experience unit tracks the user's gaze and facial expressions, allowing the generation AI to provide real-time feedback. For example, when a user wears a VR headset and gives a presentation in a virtual conference room, the virtual presentation experience unit uses gaze tracking technology to analyze the user's gaze movements. The generation AI provides real-time feedback on appropriate gaze distribution and key points based on the gaze movements. The unit also captures the user's facial expressions during the presentation with a camera and uses facial expression analysis technology to estimate the user's emotional state. The generation AI provides real-time advice to reduce tension and anxiety based on the user's facial expressions. Furthermore, the generation AI integrates the user's gaze and facial expression data to provide feedback based on the progress of the presentation. For example, if the user's gaze is not focused on a particular slide or their facial expression is stiff, the generation AI will point out appropriate areas for improvement. This improves the quality of presentations by providing real-time feedback based on the user's gaze and facial expressions.

[0052] The virtual presentation experience unit simulates audience reactions within a virtual environment, allowing users to practice responding appropriately to those reactions. In the virtual presentation experience unit, for example, a generation AI simulates audience reactions within the virtual environment. For example, it recreates reactions when the audience is interested or bored, allowing users to practice responding appropriately to each situation. The generation AI also adds a function to the audience simulation that allows users to randomly ask questions, allowing users to practice responding appropriately to unexpected questions. Furthermore, the audience reactions within the virtual environment can be changed in real time. For example, the audience's facial expressions and attitudes can change depending on the content and speaking style of the user's presentation, allowing users to practice responding to those changes. This allows users to practice responding appropriately to audience reactions, helping them prepare for actual presentations.

[0053] The virtual presentation experience unit uses the emotion estimation function to measure the user's level of tension and confidence, and based on that, can provide relaxation techniques and advice to boost confidence. For example, the virtual presentation experience unit uses the emotion estimation function to measure the user's level of tension in real time. For example, it analyzes heart rate and facial expression changes and suggests relaxation techniques if the level of tension is high. It also analyzes the tone and speed of voice used during the presentation to measure the user's level of confidence. If the level of confidence is low, the generation AI provides advice to improve the tone of voice and speaking style. Furthermore, the user's level of tension and confidence is visualized based on the emotion estimation data. For example, graphs and charts can be used to help the user understand their own emotional state. This allows the user's level of tension and confidence to be measured and, based on that, provides relaxation techniques and advice to boost confidence, thereby improving the quality of the presentation.

[0054] The virtual presentation experience can be made multilingual to accommodate audiences of different cultures and languages. To make the virtual presentation experience multilingual, for example, the generation AI adds a function to translate the presentation content in real time. For example, it can support multiple languages, such as English, Japanese, and Chinese. Furthermore, the generation AI provides advice that takes cultural backgrounds and customs into account, so that appropriate presentations can be made to audiences of different cultures. For example, it can point out differences in gestures and expressions. Furthermore, to realize a multilingual virtual presentation experience, a function can be added to automatically switch the text and audio in the virtual environment according to the language selected by the user. This will enable global presentations by making it possible to accommodate audiences of different cultures and languages.

[0055] The virtual presentation experience unit can add a function that records presentations in a virtual environment and allows the generation AI to provide detailed feedback later. The virtual presentation experience unit, for example, adds a function that records presentations in a virtual environment and allows users to play back and review the recording later. The generation AI provides detailed feedback based on the recorded content. The generation AI also analyzes the recorded presentation content and points out specific areas for improvement. For example, it provides advice on how to speak or use slides. Furthermore, a function is added that allows the recorded presentation content to be shared with other users and receive feedback. This allows opinions from other perspectives to be incorporated. This allows presentations in a virtual environment to be recorded and detailed feedback to be provided later, improving the quality of presentations.

[0056] The virtual presentation experience unit uses an emotion estimation function to display the emotional reactions of the audience in the virtual environment in real time, allowing the user to adjust the presentation based on those reactions. The virtual presentation experience unit, for example, uses the emotion estimation function to display the emotional reactions of the audience in the virtual environment in real time. For example, an indicator showing whether the audience is interested is displayed. In addition, a function is added that allows the user to adjust the content of the presentation in real time based on the audience's emotional reactions. For example, if interest wanes, the user can take action such as changing the topic. Furthermore, the audience's reactions are visualized based on the emotion estimation data. For example, graphs and charts can be used to display changes in the audience's emotions, allowing the user to improve the presentation based on that data. This allows the audience's emotional reactions to be displayed in real time, allowing the user to adjust the presentation based on those reactions, enabling more effective presentations.

