system

Abstract

Provide a system. 【Solution means】Means for a user to log in with initial settings, Means for obtaining user profile information, Means for a user to select an interlocutor, Means for the server to generate a dialogue scenario based on the interlocutor, Means for displaying the dialogue scenario on the user's terminal, Means for sending the user's input to the server, Means for the server to analyze the user input using multimodal AI, Means for the server to generate an appropriate response based on the analysis result, Means for sending the response to the user's terminal, Means for displaying or playing back the response on the user's terminal, A system including means for saving and evaluating the user's dialogue data.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that there is a lack of means for effectively improving the communication ability necessary for people with developmental disabilities and people with a poor sense of communication to smoothly conduct social life. In particular, there is a demand for a tool that fosters the ability to respond in actual situations by providing a realistic dialogue experience, but there are many technical problems to achieve this. The object of this invention is to provide an environment in which a user can simulate a realistic dialogue without difficulty and to provide an effective means for improving communication skills.

Means for Solving the Problems

[0005] The present invention provides an interactive education system that includes means for a user to log in with initial settings, means for obtaining user profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, and means for saving and evaluating the user's conversation data. Furthermore, by including means for providing feedback based on the user's progress, and means for the user to select from multiple options such as parents, siblings, teachers, and friends as conversation partners, the system can handle a variety of communication situations.

[0006] "User" refers to an individual who uses this system to conduct dialogue simulations.

[0007] A "terminal" refers to an electronic device used by a user to conduct dialogue simulations.

[0008] A "server" refers to a central computer that works in conjunction with terminals to generate dialogue scenarios and analyze user input.

[0009] "Logging in" refers to the authentication process required for a user to access a system.

[0010] "Profile information" refers to data such as personal information, preferences, and conversation history about the user.

[0011] "Conversation partner" refers to the person with whom the user virtually converses in a dialogue scenario.

[0012] A "dialogue scenario" refers to a script containing a series of questions and answers designed to simulate a conversation with a specific person.

[0013] "Multimodal AI" refers to artificial intelligence that combines and analyzes multiple pieces of information, such as voice, text, and facial expressions, to generate appropriate responses.

[0014] "Analysis" refers to data processing that involves understanding data entered by the user and deriving an appropriate response.

[0015] "Response" refers to the reply or reaction generated by the multimodal AI.

[0016] "Feedback" refers to evaluations and suggestions for improvement provided in response to a user's conversational behavior.

[0017] "Progress" refers to the learning progress achieved by the user through the dialogue simulation. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0020] First, the terms used in the following description will be described.

[0021] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the 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.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0032] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention is an educational system designed to help users improve their communication skills through interactive dialogue, and is primarily intended for individuals with developmental disabilities or those who have difficulty communicating. The system is implemented in the following manner.

[0040] Program processing and explanation

[0041] 1. Initial Setup and User Authentication

[0042] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[0043] 2. Selection of a dialogue scenario

[0044] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0045] 3. Start the dialogue simulation.

[0046] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0047] 4. Input Analysis and Response Generation

[0048] The server analyzes the received user input data using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis. Based on the analysis results, the server generates an appropriate response and sends it to the terminal.

[0049] 5. Display of responses and feedback

[0050] The device displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile.

[0051] Example: Dialogue simulation with a friend

[0052] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0053] Terminal: "Hello! How are you doing today?"

[0054] User: "I took a test at school." (input)

[0055] The terminal sends this input to the server.

[0056] 2. The server analyzes the user's input and generates an appropriate response.

[0057] Server (response generation): "That must have been tough. Are you curious about the test results?"

[0058] The server sends the generated response to the terminal.

[0059] 3. The terminal displays the received response to the user.

[0060] Terminal: "That must have been tough. Are you curious about your test results?"

[0061] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[0062] The terminal sends the next input to the server again.

[0063] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] The user starts up the device and enters their username and password on the login screen.

[0067] Step 2:

[0068] The terminal sends the entered username and password to the server.

[0069] Step 3:

[0070] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[0071] Step 4:

[0072] The server sends the user's profile information to the device.

[0073] Step 5:

[0074] The device displays a screen for the user to select a conversation partner.

[0075] Step 6:

[0076] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[0077] Step 7:

[0078] The device sends the user's selections to the server.

[0079] Step 8:

[0080] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[0081] Step 9:

[0082] The server sends the acquired dialogue scenario to the terminal.

[0083] Step 10:

[0084] The device displays the conversation scenario it received to the user.

[0085] Step 11:

[0086] The user follows the displayed prompts and makes their initial input via text or voice.

[0087] Step 12:

[0088] The terminal sends user input to the server.

[0089] Step 13:

[0090] The server receives user input data and analyzes it using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis.

[0091] Step 14:

[0092] The server generates an appropriate response based on the analysis results.

[0093] Step 15:

[0094] The server sends the generated response to the terminal.

[0095] Step 16:

[0096] The device displays or plays aloud the response it receives to the user.

[0097] Step 17:

[0098] The user enters the information again.

[0099] Step 18:

[0100] The terminal sends the user's next input back to the server.

[0101] Step 19:

[0102] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[0103] Step 20:

[0104] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[0105] Step 21:

[0106] The server saves the evaluation results and feedback to the user's learning profile.

[0107] Step 22:

[0108] The device displays the evaluation results and feedback to the user.

[0109] (Example 1)

[0110] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0111] This invention relates to an educational system for improving users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. The goal is to provide an environment in which such individuals can learn effectively and to improve their communication skills through real-world dialogue.

[0112] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0113] In this invention, the server includes means for storing and evaluating user dialogue data, means for evaluating the user's overall performance after the dialogue ends and providing feedback, and means for storing the feedback results in the user's learning profile. This makes it possible for the user to utilize the feedback obtained through the dialogue and continuously improve their communication skills.

[0114] "Users" refer to people who use the dialogue simulation system, and it is particularly targeted at people with developmental disabilities or those who have difficulty communicating.

[0115] A "terminal" refers to an electronic device used by a user that has the functions of logging in, displaying dialogue scenarios, receiving user input, and communicating with a server.

[0116] A "server" refers to a central computer system that analyzes user input data, generates appropriate responses, and provides feedback.

[0117] A "dialogue scenario" refers to a sequence of conversations generated by the server and designed based on the user's chosen conversation partner.

[0118] "Multimodal AI" refers to artificial intelligence technology used to analyze multiple sources of information, such as text, audio, and video, and to comprehensively understand and make judgments based on that information.

[0119] "Feedback" refers to evaluations and advice provided based on the results of user interactions, and plays a role in supporting the improvement of users' communication skills.

[0120] A "learning profile" is a database containing a user's past conversation data, evaluation results, and feedback information, and refers to a profile used to record and manage the user's progress and skill improvement.

[0121] This invention is an educational system designed to improve users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. This system provides an environment in which users can learn naturally through dialogue.

[0122] Hardware and software to be used

[0123] The system's main components are user terminals, a server that performs central processing, and a network that facilitates communication between them. Terminals are devices that receive user input and send it to the server, including personal computers, tablets, and smartphones. The server possesses the computing power and database necessary to analyze user input data and generate appropriate responses. Multimodal AI is used for analysis, including a speech recognition engine, a text analysis engine, and a sentiment analysis module.

[0124] Program processing

[0125] Initial setup and user authentication

[0126] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing it against the database. If authentication is successful, the server sends the user's profile information to the device.

[0127] Selection of a dialogue scenario

[0128] The user selects a conversation partner on the device screen. Options for conversation partners include parents, siblings, teachers, and friends. The device sends the user's selection to the server. Based on the selected conversation partner, the server retrieves an appropriate conversation scenario from its database and sends it to the device.

[0129] Start of dialogue simulation

[0130] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[0131] Input analysis and response generation

[0132] The server analyzes the received user input data using multimodal AI (e.g., a speech recognition engine, sentiment analysis module, text analysis engine, etc.). In the case of voice input, it is first converted into text by the speech recognition engine. Next, the text analysis engine understands the context, and the sentiment analysis module determines the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[0133] Display of response and feedback

[0134] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[0135] Specific example

[0136] Dialogue simulation with friends

[0137] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0138] Terminal: "Hello! How are you doing today?"

[0139] User: "I took a test at school." (input)

[0140] The terminal sends this input to the server.

[0141] 2. The server analyzes the user's input and generates an appropriate response.

[0142] Server (response generation): "That must have been tough. Are you curious about the test results?"

[0143] The server sends the generated response to the terminal.

[0144] 3. The terminal displays the received response to the user.

[0145] Terminal: "That must have been tough. Are you curious about your test results?"

[0146] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[0147] The terminal sends the next input to the server again.

[0148] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[0149] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0150] Step 1: Initial Setup and User Authentication

[0151] The user starts up the device and enters their username and password on the login screen. The device sends the entered username and password to the server. The server compares the received username and password with the information stored in the database. If authentication is successful, the server sends the user's profile information (e.g., name, age, learning history, etc.) to the device. If authentication fails, an error message is sent to the device.

[0152] Input: Username, Password

[0153] Data processing and calculation: Database matching

[0154] Output: User profile information (on success) or error message (on failure)

[0155] Step 2: Selecting a dialogue scenario

[0156] The user selects a conversation partner on the device screen. Options include parent, sibling, teacher, friend, etc. The device sends information about the selected conversation partner to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0157] Input: Select conversation partner

[0158] Data processing and calculation: Retrieving dialogue scenarios from databases

[0159] Output: Dialogue Scenario

[0160] Step 3: Start the dialogue simulation

[0161] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[0162] Input: Dialogue scenario, user response

[0163] Data processing / calculations: None

[0164] Output: User input data

[0165] Step 4: Input analysis and response generation

[0166] The server analyzes the received user input data using multimodal AI. In the case of voice input, it first converts it to text using a speech recognition engine. Next, it uses a text analysis engine to understand the context and an emotion analysis module to determine the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[0167] Input: User input data

[0168] Data processing and computation: speech recognition, text analysis, sentiment analysis

[0169] Output: Response data

[0170] Step 5: Displaying responses and providing feedback

[0171] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[0172] Input: Response data, user feedback

[0173] Data processing and computation: Performance evaluation, feedback generation.

[0174] Output: Feedback data

[0175] (Application Example 1)

[0176] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0177] In recent years, while the introduction of robots in factories has progressed, efficiency and precision are required in communication between workers and robots. However, the lack of training and support systems for workers to interact smoothly with robots can lead to decreased work efficiency and safety. In particular, the inability to take immediate and appropriate action when giving specific work instructions or resolving problems is a major challenge.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0179] In this invention, the server includes means for the user to log in with initial settings, means for obtaining the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for saving and evaluating user conversation data, means for providing a conversation simulation for factory workers to efficiently give work instructions and solve problems with robots, and means for speech recognition and text conversion of user input in the simulation dialogue and playing a response based on that in audio. This makes it possible to improve the efficiency and safety of communication between factory workers and robots.

[0180] "User" refers to a person who uses this system.

[0181] "Profile information" refers to data that includes personal information, specific characteristics, and settings about a user.

[0182] "Dialogue partner" refers to the person with whom the user engages in simulated dialogue.

[0183] A "dialogue scenario" refers to a scenario that outlines the flow and content of a dialogue generated based on the selected dialogue partner.

[0184] A "terminal" refers to a device used by a user to perform a dialogue simulation.

[0185] A "server" refers to a computer system responsible for managing the entire system, analyzing data, and generating responses.

[0186] "Multimodal AI" refers to artificial intelligence that analyzes data in multiple formats, such as text, audio, and video, and generates appropriate responses.

[0187] "Response" refers to the reply or explanation that the server generates based on the user's input data.

[0188] "Dialogue data" refers to all the data collected during the process of interacting with the user.

[0189] "Factory worker" refers to a worker who performs duties in a factory.

[0190] A "robot" refers to a mechanical device used to assist or automate tasks within a factory.

[0191] "Speech recognition" refers to the technology that converts speech data input through a microphone into text data.

[0192] "Text conversion" refers to the process of representing non-text data, such as audio data, as text.

[0193] "Dialogue simulation" refers to the process by which users train and test their interaction skills using a system before actually performing a task.

[0194] "Playing as audio" refers to outputting the generated response as audio data and letting the user listen to it.

[0195] This invention is an educational system that supports interaction between factory workers and robots, with the aim of enabling workers to communicate efficiently with robots and improve work efficiency and safety. The system is designed for users to log in for initial setup, select an interaction partner, and conduct a dialogue simulation.

[0196] System Overview

[0197] The system consists of the following main elements:

[0198] 1. Terminal: A device used by workers, which displays the scenario and plays audio.

[0199] 2. Server: A computer system that manages the entire system and performs data analysis and response generation.

[0200] 3. Multimodal AI: Artificial intelligence that performs speech recognition and text analysis to generate appropriate responses based on user input.

[0201] Hardware and software to be used

[0202] Hardware: Microphone, speaker, device (PC, tablet, etc.)

[0203] Software: Python, speech_recognition library, gTTS library, audio playback program (e.g., mpg321)

[0204] Data processing and data calculation

[0205] The server processes and performs calculations on the data using the following steps:

[0206] 1. Login and User Authentication: The username and password information sent from the terminal is sent to the server, which authenticates the user by comparing it with the database.

