System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a text prompt; means for parsing the input text prompt and generating a virtual space; means for displaying the generated virtual space; means for recording and transmitting user actions in real-time; and means for generating and providing feedback based on the parsed user data.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] There is a lack of opportunities to practice real-life presentations and conversations, and as a result, many people suffer from anxiety and worry, such as "I get nervous and can't speak in front of others," "I can't demonstrate my true abilities," and "I'll mismanage my time." These problems stem from a lack of effective advance preparation. Therefore, the purpose of this invention is to provide a realistic rehearsal environment to resolve these problems and support people so that they can demonstrate their true abilities. [Means for solving the problem]

[0005] In the present invention, the above problems are solved by taking the following measures.

[0006] A system is provided that includes a means for inputting a text prompt, a means for analyzing the input text prompt and generating a virtual space, a means for displaying the generated virtual space, a means for recording and transmitting a user's actions in real time, and a means for generating and providing feedback based on the analyzed user data. This system allows a user to rehearse in a realistic virtual environment and receive appropriate feedback, thereby enabling sufficient advance preparation.

[0007] A "text prompt" refers to a string of characters that a user describes in natural language the situation or desired scene and enters into the system.

[0008] "Virtual space" refers to a three-dimensional virtual environment generated by a computer, a digital space in which users can carry out various activities.

[0009] "User actions" refers to the movements and actions that the user makes and speaks in the virtual space, which are recorded and analyzed by the system.

[0010] "Real-time" means that user actions and system responses occur almost simultaneously and are processed immediately, without delay.

[0011] "Feedback" refers to the evaluation and advice the system provides based on the user's behavior and performance.

[0012] "Analysis" refers to the computer processing used to examine input data in detail and understand its content and characteristics.

[0013] "Means" refers to a method, device, etc. used to achieve a specific purpose.

[0014] "System" refers to a set of multiple means or components working together to provide a specific function. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system has the function of allowing users to input text prompts, and based on these, a generation AI realistically recreates a specified situation. A specific embodiment of this system is described below.

[0037] System Overview

[0038] The system consists of a user's device, a server, and a generative AI model. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts and generates a virtual space using the generative AI model. The generated virtual space is displayed on the user's device, where the user can rehearse. The system records the user's actions in real time and provides appropriate feedback.

[0039] Processing Details

[0040] 1. Enter the situation

[0041] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[0042] 2. Data transmission

[0043] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[0044] 3. Situation creation request

[0045] The server passes the received text prompt to the generative AI model and requests it to generate a situation. The generative AI model generates the necessary virtual space based on the input prompt.

[0046] 4. Virtual Space Creation

[0047] The generative AI model generates a virtual space corresponding to a specified situation, including a conference room, participant avatars, a presentation screen, etc. For example, in the case of a business meeting, the layout of the conference room and participant avatars are generated.

[0048] 5. Return of generated data

[0049] The server sends the generated virtual space data to the device, which receives the data, renders the virtual space, and displays it to the user.

[0050] 6. Performing a rehearsal

[0051] The user begins rehearsing in the virtual space. The device records the user's voice and movements and transmits them to the server in real time.

[0052] 7. Generate feedback

[0053] The server then analyzes the received data using a generative AI model to generate feedback based on the user's performance, such as the user's speaking speed, time allocation, and keyword usage.

[0054] 8. Providing Feedback

[0055] The generative AI model generates feedback that is sent to the device via the server, and the device displays this feedback to the user, allowing them to see areas for improvement.

[0056] Specific examples

[0057] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[0058] 1. Enter the situation

[0059] The user types text into the interface: "Presenting a new product at a large business meeting."

[0060] 2. Data transmission

[0061] The terminal sends this input to the server.

[0062] 3. Situation creation request

[0063] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[0064] 4. Virtual Space Creation

[0065] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[0066] 5. Return of generated data

[0067] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[0068] 6. Performing a rehearsal

[0069] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[0070] 7. Generate feedback

[0071] The server analyzes the user's performance and generates feedback based on speaking rate, timing, and keyword use.

[0072] 8. Providing Feedback

[0073] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[0074] This allows users to rehearse efficiently and receive appropriate feedback. By enabling rehearsals in a realistic virtual environment, the present invention provides support for effective advance preparation and the ability to demonstrate one's true potential.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[0078] Step 2:

[0079] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[0080] Step 3:

[0081] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[0082] Step 4:

[0083] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[0084] Step 5:

[0085] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[0086] Step 6:

[0087] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[0088] Step 7:

[0089] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[0090] Step 8:

[0091] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[0092] Step 9:

[0093] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[0094] Step 10:

[0095] The server analyzes the received data using a generative AI model to generate feedback, specifically analyzing speaking speed, time allocation, and keyword usage.

[0096] Step 11:

[0097] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[0098] Step 12:

[0099] The device displays the received feedback to the user, specifically, by displaying advice and suggestions for improvement on the user interface.

[0100] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the real thing.

[0101] Example 1

[0102] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] Conventional rehearsal systems lack the means to efficiently generate virtual spaces and analyze user performance, making it difficult for users to receive effective feedback through realistic rehearsals. Furthermore, the generation of realistic virtual spaces tailored to specific situations is also insufficient, resulting in low quality rehearsal environments.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0105] In this invention, the server includes a means for inputting a situation that the user wants to rehearse as a text prompt, a means for analyzing the input text prompt and passing it to the generative AI model, and a means for acquiring the generated virtual space data and transmitting it to the user's terminal, thereby enabling the user to rehearse in a realistic virtual space corresponding to a specific situation and receive feedback that has been efficiently analyzed by the generative AI model.

[0106] "User" means an individual or entity that uses the System to conduct a rehearsal.

[0107] "Terminal" refers to the electronic device used by a user to operate the system, including computers, smartphones, tablets, etc.

[0108] The "server" is a central computer system that receives data sent from the user's device, interacts with the generative AI model, and generates the virtual space.

[0109] "Generative AI model" refers to an artificial intelligence model for generating a virtual space based on text prompts from a user.

[0110] A "text prompt" is a text input that specifically describes a situation that the user wants to rehearse.

[0111] A "virtual space" is a simulated environment created by a generative AI model for users to rehearse.

[0112] "Feedback" is information that evaluates a user's performance during rehearsal and provides the user with areas for improvement and appropriate advice.

[0113] "Analysis" refers to the process by which a generative AI model evaluates a user's performance based on data obtained from the user.

[0114] "Rendering" refers to the process of converting acquired virtual space data into a form that can be visually displayed on a device.

[0115] "Real-time" means that user actions and voice are processed and transmitted immediately, without delay.

[0116] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system consists of a user terminal, a server, and a generative AI model. Specific embodiments of the system are described below.

[0117] System Overview

[0118] 1. User's device:

[0119] The user's device provides an interface for rehearsals. This interface includes a text input field for the user to enter a situation. Specific hardware examples include computers, smartphones, and tablets. The device also includes a speech recognition engine and a video capture library, and records the user's movements and voice using, for example, a microphone or camera.

[0120] 2. Server:

[0121] The server receives the data sent by the user and uses the generative AI model to generate the virtual space. The specific hardware used is likely to be a server equipped with a high-performance CPU and GPU. The software includes a runtime environment and API for running the generative AI model (e.g., OpenAI GPT-4).

[0122] 3. Generative AI Model:

[0123] Generative AI models, such as OpenAI's GPT-4, create virtual spaces based on generated text prompts, generating meeting room layouts, participant avatars, presentation screens, and more, tailored to the user's situation.

[0124] System Operation

[0125] The user enters a situation into the device's interface, such as "presenting a new product at a large business meeting," and the text prompt is sent to the server via HTTPS. The server then passes the prompt to a generative AI model, requesting it to generate a specific virtual space.

[0126] The generative AI model generates a virtual space, including the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server in structured format such as JSON. The server then sends this generated virtual space data to the device. The device then uses a 3D rendering engine (e.g., Unity or Unreal Engine) to display the virtual space based on the received data.

[0127] Users begin rehearsing in the virtual space, recording their movements and voice using a microphone and camera. The device captures this data in real time and sends it to the server. The server then analyzes the received user performance data using a generative AI model, generating feedback based on speaking speed, time allocation, keyword usage, and other factors, and sends it to the device. The device then displays this feedback to the user, allowing them to see areas for improvement.

[0128] Specific examples

[0129] As an example, consider the case where a user is rehearsing a situation in which he or she is to give a presentation on a new product at a large business meeting.

[0130] User Input:

[0131] The user types into the device interface, "Presenting a new product at a large business meeting."

[0132] Sending data:

[0133] The terminal sends this input data to the server.

[0134] Virtual space generation:

[0135] The server passes text prompts to a generative AI model, which generates a virtual conference room, participant avatars, presentation screens, and more.

[0136] Returning generated data:

[0137] The server sends the generated virtual space data to the terminal, which then renders it and displays it to the user.

[0138] Run a rehearsal:

[0139] The user rehearses in the virtual space, and the device records the voice and movements and sends them to the server.

[0140] Generate and provide feedback:

[0141] The server analyzes the user's performance, generates feedback, sends it to the device, and displays it to the user.

[0142] In this way, the present invention provides a virtual rehearsal environment in which users can efficiently rehearse and receive appropriate feedback.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1: Enter the situation

[0145] The user enters the situation they want to rehearse as a text prompt into the device's interface. For example, they might enter "Presenting a new product at a large business meeting." The device accepts this input text.

[0146] Input: The text prompt for the situation entered by the user

[0147] Output: The text prompt that the terminal gets

[0148] Step 2: Sending data

[0149] The terminal sends the entered text prompt to the server using the HTTPS protocol. Specifically, it generates an HTTP POST request and sends a payload containing the input data to the server. The server receives this request and parses the text prompt data.

[0150] Input: Text prompt

[0151] Data processing: Generating an HTTP POST request

[0152] Data Calculation: Sending a Request

[0153] Output: The text prompt data sent to the server.

[0154] Step 3: Situation generation request

[0155] The server passes the received text prompt to the generative AI model, requesting it to generate a specific situation. The server then sends an API request to the generative AI model, which then generates a virtual space based on the specified situation.

[0156] Input: Text prompt sent to the server

[0157] Data processing: Analysis of text prompts

[0158] Data Computation: Making API requests to generative AI models

[0159] Output: Sending an API request

[0160] Step 4: Creating the virtual space

[0161] The generative AI model generates a virtual space corresponding to a specific situation, such as the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server as structured data in JSON format.

[0162] Input: API request

[0163] Data processing: Creation of virtual space

[0164] Data calculation: Generation of meeting room layouts, participant avatars, presentation screens, etc.

[0165] Output: Generated virtual space data (JSON format)

[0166] Step 5: Returning generated data

[0167] The server sends the virtual space data received from the generated AI model to the device. Specifically, the server returns the data to the device as an HTTP response, and the device receives this data.

[0168] Input: Generated virtual space data

[0169] Data operations: generating and sending HTTP responses

[0170] Output: Virtual space data sent to the device

[0171] Step 6: Displaying the virtual space

[0172] The device performs 3D rendering based on the received virtual space data and displays the virtual space to the user. Specifically, the device uses a 3D rendering engine (e.g., Unity or Unreal Engine) to visually display the virtual space.

[0173] Input: Received virtual space data

[0174] Data processing: preparing for 3D rendering

[0175] Data calculation: rendering of virtual space

[0176] Output: The virtual space that is displayed to the user

[0177] Step 7: Run a rehearsal

[0178] The user begins rehearsing in the virtual space, and the device records the user's movements and voice in real time. The device uses a microphone and camera to capture this data and transmits it to the server in real time.

[0179] Input: User movements and voice

[0180] Data Processing: Voice and Motion Capture

[0181] Data calculation: Real-time data transmission

[0182] Output: User voice and movement data sent to the server

[0183] Step 8: Generate feedback

[0184] The server receives the user's performance data and analyzes it with a generative AI model, evaluating things like speaking speed, time allocation, and keyword usage to generate specific feedback.

[0185] Input: User voice and motion data

[0186] Data processing: analysis of performance data

[0187] Data Calculation: Feedback Generation

[0188] Output: Generated feedback

[0189] Step 9: Provide feedback

[0190] The server generates feedback and sends it to the device, which then displays it to the user. The device displays the feedback in the form of a pop-up or notification, allowing the user to see what improvements need to be made.

[0191] Input: Generated feedback

[0192] Data Calculation: Sending Feedback

[0193] Output: Feedback that is displayed to the user

[0194] This process allows users to rehearse in a realistic virtual space tailored to a specific situation and receive feedback that is efficiently analyzed by a generative AI model.

[0195] (Application example 1)

[0196] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0197] In recent years, as autonomous vehicles have become more widely used, new tools are needed to make effective use of travel time. However, current systems do not provide an environment where users can efficiently rehearse while traveling, preventing users from making full use of their valuable time. Furthermore, there are limited technologies that provide virtual environments that can adapt to environmental changes while traveling. The purpose of this invention is to solve these problems and provide a realistic rehearsal environment while traveling.

[0198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0199] In this invention, the server includes means for inputting text prompts, means for analyzing the input text prompts and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting user actions in real time, means for generating and providing feedback based on the analyzed user data, and means for displaying the virtual environment and rehearsing within the vehicle, thereby enabling efficient and effective rehearsals and feedback to be received even while on the move.

[0200] A "text prompt" is text data that a user inputs in sentence form to describe the situation or content they want to rehearse.

[0201] A "virtual environment" is a simulated space created by a generative AI model based on text prompts, providing a virtual space for users to rehearse.

[0202] "Display means" means the device or software that visually displays the generated virtual environment on the user's device.

[0203] The "behavior recording means" is a function that records the user's behavior and voice in real time and sends the data to a server for analysis and feedback.

[0204] A "feedback generation means" is a system that analyzes recorded user behavioral data and provides evaluations and advice on the user's performance.

[0205] "In-vehicle display means" refers to a display means for providing a virtual environment to a user and for rehearsing in a moving environment such as inside an autonomous vehicle.

[0206] The present invention relates to a system for efficiently rehearsing presentations and dialogues in an autonomous vehicle. This system allows users to rehearse in a virtual environment while on the move and receive feedback in real time. Detailed embodiments of this system are described below.

[0207] System configuration

[0208] The system consists of a user's smartphone, a server, and a generative AI model. The smartphone provides an interface for inputting text prompts and acts as a display means. The server analyzes the input text prompts and generates a virtual environment using the generative AI model.

[0209] Hardware and Software Use

[0210] A user interface application runs on the smartphone, allowing the user to input text prompts for situations they want to rehearse. The server receives the prompts from the smartphone via an HTTP POST request and passes this data to the generative AI model. Based on the prompts, the generative AI model generates virtual environments such as a conference room, participant avatars, and a presentation screen.

[0211] The server sends the generated virtual environment data to a smartphone, which can then render and display this data to the user. As the user rehearses in the virtual environment, their voice and movements are recorded and sent in real time to the server. The server analyzes the received data and generates feedback based on the user's speaking rate, timing, and keyword usage. This feedback is then sent back to the smartphone, allowing the user to see areas for improvement.

[0212] Specific examples

[0213] For example, consider a sales user who wants to rehearse an "important presentation for the next business trip." The user enters the following text prompt into the smartphone app:

[0214] Example prompt sentence:

[0215] "An important presentation on my next business trip"

[0216] Based on this prompt, the system uses a generative AI model to generate environments such as a virtual conference room or virtual audience. Users can then rehearse in this virtual environment while inside their self-driving vehicle. Data recorded during the rehearsal is sent to a server in real time, and the generative AI model analyzes the data and provides feedback to the user. This allows users to make effective use of their travel time and improve their rehearsal content by receiving appropriate feedback.

[0217] In this way, the present invention enables rehearsals in a realistic virtual environment and provides support for users to prepare more efficiently.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] The user accesses the device (smartphone) interface and inputs the situation they want to rehearse as a text prompt. The input text prompt contains specific content such as "an important presentation on my next business trip." The input data is temporarily saved on the device.

[0221] Step 2:

[0222] The terminal sends the temporarily saved text prompt to the server via an HTTP POST request. The input is the text prompt data (character string), and the output is a request to analyze this data.

[0223] Step 3:

[0224] The server analyzes the received text prompt and issues a situation generation request to input to the generative AI model. Specific operations include data processing, such as analyzing the format of the text data and converting it into an appropriate data format for the generative model.

[0225] Step 4:

[0226] The generative AI model generates a virtual environment based on a situation generation request received from the server. The generated virtual environment has components such as a conference room, participant avatars, and a presentation screen. The input is analysis data of text prompts, and the output is expressed as virtual environment data.

[0227] Step 5:

[0228] The server returns the generated virtual environment data to the device. Specifically, it sends the virtual environment data to the smartphone via an HTTP response. The input is the virtual environment data generated by the generative AI model, and the output is data sent to the device.

[0229] Step 6:

[0230] The device renders the received virtual environment data and displays it to the user. Specifically, a rendering process is performed to draw the virtual environment data as a 3D model. The input is virtual environment data, and the output is a virtual space display that the user can visually confirm.

[0231] Step 7:

[0232] The user begins rehearsing in the virtual environment, and the device records the user's voice and movements in real time. This recorded data is sent to the server in real time. The input is the user's voice and movements, and the output is the recorded data sent to the server.

[0233] Step 8:

[0234] The server analyzes the received recorded data using a generative AI model and generates feedback. The analysis targets the user's speaking speed, time allocation, keyword usage, etc. The input is the recorded data sent to the server, and the output is feedback data based on the analysis results.

[0235] Step 9:

[0236] The server returns the generated feedback data to the device. Specifically, it sends the feedback data to the smartphone as an HTTP response. The input is the feedback data analyzed by the generative AI model, and the output is the feedback data sent to the device.

[0237] Step 10:

[0238] The device displays the received feedback data to the user. Specific operations include updating the UI to display the feedback information in a visually understandable manner. The input is the feedback data, and the output is a feedback display that the user can visually confirm.