[0057] When creating presentation materials, the material creation unit allows the generation AI to refer to past success stories and propose the optimal material structure. For example, when creating presentation materials, the material creation unit allows the generation AI to search a database for past success stories and propose the optimal material structure. For example, presentation materials on the same topic are used as reference. The generation AI also analyzes past success stories and extracts their commonalities and characteristics. Based on this, it proposes the optimal material structure to the user. Furthermore, during the presentation material creation process, the generation AI presents past success stories in real time, allowing the user to create materials while referring to them. This allows effective presentation materials to be created by referring to past success stories and proposing the optimal material structure.

[0058] The material creation unit can simulate the audience's level of comprehension of presentation materials and provide advice for creating easy-to-understand materials. For example, the material creation unit has the generation AI analyze presentation materials and simulate the audience's level of comprehension. For example, if difficult terminology or complex diagrams are included, the unit provides advice to change them to simpler expressions and easier-to-understand diagrams. To simulate the audience's level of comprehension, the generation AI also references past presentation materials and their evaluation data. Based on this, the unit provides the user with advice on creating optimal materials. Furthermore, during the presentation material creation process, the generation AI simulates the audience's level of comprehension in real time, allowing the user to improve the materials while referring to the results. This allows the creation of effective presentation materials by simulating the audience's level of comprehension and providing advice for creating easy-to-understand materials.

[0059] The document creation unit can add a function to automatically convert presentation materials into different formats. For example, the document creation unit adds a function to automatically convert presentation materials created by the generation AI into PDF format, allowing users to easily share the materials. It also adds a function to convert presentation materials into PPT format, allowing users to give presentations in slideshow format. The generation AI also automatically sets the slide design and animation. It also adds a function to convert presentation materials into video format, allowing users to create video presentations. The generation AI also automatically adds audio narration and subtitles. This allows presentation materials to be automatically converted into different formats, allowing for flexible sharing of materials and presentation formats.

[0060] The document creation unit allows the generation AI to automatically search for the latest relevant research and data when creating presentation materials and reflect them in the materials. For example, when creating presentation materials, the document creation unit allows the generation AI to automatically search for the latest research papers and data on the Internet and reflect them in the materials. For example, the latest statistical data and research results are added to slides. The generation AI also searches relevant databases and automatically collects information necessary for the presentation materials. For example, industry reports and market research data are reflected in the materials. Furthermore, during the process of creating presentation materials, the generation AI searches for the latest research and data in real time, allowing the user to create materials while referring to them. This allows the generation AI to automatically search for the latest research and data and reflect them in the materials, making it possible to create highly reliable presentation materials.

[0061] The document creation unit can use the emotion estimation function to analyze the user's emotions while creating documents and provide an interface for eliciting positive emotions. The document creation unit, for example, uses the emotion estimation function to analyze the user's emotions while creating documents in real time. For example, if the positive emotions are low, an encouraging message or a success story is displayed. The document creation unit also analyzes the user's emotional state and provides an interface for eliciting positive emotions. For example, a function for playing relaxing music or a function for visualizing work progress may be added. Furthermore, the user's emotional state is visualized based on the emotion estimation data. For example, graphs and charts may be used to make it easier for the user to understand their own emotional state. This allows for effective document creation by analyzing the user's emotions and providing an interface for eliciting positive emotions.

[0062] The presentation skill coaching department can add a generation AI function that analyzes the user's voice tone and speed and suggests the optimal speaking style. For example, the generation AI analyzes the user's voice tone and speed in real time and suggests the optimal speaking style. For example, if the voice tone is monotonous, it will provide advice to add intonation. The generation AI also records the user's speaking style and analyzes the data to point out areas for improvement. For example, if the speaking speed is too fast, it will provide advice to speak more slowly. Furthermore, the generation AI analyzes the user's voice tone and speed and suggests the optimal speaking style based on the content of the presentation. For example, it will advise on how to speak to emphasize important points. In this way, the quality of presentations is improved by analyzing the user's voice tone and speed and suggesting the optimal speaking style.