[0207] 2. Retrieving the dialogue scenario: When the user selects a dialogue partner, that information is sent to the server, and the server retrieves an appropriate dialogue scenario from the database and sends it to the terminal.

[0208] 3. Initiating the dialogue simulation: As the user inputs according to the scenario, the content is converted to text via speech recognition and sent to the server. The server uses multimodal AI to analyze the user input.

[0209] 4. Response generation and display: An appropriate response is generated based on the analysis results and sent to the terminal. The terminal plays the received response aloud.

[0210] Specific example

[0211] The following is a specific example of how the system works:

[0212] 1. Initial setup and selecting a conversation partner:

[0213] The user starts up the device and enters their information on the login screen.

[0214] The user selects a dialogue scenario with a "factory robot".

[0215] Terminal: "Please begin the operation."

[0216] User: "Understood, I'll start." (typed).

[0217] The terminal sends this input to the server.

[0218] 2. Input analysis and response reproduction:

[0219] Server (analysis): "The user has indicated an intention to start the task."

[0220] Server (response generation): "Starting work. Please provide details of the steps."

[0221] The device plays the received response aloud.

[0222] Example of the above prompt message:

[0223] User: "Please begin the task."

[0224] Generated AI model response: "I will begin working. Please provide details of the steps."

[0225] This system simulates training for users to interact with robots, supporting them in efficiently performing tasks in real work environments. Furthermore, by utilizing speech recognition and text conversion to provide dialogue scenarios that may be encountered in actual work situations, it contributes to improving workers' skills.

[0226] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0227] Step 1:

[0228] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. The input is the user's login information, and the output is the authentication result and profile information.

[0229] Step 2:

[0230] The user selects a conversation partner on the terminal screen. The terminal sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the terminal. The input is the user's selection of a conversation partner, and the output is the conversation scenario.

[0231] Step 3:

[0232] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and makes initial input in text or voice. The input voice is recognized by the terminal and converted into text data. The converted text data is sent to the server. Input is the user's voice input, and output is text data.

[0233] Step 4:

[0234] The server analyzes the received text data. This analysis includes speech recognition, sentiment analysis, and text analysis. The server then generates an appropriate response based on the analysis results and sends it to the terminal. The input is the user's text data, and the output is the generated response.

[0235] Step 5:

[0236] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile. The input is the response from the server, and the output is the evaluation and feedback.

[0237] example

[0238] For example, if a factory worker instructs the robot they are interacting with by saying, "Please begin work," the server's generated AI model will produce a response saying, "I will begin work. Please tell me the details of the steps," send it to the terminal, and play it back aloud. The user then continues to give instructions such as, "Okay, please start by attaching part A." This dialogue continues through the system, training the worker to communicate smoothly with the robot.

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

[0240] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, primarily targeting individuals with developmental disabilities or those who struggle with communication. The system incorporates an emotion engine that recognizes and responds to user emotions in real time, thereby achieving even more realistic dialogue. This system is implemented in the following manner.

[0241] Program processing and explanation

[0242] 1. Initial Setup and User Authentication

[0243] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[0244] 2. Selection of a dialogue scenario

[0245] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0246] 3. Start the dialogue simulation.

[0247] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0248] 4. Input Analysis and Emotion Recognition

[0249] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (e.g., joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[0250] 5. Response generation and display

[0251] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state, and an appropriate response is taken that is empathetic to the user's feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[0252] 6. Display of responses and feedback

[0253] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and emotional expression, and is a comprehensive assessment to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[0254] Example: Dialogue simulation with a friend

[0255] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0256] Terminal: "Hello! How are you doing today?"

[0257] User: "I took a test at school." (input)

[0258] The terminal sends this input to the server.

[0259] 2. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[0260] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[0261] The server sends the generated response to the terminal.

[0262] 3. The terminal displays the received response to the user.

[0263] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[0264] The user enters the following information:

[0265] The terminal sends the next input to the server again.

[0266] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, thereby realizing more effective communication learning.

[0267] The following describes the processing flow.

[0268] Step 1:

[0269] The user starts up the device and enters their username and password on the login screen.

[0270] Step 2:

[0271] The terminal sends the entered username and password to the server.

[0272] Step 3:

[0273] The server compares the user information it received with the database for authentication. If the authentication is successful, it retrieves the user's profile information. If the authentication fails, the server sends an error message to the terminal.

[0274] Step 4:

[0275] The server sends the user's profile information to the terminal.

[0276] Step 5:

[0277] The terminal displays a screen for the user to select an interlocutor.

[0278] Step 6:

[0279] The user selects an interlocutor (such as parent, sibling, teacher, friend, etc.).

[0280] Step 7:

[0281] The terminal sends the user's selection to the server.

[0282] Step 8:

[0283] The server retrieves an appropriate conversation scenario from the database based on the selected interlocutor.

[0284] Step 9:

[0285] The server sends the retrieved conversation scenario to the terminal.

[0286] Step 10:

[0287] The terminal displays the received conversation scenario to the user.

[0288] Step 11:

[0289] The user follows the displayed prompts and makes their initial input via text or voice.

[0290] Step 12:

[0291] The terminal sends user input to the server.

[0292] Step 13:

[0293] The server receives user input data and analyzes it using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and performs speech recognition, sentiment analysis, and text analysis.

[0294] Step 14:

[0295] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state.

[0296] Step 15:

[0297] The server sends the generated response to the terminal.

[0298] Step 16:

[0299] The device displays or plays aloud the response it receives to the user.

[0300] Step 17:

[0301] The user enters the information again.

[0302] Step 18:

[0303] The terminal sends the user's next input back to the server.

[0304] Step 19:

[0305] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[0306] Step 20:

[0307] After the conversation ends, the server evaluates the user's overall performance. The evaluation items include the fluency of the conversation, the frequency of appropriate responses, the expression of emotions, etc.

[0308] Step 21:

[0309] The server saves the evaluation results and feedback to the user's learning profile.

[0310] Step 22:

[0311] The terminal displays the evaluation results and feedback to the user.

[0312] (Example 2)

[0313] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0314] In modern society, there is a need for an educational system that enables people with developmental disabilities or those who have difficulty communicating to improve their communication skills at their own pace without difficulty. However, in conventional educational systems, it has been difficult to recognize the user's emotions in real time and generate appropriate responses accordingly. Therefore, it has been impossible to realize a conversation that adapts to the user's emotional state, and effective communication learning has been difficult. To solve such problems, a system that utilizes an emotion recognition engine and multimodal artificial intelligence is required.

[0315] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing user input, means for identifying the user's emotional state using an emotion recognition engine, and means for generating an appropriate response based on the analysis results. This enables real-time dialogue that is in line with the user's emotional state.

[0316] "Logging in" is the process by which a user enters the necessary credentials to access a system and undergoes authentication.

[0317] "Profile information" refers to a collection of data that includes personal information about the user, past activity history, learning progress, and more.

[0318] A "dialogue partner" refers to a virtual or real entity with which the user engages in dialogue, and includes parents, siblings, teachers, friends, and others.

[0319] A "dialogue scenario" is a script that includes prompts and example responses to anticipate the flow of a conversation based on a specific situation.

[0320] "Multimodal artificial intelligence" is an artificial intelligence technology that has the ability to process and analyze multiple data formats, such as text, audio, and images.

[0321] An "emotion recognition engine" is a software component that identifies emotional states (such as joy, sadness, anger, or anxiety) from user input.

[0322] A "response" is a text or audio response generated by the system in response to user input.

[0323] "Feedback" is the process of providing evaluations and advice regarding user behavior and performance.

[0324] "Dialogue data" refers to a collection of data that contains all information related to the dialogue that took place between the user and the system.

[0325] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, and is particularly aimed at individuals with developmental disabilities or those who have difficulty communicating. This system achieves more realistic dialogue by incorporating an emotion engine that recognizes and responds to the user's emotions in real time. Specific embodiments of this system are described below.

[0326] First, the user starts up the device and enters their username and password on the login screen. This information is sent from the device to the server using a secure communication method (e.g., HTTPS). The server compares the received user information with its internal database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[0327] Next, the user selects a conversation partner on the device screen. These partners include parents, siblings, teachers, and friends, and the scenario changes accordingly. The user's selection information is sent to the server, which retrieves an appropriate conversation scenario from its database based on the selected partner and sends it to the device.

[0328] The received dialogue scenario is displayed on the terminal. The user follows the displayed prompts and makes the initial input in text or voice. In the case of voice input, the terminal uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[0329] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The emotion engine has functions such as speech recognition, text analysis, and sentiment analysis to identify the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.). Microsoft Azure Cognitive Services can be used for this purpose.

[0330] Based on the analysis results, the server generates an appropriate response. Using generative AI models such as OpenAI's GPT-3, it generates text or audio responses that reflect the user's emotional state. The generated response is sent from the server to the terminal and displayed or played aloud to the user.

[0331] Finally, once the user's interaction concludes, the server evaluates the overall performance and provides feedback. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression, and is used as a comprehensive assessment to help improve the user's communication skills. The evaluation results are also saved in the user's learning profile.

[0332] Below is a concrete example of a conversation simulation with a friend.

[0333] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0334] Terminal: "Hello! How are you doing today?"

[0335] User: "I took a test at school." (input)

[0336] 2. The terminal sends this input to the server.

[0337] 3. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[0338] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[0339] The server sends the generated response to the terminal.

[0340] 4. The terminal displays the received response to the user.

[0341] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[0342] The user then enters the next command, and the terminal sends the input back to the server.

[0343] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The user starts up the device and enters their username and password on the login screen. This entered information, which includes the user's credentials, is sent from the device to the server.

[0347] Step 2:

[0348] The device sends the username and password to the server. HTTPS communication is used for transmission to ensure security.

[0349] Step 3:

[0350] The server compares the received user information with the information in the database and performs authentication. The database stores the username and hashed password. If authentication is successful, it generates an authentication result and retrieves the user's profile information from the database.

[0351] Step 4:

[0352] Upon successful authentication, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[0353] Step 5:

[0354] The user selects a conversation partner on the device screen. This partner may include parents, siblings, teachers, or friends. This selection becomes the input data.

[0355] Step 6:

[0356] The terminal sends information about the selected conversation partner to the server. The transmitted data includes the identification information of the conversation partner selected by the user.

[0357] Step 7:

[0358] The server retrieves an appropriate dialogue scenario from the database based on the information of the selected dialogue partner. This scenario includes situation-specific prompts and possible response examples. The retrieved scenario data is generated and sent to the terminal.

[0359] Step 8:

[0360] The terminal displays the received dialogue scenario to the user. The first prompt appears on the screen. This prompt becomes the input data.

[0361] Step 9:

[0362] The user follows the displayed prompts and makes their initial input in text or voice. In the case of voice input, the device uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[0363] Step 10:

[0364] The terminal sends user input data to the server. The transmitted data includes user input.

[0365] Step 11:

[0366] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The analysis includes speech recognition, text analysis, and sentiment analysis. As a result of the analysis, the server identifies the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.).

[0367] Step 12:

[0368] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., GPT-3), it generates text and audio responses that reflect the user's emotional state. The generated responses become response data.

[0369] Step 13:

[0370] The server sends the generated response data to the terminal. The transmitted data contains the appropriate response.

[0371] Step 14:

[0372] The terminal displays or plays the received response to the user. The displayed response or played audio becomes the display data.

[0373] Step 15:

[0374] The user reviews the displayed response and then provides the next input. This loop continues until the interaction is complete.

[0375] Step 16:

[0376] Once the interaction ends, the server evaluates the user's overall performance. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression. The evaluation results are generated and stored in the user's learning profile. The evaluation data reflects the user's progress.

[0377] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[0378] (Application Example 2)

[0379] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0380] In modern society, individuals with developmental disabilities or those who struggle with communication often face difficulties in engaging in smooth conversations in their daily lives and work. This is particularly true in virtual environments where real-time interaction with others presents challenges in recognizing emotions and generating appropriate responses. Therefore, there is a need for support systems that enable users to more effectively improve their communication skills.

[0381] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0382] In this invention, the server includes means for the user to log in with initial settings, means for acquiring the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for simulating the user searching for, asking questions about, and purchasing products while interacting with others in a virtual environment, means for recognizing the user's emotions and generating and displaying a corresponding response in real time, and means for saving and evaluating the user's conversation data. This makes it possible for users to effectively improve their communication skills through realistic conversations even in a virtual environment.

[0383] "Users" refer to individuals who use this system with the aim of improving their communication skills.

[0384] "Initial login process" refers to the process of entering a username and password and performing authentication necessary for a user to begin using the system.

[0385] "Means of obtaining profile information" refers to the process of obtaining a user's personal information, past conversation history, etc., from the server.

[0386] "Means of selecting a dialogue partner" refers to a function that allows users to choose who they will interact with within the system (e.g., parents, siblings, teachers, friends, virtual customers, virtual shop assistants).

[0387] "Means for generating dialogue scenarios" refers to the process by which the server generates and configures an appropriate dialogue scenario based on the selected dialogue partner.

