System

Abstract

To provide a system capable of realizing effective presentation.SOLUTION: The system includes an information inputting means, a material generating means for automatically generating a presentation material based on inputted information using a AI model, an evaluating means for checking the generated material and guaranteeing an appropriate design and expression, a displaying means for displaying the checked material, an avatar generating means for performing a pre-practice of the presentation and simulating an actual presentation partner, a feedback generating means for analyzing a result of the pre-practice and generating a feedback, a progress managing means for monitoring a progress of the presentation in real time during the presentation and providing an alert and a notification, and a question analyzing means for analyzing a question during the presentation and generating an appropriate answer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional presentations have the following problems: for example, performance is impaired due to nervousness during the actual presentation, it is difficult to convey the main points that the speaker wants to convey, it is difficult to prepare presentation materials, and it is difficult to respond appropriately to unexpected questions. These problems often result in a decline in the quality of the presentation and an inability to effectively communicate information. The present invention aims to solve these problems and realize effective presentations. [Means for solving the problem]

[0005] The present invention provides a system that includes an information input device, a material generation device that automatically generates presentation materials based on the input information using an AI model, an evaluation device that checks the generated materials to ensure appropriate design and expression, a display device that displays the checked materials, an avatar generation device that simulates the actual presentation audience by practicing the presentation in a virtual environment, a feedback generation device that analyzes the results of the practice and generates feedback, a progress management device that monitors the progress of the presentation in real time during the actual presentation and provides alerts and notifications, and a question analysis device that analyzes questions asked during the presentation and generates appropriate answers. Using this system, the automatic generation of presentation materials, practice sessions, and feedback can improve the quality of presentations, reduce tension, and enable appropriate responses to questions. This allows for effective information communication.

[0006] The "information input means" is an interface that allows the user to input information such as the audience and purpose of the presentation, presentation time, audience knowledge level, industry, etc.

[0007] The "material generation means" is a function that automatically generates presentation materials using an AI model based on input information.

[0008] "Evaluation tools" are algorithms and processes that check the generated presentation materials to ensure appropriate design and presentation.

[0009] The "display means" refers to a display device or software for visually displaying the checked presentation materials to the user.

[0010] The "avatar generation means" is a function that generates an avatar that simulates the actual presentation partner during a pre-practice session in a virtual environment.

[0011] The "feedback generation means" is a function that analyzes the results of the pre-practice and provides the user with points to improve and an evaluation of the presentation.

[0012] The "progress management means" is a function that monitors the progress of the presentation in real time during the actual presentation and provides alerts and notifications to the user.

[0013] The "question analysis means" is a natural language processing function that analyzes questions received during a presentation, understands their intent, and generates appropriate answers. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] An embodiment of the AI ​​presentation support system will be described below.

[0036] Information input method

[0037] User: Enters information about the presentation (presentee, purpose, presentation time, audience level, industry, etc.) from the device. This information is the basic information required to generate presentation materials and configure avatars.

[0038] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[0039] Material generation means

[0040] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0041] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[0042] Evaluation methods

[0043] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0044] Display means

[0045] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[0046] Avatar generation method

[0047] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0048] Device: The user begins pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to an audience.

[0049] Feedback Generation Method

[0050] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[0051] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[0052] Progress management means

[0053] Device: Monitors the progress of the presentation in real time and provides notifications and alerts to the user, including checking the progress of the presentation and time management.

[0054] Example: Displaying a notification such as "10 minutes remaining" helps users manage their time better.

[0055] Question analysis means

[0056] Server: Analyzes questions asked by users during a presentation in real time, and determines the intent of the question and recommends appropriate answers.

[0057] On the device: Suggested answers are presented to the user, allowing the user to proceed with the Q&A while referring to the suggested answers.

[0058] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data, generates an appropriate answer such as, "The market size of this new product is approximately 50 billion yen," and displays it on the device.

[0059] The processing flow will be explained below.

[0060] Program processing steps

[0061] Information input and document generation

[0062] Step 1: Enter your information

[0063] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[0064] Terminal: Sends the entered information to the server, where it is stored in a database.

[0065] Step 2: Automatically generate materials

[0066] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[0067] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[0068] Step 3: Document evaluation

[0069] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[0070] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[0071] Step 4: Display the material

[0072] Terminal: Visually displays to the user the base material that has passed the check.

[0073] User: Review the displayed material and make corrections as necessary.

[0074] Pre-practice and feedback

[0075] Step 5: Avatar Creation and Practice

[0076] Server: Generates an avatar simulating the person giving the presentation based on the materials created by the user.

[0077] Terminal: The user begins a pre-practice session in the virtual environment and makes a presentation facing an avatar.

[0078] Step 6: Practice analysis and feedback generation

[0079] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[0080] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[0081] Step 7: Provide feedback

[0082] Server: Generates feedback based on the analysis results, summarizing specific improvements and enhancements.

[0083] Terminal: Display feedback to the user.

[0084] Users: Review feedback and improve their presentation.

[0085] Follow-up on the actual event

[0086] Step 8: Progress Management

[0087] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[0088] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[0089] Step 9: Question Analysis

[0090] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[0091] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[0092] Step 10: Answer Generation

[0093] Server: Understands the intent of the question and generates an appropriate answer.

[0094] On the device: Present the suggested answers to the user.

[0095] User: Use the suggested answers to guide your Q&A.

[0096] Example 1

[0097] 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."

[0098] In today's business environment, precise and effective presentations are required, but preparing them takes time and effort. Furthermore, it is difficult to provide advance feedback, rehearse, and respond quickly and appropriately to questions and answers to improve the quality of presentations. Furthermore, it is not easy to evaluate and improve the appropriateness of presentation design and content.

[0099] 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.

[0100] In this invention, the server includes an information input means, a material generation means that uses a generative AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time during the actual presentation and provides alerts and notifications, and a question analysis means that analyzes questions asked during the presentation, understands the intent of the questions, and generates appropriate answers. This enables the entire process from presentation preparation to the actual presentation to be carried out efficiently, enabling high-quality presentations.

[0101] The "information input means" is a device or system that allows a user to input presentation status information into a terminal and transmit it to a server.

[0102] A "material generation means" is a device or system that automatically generates presentation materials based on input information using a generative AI model.

[0103] An "evaluation tool" is a device or system that checks the generated presentation materials to ensure appropriate design and presentation.

[0104] The "display means" is a device or system that allows the user to visually check the generated presentation materials and their evaluation results.

[0105] The "avatar generation means" is a device or system that generates an avatar that simulates an actual presentation partner in a virtual environment based on information input by the user, for use in practicing the presentation in advance.

[0106] The "feedback generation means" is a device or system that analyzes the results of the preliminary practice and generates feedback that provides the user with suggestions and points for improvement.

[0107] A "progress management means" is a device or system that monitors the progress of a presentation in real time and provides alerts and notifications to the user.

[0108] The "question analysis means" is a device or system that analyzes questions asked by users during a presentation, understands the intent of the questions, and generates appropriate answers.

[0109] A "generative AI model" is an artificial intelligence model used to automatically generate presentation materials based on input information.

[0110] A "prompt" is text information entered by the user to specify the requirements and content of the presentation materials to the generative AI model.

[0111] An embodiment of a presentation support system according to the present invention will be described below. This system automates and supports a series of processes required when a user prepares and gives a presentation.

[0112] System Configuration

[0113] This system includes a device operated by the user, a server that processes data, and an AI model. The main hardware components are PCs, smartphones, and tablets, while the server is a cloud server with advanced computing capabilities. The main software used is a generative AI model such as OpenAI's GPT-4.

[0114] Information input method

[0115] User: Enter presentation information (e.g., audience, purpose, presentation time, audience knowledge level, industry, etc.) from the terminal.

[0116] Terminal: The information entered by the user is sent to the server, where it is stored in a database and used to generate presentation materials and configure avatars.

[0117] As a specific example, a user may input, "I am giving a presentation to engineers and would like materials that emphasize the technical details of the product."

[0118] Material generation means

[0119] Server: Based on the received information, a generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0120] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[0121] Example prompt: "Create a presentation to highlight the technical details of a new product for a customer presentation. You will be a product specialist and your audience will be engineers. Your presentation will last 20 minutes."

[0122] Evaluation methods

[0123] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0124] Terminal: The base materials that have passed the check are visually displayed to the user, allowing the user to check the overall picture and specific content of the materials.

[0125] Avatar generation method

[0126] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0127] Device: Starts pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to a person. For example, if the person they are presenting to is set to be a "CEO," the avatar will act like the CEO.

[0128] Feedback Generation Method

[0129] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[0130] Device: Display feedback to let the user know the results of the practice. For example, "The content of slide 5 is difficult to understand."

[0131] Progress management means

[0132] Terminal: Monitors the progress of a presentation in real time and provides notifications and alerts to the user. This includes checking the progress of the presentation and time management. For example, during a presentation, a notification such as "10 minutes remaining" is displayed.

[0133] Question analysis means

[0134] Server: Analyzes questions asked by users during a presentation in real time, and recommends the intent of the question and appropriate answers. For example, if a user asks, "What is the market size of this new product?", the server uses a generative AI model to analyze market size data and generate an appropriate answer, such as, "The market size of this new product is approximately 50 billion yen."

[0135] Terminal: Suggested answers are presented to the user, who can then use the suggested answers to proceed with the Q&A.

[0136] By using the above means, the user can efficiently carry out the entire process from preparation to the actual presentation, thereby enabling a high-quality presentation.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] User: Enters information about the presentation situation from the device. The user enters information such as the audience, purpose, presentation time, audience knowledge level, industry, etc. into the input form on the device. This is input data.

[0140] Terminal: The terminal sends the entered information to the server, which saves it in a database. For example, a user might enter "Presentation recipient: engineers, Purpose: new product introduction, Presentation time: 20 minutes." The server receives and saves the entered data.

[0141] Step 2:

[0142] Server: Based on the received information, retrieves user information from a database and inputs this information into a generative AI model (e.g., OpenAI GPT-4). The input data includes information such as the recipient's occupation and the purpose of the presentation.

[0143] Server: The AI ​​model analyzes the information and automatically generates presentation materials. The generated slides are the output data. For example, the AI ​​model automatically generates slides that highlight the technical features of a new product.

[0144] Terminal: The terminal displays the generated slides to the user.

[0145] Step 3:

[0146] Server: Passes the generated slides to the evaluation algorithm. The input data is the generated slides.

[0147] Server: The algorithm checks the consistency of colors and fonts on the slides, as well as the appropriateness of the content. The evaluation results are output data. For example, it detects problems such as "color mismatch" or "font inconsistency" on the slides.

[0148] Terminal: The evaluation results are displayed to the user, who can then modify the slides.

[0149] Step 4:

[0150] Server: Retrieves user and slide information from the database and inputs it into the avatar generation process. The input data is the presentation recipient's information and the slide content.

[0151] Server: Uses an AI model to generate an avatar that simulates the person you are presenting to. The generated avatar is the output data. For example, the server generates an avatar of a "CEO."

[0152] Terminal: The avatar is displayed to the user, and the user begins practicing in the virtual environment. The user uses the avatar to rehearse the presentation in front of a real audience.

[0153] Step 5:

[0154] Server: Records the user's presentation in a virtual environment. The input data is the presentation's audio and movements.

[0155] Server: The recorded presentation content is passed to an analysis algorithm. The algorithm analyzes the content and generates feedback. This is the output data. For example, it generates specific feedback such as "The content of slide 5 is difficult to understand."

[0156] Device: Provides feedback to the user, who can then use it to refine their presentation.

[0157] Step 6:

[0158] Terminal: Monitors the progress of the presentation in real time. Input data is the progress of the presentation.

[0159] Terminal: Checks the progress based on the set time and provides notifications and alerts to the user. This is the output data. For example, it sends a notification to the user saying "10 minutes left."

[0160] Step 7:

[0161] Terminal: The terminal converts questions received from users during a presentation into text using speech recognition. The input data is the voice of the question.

[0162] Terminal: Sends a textual question to the server.

[0163] Server: Analyzes the question and generates an appropriate answer. The input data is the textual question, and the output data is the recommended answer. For example, in response to the question, "What is the market size for this new product?", the server responds, "The market size is approximately 50 billion yen."

[0164] Terminal: Presents suggested answers to the user, allowing them to proceed with the Q&A session.

[0165] (Application example 1)

[0166] 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."

[0167] Conventional presentation and educational support systems face challenges in ensuring the efficiency and effectiveness of specific work instructions and educational training. In particular, it is difficult to effectively learn and practice in a virtual environment when it comes to improving staff skills or providing work instructions in physical stores. Furthermore, they lack functionality for real-time progress management and question analysis. They also lack the quality of feedback and mechanisms for reflecting learning results.

[0168] 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.

[0169] In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, a display means that displays the checked materials, a virtual character generation means that conducts pre-practice in a virtual environment and simulates an actual interlocutor, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors progress in real time and provides alerts and notifications, a question analysis means that analyzes questions and generates appropriate answers, a work instruction support means that conducts simulations to generate and present materials for work instructions and training, and a skill improvement support means that supports skill improvement based on feedback. This significantly improves the efficiency and effectiveness of work instructions and training, and enables practice in a virtual environment, real-time progress management, and question analysis.

[0170] "Information input means" refers to a device or method for inputting user-specified information into the system.

[0171] An "AI model" is a mathematical or algorithmic model that uses artificial intelligence to accomplish a specific task.

[0172] The "material generation means" is a function or device that automatically creates materials based on input information.

[0173] "Evaluation means" refers to a function or device that checks and evaluates the quality and appropriateness of the generated materials.

[0174] "Display means" means a device or method for visually presenting the checked material to a user.

[0175] "Virtual character generation means" refers to a function or device that generates a character for a user to practice or simulate in a virtual environment.

[0176] The "feedback generation means" is a function or device that analyzes the results of the preliminary practice and generates feedback to be provided to the user based on the results.

[0177] A "progress management means" is a function or device that monitors progress in real time and provides alerts or notifications as needed.

[0178] The "question analysis means" is a function or device that analyzes questions asked by users during a presentation and generates appropriate answers.

[0179] The "work instruction support means" is a function or device that performs simulations to generate and present work instructions and training materials.

[0180] A "skill improvement support means" is a function or device that supports the user in improving their skills based on feedback.

[0181] The system of the present invention includes an information input means, a material generation means, an evaluation means, a display means, a virtual character generation means, a feedback generation means, a progress management means, a question analysis means, a work instruction support means, and a skill improvement support means.

[0182] Information input method

[0183] The user inputs the information necessary for training and work instructions from the terminal. This information includes the purpose of the training, the target person's job title, knowledge level, work experience, etc. This information is sent to the server and stored in a database.

[0184] Material generation means

[0185] The server automatically generates training materials using a generative AI model based on the received information. The generated materials are then optimized for design and content by the AI ​​model, using machine learning libraries such as PyTorch and TensorFlow.

[0186] Evaluation methods

[0187] The server evaluates the quality of the design and content of the generated materials. Evaluation algorithms check the color and font consistency of the generated materials, as well as the consistency of the content. For example, they use evaluation tools such as scikit-learn.

[0188] Display means

[0189] The terminal visually displays the assessed material, and the user can review it and make corrections as needed using a device such as a tablet or smart glasses, for example, an iPad or Google Glass.

[0190] Virtual character generation means

[0191] The server generates a virtual training instructor (virtual character) based on the input information. This virtual character reflects the position and knowledge level according to the information input by the user. For example, Unity or Blender is used.

[0192] Feedback Generation Method

[0193] The server analyzes the results of the pre-practice and generates feedback to provide to the user. The analyzed data is used to evaluate the consistency and comprehension of the user's presentation. The feedback results contribute to improving the user's skills.

[0194] Progress management means

[0195] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications as the presentation or training progresses, helping users manage their time allocation.

[0196] Question analysis means

[0197] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generate the most appropriate answer.

[0198] Work instruction support means

[0199] The server runs simulations, generates work instructions and training materials, and displays them on the terminals, providing an environment where staff can effectively learn about new work procedures and products.

[0200] Skill improvement support measures

[0201] The server uses the generated feedback to help users improve their skills, particularly by providing specific advice to strengthen individual staff weaknesses and improve overall performance.

[0202] Specific examples

[0203] For example, when training staff with less than one year of experience on how to sell a new smartphone product, the following prompt might be used:

[0204] "Create training materials for staff with less than one year of experience about the features of a newly released smartphone and how to sell it. Then, simulate the training using virtual characters."

[0205] This allows store staff to effectively learn about new products and sales techniques and apply them to their actual work.

[0206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0207] Step 1: Enter your information

[0208] The user inputs information about the training and work instructions (objectives, target person's job title, knowledge level, work experience, etc.) from the terminal. This information is sent to the server by the terminal and stored in the system's database. The input information is used in each subsequent processing step.

[0209] Step 2: Generate materials

[0210] The server automatically generates training materials using a generative AI model based on the received information. Specifically, it uses PyTorch and TensorFlow to create materials with appropriate design and content. The generated materials are stored in a database and sent to the next evaluation step.

[0211] Step 3: Document evaluation

[0212] The server evaluates the quality of the design and content of the generated materials, using evaluation algorithms (e.g., scikit-learn) to check color and font matching, content consistency, etc. The evaluation results are stored in a database, and the user is notified if any corrections are required.

[0213] Step 4: Display the material

[0214] The terminal visually displays the assessed material to the user, who can then review it using a device such as a tablet or smart glasses, such as an iPad or Google Glass, and provide corrections or feedback as needed.

[0215] Step 5: Virtual Character Generation

[0216] The server generates a virtual training person (virtual character) based on the input information. Using tools such as Unity or Blender, a character that reflects the information input by the user is created. The generated virtual character is saved within the system and used in the next simulation step.

[0217] Step 6: Run the simulation

[0218] The user simulates training using a virtual character. The virtual environment is displayed on the device, and the user receives instructions and feedback while simulating real-world tasks. The results of this step are sent to the server for analysis.

[0219] Step 7: Feedback Generation

[0220] The server analyzes the simulation results and generates feedback for the user. The analysis uses algorithms that evaluate the consistency and comprehension of the user's presentation. The generated feedback is displayed on the device and can be used by the user to improve their skills.

[0221] Step 8: Progress Management

[0222] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications during presentations and training sessions, and helping users manage their time allocation, thereby enabling effective learning.

[0223] Step 9: Question Analysis

[0224] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generates the most appropriate answer. The generated answer is displayed on the device for the user's reference.

[0225] Step 10: Work instructions and skill development support

[0226] The server runs simulations, generates work instructions and training materials, and displays them on the terminal. It also supports users in improving their skills based on the generated feedback. This allows staff to learn and improve their skills effectively.

[0227] 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.

[0228] Below, we will explain an embodiment of an AI presentation support system that combines an emotion engine.

[0229] Information input method

[0230] Process Overview

[0231] User: Enters information about the presentation (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device. This information is used as basic information required to generate presentation materials and configure avatars.

[0232] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[0233] Material generation means

[0234] Process Overview

[0235] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0236] Terminal: The initially generated base materials are displayed to the user in a format that is easy for the user to review.