[0239] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0240] This invention is a virtual space rehearsal system combined with an emotion engine. It recognizes emotions from the user's facial expressions and voice, and provides feedback based on that emotion data, maximizing the effectiveness of the rehearsal. This system has the function of allowing the user to input text prompts, and based on those prompts, the generation AI realistically recreates the specified situation. Furthermore, the emotion engine analyzes the user's emotions and reflects the results in the feedback.

[0241] System Overview

[0242] This system consists of a user's device, a server, a generative AI model, and an emotion engine. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts to generate a virtual space. The generated virtual space is displayed on the user's device. The emotion engine recognizes emotions from the user's facial expressions and voice, and provides feedback based on that data.

[0243] Processing Details

[0244] 1. Enter the situation

[0245] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[0246] 2. Data transmission

[0247] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[0248] 3. Situation creation request

[0249] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[0250] 4. Virtual Space Creation

[0251] Based on the input prompts, the generative AI model generates the necessary virtual space, including the conference room, participant avatars, and presentation screen.

[0252] 5. Return of generated data

[0253] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[0254] 6. Recognizing and transmitting emotional data

[0255] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data, which the device then transmits to the server.

[0256] 7. Performing a rehearsal

[0257] The user begins rehearsing in the virtual space. The device records the user's voice and movement data and transmits it to the server in real time.

[0258] 8. Generate feedback

[0259] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback.

[0260] 9. Providing Feedback

[0261] The server sends the generated feedback back to the device, and the device displays it to the user. Specifically, the device displays the feedback details on the user interface and provides suggestions for improvement and advice.

[0262] Specific examples

[0263] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[0264] 1. Enter the situation

[0265] The user types text into the interface: "Presenting a new product at a large business meeting."

[0266] 2. Data transmission

[0267] The terminal sends this input to the server.

[0268] 3. Situation creation request

[0269] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[0270] 4. Virtual Space Creation

[0271] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[0272] 5. Return of generated data

[0273] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[0274] 6. Recognizing and transmitting emotional data

[0275] The emotion engine analyzes the user's facial expressions and voice, generates emotion data in real time, and the device sends this to the server.

[0276] 7. Performing a rehearsal

[0277] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[0278] 8. Generate feedback

[0279] The server analyzes the user's performance and generates feedback by integrating data from the emotion engine, such as based on speaking speed, time allocation, and emotional changes.

[0280] 9. Providing Feedback

[0281] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[0282] This invention allows users to rehearse efficiently and receive appropriate feedback. Furthermore, by using emotion analysis by an emotion engine, it is possible to provide optimal advice according to the user's feelings, maximizing the effectiveness of rehearsals.

[0283] The processing flow will be explained below.

[0284] Step 1:

[0285] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[0286] Step 2:

[0287] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[0288] Step 3:

[0289] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[0290] Step 4:

[0291] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[0292] Step 5:

[0293] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[0294] Step 6:

[0295] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[0296] Step 7:

[0297] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[0298] Step 8:

[0299] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data. The device then sends this emotion data to a server. Specifically, emotion data is extracted using facial recognition and voice analysis technologies.

[0300] Step 9:

[0301] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[0302] Step 10:

[0303] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[0304] Step 11:

[0305] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback. For example, it analyzes the user's speaking speed, time allocation, and emotional changes.

[0306] Step 12:

[0307] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[0308] Step 13:

[0309] The device displays the received feedback to the user. Specifically, the device displays the details of the feedback on the user interface and provides suggestions for improvement and advice.

[0310] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the performance. Analysis by the emotion engine also provides personalized advice based on the user's emotional state, enabling more effective rehearsals.

[0311] Example 2

[0312] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] Conventional rehearsal systems have the drawback of being difficult to generate a realistic virtual space for users to effectively practice specific situations, and the feedback they provide is limited to a general evaluation, without suggesting specific areas for improvement. Furthermore, they do not provide feedback that takes into account the user's emotional state, limiting the actual effectiveness of rehearsals.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting a text prompt, means for analyzing the input text prompt and generating a virtual space, means for displaying the generated virtual space, means for analyzing the user's facial expression and voice data and generating emotional data, means for recording and transmitting the user's actions in real time, and means for generating and providing feedback based on the analyzed user data and emotional data. This allows the user to rehearse a specific situation in a realistic virtual space and receive specific feedback according to the user's emotional state.

[0315] A "text prompt" is textual data that a user enters to specify a rehearsal or simulation situation.

[0316] A "virtual space" is a simulated environment generated by a generative AI model for users to rehearse.

[0317] "Analysis" is the process of analyzing input data, the user's facial expressions, and voice data to extract meaning and emotional insights.

[0318] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's facial expression and voice data.

[0319] An "action" is a movement or speech that a user makes within a virtual space.

[0320] "Feedback" is information about guidance and improvements provided to the user after the rehearsal.

[0321] This invention is a system that generates a virtual space based on text prompts entered by the user, and allows rehearsals to be conducted within it, enhancing the effectiveness of practice for real situations. The main components of this system are the user's device, a server, a generative AI model, and an emotion engine.

[0322] The user's device provides an interface for the user to input text prompts. For example, the user inputs a situation such as "presenting a new product at a large business meeting" in text format. This input interface is designed with user convenience in mind, and supports not only text input but also voice recognition and touchscreen operation.

[0323] The device sends the entered text prompt to the server via an HTTP POST request. This HTTP communication uses a common protocol, often in JSON format. The server analyzes the received text prompt and sends an API request to the generative AI model to generate the virtual space. This analysis process uses a natural language processing algorithm.

[0324] The generative AI model generates a virtual space based on the prompt text entered by the user. Specifically, it creates a virtual space that includes the layout of the conference room, participant avatars, a presentation screen, and more. The generated virtual space data is sent back to the server, which then sends it to the device. The device then performs 3D rendering based on the returned data and displays it to the user. Typical software used in this process include Unity and Unreal Engine.

[0325] Next, the user begins rehearsing in the virtual space. The device recognizes the user's facial expressions and voice data in real time and passes them to the emotion engine. The emotion engine analyzes this data and extracts the user's emotional state. The extracted emotion data is then sent back to the server.

[0326] While the user is rehearsing, the device records the user's voice and movement data and sends it to the server in real time. Based on the received data, the server integrates and analyzes the data generated by the generative AI model and emotion engine to generate appropriate feedback. The feedback specifically indicates areas for improvement and evaluation points for the user, including specific advice such as "You speak too fast" or "You do not express emotions enough." This feedback is displayed on the device so that the user can easily check it.

[0327] This invention stands out from conventional simulation systems in that it provides feedback that takes into account the user's emotions. Furthermore, by incorporating real-time analysis using an emotion engine, it is possible to comprehensively evaluate the user's performance and maximize the effectiveness of rehearsals. In this way, this invention allows users to rehearse efficiently and more effectively prepare for real-life situations.

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1:

[0330] The user enters a text prompt.

[0331] Input: User's rehearsal situation

[0332] Specific operation: The user types into the terminal in text format, "Present new product at large business meeting."

[0333] Output: Text prompt data

[0334] Step 2:

[0335] The terminal sends the entered text prompt to the server.

[0336] Input: Text prompt data

[0337] Specific behavior: The device serializes the text prompt into JSON format and sends it to the server as an HTTP POST request.

[0338] Output: Prompt data sent to the server

[0339] Step 3:

[0340] The server analyzes the received text prompt and creates a request to generate a virtual space.

[0341] Input: prompt data

[0342] Specific operation: The server uses a natural language processing algorithm to analyze the text prompt and extract the parameters necessary to generate the virtual space.

[0343] Output: API request data to the generative AI model

[0344] Step 4:

[0345] The generative AI model generates a virtual space based on requests from the server.

[0346] Input: API request data

[0347] Specific operation: The generative AI model generates the conference room layout, participant avatars, presentation screens, etc., and sends the virtual space data back to the server.

[0348] Output: Generated virtual space data

[0349] Step 5:

[0350] The server returns the generated virtual space data to the terminal.

[0351] Input: Virtual space data

[0352] Specific operation: The server sends the virtual space data to the terminal as an HTTP response.

[0353] Output: Virtual space data sent to the device

[0354] Step 6:

[0355] The terminal renders the generated virtual space data and displays it to the user.

[0356] Input: Virtual space data

[0357] How it works: The device generates 3D graphics based on virtual space data and displays them to the user. This process uses software such as Unity or Unreal Engine.

[0358] Output: Displayed virtual space

[0359] Step 7:

[0360] The user's facial expression and voice data are sent to the emotion engine.

[0361] Input: User's facial expression data and voice data

[0362] Specific operation: The device uses the camera and microphone to capture the user's facial expressions and voice in real time and send them to the emotion engine.

[0363] Output: Data sent to the emotion engine

[0364] Step 8:

[0365] An emotion engine generates emotion data from the user's facial expression and voice data.

[0366] Input: User's facial and voice data

[0367] Specific operation: The emotion engine uses machine learning algorithms to analyze the user's emotions and generate emotion data.

[0368] Output: Generated emotion data

[0369] Step 9:

[0370] The user rehearses in the virtual space.

[0371] Input: Virtual space, user actions

[0372] Specific operation: The user gives a presentation in the virtual space, and the terminal records the user's voice and actions.

[0373] Output: Recorded user performance data

[0374] Step 10:

[0375] The terminal transmits the user's performance data to the server.

[0376] Input: User performance data

[0377] Specific operation: The terminal transmits user performance data to the server in real time.

[0378] Output: Performance data sent to the server

[0379] Step 11:

[0380] The server generates feedback based on the received data.

[0381] Input: User performance and emotional data

[0382] Specific operation: The server integrates data from the generative AI model and emotion engine to generate feedback based on the user's speaking rate, time allocation, and emotional changes.

[0383] Output: Generated feedback

[0384] Step 12:

[0385] The server sends the feedback to the terminal, which displays it to the user.

[0386] Input: Generated feedback

[0387] Specific operation: The server sends the feedback to the device in an HTTP response, and the device displays it in its user interface. For example, it presents the feedback to the user in the form of a pop-up notification or a detailed report.

[0388] Output: Feedback provided to the user.

[0389] (Application example 2)

[0390] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0391] In conventional security training, it is difficult to simulate real-world situations, and there are no efficient methods to develop realistic response capabilities. Furthermore, there is a lack of systems to provide appropriate feedback on users' emotions and behavioral data during training. This makes it difficult to specifically understand how users should improve.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0393] In this invention, the server includes means for inputting a scenario, means for analyzing the input scenario and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting the user's actions in real time, means for generating and providing feedback based on the analyzed user data, means for recognizing emotions from the user's facial expressions and voice, and means for generating feedback by integrating the emotion data. This enables the user to train in a situation close to reality and receive specific feedback based on their emotions and actions in real time.

[0394] A "means for inputting a scenario" is a device or interface that allows a user to input a specific situation for training or simulation in text format.

[0395] The "means for analyzing the input scenario and generating a virtual environment" refers to software and hardware that realistically recreates the situation in a virtual space based on text data input by the user.

[0396] "Means for displaying the generated virtual environment" refers to a display device such as a display device, projector, or head-mounted display (HMD) for visually presenting the generated virtual space to the user.

[0397] "Means for recording and transmitting user actions in real time" refers to devices or systems that record actions and statements made by users in virtual space in real time and transmit them to a server. Specifically, this includes cameras, microphones, and sensors.

[0398] "Means for generating and providing feedback based on analyzed user data" refers to software and systems that analyze data on users' actions and statements, generate corresponding feedback, and provide it to users.

[0399] "Means for recognizing emotions from a user's facial expressions and voice" refers to technology that uses a camera or microphone to capture a user's facial expressions and voice, and then analyzes them to infer emotions.

[0400] The "means for integrating emotional data to generate feedback" is a system that combines emotional data and behavioral data obtained from users to generate training improvements and advice.

[0401] The system of the present invention is a system for effectively conducting security training in a virtual space. This system consists of a user terminal, a server, a generative AI model, and an emotion engine.

[0402] Detailed system configuration

[0403] 1. How to input a scenario

[0404] The user uses an interface to input a scenario. For example, the user inputs a text-based scenario such as "How should I respond when I discover a suspicious person?" into the terminal interface.

[0405] 2. A method for analyzing input scenarios and generating virtual environments

[0406] The device sends the input scenario to the server as an HTTP POST request, which the server analyzes and generates a request to the generative AI model. The generative AI model (e.g., OpenAI's GPT-3) generates a virtual environment based on the scenario.

[0407] 3. How to view the generated virtual environment

[0408] The server receives the virtual environment data from the generative AI model and sends it back to the device, which then uses this data to render the virtual space and display it to the user using a display or head-mounted display (HMD).

[0409] 4. Means of recording and transmitting user actions in real time

[0410] The device's camera and microphone are used to capture the user's facial expressions and voice in real time, and this data is sent to the server as user movement data.

[0411] 5. Means for generating and providing feedback based on analyzed user data

[0412] The server analyzes the acquired motion data and emotion data using an emotion engine (e.g., Emotion SDK). Using a generative AI model, it integrates the motion data and emotion data to generate feedback and provide suggestions for improvement.

[0413] Hardware and software used

[0414] Hardware

[0415] Camera and microphone: To capture the user's facial expressions and voice

[0416] Display or Head-Mounted Display (HMD): for viewing the virtual environment

[0417] software

[0418] Emotion engine (e.g., Emotion SDK): To recognize emotions from the user's facial expressions and voice

[0419] Generative AI models (e.g., GPT-3 by OpenAI) to generate scenario-based virtual environments and generate feedback

[0420] HTTP request processing (e.g., requests library): Used to send data from the device to the server

[0421] Examples of specific examples and prompts

[0422] As a concrete example, consider the case where a security guard practices "how to respond when a suspicious person is spotted." The user inputs the following into the scenario input interface:

[0423] How to spot a suspicious person and respond

[0424] Based on this scenario, the server and generative AI model generate a specific virtual environment (e.g., a scene in which a suspicious person appears at the entrance of an office building), and users train in that virtual space.

[0425] The emotion engine analyzes the user's facial expressions and voice data, and works with the generative AI model to provide real-time feedback based on the emotional data. For example, it provides feedback such as, "You speak slowly and calmly, but your facial expression shows signs of tension."

[0426] This allows users to develop the ability to respond to situations that are close to reality and to identify specific and appropriate areas for improvement.

[0427] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0428] Step 1:

[0429] The user enters the training situation into the interface.

[0430] Input: The user inputs a specific situation (e.g., "I discovered a suspicious person and how would I respond?") in text format.

[0431] Output: The input text data.

[0432] Specific operation: The user inputs the situation using the device's keyboard or touch screen.

[0433] Step 2:

[0434] The terminal sends the entered text prompt to the server as an HTTP POST request.

[0435] Input: The text data entered in step 1.

[0436] Output: The HTTP request sent to the server.

[0437] Specific operation: The device sends an HTTP request to the server via the network, using a network communication module such as the requests library.

[0438] Step 3:

[0439] The server parses the received text prompt and generates a request to the generative AI model.

[0440] Input: Text data sent from the terminal.

[0441] Output: Request data to the generative AI model.

[0442] Specific operation: The server analyzes the received text data and formats the prompt sentence as an API request to the generative AI model.

[0443] Step 4:

[0444] A generative AI model generates a virtual environment based on input prompts.

[0445] Input: The parsed text prompt.

[0446] Output: Generated virtual environment data (e.g., virtual environment scene data and object data).

[0447] How it works: Based on the input prompt data, the generative AI model automatically generates a virtual environment that corresponds to the specified situation. For example, it uses OpenAI's GPT-3 to generate detailed settings based on the scenario.

[0448] Step 5:

[0449] The server sends the virtual environment data received from the generated AI model back to the device.

[0450] Input: Virtual environment data returned from a generative AI model.

[0451] Output: Virtual environment data sent to the device.

[0452] Specific operation: The server sends the virtual environment data received from the generative AI model to the terminal as an HTTP response.

[0453] Step 6:

[0454] The device renders the virtual environment and displays it to the user.

[0455] Input: Virtual environment data received from the server.

[0456] Output: The virtual environment that is displayed to the user.

[0457] Specific operation: The terminal uses the received virtual environment data to display the virtual environment using a display or head-mounted display.

[0458] Step 7:

[0459] The device's camera and microphone are used to record the user's actions and voice in real time.

[0460] Input: User movements and voice.

[0461] Output: Real-time recorded motion and audio data.

[0462] How it works: The camera and microphone connected to the device capture the user's facial expressions and voice and record them in real time.

[0463] Step 8:

[0464] The terminal transmits the recorded data to the server.

[0465] Input: Recorded motion and audio data.

[0466] Output: User data sent to the server.

[0467] Specific operation: The device sends the recorded data to the server via the network.

[0468] Step 9:

[0469] The server analyzes the received data and generates feedback.

[0470] Input: Motion and audio data sent from the device.

[0471] Output: Parsed feedback data.

[0472] Specific behavior: The server analyzes the behavior data and emotion data using the emotion engine, and generates appropriate feedback using the generative AI model.

[0473] Step 10:

[0474] The server transmits the generated feedback to the terminal.

[0475] Input: Generated feedback data.

[0476] Output: Feedback data sent to the device.

[0477] Specific operation: The server generates feedback and sends it to the device as an HTTP response.

[0478] Step 11:

[0479] The device displays the feedback to the user.

[0480] Input: Feedback data sent by the server.

[0481] Output: The feedback that is displayed to the user.

[0482] Specific operation: The terminal displays the received feedback data on the user interface for the user to check.

[0483] 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.

[0484] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0485] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0486] [Second embodiment]

[0487] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0488] 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.

[0489] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0490] 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.

[0491] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0492] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0493] 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.

[0494] 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.

[0495] 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 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.

[0496] 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.

[0497] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0498] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0499] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system has the function of allowing users to input text prompts, and based on these, a generation AI realistically recreates a specified situation. A specific embodiment of this system is described below.

[0500] System Overview

[0501] The system consists of a user's device, a server, and a generative AI model. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts and generates a virtual space using the generative AI model. The generated virtual space is displayed on the user's device, where the user can rehearse. The system records the user's actions in real time and provides appropriate feedback.

[0502] Processing Details

[0503] 1. Enter the situation

[0504] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[0505] 2. Data transmission

[0506] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[0507] 3. Situation creation request

[0508] The server passes the received text prompt to the generative AI model and requests it to generate a situation. The generative AI model generates the necessary virtual space based on the input prompt.