[0388] "Means for displaying dialogue scenarios" refers to a function that presents the generated dialogue scenario to the user's terminal either visually or audibly.

[0389] "Means for sending user input to the server" refers to a function that sends the content of a conversation entered by the user in text or voice to the server.

[0390] "Methods for analyzing user input using multimodal AI" refers to the process of understanding user input using AI technology that analyzes multiple data formats, such as speech and text.

[0391] "Means for generating appropriate responses" refers to the process of generating appropriate responses to user input based on analysis results.

[0392] "Means for displaying or playing a response" refers to a function that displays the generated response on the user's device or plays it back as audio.

[0393] "Means of conducting dialogue simulations within a virtual environment" refers to the process by which users perform actions such as searching for products, asking questions, and making purchases while simulating dialogues with others within a virtual environment.

[0394] "Means for recognizing emotions and generating responses in real time" refers to a function that recognizes the user's emotional state in real time and generates a response that corresponds to those emotions.

[0395] "Means for saving and evaluating dialogue data" refers to a process of saving a user's dialogue history and later evaluating the user's communication skills based on that data.

[0396] This invention is an educational system designed to help users improve their communication skills through interactive learning. This system is particularly intended for individuals with developmental disabilities or those who have difficulty communicating. Specific embodiments of this system are described below.

[0397] Hardware and software to be used

[0398] Hardware:

[0399] smartphone

[0400] Head-mounted display (HMD)

[0401] software:

[0402] Python programming language

[0403] Speech recognition API

[0404] Emotion recognition API

[0405] Multimodal AI (for analyzing multiple data formats)

[0406] System Configuration

[0407] This system includes the following components.

[0408] 1. Initial setup and user authentication methods:

[0409] To begin using the system, a user first starts up their device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing the received user information with its database. If authentication is successful, the server sends the user's profile information to the device.

[0410] 2. Means of selecting a dialogue partner:

[0411] The user selects a conversation partner (such as a parent, sibling, teacher, friend, virtual customer, or virtual shop assistant) on the device screen. The device sends this selection to the server, which generates an appropriate conversation scenario based on the selected conversation partner and sends it back to the device.

[0412] 3. Start the dialogue simulation:

[0413] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0414] 4. Input analysis and emotion recognition methods:

[0415] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[0416] 5. Response generation and display means:

[0417] The server generates an appropriate response based on the analysis results. This response reflects the user's emotional state, and an appropriate response is taken that is sensitive to those feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[0418] 6. Display of responses and feedback:

[0419] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. The evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion, to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[0420] Specific example: Dialogue simulation in a virtual store

[0421] In a virtual store interaction simulation, users engage in activities such as searching for products, asking questions, and making purchases while interacting with store staff and other customers. Examples of prompts are shown below.

[0422] 1. Application:

[0423] "Hello, what are you looking for today?"

[0424] 2. User:

[0425] "I came to check out the new smartphone."

[0426] 3. Application: Based on the selected emotion ("anxiety")

[0427] "Having trouble choosing a smartphone? Don't worry, we'll guide you."

[0428] In this way, this system can effectively improve users' communication skills through realistic dialogue, even in a virtual environment.

[0429] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0430] Step 1:

[0431] The user logs in with the initial settings.

[0432] Input: Username, Password

[0433] Data processing: The terminal sends this information to the server, and the server authenticates the user by comparing the received user information with the database.

[0434] Output: Authentication result, user profile information

[0435] Specific operation: The user enters their username and password on the device's login screen, and the device sends this information to the server. The server verifies this against the database, and if authentication is successful, sends the user's profile information to the device.

[0436] Step 2:

[0437] The user selects their conversation partner.

[0438] Input: Select your conversation partner (e.g., parent, sibling, teacher, friend, virtual customer, virtual shop assistant)

[0439] Data processing: The terminal sends information about the selected conversation partner to the server, and the server generates an appropriate conversation scenario based on the selection.

[0440] Output: Dialogue Scenario

[0441] Specific operation: The user selects a conversation partner on the device screen, and the device sends this information to the server. The server generates a corresponding conversation scenario and sends it to the device.

[0442] Step 3:

[0443] The dialogue simulation will now begin.

[0444] Input: Dialogue scenario, user's initial input (e.g., text, voice)

[0445] Data processing: The terminal displays a dialogue scenario to the user and sends the user's input to the server.

[0446] Output: User input data sent to the server

[0447] Specific operation: The terminal displays a dialogue scenario, and the user provides initial input via text or voice. The terminal sends this input to the server.

[0448] Step 4:

[0449] The server analyzes user input and recognizes emotions.

[0450] Input: User input data

[0451] Data processing: The server uses multimodal AI to analyze user input data and extracts emotional information using an emotion engine.

[0452] Output: Emotional state (e.g., joy, sadness, anger, anxiety)

[0453] Specific operation: The server analyzes the received user input data through speech recognition, sentiment analysis, and text analysis to identify the user's emotional state.

[0454] Step 5:

[0455] The server generates an appropriate response.

[0456] Input: Emotional state, user input data

[0457] Data processing: Based on emotional state, the server generates an appropriate response.

[0458] Output: Generated response

[0459] Specific operation: The server generates an appropriate response that reflects the user's emotional state and sends it to the terminal. For example, if the user is feeling "anxious," the server will generate an encouraging response.

[0460] Step 6:

[0461] Display or play the response on the user's terminal.

[0462] Input: Generated response

[0463] Data processing: The terminal displays or plays the received response to the user.

[0464] Output: Displayed response, played audio

[0465] Specific actions: The terminal displays the response received from the server on the screen or plays it aloud. The user confirms this response and then makes the next input.

[0466] Step 7:

[0467] Save and evaluate user interaction data.

[0468] Input: User interaction history, simulation results

[0469] Data processing: The server saves the interaction history and evaluates the user's overall performance.

[0470] Output: User ratings, feedback

[0471] Specific operation: After the conversation ends, the server saves the conversation data, evaluates the fluency of the conversation, the frequency of appropriate responses, the expression of emotion, etc., and generates feedback. The feedback is saved in the user's learning profile.

[0472] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0473] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0474] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0475] [Second Embodiment]

[0476] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0477] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0478] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0479] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0480] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0481] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0482] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0483] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0484] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0485] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0486] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0487] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0488] This invention is an educational system designed to help users improve their communication skills through interactive dialogue, and is primarily intended for individuals with developmental disabilities or those who have difficulty communicating. The system is implemented in the following manner.

[0489] Program processing and explanation

[0490] 1. Initial Setup and User Authentication

[0491] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[0492] 2. Selection of a dialogue scenario

[0493] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0494] 3. Start the dialogue simulation.

[0495] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0496] 4. Input Analysis and Response Generation

[0497] The server analyzes the received user input data using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis. Based on the analysis results, the server generates an appropriate response and sends it to the terminal.

[0498] 5. Display of responses and feedback

[0499] The device displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile.

[0500] Example: Dialogue simulation with a friend

[0501] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0502] Terminal: "Hello! How are you doing today?"

[0503] User: "I took a test at school." (input)

[0504] The terminal sends this input to the server.

[0505] 2. The server analyzes the user's input and generates an appropriate response.

[0506] Server (response generation): "That must have been tough. Are you curious about the test results?"

[0507] The server sends the generated response to the terminal.

[0508] 3. The terminal displays the received response to the user.

[0509] Terminal: "That must have been tough. Are you curious about your test results?"

[0510] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[0511] The terminal sends the next input to the server again.

[0512] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The user starts up the device and enters their username and password on the login screen.

[0516] Step 2:

[0517] The terminal sends the entered username and password to the server.

[0518] Step 3:

[0519] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[0520] Step 4:

[0521] The server sends the user's profile information to the device.

[0522] Step 5:

[0523] The device displays a screen for the user to select a conversation partner.

[0524] Step 6:

[0525] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[0526] Step 7:

[0527] The device sends the user's selections to the server.

[0528] Step 8:

[0529] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[0530] Step 9:

[0531] The server sends the acquired dialogue scenario to the terminal.

[0532] Step 10:

[0533] The device displays the conversation scenario it received to the user.

[0534] Step 11:

[0535] The user follows the displayed prompts and makes their initial input via text or voice.

[0536] Step 12:

[0537] The terminal sends user input to the server.

[0538] Step 13:

[0539] The server receives user input data and analyzes it using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis.

[0540] Step 14:

[0541] The server generates an appropriate response based on the analysis results.

[0542] Step 15:

[0543] The server sends the generated response to the terminal.

[0544] Step 16:

[0545] The device displays or plays aloud the response it receives to the user.

[0546] Step 17:

[0547] The user enters the information again.

[0548] Step 18:

[0549] The terminal sends the user's next input back to the server.

[0550] Step 19:

[0551] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[0552] Step 20:

[0553] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[0554] Step 21:

[0555] The server saves the evaluation results and feedback to the user's learning profile.

[0556] Step 22:

[0557] The device displays the evaluation results and feedback to the user.

[0558] (Example 1)

[0559] Next, we will describe Example 1. 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".

[0560] This invention relates to an educational system for improving users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. The goal is to provide an environment in which such individuals can learn effectively and to improve their communication skills through real-world dialogue.

[0561] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0562] In this invention, the server includes means for storing and evaluating user dialogue data, means for evaluating the user's overall performance after the dialogue ends and providing feedback, and means for storing the feedback results in the user's learning profile. This makes it possible for the user to utilize the feedback obtained through the dialogue and continuously improve their communication skills.

[0563] "Users" refer to people who use the dialogue simulation system, and it is particularly targeted at people with developmental disabilities or those who have difficulty communicating.

[0564] A "terminal" refers to an electronic device used by a user that has the functions of logging in, displaying dialogue scenarios, receiving user input, and communicating with a server.

[0565] A "server" refers to a central computer system that analyzes user input data, generates appropriate responses, and provides feedback.

[0566] A "dialogue scenario" refers to a sequence of conversations generated by the server and designed based on the user's chosen conversation partner.

[0567] "Multimodal AI" refers to artificial intelligence technology used to analyze multiple sources of information, such as text, audio, and video, and to comprehensively understand and make judgments based on that information.

[0568] "Feedback" refers to evaluations and advice provided based on the results of user interactions, and plays a role in supporting the improvement of users' communication skills.

[0569] A "learning profile" is a database containing a user's past conversation data, evaluation results, and feedback information, and refers to a profile used to record and manage the user's progress and skill improvement.

[0570] This invention is an educational system designed to improve users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. This system provides an environment in which users can learn naturally through dialogue.

[0571] Hardware and software to be used

[0572] The system's main components are user terminals, a server that performs central processing, and a network that facilitates communication between them. Terminals are devices that receive user input and send it to the server, including personal computers, tablets, and smartphones. The server possesses the computing power and database necessary to analyze user input data and generate appropriate responses. Multimodal AI is used for analysis, including a speech recognition engine, a text analysis engine, and a sentiment analysis module.

[0573] Program processing

[0574] Initial setup and user authentication

[0575] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing it against the database. If authentication is successful, the server sends the user's profile information to the device.

[0576] Selection of a dialogue scenario

[0577] The user selects a conversation partner on the device screen. Options for conversation partners include parents, siblings, teachers, and friends. The device sends the user's selection to the server. Based on the selected conversation partner, the server retrieves an appropriate conversation scenario from its database and sends it to the device.

[0578] Start of dialogue simulation

[0579] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[0580] Input analysis and response generation

[0581] The server analyzes the received user input data using multimodal AI (e.g., a speech recognition engine, sentiment analysis module, text analysis engine, etc.). In the case of voice input, it is first converted into text by the speech recognition engine. Next, the text analysis engine understands the context, and the sentiment analysis module determines the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[0582] Display of response and feedback

[0583] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[0584] Specific example

[0585] Dialogue simulation with friends

[0586] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0587] Terminal: "Hello! How are you doing today?"

[0588] User: "I took a test at school." (input)

[0589] The terminal sends this input to the server.

[0590] 2. The server analyzes the user's input and generates an appropriate response.

[0591] Server (response generation): "That must have been tough. Are you curious about the test results?"

[0592] The server sends the generated response to the terminal.

[0593] 3. The terminal displays the received response to the user.

[0594] Terminal: "That must have been tough. Are you curious about your test results?"

[0595] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[0596] The terminal sends the next input to the server again.

[0597] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[0598] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0599] Step 1: Initial Setup and User Authentication

[0600] The user starts up the device and enters their username and password on the login screen. The device sends the entered username and password to the server. The server compares the received username and password with the information stored in the database. If authentication is successful, the server sends the user's profile information (e.g., name, age, learning history, etc.) to the device. If authentication fails, an error message is sent to the device.