[0237] Specific examples

[0238] Example: When a user inputs information for creating a presentation about "sales strategy for a new product," the server automatically generates relevant charts and text based on that information and compiles them into slides.

[0239] Evaluation methods

[0240] Process Overview

[0241] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0242] Display means

[0243] Process Overview

[0244] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[0245] Avatar generation method

[0246] Process Overview

[0247] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0248] Device: Start practicing in a virtual environment using an avatar, and practice as if you were giving a presentation to a user.

[0249] Specific examples

[0250] Example: If a user specifies "Presentation for Senior Executives," the avatar will reflect the appearance, tone, and question patterns of a senior executive.

[0251] Emotion Engine

[0252] Process Overview

[0253] Server: Contains an emotion engine that recognizes emotions by analyzing the user's facial expressions and tone of voice. The emotion engine uses the camera and microphone to analyze the user's real-time reactions.

[0254] Device: Based on the recognized emotions, feedback is provided in real time according to the content and progress of the presentation, allowing users to proceed with the presentation while being aware of their own emotional state.

[0255] Specific examples

[0256] Example: If a user is nervous while practicing a presentation, the emotion engine can detect this state and display alerts such as "take a deep breath" or "relax."

[0257] Feedback Generation Method

[0258] Process Overview

[0259] Server: Analyzes the user's presentation, identifies consistency between the materials and statements, points out any unclear parts, and generates feedback.

[0260] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[0261] Progress management means

[0262] Process Overview

[0263] Device: Monitors progress in real time during the presentation and provides notifications and alerts to users. Progress management includes timekeeping functions.

[0264] Specific examples

[0265] For example, display notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[0266] Question analysis means

[0267] Process Overview

[0268] Server: Analyzes questions received during the presentation and performs natural language processing (NLP) to understand their intent. It performs text analysis of the questions and extracts related keywords and context.

[0269] Device: Understands the intent of the question, generates an appropriate answer, and presents it to the user.

[0270] Specific examples

[0271] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data and answers, "The market size of this new product is approximately 50 billion yen."

[0272] In this way, the AI ​​presentation support system that combines an emotion engine is multifunctional, providing consistent support from presentation preparation to the actual presentation, and is equipped with all the elements necessary to deliver an effective presentation.

[0273] The processing flow will be explained below.

[0274] Program processing steps

[0275] Information input and document generation

[0276] Step 1: Enter your information

[0277] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[0278] Terminal: Sends the entered information to the server, where it is stored in a database.

[0279] Step 2: Automatically generate materials

[0280] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[0281] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[0282] Step 3: Document evaluation

[0283] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[0284] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[0285] Step 4: Display the material

[0286] Terminal: Visually displays to the user the base material that has passed the check.

[0287] User: Review the displayed material and make corrections as necessary.

[0288] Pre-practice and feedback

[0289] Step 5: Avatar Creation and Practice

[0290] Server: Generates an avatar that simulates the person giving the presentation based on the materials created by the user.

[0291] Terminal: The user begins a pre-practice session in the virtual environment and gives a presentation facing the avatar.

[0292] Specific behavior: Simulates avatar reactions and questions to provide users with an interactive experience.

[0293] Step 6: Practice analysis and feedback generation

[0294] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[0295] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[0296] Step 7: Emotion Engine Analysis

[0297] Server: Uses a camera and microphone to analyze the user's facial expressions and voice in real time to recognize emotions.

[0298] Specific behavior: Analyzes the user's facial expressions and tone of voice in real time to identify emotions such as tension or impatience.

[0299] Step 8: Provide feedback

[0300] Server: Generates feedback based on the analysis, including specific recommendations for improvement and emotional responses.

[0301] Terminal: Display feedback to the user.

[0302] User: Review the feedback and improve your presentation and manage your emotions.

[0303] Follow-up on the actual event

[0304] Step 9: Progress Management

[0305] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[0306] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[0307] Step 10: Question Analysis

[0308] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[0309] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[0310] Step 11: Answer Generation

[0311] Server: Understands the intent of the question and generates an appropriate answer.

[0312] On the device: Present the suggested answers to the user.

[0313] User: Use the suggested answers to guide your Q&A.

[0314] In this way, the AI ​​presentation support system, which combines an emotion engine, supports users at every step, from inputting information to creating materials, practicing, providing feedback, and following up on the actual presentation, providing effective presentations.

[0315] Example 2

[0316] 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."

[0317] In modern presentations, efficient and effective preparation of materials is essential, but it requires a lot of time and effort. Furthermore, progress management during the presentation, answering questions, and real-time feedback based on the user's emotional state are not yet fully supported. To address these issues, a system is needed that allows users to deliver high-quality presentations with less effort.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that automatically generates presentation materials based on the input information using a generative AI model, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation, an emotion engine means that analyzes the user's facial expressions and tone of voice during the presentation and recognizes their emotions, a means that provides real-time feedback according to the presentation content and progress based on the recognized emotions, and a question analysis means that analyzes questions during the presentation and generates appropriate answers. This enables a user to efficiently prepare presentation materials, manage the progress and respond to questions during the presentation, and understand their own emotional state, thereby delivering a more effective presentation.

[0319] The "information input means" is a means for a user to input detailed information about a presentation through a terminal.

[0320] A "generative AI model" is an artificial intelligence algorithm used to automatically generate presentation materials based on input information.

[0321] "Material generation means" refers to a means for automatically generating presentation materials based on input information using a generative AI model.

[0322] "Evaluation measures" are measures to check the design and expression of the created presentation materials and ensure their appropriateness.

[0323] The "display means" is a means for visually displaying the checked presentation materials to the user.

[0324] The "avatar generation means" is a means for generating an avatar in a virtual environment that supports the user in practicing a presentation in advance and simulates the actual person to whom the presentation will be given.

[0325] The "feedback generating means" is a means for analyzing the results of the pre-practice and providing feedback to the user.

[0326] A "progress management means" is a means for monitoring the progress of a presentation in real time and providing alerts and notifications during the presentation.

[0327] The "emotion engine means" is a means for analyzing the facial expressions and tone of voice of the user during the presentation and recognizing their emotions.

[0328] The "question analysis means" is a means for analyzing questions received during a presentation, understanding their intent, and generating appropriate answers.

[0329] This invention relates to an AI presentation support system that combines an emotion engine, and is a multifunctional system that supports everything from presentation preparation to the actual presentation.

[0330] Embodiment of information input means

[0331] The user inputs context information about the presentation from the terminal. This context information includes the audience, purpose, presentation time, audience knowledge level, industry, etc. For example, the user might input, "I need a presentation document on a new product sales strategy for senior management."

[0332] The terminal transmits the information input by the user to the server, where it is stored in a database and used for subsequent processing.

[0333] Embodiment of material generating means

[0334] The server automatically generates presentation materials using a generative AI model (e.g., OpenAI's GPT-4) based on information extracted from the database. The generative AI model analyzes the situational information entered by the user and generates slides with optimal design and content.

[0335] The terminal visually displays the presentation materials sent from the server to the user, who can then review the materials and make any necessary corrections.

[0336] Embodiments of the evaluation means

[0337] The server uses internal algorithms to evaluate the generated presentation, including slide color and font matching, and content consistency.

[0338] Display Means Embodiment

[0339] The terminal visually displays presentation materials that have passed the evaluation, allowing the user to review the materials in detail and make final adjustments.

[0340] Embodiment of Avatar Generation Means

[0341] The server generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior management," the avatar will reflect the appearance and tone of a senior manager.

[0342] The device will then begin practicing a presentation in a virtual environment using an avatar. The user can then give a presentation to the avatar in the virtual environment, providing a rehearsal for the actual presentation.

[0343] Embodiment of Emotion Engine

[0344] The server has an emotion engine that uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time. The emotion engine analyzes the user's emotional state during a presentation. For example, if the user is nervous, it will detect that state.

[0345] Based on the results of emotion analysis, the device provides real-time feedback to the user, such as "take a deep breath" or "relax," allowing the user to proceed with the presentation while being aware of their own emotional state.

[0346] Feedback Generation Means Embodiment

[0347] The server analyzes the content of the user's presentation and generates feedback that points out the consistency between the materials and the comments and any unclear parts. Based on this feedback, the user can revise and improve the content of their presentation.

[0348] The terminal visually displays the feedback sent from the server to the user.

[0349] Embodiment of progress management means

[0350] The device monitors the progress of the presentation in real time, displaying notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[0351] Embodiment of the Question Analysis Means

[0352] The server analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to the question, "What is the market size for this new product?", it analyzes market size data and generates the answer, "The market size for this new product is approximately 50 billion yen."

[0353] The terminal visually displays the generated answer to the user, who can refer to the answer to make an appropriate response to the question.

[0354] Prompt Sentence Examples

[0355] "I'd like to create a presentation on a sales strategy for a new product. The presentation is for senior management, and I have 30 minutes to give. The audience has a high level of knowledge and is in the IT industry. I'd like feedback on the design, content, avatar generation, and sentiment analysis of the presentation."

[0356] As described above, this system provides consistent support from presentation preparation to the actual presentation, and offers multifunctional assistance to help users give effective presentations.

[0357] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0358] Step 1:

[0359] User: Enters details of the presentation (such as audience, purpose, presentation time, audience knowledge level, industry, etc.) into an input form on the device. For example, the input might include, "I need a presentation document for a new product sales strategy for senior management."

[0360] Input: Presentation details

[0361] Output: Information data prepared for transmission by the terminal.

[0362] Step 2:

[0363] Terminal: Sends the information entered by the user to the server. Using the API, the information is sent in JSON format, for example.

[0364] Input: Details entered by the user

[0365] Output: Information data sent to the server

[0366] Step 3:

[0367] Server: Receives the transmitted information and stores it in a database, such as an SQL database.

[0368] Input: Information data sent from the terminal

[0369] Output: Information stored in the database

[0370] Step 4:

[0371] Server: Extracts information from the database and automatically generates presentation materials using a generative AI model (e.g., GPT-4). Optimizes the design and content.

[0372] Input: Information stored in a database

[0373] Output: Auto-generated presentation materials

[0374] Step 5:

[0375] Terminal: Visually displays the data sent from the server to the user, allowing the user to check and modify it.

[0376] Input: Presentation materials sent from the server

[0377] Output: Visual presentation material

[0378] Step 6:

[0379] Server: Evaluates the generated materials using internal algorithms, checking for color and font consistency of slides, content consistency, etc.

[0380] Input: Auto-generated presentation material

[0381] Output: Evaluation result

[0382] Step 7:

[0383] Terminal: Visually displays materials that have passed evaluation to users, allowing them to see the overall picture and specific content of the materials.

[0384] Input: Presentation materials that have passed evaluation

[0385] Output: Presentation materials for final review

[0386] Step 8:

[0387] Server: Generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior executives," the avatar will be configured to look and speak like a senior executive.

[0388] Input: Finalized presentation materials and contact information

[0389] Output: Simulated avatar

[0390] Step 9:

[0391] Device: Start practicing your presentation in a virtual environment using an avatar. Users practice in a real environment.

[0392] Input: Generated avatar and presentation materials

[0393] Output: A practice session in a virtual environment

[0394] Step 10:

[0395] Server: The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotions. For example, it can detect if the user is nervous.

[0396] Input: Camera video and microphone audio data

[0397] Output: Recognized emotion information

[0398] Step 11:

[0399] On the device: Based on the recognized emotion, the device provides the user with real-time suggested feedback, such as an alert to "take a deep breath" or "relax."

[0400] Input: Recognized emotion information

[0401] Output: Real-time feedback

[0402] Step 12:

[0403] Server: Analyzes the presentation content and generates feedback pointing out the consistency between the materials and statements and any unclear parts.

[0404] Input: Presentation materials and user comments

[0405] Output: Feedback for content correction and improvement

[0406] Step 13:

[0407] Device: Displays the feedback sent from the server and informs the user of the results of their practice. The user can then revise and improve their presentation based on the feedback.

[0408] Input: Feedback sent by the server

[0409] Output: Feedback showing corrections and improvements

[0410] Step 14:

[0411] Device: Monitors progress in real time during a live presentation and provides alerts and notifications, such as "You're running out of time on this slide" or "You have 10 minutes left."

[0412] Input: Progress information during the presentation

[0413] Output: Alerts and notifications

[0414] Step 15:

[0415] Server: Analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to a question like, "What is the market size for this new product?", the server analyzes market size data and generates an appropriate answer.

[0416] Input: Question data during presentation

[0417] Output: Answers generated through natural language processing

[0418] Step 16:

[0419] Terminal: The generated answer is visually displayed to the user, who can use it to respond appropriately to the question.

[0420] Input: Response data sent from the server

[0421] Output: A visual representation of the answer

[0422] (Application example 2)

[0423] 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."

[0424] In today's virtual stores, staff lack the tools to efficiently and effectively explain the features and usage of products. It is also difficult for them to quickly and accurately respond to customer questions during presentations. Furthermore, there is a need for a system that provides pre-practice and feedback to help staff improve their presentation skills.

[0425] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that simulates the actual presentation audience by practicing the presentation in a virtual environment, a feedback generation means that analyzes the results of the practice and generates feedback, an emotion analysis means that analyzes the user's facial expressions and tone of voice during the presentation and provides emotion-based feedback, a question analysis means that analyzes questions from customers and generates appropriate answers when staff members present product features and specifications in a virtual store, and a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation. This improves the quality of presentations and strengthens customer responsiveness.

[0426] The "information input means" is a means by which the user inputs information necessary for the presentation and transmits it to the server.

[0427] An "AI model" is an algorithmic model that uses artificial intelligence technology to automate specific tasks.

[0428] "Material generation means" refers to a means for automatically generating presentation materials using an AI model based on input information.

[0429] "Evaluation measures" are measures to check the generated presentation materials and ensure appropriate design and expression.

[0430] The "display means" is a means for visually displaying the evaluated presentation materials to the user.

[0431] The "avatar generation means" is a means for practicing a presentation in advance in a virtual environment and generating an avatar that simulates the actual person being presented to.

[0432] The "feedback generation means" is a means for analyzing the results of the preliminary practice and generating feedback that provides the user with areas for improvement and advice.

[0433] An "emotion analysis means" is a means of analyzing a user's facial expressions and tone of voice when giving a presentation and providing feedback based on their emotions.

[0434] The "question analysis means" is a means for analyzing questions from customers and generating appropriate answers when staff members present product features and specifications in a virtual store.

[0435] A "progress management means" is a means for monitoring the progress of a presentation in real time during the presentation and providing alerts and notifications.

[0436] This invention relates to an AI presentation support system for efficiently and effectively presenting products in a virtual store.

[0437] First, the user uses the information input means to input basic information into the terminal, such as the person to whom the presentation will be given, the features of the product, the purpose of the presentation, etc. This information is sent to the server and stored in a database.

[0438] Next, a material generation tool using an AI model automatically generates presentation materials based on the input information, such as product explanation slides and videos showing how to use the product.

[0439] The server then uses evaluation tools to check the generated materials to ensure that the design and presentation are appropriate. This evaluation process ensures the quality of the presentation.

[0440] The materials that pass the evaluation are displayed to the user through the display means, so that the user can check the contents of the generated materials.

[0441] Next, the avatar generation means is activated to allow a user to practice the presentation in advance in the virtual environment. This means generates an avatar according to the position and knowledge level of the person to whom the user is going to give the presentation, and realizes a presentation simulation in the virtual environment.

[0442] Furthermore, the results of the pre-practice are analyzed, and the feedback generation means provides the user with suggestions and advice on how to improve, allowing the user to improve their presentation skills.

[0443] When giving a presentation, the emotion analyzer analyzes the user's facial expressions and tone of voice and provides real-time feedback based on their emotions. For example, if they are nervous, the device will display instructions such as "Relax."

[0444] During presentations in the virtual store, the question analysis tool analyzes customer questions in real time and generates appropriate answers, allowing staff to provide accurate answers immediately.

[0445] During the live presentation, the progress management tool monitors the progress of the presentation in real time and provides alerts and notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[0446] To realize this invention, a smartphone equipped with a camera and microphone, smart glasses, or a head-mounted display (e.g., Oculus Quest 2) is used. PyTorch or TensorFlow is used as the AI ​​model, and GPT-4 (OpenAI) is used for natural language processing. Microsoft Azure Cognitive Services or Google Cloud Machine Learning API is used as the sentiment analysis engine, and WebRTC is used for real-time communication.

[0447] As a concrete example, consider a scenario in which a new smartphone product is explained in a virtual store. The user inputs the smartphone's features (e.g., high-resolution camera, voice recognition function), and the material generation means generates presentation materials based on that information. The generated materials are checked by the evaluation means and displayed to the user through the display means. During practice, the avatar generation means generates an avatar that resembles the customer, and the presentation is simulated in the virtual environment.

[0448] An example of a prompt sentence that may be used is:

[0449] ---

[0450] Prompt: "Prompt to generate a presentation document that explains the product's features."

[0451] Please create a presentation about a new product equipped with a high-resolution camera and voice recognition function. The purpose of the presentation is to increase customer desire to purchase.

[0452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0453] Step 1:

[0454] The user uses a terminal to input information necessary for the presentation. The input information includes the attributes of the person being presented to (e.g., job title and knowledge level), product features, and the purpose of the presentation. This input data is sent from the terminal to the server.

[0455] Input: Information such as the attributes of the person you are presenting to, product features, and the purpose of the presentation

[0456] Output: User-entered data sent to the server

[0457] Step 2:

[0458] The server stores the received input data in a database, which is used for subsequent processing.

[0459] Input: User-entered data

[0460] Output: Data stored in the database

[0461] Step 3:

[0462] The server automatically generates presentation materials using an AI model based on the stored user input data. Specifically, the AI ​​model generates presentation materials (slides, videos, text, etc.) that include product features and usage instructions based on prompt statements.

[0463] Input: User-entered data

[0464] Output: Generated presentation materials

[0465] Step 4:

[0466] The server evaluates the generated presentation materials for appropriateness of design and expression, checking the color and font consistency of the slides, and content consistency.

[0467] Input: Generated presentation material

[0468] Output: Evaluated presentation materials

[0469] Step 5:

[0470] The terminal displays the evaluated presentation materials to the user, allowing the user to review the generated materials and identify any corrections needed.

[0471] Input: Evaluated presentation materials

[0472] Output: Presentation materials displayed on the device

[0473] Step 6:

[0474] To allow users to practice their presentation in a virtual environment, the server uses an avatar generation unit to generate an avatar that simulates the person they are presenting to. This avatar is customized according to the attributes of the person they are presenting to (e.g., their job title or knowledge level) specified by the user.

[0475] Input: User input data, presentation partner attributes

[0476] Output: The generated avatar

[0477] Step 7:

[0478] The user performs a pre-practice in the virtual environment using the generated avatar. The server analyzes the results of the pre-practice and generates feedback using a feedback generation means. The feedback provides the user with suggestions for improvement and advice.

[0479] Input: Pre-practice result data

[0480] Output: Generated feedback

[0481] Step 8:

[0482] During the presentation, the server uses emotion analysis to analyze the user's facial expressions and tone of voice, and provides real-time feedback based on their emotions. For example, if the user is nervous, it will display instructions such as "Relax."