[0509] 4. Virtual Space Creation

[0510] The generative AI model generates a virtual space corresponding to a specified situation, including a conference room, participant avatars, a presentation screen, etc. For example, in the case of a business meeting, the layout of the conference room and participant avatars are generated.

[0511] 5. Return of generated data

[0512] The server sends the generated virtual space data to the device, which receives the data, renders the virtual space, and displays it to the user.

[0513] 6. Performing a rehearsal

[0514] The user begins rehearsing in the virtual space. The device records the user's voice and movements and transmits them to the server in real time.

[0515] 7. Generate feedback

[0516] The server then analyzes the received data using a generative AI model to generate feedback based on the user's performance, such as the user's speaking speed, time allocation, and keyword usage.

[0517] 8. Providing Feedback

[0518] The generative AI model generates feedback that is sent to the device via the server, and the device displays this feedback to the user, allowing them to see areas for improvement.

[0519] Specific examples

[0520] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[0521] 1. Enter the situation

[0522] The user types text into the interface: "Presenting a new product at a large business meeting."

[0523] 2. Data transmission

[0524] The terminal sends this input to the server.

[0525] 3. Situation creation request

[0526] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[0527] 4. Virtual Space Creation

[0528] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[0529] 5. Return of generated data

[0530] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[0531] 6. Performing a rehearsal

[0532] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[0533] 7. Generate feedback

[0534] The server analyzes the user's performance and generates feedback based on speaking rate, timing, and keyword use.

[0535] 8. Providing Feedback

[0536] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[0537] This allows users to rehearse efficiently and receive appropriate feedback. By enabling rehearsals in a realistic virtual environment, the present invention provides support for effective advance preparation and the ability to demonstrate one's true potential.

[0538] The processing flow will be explained below.

[0539] Step 1:

[0540] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[0541] Step 2:

[0542] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[0543] Step 3:

[0544] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[0545] Step 4:

[0546] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[0547] Step 5:

[0548] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[0549] Step 6:

[0550] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[0551] Step 7:

[0552] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[0553] Step 8:

[0554] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[0555] Step 9:

[0556] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[0557] Step 10:

[0558] The server analyzes the received data using a generative AI model to generate feedback, specifically analyzing speaking speed, time allocation, and keyword usage.

[0559] Step 11:

[0560] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[0561] Step 12:

[0562] The device displays the received feedback to the user, specifically, by displaying advice and suggestions for improvement on the user interface.

[0563] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the real thing.

[0564] Example 1

[0565] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0566] Conventional rehearsal systems lack the means to efficiently generate virtual spaces and analyze user performance, making it difficult for users to receive effective feedback through realistic rehearsals. Furthermore, the generation of realistic virtual spaces tailored to specific situations is also insufficient, resulting in low quality rehearsal environments.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0568] In this invention, the server includes a means for inputting a situation that the user wants to rehearse as a text prompt, a means for analyzing the input text prompt and passing it to the generative AI model, and a means for acquiring the generated virtual space data and transmitting it to the user's terminal, thereby enabling the user to rehearse in a realistic virtual space corresponding to a specific situation and receive feedback that has been efficiently analyzed by the generative AI model.

[0569] "User" means an individual or entity that uses the System to conduct a rehearsal.

[0570] "Terminal" refers to the electronic device used by a user to operate the system, including computers, smartphones, tablets, etc.

[0571] The "server" is a central computer system that receives data sent from the user's device, interacts with the generative AI model, and generates the virtual space.

[0572] "Generative AI model" refers to an artificial intelligence model for generating a virtual space based on text prompts from a user.

[0573] A "text prompt" is a text input that specifically describes a situation that the user wants to rehearse.

[0574] A "virtual space" is a simulated environment created by a generative AI model for users to rehearse.

[0575] "Feedback" is information that evaluates a user's performance during rehearsal and provides the user with areas for improvement and appropriate advice.

[0576] "Analysis" refers to the process by which a generative AI model evaluates a user's performance based on data obtained from the user.

[0577] "Rendering" refers to the process of converting acquired virtual space data into a form that can be visually displayed on a device.

[0578] "Real-time" means that user actions and voice are processed and transmitted immediately, without delay.

[0579] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system consists of a user terminal, a server, and a generative AI model. Specific embodiments of the system are described below.

[0580] System Overview

[0581] 1. User's device:

[0582] The user's device provides an interface for rehearsals. This interface includes a text input field for the user to enter a situation. Specific hardware examples include computers, smartphones, and tablets. The device also includes a speech recognition engine and a video capture library, and records the user's movements and voice using, for example, a microphone or camera.

[0583] 2. Server:

[0584] The server receives the data sent by the user and uses the generative AI model to generate the virtual space. The specific hardware used is likely to be a server equipped with a high-performance CPU and GPU. The software includes a runtime environment and API for running the generative AI model (e.g., OpenAI GPT-4).

[0585] 3. Generative AI Model:

[0586] Generative AI models, such as OpenAI's GPT-4, create virtual spaces based on generated text prompts, generating meeting room layouts, participant avatars, presentation screens, and more, tailored to the user's situation.

[0587] System Operation

[0588] The user enters a situation into the device's interface, such as "presenting a new product at a large business meeting," and the text prompt is sent to the server via HTTPS. The server then passes the prompt to a generative AI model, requesting it to generate a specific virtual space.

[0589] The generative AI model generates a virtual space, including the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server in structured format such as JSON. The server then sends this generated virtual space data to the device. The device then uses a 3D rendering engine (e.g., Unity or Unreal Engine) to display the virtual space based on the received data.

[0590] Users begin rehearsing in the virtual space, recording their movements and voice using a microphone and camera. The device captures this data in real time and sends it to the server. The server then analyzes the received user performance data using a generative AI model, generating feedback based on speaking speed, time allocation, keyword usage, and other factors, and sends it to the device. The device then displays this feedback to the user, allowing them to see areas for improvement.

[0591] Specific examples

[0592] As an example, consider the case where a user is rehearsing a situation in which he or she is to give a presentation on a new product at a large business meeting.

[0593] User Input:

[0594] The user types into the device interface, "Presenting a new product at a large business meeting."

[0595] Sending data:

[0596] The terminal sends this input data to the server.

[0597] Virtual space generation:

[0598] The server passes text prompts to a generative AI model, which generates a virtual conference room, participant avatars, presentation screens, and more.

[0599] Returning generated data:

[0600] The server sends the generated virtual space data to the terminal, which then renders it and displays it to the user.

[0601] Run a rehearsal:

[0602] The user rehearses in the virtual space, and the device records the voice and movements and sends them to the server.

[0603] Generate and provide feedback:

[0604] The server analyzes the user's performance, generates feedback, sends it to the device, and displays it to the user.

[0605] In this way, the present invention provides a virtual rehearsal environment in which users can efficiently rehearse and receive appropriate feedback.

[0606] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0607] Step 1: Enter the situation

[0608] The user enters the situation they want to rehearse as a text prompt into the device's interface. For example, they might enter "Presenting a new product at a large business meeting." The device accepts this input text.

[0609] Input: The text prompt for the situation entered by the user

[0610] Output: The text prompt that the terminal gets

[0611] Step 2: Sending data

[0612] The terminal sends the entered text prompt to the server using the HTTPS protocol. Specifically, it generates an HTTP POST request and sends a payload containing the input data to the server. The server receives this request and parses the text prompt data.

[0613] Input: Text prompt

[0614] Data processing: Generating an HTTP POST request

[0615] Data Calculation: Sending a Request

[0616] Output: The text prompt data sent to the server.

[0617] Step 3: Situation generation request

[0618] The server passes the received text prompt to the generative AI model, requesting it to generate a specific situation. The server then sends an API request to the generative AI model, which then generates a virtual space based on the specified situation.

[0619] Input: Text prompt sent to the server

[0620] Data processing: Analysis of text prompts

[0621] Data Computation: Making API requests to generative AI models

[0622] Output: Sending an API request

[0623] Step 4: Creating the virtual space

[0624] The generative AI model generates a virtual space corresponding to a specific situation, such as the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server as structured data in JSON format.

[0625] Input: API request

[0626] Data processing: Creation of virtual space

[0627] Data calculation: Generation of meeting room layouts, participant avatars, presentation screens, etc.

[0628] Output: Generated virtual space data (JSON format)

[0629] Step 5: Returning generated data

[0630] The server sends the virtual space data received from the generated AI model to the device. Specifically, the server returns the data to the device as an HTTP response, and the device receives this data.

[0631] Input: Generated virtual space data

[0632] Data operations: generating and sending HTTP responses

[0633] Output: Virtual space data sent to the device

[0634] Step 6: Displaying the virtual space

[0635] The device performs 3D rendering based on the received virtual space data and displays the virtual space to the user. Specifically, the device uses a 3D rendering engine (e.g., Unity or Unreal Engine) to visually display the virtual space.

[0636] Input: Received virtual space data

[0637] Data processing: preparing for 3D rendering

[0638] Data calculation: rendering of virtual space

[0639] Output: The virtual space that is displayed to the user

[0640] Step 7: Run a rehearsal

[0641] The user begins rehearsing in the virtual space, and the device records the user's movements and voice in real time. The device uses a microphone and camera to capture this data and transmits it to the server in real time.

[0642] Input: User movements and voice

[0643] Data Processing: Voice and Motion Capture

[0644] Data calculation: Real-time data transmission

[0645] Output: User voice and movement data sent to the server

[0646] Step 8: Generate feedback

[0647] The server receives the user's performance data and analyzes it with a generative AI model, evaluating things like speaking speed, time allocation, and keyword usage to generate specific feedback.

[0648] Input: User voice and motion data

[0649] Data processing: analysis of performance data

[0650] Data Calculation: Feedback Generation

[0651] Output: Generated feedback

[0652] Step 9: Provide feedback

[0653] The server generates feedback and sends it to the device, which then displays it to the user. The device displays the feedback in the form of a pop-up or notification, allowing the user to see what improvements need to be made.

[0654] Input: Generated feedback

[0655] Data Calculation: Sending Feedback

[0656] Output: Feedback that is displayed to the user

[0657] This process allows users to rehearse in a realistic virtual space tailored to a specific situation and receive feedback that is efficiently analyzed by a generative AI model.

[0658] (Application example 1)

[0659] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0660] In recent years, as autonomous vehicles have become more widely used, new tools are needed to make effective use of travel time. However, current systems do not provide an environment where users can efficiently rehearse while traveling, preventing users from making full use of their valuable time. Furthermore, there are limited technologies that provide virtual environments that can adapt to environmental changes while traveling. The purpose of this invention is to solve these problems and provide a realistic rehearsal environment while traveling.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0662] In this invention, the server includes means for inputting text prompts, means for analyzing the input text prompts and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting user actions in real time, means for generating and providing feedback based on the analyzed user data, and means for displaying the virtual environment and rehearsing within the vehicle, thereby enabling efficient and effective rehearsals and feedback to be received even while on the move.

[0663] A "text prompt" is text data that a user inputs in sentence form to describe the situation or content they want to rehearse.

[0664] A "virtual environment" is a simulated space created by a generative AI model based on text prompts, providing a virtual space for users to rehearse.

[0665] "Display means" means the device or software that visually displays the generated virtual environment on the user's device.

[0666] The "behavior recording means" is a function that records the user's behavior and voice in real time and sends the data to a server for analysis and feedback.

[0667] A "feedback generation means" is a system that analyzes recorded user behavioral data and provides evaluations and advice on the user's performance.

[0668] "In-vehicle display means" refers to a display means for providing a virtual environment to a user and for rehearsing in a moving environment such as inside an autonomous vehicle.

[0669] The present invention relates to a system for efficiently rehearsing presentations and dialogues in an autonomous vehicle. This system allows users to rehearse in a virtual environment while on the move and receive feedback in real time. Detailed embodiments of this system are described below.

[0670] System configuration

[0671] The system consists of a user's smartphone, a server, and a generative AI model. The smartphone provides an interface for inputting text prompts and acts as a display means. The server analyzes the input text prompts and generates a virtual environment using the generative AI model.

[0672] Hardware and Software Use

[0673] A user interface application runs on the smartphone, allowing the user to input text prompts for situations they want to rehearse. The server receives the prompts from the smartphone via an HTTP POST request and passes this data to the generative AI model. Based on the prompts, the generative AI model generates virtual environments such as a conference room, participant avatars, and a presentation screen.

[0674] The server sends the generated virtual environment data to a smartphone, which can then render and display this data to the user. As the user rehearses in the virtual environment, their voice and movements are recorded and sent in real time to the server. The server analyzes the received data and generates feedback based on the user's speaking rate, timing, and keyword usage. This feedback is then sent back to the smartphone, allowing the user to see areas for improvement.

[0675] Specific examples

[0676] For example, consider a sales user who wants to rehearse an "important presentation for the next business trip." The user enters the following text prompt into the smartphone app:

[0677] Example prompt sentence:

[0678] "An important presentation on my next business trip"

[0679] Based on this prompt, the system uses a generative AI model to generate environments such as a virtual conference room or virtual audience. Users can then rehearse in this virtual environment while inside their self-driving vehicle. Data recorded during the rehearsal is sent to a server in real time, and the generative AI model analyzes the data and provides feedback to the user. This allows users to make effective use of their travel time and improve their rehearsal content by receiving appropriate feedback.

[0680] In this way, the present invention enables rehearsals in a realistic virtual environment and provides support for users to prepare more efficiently.

[0681] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0682] Step 1:

[0683] The user accesses the device (smartphone) interface and inputs the situation they want to rehearse as a text prompt. The input text prompt contains specific content such as "an important presentation on my next business trip." The input data is temporarily saved on the device.

[0684] Step 2:

[0685] The terminal sends the temporarily saved text prompt to the server via an HTTP POST request. The input is the text prompt data (character string), and the output is a request to analyze this data.

[0686] Step 3:

[0687] The server analyzes the received text prompt and issues a situation generation request to input to the generative AI model. Specific operations include data processing, such as analyzing the format of the text data and converting it into an appropriate data format for the generative model.

[0688] Step 4:

[0689] The generative AI model generates a virtual environment based on a situation generation request received from the server. The generated virtual environment has components such as a conference room, participant avatars, and a presentation screen. The input is analysis data of text prompts, and the output is expressed as virtual environment data.

[0690] Step 5:

[0691] The server returns the generated virtual environment data to the device. Specifically, it sends the virtual environment data to the smartphone via an HTTP response. The input is the virtual environment data generated by the generative AI model, and the output is data sent to the device.

[0692] Step 6:

[0693] The device renders the received virtual environment data and displays it to the user. Specifically, a rendering process is performed to draw the virtual environment data as a 3D model. The input is virtual environment data, and the output is a virtual space display that the user can visually confirm.

[0694] Step 7:

[0695] The user begins rehearsing in the virtual environment, and the device records the user's voice and movements in real time. This recorded data is sent to the server in real time. The input is the user's voice and movements, and the output is the recorded data sent to the server.

[0696] Step 8:

[0697] The server analyzes the received recorded data using a generative AI model and generates feedback. The analysis targets the user's speaking speed, time allocation, keyword usage, etc. The input is the recorded data sent to the server, and the output is feedback data based on the analysis results.

[0698] Step 9:

[0699] The server returns the generated feedback data to the device. Specifically, it sends the feedback data to the smartphone as an HTTP response. The input is the feedback data analyzed by the generative AI model, and the output is the feedback data sent to the device.

[0700] Step 10:

[0701] The device displays the received feedback data to the user. Specific operations include updating the UI to display the feedback information in a visually understandable manner. The input is the feedback data, and the output is a feedback display that the user can visually confirm.

[0702] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0703] This invention is a virtual space rehearsal system combined with an emotion engine. It recognizes emotions from the user's facial expressions and voice, and provides feedback based on that emotion data, maximizing the effectiveness of the rehearsal. This system has the function of allowing the user to input text prompts, and based on those prompts, the generation AI realistically recreates the specified situation. Furthermore, the emotion engine analyzes the user's emotions and reflects the results in the feedback.

[0704] System Overview

[0705] This system consists of a user's device, a server, a generative AI model, and an emotion engine. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts to generate a virtual space. The generated virtual space is displayed on the user's device. The emotion engine recognizes emotions from the user's facial expressions and voice, and provides feedback based on that data.

[0706] Processing Details

[0707] 1. Enter the situation

[0708] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[0709] 2. Data transmission

[0710] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[0711] 3. Situation creation request

[0712] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[0713] 4. Virtual Space Creation

[0714] Based on the input prompts, the generative AI model generates the necessary virtual space, including the conference room, participant avatars, and presentation screen.

[0715] 5. Return of generated data

[0716] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[0717] 6. Recognizing and transmitting emotional data

[0718] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data, which the device then transmits to the server.

[0719] 7. Performing a rehearsal

[0720] The user begins rehearsing in the virtual space. The device records the user's voice and movement data and transmits it to the server in real time.

[0721] 8. Generate feedback

[0722] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback.

[0723] 9. Providing Feedback

[0724] The server sends the generated feedback back to the device, and the device displays it to the user. Specifically, the device displays the feedback details on the user interface and provides suggestions for improvement and advice.

[0725] Specific examples

[0726] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[0727] 1. Enter the situation

[0728] The user types text into the interface: "Presenting a new product at a large business meeting."

[0729] 2. Data transmission

[0730] The terminal sends this input to the server.

[0731] 3. Situation creation request

[0732] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[0733] 4. Virtual Space Creation

[0734] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[0735] 5. Return of generated data

[0736] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[0737] 6. Recognizing and transmitting emotional data

[0738] The emotion engine analyzes the user's facial expressions and voice, generates emotion data in real time, and the device sends this to the server.

[0739] 7. Performing a rehearsal

[0740] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[0741] 8. Generate feedback

[0742] The server analyzes the user's performance and generates feedback by integrating data from the emotion engine, such as based on speaking speed, time allocation, and emotional changes.