[0601] Input: Username, Password

[0602] Data processing and calculation: Database matching

[0603] Output: User profile information (on success) or error message (on failure)

[0604] Step 2: Selecting a dialogue scenario

[0605] The user selects a conversation partner on the device screen. Options include parent, sibling, teacher, friend, etc. The device sends information about the selected conversation partner to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0606] Input: Select conversation partner

[0607] Data processing and calculation: Retrieving dialogue scenarios from databases

[0608] Output: Dialogue Scenario

[0609] Step 3: Start the dialogue simulation

[0610] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[0611] Input: Dialogue scenario, user response

[0612] Data processing / calculations: None

[0613] Output: User input data

[0614] Step 4: Input analysis and response generation

[0615] The server analyzes the received user input data using multimodal AI. In the case of voice input, it first converts it to text using a speech recognition engine. Next, it uses a text analysis engine to understand the context and an emotion analysis module to determine the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[0616] Input: User input data

[0617] Data processing and computation: speech recognition, text analysis, sentiment analysis

[0618] Output: Response data

[0619] Step 5: Displaying responses and providing feedback

[0620] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[0621] Input: Response data, user feedback

[0622] Data processing and computation: Performance evaluation, feedback generation.

[0623] Output: Feedback data

[0624] (Application Example 1)

[0625] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0626] In recent years, while the introduction of robots in factories has progressed, efficiency and precision are required in communication between workers and robots. However, the lack of training and support systems for workers to interact smoothly with robots can lead to decreased work efficiency and safety. In particular, the inability to take immediate and appropriate action when giving specific work instructions or resolving problems is a major challenge.

[0627] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0628] In this invention, the server includes means for the user to log in with initial settings, means for obtaining the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for saving and evaluating user conversation data, means for providing a conversation simulation for factory workers to efficiently give work instructions and solve problems with robots, and means for speech recognition and text conversion of user input in the simulation dialogue and playing a response based on that in audio. This makes it possible to improve the efficiency and safety of communication between factory workers and robots.

[0629] "User" refers to a person who uses this system.

[0630] "Profile information" refers to data that includes personal information, specific characteristics, and settings about a user.

[0631] "Dialogue partner" refers to the person with whom the user engages in simulated dialogue.

[0632] A "dialogue scenario" refers to a scenario that outlines the flow and content of a dialogue generated based on the selected dialogue partner.

[0633] A "terminal" refers to a device used by a user to perform a dialogue simulation.

[0634] A "server" refers to a computer system responsible for managing the entire system, analyzing data, and generating responses.

[0635] "Multimodal AI" refers to artificial intelligence that analyzes data in multiple formats, such as text, audio, and video, and generates appropriate responses.

[0636] "Response" refers to the reply or explanation that the server generates based on the user's input data.

[0637] "Dialogue data" refers to all the data collected during the process of interacting with the user.

[0638] "Factory worker" refers to a worker who performs duties in a factory.

[0639] A "robot" refers to a mechanical device used to assist or automate tasks within a factory.

[0640] "Speech recognition" refers to the technology that converts speech data input through a microphone into text data.

[0641] "Text conversion" refers to the process of representing non-text data, such as audio data, as text.

[0642] "Dialogue simulation" refers to the process by which users train and test their interaction skills using a system before actually performing a task.

[0643] "Playing as audio" refers to outputting the generated response as audio data and letting the user listen to it.

[0644] This invention is an educational system that supports interaction between factory workers and robots, with the aim of enabling workers to communicate efficiently with robots and improve work efficiency and safety. The system is designed for users to log in for initial setup, select an interaction partner, and conduct a dialogue simulation.

[0645] System Overview

[0646] The system consists of the following main elements:

[0647] 1. Terminal: A device used by workers, which displays the scenario and plays audio.

[0648] 2. Server: A computer system that manages the entire system and performs data analysis and response generation.

[0649] 3. Multimodal AI: Artificial intelligence that performs speech recognition and text analysis to generate appropriate responses based on user input.

[0650] Hardware and software to be used

[0651] Hardware: Microphone, speaker, device (PC, tablet, etc.)

[0652] Software: Python, speech_recognition library, gTTS library, audio playback program (e.g., mpg321)

[0653] Data processing and data calculation

[0654] The server processes and performs calculations on the data using the following steps:

[0655] 1. Login and User Authentication: The username and password information sent from the terminal is sent to the server, which authenticates the user by comparing it with the database.

[0656] 2. Retrieving the dialogue scenario: When the user selects a dialogue partner, that information is sent to the server, and the server retrieves an appropriate dialogue scenario from the database and sends it to the terminal.

[0657] 3. Initiating the dialogue simulation: As the user inputs according to the scenario, the content is converted to text via speech recognition and sent to the server. The server uses multimodal AI to analyze the user input.

[0658] 4. Response generation and display: An appropriate response is generated based on the analysis results and sent to the terminal. The terminal plays the received response aloud.

[0659] Specific example

[0660] The following is a specific example of how the system works:

[0661] 1. Initial setup and selecting a conversation partner:

[0662] The user starts up the device and enters their information on the login screen.

[0663] The user selects a dialogue scenario with a "factory robot".

[0664] Terminal: "Please begin the operation."

[0665] User: "Understood, I'll start." (typed).

[0666] The terminal sends this input to the server.

[0667] 2. Input analysis and response reproduction:

[0668] Server (analysis): "The user has indicated an intention to start the task."

[0669] Server (response generation): "Starting work. Please provide details of the steps."

[0670] The device plays the received response aloud.

[0671] Example of the above prompt message:

[0672] User: "Please begin the task."

[0673] Generated AI model response: "I will begin working. Please provide details of the steps."

[0674] This system simulates training for users to interact with robots, supporting them in efficiently performing tasks in real work environments. Furthermore, by utilizing speech recognition and text conversion to provide dialogue scenarios that may be encountered in actual work situations, it contributes to improving workers' skills.

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. The input is the user's login information, and the output is the authentication result and profile information.

[0678] Step 2:

[0679] The user selects a conversation partner on the terminal screen. The terminal sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the terminal. The input is the user's selection of a conversation partner, and the output is the conversation scenario.

[0680] Step 3:

[0681] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and makes initial input in text or voice. The input voice is recognized by the terminal and converted into text data. The converted text data is sent to the server. Input is the user's voice input, and output is text data.

[0682] Step 4:

[0683] The server analyzes the received text data. This analysis includes speech recognition, sentiment analysis, and text analysis. The server then generates an appropriate response based on the analysis results and sends it to the terminal. The input is the user's text data, and the output is the generated response.

[0684] Step 5:

[0685] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile. The input is the response from the server, and the output is the evaluation and feedback.

[0686] example

[0687] For example, if a factory worker instructs the robot they are interacting with by saying, "Please begin work," the server's generated AI model will produce a response saying, "I will begin work. Please tell me the details of the steps," send it to the terminal, and play it back aloud. The user then continues to give instructions such as, "Okay, please start by attaching part A." This dialogue continues through the system, training the worker to communicate smoothly with the robot.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, primarily targeting individuals with developmental disabilities or those who struggle with communication. The system incorporates an emotion engine that recognizes and responds to user emotions in real time, thereby achieving even more realistic dialogue. This system is implemented in the following manner.

[0690] Program processing and explanation

[0691] 1. Initial Setup and User Authentication

[0692] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[0693] 2. Selection of a dialogue scenario

[0694] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0695] 3. Start the dialogue simulation.

[0696] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0697] 4. Input Analysis and Emotion Recognition

[0698] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (e.g., joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[0699] 5. Response generation and display

[0700] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state, and an appropriate response is taken that is empathetic to the user's feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[0701] 6. Display of responses and feedback

[0702] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and emotional expression, and is a comprehensive assessment to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[0703] Example: Dialogue simulation with a friend

[0704] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0705] Terminal: "Hello! How are you doing today?"

[0706] User: "I took a test at school." (input)

[0707] The terminal sends this input to the server.

[0708] 2. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[0709] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[0710] The server sends the generated response to the terminal.

[0711] 3. The terminal displays the received response to the user.

[0712] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[0713] The user enters the following information:

[0714] The terminal sends the next input to the server again.

[0715] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, thereby realizing more effective communication learning.

[0716] The following describes the processing flow.

[0717] Step 1:

[0718] The user starts up the device and enters their username and password on the login screen.

[0719] Step 2:

[0720] The terminal sends the entered username and password to the server.

[0721] Step 3:

[0722] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[0723] Step 4:

[0724] The server sends the user's profile information to the device.

[0725] Step 5:

[0726] The device displays a screen for the user to select a conversation partner.

[0727] Step 6:

[0728] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[0729] Step 7:

[0730] The device sends the user's selections to the server.

[0731] Step 8:

[0732] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[0733] Step 9:

[0734] The server sends the acquired dialogue scenario to the terminal.

[0735] Step 10:

[0736] The device displays the conversation scenario it received to the user.

[0737] Step 11:

[0738] The user follows the displayed prompts and makes their initial input via text or voice.

[0739] Step 12:

[0740] The terminal sends user input to the server.

[0741] Step 13:

[0742] The server receives user input data and analyzes it using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and performs speech recognition, sentiment analysis, and text analysis.

[0743] Step 14:

[0744] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state.

[0745] Step 15:

[0746] The server sends the generated response to the terminal.

[0747] Step 16:

[0748] The device displays or plays aloud the response it receives to the user.

[0749] Step 17:

[0750] The user enters the information again.

[0751] Step 18:

[0752] The terminal sends the user's next input back to the server.

[0753] Step 19:

[0754] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[0755] Step 20:

[0756] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[0757] Step 21:

[0758] The server saves the evaluation results and feedback to the user's learning profile.

[0759] Step 22:

[0760] The device displays the evaluation results and feedback to the user.

[0761] (Example 2)

[0762] Next, we will describe Example 2. 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".

[0763] In modern society, there is a need for educational systems that allow people with developmental disabilities and those who struggle with communication to improve their communication skills at their own pace without undue pressure. However, conventional educational systems have struggled to recognize users' emotions in real time and generate appropriate responses accordingly. As a result, they have been unable to realize dialogue that is in line with the user's emotional state, making effective communication learning difficult. To solve these problems, a system utilizing an emotion recognition engine and multimodal artificial intelligence is necessary.

[0764] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing user input, means for identifying the user's emotional state using an emotion recognition engine, and means for generating an appropriate response based on the analysis results. This enables real-time dialogue that is in line with the user's emotional state.

[0765] "Logging in" is the process by which a user enters the necessary credentials to access a system and undergoes authentication.

[0766] "Profile information" refers to a collection of data that includes personal information about the user, past activity history, learning progress, and more.

[0767] A "dialogue partner" refers to a virtual or real entity with which the user engages in dialogue, and includes parents, siblings, teachers, friends, and others.

[0768] A "dialogue scenario" is a script that includes prompts and example responses to anticipate the flow of a conversation based on a specific situation.

[0769] "Multimodal artificial intelligence" is an artificial intelligence technology that has the ability to process and analyze multiple data formats, such as text, audio, and images.

[0770] An "emotion recognition engine" is a software component that identifies emotional states (such as joy, sadness, anger, or anxiety) from user input.

[0771] A "response" is a text or audio response generated by the system in response to user input.

[0772] "Feedback" is the process of providing evaluations and advice regarding user behavior and performance.

[0773] "Dialogue data" refers to a collection of data that contains all information related to the dialogue that took place between the user and the system.

[0774] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, and is particularly aimed at individuals with developmental disabilities or those who have difficulty communicating. This system achieves more realistic dialogue by incorporating an emotion engine that recognizes and responds to the user's emotions in real time. Specific embodiments of this system are described below.

[0775] First, the user starts up the device and enters their username and password on the login screen. This information is sent from the device to the server using a secure communication method (e.g., HTTPS). The server compares the received user information with its internal database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[0776] Next, the user selects a conversation partner on the device screen. These partners include parents, siblings, teachers, and friends, and the scenario changes accordingly. The user's selection information is sent to the server, which retrieves an appropriate conversation scenario from its database based on the selected partner and sends it to the device.

[0777] The received dialogue scenario is displayed on the terminal. The user follows the displayed prompts and makes the initial input in text or voice. In the case of voice input, the terminal uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[0778] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The emotion engine has functions such as speech recognition, text analysis, and sentiment analysis to identify the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.). Microsoft Azure Cognitive Services, etc., can be used.

[0779] Based on the analysis results, the server generates an appropriate response. Using generative AI models such as OpenAI's GPT-3, it generates text or audio responses that reflect the user's emotional state. The generated response is sent from the server to the terminal and displayed or played aloud to the user.

[0780] Finally, once the user's interaction concludes, the server evaluates the overall performance and provides feedback. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression, and is used as a comprehensive assessment to help improve the user's communication skills. The evaluation results are also saved in the user's learning profile.

[0781] Below is a concrete example of a conversation simulation with a friend.

[0782] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0783] Terminal: "Hello! How are you doing today?"

[0784] User: "I took a test at school." (input)

[0785] 2. The terminal sends this input to the server.

[0786] 3. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[0787] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[0788] The server sends the generated response to the terminal.

[0789] 4. The terminal displays the received response to the user.

[0790] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[0791] The user then enters the next command, and the terminal sends the input back to the server.

[0792] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[0793] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0794] Step 1:

[0795] The user starts up the device and enters their username and password on the login screen. This entered information, which includes the user's credentials, is sent from the device to the server.

[0796] Step 2:

[0797] The device sends the username and password to the server. HTTPS communication is used for transmission to ensure security.