[0483] Input: User's facial expression data, voice tone data

[0484] Output: Real-time feedback

[0485] Step 9:

[0486] During the presentation in the virtual store, the server uses a question analysis means to analyze questions from customers in real time and generates appropriate answers using natural language processing, allowing the user to provide accurate answers immediately.

[0487] Input: Question data from customers

[0488] Output: The generated answer

[0489] Step 10:

[0490] During the live presentation, the server uses the progress management means to monitor the progress of the presentation in real time and provide alerts and notifications to the user, such as "You're running out of time on this slide" or "You have 10 minutes left."

[0491] Input: Presentation progress data

[0492] Output: Alerts, notifications

[0493] 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.

[0494] 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.

[0495] 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.

[0496] [Second embodiment]

[0497] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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).

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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."

[0509] An embodiment of the AI ​​presentation support system will be described below.

[0510] Information input method

[0511] User: Enters information about the presentation (presentee, purpose, presentation time, audience level, industry, etc.) from the device. This information is the basic information required to generate presentation materials and configure avatars.

[0512] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[0513] Material generation means

[0514] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0515] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[0516] Evaluation methods

[0517] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0518] Display means

[0519] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[0520] Avatar generation method

[0521] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0522] Device: The user begins pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to an audience.

[0523] Feedback Generation Method

[0524] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[0525] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[0526] Progress management means

[0527] Device: Monitors the progress of the presentation in real time and provides notifications and alerts to the user, including checking the progress of the presentation and time management.

[0528] Example: Displaying a notification such as "10 minutes remaining" helps users manage their time better.

[0529] Question analysis means

[0530] Server: Analyzes questions asked by users during a presentation in real time, and determines the intent of the question and recommends appropriate answers.

[0531] On the device: Suggested answers are presented to the user, allowing the user to proceed with the Q&A while referring to the suggested answers.

[0532] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data, generates an appropriate answer such as, "The market size of this new product is approximately 50 billion yen," and displays it on the device.

[0533] The processing flow will be explained below.

[0534] Program processing steps

[0535] Information input and document generation

[0536] Step 1: Enter your information

[0537] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[0538] Terminal: Sends the entered information to the server, where it is stored in a database.

[0539] Step 2: Automatically generate materials

[0540] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[0541] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[0542] Step 3: Document evaluation

[0543] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[0544] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[0545] Step 4: Display the material

[0546] Terminal: Visually displays to the user the base material that has passed the check.

[0547] User: Review the displayed material and make corrections as necessary.

[0548] Pre-practice and feedback

[0549] Step 5: Avatar Creation and Practice

[0550] Server: Generates an avatar simulating the person giving the presentation based on the materials created by the user.

[0551] Terminal: The user begins a pre-practice session in the virtual environment and makes a presentation facing an avatar.

[0552] Step 6: Practice analysis and feedback generation

[0553] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[0554] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[0555] Step 7: Provide feedback

[0556] Server: Generates feedback based on the analysis results, summarizing specific improvements and enhancements.

[0557] Terminal: Display feedback to the user.

[0558] Users: Review feedback and improve their presentation.

[0559] Follow-up on the actual event

[0560] Step 8: Progress Management

[0561] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[0562] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[0563] Step 9: Question Analysis

[0564] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[0565] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[0566] Step 10: Answer Generation

[0567] Server: Understands the intent of the question and generates an appropriate answer.

[0568] On the device: Present the suggested answers to the user.

[0569] User: Use the suggested answers to guide your Q&A.

[0570] Example 1

[0571] 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."

[0572] In today's business environment, precise and effective presentations are required, but preparing them takes time and effort. Furthermore, it is difficult to provide advance feedback, rehearse, and respond quickly and appropriately to questions and answers to improve the quality of presentations. Furthermore, it is not easy to evaluate and improve the appropriateness of presentation design and content.

[0573] 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.

[0574] In this invention, the server includes an information input means, a material generation means that uses a generative AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time during the actual presentation and provides alerts and notifications, and a question analysis means that analyzes questions asked during the presentation, understands the intent of the questions, and generates appropriate answers. This enables the entire process from presentation preparation to the actual presentation to be carried out efficiently, enabling high-quality presentations.

[0575] The "information input means" is a device or system that allows a user to input presentation status information into a terminal and transmit it to a server.

[0576] A "material generation means" is a device or system that automatically generates presentation materials based on input information using a generative AI model.

[0577] An "evaluation tool" is a device or system that checks the generated presentation materials to ensure appropriate design and presentation.

[0578] The "display means" is a device or system that allows the user to visually check the generated presentation materials and their evaluation results.

[0579] The "avatar generation means" is a device or system that generates an avatar that simulates an actual presentation partner in a virtual environment based on information input by the user, for use in practicing the presentation in advance.

[0580] The "feedback generation means" is a device or system that analyzes the results of the preliminary practice and generates feedback that provides the user with suggestions and points for improvement.

[0581] A "progress management means" is a device or system that monitors the progress of a presentation in real time and provides alerts and notifications to the user.

[0582] The "question analysis means" is a device or system that analyzes questions asked by users during a presentation, understands the intent of the questions, and generates appropriate answers.

[0583] A "generative AI model" is an artificial intelligence model used to automatically generate presentation materials based on input information.

[0584] A "prompt" is text information entered by the user to specify the requirements and content of the presentation materials to the generative AI model.

[0585] An embodiment of a presentation support system according to the present invention will be described below. This system automates and supports a series of processes required when a user prepares and gives a presentation.

[0586] System Configuration

[0587] This system includes a device operated by the user, a server that processes data, and an AI model. The main hardware components are PCs, smartphones, and tablets, while the server is a cloud server with advanced computing capabilities. The main software used is a generative AI model such as OpenAI's GPT-4.

[0588] Information input method

[0589] User: Enter presentation information (e.g., audience, purpose, presentation time, audience knowledge level, industry, etc.) from the terminal.

[0590] Terminal: The information entered by the user is sent to the server, where it is stored in a database and used to generate presentation materials and configure avatars.

[0591] As a specific example, a user may input, "I am giving a presentation to engineers and would like materials that emphasize the technical details of the product."

[0592] Material generation means

[0593] Server: Based on the received information, a generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0594] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[0595] Example prompt: "Create a presentation to highlight the technical details of a new product for a customer presentation. You will be a product specialist and your audience will be engineers. Your presentation will last 20 minutes."

[0596] Evaluation methods

[0597] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0598] Terminal: The base materials that have passed the check are visually displayed to the user, allowing the user to check the overall picture and specific content of the materials.

[0599] Avatar generation method

[0600] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0601] Device: Starts pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to a person. For example, if the person they are presenting to is set to be a "CEO," the avatar will act like the CEO.

[0602] Feedback Generation Method

[0603] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[0604] Device: Display feedback to let the user know the results of the practice. For example, "The content of slide 5 is difficult to understand."

[0605] Progress management means

[0606] Terminal: Monitors the progress of a presentation in real time and provides notifications and alerts to the user. This includes checking the progress of the presentation and time management. For example, during a presentation, a notification such as "10 minutes remaining" is displayed.

[0607] Question analysis means

[0608] Server: Analyzes questions asked by users during a presentation in real time, and recommends the intent of the question and appropriate answers. For example, if a user asks, "What is the market size of this new product?", the server uses a generative AI model to analyze market size data and generate an appropriate answer, such as, "The market size of this new product is approximately 50 billion yen."

[0609] Terminal: Suggested answers are presented to the user, who can then use the suggested answers to proceed with the Q&A.

[0610] By using the above means, the user can efficiently carry out the entire process from preparation to the actual presentation, thereby enabling a high-quality presentation.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] User: Enters information about the presentation situation from the device. The user enters information such as the audience, purpose, presentation time, audience knowledge level, industry, etc. into the input form on the device. This is input data.

[0614] Terminal: The terminal sends the entered information to the server, which saves it in a database. For example, a user might enter "Presentation recipient: engineers, Purpose: new product introduction, Presentation time: 20 minutes." The server receives and saves the entered data.

[0615] Step 2:

[0616] Server: Based on the received information, retrieves user information from a database and inputs this information into a generative AI model (e.g., OpenAI GPT-4). The input data includes information such as the recipient's occupation and the purpose of the presentation.

[0617] Server: The AI ​​model analyzes the information and automatically generates presentation materials. The generated slides are the output data. For example, the AI ​​model automatically generates slides that highlight the technical features of a new product.

[0618] Terminal: The terminal displays the generated slides to the user.

[0619] Step 3:

[0620] Server: Passes the generated slides to the evaluation algorithm. The input data is the generated slides.

[0621] Server: The algorithm checks the consistency of colors and fonts on the slides, as well as the appropriateness of the content. The evaluation results are output data. For example, it detects problems such as "color mismatch" or "font inconsistency" on the slides.

[0622] Terminal: The evaluation results are displayed to the user, who can then modify the slides.

[0623] Step 4:

[0624] Server: Retrieves user and slide information from the database and inputs it into the avatar generation process. The input data is the presentation recipient's information and the slide content.

[0625] Server: Uses an AI model to generate an avatar that simulates the person you are presenting to. The generated avatar is the output data. For example, the server generates an avatar of a "CEO."

[0626] Terminal: The avatar is displayed to the user, and the user begins practicing in the virtual environment. The user uses the avatar to rehearse the presentation in front of a real audience.

[0627] Step 5:

[0628] Server: Records the user's presentation in a virtual environment. The input data is the presentation's audio and movements.

[0629] Server: The recorded presentation content is passed to an analysis algorithm. The algorithm analyzes the content and generates feedback. This is the output data. For example, it generates specific feedback such as "The content of slide 5 is difficult to understand."

[0630] Device: Provides feedback to the user, who can then use it to refine their presentation.

[0631] Step 6:

[0632] Terminal: Monitors the progress of the presentation in real time. Input data is the progress of the presentation.

[0633] Terminal: Checks the progress based on the set time and provides notifications and alerts to the user. This is the output data. For example, it sends a notification to the user saying "10 minutes left."

[0634] Step 7:

[0635] Terminal: The terminal converts questions received from users during a presentation into text using speech recognition. The input data is the voice of the question.

[0636] Terminal: Sends a textual question to the server.

[0637] Server: Analyzes the question and generates an appropriate answer. The input data is the textual question, and the output data is the recommended answer. For example, in response to the question, "What is the market size for this new product?", the server responds, "The market size is approximately 50 billion yen."

[0638] Terminal: Presents suggested answers to the user, allowing them to proceed with the Q&A session.

[0639] (Application example 1)

[0640] 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."

[0641] Conventional presentation and educational support systems face challenges in ensuring the efficiency and effectiveness of specific work instructions and educational training. In particular, it is difficult to effectively learn and practice in a virtual environment when it comes to improving staff skills or providing work instructions in physical stores. Furthermore, they lack functionality for real-time progress management and question analysis. They also lack the quality of feedback and mechanisms for reflecting learning results.

[0642] 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.

[0643] In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, a display means that displays the checked materials, a virtual character generation means that conducts pre-practice in a virtual environment and simulates an actual interlocutor, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors progress in real time and provides alerts and notifications, a question analysis means that analyzes questions and generates appropriate answers, a work instruction support means that conducts simulations to generate and present materials for work instructions and training, and a skill improvement support means that supports skill improvement based on feedback. This significantly improves the efficiency and effectiveness of work instructions and training, and enables practice in a virtual environment, real-time progress management, and question analysis.

[0644] "Information input means" refers to a device or method for inputting user-specified information into the system.

[0645] An "AI model" is a mathematical or algorithmic model that uses artificial intelligence to accomplish a specific task.

[0646] The "material generation means" is a function or device that automatically creates materials based on input information.

[0647] "Evaluation means" refers to a function or device that checks and evaluates the quality and appropriateness of the generated materials.

[0648] "Display means" means a device or method for visually presenting the checked material to a user.

[0649] "Virtual character generation means" refers to a function or device that generates a character for a user to practice or simulate in a virtual environment.

[0650] The "feedback generation means" is a function or device that analyzes the results of the preliminary practice and generates feedback to be provided to the user based on the results.

[0651] A "progress management means" is a function or device that monitors progress in real time and provides alerts or notifications as needed.

[0652] The "question analysis means" is a function or device that analyzes questions asked by users during a presentation and generates appropriate answers.

[0653] The "work instruction support means" is a function or device that performs simulations to generate and present work instructions and training materials.

[0654] A "skill improvement support means" is a function or device that supports the user in improving their skills based on feedback.

[0655] The system of the present invention includes an information input means, a material generation means, an evaluation means, a display means, a virtual character generation means, a feedback generation means, a progress management means, a question analysis means, a work instruction support means, and a skill improvement support means.

[0656] Information input method

[0657] The user inputs the information necessary for training and work instructions from the terminal. This information includes the purpose of the training, the target person's job title, knowledge level, work experience, etc. This information is sent to the server and stored in a database.

[0658] Material generation means

[0659] The server automatically generates training materials using a generative AI model based on the received information. The generated materials are then optimized for design and content by the AI ​​model, using machine learning libraries such as PyTorch and TensorFlow.

[0660] Evaluation methods

[0661] The server evaluates the quality of the design and content of the generated materials. Evaluation algorithms check the color and font consistency of the generated materials, as well as the consistency of the content. For example, they use evaluation tools such as scikit-learn.

[0662] Display means

[0663] The terminal visually displays the assessed material, and the user can review it and make corrections as needed using a device such as a tablet or smart glasses, for example, an iPad or Google Glass.

[0664] Virtual character generation means

[0665] The server generates a virtual training instructor (virtual character) based on the input information. This virtual character reflects the position and knowledge level according to the information input by the user. For example, Unity or Blender is used.

[0666] Feedback Generation Method

[0667] The server analyzes the results of the pre-practice and generates feedback to provide to the user. The analyzed data is used to evaluate the consistency and comprehension of the user's presentation. The feedback results contribute to improving the user's skills.

[0668] Progress management means

[0669] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications as the presentation or training progresses, helping users manage their time allocation.

[0670] Question analysis means

[0671] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generate the most appropriate answer.

[0672] Work instruction support means

[0673] The server runs simulations, generates work instructions and training materials, and displays them on the terminals, providing an environment where staff can effectively learn about new work procedures and products.

[0674] Skill improvement support measures

[0675] The server uses the generated feedback to help users improve their skills, particularly by providing specific advice to strengthen individual staff weaknesses and improve overall performance.

[0676] Specific examples

[0677] For example, when training staff with less than one year of experience on how to sell a new smartphone product, the following prompt might be used:

[0678] "Create training materials for staff with less than one year of experience about the features of a newly released smartphone and how to sell it. Then, simulate the training using virtual characters."

[0679] This allows store staff to effectively learn about new products and sales techniques and apply them to their actual work.

[0680] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0681] Step 1: Enter your information

[0682] The user inputs information about the training and work instructions (objectives, target person's job title, knowledge level, work experience, etc.) from the terminal. This information is sent to the server by the terminal and stored in the system's database. The input information is used in each subsequent processing step.

[0683] Step 2: Generate materials

[0684] The server automatically generates training materials using a generative AI model based on the received information. Specifically, it uses PyTorch and TensorFlow to create materials with appropriate design and content. The generated materials are stored in a database and sent to the next evaluation step.

[0685] Step 3: Document evaluation

[0686] The server evaluates the quality of the design and content of the generated materials, using evaluation algorithms (e.g., scikit-learn) to check color and font matching, content consistency, etc. The evaluation results are stored in a database, and the user is notified if any corrections are required.

[0687] Step 4: Display the material

[0688] The terminal visually displays the assessed material to the user, who can then review it using a device such as a tablet or smart glasses, such as an iPad or Google Glass, and provide corrections or feedback as needed.

[0689] Step 5: Virtual Character Generation

[0690] The server generates a virtual training person (virtual character) based on the input information. Using tools such as Unity or Blender, a character that reflects the information input by the user is created. The generated virtual character is saved within the system and used in the next simulation step.

[0691] Step 6: Run the simulation

[0692] The user simulates training using a virtual character. The virtual environment is displayed on the device, and the user receives instructions and feedback while simulating real-world tasks. The results of this step are sent to the server for analysis.

[0693] Step 7: Feedback Generation

[0694] The server analyzes the simulation results and generates feedback for the user. The analysis uses algorithms that evaluate the consistency and comprehension of the user's presentation. The generated feedback is displayed on the device and can be used by the user to improve their skills.

[0695] Step 8: Progress Management

[0696] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications during presentations and training sessions, and helping users manage their time allocation, thereby enabling effective learning.

[0697] Step 9: Question Analysis

[0698] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generates the most appropriate answer. The generated answer is displayed on the device for the user's reference.

[0699] Step 10: Work instructions and skill development support

[0700] The server runs simulations, generates work instructions and training materials, and displays them on the terminal. It also supports users in improving their skills based on the generated feedback. This allows staff to learn and improve their skills effectively.

[0701] 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.

[0702] Below, we will explain an embodiment of an AI presentation support system that combines an emotion engine.

[0703] Information input method

[0704] Process Overview

[0705] User: Enters information about the presentation (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device. This information is used as basic information required to generate presentation materials and configure avatars.

[0706] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[0707] Material generation means

[0708] Process Overview

[0709] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0710] Terminal: The initially generated base materials are displayed to the user in a format that is easy for the user to review.

[0711] Specific examples

[0712] Example: When a user inputs information for creating a presentation about "sales strategy for a new product," the server automatically generates relevant charts and text based on that information and compiles them into slides.

[0713] Evaluation methods

[0714] Process Overview

[0715] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0716] Display means

[0717] Process Overview

[0718] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[0719] Avatar generation method

[0720] Process Overview

[0721] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0722] Device: Start practicing in a virtual environment using an avatar, and practice as if you were giving a presentation to a user.

[0723] Specific examples

[0724] Example: If a user specifies "Presentation for Senior Executives," the avatar will reflect the appearance, tone, and question patterns of a senior executive.

[0725] Emotion Engine

[0726] Process Overview

[0727] Server: Contains an emotion engine that recognizes emotions by analyzing the user's facial expressions and tone of voice. The emotion engine uses the camera and microphone to analyze the user's real-time reactions.

[0728] Device: Based on the recognized emotions, feedback is provided in real time according to the content and progress of the presentation, allowing users to proceed with the presentation while being aware of their own emotional state.

[0729] Specific examples

[0730] Example: If a user is nervous while practicing a presentation, the emotion engine can detect this state and display alerts such as "take a deep breath" or "relax."

[0731] Feedback Generation Method

[0732] Process Overview

[0733] Server: Analyzes the user's presentation, identifies consistency between the materials and statements, points out any unclear parts, and generates feedback.

[0734] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[0735] Progress management means

[0736] Process Overview

[0737] Device: Monitors progress in real time during the presentation and provides notifications and alerts to users. Progress management includes timekeeping functions.

[0738] Specific examples

[0739] For example, display notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[0740] Question analysis means

[0741] Process Overview

[0742] Server: Analyzes questions received during the presentation and performs natural language processing (NLP) to understand their intent. It performs text analysis of the questions and extracts related keywords and context.