[0743] 9. Providing Feedback

[0744] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[0745] This invention allows users to rehearse efficiently and receive appropriate feedback. Furthermore, by using emotion analysis by an emotion engine, it is possible to provide optimal advice according to the user's feelings, maximizing the effectiveness of rehearsals.

[0746] The processing flow will be explained below.

[0747] Step 1:

[0748] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[0749] Step 2:

[0750] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[0751] Step 3:

[0752] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[0753] Step 4:

[0754] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[0755] Step 5:

[0756] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[0757] Step 6:

[0758] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[0759] Step 7:

[0760] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[0761] Step 8:

[0762] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data. The device then sends this emotion data to a server. Specifically, emotion data is extracted using facial recognition and voice analysis technologies.

[0763] Step 9:

[0764] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[0765] Step 10:

[0766] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[0767] Step 11:

[0768] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback. For example, it analyzes the user's speaking speed, time allocation, and emotional changes.

[0769] Step 12:

[0770] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[0771] Step 13:

[0772] The device displays the received feedback to the user. Specifically, the device displays the details of the feedback on the user interface and provides suggestions for improvement and advice.

[0773] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the performance. Analysis by the emotion engine also provides personalized advice based on the user's emotional state, enabling more effective rehearsals.

[0774] Example 2

[0775] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0776] Conventional rehearsal systems have the drawback of being difficult to generate a realistic virtual space for users to effectively practice specific situations, and the feedback they provide is limited to a general evaluation, without suggesting specific areas for improvement. Furthermore, they do not provide feedback that takes into account the user's emotional state, limiting the actual effectiveness of rehearsals.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting a text prompt, means for analyzing the input text prompt and generating a virtual space, means for displaying the generated virtual space, means for analyzing the user's facial expression and voice data and generating emotional data, means for recording and transmitting the user's actions in real time, and means for generating and providing feedback based on the analyzed user data and emotional data. This allows the user to rehearse a specific situation in a realistic virtual space and receive specific feedback according to the user's emotional state.

[0778] A "text prompt" is textual data that a user enters to specify a rehearsal or simulation situation.

[0779] A "virtual space" is a simulated environment generated by a generative AI model for users to rehearse.

[0780] "Analysis" is the process of analyzing input data, the user's facial expressions, and voice data to extract meaning and emotional insights.

[0781] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's facial expression and voice data.

[0782] An "action" is a movement or speech that a user makes within a virtual space.

[0783] "Feedback" is information about guidance and improvements provided to the user after the rehearsal.

[0784] This invention is a system that generates a virtual space based on text prompts entered by the user, and allows rehearsals to be conducted within it, enhancing the effectiveness of practice for real situations. The main components of this system are the user's device, a server, a generative AI model, and an emotion engine.

[0785] The user's device provides an interface for the user to input text prompts. For example, the user inputs a situation such as "presenting a new product at a large business meeting" in text format. This input interface is designed with user convenience in mind, and supports not only text input but also voice recognition and touchscreen operation.

[0786] The device sends the entered text prompt to the server via an HTTP POST request. This HTTP communication uses a common protocol, often in JSON format. The server analyzes the received text prompt and sends an API request to the generative AI model to generate the virtual space. This analysis process uses a natural language processing algorithm.

[0787] The generative AI model generates a virtual space based on the prompt text entered by the user. Specifically, it creates a virtual space that includes the layout of the conference room, participant avatars, a presentation screen, and more. The generated virtual space data is sent back to the server, which then sends it to the device. The device then performs 3D rendering based on the returned data and displays it to the user. Typical software used in this process include Unity and Unreal Engine.

[0788] Next, the user begins rehearsing in the virtual space. The device recognizes the user's facial expressions and voice data in real time and passes them to the emotion engine. The emotion engine analyzes this data and extracts the user's emotional state. The extracted emotion data is then sent back to the server.

[0789] While the user is rehearsing, the device records the user's voice and movement data and sends it to the server in real time. Based on the received data, the server integrates and analyzes the data generated by the generative AI model and emotion engine to generate appropriate feedback. The feedback specifically indicates areas for improvement and evaluation points for the user, including specific advice such as "You speak too fast" or "You do not express emotions enough." This feedback is displayed on the device so that the user can easily check it.

[0790] This invention stands out from conventional simulation systems in that it provides feedback that takes into account the user's emotions. Furthermore, by incorporating real-time analysis using an emotion engine, it is possible to comprehensively evaluate the user's performance and maximize the effectiveness of rehearsals. In this way, this invention allows users to rehearse efficiently and more effectively prepare for real-life situations.

[0791] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0792] Step 1:

[0793] The user enters a text prompt.

[0794] Input: User's rehearsal situation

[0795] Specific operation: The user types into the terminal in text format, "Present new product at large business meeting."

[0796] Output: Text prompt data

[0797] Step 2:

[0798] The terminal sends the entered text prompt to the server.

[0799] Input: Text prompt data

[0800] Specific behavior: The device serializes the text prompt into JSON format and sends it to the server as an HTTP POST request.

[0801] Output: Prompt data sent to the server

[0802] Step 3:

[0803] The server analyzes the received text prompt and creates a request to generate a virtual space.

[0804] Input: prompt data

[0805] Specific operation: The server uses a natural language processing algorithm to analyze the text prompt and extract the parameters necessary to generate the virtual space.

[0806] Output: API request data to the generative AI model

[0807] Step 4:

[0808] The generative AI model generates a virtual space based on requests from the server.

[0809] Input: API request data

[0810] Specific operation: The generative AI model generates the conference room layout, participant avatars, presentation screens, etc., and sends the virtual space data back to the server.

[0811] Output: Generated virtual space data

[0812] Step 5:

[0813] The server returns the generated virtual space data to the terminal.

[0814] Input: Virtual space data

[0815] Specific operation: The server sends the virtual space data to the terminal as an HTTP response.

[0816] Output: Virtual space data sent to the device

[0817] Step 6:

[0818] The terminal renders the generated virtual space data and displays it to the user.

[0819] Input: Virtual space data

[0820] How it works: The device generates 3D graphics based on virtual space data and displays them to the user. This process uses software such as Unity or Unreal Engine.

[0821] Output: Displayed virtual space

[0822] Step 7:

[0823] The user's facial expression and voice data are sent to the emotion engine.

[0824] Input: User's facial expression data and voice data

[0825] Specific operation: The device uses the camera and microphone to capture the user's facial expressions and voice in real time and send them to the emotion engine.

[0826] Output: Data sent to the emotion engine

[0827] Step 8:

[0828] An emotion engine generates emotion data from the user's facial expression and voice data.

[0829] Input: User's facial and voice data

[0830] Specific operation: The emotion engine uses machine learning algorithms to analyze the user's emotions and generate emotion data.

[0831] Output: Generated emotion data

[0832] Step 9:

[0833] The user rehearses in the virtual space.

[0834] Input: Virtual space, user actions

[0835] Specific operation: The user gives a presentation in the virtual space, and the terminal records the user's voice and actions.

[0836] Output: Recorded user performance data

[0837] Step 10:

[0838] The terminal transmits the user's performance data to the server.

[0839] Input: User performance data

[0840] Specific operation: The terminal transmits user performance data to the server in real time.

[0841] Output: Performance data sent to the server

[0842] Step 11:

[0843] The server generates feedback based on the received data.

[0844] Input: User performance and emotional data

[0845] Specific operation: The server integrates data from the generative AI model and emotion engine to generate feedback based on the user's speaking rate, time allocation, and emotional changes.

[0846] Output: Generated feedback

[0847] Step 12:

[0848] The server sends the feedback to the terminal, which displays it to the user.

[0849] Input: Generated feedback

[0850] Specific operation: The server sends the feedback to the device in an HTTP response, and the device displays it in its user interface. For example, it presents the feedback to the user in the form of a pop-up notification or a detailed report.

[0851] Output: Feedback provided to the user.

[0852] (Application example 2)

[0853] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0854] In conventional security training, it is difficult to simulate real-world situations, and there are no efficient methods to develop realistic response capabilities. Furthermore, there is a lack of systems to provide appropriate feedback on users' emotions and behavioral data during training. This makes it difficult to specifically understand how users should improve.

[0855] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0856] In this invention, the server includes means for inputting a scenario, means for analyzing the input scenario and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting the user's actions in real time, means for generating and providing feedback based on the analyzed user data, means for recognizing emotions from the user's facial expressions and voice, and means for generating feedback by integrating the emotion data. This enables the user to train in a situation close to reality and receive specific feedback based on their emotions and actions in real time.

[0857] A "means for inputting a scenario" is a device or interface that allows a user to input a specific situation for training or simulation in text format.

[0858] The "means for analyzing the input scenario and generating a virtual environment" refers to software and hardware that realistically recreates the situation in a virtual space based on text data input by the user.

[0859] "Means for displaying the generated virtual environment" refers to a display device such as a display device, projector, or head-mounted display (HMD) for visually presenting the generated virtual space to the user.

[0860] "Means for recording and transmitting user actions in real time" refers to devices or systems that record actions and statements made by users in virtual space in real time and transmit them to a server. Specifically, this includes cameras, microphones, and sensors.

[0861] "Means for generating and providing feedback based on analyzed user data" refers to software and systems that analyze data on users' actions and statements, generate corresponding feedback, and provide it to users.

[0862] "Means for recognizing emotions from a user's facial expressions and voice" refers to technology that uses a camera or microphone to capture a user's facial expressions and voice, and then analyzes them to infer emotions.

[0863] The "means for integrating emotional data to generate feedback" is a system that combines emotional data and behavioral data obtained from users to generate training improvements and advice.

[0864] The system of the present invention is a system for effectively conducting security training in a virtual space. This system consists of a user terminal, a server, a generative AI model, and an emotion engine.

[0865] Detailed system configuration

[0866] 1. How to input a scenario

[0867] The user uses an interface to input a scenario. For example, the user inputs a text-based scenario such as "How should I respond when I discover a suspicious person?" into the terminal interface.

[0868] 2. A method for analyzing input scenarios and generating virtual environments

[0869] The device sends the input scenario to the server as an HTTP POST request, which the server analyzes and generates a request to the generative AI model. The generative AI model (e.g., OpenAI's GPT-3) generates a virtual environment based on the scenario.

[0870] 3. How to view the generated virtual environment

[0871] The server receives the virtual environment data from the generative AI model and sends it back to the device, which then uses this data to render the virtual space and display it to the user using a display or head-mounted display (HMD).

[0872] 4. Means of recording and transmitting user actions in real time

[0873] The device's camera and microphone are used to capture the user's facial expressions and voice in real time, and this data is sent to the server as user movement data.

[0874] 5. Means for generating and providing feedback based on analyzed user data

[0875] The server analyzes the acquired motion data and emotion data using an emotion engine (e.g., Emotion SDK). Using a generative AI model, it integrates the motion data and emotion data to generate feedback and provide suggestions for improvement.

[0876] Hardware and software used

[0877] Hardware

[0878] Camera and microphone: To capture the user's facial expressions and voice

[0879] Display or Head-Mounted Display (HMD): for viewing the virtual environment

[0880] software

[0881] Emotion engine (e.g., Emotion SDK): To recognize emotions from the user's facial expressions and voice

[0882] Generative AI models (e.g., GPT-3 by OpenAI) to generate scenario-based virtual environments and generate feedback

[0883] HTTP request processing (e.g., requests library): Used to send data from the device to the server

[0884] Examples of specific examples and prompts

[0885] As a concrete example, consider the case where a security guard practices "how to respond when a suspicious person is spotted." The user inputs the following into the scenario input interface:

[0886] How to spot a suspicious person and respond

[0887] Based on this scenario, the server and generative AI model generate a specific virtual environment (e.g., a scene in which a suspicious person appears at the entrance of an office building), and users train in that virtual space.

[0888] The emotion engine analyzes the user's facial expressions and voice data, and works with the generative AI model to provide real-time feedback based on the emotional data. For example, it provides feedback such as, "You speak slowly and calmly, but your facial expression shows signs of tension."

[0889] This allows users to develop the ability to respond to situations that are close to reality and to identify specific and appropriate areas for improvement.

[0890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0891] Step 1:

[0892] The user enters the training situation into the interface.

[0893] Input: The user inputs a specific situation (e.g., "I discovered a suspicious person and how would I respond?") in text format.

[0894] Output: The input text data.

[0895] Specific operation: The user inputs the situation using the device's keyboard or touch screen.

[0896] Step 2:

[0897] The terminal sends the entered text prompt to the server as an HTTP POST request.

[0898] Input: The text data entered in step 1.

[0899] Output: The HTTP request sent to the server.

[0900] Specific operation: The device sends an HTTP request to the server via the network, using a network communication module such as the requests library.

[0901] Step 3:

[0902] The server parses the received text prompt and generates a request to the generative AI model.

[0903] Input: Text data sent from the terminal.

[0904] Output: Request data to the generative AI model.

[0905] Specific operation: The server analyzes the received text data and formats the prompt sentence as an API request to the generative AI model.

[0906] Step 4:

[0907] A generative AI model generates a virtual environment based on input prompts.

[0908] Input: The parsed text prompt.

[0909] Output: Generated virtual environment data (e.g., virtual environment scene data and object data).

[0910] How it works: Based on the input prompt data, the generative AI model automatically generates a virtual environment that corresponds to the specified situation. For example, it uses OpenAI's GPT-3 to generate detailed settings based on the scenario.

[0911] Step 5:

[0912] The server sends the virtual environment data received from the generated AI model back to the device.

[0913] Input: Virtual environment data returned from a generative AI model.

[0914] Output: Virtual environment data sent to the device.

[0915] Specific operation: The server sends the virtual environment data received from the generative AI model to the terminal as an HTTP response.

[0916] Step 6:

[0917] The device renders the virtual environment and displays it to the user.

[0918] Input: Virtual environment data received from the server.

[0919] Output: The virtual environment that is displayed to the user.

[0920] Specific operation: The terminal uses the received virtual environment data to display the virtual environment using a display or head-mounted display.

[0921] Step 7:

[0922] The device's camera and microphone are used to record the user's actions and voice in real time.

[0923] Input: User movements and voice.

[0924] Output: Real-time recorded motion and audio data.

[0925] How it works: The camera and microphone connected to the device capture the user's facial expressions and voice and record them in real time.

[0926] Step 8:

[0927] The terminal transmits the recorded data to the server.

[0928] Input: Recorded motion and audio data.

[0929] Output: User data sent to the server.

[0930] Specific operation: The device sends the recorded data to the server via the network.

[0931] Step 9:

[0932] The server analyzes the received data and generates feedback.

[0933] Input: Motion and audio data sent from the device.

[0934] Output: Parsed feedback data.

[0935] Specific behavior: The server analyzes the behavior data and emotion data using the emotion engine, and generates appropriate feedback using the generative AI model.

[0936] Step 10:

[0937] The server transmits the generated feedback to the terminal.

[0938] Input: Generated feedback data.

[0939] Output: Feedback data sent to the device.

[0940] Specific operation: The server generates feedback and sends it to the device as an HTTP response.

[0941] Step 11:

[0942] The device displays the feedback to the user.

[0943] Input: Feedback data sent by the server.

[0944] Output: The feedback that is displayed to the user.

[0945] Specific operation: The terminal displays the received feedback data on the user interface for the user to check.

[0946] 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.

[0947] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0948] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0949] [Third embodiment]

[0950] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0951] 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.

[0952] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0953] 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.

[0954] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0955] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0956] 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.

[0957] 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.

[0958] 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 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.

[0959] 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.

[0960] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0961] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0962] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system has the function of allowing users to input text prompts, and based on these, a generation AI realistically recreates a specified situation. A specific embodiment of this system is described below.

[0963] System Overview

[0964] The system consists of a user's device, a server, and a generative AI model. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts and generates a virtual space using the generative AI model. The generated virtual space is displayed on the user's device, where the user can rehearse. The system records the user's actions in real time and provides appropriate feedback.

[0965] Processing Details

[0966] 1. Enter the situation

[0967] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[0968] 2. Data transmission

[0969] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[0970] 3. Situation creation request

[0971] The server passes the received text prompt to the generative AI model and requests it to generate a situation. The generative AI model generates the necessary virtual space based on the input prompt.

[0972] 4. Virtual Space Creation

[0973] The generative AI model generates a virtual space corresponding to a specified situation, including a conference room, participant avatars, a presentation screen, etc. For example, in the case of a business meeting, the layout of the conference room and participant avatars are generated.

[0974] 5. Return of generated data

[0975] The server sends the generated virtual space data to the device, which receives the data, renders the virtual space, and displays it to the user.

[0976] 6. Performing a rehearsal

[0977] The user begins rehearsing in the virtual space. The device records the user's voice and movements and transmits them to the server in real time.

[0978] 7. Generate feedback

[0979] The server then analyzes the received data using a generative AI model to generate feedback based on the user's performance, such as the user's speaking speed, time allocation, and keyword usage.

[0980] 8. Providing Feedback

[0981] The generative AI model generates feedback that is sent to the device via the server, and the device displays this feedback to the user, allowing them to see areas for improvement.

[0982] Specific examples

[0983] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[0984] 1. Enter the situation

[0985] The user types text into the interface: "Presenting a new product at a large business meeting."

[0986] 2. Data transmission

[0987] The terminal sends this input to the server.

[0988] 3. Situation creation request

[0989] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[0990] 4. Virtual Space Creation

[0991] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[0992] 5. Return of generated data

[0993] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[0994] 6. Performing a rehearsal

[0995] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[0996] 7. Generate feedback

[0997] The server analyzes the user's performance and generates feedback based on speaking rate, timing, and keyword use.

[0998] 8. Providing Feedback

[0999] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[1000] This allows users to rehearse efficiently and receive appropriate feedback. By enabling rehearsals in a realistic virtual environment, the present invention provides support for effective advance preparation and the ability to demonstrate one's true potential.

[1001] The processing flow will be explained below.

[1002] Step 1:

[1003] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[1004] Step 2:

[1005] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[1006] Step 3:

[1007] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[1008] Step 4:

[1009] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[1010] Step 5:

[1011] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[1012] Step 6:

[1013] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[1014] Step 7:

[1015] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[1016] Step 8:

[1017] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[1018] Step 9:

[1019] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[1020] Step 10:

[1021] The server analyzes the received data using a generative AI model to generate feedback, specifically analyzing speaking speed, time allocation, and keyword usage.