[0798] Step 3:

[0799] The server compares the received user information with the information in the database and performs authentication. The database stores the username and hashed password. If authentication is successful, it generates an authentication result and retrieves the user's profile information from the database.

[0800] Step 4:

[0801] Upon successful authentication, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[0802] Step 5:

[0803] The user selects a conversation partner on the device screen. This partner may include parents, siblings, teachers, or friends. This selection becomes the input data.

[0804] Step 6:

[0805] The terminal sends information about the selected conversation partner to the server. The transmitted data includes the identification information of the conversation partner selected by the user.

[0806] Step 7:

[0807] The server retrieves an appropriate dialogue scenario from the database based on the information of the selected dialogue partner. This scenario includes situation-specific prompts and possible response examples. The retrieved scenario data is generated and sent to the terminal.

[0808] Step 8:

[0809] The terminal displays the received dialogue scenario to the user. The first prompt appears on the screen. This prompt becomes the input data.

[0810] Step 9:

[0811] The user follows the displayed prompts and makes their initial input in text or voice. In the case of voice input, the device uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[0812] Step 10:

[0813] The terminal sends user input data to the server. The transmitted data includes user input.

[0814] Step 11:

[0815] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The analysis includes speech recognition, text analysis, and sentiment analysis. As a result of the analysis, the server identifies the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.).

[0816] Step 12:

[0817] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., GPT-3), it generates text and audio responses that reflect the user's emotional state. The generated responses become response data.

[0818] Step 13:

[0819] The server sends the generated response data to the terminal. The transmitted data contains the appropriate response.

[0820] Step 14:

[0821] The terminal displays or plays the received response to the user. The displayed response or played audio becomes the display data.

[0822] Step 15:

[0823] The user reviews the displayed response and then provides the next input. This loop continues until the interaction is complete.

[0824] Step 16:

[0825] Once the interaction ends, the server evaluates the user's overall performance. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression. The evaluation results are generated and stored in the user's learning profile. The evaluation data reflects the user's progress.

[0826] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[0827] (Application Example 2)

[0828] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0829] In modern society, individuals with developmental disabilities or those who struggle with communication often face difficulties in engaging in smooth conversations in their daily lives and work. This is particularly true in virtual environments where real-time interaction with others presents challenges in recognizing emotions and generating appropriate responses. Therefore, there is a need for support systems that enable users to more effectively improve their communication skills.

[0830] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0831] In this invention, the server includes means for the user to log in with initial settings, means for acquiring the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for simulating the user searching for, asking questions about, and purchasing products while interacting with others in a virtual environment, means for recognizing the user's emotions and generating and displaying a corresponding response in real time, and means for saving and evaluating the user's conversation data. This makes it possible for users to effectively improve their communication skills through realistic conversations even in a virtual environment.

[0832] "Users" refer to individuals who use this system with the aim of improving their communication skills.

[0833] "Initial login process" refers to the process of entering a username and password and performing authentication necessary for a user to begin using the system.

[0834] "Means of obtaining profile information" refers to the process of obtaining a user's personal information, past conversation history, etc., from the server.

[0835] "Means of selecting a dialogue partner" refers to a function that allows users to choose who they will interact with within the system (e.g., parents, siblings, teachers, friends, virtual customers, virtual shop assistants).

[0836] "Means for generating dialogue scenarios" refers to the process by which the server generates and configures an appropriate dialogue scenario based on the selected dialogue partner.

[0837] "Means for displaying dialogue scenarios" refers to a function that presents the generated dialogue scenario to the user's terminal either visually or audibly.

[0838] "Means for sending user input to the server" refers to a function that sends the content of a conversation entered by the user in text or voice to the server.

[0839] "Methods for analyzing user input using multimodal AI" refers to the process of understanding user input using AI technology that analyzes multiple data formats, such as speech and text.

[0840] "Means for generating appropriate responses" refers to the process of generating appropriate responses to user input based on analysis results.

[0841] "Means for displaying or playing a response" refers to a function that displays the generated response on the user's device or plays it back as audio.

[0842] "Means of conducting dialogue simulations within a virtual environment" refers to the process by which users perform actions such as searching for products, asking questions, and making purchases while simulating dialogues with others within a virtual environment.

[0843] "Means for recognizing emotions and generating responses in real time" refers to a function that recognizes the user's emotional state in real time and generates a response that corresponds to those emotions.

[0844] "Means for saving and evaluating dialogue data" refers to a process of saving a user's dialogue history and later evaluating the user's communication skills based on that data.

[0845] This invention is an educational system designed to help users improve their communication skills through interactive learning. This system is particularly intended for individuals with developmental disabilities or those who have difficulty communicating. Specific embodiments of this system are described below.

[0846] Hardware and software to be used

[0847] Hardware:

[0848] smartphone

[0849] Head-mounted display (HMD)

[0850] software:

[0851] Python programming language

[0852] Speech recognition API

[0853] Emotion recognition API

[0854] Multimodal AI (for analyzing multiple data formats)

[0855] System Configuration

[0856] This system includes the following components.

[0857] 1. Initial setup and user authentication methods:

[0858] To begin using the system, a user first starts up their device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing the received user information with its database. If authentication is successful, the server sends the user's profile information to the device.

[0859] 2. Means of selecting a dialogue partner:

[0860] The user selects a conversation partner (such as a parent, sibling, teacher, friend, virtual customer, or virtual shop assistant) on the device screen. The device sends this selection to the server, which generates an appropriate conversation scenario based on the selected conversation partner and sends it back to the device.

[0861] 3. Start the dialogue simulation:

[0862] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0863] 4. Input analysis and emotion recognition methods:

[0864] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[0865] 5. Response generation and display means:

[0866] The server generates an appropriate response based on the analysis results. This response reflects the user's emotional state, and an appropriate response is taken that is sensitive to those feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[0867] 6. Display of responses and feedback:

[0868] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. The evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion, to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[0869] Example: Dialogue simulation in a virtual store

[0870] In a virtual store interaction simulation, users engage in activities such as searching for products, asking questions, and making purchases while interacting with store staff and other customers. Examples of prompts are shown below.

[0871] 1. Application:

[0872] "Hello, what are you looking for today?"

[0873] 2. User:

[0874] "I came to check out the new smartphone."

[0875] 3. Application: Based on the selected emotion ("anxiety")

[0876] "Having trouble choosing a smartphone? Don't worry, we'll guide you."

[0877] In this way, this system can effectively improve users' communication skills through realistic dialogue, even in a virtual environment.

[0878] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0879] Step 1:

[0880] The user logs in with the initial settings.

[0881] Input: Username, Password

[0882] Data processing: The terminal sends this information to the server, and the server authenticates the user by comparing the received user information with the database.

[0883] Output: Authentication result, user profile information

[0884] Specific operation: The user enters their username and password on the device's login screen, and the device sends this information to the server. The server verifies this against the database, and if authentication is successful, sends the user's profile information to the device.

[0885] Step 2:

[0886] The user selects their conversation partner.

[0887] Input: Select your conversation partner (e.g., parent, sibling, teacher, friend, virtual customer, virtual shop assistant)

[0888] Data processing: The terminal sends information about the selected conversation partner to the server, and the server generates an appropriate conversation scenario based on the selection.

[0889] Output: Dialogue Scenario

[0890] Specific operation: The user selects a conversation partner on the device screen, and the device sends this information to the server. The server generates a corresponding conversation scenario and sends it to the device.

[0891] Step 3:

[0892] The dialogue simulation will now begin.

[0893] Input: Dialogue scenario, user's initial input (e.g., text, voice)

[0894] Data processing: The terminal displays a dialogue scenario to the user and sends the user's input to the server.

[0895] Output: User input data sent to the server

[0896] Specific operation: The terminal displays a dialogue scenario, and the user provides initial input via text or voice. The terminal sends this input to the server.

[0897] Step 4:

[0898] The server analyzes user input and recognizes emotions.

[0899] Input: User input data

[0900] Data processing: The server uses multimodal AI to analyze user input data and extracts emotional information using an emotion engine.

[0901] Output: Emotional state (e.g., joy, sadness, anger, anxiety)

[0902] Specific operation: The server analyzes the received user input data through speech recognition, sentiment analysis, and text analysis to identify the user's emotional state.

[0903] Step 5:

[0904] The server generates an appropriate response.

[0905] Input: Emotional state, user input data

[0906] Data processing: Based on emotional state, the server generates an appropriate response.

[0907] Output: Generated response

[0908] Specific operation: The server generates an appropriate response that reflects the user's emotional state and sends it to the terminal. For example, if the user is feeling "anxious," the server will generate an encouraging response.

[0909] Step 6:

[0910] Display or play the response on the user's terminal.

[0911] Input: Generated response

[0912] Data processing: The terminal displays or plays the received response to the user.

[0913] Output: Displayed response, played audio

[0914] Specific actions: The terminal displays the response received from the server on the screen or plays it aloud. The user confirms this response and then makes the next input.

[0915] Step 7:

[0916] Save and evaluate user interaction data.

[0917] Input: User interaction history, simulation results

[0918] Data processing: The server saves the interaction history and evaluates the user's overall performance.

[0919] Output: User ratings, feedback

[0920] Specific operation: After the conversation ends, the server saves the conversation data, evaluates the fluency of the conversation, the frequency of appropriate responses, the expression of emotion, etc., and generates feedback. The feedback is saved in the user's learning profile.

[0921] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0922] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0923] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0924] [Third Embodiment]

[0925] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0926] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0927] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0928] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0929] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0930] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0931] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0932] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0933] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0934] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0935] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0936] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0937] This invention is an educational system designed to help users improve their communication skills through interactive dialogue, and is primarily intended for individuals with developmental disabilities or those who have difficulty communicating. The system is implemented in the following manner.

[0938] Program processing and explanation

[0939] 1. Initial Setup and User Authentication

[0940] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[0941] 2. Selection of a dialogue scenario

[0942] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[0943] 3. Start the dialogue simulation.

[0944] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[0945] 4. Input Analysis and Response Generation

[0946] The server analyzes the received user input data using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis. Based on the analysis results, the server generates an appropriate response and sends it to the terminal.

[0947] 5. Display of responses and feedback

[0948] The device displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile.

[0949] Example: Dialogue simulation with a friend

[0950] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[0951] Terminal: "Hello! How are you doing today?"

[0952] User: "I took a test at school." (input)

[0953] The terminal sends this input to the server.

[0954] 2. The server analyzes the user's input and generates an appropriate response.

[0955] Server (response generation): "That must have been tough. Are you curious about the test results?"

[0956] The server sends the generated response to the terminal.

[0957] 3. The terminal displays the received response to the user.

[0958] Terminal: "That must have been tough. Are you curious about your test results?"

[0959] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[0960] The terminal sends the next input to the server again.

[0961] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[0962] The following describes the processing flow.

[0963] Step 1:

[0964] The user starts up the device and enters their username and password on the login screen.

[0965] Step 2:

[0966] The terminal sends the entered username and password to the server.

[0967] Step 3:

[0968] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[0969] Step 4:

[0970] The server sends the user's profile information to the device.

[0971] Step 5:

[0972] The device displays a screen for the user to select a conversation partner.

[0973] Step 6:

[0974] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[0975] Step 7:

[0976] The device sends the user's selections to the server.

[0977] Step 8:

[0978] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[0979] Step 9:

[0980] The server sends the acquired dialogue scenario to the terminal.

[0981] Step 10:

[0982] The device displays the conversation scenario it received to the user.

[0983] Step 11:

[0984] The user follows the displayed prompts and makes their initial input via text or voice.

[0985] Step 12:

[0986] The terminal sends user input to the server.

[0987] Step 13:

[0988] The server receives user input data and analyzes it using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis.

[0989] Step 14:

[0990] The server generates an appropriate response based on the analysis results.

[0991] Step 15:

[0992] The server sends the generated response to the terminal.

[0993] Step 16:

[0994] The device displays or plays aloud the response it receives to the user.

[0995] Step 17:

[0996] The user enters the information again.

[0997] Step 18:

[0998] The terminal sends the user's next input back to the server.

[0999] Step 19:

[1000] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[1001] Step 20:

[1002] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[1003] Step 21:

[1004] The server saves the evaluation results and feedback to the user's learning profile.

[1005] Step 22:

[1006] The device displays the evaluation results and feedback to the user.

[1007] (Example 1)

[1008] Next, we will describe Example 1. 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."

[1009] This invention relates to an educational system for improving users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. The goal is to provide an environment in which such individuals can learn effectively and to improve their communication skills through real-world dialogue.

[1010] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1011] In this invention, the server includes means for storing and evaluating user dialogue data, means for evaluating the user's overall performance after the dialogue ends and providing feedback, and means for storing the feedback results in the user's learning profile. This makes it possible for the user to utilize the feedback obtained through the dialogue and continuously improve their communication skills.

[1012] "Users" refer to people who use the dialogue simulation system, and it is particularly targeted at people with developmental disabilities or those who have difficulty communicating.

[1013] A "terminal" refers to an electronic device used by a user that has the functions of logging in, displaying dialogue scenarios, receiving user input, and communicating with a server.

[1014] A "server" refers to a central computer system that analyzes user input data, generates appropriate responses, and provides feedback.