[0743] Device: Understands the intent of the question, generates an appropriate answer, and presents it to the user.

[0744] Specific examples

[0745] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data and answers, "The market size of this new product is approximately 50 billion yen."

[0746] In this way, the AI ​​presentation support system that combines an emotion engine is multifunctional, providing consistent support from presentation preparation to the actual presentation, and is equipped with all the elements necessary to deliver an effective presentation.

[0747] The processing flow will be explained below.

[0748] Program processing steps

[0749] Information input and document generation

[0750] Step 1: Enter your information

[0751] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[0752] Terminal: Sends the entered information to the server, where it is stored in a database.

[0753] Step 2: Automatically generate materials

[0754] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[0755] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[0756] Step 3: Document evaluation

[0757] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[0758] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[0759] Step 4: Display the material

[0760] Terminal: Visually displays to the user the base material that has passed the check.

[0761] User: Review the displayed material and make corrections as necessary.

[0762] Pre-practice and feedback

[0763] Step 5: Avatar Creation and Practice

[0764] Server: Generates an avatar that simulates the person giving the presentation based on the materials created by the user.

[0765] Terminal: The user begins a pre-practice session in the virtual environment and gives a presentation facing the avatar.

[0766] Specific behavior: Simulates avatar reactions and questions to provide users with an interactive experience.

[0767] Step 6: Practice analysis and feedback generation

[0768] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[0769] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[0770] Step 7: Emotion Engine Analysis

[0771] Server: Uses a camera and microphone to analyze the user's facial expressions and voice in real time to recognize emotions.

[0772] Specific behavior: Analyzes the user's facial expressions and tone of voice in real time to identify emotions such as tension or impatience.

[0773] Step 8: Provide feedback

[0774] Server: Generates feedback based on the analysis, including specific recommendations for improvement and emotional responses.

[0775] Terminal: Display feedback to the user.

[0776] User: Review the feedback and improve your presentation and manage your emotions.

[0777] Follow-up on the actual event

[0778] Step 9: Progress Management

[0779] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[0780] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[0781] Step 10: Question Analysis

[0782] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[0783] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[0784] Step 11: Answer Generation

[0785] Server: Understands the intent of the question and generates an appropriate answer.

[0786] On the device: Present the suggested answers to the user.

[0787] User: Use the suggested answers to guide your Q&A.

[0788] In this way, the AI ​​presentation support system, which combines an emotion engine, supports users at every step, from inputting information to creating materials, practicing, providing feedback, and following up on the actual presentation, providing effective presentations.

[0789] Example 2

[0790] 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."

[0791] In modern presentations, efficient and effective preparation of materials is essential, but it requires a lot of time and effort. Furthermore, progress management during the presentation, answering questions, and real-time feedback based on the user's emotional state are not yet fully supported. To address these issues, a system is needed that allows users to deliver high-quality presentations with less effort.

[0792] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that automatically generates presentation materials based on the input information using a generative AI model, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation, an emotion engine means that analyzes the user's facial expressions and tone of voice during the presentation and recognizes their emotions, a means that provides real-time feedback according to the presentation content and progress based on the recognized emotions, and a question analysis means that analyzes questions during the presentation and generates appropriate answers. This enables a user to efficiently prepare presentation materials, manage the progress and respond to questions during the presentation, and understand their own emotional state, thereby delivering a more effective presentation.

[0793] The "information input means" is a means for a user to input detailed information about a presentation through a terminal.

[0794] A "generative AI model" is an artificial intelligence algorithm used to automatically generate presentation materials based on input information.

[0795] "Material generation means" refers to a means for automatically generating presentation materials based on input information using a generative AI model.

[0796] "Evaluation measures" are measures to check the design and expression of the created presentation materials and ensure their appropriateness.

[0797] The "display means" is a means for visually displaying the checked presentation materials to the user.

[0798] The "avatar generation means" is a means for generating an avatar in a virtual environment that supports the user in practicing a presentation in advance and simulates the actual person to whom the presentation will be given.

[0799] The "feedback generating means" is a means for analyzing the results of the pre-practice and providing feedback to the user.

[0800] A "progress management means" is a means for monitoring the progress of a presentation in real time and providing alerts and notifications during the presentation.

[0801] The "emotion engine means" is a means for analyzing the facial expressions and tone of voice of the user during the presentation and recognizing their emotions.

[0802] The "question analysis means" is a means for analyzing questions received during a presentation, understanding their intent, and generating appropriate answers.

[0803] This invention relates to an AI presentation support system that combines an emotion engine, and is a multifunctional system that supports everything from presentation preparation to the actual presentation.

[0804] Embodiment of information input means

[0805] The user inputs context information about the presentation from the terminal. This context information includes the audience, purpose, presentation time, audience knowledge level, industry, etc. For example, the user might input, "I need a presentation document on a new product sales strategy for senior management."

[0806] The terminal transmits the information input by the user to the server, where it is stored in a database and used for subsequent processing.

[0807] Embodiment of material generating means

[0808] The server automatically generates presentation materials using a generative AI model (e.g., OpenAI's GPT-4) based on information extracted from the database. The generative AI model analyzes the situational information entered by the user and generates slides with optimal design and content.

[0809] The terminal visually displays the presentation materials sent from the server to the user, who can then review the materials and make any necessary corrections.

[0810] Embodiments of the evaluation means

[0811] The server uses internal algorithms to evaluate the generated presentation, including slide color and font matching, and content consistency.

[0812] Display Means Embodiment

[0813] The terminal visually displays presentation materials that have passed the evaluation, allowing the user to review the materials in detail and make final adjustments.

[0814] Embodiment of Avatar Generation Means

[0815] The server generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior management," the avatar will reflect the appearance and tone of a senior manager.

[0816] The device will then begin practicing a presentation in a virtual environment using an avatar. The user can then give a presentation to the avatar in the virtual environment, providing a rehearsal for the actual presentation.

[0817] Embodiment of Emotion Engine

[0818] The server has an emotion engine that uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time. The emotion engine analyzes the user's emotional state during a presentation. For example, if the user is nervous, it will detect that state.

[0819] Based on the results of emotion analysis, the device provides real-time feedback to the user, such as "take a deep breath" or "relax," allowing the user to proceed with the presentation while being aware of their own emotional state.

[0820] Feedback Generation Means Embodiment

[0821] The server analyzes the content of the user's presentation and generates feedback that points out the consistency between the materials and the comments and any unclear parts. Based on this feedback, the user can revise and improve the content of their presentation.

[0822] The terminal visually displays the feedback sent from the server to the user.

[0823] Embodiment of progress management means

[0824] The device monitors the progress of the presentation in real time, displaying notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[0825] Embodiment of the Question Analysis Means

[0826] The server analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to the question, "What is the market size for this new product?", it analyzes market size data and generates the answer, "The market size for this new product is approximately 50 billion yen."

[0827] The terminal visually displays the generated answer to the user, who can refer to the answer to make an appropriate response to the question.

[0828] Prompt Sentence Examples

[0829] "I'd like to create a presentation on a sales strategy for a new product. The presentation is for senior management, and I have 30 minutes to give. The audience has a high level of knowledge and is in the IT industry. I'd like feedback on the design, content, avatar generation, and sentiment analysis of the presentation."

[0830] As described above, this system provides consistent support from presentation preparation to the actual presentation, and offers multifunctional assistance to help users give effective presentations.

[0831] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0832] Step 1:

[0833] User: Enters details of the presentation (such as audience, purpose, presentation time, audience knowledge level, industry, etc.) into an input form on the device. For example, the input might include, "I need a presentation document for a new product sales strategy for senior management."

[0834] Input: Presentation details

[0835] Output: Information data prepared for transmission by the terminal.

[0836] Step 2:

[0837] Terminal: Sends the information entered by the user to the server. Using the API, the information is sent in JSON format, for example.

[0838] Input: Details entered by the user

[0839] Output: Information data sent to the server

[0840] Step 3:

[0841] Server: Receives the transmitted information and stores it in a database, such as an SQL database.

[0842] Input: Information data sent from the terminal

[0843] Output: Information stored in the database

[0844] Step 4:

[0845] Server: Extracts information from the database and automatically generates presentation materials using a generative AI model (e.g., GPT-4). Optimizes the design and content.

[0846] Input: Information stored in a database

[0847] Output: Auto-generated presentation materials

[0848] Step 5:

[0849] Terminal: Visually displays the data sent from the server to the user, allowing the user to check and modify it.

[0850] Input: Presentation materials sent from the server

[0851] Output: Visual presentation material

[0852] Step 6:

[0853] Server: Evaluates the generated materials using internal algorithms, checking for color and font consistency of slides, content consistency, etc.

[0854] Input: Auto-generated presentation material

[0855] Output: Evaluation result

[0856] Step 7:

[0857] Terminal: Visually displays materials that have passed evaluation to users, allowing them to see the overall picture and specific content of the materials.

[0858] Input: Presentation materials that have passed evaluation

[0859] Output: Presentation materials for final review

[0860] Step 8:

[0861] Server: Generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior executives," the avatar will be configured to look and speak like a senior executive.

[0862] Input: Finalized presentation materials and contact information

[0863] Output: Simulated avatar

[0864] Step 9:

[0865] Device: Start practicing your presentation in a virtual environment using an avatar. Users practice in a real environment.

[0866] Input: Generated avatar and presentation materials

[0867] Output: A practice session in a virtual environment

[0868] Step 10:

[0869] Server: The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotions. For example, it can detect if the user is nervous.

[0870] Input: Camera video and microphone audio data

[0871] Output: Recognized emotion information

[0872] Step 11:

[0873] On the device: Based on the recognized emotion, the device provides the user with real-time suggested feedback, such as an alert to "take a deep breath" or "relax."

[0874] Input: Recognized emotion information

[0875] Output: Real-time feedback

[0876] Step 12:

[0877] Server: Analyzes the presentation content and generates feedback pointing out the consistency between the materials and statements and any unclear parts.

[0878] Input: Presentation materials and user comments

[0879] Output: Feedback for content correction and improvement

[0880] Step 13:

[0881] Device: Displays the feedback sent from the server and informs the user of the results of their practice. The user can then revise and improve their presentation based on the feedback.

[0882] Input: Feedback sent by the server

[0883] Output: Feedback showing corrections and improvements

[0884] Step 14:

[0885] Device: Monitors progress in real time during a live presentation and provides alerts and notifications, such as "You're running out of time on this slide" or "You have 10 minutes left."

[0886] Input: Progress information during the presentation

[0887] Output: Alerts and notifications

[0888] Step 15:

[0889] Server: Analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to a question like, "What is the market size for this new product?", the server analyzes market size data and generates an appropriate answer.

[0890] Input: Question data during presentation

[0891] Output: Answers generated through natural language processing

[0892] Step 16:

[0893] Terminal: The generated answer is visually displayed to the user, who can use it to respond appropriately to the question.

[0894] Input: Response data sent from the server

[0895] Output: A visual representation of the answer

[0896] (Application example 2)

[0897] 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."

[0898] In today's virtual stores, staff lack the tools to efficiently and effectively explain the features and usage of products. It is also difficult for them to quickly and accurately respond to customer questions during presentations. Furthermore, there is a need for a system that provides pre-practice and feedback to help staff improve their presentation skills.

[0899] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that simulates the actual presentation audience by practicing the presentation in a virtual environment, a feedback generation means that analyzes the results of the practice and generates feedback, an emotion analysis means that analyzes the user's facial expressions and tone of voice during the presentation and provides emotion-based feedback, a question analysis means that analyzes questions from customers and generates appropriate answers when staff members present product features and specifications in a virtual store, and a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation. This improves the quality of presentations and strengthens customer responsiveness.

[0900] The "information input means" is a means by which the user inputs information necessary for the presentation and transmits it to the server.

[0901] An "AI model" is an algorithmic model that uses artificial intelligence technology to automate specific tasks.

[0902] "Material generation means" refers to a means for automatically generating presentation materials using an AI model based on input information.

[0903] "Evaluation measures" are measures to check the generated presentation materials and ensure appropriate design and expression.

[0904] The "display means" is a means for visually displaying the evaluated presentation materials to the user.

[0905] The "avatar generation means" is a means for practicing a presentation in advance in a virtual environment and generating an avatar that simulates the actual person being presented to.

[0906] The "feedback generation means" is a means for analyzing the results of the preliminary practice and generating feedback that provides the user with areas for improvement and advice.

[0907] An "emotion analysis means" is a means of analyzing a user's facial expressions and tone of voice when giving a presentation and providing feedback based on their emotions.

[0908] The "question analysis means" is a means for analyzing questions from customers and generating appropriate answers when staff members present product features and specifications in a virtual store.

[0909] A "progress management means" is a means for monitoring the progress of a presentation in real time during the presentation and providing alerts and notifications.

[0910] This invention relates to an AI presentation support system for efficiently and effectively presenting products in a virtual store.

[0911] First, the user uses the information input means to input basic information into the terminal, such as the person to whom the presentation will be given, the features of the product, the purpose of the presentation, etc. This information is sent to the server and stored in a database.

[0912] Next, a material generation tool using an AI model automatically generates presentation materials based on the input information, such as product explanation slides and videos showing how to use the product.

[0913] The server then uses evaluation tools to check the generated materials to ensure that the design and presentation are appropriate. This evaluation process ensures the quality of the presentation.

[0914] The materials that pass the evaluation are displayed to the user through the display means, so that the user can check the contents of the generated materials.

[0915] Next, the avatar generation means is activated to allow a user to practice the presentation in advance in the virtual environment. This means generates an avatar according to the position and knowledge level of the person to whom the user is going to give the presentation, and realizes a presentation simulation in the virtual environment.

[0916] Furthermore, the results of the pre-practice are analyzed, and the feedback generation means provides the user with suggestions and advice on how to improve, allowing the user to improve their presentation skills.

[0917] When giving a presentation, the emotion analyzer analyzes the user's facial expressions and tone of voice and provides real-time feedback based on their emotions. For example, if they are nervous, the device will display instructions such as "Relax."

[0918] During presentations in the virtual store, the question analysis tool analyzes customer questions in real time and generates appropriate answers, allowing staff to provide accurate answers immediately.

[0919] During the live presentation, the progress management tool monitors the progress of the presentation in real time and provides alerts and notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[0920] To realize this invention, a smartphone equipped with a camera and microphone, smart glasses, or a head-mounted display (e.g., Oculus Quest 2) is used. PyTorch or TensorFlow is used as the AI ​​model, and GPT-4 (OpenAI) is used for natural language processing. Microsoft Azure Cognitive Services or Google Cloud Machine Learning API is used as the sentiment analysis engine, and WebRTC is used for real-time communication.

[0921] As a concrete example, consider a scenario in which a new smartphone product is explained in a virtual store. The user inputs the smartphone's features (e.g., high-resolution camera, voice recognition function), and the material generation means generates presentation materials based on that information. The generated materials are checked by the evaluation means and displayed to the user through the display means. During practice, the avatar generation means generates an avatar that resembles the customer, and the presentation is simulated in the virtual environment.

[0922] An example of a prompt sentence that may be used is:

[0923] ---

[0924] Prompt: "Prompt to generate a presentation document that explains the product's features."

[0925] Please create a presentation about a new product equipped with a high-resolution camera and voice recognition function. The purpose of the presentation is to increase customer desire to purchase.

[0926] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0927] Step 1:

[0928] The user uses a terminal to input information necessary for the presentation. The input information includes the attributes of the person being presented to (e.g., job title and knowledge level), product features, and the purpose of the presentation. This input data is sent from the terminal to the server.

[0929] Input: Information such as the attributes of the person you are presenting to, product features, and the purpose of the presentation

[0930] Output: User-entered data sent to the server

[0931] Step 2:

[0932] The server stores the received input data in a database, which is used for subsequent processing.

[0933] Input: User-entered data

[0934] Output: Data stored in the database

[0935] Step 3:

[0936] The server automatically generates presentation materials using an AI model based on the stored user input data. Specifically, the AI ​​model generates presentation materials (slides, videos, text, etc.) that include product features and usage instructions based on prompt statements.

[0937] Input: User-entered data

[0938] Output: Generated presentation materials

[0939] Step 4:

[0940] The server evaluates the generated presentation materials for appropriateness of design and expression, checking the color and font consistency of the slides, and content consistency.

[0941] Input: Generated presentation material

[0942] Output: Evaluated presentation materials

[0943] Step 5:

[0944] The terminal displays the evaluated presentation materials to the user, allowing the user to review the generated materials and identify any corrections needed.

[0945] Input: Evaluated presentation materials

[0946] Output: Presentation materials displayed on the device

[0947] Step 6:

[0948] To allow users to practice their presentation in a virtual environment, the server uses an avatar generation unit to generate an avatar that simulates the person they are presenting to. This avatar is customized according to the attributes of the person they are presenting to (e.g., their job title or knowledge level) specified by the user.

[0949] Input: User input data, presentation partner attributes

[0950] Output: The generated avatar

[0951] Step 7:

[0952] The user performs a pre-practice in the virtual environment using the generated avatar. The server analyzes the results of the pre-practice and generates feedback using a feedback generation means. The feedback provides the user with suggestions for improvement and advice.

[0953] Input: Pre-practice result data

[0954] Output: Generated feedback

[0955] Step 8:

[0956] During the presentation, the server uses emotion analysis to analyze the user's facial expressions and tone of voice, and provides real-time feedback based on their emotions. For example, if the user is nervous, it will display instructions such as "Relax."

[0957] Input: User's facial expression data, voice tone data

[0958] Output: Real-time feedback

[0959] Step 9:

[0960] During the presentation in the virtual store, the server uses a question analysis means to analyze questions from customers in real time and generates appropriate answers using natural language processing, allowing the user to provide accurate answers immediately.

[0961] Input: Question data from customers

[0962] Output: The generated answer

[0963] Step 10:

[0964] During the live presentation, the server uses the progress management means to monitor the progress of the presentation in real time and provide alerts and notifications to the user, such as "You're running out of time on this slide" or "You have 10 minutes left."

[0965] Input: Presentation progress data

[0966] Output: Alerts, notifications

[0967] 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.

[0968] 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.

[0969] 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.

[0970] [Third embodiment]

[0971] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0972] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0973] 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).

[0974] 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.

[0975] 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.

[0976] 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).

[0977] 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.

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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."

[0983] An embodiment of the AI ​​presentation support system will be described below.

[0984] Information input method

[0985] User: Enters information about the presentation (presentee, purpose, presentation time, audience level, industry, etc.) from the device. This information is the basic information required to generate presentation materials and configure avatars.

[0986] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[0987] Material generation means

[0988] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[0989] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[0990] Evaluation methods

[0991] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[0992] Display means

[0993] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[0994] Avatar generation method

[0995] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[0996] Device: The user begins pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to an audience.

[0997] Feedback Generation Method

[0998] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[0999] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[1000] Progress management means

[1001] Device: Monitors the progress of the presentation in real time and provides notifications and alerts to the user, including checking the progress of the presentation and time management.

[1002] Example: Displaying a notification such as "10 minutes remaining" helps users manage their time better.