[0063] The presentation skills coaching unit can track gestures and posture during a presentation and provide feedback on areas for improvement in real time. For example, the generation AI uses a camera to track the user's gestures and posture and provide real-time feedback. For example, if there are few hand movements, it will offer advice to increase gestures. The generation AI can also analyze the user's posture and provide advice on maintaining appropriate posture. For example, it may instruct the user to keep their back straight. Furthermore, based on gesture and posture data, the generation AI can point out specific areas for improvement to improve the quality of the presentation. For example, it can offer advice on eye distribution and the timing of hand movements. In this way, the quality of presentations can be improved by tracking gestures and posture during a presentation and providing real-time feedback.

[0064] The presentation skill coaching unit can use the emotion estimation function to analyze the user's emotional changes during a presentation and provide specific advice to relieve tension. The presentation skill coaching unit, for example, uses the emotion estimation function to analyze the user's emotional changes during a presentation in real time. For example, if the user is feeling nervous, it can provide advice to encourage deep breathing. It can also analyze the user's emotional state and provide specific advice to relieve tension. For example, it can suggest simple exercises to help the user relax. Furthermore, it can visualize the user's emotional changes based on the emotion estimation data. For example, it can use graphs and charts to make it easier for the user to understand their own emotional state. This allows the quality of the presentation to be improved by analyzing the user's emotional changes during a presentation and providing specific advice to relieve tension.

[0065] The presentation skills training unit can provide presentation skills training as a customizable training program specialized for different industries or occupations. In the presentation skills training unit, for example, the generation AI provides a presentation skills training program specialized for different industries or occupations. For example, it customizes presentation training for sales positions or presentation training for technical positions. In addition, a function is added that allows users to select their own industry or occupation and receive a presentation skills training program that suits them. For example, it provides presentation training for the medical industry. Furthermore, the generation AI automatically creates training programs specialized for different industries or occupations, allowing users to select one that suits their needs. For example, it provides presentation training for the education industry. In this way, by providing customizable training programs specialized for different industries and occupations, it becomes possible to improve presentation skills according to the users' needs.

[0066] The presentation skill coaching department can gamify the improvement of presentation skills, allowing users to hone their skills while having fun. The presentation skill coaching department can, for example, gamify presentation skill training, allowing users to hone their skills while having fun. For example, a presentation mini-game can be used to practice speaking and gestures. A feature can also be added that allows users to compete for presentation scores within the game, allowing users to improve their skills while competing with other players. For example, a ranking function can be introduced. Furthermore, gamifying presentation skill training makes it easier for users to train continuously. For example, a system can be introduced in which users can set daily training goals and receive rewards for achieving them. In this way, gamifying the improvement of presentation skills allows users to hone their skills while having fun.

[0067] The presentation skill coaching department can use the emotion estimation function to provide feedback on the user's presentation from an emotional perspective, aiming to create a presentation that is more likely to resonate with the audience. The presentation skill coaching department, for example, uses the emotion estimation function to provide feedback on the user's presentation from an emotional perspective. For example, it can analyze changes in emotions during the presentation and offer advice on how to speak in a way that is more likely to resonate with the audience. In addition, to provide emotional feedback on the user's presentation, the generation AI can point out specific areas for improvement based on the emotion analysis data. For example, it can suggest ways of speaking and expressing emotions that incorporate emotion. Furthermore, it can analyze the emotional impact the user's presentation has on the audience based on the emotion estimation data and offer advice to create a presentation that is more likely to resonate with the audience. For example, it can suggest incorporating emotional anecdotes. In this way, by using the emotion estimation function to provide feedback on the user's presentation from an emotional perspective, it becomes possible to create a presentation that is more likely to resonate with the audience.

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

[0069] The presentation support system can also add a generation AI function that analyzes the user's voice tone and speed and suggests the optimal speaking style. For example, if the voice tone is monotonous, advice is given to add more inflection. The generation AI can also record the user's speech and analyze the data to point out areas for improvement. For example, if the speech speed is too fast, advice is given to speak more slowly. The generation AI can also analyze the user's voice tone and speed and suggest the optimal speaking style based on the content of the presentation. For example, advice is given on how to speak to emphasize important points. This allows the quality of presentations to be improved by analyzing the user's voice tone and speed and suggesting the optimal speaking style.