[1015] A "dialogue scenario" refers to a sequence of conversations generated by the server and designed based on the user's chosen conversation partner.

[1016] "Multimodal AI" refers to artificial intelligence technology used to analyze multiple sources of information, such as text, audio, and video, and to comprehensively understand and make judgments based on that information.

[1017] "Feedback" refers to evaluations and advice provided based on the results of user interactions, and plays a role in supporting the improvement of users' communication skills.

[1018] A "learning profile" is a database containing a user's past conversation data, evaluation results, and feedback information, and refers to a profile used to record and manage the user's progress and skill improvement.

[1019] This invention is an educational system designed to improve users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. This system provides an environment in which users can learn naturally through dialogue.

[1020] Hardware and software to be used

[1021] The system's main components are user terminals, a server that performs central processing, and a network that facilitates communication between them. Terminals are devices that receive user input and send it to the server, including personal computers, tablets, and smartphones. The server possesses the computing power and database necessary to analyze user input data and generate appropriate responses. Multimodal AI is used for analysis, including a speech recognition engine, a text analysis engine, and a sentiment analysis module.

[1022] Program processing

[1023] Initial setup and user authentication

[1024] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing it against the database. If authentication is successful, the server sends the user's profile information to the device.

[1025] Selection of a dialogue scenario

[1026] The user selects a conversation partner on the device screen. Options for conversation partners include parents, siblings, teachers, and friends. The device sends the user's selection to the server. Based on the selected conversation partner, the server retrieves an appropriate conversation scenario from its database and sends it to the device.

[1027] Start of dialogue simulation

[1028] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[1029] Input analysis and response generation

[1030] The server analyzes the received user input data using multimodal AI (e.g., a speech recognition engine, sentiment analysis module, text analysis engine, etc.). In the case of voice input, it is first converted into text by the speech recognition engine. Next, the text analysis engine understands the context, and the sentiment analysis module determines the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[1031] Display of response and feedback

[1032] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[1033] Specific example

[1034] Dialogue simulation with friends

[1035] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1036] Terminal: "Hello! How are you doing today?"

[1037] User: "I took a test at school." (input)

[1038] The terminal sends this input to the server.

[1039] 2. The server analyzes the user's input and generates an appropriate response.

[1040] Server (response generation): "That must have been tough. Are you curious about the test results?"

[1041] The server sends the generated response to the terminal.

[1042] 3. The terminal displays the received response to the user.

[1043] Terminal: "That must have been tough. Are you curious about your test results?"

[1044] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[1045] The terminal sends the next input to the server again.

[1046] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[1047] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1048] Step 1: Initial Setup and User Authentication

[1049] The user starts up the device and enters their username and password on the login screen. The device sends the entered username and password to the server. The server compares the received username and password with the information stored in the database. If authentication is successful, the server sends the user's profile information (e.g., name, age, learning history, etc.) to the device. If authentication fails, an error message is sent to the device.

[1050] Input: Username, Password

[1051] Data processing and calculation: Database matching

[1052] Output: User profile information (on success) or error message (on failure)

[1053] Step 2: Selecting a dialogue scenario

[1054] The user selects a conversation partner on the device screen. Options include parent, sibling, teacher, friend, etc. The device sends information about the selected conversation partner to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[1055] Input: Select conversation partner

[1056] Data processing and calculation: Retrieving dialogue scenarios from databases

[1057] Output: Dialogue Scenario

[1058] Step 3: Start the dialogue simulation

[1059] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[1060] Input: Dialogue scenario, user response

[1061] Data processing / calculations: None

[1062] Output: User input data

[1063] Step 4: Input analysis and response generation

[1064] The server analyzes the received user input data using multimodal AI. In the case of voice input, it first converts it to text using a speech recognition engine. Next, it uses a text analysis engine to understand the context and an emotion analysis module to determine the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[1065] Input: User input data

[1066] Data processing and computation: speech recognition, text analysis, sentiment analysis

[1067] Output: Response data

[1068] Step 5: Displaying responses and providing feedback

[1069] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[1070] Input: Response data, user feedback

[1071] Data processing and computation: Performance evaluation, feedback generation.

[1072] Output: Feedback data

[1073] (Application Example 1)

[1074] Next, we will explain Application Example 1. In the following explanation, 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."

[1075] In recent years, while the introduction of robots in factories has progressed, efficiency and precision are required in communication between workers and robots. However, the lack of training and support systems for workers to interact smoothly with robots can lead to decreased work efficiency and safety. In particular, the inability to take immediate and appropriate action when giving specific work instructions or resolving problems is a major challenge.

[1076] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1077] In this invention, the server includes means for the user to log in with initial settings, means for obtaining the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for saving and evaluating user conversation data, means for providing a conversation simulation for factory workers to efficiently give work instructions and solve problems with robots, and means for speech recognition and text conversion of user input in the simulation dialogue and playing a response based on that in audio. This makes it possible to improve the efficiency and safety of communication between factory workers and robots.

[1078] "User" refers to a person who uses this system.

[1079] "Profile information" refers to data that includes personal information, specific characteristics, and settings about a user.

[1080] "Dialogue partner" refers to the person with whom the user engages in simulated dialogue.

[1081] A "dialogue scenario" refers to a scenario that outlines the flow and content of a dialogue generated based on the selected dialogue partner.

[1082] A "terminal" refers to a device used by a user to perform a dialogue simulation.

[1083] A "server" refers to a computer system responsible for managing the entire system, analyzing data, and generating responses.

[1084] "Multimodal AI" refers to artificial intelligence that analyzes data in multiple formats, such as text, audio, and video, and generates appropriate responses.

[1085] "Response" refers to the reply or explanation that the server generates based on the user's input data.

[1086] "Dialogue data" refers to all the data collected during the process of interacting with the user.

[1087] "Factory worker" refers to a worker who performs duties in a factory.

[1088] A "robot" refers to a mechanical device used to assist or automate tasks within a factory.

[1089] "Speech recognition" refers to the technology that converts speech data input through a microphone into text data.

[1090] "Text conversion" refers to the process of representing non-text data, such as audio data, as text.

[1091] "Dialogue simulation" refers to the process by which users train and test their interaction skills using a system before actually performing a task.

[1092] "Playing as audio" refers to outputting the generated response as audio data and letting the user listen to it.

[1093] This invention is an educational system that supports interaction between factory workers and robots, with the aim of enabling workers to communicate efficiently with robots and improve work efficiency and safety. The system is designed for users to log in for initial setup, select an interaction partner, and conduct a dialogue simulation.

[1094] System Overview

[1095] The system consists of the following main elements:

[1096] 1. Terminal: A device used by workers, which displays the scenario and plays audio.

[1097] 2. Server: A computer system that manages the entire system and performs data analysis and response generation.

[1098] 3. Multimodal AI: Artificial intelligence that performs speech recognition and text analysis to generate appropriate responses based on user input.

[1099] Hardware and software to be used

[1100] Hardware: Microphone, speaker, device (PC, tablet, etc.)

[1101] Software: Python, speech_recognition library, gTTS library, audio playback program (e.g., mpg321)

[1102] Data processing and data calculation

[1103] The server processes and performs calculations on the data using the following steps:

[1104] 1. Login and User Authentication: The username and password information sent from the terminal is sent to the server, which authenticates the user by comparing it with the database.

[1105] 2. Retrieving the dialogue scenario: When the user selects a dialogue partner, that information is sent to the server, and the server retrieves an appropriate dialogue scenario from the database and sends it to the terminal.

[1106] 3. Initiating the dialogue simulation: As the user inputs according to the scenario, the content is converted to text via speech recognition and sent to the server. The server uses multimodal AI to analyze the user input.

[1107] 4. Response generation and display: An appropriate response is generated based on the analysis results and sent to the terminal. The terminal plays the received response aloud.

[1108] Specific example

[1109] The following is a specific example of how the system works:

[1110] 1. Initial setup and selecting a conversation partner:

[1111] The user starts up the device and enters their information on the login screen.

[1112] The user selects a dialogue scenario with a "factory robot".

[1113] Terminal: "Please begin the operation."

[1114] User: "Understood, I'll start." (typed).

[1115] The terminal sends this input to the server.

[1116] 2. Input analysis and response reproduction:

[1117] Server (analysis): "The user has indicated an intention to start the task."

[1118] Server (response generation): "Starting work. Please provide details of the steps."

[1119] The device plays the received response aloud.

[1120] Example of the above prompt message:

[1121] User: "Please begin the task."

[1122] Generated AI model response: "I will begin working. Please provide details of the steps."

[1123] This system simulates training for users to interact with robots, supporting them in efficiently performing tasks in real work environments. Furthermore, by utilizing speech recognition and text conversion to provide dialogue scenarios that may be encountered in actual work situations, it contributes to improving workers' skills.

[1124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1125] Step 1:

[1126] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. The input is the user's login information, and the output is the authentication result and profile information.

[1127] Step 2:

[1128] The user selects a conversation partner on the terminal screen. The terminal sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the terminal. The input is the user's selection of a conversation partner, and the output is the conversation scenario.

[1129] Step 3:

[1130] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and makes initial input in text or voice. The input voice is recognized by the terminal and converted into text data. The converted text data is sent to the server. Input is the user's voice input, and output is text data.

[1131] Step 4:

[1132] The server analyzes the received text data. This analysis includes speech recognition, sentiment analysis, and text analysis. The server then generates an appropriate response based on the analysis results and sends it to the terminal. The input is the user's text data, and the output is the generated response.

[1133] Step 5:

[1134] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile. The input is the response from the server, and the output is the evaluation and feedback.

[1135] example

[1136] For example, if a factory worker instructs the robot they are interacting with by saying, "Please begin work," the server's generated AI model will produce a response saying, "I will begin work. Please tell me the details of the steps," send it to the terminal, and play it back aloud. The user then continues to give instructions such as, "Okay, please start by attaching part A." This dialogue continues through the system, training the worker to communicate smoothly with the robot.

[1137] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1138] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, primarily targeting individuals with developmental disabilities or those who struggle with communication. The system incorporates an emotion engine that recognizes and responds to user emotions in real time, thereby achieving even more realistic dialogue. This system is implemented in the following manner.

[1139] Program processing and explanation

[1140] 1. Initial Setup and User Authentication

[1141] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[1142] 2. Selection of a dialogue scenario

[1143] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[1144] 3. Start the dialogue simulation.

[1145] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[1146] 4. Input Analysis and Emotion Recognition

[1147] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (e.g., joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[1148] 5. Response generation and display

[1149] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state, and an appropriate response is taken that is empathetic to the user's feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[1150] 6. Display of responses and feedback

[1151] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and emotional expression, and is a comprehensive assessment to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[1152] Example: Dialogue simulation with a friend

[1153] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1154] Terminal: "Hello! How are you doing today?"

[1155] User: "I took a test at school." (input)

[1156] The terminal sends this input to the server.

[1157] 2. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[1158] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[1159] The server sends the generated response to the terminal.

[1160] 3. The terminal displays the received response to the user.

[1161] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[1162] The user enters the following information:

[1163] The terminal sends the next input to the server again.

[1164] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, thereby realizing more effective communication learning.

[1165] The following describes the processing flow.

[1166] Step 1:

[1167] The user starts up the device and enters their username and password on the login screen.

[1168] Step 2:

[1169] The terminal sends the entered username and password to the server.

[1170] Step 3:

[1171] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[1172] Step 4:

[1173] The server sends the user's profile information to the device.

[1174] Step 5:

[1175] The device displays a screen for the user to select a conversation partner.

[1176] Step 6:

[1177] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[1178] Step 7:

[1179] The device sends the user's selections to the server.

[1180] Step 8:

[1181] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[1182] Step 9:

[1183] The server sends the acquired dialogue scenario to the terminal.

[1184] Step 10:

[1185] The device displays the conversation scenario it received to the user.

[1186] Step 11:

[1187] The user follows the displayed prompts and makes their initial input via text or voice.

[1188] Step 12:

[1189] The terminal sends user input to the server.

[1190] Step 13:

[1191] The server receives user input data and analyzes it using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and performs speech recognition, sentiment analysis, and text analysis.

[1192] Step 14:

[1193] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state.

[1194] Step 15:

[1195] The server sends the generated response to the terminal.

[1196] Step 16:

[1197] The device displays or plays aloud the response it receives to the user.

[1198] Step 17:

[1199] The user enters the information again.

[1200] Step 18:

[1201] The terminal sends the user's next input back to the server.

[1202] Step 19:

[1203] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[1204] Step 20:

[1205] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[1206] Step 21:

[1207] The server saves the evaluation results and feedback to the user's learning profile.

[1208] Step 22:

[1209] The device displays the evaluation results and feedback to the user.

[1210] (Example 2)

[1211] Next, we will describe Example 2. 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."

[1212] In modern society, there is a need for educational systems that allow people with developmental disabilities and those who struggle with communication to improve their communication skills at their own pace without undue pressure. However, conventional educational systems have struggled to recognize users' emotions in real time and generate appropriate responses accordingly. As a result, they have been unable to realize dialogue that is in line with the user's emotional state, making effective communication learning difficult. To solve these problems, a system utilizing an emotion recognition engine and multimodal artificial intelligence is necessary.