[1022] Step 11:

[1023] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[1024] Step 12:

[1025] The device displays the received feedback to the user, specifically, by displaying advice and suggestions for improvement on the user interface.

[1026] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the real thing.

[1027] Example 1

[1028] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1029] Conventional rehearsal systems lack the means to efficiently generate virtual spaces and analyze user performance, making it difficult for users to receive effective feedback through realistic rehearsals. Furthermore, the generation of realistic virtual spaces tailored to specific situations is also insufficient, resulting in low quality rehearsal environments.

[1030] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1031] In this invention, the server includes a means for inputting a situation that the user wants to rehearse as a text prompt, a means for analyzing the input text prompt and passing it to the generative AI model, and a means for acquiring the generated virtual space data and transmitting it to the user's terminal, thereby enabling the user to rehearse in a realistic virtual space corresponding to a specific situation and receive feedback that has been efficiently analyzed by the generative AI model.

[1032] "User" means an individual or entity that uses the System to conduct a rehearsal.

[1033] "Terminal" refers to the electronic device used by a user to operate the system, including computers, smartphones, tablets, etc.

[1034] The "server" is a central computer system that receives data sent from the user's device, interacts with the generative AI model, and generates the virtual space.

[1035] "Generative AI model" refers to an artificial intelligence model for generating a virtual space based on text prompts from a user.

[1036] A "text prompt" is a text input that specifically describes a situation that the user wants to rehearse.

[1037] A "virtual space" is a simulated environment created by a generative AI model for users to rehearse.

[1038] "Feedback" is information that evaluates a user's performance during rehearsal and provides the user with areas for improvement and appropriate advice.

[1039] "Analysis" refers to the process by which a generative AI model evaluates a user's performance based on data obtained from the user.

[1040] "Rendering" refers to the process of converting acquired virtual space data into a form that can be visually displayed on a device.

[1041] "Real-time" means that user actions and voice are processed and transmitted immediately, without delay.

[1042] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system consists of a user terminal, a server, and a generative AI model. Specific embodiments of the system are described below.

[1043] System Overview

[1044] 1. User's device:

[1045] The user's device provides an interface for rehearsals. This interface includes a text input field for the user to enter a situation. Specific hardware examples include computers, smartphones, and tablets. The device also includes a speech recognition engine and a video capture library, and records the user's movements and voice using, for example, a microphone or camera.

[1046] 2. Server:

[1047] The server receives the data sent by the user and uses the generative AI model to generate the virtual space. The specific hardware used is likely to be a server equipped with a high-performance CPU and GPU. The software includes a runtime environment and API for running the generative AI model (e.g., OpenAI GPT-4).

[1048] 3. Generative AI Model:

[1049] Generative AI models, such as OpenAI's GPT-4, create virtual spaces based on generated text prompts, generating meeting room layouts, participant avatars, presentation screens, and more, tailored to the user's situation.

[1050] System Operation

[1051] The user enters a situation into the device's interface, such as "presenting a new product at a large business meeting," and the text prompt is sent to the server via HTTPS. The server then passes the prompt to a generative AI model, requesting it to generate a specific virtual space.

[1052] The generative AI model generates a virtual space, including the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server in structured format such as JSON. The server then sends this generated virtual space data to the device. The device then uses a 3D rendering engine (e.g., Unity or Unreal Engine) to display the virtual space based on the received data.

[1053] Users begin rehearsing in the virtual space, recording their movements and voice using a microphone and camera. The device captures this data in real time and sends it to the server. The server then analyzes the received user performance data using a generative AI model, generating feedback based on speaking speed, time allocation, keyword usage, and other factors, and sends it to the device. The device then displays this feedback to the user, allowing them to see areas for improvement.

[1054] Specific examples

[1055] As an example, consider the case where a user is rehearsing a situation in which he or she is to give a presentation on a new product at a large business meeting.

[1056] User Input:

[1057] The user types into the device interface, "Presenting a new product at a large business meeting."

[1058] Sending data:

[1059] The terminal sends this input data to the server.

[1060] Virtual space generation:

[1061] The server passes text prompts to a generative AI model, which generates a virtual conference room, participant avatars, presentation screens, and more.

[1062] Returning generated data:

[1063] The server sends the generated virtual space data to the terminal, which then renders it and displays it to the user.

[1064] Run a rehearsal:

[1065] The user rehearses in the virtual space, and the device records the voice and movements and sends them to the server.

[1066] Generate and provide feedback:

[1067] The server analyzes the user's performance, generates feedback, sends it to the device, and displays it to the user.

[1068] In this way, the present invention provides a virtual rehearsal environment in which users can efficiently rehearse and receive appropriate feedback.

[1069] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1070] Step 1: Enter the situation

[1071] The user enters the situation they want to rehearse as a text prompt into the device's interface. For example, they might enter "Presenting a new product at a large business meeting." The device accepts this input text.

[1072] Input: The text prompt for the situation entered by the user

[1073] Output: The text prompt that the terminal gets

[1074] Step 2: Sending data

[1075] The terminal sends the entered text prompt to the server using the HTTPS protocol. Specifically, it generates an HTTP POST request and sends a payload containing the input data to the server. The server receives this request and parses the text prompt data.

[1076] Input: Text prompt

[1077] Data processing: Generating an HTTP POST request

[1078] Data Calculation: Sending a Request

[1079] Output: The text prompt data sent to the server.

[1080] Step 3: Situation generation request

[1081] The server passes the received text prompt to the generative AI model, requesting it to generate a specific situation. The server then sends an API request to the generative AI model, which then generates a virtual space based on the specified situation.

[1082] Input: Text prompt sent to the server

[1083] Data processing: Analysis of text prompts

[1084] Data Computation: Making API requests to generative AI models

[1085] Output: Sending an API request

[1086] Step 4: Creating the virtual space

[1087] The generative AI model generates a virtual space corresponding to a specific situation, such as the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server as structured data in JSON format.

[1088] Input: API request

[1089] Data processing: Creation of virtual space

[1090] Data calculation: Generation of meeting room layouts, participant avatars, presentation screens, etc.

[1091] Output: Generated virtual space data (JSON format)

[1092] Step 5: Returning generated data

[1093] The server sends the virtual space data received from the generated AI model to the device. Specifically, the server returns the data to the device as an HTTP response, and the device receives this data.

[1094] Input: Generated virtual space data

[1095] Data operations: generating and sending HTTP responses

[1096] Output: Virtual space data sent to the device

[1097] Step 6: Displaying the virtual space

[1098] The device performs 3D rendering based on the received virtual space data and displays the virtual space to the user. Specifically, the device uses a 3D rendering engine (e.g., Unity or Unreal Engine) to visually display the virtual space.

[1099] Input: Received virtual space data

[1100] Data processing: preparing for 3D rendering

[1101] Data calculation: rendering of virtual space

[1102] Output: The virtual space that is displayed to the user

[1103] Step 7: Run a rehearsal

[1104] The user begins rehearsing in the virtual space, and the device records the user's movements and voice in real time. The device uses a microphone and camera to capture this data and transmits it to the server in real time.

[1105] Input: User movements and voice

[1106] Data Processing: Voice and Motion Capture

[1107] Data calculation: Real-time data transmission

[1108] Output: User voice and movement data sent to the server

[1109] Step 8: Generate feedback

[1110] The server receives the user's performance data and analyzes it with a generative AI model, evaluating things like speaking speed, time allocation, and keyword usage to generate specific feedback.

[1111] Input: User voice and motion data

[1112] Data processing: analysis of performance data

[1113] Data Calculation: Feedback Generation

[1114] Output: Generated feedback

[1115] Step 9: Provide feedback

[1116] The server generates feedback and sends it to the device, which then displays it to the user. The device displays the feedback in the form of a pop-up or notification, allowing the user to see what improvements need to be made.

[1117] Input: Generated feedback

[1118] Data Calculation: Sending Feedback

[1119] Output: Feedback that is displayed to the user

[1120] This process allows users to rehearse in a realistic virtual space tailored to a specific situation and receive feedback that is efficiently analyzed by a generative AI model.

[1121] (Application example 1)

[1122] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1123] In recent years, as autonomous vehicles have become more widely used, new tools are needed to make effective use of travel time. However, current systems do not provide an environment where users can efficiently rehearse while traveling, preventing users from making full use of their valuable time. Furthermore, there are limited technologies that provide virtual environments that can adapt to environmental changes while traveling. The purpose of this invention is to solve these problems and provide a realistic rehearsal environment while traveling.

[1124] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1125] In this invention, the server includes means for inputting text prompts, means for analyzing the input text prompts and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting user actions in real time, means for generating and providing feedback based on the analyzed user data, and means for displaying the virtual environment and rehearsing within the vehicle, thereby enabling efficient and effective rehearsals and feedback to be received even while on the move.

[1126] A "text prompt" is text data that a user inputs in sentence form to describe the situation or content they want to rehearse.

[1127] A "virtual environment" is a simulated space created by a generative AI model based on text prompts, providing a virtual space for users to rehearse.

[1128] "Display means" means the device or software that visually displays the generated virtual environment on the user's device.

[1129] The "behavior recording means" is a function that records the user's behavior and voice in real time and sends the data to a server for analysis and feedback.

[1130] A "feedback generation means" is a system that analyzes recorded user behavioral data and provides evaluations and advice on the user's performance.

[1131] "In-vehicle display means" refers to a display means for providing a virtual environment to a user and for rehearsing in a moving environment such as inside an autonomous vehicle.

[1132] The present invention relates to a system for efficiently rehearsing presentations and dialogues in an autonomous vehicle. This system allows users to rehearse in a virtual environment while on the move and receive feedback in real time. Detailed embodiments of this system are described below.

[1133] System configuration

[1134] The system consists of a user's smartphone, a server, and a generative AI model. The smartphone provides an interface for inputting text prompts and acts as a display means. The server analyzes the input text prompts and generates a virtual environment using the generative AI model.

[1135] Hardware and Software Use

[1136] A user interface application runs on the smartphone, allowing the user to input text prompts for situations they want to rehearse. The server receives the prompts from the smartphone via an HTTP POST request and passes this data to the generative AI model. Based on the prompts, the generative AI model generates virtual environments such as a conference room, participant avatars, and a presentation screen.

[1137] The server sends the generated virtual environment data to a smartphone, which can then render and display this data to the user. As the user rehearses in the virtual environment, their voice and movements are recorded and sent in real time to the server. The server analyzes the received data and generates feedback based on the user's speaking rate, timing, and keyword usage. This feedback is then sent back to the smartphone, allowing the user to see areas for improvement.

[1138] Specific examples

[1139] For example, consider a sales user who wants to rehearse an "important presentation for the next business trip." The user enters the following text prompt into the smartphone app:

[1140] Example prompt sentence:

[1141] "An important presentation on my next business trip"

[1142] Based on this prompt, the system uses a generative AI model to generate environments such as a virtual conference room or virtual audience. Users can then rehearse in this virtual environment while inside their self-driving vehicle. Data recorded during the rehearsal is sent to a server in real time, and the generative AI model analyzes the data and provides feedback to the user. This allows users to make effective use of their travel time and improve their rehearsal content by receiving appropriate feedback.

[1143] In this way, the present invention enables rehearsals in a realistic virtual environment and provides support for users to prepare more efficiently.

[1144] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1145] Step 1:

[1146] The user accesses the device (smartphone) interface and inputs the situation they want to rehearse as a text prompt. The input text prompt contains specific content such as "an important presentation on my next business trip." The input data is temporarily saved on the device.

[1147] Step 2:

[1148] The terminal sends the temporarily saved text prompt to the server via an HTTP POST request. The input is the text prompt data (character string), and the output is a request to analyze this data.

[1149] Step 3:

[1150] The server analyzes the received text prompt and issues a situation generation request to input to the generative AI model. Specific operations include data processing, such as analyzing the format of the text data and converting it into an appropriate data format for the generative model.

[1151] Step 4:

[1152] The generative AI model generates a virtual environment based on a situation generation request received from the server. The generated virtual environment has components such as a conference room, participant avatars, and a presentation screen. The input is analysis data of text prompts, and the output is expressed as virtual environment data.

[1153] Step 5:

[1154] The server returns the generated virtual environment data to the device. Specifically, it sends the virtual environment data to the smartphone via an HTTP response. The input is the virtual environment data generated by the generative AI model, and the output is data sent to the device.

[1155] Step 6:

[1156] The device renders the received virtual environment data and displays it to the user. Specifically, a rendering process is performed to draw the virtual environment data as a 3D model. The input is virtual environment data, and the output is a virtual space display that the user can visually confirm.

[1157] Step 7:

[1158] The user begins rehearsing in the virtual environment, and the device records the user's voice and movements in real time. This recorded data is sent to the server in real time. The input is the user's voice and movements, and the output is the recorded data sent to the server.

[1159] Step 8:

[1160] The server analyzes the received recorded data using a generative AI model and generates feedback. The analysis targets the user's speaking speed, time allocation, keyword usage, etc. The input is the recorded data sent to the server, and the output is feedback data based on the analysis results.

[1161] Step 9:

[1162] The server returns the generated feedback data to the device. Specifically, it sends the feedback data to the smartphone as an HTTP response. The input is the feedback data analyzed by the generative AI model, and the output is the feedback data sent to the device.

[1163] Step 10:

[1164] The device displays the received feedback data to the user. Specific operations include updating the UI to display the feedback information in a visually understandable manner. The input is the feedback data, and the output is a feedback display that the user can visually confirm.

[1165] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1166] This invention is a virtual space rehearsal system combined with an emotion engine. It recognizes emotions from the user's facial expressions and voice, and provides feedback based on that emotion data, maximizing the effectiveness of the rehearsal. This system has the function of allowing the user to input text prompts, and based on those prompts, the generation AI realistically recreates the specified situation. Furthermore, the emotion engine analyzes the user's emotions and reflects the results in the feedback.

[1167] System Overview

[1168] This system consists of a user's device, a server, a generative AI model, and an emotion engine. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts to generate a virtual space. The generated virtual space is displayed on the user's device. The emotion engine recognizes emotions from the user's facial expressions and voice, and provides feedback based on that data.

[1169] Processing Details

[1170] 1. Enter the situation

[1171] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[1172] 2. Data transmission

[1173] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[1174] 3. Situation creation request

[1175] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[1176] 4. Virtual Space Creation

[1177] Based on the input prompts, the generative AI model generates the necessary virtual space, including the conference room, participant avatars, and presentation screen.

[1178] 5. Return of generated data

[1179] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[1180] 6. Recognizing and transmitting emotional data

[1181] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data, which the device then transmits to the server.

[1182] 7. Performing a rehearsal

[1183] The user begins rehearsing in the virtual space. The device records the user's voice and movement data and transmits it to the server in real time.

[1184] 8. Generate feedback

[1185] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback.

[1186] 9. Providing Feedback

[1187] The server sends the generated feedback back to the device, and the device displays it to the user. Specifically, the device displays the feedback details on the user interface and provides suggestions for improvement and advice.

[1188] Specific examples

[1189] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[1190] 1. Enter the situation

[1191] The user types text into the interface: "Presenting a new product at a large business meeting."

[1192] 2. Data transmission

[1193] The terminal sends this input to the server.

[1194] 3. Situation creation request

[1195] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[1196] 4. Virtual Space Creation

[1197] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[1198] 5. Return of generated data

[1199] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[1200] 6. Recognizing and transmitting emotional data

[1201] The emotion engine analyzes the user's facial expressions and voice, generates emotion data in real time, and the device sends this to the server.

[1202] 7. Performing a rehearsal

[1203] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[1204] 8. Generate feedback

[1205] The server analyzes the user's performance and generates feedback by integrating data from the emotion engine, such as based on speaking speed, time allocation, and emotional changes.

[1206] 9. Providing Feedback

[1207] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[1208] This invention allows users to rehearse efficiently and receive appropriate feedback. Furthermore, by using emotion analysis by an emotion engine, it is possible to provide optimal advice according to the user's feelings, maximizing the effectiveness of rehearsals.

[1209] The processing flow will be explained below.

[1210] Step 1:

[1211] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[1212] Step 2:

[1213] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[1214] Step 3:

[1215] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[1216] Step 4:

[1217] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[1218] Step 5:

[1219] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[1220] Step 6:

[1221] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[1222] Step 7:

[1223] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[1224] Step 8:

[1225] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data. The device then sends this emotion data to a server. Specifically, emotion data is extracted using facial recognition and voice analysis technologies.

[1226] Step 9:

[1227] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[1228] Step 10:

[1229] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[1230] Step 11:

[1231] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback. For example, it analyzes the user's speaking speed, time allocation, and emotional changes.

[1232] Step 12:

[1233] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[1234] Step 13:

[1235] The device displays the received feedback to the user. Specifically, the device displays the details of the feedback on the user interface and provides suggestions for improvement and advice.

[1236] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the performance. Analysis by the emotion engine also provides personalized advice based on the user's emotional state, enabling more effective rehearsals.

[1237] Example 2

[1238] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1239] Conventional rehearsal systems have the drawback of being difficult to generate a realistic virtual space for users to effectively practice specific situations, and the feedback they provide is limited to a general evaluation, without suggesting specific areas for improvement. Furthermore, they do not provide feedback that takes into account the user's emotional state, limiting the actual effectiveness of rehearsals.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting a text prompt, means for analyzing the input text prompt and generating a virtual space, means for displaying the generated virtual space, means for analyzing the user's facial expression and voice data and generating emotional data, means for recording and transmitting the user's actions in real time, and means for generating and providing feedback based on the analyzed user data and emotional data. This allows the user to rehearse a specific situation in a realistic virtual space and receive specific feedback according to the user's emotional state.

[1241] A "text prompt" is textual data that a user enters to specify a rehearsal or simulation situation.

[1242] A "virtual space" is a simulated environment generated by a generative AI model for users to rehearse.

[1243] "Analysis" is the process of analyzing input data, the user's facial expressions, and voice data to extract meaning and emotional insights.

[1244] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's facial expression and voice data.