[1003] Question analysis means

[1004] Server: Analyzes questions asked by users during a presentation in real time, and determines the intent of the question and recommends appropriate answers.

[1005] On the device: Suggested answers are presented to the user, allowing the user to proceed with the Q&A while referring to the suggested answers.

[1006] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data, generates an appropriate answer such as, "The market size of this new product is approximately 50 billion yen," and displays it on the device.

[1007] The processing flow will be explained below.

[1008] Program processing steps

[1009] Information input and document generation

[1010] Step 1: Enter your information

[1011] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[1012] Terminal: Sends the entered information to the server, where it is stored in a database.

[1013] Step 2: Automatically generate materials

[1014] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[1015] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[1016] Step 3: Document evaluation

[1017] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[1018] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[1019] Step 4: Display the material

[1020] Terminal: Visually displays to the user the base material that has passed the check.

[1021] User: Review the displayed material and make corrections as necessary.

[1022] Pre-practice and feedback

[1023] Step 5: Avatar Creation and Practice

[1024] Server: Generates an avatar simulating the person giving the presentation based on the materials created by the user.

[1025] Terminal: The user begins a pre-practice session in the virtual environment and makes a presentation facing an avatar.

[1026] Step 6: Practice analysis and feedback generation

[1027] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[1028] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[1029] Step 7: Provide feedback

[1030] Server: Generates feedback based on the analysis results, summarizing specific improvements and enhancements.

[1031] Terminal: Display feedback to the user.

[1032] Users: Review feedback and improve their presentation.

[1033] Follow-up on the actual event

[1034] Step 8: Progress Management

[1035] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[1036] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[1037] Step 9: Question Analysis

[1038] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[1039] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[1040] Step 10: Answer Generation

[1041] Server: Understands the intent of the question and generates an appropriate answer.

[1042] On the device: Present the suggested answers to the user.

[1043] User: Use the suggested answers to guide your Q&A.

[1044] Example 1

[1045] 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."

[1046] In today's business environment, precise and effective presentations are required, but preparing them takes time and effort. Furthermore, it is difficult to provide advance feedback, rehearse, and respond quickly and appropriately to questions and answers to improve the quality of presentations. Furthermore, it is not easy to evaluate and improve the appropriateness of presentation design and content.

[1047] 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.

[1048] In this invention, the server includes an information input means, a material generation means that uses a generative AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time during the actual presentation and provides alerts and notifications, and a question analysis means that analyzes questions asked during the presentation, understands the intent of the questions, and generates appropriate answers. This enables the entire process from presentation preparation to the actual presentation to be carried out efficiently, enabling high-quality presentations.

[1049] The "information input means" is a device or system that allows a user to input presentation status information into a terminal and transmit it to a server.

[1050] A "material generation means" is a device or system that automatically generates presentation materials based on input information using a generative AI model.

[1051] An "evaluation tool" is a device or system that checks the generated presentation materials to ensure appropriate design and presentation.

[1052] The "display means" is a device or system that allows the user to visually check the generated presentation materials and their evaluation results.

[1053] The "avatar generation means" is a device or system that generates an avatar that simulates an actual presentation partner in a virtual environment based on information input by the user, for use in practicing the presentation in advance.

[1054] The "feedback generation means" is a device or system that analyzes the results of the preliminary practice and generates feedback that provides the user with suggestions and points for improvement.

[1055] A "progress management means" is a device or system that monitors the progress of a presentation in real time and provides alerts and notifications to the user.

[1056] The "question analysis means" is a device or system that analyzes questions asked by users during a presentation, understands the intent of the questions, and generates appropriate answers.

[1057] A "generative AI model" is an artificial intelligence model used to automatically generate presentation materials based on input information.

[1058] A "prompt" is text information entered by the user to specify the requirements and content of the presentation materials to the generative AI model.

[1059] An embodiment of a presentation support system according to the present invention will be described below. This system automates and supports a series of processes required when a user prepares and gives a presentation.

[1060] System Configuration

[1061] This system includes a device operated by the user, a server that processes data, and an AI model. The main hardware components are PCs, smartphones, and tablets, while the server is a cloud server with advanced computing capabilities. The main software used is a generative AI model such as OpenAI's GPT-4.

[1062] Information input method

[1063] User: Enter presentation information (e.g., audience, purpose, presentation time, audience knowledge level, industry, etc.) from the terminal.

[1064] Terminal: The information entered by the user is sent to the server, where it is stored in a database and used to generate presentation materials and configure avatars.

[1065] As a specific example, a user may input, "I am giving a presentation to engineers and would like materials that emphasize the technical details of the product."

[1066] Material generation means

[1067] Server: Based on the received information, a generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[1068] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[1069] Example prompt: "Create a presentation to highlight the technical details of a new product for a customer presentation. You will be a product specialist and your audience will be engineers. Your presentation will last 20 minutes."

[1070] Evaluation methods

[1071] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[1072] Terminal: The base materials that have passed the check are visually displayed to the user, allowing the user to check the overall picture and specific content of the materials.

[1073] Avatar generation method

[1074] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[1075] Device: Starts pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to a person. For example, if the person they are presenting to is set to be a "CEO," the avatar will act like the CEO.

[1076] Feedback Generation Method

[1077] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[1078] Device: Display feedback to let the user know the results of the practice. For example, "The content of slide 5 is difficult to understand."

[1079] Progress management means

[1080] Terminal: Monitors the progress of a presentation in real time and provides notifications and alerts to the user. This includes checking the progress of the presentation and time management. For example, during a presentation, a notification such as "10 minutes remaining" is displayed.

[1081] Question analysis means

[1082] Server: Analyzes questions asked by users during a presentation in real time, and recommends the intent of the question and appropriate answers. For example, if a user asks, "What is the market size of this new product?", the server uses a generative AI model to analyze market size data and generate an appropriate answer, such as, "The market size of this new product is approximately 50 billion yen."

[1083] Terminal: Suggested answers are presented to the user, who can then use the suggested answers to proceed with the Q&A.

[1084] By using the above means, the user can efficiently carry out the entire process from preparation to the actual presentation, thereby enabling a high-quality presentation.

[1085] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1086] Step 1:

[1087] User: Enters information about the presentation situation from the device. The user enters information such as the audience, purpose, presentation time, audience knowledge level, industry, etc. into the input form on the device. This is input data.

[1088] Terminal: The terminal sends the entered information to the server, which saves it in a database. For example, a user might enter "Presentation recipient: engineers, Purpose: new product introduction, Presentation time: 20 minutes." The server receives and saves the entered data.

[1089] Step 2:

[1090] Server: Based on the received information, retrieves user information from a database and inputs this information into a generative AI model (e.g., OpenAI GPT-4). The input data includes information such as the recipient's occupation and the purpose of the presentation.

[1091] Server: The AI ​​model analyzes the information and automatically generates presentation materials. The generated slides are the output data. For example, the AI ​​model automatically generates slides that highlight the technical features of a new product.

[1092] Terminal: The terminal displays the generated slides to the user.

[1093] Step 3:

[1094] Server: Passes the generated slides to the evaluation algorithm. The input data is the generated slides.

[1095] Server: The algorithm checks the consistency of colors and fonts on the slides, as well as the appropriateness of the content. The evaluation results are output data. For example, it detects problems such as "color mismatch" or "font inconsistency" on the slides.

[1096] Terminal: The evaluation results are displayed to the user, who can then modify the slides.

[1097] Step 4:

[1098] Server: Retrieves user and slide information from the database and inputs it into the avatar generation process. The input data is the presentation recipient's information and the slide content.

[1099] Server: Uses an AI model to generate an avatar that simulates the person you are presenting to. The generated avatar is the output data. For example, the server generates an avatar of a "CEO."

[1100] Terminal: The avatar is displayed to the user, and the user begins practicing in the virtual environment. The user uses the avatar to rehearse the presentation in front of a real audience.

[1101] Step 5:

[1102] Server: Records the user's presentation in a virtual environment. The input data is the presentation's audio and movements.

[1103] Server: The recorded presentation content is passed to an analysis algorithm. The algorithm analyzes the content and generates feedback. This is the output data. For example, it generates specific feedback such as "The content of slide 5 is difficult to understand."

[1104] Device: Provides feedback to the user, who can then use it to refine their presentation.

[1105] Step 6:

[1106] Terminal: Monitors the progress of the presentation in real time. Input data is the progress of the presentation.

[1107] Terminal: Checks the progress based on the set time and provides notifications and alerts to the user. This is the output data. For example, it sends a notification to the user saying "10 minutes left."

[1108] Step 7:

[1109] Terminal: The terminal converts questions received from users during a presentation into text using speech recognition. The input data is the voice of the question.

[1110] Terminal: Sends a textual question to the server.

[1111] Server: Analyzes the question and generates an appropriate answer. The input data is the textual question, and the output data is the recommended answer. For example, in response to the question, "What is the market size for this new product?", the server responds, "The market size is approximately 50 billion yen."

[1112] Terminal: Presents suggested answers to the user, allowing them to proceed with the Q&A session.

[1113] (Application example 1)

[1114] 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."

[1115] Conventional presentation and educational support systems face challenges in ensuring the efficiency and effectiveness of specific work instructions and educational training. In particular, it is difficult to effectively learn and practice in a virtual environment when it comes to improving staff skills or providing work instructions in physical stores. Furthermore, they lack functionality for real-time progress management and question analysis. They also lack the quality of feedback and mechanisms for reflecting learning results.

[1116] 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.

[1117] In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, a display means that displays the checked materials, a virtual character generation means that conducts pre-practice in a virtual environment and simulates an actual interlocutor, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors progress in real time and provides alerts and notifications, a question analysis means that analyzes questions and generates appropriate answers, a work instruction support means that conducts simulations to generate and present materials for work instructions and training, and a skill improvement support means that supports skill improvement based on feedback. This significantly improves the efficiency and effectiveness of work instructions and training, and enables practice in a virtual environment, real-time progress management, and question analysis.

[1118] "Information input means" refers to a device or method for inputting user-specified information into the system.

[1119] An "AI model" is a mathematical or algorithmic model that uses artificial intelligence to accomplish a specific task.

[1120] The "material generation means" is a function or device that automatically creates materials based on input information.

[1121] "Evaluation means" refers to a function or device that checks and evaluates the quality and appropriateness of the generated materials.

[1122] "Display means" means a device or method for visually presenting the checked material to a user.

[1123] "Virtual character generation means" refers to a function or device that generates a character for a user to practice or simulate in a virtual environment.

[1124] The "feedback generation means" is a function or device that analyzes the results of the preliminary practice and generates feedback to be provided to the user based on the results.

[1125] A "progress management means" is a function or device that monitors progress in real time and provides alerts or notifications as needed.

[1126] The "question analysis means" is a function or device that analyzes questions asked by users during a presentation and generates appropriate answers.

[1127] The "work instruction support means" is a function or device that performs simulations to generate and present work instructions and training materials.

[1128] A "skill improvement support means" is a function or device that supports the user in improving their skills based on feedback.

[1129] The system of the present invention includes an information input means, a material generation means, an evaluation means, a display means, a virtual character generation means, a feedback generation means, a progress management means, a question analysis means, a work instruction support means, and a skill improvement support means.

[1130] Information input method

[1131] The user inputs the information necessary for training and work instructions from the terminal. This information includes the purpose of the training, the target person's job title, knowledge level, work experience, etc. This information is sent to the server and stored in a database.

[1132] Material generation means

[1133] The server automatically generates training materials using a generative AI model based on the received information. The generated materials are then optimized for design and content by the AI ​​model, using machine learning libraries such as PyTorch and TensorFlow.

[1134] Evaluation methods

[1135] The server evaluates the quality of the design and content of the generated materials. Evaluation algorithms check the color and font consistency of the generated materials, as well as the consistency of the content. For example, they use evaluation tools such as scikit-learn.

[1136] Display means

[1137] The terminal visually displays the assessed material, and the user can review it and make corrections as needed using a device such as a tablet or smart glasses, for example, an iPad or Google Glass.

[1138] Virtual character generation means

[1139] The server generates a virtual training instructor (virtual character) based on the input information. This virtual character reflects the position and knowledge level according to the information input by the user. For example, Unity or Blender is used.

[1140] Feedback Generation Method

[1141] The server analyzes the results of the pre-practice and generates feedback to provide to the user. The analyzed data is used to evaluate the consistency and comprehension of the user's presentation. The feedback results contribute to improving the user's skills.

[1142] Progress management means

[1143] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications as the presentation or training progresses, helping users manage their time allocation.

[1144] Question analysis means

[1145] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generate the most appropriate answer.

[1146] Work instruction support means

[1147] The server runs simulations, generates work instructions and training materials, and displays them on the terminals, providing an environment where staff can effectively learn about new work procedures and products.

[1148] Skill improvement support measures

[1149] The server uses the generated feedback to help users improve their skills, particularly by providing specific advice to strengthen individual staff weaknesses and improve overall performance.

[1150] Specific examples

[1151] For example, when training staff with less than one year of experience on how to sell a new smartphone product, the following prompt might be used:

[1152] "Create training materials for staff with less than one year of experience about the features of a newly released smartphone and how to sell it. Then, simulate the training using virtual characters."

[1153] This allows store staff to effectively learn about new products and sales techniques and apply them to their actual work.

[1154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1155] Step 1: Enter your information

[1156] The user inputs information about the training and work instructions (objectives, target person's job title, knowledge level, work experience, etc.) from the terminal. This information is sent to the server by the terminal and stored in the system's database. The input information is used in each subsequent processing step.

[1157] Step 2: Generate materials

[1158] The server automatically generates training materials using a generative AI model based on the received information. Specifically, it uses PyTorch and TensorFlow to create materials with appropriate design and content. The generated materials are stored in a database and sent to the next evaluation step.

[1159] Step 3: Document evaluation

[1160] The server evaluates the quality of the design and content of the generated materials, using evaluation algorithms (e.g., scikit-learn) to check color and font matching, content consistency, etc. The evaluation results are stored in a database, and the user is notified if any corrections are required.

[1161] Step 4: Display the material

[1162] The terminal visually displays the assessed material to the user, who can then review it using a device such as a tablet or smart glasses, such as an iPad or Google Glass, and provide corrections or feedback as needed.

[1163] Step 5: Virtual Character Generation

[1164] The server generates a virtual training person (virtual character) based on the input information. Using tools such as Unity or Blender, a character that reflects the information input by the user is created. The generated virtual character is saved within the system and used in the next simulation step.

[1165] Step 6: Run the simulation

[1166] The user simulates training using a virtual character. The virtual environment is displayed on the device, and the user receives instructions and feedback while simulating real-world tasks. The results of this step are sent to the server for analysis.

[1167] Step 7: Feedback Generation

[1168] The server analyzes the simulation results and generates feedback for the user. The analysis uses algorithms that evaluate the consistency and comprehension of the user's presentation. The generated feedback is displayed on the device and can be used by the user to improve their skills.

[1169] Step 8: Progress Management

[1170] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications during presentations and training sessions, and helping users manage their time allocation, thereby enabling effective learning.

[1171] Step 9: Question Analysis

[1172] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generates the most appropriate answer. The generated answer is displayed on the device for the user's reference.

[1173] Step 10: Work instructions and skill development support

[1174] The server runs simulations, generates work instructions and training materials, and displays them on the terminal. It also supports users in improving their skills based on the generated feedback. This allows staff to learn and improve their skills effectively.

[1175] 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.

[1176] Below, we will explain an embodiment of an AI presentation support system that combines an emotion engine.

[1177] Information input method

[1178] Process Overview

[1179] User: Enters information about the presentation (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device. This information is used as basic information required to generate presentation materials and configure avatars.

[1180] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[1181] Material generation means

[1182] Process Overview

[1183] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[1184] Terminal: The initially generated base materials are displayed to the user in a format that is easy for the user to review.

[1185] Specific examples

[1186] Example: When a user inputs information for creating a presentation about "sales strategy for a new product," the server automatically generates relevant charts and text based on that information and compiles them into slides.

[1187] Evaluation methods

[1188] Process Overview

[1189] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[1190] Display means

[1191] Process Overview

[1192] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[1193] Avatar generation method

[1194] Process Overview

[1195] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[1196] Device: Start practicing in a virtual environment using an avatar, and practice as if you were giving a presentation to a user.

[1197] Specific examples

[1198] Example: If a user specifies "Presentation for Senior Executives," the avatar will reflect the appearance, tone, and question patterns of a senior executive.

[1199] Emotion Engine

[1200] Process Overview

[1201] Server: Contains an emotion engine that recognizes emotions by analyzing the user's facial expressions and tone of voice. The emotion engine uses the camera and microphone to analyze the user's real-time reactions.

[1202] Device: Based on the recognized emotions, feedback is provided in real time according to the content and progress of the presentation, allowing users to proceed with the presentation while being aware of their own emotional state.

[1203] Specific examples

[1204] Example: If a user is nervous while practicing a presentation, the emotion engine can detect this state and display alerts such as "take a deep breath" or "relax."

[1205] Feedback Generation Method

[1206] Process Overview

[1207] Server: Analyzes the user's presentation, identifies consistency between the materials and statements, points out any unclear parts, and generates feedback.

[1208] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[1209] Progress management means

[1210] Process Overview

[1211] Device: Monitors progress in real time during the presentation and provides notifications and alerts to users. Progress management includes timekeeping functions.

[1212] Specific examples

[1213] For example, display notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[1214] Question analysis means

[1215] Process Overview

[1216] Server: Analyzes questions received during the presentation and performs natural language processing (NLP) to understand their intent. It performs text analysis of the questions and extracts related keywords and context.

[1217] Device: Understands the intent of the question, generates an appropriate answer, and presents it to the user.

[1218] Specific examples

[1219] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data and answers, "The market size of this new product is approximately 50 billion yen."

[1220] In this way, the AI ​​presentation support system that combines an emotion engine is multifunctional, providing consistent support from presentation preparation to the actual presentation, and is equipped with all the elements necessary to deliver an effective presentation.

[1221] The processing flow will be explained below.

[1222] Program processing steps

[1223] Information input and document generation

[1224] Step 1: Enter your information

[1225] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[1226] Terminal: Sends the entered information to the server, where it is stored in a database.

[1227] Step 2: Automatically generate materials

[1228] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[1229] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[1230] Step 3: Document evaluation

[1231] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[1232] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[1233] Step 4: Display the material

[1234] Terminal: Visually displays to the user the base material that has passed the check.

[1235] User: Review the displayed material and make corrections as necessary.

[1236] Pre-practice and feedback

[1237] Step 5: Avatar Creation and Practice

[1238] Server: Generates an avatar that simulates the person giving the presentation based on the materials created by the user.

[1239] Terminal: The user begins a pre-practice session in the virtual environment and gives a presentation facing the avatar.

[1240] Specific behavior: Simulates avatar reactions and questions to provide users with an interactive experience.

[1241] Step 6: Practice analysis and feedback generation

[1242] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[1243] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[1244] Step 7: Emotion Engine Analysis

[1245] Server: Uses a camera and microphone to analyze the user's facial expressions and voice in real time to recognize emotions.