[1213] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing user input, means for identifying the user's emotional state using an emotion recognition engine, and means for generating an appropriate response based on the analysis results. This enables real-time dialogue that is in line with the user's emotional state.

[1214] "Logging in" is the process by which a user enters the necessary credentials to access a system and undergoes authentication.

[1215] "Profile information" refers to a collection of data that includes personal information about the user, past activity history, learning progress, and more.

[1216] A "dialogue partner" refers to a virtual or real entity with which the user engages in dialogue, and includes parents, siblings, teachers, friends, and others.

[1217] A "dialogue scenario" is a script that includes prompts and example responses to anticipate the flow of a conversation based on a specific situation.

[1218] "Multimodal artificial intelligence" is an artificial intelligence technology that has the ability to process and analyze multiple data formats, such as text, audio, and images.

[1219] An "emotion recognition engine" is a software component that identifies emotional states (such as joy, sadness, anger, or anxiety) from user input.

[1220] A "response" is a text or audio response generated by the system in response to user input.

[1221] "Feedback" is the process of providing evaluations and advice regarding user behavior and performance.

[1222] "Dialogue data" refers to a collection of data that contains all information related to the dialogue that took place between the user and the system.

[1223] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, and is particularly aimed at individuals with developmental disabilities or those who have difficulty communicating. This system achieves more realistic dialogue by incorporating an emotion engine that recognizes and responds to the user's emotions in real time. Specific embodiments of this system are described below.

[1224] First, the user starts up the device and enters their username and password on the login screen. This information is sent from the device to the server using a secure communication method (e.g., HTTPS). The server compares the received user information with its internal database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[1225] Next, the user selects a conversation partner on the device screen. These partners include parents, siblings, teachers, and friends, and the scenario changes accordingly. The user's selection information is sent to the server, which retrieves an appropriate conversation scenario from its database based on the selected partner and sends it to the device.

[1226] The received dialogue scenario is displayed on the terminal. The user follows the displayed prompts and makes the initial input in text or voice. In the case of voice input, the terminal uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[1227] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The emotion engine has functions such as speech recognition, text analysis, and sentiment analysis to identify the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.). Microsoft Azure Cognitive Services, etc., can be used.

[1228] Based on the analysis results, the server generates an appropriate response. Using generative AI models such as OpenAI's GPT-3, it generates text or audio responses that reflect the user's emotional state. The generated response is sent from the server to the terminal and displayed or played aloud to the user.

[1229] Finally, once the user's interaction concludes, the server evaluates the overall performance and provides feedback. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression, and is used as a comprehensive assessment to help improve the user's communication skills. The evaluation results are also saved in the user's learning profile.

[1230] Below is a concrete example of a conversation simulation with a friend.

[1231] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1232] Terminal: "Hello! How are you doing today?"

[1233] User: "I took a test at school." (input)

[1234] 2. The terminal sends this input to the server.

[1235] 3. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[1236] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[1237] The server sends the generated response to the terminal.

[1238] 4. The terminal displays the received response to the user.

[1239] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[1240] The user then enters the next command, and the terminal sends the input back to the server.

[1241] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[1242] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1243] Step 1:

[1244] The user starts up the device and enters their username and password on the login screen. This entered information, which includes the user's credentials, is sent from the device to the server.

[1245] Step 2:

[1246] The device sends the username and password to the server. HTTPS communication is used for transmission to ensure security.

[1247] Step 3:

[1248] The server compares the received user information with the information in the database and performs authentication. The database stores the username and hashed password. If authentication is successful, it generates an authentication result and retrieves the user's profile information from the database.

[1249] Step 4:

[1250] Upon successful authentication, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[1251] Step 5:

[1252] The user selects a conversation partner on the device screen. This partner may include parents, siblings, teachers, or friends. This selection becomes the input data.

[1253] Step 6:

[1254] The terminal sends information about the selected conversation partner to the server. The transmitted data includes the identification information of the conversation partner selected by the user.

[1255] Step 7:

[1256] The server retrieves an appropriate dialogue scenario from the database based on the information of the selected dialogue partner. This scenario includes situation-specific prompts and possible response examples. The retrieved scenario data is generated and sent to the terminal.

[1257] Step 8:

[1258] The terminal displays the received dialogue scenario to the user. The first prompt appears on the screen. This prompt becomes the input data.

[1259] Step 9:

[1260] The user follows the displayed prompts and makes their initial input in text or voice. In the case of voice input, the device uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[1261] Step 10:

[1262] The terminal sends user input data to the server. The transmitted data includes user input.

[1263] Step 11:

[1264] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The analysis includes speech recognition, text analysis, and sentiment analysis. As a result of the analysis, the server identifies the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.).

[1265] Step 12:

[1266] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., GPT-3), it generates text and audio responses that reflect the user's emotional state. The generated responses become response data.

[1267] Step 13:

[1268] The server sends the generated response data to the terminal. The transmitted data contains the appropriate response.

[1269] Step 14:

[1270] The terminal displays or plays the received response to the user. The displayed response or played audio becomes the display data.

[1271] Step 15:

[1272] The user reviews the displayed response and then provides the next input. This loop continues until the interaction is complete.

[1273] Step 16:

[1274] Once the interaction ends, the server evaluates the user's overall performance. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression. The evaluation results are generated and stored in the user's learning profile. The evaluation data reflects the user's progress.

[1275] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[1276] (Application Example 2)

[1277] Next, we will explain application example 2. In the following explanation, 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."

[1278] In modern society, individuals with developmental disabilities or those who struggle with communication often face difficulties in engaging in smooth conversations in their daily lives and work. This is particularly true in virtual environments where real-time interaction with others presents challenges in recognizing emotions and generating appropriate responses. Therefore, there is a need for support systems that enable users to more effectively improve their communication skills.

[1279] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1280] In this invention, the server includes means for the user to log in with initial settings, means for acquiring the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for simulating the user searching for, asking questions about, and purchasing products while interacting with others in a virtual environment, means for recognizing the user's emotions and generating and displaying a corresponding response in real time, and means for saving and evaluating the user's conversation data. This makes it possible for users to effectively improve their communication skills through realistic conversations even in a virtual environment.

[1281] "Users" refer to individuals who use this system with the aim of improving their communication skills.

[1282] "Initial login process" refers to the process of entering a username and password and performing authentication necessary for a user to begin using the system.

[1283] "Means of obtaining profile information" refers to the process of obtaining a user's personal information, past conversation history, etc., from the server.

[1284] "Means of selecting a dialogue partner" refers to a function that allows users to choose who they will interact with within the system (e.g., parents, siblings, teachers, friends, virtual customers, virtual shop assistants).

[1285] "Means for generating dialogue scenarios" refers to the process by which the server generates and configures an appropriate dialogue scenario based on the selected dialogue partner.

[1286] "Means for displaying dialogue scenarios" refers to a function that presents the generated dialogue scenario to the user's terminal either visually or audibly.

[1287] "Means for sending user input to the server" refers to a function that sends the content of a conversation entered by the user in text or voice to the server.

[1288] "Methods for analyzing user input using multimodal AI" refers to the process of understanding user input using AI technology that analyzes multiple data formats, such as speech and text.

[1289] "Means for generating appropriate responses" refers to the process of generating appropriate responses to user input based on analysis results.

[1290] "Means for displaying or playing a response" refers to a function that displays the generated response on the user's device or plays it back as audio.

[1291] "Means of conducting dialogue simulations within a virtual environment" refers to the process by which users perform actions such as searching for products, asking questions, and making purchases while simulating dialogues with others within a virtual environment.

[1292] "Means for recognizing emotions and generating responses in real time" refers to a function that recognizes the user's emotional state in real time and generates a response that corresponds to those emotions.

[1293] "Means for saving and evaluating dialogue data" refers to a process of saving a user's dialogue history and later evaluating the user's communication skills based on that data.

[1294] This invention is an educational system designed to help users improve their communication skills through interactive learning. This system is particularly intended for individuals with developmental disabilities or those who have difficulty communicating. Specific embodiments of this system are described below.

[1295] Hardware and software to be used

[1296] Hardware:

[1297] smartphone

[1298] Head-mounted display (HMD)

[1299] software:

[1300] Python programming language

[1301] Speech recognition API

[1302] Emotion recognition API

[1303] Multimodal AI (for analyzing multiple data formats)

[1304] System Configuration

[1305] This system includes the following components.

[1306] 1. Initial setup and user authentication methods:

[1307] To begin using the system, a user first starts up their device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing the received user information with its database. If authentication is successful, the server sends the user's profile information to the device.

[1308] 2. Means of selecting a dialogue partner:

[1309] The user selects a conversation partner (such as a parent, sibling, teacher, friend, virtual customer, or virtual shop assistant) on the device screen. The device sends this selection to the server, which generates an appropriate conversation scenario based on the selected conversation partner and sends it back to the device.

[1310] 3. Start the dialogue simulation:

[1311] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[1312] 4. Input analysis and emotion recognition methods:

[1313] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[1314] 5. Response generation and display means:

[1315] The server generates an appropriate response based on the analysis results. This response reflects the user's emotional state, and an appropriate response is taken that is sensitive to those feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[1316] 6. Display of responses and feedback:

[1317] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. The evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion, to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[1318] Example: Dialogue simulation in a virtual store

[1319] In a virtual store interaction simulation, users engage in activities such as searching for products, asking questions, and making purchases while interacting with store staff and other customers. Examples of prompts are shown below.

[1320] 1. Application:

[1321] "Hello, what are you looking for today?"

[1322] 2. User:

[1323] "I came to check out the new smartphone."

[1324] 3. Application: Based on the selected emotion ("anxiety")

[1325] "Having trouble choosing a smartphone? Don't worry, we'll guide you."

[1326] In this way, this system can effectively improve users' communication skills through realistic dialogue, even in a virtual environment.

[1327] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1328] Step 1:

[1329] The user logs in with the initial settings.

[1330] Input: Username, Password

[1331] Data processing: The terminal sends this information to the server, and the server authenticates the user by comparing the received user information with the database.

[1332] Output: Authentication result, user profile information

[1333] Specific operation: The user enters their username and password on the device's login screen, and the device sends this information to the server. The server verifies this against the database, and if authentication is successful, sends the user's profile information to the device.

[1334] Step 2:

[1335] The user selects their conversation partner.

[1336] Input: Select your conversation partner (e.g., parent, sibling, teacher, friend, virtual customer, virtual shop assistant)

[1337] Data processing: The terminal sends information about the selected conversation partner to the server, and the server generates an appropriate conversation scenario based on the selection.

[1338] Output: Dialogue Scenario

[1339] Specific operation: The user selects a conversation partner on the device screen, and the device sends this information to the server. The server generates a corresponding conversation scenario and sends it to the device.

[1340] Step 3:

[1341] The dialogue simulation will now begin.

[1342] Input: Dialogue scenario, user's initial input (e.g., text, voice)

[1343] Data processing: The terminal displays a dialogue scenario to the user and sends the user's input to the server.

[1344] Output: User input data sent to the server

[1345] Specific operation: The terminal displays a dialogue scenario, and the user provides initial input via text or voice. The terminal sends this input to the server.

[1346] Step 4:

[1347] The server analyzes user input and recognizes emotions.

[1348] Input: User input data

[1349] Data processing: The server uses multimodal AI to analyze user input data and extracts emotional information using an emotion engine.

[1350] Output: Emotional state (e.g., joy, sadness, anger, anxiety)

[1351] Specific operation: The server analyzes the received user input data through speech recognition, sentiment analysis, and text analysis to identify the user's emotional state.

[1352] Step 5:

[1353] The server generates an appropriate response.

[1354] Input: Emotional state, user input data

[1355] Data processing: Based on emotional state, the server generates an appropriate response.

[1356] Output: Generated response

[1357] Specific operation: The server generates an appropriate response that reflects the user's emotional state and sends it to the terminal. For example, if the user is feeling "anxious," the server will generate an encouraging response.

[1358] Step 6:

[1359] Display or play the response on the user's terminal.

[1360] Input: Generated response

[1361] Data processing: The terminal displays or plays the received response to the user.

[1362] Output: Displayed response, played audio

[1363] Specific actions: The terminal displays the response received from the server on the screen or plays it aloud. The user confirms this response and then makes the next input.

[1364] Step 7:

[1365] Save and evaluate user interaction data.

[1366] Input: User interaction history, simulation results

[1367] Data processing: The server saves the interaction history and evaluates the user's overall performance.

[1368] Output: User ratings, feedback

[1369] Specific operation: After the conversation ends, the server saves the conversation data, evaluates the fluency of the conversation, the frequency of appropriate responses, the expression of emotion, etc., and generates feedback. The feedback is saved in the user's learning profile.

[1370] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1371] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1372] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1373] [Fourth Embodiment]

[1374] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1375] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1376] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1377] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1378] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1379] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1380] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1381] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1382] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1383] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1384] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1385] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1386] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1387] This invention is an educational system designed to help users improve their communication skills through interactive dialogue, and is primarily intended for individuals with developmental disabilities or those who have difficulty communicating. The system is implemented in the following manner.