[1245] An "action" is a movement or speech that a user makes within a virtual space.

[1246] "Feedback" is information about guidance and improvements provided to the user after the rehearsal.

[1247] This invention is a system that generates a virtual space based on text prompts entered by the user, and allows rehearsals to be conducted within it, enhancing the effectiveness of practice for real situations. The main components of this system are the user's device, a server, a generative AI model, and an emotion engine.

[1248] The user's device provides an interface for the user to input text prompts. For example, the user inputs a situation such as "presenting a new product at a large business meeting" in text format. This input interface is designed with user convenience in mind, and supports not only text input but also voice recognition and touchscreen operation.

[1249] The device sends the entered text prompt to the server via an HTTP POST request. This HTTP communication uses a common protocol, often in JSON format. The server analyzes the received text prompt and sends an API request to the generative AI model to generate the virtual space. This analysis process uses a natural language processing algorithm.

[1250] The generative AI model generates a virtual space based on the prompt text entered by the user. Specifically, it creates a virtual space that includes the layout of the conference room, participant avatars, a presentation screen, and more. The generated virtual space data is sent back to the server, which then sends it to the device. The device then performs 3D rendering based on the returned data and displays it to the user. Typical software used in this process include Unity and Unreal Engine.

[1251] Next, the user begins rehearsing in the virtual space. The device recognizes the user's facial expressions and voice data in real time and passes them to the emotion engine. The emotion engine analyzes this data and extracts the user's emotional state. The extracted emotion data is then sent back to the server.

[1252] While the user is rehearsing, the device records the user's voice and movement data and sends it to the server in real time. Based on the received data, the server integrates and analyzes the data generated by the generative AI model and emotion engine to generate appropriate feedback. The feedback specifically indicates areas for improvement and evaluation points for the user, including specific advice such as "You speak too fast" or "You do not express emotions enough." This feedback is displayed on the device so that the user can easily check it.

[1253] This invention stands out from conventional simulation systems in that it provides feedback that takes into account the user's emotions. Furthermore, by incorporating real-time analysis using an emotion engine, it is possible to comprehensively evaluate the user's performance and maximize the effectiveness of rehearsals. In this way, this invention allows users to rehearse efficiently and more effectively prepare for real-life situations.

[1254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1255] Step 1:

[1256] The user enters a text prompt.

[1257] Input: User's rehearsal situation

[1258] Specific operation: The user types into the terminal in text format, "Present new product at large business meeting."

[1259] Output: Text prompt data

[1260] Step 2:

[1261] The terminal sends the entered text prompt to the server.

[1262] Input: Text prompt data

[1263] Specific behavior: The device serializes the text prompt into JSON format and sends it to the server as an HTTP POST request.

[1264] Output: Prompt data sent to the server

[1265] Step 3:

[1266] The server analyzes the received text prompt and creates a request to generate a virtual space.

[1267] Input: prompt data

[1268] Specific operation: The server uses a natural language processing algorithm to analyze the text prompt and extract the parameters necessary to generate the virtual space.

[1269] Output: API request data to the generative AI model

[1270] Step 4:

[1271] The generative AI model generates a virtual space based on requests from the server.

[1272] Input: API request data

[1273] Specific operation: The generative AI model generates the conference room layout, participant avatars, presentation screens, etc., and sends the virtual space data back to the server.

[1274] Output: Generated virtual space data

[1275] Step 5:

[1276] The server returns the generated virtual space data to the terminal.

[1277] Input: Virtual space data

[1278] Specific operation: The server sends the virtual space data to the terminal as an HTTP response.

[1279] Output: Virtual space data sent to the device

[1280] Step 6:

[1281] The terminal renders the generated virtual space data and displays it to the user.

[1282] Input: Virtual space data

[1283] How it works: The device generates 3D graphics based on virtual space data and displays them to the user. This process uses software such as Unity or Unreal Engine.

[1284] Output: Displayed virtual space

[1285] Step 7:

[1286] The user's facial expression and voice data are sent to the emotion engine.

[1287] Input: User's facial expression data and voice data

[1288] Specific operation: The device uses the camera and microphone to capture the user's facial expressions and voice in real time and send them to the emotion engine.

[1289] Output: Data sent to the emotion engine

[1290] Step 8:

[1291] An emotion engine generates emotion data from the user's facial expression and voice data.

[1292] Input: User's facial and voice data

[1293] Specific operation: The emotion engine uses machine learning algorithms to analyze the user's emotions and generate emotion data.

[1294] Output: Generated emotion data

[1295] Step 9:

[1296] The user rehearses in the virtual space.

[1297] Input: Virtual space, user actions

[1298] Specific operation: The user gives a presentation in the virtual space, and the terminal records the user's voice and actions.

[1299] Output: Recorded user performance data

[1300] Step 10:

[1301] The terminal transmits the user's performance data to the server.

[1302] Input: User performance data

[1303] Specific operation: The terminal transmits user performance data to the server in real time.

[1304] Output: Performance data sent to the server

[1305] Step 11:

[1306] The server generates feedback based on the received data.

[1307] Input: User performance and emotional data

[1308] Specific operation: The server integrates data from the generative AI model and emotion engine to generate feedback based on the user's speaking rate, time allocation, and emotional changes.

[1309] Output: Generated feedback

[1310] Step 12:

[1311] The server sends the feedback to the terminal, which displays it to the user.

[1312] Input: Generated feedback

[1313] Specific operation: The server sends the feedback to the device in an HTTP response, and the device displays it in its user interface. For example, it presents the feedback to the user in the form of a pop-up notification or a detailed report.

[1314] Output: Feedback provided to the user.

[1315] (Application example 2)

[1316] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1317] In conventional security training, it is difficult to simulate real-world situations, and there are no efficient methods to develop realistic response capabilities. Furthermore, there is a lack of systems to provide appropriate feedback on users' emotions and behavioral data during training. This makes it difficult to specifically understand how users should improve.

[1318] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1319] In this invention, the server includes means for inputting a scenario, means for analyzing the input scenario and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting the user's actions in real time, means for generating and providing feedback based on the analyzed user data, means for recognizing emotions from the user's facial expressions and voice, and means for generating feedback by integrating the emotion data. This enables the user to train in a situation close to reality and receive specific feedback based on their emotions and actions in real time.

[1320] A "means for inputting a scenario" is a device or interface that allows a user to input a specific situation for training or simulation in text format.

[1321] The "means for analyzing the input scenario and generating a virtual environment" refers to software and hardware that realistically recreates the situation in a virtual space based on text data input by the user.

[1322] "Means for displaying the generated virtual environment" refers to a display device such as a display device, projector, or head-mounted display (HMD) for visually presenting the generated virtual space to the user.

[1323] "Means for recording and transmitting user actions in real time" refers to devices or systems that record actions and statements made by users in virtual space in real time and transmit them to a server. Specifically, this includes cameras, microphones, and sensors.

[1324] "Means for generating and providing feedback based on analyzed user data" refers to software and systems that analyze data on users' actions and statements, generate corresponding feedback, and provide it to users.

[1325] "Means for recognizing emotions from a user's facial expressions and voice" refers to technology that uses a camera or microphone to capture a user's facial expressions and voice, and then analyzes them to infer emotions.

[1326] The "means for integrating emotional data to generate feedback" is a system that combines emotional data and behavioral data obtained from users to generate training improvements and advice.

[1327] The system of the present invention is a system for effectively conducting security training in a virtual space. This system consists of a user terminal, a server, a generative AI model, and an emotion engine.

[1328] Detailed system configuration

[1329] 1. How to input a scenario

[1330] The user uses an interface to input a scenario. For example, the user inputs a text-based scenario such as "How should I respond when I discover a suspicious person?" into the terminal interface.

[1331] 2. A method for analyzing input scenarios and generating virtual environments

[1332] The device sends the input scenario to the server as an HTTP POST request, which the server analyzes and generates a request to the generative AI model. The generative AI model (e.g., OpenAI's GPT-3) generates a virtual environment based on the scenario.

[1333] 3. How to view the generated virtual environment

[1334] The server receives the virtual environment data from the generative AI model and sends it back to the device, which then uses this data to render the virtual space and display it to the user using a display or head-mounted display (HMD).

[1335] 4. Means of recording and transmitting user actions in real time

[1336] The device's camera and microphone are used to capture the user's facial expressions and voice in real time, and this data is sent to the server as user movement data.

[1337] 5. Means for generating and providing feedback based on analyzed user data

[1338] The server analyzes the acquired motion data and emotion data using an emotion engine (e.g., Emotion SDK). Using a generative AI model, it integrates the motion data and emotion data to generate feedback and provide suggestions for improvement.

[1339] Hardware and software used

[1340] Hardware

[1341] Camera and microphone: To capture the user's facial expressions and voice

[1342] Display or Head-Mounted Display (HMD): for viewing the virtual environment

[1343] software

[1344] Emotion engine (e.g., Emotion SDK): To recognize emotions from the user's facial expressions and voice

[1345] Generative AI models (e.g., GPT-3 by OpenAI) to generate scenario-based virtual environments and generate feedback

[1346] HTTP request processing (e.g., requests library): Used to send data from the device to the server

[1347] Examples of specific examples and prompts

[1348] As a concrete example, consider the case where a security guard practices "how to respond when a suspicious person is spotted." The user inputs the following into the scenario input interface:

[1349] How to spot a suspicious person and respond

[1350] Based on this scenario, the server and generative AI model generate a specific virtual environment (e.g., a scene in which a suspicious person appears at the entrance of an office building), and users train in that virtual space.

[1351] The emotion engine analyzes the user's facial expressions and voice data, and works with the generative AI model to provide real-time feedback based on the emotional data. For example, it provides feedback such as, "You speak slowly and calmly, but your facial expression shows signs of tension."

[1352] This allows users to develop the ability to respond to situations that are close to reality and to identify specific and appropriate areas for improvement.

[1353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1354] Step 1:

[1355] The user enters the training situation into the interface.

[1356] Input: The user inputs a specific situation (e.g., "I discovered a suspicious person and how would I respond?") in text format.

[1357] Output: The input text data.

[1358] Specific operation: The user inputs the situation using the device's keyboard or touch screen.

[1359] Step 2:

[1360] The terminal sends the entered text prompt to the server as an HTTP POST request.

[1361] Input: The text data entered in step 1.

[1362] Output: The HTTP request sent to the server.

[1363] Specific operation: The device sends an HTTP request to the server via the network, using a network communication module such as the requests library.

[1364] Step 3:

[1365] The server parses the received text prompt and generates a request to the generative AI model.

[1366] Input: Text data sent from the terminal.

[1367] Output: Request data to the generative AI model.

[1368] Specific operation: The server analyzes the received text data and formats the prompt sentence as an API request to the generative AI model.

[1369] Step 4:

[1370] A generative AI model generates a virtual environment based on input prompts.

[1371] Input: The parsed text prompt.

[1372] Output: Generated virtual environment data (e.g., virtual environment scene data and object data).

[1373] How it works: Based on the input prompt data, the generative AI model automatically generates a virtual environment that corresponds to the specified situation. For example, it uses OpenAI's GPT-3 to generate detailed settings based on the scenario.

[1374] Step 5:

[1375] The server sends the virtual environment data received from the generated AI model back to the device.

[1376] Input: Virtual environment data returned from a generative AI model.

[1377] Output: Virtual environment data sent to the device.

[1378] Specific operation: The server sends the virtual environment data received from the generative AI model to the terminal as an HTTP response.

[1379] Step 6:

[1380] The device renders the virtual environment and displays it to the user.

[1381] Input: Virtual environment data received from the server.

[1382] Output: The virtual environment that is displayed to the user.

[1383] Specific operation: The terminal uses the received virtual environment data to display the virtual environment using a display or head-mounted display.

[1384] Step 7:

[1385] The device's camera and microphone are used to record the user's actions and voice in real time.

[1386] Input: User movements and voice.

[1387] Output: Real-time recorded motion and audio data.

[1388] How it works: The camera and microphone connected to the device capture the user's facial expressions and voice and record them in real time.

[1389] Step 8:

[1390] The terminal transmits the recorded data to the server.

[1391] Input: Recorded motion and audio data.

[1392] Output: User data sent to the server.

[1393] Specific operation: The device sends the recorded data to the server via the network.

[1394] Step 9:

[1395] The server analyzes the received data and generates feedback.

[1396] Input: Motion and audio data sent from the device.

[1397] Output: Parsed feedback data.

[1398] Specific behavior: The server analyzes the behavior data and emotion data using the emotion engine, and generates appropriate feedback using the generative AI model.

[1399] Step 10:

[1400] The server transmits the generated feedback to the terminal.

[1401] Input: Generated feedback data.

[1402] Output: Feedback data sent to the device.

[1403] Specific operation: The server generates feedback and sends it to the device as an HTTP response.

[1404] Step 11:

[1405] The device displays the feedback to the user.

[1406] Input: Feedback data sent by the server.

[1407] Output: The feedback that is displayed to the user.

[1408] Specific operation: The terminal displays the received feedback data on the user interface for the user to check.

[1409] 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.

[1410] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1411] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1412] [Fourth embodiment]

[1413] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1414] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1415] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1416] 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.

[1417] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1418] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1419] 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.

[1420] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1421] 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.

[1422] 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 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.

[1423] 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.

[1424] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1425] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1426] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system has the function of allowing users to input text prompts, and based on these, a generation AI realistically recreates a specified situation. A specific embodiment of this system is described below.

[1427] System Overview

[1428] The system consists of a user's device, a server, and a generative AI model. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts and generates a virtual space using the generative AI model. The generated virtual space is displayed on the user's device, where the user can rehearse. The system records the user's actions in real time and provides appropriate feedback.

[1429] Processing Details

[1430] 1. Enter the situation

[1431] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[1432] 2. Data transmission

[1433] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[1434] 3. Situation creation request

[1435] The server passes the received text prompt to the generative AI model and requests it to generate a situation. The generative AI model generates the necessary virtual space based on the input prompt.

[1436] 4. Virtual Space Creation

[1437] The generative AI model generates a virtual space corresponding to a specified situation, including a conference room, participant avatars, a presentation screen, etc. For example, in the case of a business meeting, the layout of the conference room and participant avatars are generated.

[1438] 5. Return of generated data

[1439] The server sends the generated virtual space data to the device, which receives the data, renders the virtual space, and displays it to the user.

[1440] 6. Performing a rehearsal

[1441] The user begins rehearsing in the virtual space. The device records the user's voice and movements and transmits them to the server in real time.

[1442] 7. Generate feedback

[1443] The server then analyzes the received data using a generative AI model to generate feedback based on the user's performance, such as the user's speaking speed, time allocation, and keyword usage.

[1444] 8. Providing Feedback

[1445] The generative AI model generates feedback that is sent to the device via the server, and the device displays this feedback to the user, allowing them to see areas for improvement.

[1446] Specific examples

[1447] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[1448] 1. Enter the situation

[1449] The user types text into the interface: "Presenting a new product at a large business meeting."

[1450] 2. Data transmission

[1451] The terminal sends this input to the server.

[1452] 3. Situation creation request

[1453] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[1454] 4. Virtual Space Creation

[1455] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[1456] 5. Return of generated data

[1457] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[1458] 6. Performing a rehearsal

[1459] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[1460] 7. Generate feedback

[1461] The server analyzes the user's performance and generates feedback based on speaking rate, timing, and keyword use.

[1462] 8. Providing Feedback

[1463] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[1464] This allows users to rehearse efficiently and receive appropriate feedback. By enabling rehearsals in a realistic virtual environment, the present invention provides support for effective advance preparation and the ability to demonstrate one's true potential.

[1465] The processing flow will be explained below.

[1466] Step 1:

[1467] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[1468] Step 2:

[1469] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[1470] Step 3:

[1471] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[1472] Step 4:

[1473] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[1474] Step 5:

[1475] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[1476] Step 6:

[1477] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[1478] Step 7:

[1479] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[1480] Step 8:

[1481] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[1482] Step 9:

[1483] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[1484] Step 10:

[1485] The server analyzes the received data using a generative AI model to generate feedback, specifically analyzing speaking speed, time allocation, and keyword usage.

[1486] Step 11:

[1487] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[1488] Step 12:

[1489] The device displays the received feedback to the user, specifically, by displaying advice and suggestions for improvement on the user interface.

[1490] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the real thing.

[1491] Example 1

[1492] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1493] Conventional rehearsal systems lack the means to efficiently generate virtual spaces and analyze user performance, making it difficult for users to receive effective feedback through realistic rehearsals. Furthermore, the generation of realistic virtual spaces tailored to specific situations is also insufficient, resulting in low quality rehearsal environments.

[1494] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1495] In this invention, the server includes a means for inputting a situation that the user wants to rehearse as a text prompt, a means for analyzing the input text prompt and passing it to the generative AI model, and a means for acquiring the generated virtual space data and transmitting it to the user's terminal, thereby enabling the user to rehearse in a realistic virtual space corresponding to a specific situation and receive feedback that has been efficiently analyzed by the generative AI model.

[1496] "User" means an individual or entity that uses the System to conduct a rehearsal.

[1497] "Terminal" refers to the electronic device used by a user to operate the system, including computers, smartphones, tablets, etc.

[1498] The "server" is a central computer system that receives data sent from the user's device, interacts with the generative AI model, and generates the virtual space.

[1499] "Generative AI model" refers to an artificial intelligence model for generating a virtual space based on text prompts from a user.

[1500] A "text prompt" is a text input that specifically describes a situation that the user wants to rehearse.

[1501] A "virtual space" is a simulated environment created by a generative AI model for users to rehearse.

[1502] "Feedback" is information that evaluates a user's performance during rehearsal and provides the user with areas for improvement and appropriate advice.

[1503] "Analysis" refers to the process by which a generative AI model evaluates a user's performance based on data obtained from the user.

[1504] "Rendering" refers to the process of converting acquired virtual space data into a form that can be visually displayed on a device.

[1505] "Real-time" means that user actions and voice are processed and transmitted immediately, without delay.

[1506] The present invention is a system that provides a virtual environment for users to efficiently rehearse presentations and dialogues. This system consists of a user terminal, a server, and a generative AI model. Specific embodiments of the system are described below.