[1246] Specific behavior: Analyzes the user's facial expressions and tone of voice in real time to identify emotions such as tension or impatience.

[1247] Step 8: Provide feedback

[1248] Server: Generates feedback based on the analysis, including specific recommendations for improvement and emotional responses.

[1249] Terminal: Display feedback to the user.

[1250] User: Review the feedback and improve your presentation and manage your emotions.

[1251] Follow-up on the actual event

[1252] Step 9: Progress Management

[1253] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[1254] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[1255] Step 10: Question Analysis

[1256] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[1257] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[1258] Step 11: Answer Generation

[1259] Server: Understands the intent of the question and generates an appropriate answer.

[1260] On the device: Present the suggested answers to the user.

[1261] User: Use the suggested answers to guide your Q&A.

[1262] In this way, the AI ​​presentation support system, which combines an emotion engine, supports users at every step, from inputting information to creating materials, practicing, providing feedback, and following up on the actual presentation, providing effective presentations.

[1263] Example 2

[1264] 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."

[1265] In modern presentations, efficient and effective preparation of materials is essential, but it requires a lot of time and effort. Furthermore, progress management during the presentation, answering questions, and real-time feedback based on the user's emotional state are not yet fully supported. To address these issues, a system is needed that allows users to deliver high-quality presentations with less effort.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that automatically generates presentation materials based on the input information using a generative AI model, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation, an emotion engine means that analyzes the user's facial expressions and tone of voice during the presentation and recognizes their emotions, a means that provides real-time feedback according to the presentation content and progress based on the recognized emotions, and a question analysis means that analyzes questions during the presentation and generates appropriate answers. This enables a user to efficiently prepare presentation materials, manage the progress and respond to questions during the presentation, and understand their own emotional state, thereby delivering a more effective presentation.

[1267] The "information input means" is a means for a user to input detailed information about a presentation through a terminal.

[1268] A "generative AI model" is an artificial intelligence algorithm used to automatically generate presentation materials based on input information.

[1269] "Material generation means" refers to a means for automatically generating presentation materials based on input information using a generative AI model.

[1270] "Evaluation measures" are measures to check the design and expression of the created presentation materials and ensure their appropriateness.

[1271] The "display means" is a means for visually displaying the checked presentation materials to the user.

[1272] The "avatar generation means" is a means for generating an avatar in a virtual environment that supports the user in practicing a presentation in advance and simulates the actual person to whom the presentation will be given.

[1273] The "feedback generating means" is a means for analyzing the results of the pre-practice and providing feedback to the user.

[1274] A "progress management means" is a means for monitoring the progress of a presentation in real time and providing alerts and notifications during the presentation.

[1275] The "emotion engine means" is a means for analyzing the facial expressions and tone of voice of the user during the presentation and recognizing their emotions.

[1276] The "question analysis means" is a means for analyzing questions received during a presentation, understanding their intent, and generating appropriate answers.

[1277] This invention relates to an AI presentation support system that combines an emotion engine, and is a multifunctional system that supports everything from presentation preparation to the actual presentation.

[1278] Embodiment of information input means

[1279] The user inputs context information about the presentation from the terminal. This context information includes the audience, purpose, presentation time, audience knowledge level, industry, etc. For example, the user might input, "I need a presentation document on a new product sales strategy for senior management."

[1280] The terminal transmits the information input by the user to the server, where it is stored in a database and used for subsequent processing.

[1281] Embodiment of material generating means

[1282] The server automatically generates presentation materials using a generative AI model (e.g., OpenAI's GPT-4) based on information extracted from the database. The generative AI model analyzes the situational information entered by the user and generates slides with optimal design and content.

[1283] The terminal visually displays the presentation materials sent from the server to the user, who can then review the materials and make any necessary corrections.

[1284] Embodiments of the evaluation means

[1285] The server uses internal algorithms to evaluate the generated presentation, including slide color and font matching, and content consistency.

[1286] Display Means Embodiment

[1287] The terminal visually displays presentation materials that have passed the evaluation, allowing the user to review the materials in detail and make final adjustments.

[1288] Embodiment of Avatar Generation Means

[1289] The server generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior management," the avatar will reflect the appearance and tone of a senior manager.

[1290] The device will then begin practicing a presentation in a virtual environment using an avatar. The user can then give a presentation to the avatar in the virtual environment, providing a rehearsal for the actual presentation.

[1291] Embodiment of Emotion Engine

[1292] The server has an emotion engine that uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time. The emotion engine analyzes the user's emotional state during a presentation. For example, if the user is nervous, it will detect that state.

[1293] Based on the results of emotion analysis, the device provides real-time feedback to the user, such as "take a deep breath" or "relax," allowing the user to proceed with the presentation while being aware of their own emotional state.

[1294] Feedback Generation Means Embodiment

[1295] The server analyzes the content of the user's presentation and generates feedback that points out the consistency between the materials and the comments and any unclear parts. Based on this feedback, the user can revise and improve the content of their presentation.

[1296] The terminal visually displays the feedback sent from the server to the user.

[1297] Embodiment of progress management means

[1298] The device monitors the progress of the presentation in real time, displaying notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[1299] Embodiment of the Question Analysis Means

[1300] The server analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to the question, "What is the market size for this new product?", it analyzes market size data and generates the answer, "The market size for this new product is approximately 50 billion yen."

[1301] The terminal visually displays the generated answer to the user, who can refer to the answer to make an appropriate response to the question.

[1302] Prompt Sentence Examples

[1303] "I'd like to create a presentation on a sales strategy for a new product. The presentation is for senior management, and I have 30 minutes to give. The audience has a high level of knowledge and is in the IT industry. I'd like feedback on the design, content, avatar generation, and sentiment analysis of the presentation."

[1304] As described above, this system provides consistent support from presentation preparation to the actual presentation, and offers multifunctional assistance to help users give effective presentations.

[1305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1306] Step 1:

[1307] User: Enters details of the presentation (such as audience, purpose, presentation time, audience knowledge level, industry, etc.) into an input form on the device. For example, the input might include, "I need a presentation document for a new product sales strategy for senior management."

[1308] Input: Presentation details

[1309] Output: Information data prepared for transmission by the terminal.

[1310] Step 2:

[1311] Terminal: Sends the information entered by the user to the server. Using the API, the information is sent in JSON format, for example.

[1312] Input: Details entered by the user

[1313] Output: Information data sent to the server

[1314] Step 3:

[1315] Server: Receives the transmitted information and stores it in a database, such as an SQL database.

[1316] Input: Information data sent from the terminal

[1317] Output: Information stored in the database

[1318] Step 4:

[1319] Server: Extracts information from the database and automatically generates presentation materials using a generative AI model (e.g., GPT-4). Optimizes the design and content.

[1320] Input: Information stored in a database

[1321] Output: Auto-generated presentation materials

[1322] Step 5:

[1323] Terminal: Visually displays the data sent from the server to the user, allowing the user to check and modify it.

[1324] Input: Presentation materials sent from the server

[1325] Output: Visual presentation material

[1326] Step 6:

[1327] Server: Evaluates the generated materials using internal algorithms, checking for color and font consistency of slides, content consistency, etc.

[1328] Input: Auto-generated presentation material

[1329] Output: Evaluation result

[1330] Step 7:

[1331] Terminal: Visually displays materials that have passed evaluation to users, allowing them to see the overall picture and specific content of the materials.

[1332] Input: Presentation materials that have passed evaluation

[1333] Output: Presentation materials for final review

[1334] Step 8:

[1335] Server: Generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior executives," the avatar will be configured to look and speak like a senior executive.

[1336] Input: Finalized presentation materials and contact information

[1337] Output: Simulated avatar

[1338] Step 9:

[1339] Device: Start practicing your presentation in a virtual environment using an avatar. Users practice in a real environment.

[1340] Input: Generated avatar and presentation materials

[1341] Output: A practice session in a virtual environment

[1342] Step 10:

[1343] Server: The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotions. For example, it can detect if the user is nervous.

[1344] Input: Camera video and microphone audio data

[1345] Output: Recognized emotion information

[1346] Step 11:

[1347] On the device: Based on the recognized emotion, the device provides the user with real-time suggested feedback, such as an alert to "take a deep breath" or "relax."

[1348] Input: Recognized emotion information

[1349] Output: Real-time feedback

[1350] Step 12:

[1351] Server: Analyzes the presentation content and generates feedback pointing out the consistency between the materials and statements and any unclear parts.

[1352] Input: Presentation materials and user comments

[1353] Output: Feedback for content correction and improvement

[1354] Step 13:

[1355] Device: Displays the feedback sent from the server and informs the user of the results of their practice. The user can then revise and improve their presentation based on the feedback.

[1356] Input: Feedback sent by the server

[1357] Output: Feedback showing corrections and improvements

[1358] Step 14:

[1359] Device: Monitors progress in real time during a live presentation and provides alerts and notifications, such as "You're running out of time on this slide" or "You have 10 minutes left."

[1360] Input: Progress information during the presentation

[1361] Output: Alerts and notifications

[1362] Step 15:

[1363] Server: Analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to a question like, "What is the market size for this new product?", the server analyzes market size data and generates an appropriate answer.

[1364] Input: Question data during presentation

[1365] Output: Answers generated through natural language processing

[1366] Step 16:

[1367] Terminal: The generated answer is visually displayed to the user, who can use it to respond appropriately to the question.

[1368] Input: Response data sent from the server

[1369] Output: A visual representation of the answer

[1370] (Application example 2)

[1371] 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."

[1372] In today's virtual stores, staff lack the tools to efficiently and effectively explain the features and usage of products. It is also difficult for them to quickly and accurately respond to customer questions during presentations. Furthermore, there is a need for a system that provides pre-practice and feedback to help staff improve their presentation skills.

[1373] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that simulates the actual presentation audience by practicing the presentation in a virtual environment, a feedback generation means that analyzes the results of the practice and generates feedback, an emotion analysis means that analyzes the user's facial expressions and tone of voice during the presentation and provides emotion-based feedback, a question analysis means that analyzes questions from customers and generates appropriate answers when staff members present product features and specifications in a virtual store, and a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation. This improves the quality of presentations and strengthens customer responsiveness.

[1374] The "information input means" is a means by which the user inputs information necessary for the presentation and transmits it to the server.

[1375] An "AI model" is an algorithmic model that uses artificial intelligence technology to automate specific tasks.

[1376] "Material generation means" refers to a means for automatically generating presentation materials using an AI model based on input information.

[1377] "Evaluation measures" are measures to check the generated presentation materials and ensure appropriate design and expression.

[1378] The "display means" is a means for visually displaying the evaluated presentation materials to the user.

[1379] The "avatar generation means" is a means for practicing a presentation in advance in a virtual environment and generating an avatar that simulates the actual person being presented to.

[1380] The "feedback generation means" is a means for analyzing the results of the preliminary practice and generating feedback that provides the user with areas for improvement and advice.

[1381] An "emotion analysis means" is a means of analyzing a user's facial expressions and tone of voice when giving a presentation and providing feedback based on their emotions.

[1382] The "question analysis means" is a means for analyzing questions from customers and generating appropriate answers when staff members present product features and specifications in a virtual store.

[1383] A "progress management means" is a means for monitoring the progress of a presentation in real time during the presentation and providing alerts and notifications.

[1384] This invention relates to an AI presentation support system for efficiently and effectively presenting products in a virtual store.

[1385] First, the user uses the information input means to input basic information into the terminal, such as the person to whom the presentation will be given, the features of the product, the purpose of the presentation, etc. This information is sent to the server and stored in a database.

[1386] Next, a material generation tool using an AI model automatically generates presentation materials based on the input information, such as product explanation slides and videos showing how to use the product.

[1387] The server then uses evaluation tools to check the generated materials to ensure that the design and presentation are appropriate. This evaluation process ensures the quality of the presentation.

[1388] The materials that pass the evaluation are displayed to the user through the display means, so that the user can check the contents of the generated materials.

[1389] Next, the avatar generation means is activated to allow a user to practice the presentation in advance in the virtual environment. This means generates an avatar according to the position and knowledge level of the person to whom the user is going to give the presentation, and realizes a presentation simulation in the virtual environment.

[1390] Furthermore, the results of the pre-practice are analyzed, and the feedback generation means provides the user with suggestions and advice on how to improve, allowing the user to improve their presentation skills.

[1391] When giving a presentation, the emotion analyzer analyzes the user's facial expressions and tone of voice and provides real-time feedback based on their emotions. For example, if they are nervous, the device will display instructions such as "Relax."

[1392] During presentations in the virtual store, the question analysis tool analyzes customer questions in real time and generates appropriate answers, allowing staff to provide accurate answers immediately.

[1393] During the live presentation, the progress management tool monitors the progress of the presentation in real time and provides alerts and notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[1394] To realize this invention, a smartphone equipped with a camera and microphone, smart glasses, or a head-mounted display (e.g., Oculus Quest 2) is used. PyTorch or TensorFlow is used as the AI ​​model, and GPT-4 (OpenAI) is used for natural language processing. Microsoft Azure Cognitive Services or Google Cloud Machine Learning API is used as the sentiment analysis engine, and WebRTC is used for real-time communication.

[1395] As a concrete example, consider a scenario in which a new smartphone product is explained in a virtual store. The user inputs the smartphone's features (e.g., high-resolution camera, voice recognition function), and the material generation means generates presentation materials based on that information. The generated materials are checked by the evaluation means and displayed to the user through the display means. During practice, the avatar generation means generates an avatar that resembles the customer, and the presentation is simulated in the virtual environment.

[1396] An example of a prompt sentence that may be used is:

[1397] ---

[1398] Prompt: "Prompt to generate a presentation document that explains the product's features."

[1399] Please create a presentation about a new product equipped with a high-resolution camera and voice recognition function. The purpose of the presentation is to increase customer desire to purchase.

[1400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1401] Step 1:

[1402] The user uses a terminal to input information necessary for the presentation. The input information includes the attributes of the person being presented to (e.g., job title and knowledge level), product features, and the purpose of the presentation. This input data is sent from the terminal to the server.

[1403] Input: Information such as the attributes of the person you are presenting to, product features, and the purpose of the presentation

[1404] Output: User-entered data sent to the server

[1405] Step 2:

[1406] The server stores the received input data in a database, which is used for subsequent processing.

[1407] Input: User-entered data

[1408] Output: Data stored in the database

[1409] Step 3:

[1410] The server automatically generates presentation materials using an AI model based on the stored user input data. Specifically, the AI ​​model generates presentation materials (slides, videos, text, etc.) that include product features and usage instructions based on prompt statements.

[1411] Input: User-entered data

[1412] Output: Generated presentation materials

[1413] Step 4:

[1414] The server evaluates the generated presentation materials for appropriateness of design and expression, checking the color and font consistency of the slides, and content consistency.

[1415] Input: Generated presentation material

[1416] Output: Evaluated presentation materials

[1417] Step 5:

[1418] The terminal displays the evaluated presentation materials to the user, allowing the user to review the generated materials and identify any corrections needed.

[1419] Input: Evaluated presentation materials

[1420] Output: Presentation materials displayed on the device

[1421] Step 6:

[1422] To allow users to practice their presentation in a virtual environment, the server uses an avatar generation unit to generate an avatar that simulates the person they are presenting to. This avatar is customized according to the attributes of the person they are presenting to (e.g., their job title or knowledge level) specified by the user.

[1423] Input: User input data, presentation partner attributes

[1424] Output: The generated avatar

[1425] Step 7:

[1426] The user performs a pre-practice in the virtual environment using the generated avatar. The server analyzes the results of the pre-practice and generates feedback using a feedback generation means. The feedback provides the user with suggestions for improvement and advice.

[1427] Input: Pre-practice result data

[1428] Output: Generated feedback

[1429] Step 8:

[1430] During the presentation, the server uses emotion analysis to analyze the user's facial expressions and tone of voice, and provides real-time feedback based on their emotions. For example, if the user is nervous, it will display instructions such as "Relax."

[1431] Input: User's facial expression data, voice tone data

[1432] Output: Real-time feedback

[1433] Step 9:

[1434] During the presentation in the virtual store, the server uses a question analysis means to analyze questions from customers in real time and generates appropriate answers using natural language processing, allowing the user to provide accurate answers immediately.

[1435] Input: Question data from customers

[1436] Output: The generated answer

[1437] Step 10:

[1438] During the live presentation, the server uses the progress management means to monitor the progress of the presentation in real time and provide alerts and notifications to the user, such as "You're running out of time on this slide" or "You have 10 minutes left."

[1439] Input: Presentation progress data

[1440] Output: Alerts, notifications

[1441] 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.

[1442] 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.

[1443] 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.

[1444] [Fourth embodiment]

[1445] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1446] 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.

[1447] 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).

[1448] 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.

[1449] 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.

[1450] 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).

[1451] 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.

[1452] 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.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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."

[1458] An embodiment of the AI ​​presentation support system will be described below.

[1459] Information input method

[1460] User: Enters information about the presentation (presentee, purpose, presentation time, audience level, industry, etc.) from the device. This information is the basic information required to generate presentation materials and configure avatars.

[1461] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[1462] Material generation means

[1463] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[1464] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[1465] Evaluation methods

[1466] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[1467] Display means

[1468] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[1469] Avatar generation method

[1470] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[1471] Device: The user begins pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to an audience.

[1472] Feedback Generation Method

[1473] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[1474] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[1475] Progress management means

[1476] Device: Monitors the progress of the presentation in real time and provides notifications and alerts to the user, including checking the progress of the presentation and time management.

[1477] Example: Displaying a notification such as "10 minutes remaining" helps users manage their time better.

[1478] Question analysis means

[1479] Server: Analyzes questions asked by users during a presentation in real time, and determines the intent of the question and recommends appropriate answers.

[1480] On the device: Suggested answers are presented to the user, allowing the user to proceed with the Q&A while referring to the suggested answers.

[1481] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data, generates an appropriate answer such as, "The market size of this new product is approximately 50 billion yen," and displays it on the device.

[1482] The processing flow will be explained below.

[1483] Program processing steps

[1484] Information input and document generation

[1485] Step 1: Enter your information

[1486] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[1487] Terminal: Sends the entered information to the server, where it is stored in a database.

[1488] Step 2: Automatically generate materials

[1489] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[1490] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[1491] Step 3: Document evaluation

[1492] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[1493] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[1494] Step 4: Display the material

[1495] Terminal: Visually displays to the user the base material that has passed the check.

[1496] User: Review the displayed material and make corrections as necessary.

[1497] Pre-practice and feedback

[1498] Step 5: Avatar Creation and Practice

[1499] Server: Generates an avatar simulating the person giving the presentation based on the materials created by the user.

[1500] Terminal: The user begins a pre-practice session in the virtual environment and makes a presentation facing an avatar.

[1501] Step 6: Practice analysis and feedback generation

[1502] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[1503] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[1504] Step 7: Provide feedback

[1505] Server: Generates feedback based on the analysis results, summarizing specific improvements and enhancements.

[1506] Terminal: Display feedback to the user.

[1507] Users: Review feedback and improve their presentation.