[0070] Presentation support systems can also be made multilingual to accommodate audiences of different cultures and languages. For example, to make the virtual presentation experience multilingual, the generation AI can add a function to translate the presentation content in real time. For example, it can support multiple languages ​​such as English, Japanese, and Chinese. Furthermore, to ensure appropriate presentations for audiences of different cultures, the generation AI can provide advice that takes cultural backgrounds and customs into account. For example, it can point out differences in gestures and expressions. Furthermore, to realize a multilingual virtual presentation experience, a function can be added to automatically switch the text and audio in the virtual environment according to the language selected by the user. This will enable global presentations by making it possible to accommodate audiences of different cultures and languages.

[0071] Furthermore, when creating presentation materials, the presentation support system's generation AI can refer to past success stories and propose the optimal material structure. For example, it can refer to presentation materials on the same topic. The generation AI can also analyze past success stories and extract commonalities and characteristics. Based on this, it can propose the optimal material structure to the user. Furthermore, during the presentation material creation process, the generation AI can present past success stories in real time, allowing the user to create their materials while referring to them. This allows for the creation of effective presentation materials by referring to past success stories and proposing the optimal material structure.

[0072] The presentation support system can also simulate the audience's level of comprehension of presentation materials and provide advice for creating easy-to-understand materials. For example, the generation AI analyzes presentation materials and simulates the audience's level of comprehension. For example, if difficult terminology or complex diagrams are included, advice is provided to change them to simpler expressions and easier-to-understand diagrams. To simulate the audience's level of comprehension, the generation AI also references past presentation materials and their evaluation data. Based on this, it provides the user with advice on creating optimal materials. Furthermore, during the presentation material creation process, the generation AI simulates the audience's level of comprehension in real time, allowing the user to improve the materials while referring to the results. This allows the creation of effective presentation materials by simulating the audience's level of comprehension and providing advice for creating easy-to-understand materials.

[0073] The presentation support system can also add a function to automatically convert presentation materials into different formats. For example, a function can be added to automatically convert presentation materials created by the generation AI into PDF format, allowing users to easily share the materials. A function can also be added to convert presentation materials into PPT format, allowing users to give presentations in slideshow format. The generation AI can also automatically set the slide design and animation. A function can also be added to convert presentation materials into video format, allowing users to create video presentations. The generation AI can also automatically add audio narration and subtitles. This allows presentation materials to be automatically converted into different formats, allowing for flexible sharing of materials and presentation formats.

[0074] The presentation support system can also use emotion estimation to measure a user's level of tension and confidence and provide relaxation techniques and advice to boost confidence based on the results. For example, the emotion estimation function can be used to measure a user's level of tension in real time. For example, it can analyze changes in heart rate and facial expressions and suggest relaxation techniques if the user is highly nervous. The system can also analyze the tone and speed of voice used during a presentation to measure the user's confidence. If the user's confidence level is low, the generation AI can provide advice on improving the tone of voice and speaking style. Furthermore, the emotion estimation data can be used to visualize the user's level of tension and confidence. For example, graphs and charts can be used to help users understand their own emotional state. This allows the system to measure a user's level of tension and confidence and provide relaxation techniques and advice to boost confidence based on the results, thereby improving the quality of presentations.

[0075] The presentation support system further uses an emotion estimation function to display the audience's emotional reactions in the virtual environment in real time, allowing the user to adjust the presentation based on those reactions. For example, the emotion estimation function can be used to display the audience's emotional reactions in real time in the virtual environment. For example, an indicator can be displayed to show whether the audience is interested. A function can also be added that allows the user to adjust the presentation content in real time based on the audience's emotional reactions. For example, if interest wanes, the system can take action such as changing the topic. Furthermore, the audience's reactions can be visualized based on the emotion estimation data. For example, graphs and charts can be used to display changes in the audience's emotions, allowing the user to improve the presentation based on that data. This allows the audience's emotional reactions to be displayed in real time, allowing the user to adjust the presentation based on those reactions, enabling more effective presentations.