[1507] System Overview

[1508] 1. User's device:

[1509] The user's device provides an interface for rehearsals. This interface includes a text input field for the user to enter a situation. Specific hardware examples include computers, smartphones, and tablets. The device also includes a speech recognition engine and a video capture library, and records the user's movements and voice using, for example, a microphone or camera.

[1510] 2. Server:

[1511] The server receives the data sent by the user and uses the generative AI model to generate the virtual space. The specific hardware used is likely to be a server equipped with a high-performance CPU and GPU. The software includes a runtime environment and API for running the generative AI model (e.g., OpenAI GPT-4).

[1512] 3. Generative AI Model:

[1513] Generative AI models, such as OpenAI's GPT-4, create virtual spaces based on generated text prompts, generating meeting room layouts, participant avatars, presentation screens, and more, tailored to the user's situation.

[1514] System Operation

[1515] The user enters a situation into the device's interface, such as "presenting a new product at a large business meeting," and the text prompt is sent to the server via HTTPS. The server then passes the prompt to a generative AI model, requesting it to generate a specific virtual space.

[1516] The generative AI model generates a virtual space, including the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server in structured format such as JSON. The server then sends this generated virtual space data to the device. The device then uses a 3D rendering engine (e.g., Unity or Unreal Engine) to display the virtual space based on the received data.

[1517] Users begin rehearsing in the virtual space, recording their movements and voice using a microphone and camera. The device captures this data in real time and sends it to the server. The server then analyzes the received user performance data using a generative AI model, generating feedback based on speaking speed, time allocation, keyword usage, and other factors, and sends it to the device. The device then displays this feedback to the user, allowing them to see areas for improvement.

[1518] Specific examples

[1519] As an example, consider the case where a user is rehearsing a situation in which he or she is to give a presentation on a new product at a large business meeting.

[1520] User Input:

[1521] The user types into the device interface, "Presenting a new product at a large business meeting."

[1522] Sending data:

[1523] The terminal sends this input data to the server.

[1524] Virtual space generation:

[1525] The server passes text prompts to a generative AI model, which generates a virtual conference room, participant avatars, presentation screens, and more.

[1526] Returning generated data:

[1527] The server sends the generated virtual space data to the terminal, which then renders it and displays it to the user.

[1528] Run a rehearsal:

[1529] The user rehearses in the virtual space, and the device records the voice and movements and sends them to the server.

[1530] Generate and provide feedback:

[1531] The server analyzes the user's performance, generates feedback, sends it to the device, and displays it to the user.

[1532] In this way, the present invention provides a virtual rehearsal environment in which users can efficiently rehearse and receive appropriate feedback.

[1533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1534] Step 1: Enter the situation

[1535] The user enters the situation they want to rehearse as a text prompt into the device's interface. For example, they might enter "Presenting a new product at a large business meeting." The device accepts this input text.

[1536] Input: The text prompt for the situation entered by the user

[1537] Output: The text prompt that the terminal gets

[1538] Step 2: Sending data

[1539] The terminal sends the entered text prompt to the server using the HTTPS protocol. Specifically, it generates an HTTP POST request and sends a payload containing the input data to the server. The server receives this request and parses the text prompt data.

[1540] Input: Text prompt

[1541] Data processing: Generating an HTTP POST request

[1542] Data Calculation: Sending a Request

[1543] Output: The text prompt data sent to the server.

[1544] Step 3: Situation generation request

[1545] The server passes the received text prompt to the generative AI model, requesting it to generate a specific situation. The server then sends an API request to the generative AI model, which then generates a virtual space based on the specified situation.

[1546] Input: Text prompt sent to the server

[1547] Data processing: Analysis of text prompts

[1548] Data Computation: Making API requests to generative AI models

[1549] Output: Sending an API request

[1550] Step 4: Creating the virtual space

[1551] The generative AI model generates a virtual space corresponding to a specific situation, such as the layout of the conference room, participant avatars, and a presentation screen, and sends this data back to the server as structured data in JSON format.

[1552] Input: API request

[1553] Data processing: Creation of virtual space

[1554] Data calculation: Generation of meeting room layouts, participant avatars, presentation screens, etc.

[1555] Output: Generated virtual space data (JSON format)

[1556] Step 5: Returning generated data

[1557] The server sends the virtual space data received from the generated AI model to the device. Specifically, the server returns the data to the device as an HTTP response, and the device receives this data.

[1558] Input: Generated virtual space data

[1559] Data operations: generating and sending HTTP responses

[1560] Output: Virtual space data sent to the device

[1561] Step 6: Displaying the virtual space

[1562] The device performs 3D rendering based on the received virtual space data and displays the virtual space to the user. Specifically, the device uses a 3D rendering engine (e.g., Unity or Unreal Engine) to visually display the virtual space.

[1563] Input: Received virtual space data

[1564] Data processing: preparing for 3D rendering

[1565] Data calculation: rendering of virtual space

[1566] Output: The virtual space that is displayed to the user

[1567] Step 7: Run a rehearsal

[1568] The user begins rehearsing in the virtual space, and the device records the user's movements and voice in real time. The device uses a microphone and camera to capture this data and transmits it to the server in real time.

[1569] Input: User movements and voice

[1570] Data Processing: Voice and Motion Capture

[1571] Data calculation: Real-time data transmission

[1572] Output: User voice and movement data sent to the server

[1573] Step 8: Generate feedback

[1574] The server receives the user's performance data and analyzes it with a generative AI model, evaluating things like speaking speed, time allocation, and keyword usage to generate specific feedback.

[1575] Input: User voice and motion data

[1576] Data processing: analysis of performance data

[1577] Data Calculation: Feedback Generation

[1578] Output: Generated feedback

[1579] Step 9: Provide feedback

[1580] The server generates feedback and sends it to the device, which then displays it to the user. The device displays the feedback in the form of a pop-up or notification, allowing the user to see what improvements need to be made.

[1581] Input: Generated feedback

[1582] Data Calculation: Sending Feedback

[1583] Output: Feedback that is displayed to the user

[1584] This process allows users to rehearse in a realistic virtual space tailored to a specific situation and receive feedback that is efficiently analyzed by a generative AI model.

[1585] (Application example 1)

[1586] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1587] In recent years, as autonomous vehicles have become more widely used, new tools are needed to make effective use of travel time. However, current systems do not provide an environment where users can efficiently rehearse while traveling, preventing users from making full use of their valuable time. Furthermore, there are limited technologies that provide virtual environments that can adapt to environmental changes while traveling. The purpose of this invention is to solve these problems and provide a realistic rehearsal environment while traveling.

[1588] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1589] In this invention, the server includes means for inputting text prompts, means for analyzing the input text prompts and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting user actions in real time, means for generating and providing feedback based on the analyzed user data, and means for displaying the virtual environment and rehearsing within the vehicle, thereby enabling efficient and effective rehearsals and feedback to be received even while on the move.

[1590] A "text prompt" is text data that a user inputs in sentence form to describe the situation or content they want to rehearse.

[1591] A "virtual environment" is a simulated space created by a generative AI model based on text prompts, providing a virtual space for users to rehearse.

[1592] "Display means" means the device or software that visually displays the generated virtual environment on the user's device.

[1593] The "behavior recording means" is a function that records the user's behavior and voice in real time and sends the data to a server for analysis and feedback.

[1594] A "feedback generation means" is a system that analyzes recorded user behavioral data and provides evaluations and advice on the user's performance.

[1595] "In-vehicle display means" refers to a display means for providing a virtual environment to a user and for rehearsing in a moving environment such as inside an autonomous vehicle.

[1596] The present invention relates to a system for efficiently rehearsing presentations and dialogues in an autonomous vehicle. This system allows users to rehearse in a virtual environment while on the move and receive feedback in real time. Detailed embodiments of this system are described below.

[1597] System configuration

[1598] The system consists of a user's smartphone, a server, and a generative AI model. The smartphone provides an interface for inputting text prompts and acts as a display means. The server analyzes the input text prompts and generates a virtual environment using the generative AI model.

[1599] Hardware and Software Use

[1600] A user interface application runs on the smartphone, allowing the user to input text prompts for situations they want to rehearse. The server receives the prompts from the smartphone via an HTTP POST request and passes this data to the generative AI model. Based on the prompts, the generative AI model generates virtual environments such as a conference room, participant avatars, and a presentation screen.

[1601] The server sends the generated virtual environment data to a smartphone, which can then render and display this data to the user. As the user rehearses in the virtual environment, their voice and movements are recorded and sent in real time to the server. The server analyzes the received data and generates feedback based on the user's speaking rate, timing, and keyword usage. This feedback is then sent back to the smartphone, allowing the user to see areas for improvement.

[1602] Specific examples

[1603] For example, consider a sales user who wants to rehearse an "important presentation for the next business trip." The user enters the following text prompt into the smartphone app:

[1604] Example prompt sentence:

[1605] "An important presentation on my next business trip"

[1606] Based on this prompt, the system uses a generative AI model to generate environments such as a virtual conference room or virtual audience. Users can then rehearse in this virtual environment while inside their self-driving vehicle. Data recorded during the rehearsal is sent to a server in real time, and the generative AI model analyzes the data and provides feedback to the user. This allows users to make effective use of their travel time and improve their rehearsal content by receiving appropriate feedback.

[1607] In this way, the present invention enables rehearsals in a realistic virtual environment and provides support for users to prepare more efficiently.

[1608] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1609] Step 1:

[1610] The user accesses the device (smartphone) interface and inputs the situation they want to rehearse as a text prompt. The input text prompt contains specific content such as "an important presentation on my next business trip." The input data is temporarily saved on the device.

[1611] Step 2:

[1612] The terminal sends the temporarily saved text prompt to the server via an HTTP POST request. The input is the text prompt data (character string), and the output is a request to analyze this data.

[1613] Step 3:

[1614] The server analyzes the received text prompt and issues a situation generation request to input to the generative AI model. Specific operations include data processing, such as analyzing the format of the text data and converting it into an appropriate data format for the generative model.

[1615] Step 4:

[1616] The generative AI model generates a virtual environment based on a situation generation request received from the server. The generated virtual environment has components such as a conference room, participant avatars, and a presentation screen. The input is analysis data of text prompts, and the output is expressed as virtual environment data.

[1617] Step 5:

[1618] The server returns the generated virtual environment data to the device. Specifically, it sends the virtual environment data to the smartphone via an HTTP response. The input is the virtual environment data generated by the generative AI model, and the output is data sent to the device.

[1619] Step 6:

[1620] The device renders the received virtual environment data and displays it to the user. Specifically, a rendering process is performed to draw the virtual environment data as a 3D model. The input is virtual environment data, and the output is a virtual space display that the user can visually confirm.

[1621] Step 7:

[1622] The user begins rehearsing in the virtual environment, and the device records the user's voice and movements in real time. This recorded data is sent to the server in real time. The input is the user's voice and movements, and the output is the recorded data sent to the server.

[1623] Step 8:

[1624] The server analyzes the received recorded data using a generative AI model and generates feedback. The analysis targets the user's speaking speed, time allocation, keyword usage, etc. The input is the recorded data sent to the server, and the output is feedback data based on the analysis results.

[1625] Step 9:

[1626] The server returns the generated feedback data to the device. Specifically, it sends the feedback data to the smartphone as an HTTP response. The input is the feedback data analyzed by the generative AI model, and the output is the feedback data sent to the device.

[1627] Step 10:

[1628] The device displays the received feedback data to the user. Specific operations include updating the UI to display the feedback information in a visually understandable manner. The input is the feedback data, and the output is a feedback display that the user can visually confirm.

[1629] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1630] This invention is a virtual space rehearsal system combined with an emotion engine. It recognizes emotions from the user's facial expressions and voice, and provides feedback based on that emotion data, maximizing the effectiveness of the rehearsal. This system has the function of allowing the user to input text prompts, and based on those prompts, the generation AI realistically recreates the specified situation. Furthermore, the emotion engine analyzes the user's emotions and reflects the results in the feedback.

[1631] System Overview

[1632] This system consists of a user's device, a server, a generative AI model, and an emotion engine. The user's device provides an interface for inputting text prompts, and the server analyzes the input prompts to generate a virtual space. The generated virtual space is displayed on the user's device. The emotion engine recognizes emotions from the user's facial expressions and voice, and provides feedback based on that data.

[1633] Processing Details

[1634] 1. Enter the situation

[1635] The user enters a text prompt describing the situation they want to rehearse, for example, "Presenting a new product at a large business meeting."

[1636] 2. Data transmission

[1637] The device sends the entered text prompt to the server via an HTTP POST request, which receives the input data provided by the user.

[1638] 3. Situation creation request

[1639] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[1640] 4. Virtual Space Creation

[1641] Based on the input prompts, the generative AI model generates the necessary virtual space, including the conference room, participant avatars, and presentation screen.

[1642] 5. Return of generated data

[1643] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[1644] 6. Recognizing and transmitting emotional data

[1645] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data, which the device then transmits to the server.

[1646] 7. Performing a rehearsal

[1647] The user begins rehearsing in the virtual space. The device records the user's voice and movement data and transmits it to the server in real time.

[1648] 8. Generate feedback

[1649] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback.

[1650] 9. Providing Feedback

[1651] The server sends the generated feedback back to the device, and the device displays it to the user. Specifically, the device displays the feedback details on the user interface and provides suggestions for improvement and advice.

[1652] Specific examples

[1653] As an example, consider the case where a user wants to rehearse a situation in which they will be presenting a new product at a large business meeting.

[1654] 1. Enter the situation

[1655] The user types text into the interface: "Presenting a new product at a large business meeting."

[1656] 2. Data transmission

[1657] The terminal sends this input to the server.

[1658] 3. Situation creation request

[1659] The server passes the text prompts to a generative AI model, which generates a virtual space including a virtual conference room, participant avatars, and presentation materials.

[1660] 4. Virtual Space Creation

[1661] A generative AI model generates meeting room layouts, participant avatars, and presentation screens.

[1662] 5. Return of generated data

[1663] The server sends the generated virtual space data to the terminal, which then renders this data and displays it to the user.

[1664] 6. Recognizing and transmitting emotional data

[1665] The emotion engine analyzes the user's facial expressions and voice, generates emotion data in real time, and the device sends this to the server.

[1666] 7. Performing a rehearsal

[1667] The user starts a presentation in the virtual space, and the device records the user's voice and actions and transmits them to the server.

[1668] 8. Generate feedback

[1669] The server analyzes the user's performance and generates feedback by integrating data from the emotion engine, such as based on speaking speed, time allocation, and emotional changes.

[1670] 9. Providing Feedback

[1671] The server generates feedback and sends it to the device, which then displays it to the user, allowing the user to see improvements.

[1672] This invention allows users to rehearse efficiently and receive appropriate feedback. Furthermore, by using emotion analysis by an emotion engine, it is possible to provide optimal advice according to the user's feelings, maximizing the effectiveness of rehearsals.

[1673] The processing flow will be explained below.

[1674] Step 1:

[1675] The user enters a text prompt for the situation they want to rehearse into a dedicated interface. Specifically, the user enters "Presenting a new product at a large business meeting."

[1676] Step 2:

[1677] The terminal sends the entered text prompt to the server. Specifically, it sends the text data to the server via an HTTP POST request.

[1678] Step 3:

[1679] The server analyzes the received text prompt and generates a request to the generative AI model, specifically, preparing an API request for situation generation.

[1680] Step 4:

[1681] The server sends a text prompt to the generative AI model, requesting it to generate a virtual space. Specifically, the prompt data is sent via an API.

[1682] Step 5:

[1683] The generative AI model generates a virtual space based on the prompts it receives, including a conference room, participant avatars, and a presentation screen.

[1684] Step 6:

[1685] The server receives the generated virtual space data and returns it to the device. Specifically, it sends the data received from the generative AI model to the device as an HTTP response.

[1686] Step 7:

[1687] The device renders the received virtual space data and displays it to the user. Specifically, it displays a virtual conference room using 3D rendering technologies such as WebGL and Unity.

[1688] Step 8:

[1689] The emotion engine analyzes the user's facial expressions and voice in real time to generate emotion data. The device then sends this emotion data to a server. Specifically, emotion data is extracted using facial recognition and voice analysis technologies.

[1690] Step 9:

[1691] The user begins rehearsing in the virtual space. Specifically, the user starts the presentation using the microphone and performs the movements.

[1692] Step 10:

[1693] The device records the user's voice and movement data in real time and transmits it to the server via streaming audio data over HTTP or WebSocket.

[1694] Step 11:

[1695] The server analyzes the received data using a generative AI model and generates feedback. It also integrates the emotional data generated by the emotion engine to generate appropriate feedback. For example, it analyzes the user's speaking speed, time allocation, and emotional changes.

[1696] Step 12:

[1697] The server sends the generated feedback to the terminal. Specifically, the server sends the feedback data as an HTTP response.

[1698] Step 13:

[1699] The device displays the received feedback to the user. Specifically, the device displays the details of the feedback on the user interface and provides suggestions for improvement and advice.

[1700] This allows users to improve their rehearsals based on detailed feedback and effectively prepare for the performance. Analysis by the emotion engine also provides personalized advice based on the user's emotional state, enabling more effective rehearsals.

[1701] Example 2

[1702] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1703] Conventional rehearsal systems have the drawback of being difficult to generate a realistic virtual space for users to effectively practice specific situations, and the feedback they provide is limited to a general evaluation, without suggesting specific areas for improvement. Furthermore, they do not provide feedback that takes into account the user's emotional state, limiting the actual effectiveness of rehearsals.

[1704] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for inputting a text prompt, means for analyzing the input text prompt and generating a virtual space, means for displaying the generated virtual space, means for analyzing the user's facial expression and voice data and generating emotional data, means for recording and transmitting the user's actions in real time, and means for generating and providing feedback based on the analyzed user data and emotional data. This allows the user to rehearse a specific situation in a realistic virtual space and receive specific feedback according to the user's emotional state.

[1705] A "text prompt" is textual data that a user enters to specify a rehearsal or simulation situation.

[1706] A "virtual space" is a simulated environment generated by a generative AI model for users to rehearse.