[1508] Follow-up on the actual event

[1509] Step 8: Progress Management

[1510] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[1511] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[1512] Step 9: Question Analysis

[1513] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[1514] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[1515] Step 10: Answer Generation

[1516] Server: Understands the intent of the question and generates an appropriate answer.

[1517] On the device: Present the suggested answers to the user.

[1518] User: Use the suggested answers to guide your Q&A.

[1519] Example 1

[1520] 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."

[1521] In today's business environment, precise and effective presentations are required, but preparing them takes time and effort. Furthermore, it is difficult to provide advance feedback, rehearse, and respond quickly and appropriately to questions and answers to improve the quality of presentations. Furthermore, it is not easy to evaluate and improve the appropriateness of presentation design and content.

[1522] 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.

[1523] In this invention, the server includes an information input means, a material generation means that uses a generative AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time during the actual presentation and provides alerts and notifications, and a question analysis means that analyzes questions asked during the presentation, understands the intent of the questions, and generates appropriate answers. This enables the entire process from presentation preparation to the actual presentation to be carried out efficiently, enabling high-quality presentations.

[1524] The "information input means" is a device or system that allows a user to input presentation status information into a terminal and transmit it to a server.

[1525] A "material generation means" is a device or system that automatically generates presentation materials based on input information using a generative AI model.

[1526] An "evaluation tool" is a device or system that checks the generated presentation materials to ensure appropriate design and presentation.

[1527] The "display means" is a device or system that allows the user to visually check the generated presentation materials and their evaluation results.

[1528] The "avatar generation means" is a device or system that generates an avatar that simulates an actual presentation partner in a virtual environment based on information input by the user, for use in practicing the presentation in advance.

[1529] The "feedback generation means" is a device or system that analyzes the results of the preliminary practice and generates feedback that provides the user with suggestions and points for improvement.

[1530] A "progress management means" is a device or system that monitors the progress of a presentation in real time and provides alerts and notifications to the user.

[1531] The "question analysis means" is a device or system that analyzes questions asked by users during a presentation, understands the intent of the questions, and generates appropriate answers.

[1532] A "generative AI model" is an artificial intelligence model used to automatically generate presentation materials based on input information.

[1533] A "prompt" is text information entered by the user to specify the requirements and content of the presentation materials to the generative AI model.

[1534] An embodiment of a presentation support system according to the present invention will be described below. This system automates and supports a series of processes required when a user prepares and gives a presentation.

[1535] System Configuration

[1536] This system includes a device operated by the user, a server that processes data, and an AI model. The main hardware components are PCs, smartphones, and tablets, while the server is a cloud server with advanced computing capabilities. The main software used is a generative AI model such as OpenAI's GPT-4.

[1537] Information input method

[1538] User: Enter presentation information (e.g., audience, purpose, presentation time, audience knowledge level, industry, etc.) from the terminal.

[1539] Terminal: The information entered by the user is sent to the server, where it is stored in a database and used to generate presentation materials and configure avatars.

[1540] As a specific example, a user may input, "I am giving a presentation to engineers and would like materials that emphasize the technical details of the product."

[1541] Material generation means

[1542] Server: Based on the received information, a generative AI model (e.g., OpenAI's GPT-4) is used to automatically generate presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[1543] Terminal: Displays the generated base material in a format that the user can check. The user can check the displayed material and make corrections as necessary.

[1544] Example prompt: "Create a presentation to highlight the technical details of a new product for a customer presentation. You will be a product specialist and your audience will be engineers. Your presentation will last 20 minutes."

[1545] Evaluation methods

[1546] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[1547] Terminal: The base materials that have passed the check are visually displayed to the user, allowing the user to check the overall picture and specific content of the materials.

[1548] Avatar generation method

[1549] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[1550] Device: Starts pre-practice in a virtual environment using an avatar. The user practices as if they were actually giving a presentation to a person. For example, if the person they are presenting to is set to be a "CEO," the avatar will act like the CEO.

[1551] Feedback Generation Method

[1552] Server: Analyzes the content of the user's presentation, points out the consistency between the materials and the remarks, points out any unclear parts, and generates feedback.

[1553] Device: Display feedback to let the user know the results of the practice. For example, "The content of slide 5 is difficult to understand."

[1554] Progress management means

[1555] Terminal: Monitors the progress of a presentation in real time and provides notifications and alerts to the user. This includes checking the progress of the presentation and time management. For example, during a presentation, a notification such as "10 minutes remaining" is displayed.

[1556] Question analysis means

[1557] Server: Analyzes questions asked by users during a presentation in real time, and recommends the intent of the question and appropriate answers. For example, if a user asks, "What is the market size of this new product?", the server uses a generative AI model to analyze market size data and generate an appropriate answer, such as, "The market size of this new product is approximately 50 billion yen."

[1558] Terminal: Suggested answers are presented to the user, who can then use the suggested answers to proceed with the Q&A.

[1559] By using the above means, the user can efficiently carry out the entire process from preparation to the actual presentation, thereby enabling a high-quality presentation.

[1560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1561] Step 1:

[1562] User: Enters information about the presentation situation from the device. The user enters information such as the audience, purpose, presentation time, audience knowledge level, industry, etc. into the input form on the device. This is input data.

[1563] Terminal: The terminal sends the entered information to the server, which saves it in a database. For example, a user might enter "Presentation recipient: engineers, Purpose: new product introduction, Presentation time: 20 minutes." The server receives and saves the entered data.

[1564] Step 2:

[1565] Server: Based on the received information, retrieves user information from a database and inputs this information into a generative AI model (e.g., OpenAI GPT-4). The input data includes information such as the recipient's occupation and the purpose of the presentation.

[1566] Server: The AI ​​model analyzes the information and automatically generates presentation materials. The generated slides are the output data. For example, the AI ​​model automatically generates slides that highlight the technical features of a new product.

[1567] Terminal: The terminal displays the generated slides to the user.

[1568] Step 3:

[1569] Server: Passes the generated slides to the evaluation algorithm. The input data is the generated slides.

[1570] Server: The algorithm checks the consistency of colors and fonts on the slides, as well as the appropriateness of the content. The evaluation results are output data. For example, it detects problems such as "color mismatch" or "font inconsistency" on the slides.

[1571] Terminal: The evaluation results are displayed to the user, who can then modify the slides.

[1572] Step 4:

[1573] Server: Retrieves user and slide information from the database and inputs it into the avatar generation process. The input data is the presentation recipient's information and the slide content.

[1574] Server: Uses an AI model to generate an avatar that simulates the person you are presenting to. The generated avatar is the output data. For example, the server generates an avatar of a "CEO."

[1575] Terminal: The avatar is displayed to the user, and the user begins practicing in the virtual environment. The user uses the avatar to rehearse the presentation in front of a real audience.

[1576] Step 5:

[1577] Server: Records the user's presentation in a virtual environment. The input data is the presentation's audio and movements.

[1578] Server: The recorded presentation content is passed to an analysis algorithm. The algorithm analyzes the content and generates feedback. This is the output data. For example, it generates specific feedback such as "The content of slide 5 is difficult to understand."

[1579] Device: Provides feedback to the user, who can then use it to refine their presentation.

[1580] Step 6:

[1581] Terminal: Monitors the progress of the presentation in real time. Input data is the progress of the presentation.

[1582] Terminal: Checks the progress based on the set time and provides notifications and alerts to the user. This is the output data. For example, it sends a notification to the user saying "10 minutes left."

[1583] Step 7:

[1584] Terminal: The terminal converts questions received from users during a presentation into text using speech recognition. The input data is the voice of the question.

[1585] Terminal: Sends a textual question to the server.

[1586] Server: Analyzes the question and generates an appropriate answer. The input data is the textual question, and the output data is the recommended answer. For example, in response to the question, "What is the market size for this new product?", the server responds, "The market size is approximately 50 billion yen."

[1587] Terminal: Presents suggested answers to the user, allowing them to proceed with the Q&A session.

[1588] (Application example 1)

[1589] 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."

[1590] Conventional presentation and educational support systems face challenges in ensuring the efficiency and effectiveness of specific work instructions and educational training. In particular, it is difficult to effectively learn and practice in a virtual environment when it comes to improving staff skills or providing work instructions in physical stores. Furthermore, they lack functionality for real-time progress management and question analysis. They also lack the quality of feedback and mechanisms for reflecting learning results.

[1591] 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.

[1592] In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate materials based on the input information, an evaluation means that checks the generated materials and ensures appropriate design and expression, a display means that displays the checked materials, a virtual character generation means that conducts pre-practice in a virtual environment and simulates an actual interlocutor, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors progress in real time and provides alerts and notifications, a question analysis means that analyzes questions and generates appropriate answers, a work instruction support means that conducts simulations to generate and present materials for work instructions and training, and a skill improvement support means that supports skill improvement based on feedback. This significantly improves the efficiency and effectiveness of work instructions and training, and enables practice in a virtual environment, real-time progress management, and question analysis.

[1593] "Information input means" refers to a device or method for inputting user-specified information into the system.

[1594] An "AI model" is a mathematical or algorithmic model that uses artificial intelligence to accomplish a specific task.

[1595] The "material generation means" is a function or device that automatically creates materials based on input information.

[1596] "Evaluation means" refers to a function or device that checks and evaluates the quality and appropriateness of the generated materials.

[1597] "Display means" means a device or method for visually presenting the checked material to a user.

[1598] "Virtual character generation means" refers to a function or device that generates a character for a user to practice or simulate in a virtual environment.

[1599] The "feedback generation means" is a function or device that analyzes the results of the preliminary practice and generates feedback to be provided to the user based on the results.

[1600] A "progress management means" is a function or device that monitors progress in real time and provides alerts or notifications as needed.

[1601] The "question analysis means" is a function or device that analyzes questions asked by users during a presentation and generates appropriate answers.

[1602] The "work instruction support means" is a function or device that performs simulations to generate and present work instructions and training materials.

[1603] A "skill improvement support means" is a function or device that supports the user in improving their skills based on feedback.

[1604] The system of the present invention includes an information input means, a material generation means, an evaluation means, a display means, a virtual character generation means, a feedback generation means, a progress management means, a question analysis means, a work instruction support means, and a skill improvement support means.

[1605] Information input method

[1606] The user inputs the information necessary for training and work instructions from the terminal. This information includes the purpose of the training, the target person's job title, knowledge level, work experience, etc. This information is sent to the server and stored in a database.

[1607] Material generation means

[1608] The server automatically generates training materials using a generative AI model based on the received information. The generated materials are then optimized for design and content by the AI ​​model, using machine learning libraries such as PyTorch and TensorFlow.

[1609] Evaluation methods

[1610] The server evaluates the quality of the design and content of the generated materials. Evaluation algorithms check the color and font consistency of the generated materials, as well as the consistency of the content. For example, they use evaluation tools such as scikit-learn.

[1611] Display means

[1612] The terminal visually displays the assessed material, and the user can review it and make corrections as needed using a device such as a tablet or smart glasses, for example, an iPad or Google Glass.

[1613] Virtual character generation means

[1614] The server generates a virtual training instructor (virtual character) based on the input information. This virtual character reflects the position and knowledge level according to the information input by the user. For example, Unity or Blender is used.

[1615] Feedback Generation Method

[1616] The server analyzes the results of the pre-practice and generates feedback to provide to the user. The analyzed data is used to evaluate the consistency and comprehension of the user's presentation. The feedback results contribute to improving the user's skills.

[1617] Progress management means

[1618] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications as the presentation or training progresses, helping users manage their time allocation.

[1619] Question analysis means

[1620] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generate the most appropriate answer.

[1621] Work instruction support means

[1622] The server runs simulations, generates work instructions and training materials, and displays them on the terminals, providing an environment where staff can effectively learn about new work procedures and products.

[1623] Skill improvement support measures

[1624] The server uses the generated feedback to help users improve their skills, particularly by providing specific advice to strengthen individual staff weaknesses and improve overall performance.

[1625] Specific examples

[1626] For example, when training staff with less than one year of experience on how to sell a new smartphone product, the following prompt might be used:

[1627] "Create training materials for staff with less than one year of experience about the features of a newly released smartphone and how to sell it. Then, simulate the training using virtual characters."

[1628] This allows store staff to effectively learn about new products and sales techniques and apply them to their actual work.

[1629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1630] Step 1: Enter your information

[1631] The user inputs information about the training and work instructions (objectives, target person's job title, knowledge level, work experience, etc.) from the terminal. This information is sent to the server by the terminal and stored in the system's database. The input information is used in each subsequent processing step.

[1632] Step 2: Generate materials

[1633] The server automatically generates training materials using a generative AI model based on the received information. Specifically, it uses PyTorch and TensorFlow to create materials with appropriate design and content. The generated materials are stored in a database and sent to the next evaluation step.

[1634] Step 3: Document evaluation

[1635] The server evaluates the quality of the design and content of the generated materials, using evaluation algorithms (e.g., scikit-learn) to check color and font matching, content consistency, etc. The evaluation results are stored in a database, and the user is notified if any corrections are required.

[1636] Step 4: Display the material

[1637] The terminal visually displays the assessed material to the user, who can then review it using a device such as a tablet or smart glasses, such as an iPad or Google Glass, and provide corrections or feedback as needed.

[1638] Step 5: Virtual Character Generation

[1639] The server generates a virtual training person (virtual character) based on the input information. Using tools such as Unity or Blender, a character that reflects the information input by the user is created. The generated virtual character is saved within the system and used in the next simulation step.

[1640] Step 6: Run the simulation

[1641] The user simulates training using a virtual character. The virtual environment is displayed on the device, and the user receives instructions and feedback while simulating real-world tasks. The results of this step are sent to the server for analysis.

[1642] Step 7: Feedback Generation

[1643] The server analyzes the simulation results and generates feedback for the user. The analysis uses algorithms that evaluate the consistency and comprehension of the user's presentation. The generated feedback is displayed on the device and can be used by the user to improve their skills.

[1644] Step 8: Progress Management

[1645] The device monitors the progress of training and work instructions in real time, providing appropriate alerts and notifications during presentations and training sessions, and helping users manage their time allocation, thereby enabling effective learning.

[1646] Step 9: Question Analysis

[1647] The server analyzes the questions received by the user in real time, and recommends the intent of the question and the appropriate answer. It uses natural language processing to analyze the meaning of the question and generates the most appropriate answer. The generated answer is displayed on the device for the user's reference.

[1648] Step 10: Work instructions and skill development support

[1649] The server runs simulations, generates work instructions and training materials, and displays them on the terminal. It also supports users in improving their skills based on the generated feedback. This allows staff to learn and improve their skills effectively.

[1650] 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.

[1651] Below, we will explain an embodiment of an AI presentation support system that combines an emotion engine.

[1652] Information input method

[1653] Process Overview

[1654] User: Enters information about the presentation (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device. This information is used as basic information required to generate presentation materials and configure avatars.

[1655] Terminal: The information entered by the user is sent to the server, where it is stored in the server's database and used for subsequent processing.

[1656] Material generation means

[1657] Process Overview

[1658] Server: Based on the received information, the AI ​​model automatically generates presentation materials. The AI ​​model analyzes the input information and generates slides with optimal design and content.

[1659] Terminal: The initially generated base materials are displayed to the user in a format that is easy for the user to review.

[1660] Specific examples

[1661] Example: When a user inputs information for creating a presentation about "sales strategy for a new product," the server automatically generates relevant charts and text based on that information and compiles them into slides.

[1662] Evaluation methods

[1663] Process Overview

[1664] Server: Generated presentations are evaluated using internal algorithms for design and presentation appropriateness, including slide color and font matching, and content consistency.

[1665] Display means

[1666] Process Overview

[1667] Terminal: The base material that has passed the check is visually displayed to the user, allowing the user to check the overall picture and specific content of the material.

[1668] Avatar generation method

[1669] Process Overview

[1670] Server: Based on the materials created by the user, the server generates an avatar that simulates the person giving the presentation. This avatar reflects the position and knowledge level according to the information entered by the user.

[1671] Device: Start practicing in a virtual environment using an avatar, and practice as if you were giving a presentation to a user.

[1672] Specific examples

[1673] Example: If a user specifies "Presentation for Senior Executives," the avatar will reflect the appearance, tone, and question patterns of a senior executive.

[1674] Emotion Engine

[1675] Process Overview

[1676] Server: Contains an emotion engine that recognizes emotions by analyzing the user's facial expressions and tone of voice. The emotion engine uses the camera and microphone to analyze the user's real-time reactions.

[1677] Device: Based on the recognized emotions, feedback is provided in real time according to the content and progress of the presentation, allowing users to proceed with the presentation while being aware of their own emotional state.

[1678] Specific examples

[1679] Example: If a user is nervous while practicing a presentation, the emotion engine can detect this state and display alerts such as "take a deep breath" or "relax."

[1680] Feedback Generation Method

[1681] Process Overview

[1682] Server: Analyzes the user's presentation, identifies consistency between the materials and statements, points out any unclear parts, and generates feedback.

[1683] Device: Displays feedback to let users know the results of their practice, allowing them to revise and improve their presentations.

[1684] Progress management means

[1685] Process Overview

[1686] Device: Monitors progress in real time during the presentation and provides notifications and alerts to users. Progress management includes timekeeping functions.

[1687] Specific examples

[1688] For example, display notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[1689] Question analysis means

[1690] Process Overview

[1691] Server: Analyzes questions received during the presentation and performs natural language processing (NLP) to understand their intent. It performs text analysis of the questions and extracts related keywords and context.

[1692] Device: Understands the intent of the question, generates an appropriate answer, and presents it to the user.

[1693] Specific examples

[1694] Example: In response to the question, "What is the market size of this new product?", the server analyzes market size data and answers, "The market size of this new product is approximately 50 billion yen."

[1695] In this way, the AI ​​presentation support system that combines an emotion engine is multifunctional, providing consistent support from presentation preparation to the actual presentation, and is equipped with all the elements necessary to deliver an effective presentation.

[1696] The processing flow will be explained below.

[1697] Program processing steps

[1698] Information input and document generation

[1699] Step 1: Enter your information

[1700] User: Enter presentation information (presentee, purpose, presentation time, audience knowledge level, industry, etc.) from the device.

[1701] Terminal: Sends the entered information to the server, where it is stored in a database.

[1702] Step 2: Automatically generate materials

[1703] Server: Based on the received information, the server uses an AI model to automatically generate presentation materials.

[1704] How it works: The AI ​​model determines the number of slides needed and the content of each slide (text, images, graphs, etc.), and then automatically generates them.

[1705] Step 3: Document evaluation

[1706] Server: Checks the generated presentation materials using design and content evaluation algorithms.

[1707] Specific actions: Check the color and font consistency of the slides, the amount of information on each slide, and visual balance.

[1708] Step 4: Display the material

[1709] Terminal: Visually displays to the user the base material that has passed the check.

[1710] User: Review the displayed material and make corrections as necessary.

[1711] Pre-practice and feedback

[1712] Step 5: Avatar Creation and Practice

[1713] Server: Generates an avatar that simulates the person giving the presentation based on the materials created by the user.

[1714] Terminal: The user begins a pre-practice session in the virtual environment and gives a presentation facing the avatar.

[1715] Specific behavior: Simulates avatar reactions and questions to provide users with an interactive experience.