[0076] The presentation support system can further use an emotion estimation function to analyze a user's emotional changes during a presentation and provide specific advice to relieve tension. For example, the emotion estimation function can be used to analyze a user's emotional changes during a presentation in real time. For example, if the user is feeling nervous, advice to take deep breaths can be provided. The system can also analyze the user's emotional state and provide specific advice to relieve tension. For example, simple exercises for relaxation can be suggested. Furthermore, the system can visualize the user's emotional changes based on the emotion estimation data. For example, graphs and charts can be used to make it easier for the user to understand their own emotional state. This allows the quality of the presentation to be improved by analyzing a user's emotional changes during a presentation and providing specific advice to relieve tension.

[0077] The presentation support system can also use an emotion estimation function to provide feedback on a user's presentation from an emotional perspective, aiming to create a presentation that is more likely to resonate with the audience. For example, the emotion estimation function can be used to provide feedback on a user's presentation from an emotional perspective. For example, it can analyze changes in emotions during a presentation and provide advice on how to speak in a way that is more likely to resonate with the audience. In addition, to provide emotional feedback on a user's presentation, the generation AI can point out specific areas for improvement based on the emotion analysis data. For example, it can suggest ways of speaking and expressing emotions that incorporate emotion. Furthermore, based on the emotion estimation data, it can analyze the emotional impact a user's presentation has on the audience and provide advice to create a presentation that is more likely to resonate with the audience. For example, it can suggest incorporating emotional anecdotes. In this way, by using the emotion estimation function to provide feedback on a user's presentation from an emotional perspective, it becomes possible to create a presentation that is more likely to resonate with the audience.

[0078] The presentation support system can further use an emotion estimation function to analyze the emotions of a user while creating materials and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotions of a user while creating materials in real time. For example, if positive emotions are low, an encouraging message or a success story can be displayed. The system can also analyze the user's emotional state and provide an interface for eliciting positive emotions. For example, a function for playing relaxing music or a function for visualizing work progress can be added. Furthermore, the user's emotional state can be visualized based on the emotion estimation data. For example, graphs and charts can be used to make it easier for the user to understand their own emotional state. This allows the system to analyze the user's emotions and provide an interface for eliciting positive emotions, thereby enabling effective material creation.

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

[0080] Step 1: The Virtual Presentation Experience Unit uses AR and VR technologies to provide a virtual presentation experience. For example, users can wear a VR headset and experience a simulation of giving a presentation in a virtual conference room or auditorium. AR technology can also be used to recreate a situation close to a real presentation environment, allowing users to receive realistic feedback. Step 2: The material creation department inputs the presentation conditions into the AI ​​and creates appropriate materials. For example, by inputting information such as the presentation theme, target audience, and time allocation, the generation AI will create optimal slides and materials based on that information. It will also provide advice on the presentation structure and delivery style. Step 3: The Presentation Skills Coaching Unit uses AI to teach presentation skills. For example, when a user gives a presentation in a virtual environment, the AI ​​provides real-time feedback and points out areas for improvement. It also provides advice on the user's speaking style and gestures to improve the quality of the presentation.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0125] In the robot 414, 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 robot 414 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.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Virtual Presentation Experience Department provides virtual presentation experiences using AR and VR technology, The materials creation department inputs the presentation conditions into the AI ​​and creates appropriate materials, A presentation skills training department where AI provides instruction on presentation skills. A system characterized by:

2. The virtual presentation experience section includes: Generative AI tracks user gaze and facial expressions to provide real-time feedback 2. The system of claim 1.

3. The virtual presentation experience section includes: Simulate audience reactions in a virtual environment and allow users to practice responding appropriately to those reactions 2. The system of claim 1.

4. The virtual presentation experience section includes: Measures the user's level of tension and confidence, and provides advice on how to relax and improve self-confidence.

2. The system of claim 1.

5. The virtual presentation experience section includes: Be multilingual to cater to audiences of different cultures and languages 2. The system of claim 1.

6. The virtual presentation experience section includes: Add the ability to record presentations in virtual environments and later use generative AI to provide detailed feedback.

2. The system of claim 1.

Images