[1707] "Analysis" is the process of analyzing input data, the user's facial expressions, and voice data to extract meaning and emotional insights.

[1708] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's facial expression and voice data.

[1709] An "action" is a movement or speech that a user makes within a virtual space.

[1710] "Feedback" is information about guidance and improvements provided to the user after the rehearsal.

[1711] This invention is a system that generates a virtual space based on text prompts entered by the user, and allows rehearsals to be conducted within it, enhancing the effectiveness of practice for real situations. The main components of this system are the user's device, a server, a generative AI model, and an emotion engine.

[1712] The user's device provides an interface for the user to input text prompts. For example, the user inputs a situation such as "presenting a new product at a large business meeting" in text format. This input interface is designed with user convenience in mind, and supports not only text input but also voice recognition and touchscreen operation.

[1713] The device sends the entered text prompt to the server via an HTTP POST request. This HTTP communication uses a common protocol, often in JSON format. The server analyzes the received text prompt and sends an API request to the generative AI model to generate the virtual space. This analysis process uses a natural language processing algorithm.

[1714] The generative AI model generates a virtual space based on the prompt text entered by the user. Specifically, it creates a virtual space that includes the layout of the conference room, participant avatars, a presentation screen, and more. The generated virtual space data is sent back to the server, which then sends it to the device. The device then performs 3D rendering based on the returned data and displays it to the user. Typical software used in this process include Unity and Unreal Engine.

[1715] Next, the user begins rehearsing in the virtual space. The device recognizes the user's facial expressions and voice data in real time and passes them to the emotion engine. The emotion engine analyzes this data and extracts the user's emotional state. The extracted emotion data is then sent back to the server.

[1716] While the user is rehearsing, the device records the user's voice and movement data and sends it to the server in real time. Based on the received data, the server integrates and analyzes the data generated by the generative AI model and emotion engine to generate appropriate feedback. The feedback specifically indicates areas for improvement and evaluation points for the user, including specific advice such as "You speak too fast" or "You do not express emotions enough." This feedback is displayed on the device so that the user can easily check it.

[1717] This invention stands out from conventional simulation systems in that it provides feedback that takes into account the user's emotions. Furthermore, by incorporating real-time analysis using an emotion engine, it is possible to comprehensively evaluate the user's performance and maximize the effectiveness of rehearsals. In this way, this invention allows users to rehearse efficiently and more effectively prepare for real-life situations.

[1718] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1719] Step 1:

[1720] The user enters a text prompt.

[1721] Input: User's rehearsal situation

[1722] Specific operation: The user types into the terminal in text format, "Present new product at large business meeting."

[1723] Output: Text prompt data

[1724] Step 2:

[1725] The terminal sends the entered text prompt to the server.

[1726] Input: Text prompt data

[1727] Specific behavior: The device serializes the text prompt into JSON format and sends it to the server as an HTTP POST request.

[1728] Output: Prompt data sent to the server

[1729] Step 3:

[1730] The server analyzes the received text prompt and creates a request to generate a virtual space.

[1731] Input: prompt data

[1732] Specific operation: The server uses a natural language processing algorithm to analyze the text prompt and extract the parameters necessary to generate the virtual space.

[1733] Output: API request data to the generative AI model

[1734] Step 4:

[1735] The generative AI model generates a virtual space based on requests from the server.

[1736] Input: API request data

[1737] Specific operation: The generative AI model generates the conference room layout, participant avatars, presentation screens, etc., and sends the virtual space data back to the server.

[1738] Output: Generated virtual space data

[1739] Step 5:

[1740] The server returns the generated virtual space data to the terminal.

[1741] Input: Virtual space data

[1742] Specific operation: The server sends the virtual space data to the terminal as an HTTP response.

[1743] Output: Virtual space data sent to the device

[1744] Step 6:

[1745] The terminal renders the generated virtual space data and displays it to the user.

[1746] Input: Virtual space data

[1747] How it works: The device generates 3D graphics based on virtual space data and displays them to the user. This process uses software such as Unity or Unreal Engine.

[1748] Output: Displayed virtual space

[1749] Step 7:

[1750] The user's facial expression and voice data are sent to the emotion engine.

[1751] Input: User's facial expression data and voice data

[1752] Specific operation: The device uses the camera and microphone to capture the user's facial expressions and voice in real time and send them to the emotion engine.

[1753] Output: Data sent to the emotion engine

[1754] Step 8:

[1755] An emotion engine generates emotion data from the user's facial expression and voice data.

[1756] Input: User's facial and voice data

[1757] Specific operation: The emotion engine uses machine learning algorithms to analyze the user's emotions and generate emotion data.

[1758] Output: Generated emotion data

[1759] Step 9:

[1760] The user rehearses in the virtual space.

[1761] Input: Virtual space, user actions

[1762] Specific operation: The user gives a presentation in the virtual space, and the terminal records the user's voice and actions.

[1763] Output: Recorded user performance data

[1764] Step 10:

[1765] The terminal transmits the user's performance data to the server.

[1766] Input: User performance data

[1767] Specific operation: The terminal transmits user performance data to the server in real time.

[1768] Output: Performance data sent to the server

[1769] Step 11:

[1770] The server generates feedback based on the received data.

[1771] Input: User performance and emotional data

[1772] Specific operation: The server integrates data from the generative AI model and emotion engine to generate feedback based on the user's speaking rate, time allocation, and emotional changes.

[1773] Output: Generated feedback

[1774] Step 12:

[1775] The server sends the feedback to the terminal, which displays it to the user.

[1776] Input: Generated feedback

[1777] Specific operation: The server sends the feedback to the device in an HTTP response, and the device displays it in its user interface. For example, it presents the feedback to the user in the form of a pop-up notification or a detailed report.

[1778] Output: Feedback provided to the user.

[1779] (Application example 2)

[1780] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1781] In conventional security training, it is difficult to simulate real-world situations, and there are no efficient methods to develop realistic response capabilities. Furthermore, there is a lack of systems to provide appropriate feedback on users' emotions and behavioral data during training. This makes it difficult to specifically understand how users should improve.

[1782] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1783] In this invention, the server includes means for inputting a scenario, means for analyzing the input scenario and generating a virtual environment, means for displaying the generated virtual environment, means for recording and transmitting the user's actions in real time, means for generating and providing feedback based on the analyzed user data, means for recognizing emotions from the user's facial expressions and voice, and means for generating feedback by integrating the emotion data. This enables the user to train in a situation close to reality and receive specific feedback based on their emotions and actions in real time.

[1784] A "means for inputting a scenario" is a device or interface that allows a user to input a specific situation for training or simulation in text format.

[1785] The "means for analyzing the input scenario and generating a virtual environment" refers to software and hardware that realistically recreates the situation in a virtual space based on text data input by the user.

[1786] "Means for displaying the generated virtual environment" refers to a display device such as a display device, projector, or head-mounted display (HMD) for visually presenting the generated virtual space to the user.

[1787] "Means for recording and transmitting user actions in real time" refers to devices or systems that record actions and statements made by users in virtual space in real time and transmit them to a server. Specifically, this includes cameras, microphones, and sensors.

[1788] "Means for generating and providing feedback based on analyzed user data" refers to software and systems that analyze data on users' actions and statements, generate corresponding feedback, and provide it to users.

[1789] "Means for recognizing emotions from a user's facial expressions and voice" refers to technology that uses a camera or microphone to capture a user's facial expressions and voice, and then analyzes them to infer emotions.

[1790] The "means for integrating emotional data to generate feedback" is a system that combines emotional data and behavioral data obtained from users to generate training improvements and advice.

[1791] The system of the present invention is a system for effectively conducting security training in a virtual space. This system consists of a user terminal, a server, a generative AI model, and an emotion engine.

[1792] Detailed system configuration

[1793] 1. How to input a scenario

[1794] The user uses an interface to input a scenario. For example, the user inputs a text-based scenario such as "How should I respond when I discover a suspicious person?" into the terminal interface.

[1795] 2. A method for analyzing input scenarios and generating virtual environments

[1796] The device sends the input scenario to the server as an HTTP POST request, which the server analyzes and generates a request to the generative AI model. The generative AI model (e.g., OpenAI's GPT-3) generates a virtual environment based on the scenario.

[1797] 3. How to view the generated virtual environment

[1798] The server receives the virtual environment data from the generative AI model and sends it back to the device, which then uses this data to render the virtual space and display it to the user using a display or head-mounted display (HMD).

[1799] 4. Means of recording and transmitting user actions in real time

[1800] The device's camera and microphone are used to capture the user's facial expressions and voice in real time, and this data is sent to the server as user movement data.

[1801] 5. Means for generating and providing feedback based on analyzed user data

[1802] The server analyzes the acquired motion data and emotion data using an emotion engine (e.g., Emotion SDK). Using a generative AI model, it integrates the motion data and emotion data to generate feedback and provide suggestions for improvement.

[1803] Hardware and software used

[1804] Hardware

[1805] Camera and microphone: To capture the user's facial expressions and voice

[1806] Display or Head-Mounted Display (HMD): for viewing the virtual environment

[1807] software

[1808] Emotion engine (e.g., Emotion SDK): To recognize emotions from the user's facial expressions and voice

[1809] Generative AI models (e.g., GPT-3 by OpenAI) to generate scenario-based virtual environments and generate feedback

[1810] HTTP request processing (e.g., requests library): Used to send data from the device to the server

[1811] Examples of specific examples and prompts

[1812] As a concrete example, consider the case where a security guard practices "how to respond when a suspicious person is spotted." The user inputs the following into the scenario input interface:

[1813] How to spot a suspicious person and respond

[1814] Based on this scenario, the server and generative AI model generate a specific virtual environment (e.g., a scene in which a suspicious person appears at the entrance of an office building), and users train in that virtual space.

[1815] The emotion engine analyzes the user's facial expressions and voice data, and works with the generative AI model to provide real-time feedback based on the emotional data. For example, it provides feedback such as, "You speak slowly and calmly, but your facial expression shows signs of tension."

[1816] This allows users to develop the ability to respond to situations that are close to reality and to identify specific and appropriate areas for improvement.

[1817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1818] Step 1:

[1819] The user enters the training situation into the interface.

[1820] Input: The user inputs a specific situation (e.g., "I discovered a suspicious person and how would I respond?") in text format.

[1821] Output: The input text data.

[1822] Specific operation: The user inputs the situation using the device's keyboard or touch screen.

[1823] Step 2:

[1824] The terminal sends the entered text prompt to the server as an HTTP POST request.

[1825] Input: The text data entered in step 1.

[1826] Output: The HTTP request sent to the server.

[1827] Specific operation: The device sends an HTTP request to the server via the network, using a network communication module such as the requests library.

[1828] Step 3:

[1829] The server parses the received text prompt and generates a request to the generative AI model.

[1830] Input: Text data sent from the terminal.

[1831] Output: Request data to the generative AI model.

[1832] Specific operation: The server analyzes the received text data and formats the prompt sentence as an API request to the generative AI model.

[1833] Step 4:

[1834] A generative AI model generates a virtual environment based on input prompts.

[1835] Input: The parsed text prompt.

[1836] Output: Generated virtual environment data (e.g., virtual environment scene data and object data).

[1837] How it works: Based on the input prompt data, the generative AI model automatically generates a virtual environment that corresponds to the specified situation. For example, it uses OpenAI's GPT-3 to generate detailed settings based on the scenario.

[1838] Step 5:

[1839] The server sends the virtual environment data received from the generated AI model back to the device.

[1840] Input: Virtual environment data returned from a generative AI model.

[1841] Output: Virtual environment data sent to the device.

[1842] Specific operation: The server sends the virtual environment data received from the generative AI model to the terminal as an HTTP response.

[1843] Step 6:

[1844] The device renders the virtual environment and displays it to the user.

[1845] Input: Virtual environment data received from the server.

[1846] Output: The virtual environment that is displayed to the user.

[1847] Specific operation: The terminal uses the received virtual environment data to display the virtual environment using a display or head-mounted display.

[1848] Step 7:

[1849] The device's camera and microphone are used to record the user's actions and voice in real time.

[1850] Input: User movements and voice.

[1851] Output: Real-time recorded motion and audio data.

[1852] How it works: The camera and microphone connected to the device capture the user's facial expressions and voice and record them in real time.

[1853] Step 8:

[1854] The terminal transmits the recorded data to the server.

[1855] Input: Recorded motion and audio data.

[1856] Output: User data sent to the server.

[1857] Specific operation: The device sends the recorded data to the server via the network.

[1858] Step 9:

[1859] The server analyzes the received data and generates feedback.

[1860] Input: Motion and audio data sent from the device.

[1861] Output: Parsed feedback data.

[1862] Specific behavior: The server analyzes the behavior data and emotion data using the emotion engine, and generates appropriate feedback using the generative AI model.

[1863] Step 10:

[1864] The server transmits the generated feedback to the terminal.

[1865] Input: Generated feedback data.

[1866] Output: Feedback data sent to the device.

[1867] Specific operation: The server generates feedback and sends it to the device as an HTTP response.

[1868] Step 11:

[1869] The device displays the feedback to the user.

[1870] Input: Feedback data sent by the server.

[1871] Output: The feedback that is displayed to the user.

[1872] Specific operation: The terminal displays the received feedback data on the user interface for the user to check.

[1873] 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.

[1874] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1875] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1876] 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.

[1877] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1878] 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.

[1879] 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).

[1880] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1881] 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."

[1882] 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.

[1883] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1884] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1885] 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.

[1886] 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.

[1887] 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.

[1888] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1889] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1890] 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.

[1891] 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.

[1892] 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.

[1893] 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.

[1894] The following is further disclosed regarding the above embodiment.

[1895] (Claim 1)

[1896] a means for inputting a text prompt;

[1897] means for analyzing an input text prompt and generating a virtual space;

[1898] a means for displaying the generated virtual space;

[1899] A means of recording and transmitting user actions in real time;

[1900] means for generating and providing feedback based on the analyzed user data;

[1901] A system including:

[1902] (Claim 2)

[1903] 2. The system according to claim 1, wherein the virtual space generating means generates an environment corresponding to a specific situation such as a large-scale meeting or dialogue practice.

[1904] (Claim 3)

[1905] 2. The system according to claim 1, wherein the feedback generating means provides appropriate advice based on the user's speaking speed, time allocation, and keyword extraction.

[1906] "Example 1"

[1907] (Claim 1)

[1908] a means for the user to input a text prompt for a situation they wish to rehearse;

[1909] A means of parsing the input text prompt and passing it to a generative AI model;

[1910] A means for acquiring the generated virtual space data and transmitting it to a user's terminal;

[1911] A means for rendering and displaying the generated virtual space on the user's device;

[1912] A means for recording the user's voice and actions in real time and transmitting them to a server;

[1913] a means for analyzing received user performance data to generate feedback;

[1914] a means for providing the generated feedback to the user;

[1915] A system including:

[1916] (Claim 2)

[1917] The system according to claim 1, wherein the virtual space generation means using the generative AI model generates a virtual space corresponding to a specific situation such as a large-scale meeting or dialogue practice.

[1918] (Claim 3)

[1919] 2. The system according to claim 1, wherein the feedback generating means provides appropriate feedback based on the user's speaking speed, time allocation, and keyword extraction.

[1920] "Application Example 1"

[1921] (Claim 1)

[1922] a means for inputting a text prompt;

[1923] means for parsing an input text prompt and generating a virtual environment;

[1924] a means for displaying the generated virtual environment;

[1925] A means for recording and transmitting user behavior in real time;

[1926] means for generating and providing feedback based on the analyzed user data;

[1927] means for displaying and rehearsing a virtual environment within the mobile body;

[1928] A system including:

[1929] (Claim 2)

[1930] 2. The system according to claim 1, wherein the virtual environment generating means generates an environment corresponding to a specific situation such as a meeting or dialogue practice.

[1931] (Claim 3)

[1932] 2. The system according to claim 1, wherein the feedback generating means provides appropriate advice based on the user's speaking speed, time allocation, and keyword extraction.

[1933] "Example 2: Combining Emotion Engines"

[1934] (Claim 1)

[1935] a means for inputting a text prompt;

[1936] means for analyzing an input text prompt and generating a virtual space;

[1937] a means for displaying the generated virtual space;

[1938] means for analyzing facial expression and voice data of a user and generating emotion data;

[1939] a means for recording and transmitting user actions in real time;

[1940] means for generating and providing feedback based on the analyzed user data and emotion data;

[1941] A system including:

[1942] (Claim 2)

[1943] 2. The system according to claim 1, wherein the virtual space generating means generates an environment corresponding to a specific situation.

[1944] (Claim 3)

[1945] 2. The system according to claim 1, wherein the feedback generating means provides appropriate advice based on the user's speaking speed, time allocation, and emotional changes.

[1946] "Application example 2 when combining emotion engines"

[1947] (Claim 1)

[1948] a means for inputting a scenario;

[1949] a means for analyzing an input scenario and generating a virtual environment;

[1950] a means for displaying the generated virtual environment;

[1951] A means for recording and transmitting user actions in real time;

[1952] means for generating and providing feedback based on the analyzed user data;

[1953] A means for recognizing emotions from a user's facial expression and voice;

[1954] a means for integrating the emotional data to generate feedback;

[1955] A system including:

[1956] (Claim 2)

[1957] 2. The system according to claim 1, wherein the virtual environment generating means generates an environment corresponding to a specific situation such as security training or crisis response.

[1958] (Claim 3)

[1959] 2. The system according to claim 1, wherein the feedback generating means provides appropriate advice based on the user's speaking speed, time allocation, and emotional changes. [Explanation of symbols]

[1960] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting a text prompt; means for analyzing an input text prompt and generating a virtual space; a means for displaying the generated virtual space; A means of recording and transmitting user actions in real time; means for generating and providing feedback based on the analyzed user data; A system including:

2. 2. The system according to claim 1, wherein the virtual space generating means generates an environment corresponding to a specific situation such as a large-scale meeting or dialogue practice.

3. 2. The system according to claim 1, wherein the feedback generating means provides appropriate advice based on the user's speaking speed, time allocation, and keyword extraction.

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