[1716] Step 6: Practice analysis and feedback generation

[1717] Server: Analyzes the user's presentation content in real time and evaluates the consistency between the materials and the speech, as well as the fluency of the speech.

[1718] Specific actions: Convert the content of the speech into text, check the consistency with the materials, and evaluate the pauses and expression.

[1719] Step 7: Emotion Engine Analysis

[1720] Server: Uses a camera and microphone to analyze the user's facial expressions and voice in real time to recognize emotions.

[1721] Specific behavior: Analyzes the user's facial expressions and tone of voice in real time to identify emotions such as tension or impatience.

[1722] Step 8: Provide feedback

[1723] Server: Generates feedback based on the analysis, including specific recommendations for improvement and emotional responses.

[1724] Terminal: Display feedback to the user.

[1725] User: Review the feedback and improve your presentation and manage your emotions.

[1726] Follow-up on the actual event

[1727] Step 9: Progress Management

[1728] Device: Monitors progress in real time during the presentation and provides notifications and alerts to the user.

[1729] Specific behavior: Display a pop-up notification such as "Your time on this slide is over" or "You have 10 minutes left."

[1730] Step 10: Question Analysis

[1731] Server: Analyzes questions asked by users during the presentation and performs natural language processing to understand their intent.

[1732] Specific operation: Analyzes the text of the question and extracts related keywords and context.

[1733] Step 11: Answer Generation

[1734] Server: Understands the intent of the question and generates an appropriate answer.

[1735] On the device: Present the suggested answers to the user.

[1736] User: Use the suggested answers to guide your Q&A.

[1737] In this way, the AI ​​presentation support system, which combines an emotion engine, supports users at every step, from inputting information to creating materials, practicing, providing feedback, and following up on the actual presentation, providing effective presentations.

[1738] Example 2

[1739] 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."

[1740] In modern presentations, efficient and effective preparation of materials is essential, but it requires a lot of time and effort. Furthermore, progress management during the presentation, answering questions, and real-time feedback based on the user's emotional state are not yet fully supported. To address these issues, a system is needed that allows users to deliver high-quality presentations with less effort.

[1741] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that automatically generates presentation materials based on the input information using a generative AI model, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that conducts a pre-practice presentation in a virtual environment and simulates the actual presentation audience, a feedback generation means that analyzes the results of the pre-practice and generates feedback, a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation, an emotion engine means that analyzes the user's facial expressions and tone of voice during the presentation and recognizes their emotions, a means that provides real-time feedback according to the presentation content and progress based on the recognized emotions, and a question analysis means that analyzes questions during the presentation and generates appropriate answers. This enables a user to efficiently prepare presentation materials, manage the progress and respond to questions during the presentation, and understand their own emotional state, thereby delivering a more effective presentation.

[1742] The "information input means" is a means for a user to input detailed information about a presentation through a terminal.

[1743] A "generative AI model" is an artificial intelligence algorithm used to automatically generate presentation materials based on input information.

[1744] "Material generation means" refers to a means for automatically generating presentation materials based on input information using a generative AI model.

[1745] "Evaluation measures" are measures to check the design and expression of the created presentation materials and ensure their appropriateness.

[1746] The "display means" is a means for visually displaying the checked presentation materials to the user.

[1747] The "avatar generation means" is a means for generating an avatar in a virtual environment that supports the user in practicing a presentation in advance and simulates the actual person to whom the presentation will be given.

[1748] The "feedback generating means" is a means for analyzing the results of the pre-practice and providing feedback to the user.

[1749] A "progress management means" is a means for monitoring the progress of a presentation in real time and providing alerts and notifications during the presentation.

[1750] The "emotion engine means" is a means for analyzing the facial expressions and tone of voice of the user during the presentation and recognizing their emotions.

[1751] The "question analysis means" is a means for analyzing questions received during a presentation, understanding their intent, and generating appropriate answers.

[1752] This invention relates to an AI presentation support system that combines an emotion engine, and is a multifunctional system that supports everything from presentation preparation to the actual presentation.

[1753] Embodiment of information input means

[1754] The user inputs context information about the presentation from the terminal. This context information includes the audience, purpose, presentation time, audience knowledge level, industry, etc. For example, the user might input, "I need a presentation document on a new product sales strategy for senior management."

[1755] The terminal transmits the information input by the user to the server, where it is stored in a database and used for subsequent processing.

[1756] Embodiment of material generating means

[1757] The server automatically generates presentation materials using a generative AI model (e.g., OpenAI's GPT-4) based on information extracted from the database. The generative AI model analyzes the situational information entered by the user and generates slides with optimal design and content.

[1758] The terminal visually displays the presentation materials sent from the server to the user, who can then review the materials and make any necessary corrections.

[1759] Embodiments of the evaluation means

[1760] The server uses internal algorithms to evaluate the generated presentation, including slide color and font matching, and content consistency.

[1761] Display Means Embodiment

[1762] The terminal visually displays presentation materials that have passed the evaluation, allowing the user to review the materials in detail and make final adjustments.

[1763] Embodiment of Avatar Generation Means

[1764] The server generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior management," the avatar will reflect the appearance and tone of a senior manager.

[1765] The device will then begin practicing a presentation in a virtual environment using an avatar. The user can then give a presentation to the avatar in the virtual environment, providing a rehearsal for the actual presentation.

[1766] Embodiment of Emotion Engine

[1767] The server has an emotion engine that uses a camera and microphone to analyze the user's facial expressions and tone of voice in real time. The emotion engine analyzes the user's emotional state during a presentation. For example, if the user is nervous, it will detect that state.

[1768] Based on the results of emotion analysis, the device provides real-time feedback to the user, such as "take a deep breath" or "relax," allowing the user to proceed with the presentation while being aware of their own emotional state.

[1769] Feedback Generation Means Embodiment

[1770] The server analyzes the content of the user's presentation and generates feedback that points out the consistency between the materials and the comments and any unclear parts. Based on this feedback, the user can revise and improve the content of their presentation.

[1771] The terminal visually displays the feedback sent from the server to the user.

[1772] Embodiment of progress management means

[1773] The device monitors the progress of the presentation in real time, displaying notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[1774] Embodiment of the Question Analysis Means

[1775] The server analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to the question, "What is the market size for this new product?", it analyzes market size data and generates the answer, "The market size for this new product is approximately 50 billion yen."

[1776] The terminal visually displays the generated answer to the user, who can refer to the answer to make an appropriate response to the question.

[1777] Prompt Sentence Examples

[1778] "I'd like to create a presentation on a sales strategy for a new product. The presentation is for senior management, and I have 30 minutes to give. The audience has a high level of knowledge and is in the IT industry. I'd like feedback on the design, content, avatar generation, and sentiment analysis of the presentation."

[1779] As described above, this system provides consistent support from presentation preparation to the actual presentation, and offers multifunctional assistance to help users give effective presentations.

[1780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1781] Step 1:

[1782] User: Enters details of the presentation (such as audience, purpose, presentation time, audience knowledge level, industry, etc.) into an input form on the device. For example, the input might include, "I need a presentation document for a new product sales strategy for senior management."

[1783] Input: Presentation details

[1784] Output: Information data prepared for transmission by the terminal.

[1785] Step 2:

[1786] Terminal: Sends the information entered by the user to the server. Using the API, the information is sent in JSON format, for example.

[1787] Input: Details entered by the user

[1788] Output: Information data sent to the server

[1789] Step 3:

[1790] Server: Receives the transmitted information and stores it in a database, such as an SQL database.

[1791] Input: Information data sent from the terminal

[1792] Output: Information stored in the database

[1793] Step 4:

[1794] Server: Extracts information from the database and automatically generates presentation materials using a generative AI model (e.g., GPT-4). Optimizes the design and content.

[1795] Input: Information stored in a database

[1796] Output: Auto-generated presentation materials

[1797] Step 5:

[1798] Terminal: Visually displays the data sent from the server to the user, allowing the user to check and modify it.

[1799] Input: Presentation materials sent from the server

[1800] Output: Visual presentation material

[1801] Step 6:

[1802] Server: Evaluates the generated materials using internal algorithms, checking for color and font consistency of slides, content consistency, etc.

[1803] Input: Auto-generated presentation material

[1804] Output: Evaluation result

[1805] Step 7:

[1806] Terminal: Visually displays materials that have passed evaluation to users, allowing them to see the overall picture and specific content of the materials.

[1807] Input: Presentation materials that have passed evaluation

[1808] Output: Presentation materials for final review

[1809] Step 8:

[1810] Server: Generates an avatar that simulates the person presenting based on the materials created by the user. For example, if the user specifies "for senior executives," the avatar will be configured to look and speak like a senior executive.

[1811] Input: Finalized presentation materials and contact information

[1812] Output: Simulated avatar

[1813] Step 9:

[1814] Device: Start practicing your presentation in a virtual environment using an avatar. Users practice in a real environment.

[1815] Input: Generated avatar and presentation materials

[1816] Output: A practice session in a virtual environment

[1817] Step 10:

[1818] Server: The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice to recognize their emotions. For example, it can detect if the user is nervous.

[1819] Input: Camera video and microphone audio data

[1820] Output: Recognized emotion information

[1821] Step 11:

[1822] On the device: Based on the recognized emotion, the device provides the user with real-time suggested feedback, such as an alert to "take a deep breath" or "relax."

[1823] Input: Recognized emotion information

[1824] Output: Real-time feedback

[1825] Step 12:

[1826] Server: Analyzes the presentation content and generates feedback pointing out the consistency between the materials and statements and any unclear parts.

[1827] Input: Presentation materials and user comments

[1828] Output: Feedback for content correction and improvement

[1829] Step 13:

[1830] Device: Displays the feedback sent from the server and informs the user of the results of their practice. The user can then revise and improve their presentation based on the feedback.

[1831] Input: Feedback sent by the server

[1832] Output: Feedback showing corrections and improvements

[1833] Step 14:

[1834] Device: Monitors progress in real time during a live presentation and provides alerts and notifications, such as "You're running out of time on this slide" or "You have 10 minutes left."

[1835] Input: Progress information during the presentation

[1836] Output: Alerts and notifications

[1837] Step 15:

[1838] Server: Analyzes questions received during the presentation and uses natural language processing (NLP) to understand their intent. For example, in response to a question like, "What is the market size for this new product?", the server analyzes market size data and generates an appropriate answer.

[1839] Input: Question data during presentation

[1840] Output: Answers generated through natural language processing

[1841] Step 16:

[1842] Terminal: The generated answer is visually displayed to the user, who can use it to respond appropriately to the question.

[1843] Input: Response data sent from the server

[1844] Output: A visual representation of the answer

[1845] (Application example 2)

[1846] 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."

[1847] In today's virtual stores, staff lack the tools to efficiently and effectively explain the features and usage of products. It is also difficult for them to quickly and accurately respond to customer questions during presentations. Furthermore, there is a need for a system that provides pre-practice and feedback to help staff improve their presentation skills.

[1848] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an information input means, a material generation means that uses an AI model to automatically generate presentation materials based on the input information, an evaluation means that checks the generated materials to ensure appropriate design and expression, a display means that displays the checked materials, an avatar generation means that simulates the actual presentation audience by practicing the presentation in a virtual environment, a feedback generation means that analyzes the results of the practice and generates feedback, an emotion analysis means that analyzes the user's facial expressions and tone of voice during the presentation and provides emotion-based feedback, a question analysis means that analyzes questions from customers and generates appropriate answers when staff members present product features and specifications in a virtual store, and a progress management means that monitors the progress of the presentation in real time and provides alerts and notifications during the actual presentation. This improves the quality of presentations and strengthens customer responsiveness.

[1849] The "information input means" is a means by which the user inputs information necessary for the presentation and transmits it to the server.

[1850] An "AI model" is an algorithmic model that uses artificial intelligence technology to automate specific tasks.

[1851] "Material generation means" refers to a means for automatically generating presentation materials using an AI model based on input information.

[1852] "Evaluation measures" are measures to check the generated presentation materials and ensure appropriate design and expression.

[1853] The "display means" is a means for visually displaying the evaluated presentation materials to the user.

[1854] The "avatar generation means" is a means for practicing a presentation in advance in a virtual environment and generating an avatar that simulates the actual person being presented to.

[1855] The "feedback generation means" is a means for analyzing the results of the preliminary practice and generating feedback that provides the user with areas for improvement and advice.

[1856] An "emotion analysis means" is a means of analyzing a user's facial expressions and tone of voice when giving a presentation and providing feedback based on their emotions.

[1857] The "question analysis means" is a means for analyzing questions from customers and generating appropriate answers when staff members present product features and specifications in a virtual store.

[1858] A "progress management means" is a means for monitoring the progress of a presentation in real time during the presentation and providing alerts and notifications.

[1859] This invention relates to an AI presentation support system for efficiently and effectively presenting products in a virtual store.

[1860] First, the user uses the information input means to input basic information into the terminal, such as the person to whom the presentation will be given, the features of the product, the purpose of the presentation, etc. This information is sent to the server and stored in a database.

[1861] Next, a material generation tool using an AI model automatically generates presentation materials based on the input information, such as product explanation slides and videos showing how to use the product.

[1862] The server then uses evaluation tools to check the generated materials to ensure that the design and presentation are appropriate. This evaluation process ensures the quality of the presentation.

[1863] The materials that pass the evaluation are displayed to the user through the display means, so that the user can check the contents of the generated materials.

[1864] Next, the avatar generation means is activated to allow a user to practice the presentation in advance in the virtual environment. This means generates an avatar according to the position and knowledge level of the person to whom the user is going to give the presentation, and realizes a presentation simulation in the virtual environment.

[1865] Furthermore, the results of the pre-practice are analyzed, and the feedback generation means provides the user with suggestions and advice on how to improve, allowing the user to improve their presentation skills.

[1866] When giving a presentation, the emotion analyzer analyzes the user's facial expressions and tone of voice and provides real-time feedback based on their emotions. For example, if they are nervous, the device will display instructions such as "Relax."

[1867] During presentations in the virtual store, the question analysis tool analyzes customer questions in real time and generates appropriate answers, allowing staff to provide accurate answers immediately.

[1868] During the live presentation, the progress management tool monitors the progress of the presentation in real time and provides alerts and notifications such as "You're running out of time on this slide" or "You have 10 minutes left."

[1869] To realize this invention, a smartphone equipped with a camera and microphone, smart glasses, or a head-mounted display (e.g., Oculus Quest 2) is used. PyTorch or TensorFlow is used as the AI ​​model, and GPT-4 (OpenAI) is used for natural language processing. Microsoft Azure Cognitive Services or Google Cloud Machine Learning API is used as the sentiment analysis engine, and WebRTC is used for real-time communication.

[1870] As a concrete example, consider a scenario in which a new smartphone product is explained in a virtual store. The user inputs the smartphone's features (e.g., high-resolution camera, voice recognition function), and the material generation means generates presentation materials based on that information. The generated materials are checked by the evaluation means and displayed to the user through the display means. During practice, the avatar generation means generates an avatar that resembles the customer, and the presentation is simulated in the virtual environment.

[1871] An example of a prompt sentence that may be used is:

[1872] ---

[1873] Prompt: "Prompt to generate a presentation document that explains the product's features."

[1874] Please create a presentation about a new product equipped with a high-resolution camera and voice recognition function. The purpose of the presentation is to increase customer desire to purchase.

[1875] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1876] Step 1:

[1877] The user uses a terminal to input information necessary for the presentation. The input information includes the attributes of the person being presented to (e.g., job title and knowledge level), product features, and the purpose of the presentation. This input data is sent from the terminal to the server.

[1878] Input: Information such as the attributes of the person you are presenting to, product features, and the purpose of the presentation

[1879] Output: User-entered data sent to the server

[1880] Step 2:

[1881] The server stores the received input data in a database, which is used for subsequent processing.

[1882] Input: User-entered data

[1883] Output: Data stored in the database

[1884] Step 3:

[1885] The server automatically generates presentation materials using an AI model based on the stored user input data. Specifically, the AI ​​model generates presentation materials (slides, videos, text, etc.) that include product features and usage instructions based on prompt statements.

[1886] Input: User-entered data

[1887] Output: Generated presentation materials

[1888] Step 4:

[1889] The server evaluates the generated presentation materials for appropriateness of design and expression, checking the color and font consistency of the slides, and content consistency.

[1890] Input: Generated presentation material

[1891] Output: Evaluated presentation materials

[1892] Step 5:

[1893] The terminal displays the evaluated presentation materials to the user, allowing the user to review the generated materials and identify any corrections needed.

[1894] Input: Evaluated presentation materials

[1895] Output: Presentation materials displayed on the device

[1896] Step 6:

[1897] To allow users to practice their presentation in a virtual environment, the server uses an avatar generation unit to generate an avatar that simulates the person they are presenting to. This avatar is customized according to the attributes of the person they are presenting to (e.g., their job title or knowledge level) specified by the user.

[1898] Input: User input data, presentation partner attributes

[1899] Output: The generated avatar

[1900] Step 7:

[1901] The user performs a pre-practice in the virtual environment using the generated avatar. The server analyzes the results of the pre-practice and generates feedback using a feedback generation means. The feedback provides the user with suggestions for improvement and advice.

[1902] Input: Pre-practice result data

[1903] Output: Generated feedback

[1904] Step 8:

[1905] During the presentation, the server uses emotion analysis to analyze the user's facial expressions and tone of voice, and provides real-time feedback based on their emotions. For example, if the user is nervous, it will display instructions such as "Relax."

[1906] Input: User's facial expression data, voice tone data

[1907] Output: Real-time feedback

[1908] Step 9:

[1909] During the presentation in the virtual store, the server uses a question analysis means to analyze questions from customers in real time and generates appropriate answers using natural language processing, allowing the user to provide accurate answers immediately.

[1910] Input: Question data from customers

[1911] Output: The generated answer

[1912] Step 10:

[1913] During the live presentation, the server uses the progress management means to monitor the progress of the presentation in real time and provide alerts and notifications to the user, such as "You're running out of time on this slide" or "You have 10 minutes left."

[1914] Input: Presentation progress data

[1915] Output: Alerts, notifications

[1916] 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.

[1917] 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.

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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).

[1923] 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.

[1924] 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."

[1925] 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.

[1926] 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).

[1927] 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.

[1928] 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.

[1929] 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.

[1930] 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.

[1931] 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 reso...

Claims

1. An information input means; a material generation means for automatically generating presentation materials based on input information using an AI model; evaluation measures to check the materials produced and ensure appropriate design and presentation; a display means for displaying the checked materials; A means for generating an avatar that allows a user to practice a presentation in advance in a virtual environment and simulate an actual presentation target; a feedback generating means for analyzing a result of the pre-practice and generating feedback; A progress management tool that monitors the progress of the presentation in real time during the live presentation and provides alerts and notifications. A system including a question analysis means for analyzing questions asked during a presentation and generating appropriate answers.

2. 2. The system according to claim 1, wherein the question analysis means performs natural language processing to understand the intent of the question.

3. 2. The system according to claim 1, wherein the avatar generating means generates an avatar according to the position and knowledge level of the person to whom the presentation is to be given.

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