[1388] Program processing and explanation

[1389] 1. Initial Setup and User Authentication

[1390] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[1391] 2. Selection of a dialogue scenario

[1392] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[1393] 3. Start the dialogue simulation.

[1394] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[1395] 4. Input Analysis and Response Generation

[1396] The server analyzes the received user input data using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis. Based on the analysis results, the server generates an appropriate response and sends it to the terminal.

[1397] 5. Display of responses and feedback

[1398] The device displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile.

[1399] Example: Dialogue simulation with a friend

[1400] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1401] Terminal: "Hello! How are you doing today?"

[1402] User: "I took a test at school." (input)

[1403] The terminal sends this input to the server.

[1404] 2. The server analyzes the user's input and generates an appropriate response.

[1405] Server (response generation): "That must have been tough. Are you curious about the test results?"

[1406] The server sends the generated response to the terminal.

[1407] 3. The terminal displays the received response to the user.

[1408] Terminal: "That must have been tough. Are you curious about your test results?"

[1409] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[1410] The terminal sends the next input to the server again.

[1411] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[1412] The following describes the processing flow.

[1413] Step 1:

[1414] The user starts up the device and enters their username and password on the login screen.

[1415] Step 2:

[1416] The terminal sends the entered username and password to the server.

[1417] Step 3:

[1418] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[1419] Step 4:

[1420] The server sends the user's profile information to the device.

[1421] Step 5:

[1422] The device displays a screen for the user to select a conversation partner.

[1423] Step 6:

[1424] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[1425] Step 7:

[1426] The device sends the user's selections to the server.

[1427] Step 8:

[1428] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[1429] Step 9:

[1430] The server sends the acquired dialogue scenario to the terminal.

[1431] Step 10:

[1432] The device displays the conversation scenario it received to the user.

[1433] Step 11:

[1434] The user follows the displayed prompts and makes their initial input via text or voice.

[1435] Step 12:

[1436] The terminal sends user input to the server.

[1437] Step 13:

[1438] The server receives user input data and analyzes it using multimodal AI. This analysis includes speech recognition, sentiment analysis, and text analysis.

[1439] Step 14:

[1440] The server generates an appropriate response based on the analysis results.

[1441] Step 15:

[1442] The server sends the generated response to the terminal.

[1443] Step 16:

[1444] The device displays or plays aloud the response it receives to the user.

[1445] Step 17:

[1446] The user enters the information again.

[1447] Step 18:

[1448] The terminal sends the user's next input back to the server.

[1449] Step 19:

[1450] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[1451] Step 20:

[1452] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[1453] Step 21:

[1454] The server saves the evaluation results and feedback to the user's learning profile.

[1455] Step 22:

[1456] The device displays the evaluation results and feedback to the user.

[1457] (Example 1)

[1458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1459] This invention relates to an educational system for improving users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. The goal is to provide an environment in which such individuals can learn effectively and to improve their communication skills through real-world dialogue.

[1460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1461] In this invention, the server includes means for storing and evaluating user dialogue data, means for evaluating the user's overall performance after the dialogue ends and providing feedback, and means for storing the feedback results in the user's learning profile. This makes it possible for the user to utilize the feedback obtained through the dialogue and continuously improve their communication skills.

[1462] "Users" refer to people who use the dialogue simulation system, and it is particularly targeted at people with developmental disabilities or those who have difficulty communicating.

[1463] A "terminal" refers to an electronic device used by a user that has the functions of logging in, displaying dialogue scenarios, receiving user input, and communicating with a server.

[1464] A "server" refers to a central computer system that analyzes user input data, generates appropriate responses, and provides feedback.

[1465] A "dialogue scenario" refers to a sequence of conversations generated by the server and designed based on the user's chosen conversation partner.

[1466] "Multimodal AI" refers to artificial intelligence technology used to analyze multiple sources of information, such as text, audio, and video, and to comprehensively understand and make judgments based on that information.

[1467] "Feedback" refers to evaluations and advice provided based on the results of user interactions, and plays a role in supporting the improvement of users' communication skills.

[1468] A "learning profile" is a database containing a user's past conversation data, evaluation results, and feedback information, and refers to a profile used to record and manage the user's progress and skill improvement.

[1469] This invention is an educational system designed to improve users' communication skills through dialogue, and is particularly aimed at people with developmental disabilities or those who have difficulty communicating. This system provides an environment in which users can learn naturally through dialogue.

[1470] Hardware and software to be used

[1471] The system's main components are user terminals, a server that performs central processing, and a network that facilitates communication between them. Terminals are devices that receive user input and send it to the server, including personal computers, tablets, and smartphones. The server possesses the computing power and database necessary to analyze user input data and generate appropriate responses. Multimodal AI is used for analysis, including a speech recognition engine, a text analysis engine, and a sentiment analysis module.

[1472] Program processing

[1473] Initial setup and user authentication

[1474] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing it against the database. If authentication is successful, the server sends the user's profile information to the device.

[1475] Selection of a dialogue scenario

[1476] The user selects a conversation partner on the device screen. Options for conversation partners include parents, siblings, teachers, and friends. The device sends the user's selection to the server. Based on the selected conversation partner, the server retrieves an appropriate conversation scenario from its database and sends it to the device.

[1477] Start of dialogue simulation

[1478] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[1479] Input analysis and response generation

[1480] The server analyzes the received user input data using multimodal AI (e.g., a speech recognition engine, sentiment analysis module, text analysis engine, etc.). In the case of voice input, it is first converted into text by the speech recognition engine. Next, the text analysis engine understands the context, and the sentiment analysis module determines the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[1481] Display of response and feedback

[1482] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[1483] Specific example

[1484] Dialogue simulation with friends

[1485] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1486] Terminal: "Hello! How are you doing today?"

[1487] User: "I took a test at school." (input)

[1488] The terminal sends this input to the server.

[1489] 2. The server analyzes the user's input and generates an appropriate response.

[1490] Server (response generation): "That must have been tough. Are you curious about the test results?"

[1491] The server sends the generated response to the terminal.

[1492] 3. The terminal displays the received response to the user.

[1493] Terminal: "That must have been tough. Are you curious about your test results?"

[1494] User: "I'm a little worried, but I did my best so I think it'll be okay." (Next input)

[1495] The terminal sends the next input to the server again.

[1496] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing multimodal AI, technology that analyzes diverse user inputs (text, voice, video, etc.) and generates optimal responses is effectively used.

[1497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1498] Step 1: Initial Setup and User Authentication

[1499] The user starts up the device and enters their username and password on the login screen. The device sends the entered username and password to the server. The server compares the received username and password with the information stored in the database. If authentication is successful, the server sends the user's profile information (e.g., name, age, learning history, etc.) to the device. If authentication fails, an error message is sent to the device.

[1500] Input: Username, Password

[1501] Data processing and calculation: Database matching

[1502] Output: User profile information (on success) or error message (on failure)

[1503] Step 2: Selecting a dialogue scenario

[1504] The user selects a conversation partner on the device screen. Options include parent, sibling, teacher, friend, etc. The device sends information about the selected conversation partner to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[1505] Input: Select conversation partner

[1506] Data processing and calculation: Retrieving dialogue scenarios from databases

[1507] Output: Dialogue Scenario

[1508] Step 3: Start the dialogue simulation

[1509] The terminal displays the received dialogue scenario to the user. For example, it might display a prompt such as, "Hello! How are you doing today?" The user responds to this prompt with text or voice input, such as, "I took a test at school." The terminal then sends the user's input to the server.

[1510] Input: Dialogue scenario, user response

[1511] Data processing / calculations: None

[1512] Output: User input data

[1513] Step 4: Input analysis and response generation

[1514] The server analyzes the received user input data using multimodal AI. In the case of voice input, it first converts it to text using a speech recognition engine. Next, it uses a text analysis engine to understand the context and an emotion analysis module to determine the user's emotions. Integrating this information, the server generates an appropriate response and sends it to the terminal.

[1515] Input: User input data

[1516] Data processing and computation: speech recognition, text analysis, sentiment analysis

[1517] Output: Response data

[1518] Step 5: Displaying responses and providing feedback

[1519] The device displays the received response to the user. For example, it might display a response such as, "That must have been tough. Are you curious about the test results?" If voice output functionality is available, it can also be played back as audio. The user then provides further input, and the conversation continues. After the conversation ends, the server evaluates the performance, including conversational fluency, frequency of appropriate responses, and expression of emotion, and provides feedback. Finally, this feedback is saved in the user's learning profile.

[1520] Input: Response data, user feedback

[1521] Data processing and computation: Performance evaluation, feedback generation.

[1522] Output: Feedback data

[1523] (Application Example 1)

[1524] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1525] In recent years, while the introduction of robots in factories has progressed, efficiency and precision are required in communication between workers and robots. However, the lack of training and support systems for workers to interact smoothly with robots can lead to decreased work efficiency and safety. In particular, the inability to take immediate and appropriate action when giving specific work instructions or resolving problems is a major challenge.

[1526] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1527] In this invention, the server includes means for the user to log in with initial settings, means for obtaining the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for saving and evaluating user conversation data, means for providing a conversation simulation for factory workers to efficiently give work instructions and solve problems with robots, and means for speech recognition and text conversion of user input in the simulation dialogue and playing a response based on that in audio. This makes it possible to improve the efficiency and safety of communication between factory workers and robots.

[1528] "User" refers to a person who uses this system.

[1529] "Profile information" refers to data that includes personal information, specific characteristics, and settings about a user.

[1530] "Dialogue partner" refers to the person with whom the user engages in simulated dialogue.

[1531] A "dialogue scenario" refers to a scenario that outlines the flow and content of a dialogue generated based on the selected dialogue partner.

[1532] A "terminal" refers to a device used by a user to perform a dialogue simulation.

[1533] A "server" refers to a computer system responsible for managing the entire system, analyzing data, and generating responses.

[1534] "Multimodal AI" refers to artificial intelligence that analyzes data in multiple formats, such as text, audio, and video, and generates appropriate responses.

[1535] "Response" refers to the reply or explanation that the server generates based on the user's input data.

[1536] "Dialogue data" refers to all the data collected during the process of interacting with the user.

[1537] "Factory worker" refers to a worker who performs duties in a factory.

[1538] A "robot" refers to a mechanical device used to assist or automate tasks within a factory.

[1539] "Speech recognition" refers to the technology that converts speech data input through a microphone into text data.

[1540] "Text conversion" refers to the process of representing non-text data, such as audio data, as text.

[1541] "Dialogue simulation" refers to the process by which users train and test their interaction skills using a system before actually performing a task.

[1542] "Playing as audio" refers to outputting the generated response as audio data and letting the user listen to it.

[1543] This invention is an educational system that supports interaction between factory workers and robots, with the aim of enabling workers to communicate efficiently with robots and improve work efficiency and safety. The system is designed for users to log in for initial setup, select an interaction partner, and conduct a dialogue simulation.

[1544] System Overview

[1545] The system consists of the following main elements:

[1546] 1. Terminal: A device used by workers, which displays the scenario and plays audio.

[1547] 2. Server: A computer system that manages the entire system and performs data analysis and response generation.

[1548] 3. Multimodal AI: Artificial intelligence that performs speech recognition and text analysis to generate appropriate responses based on user input.

[1549] Hardware and software to be used

[1550] Hardware: Microphone, speaker, device (PC, tablet, etc.)

[1551] Software: Python, speech_recognition library, gTTS library, audio playback program (e.g., mpg321)

[1552] Data processing and data calculation

[1553] The server processes and performs calculations on the data using the following steps:

[1554] 1. Login and User Authentication: The username and password information sent from the terminal is sent to the server, which authenticates the user by comparing it with the database.

[1555] 2. Retrieving the dialogue scenario: When the user selects a dialogue partner, that information is sent to the server, and the server retrieves an appropriate dialogue scenario from the database and sends it to the terminal.

[1556] 3. Initiating the dialogue simulation: As the user inputs according to the scenario, the content is converted to text via speech recognition and sent to the server. The server uses multimodal AI to analyze the user input.

[1557] 4. Response generation and display: An appropriate response is generated based on the analysis results and sent to the terminal. The terminal plays the received response aloud.

[1558] Specific example

[1559] The following is a specific example of how the system works:

[1560] 1. Initial setup and selecting a conversation partner:

[1561] The user starts up the device and enters their information on the login screen.

[1562] The user selects a dialogue scenario with a "factory robot".

[1563] Terminal: "Please begin the operation."

[1564] User: "Understood, I'll start." (typed).

[1565] The terminal sends this input to the server.

[1566] 2. Input analysis and response reproduction:

[1567] Server (analysis): "The user has indicated an intention to start the task."

[1568] Server (response generation): "Starting work. Please provide details of the steps."

[1569] The device plays the received response aloud.

[1570] Example of the above prompt message:

[1571] User: "Please begin the task."

[1572] Generated AI model response: "I will begin working. Please provide details of the steps."

[1573] This system simulates training for users to interact with robots, supporting them in efficiently performing tasks in real work environments. Furthermore, by utilizing speech recognition and text conversion to provide dialogue scenarios that may be encountered in actual work situations, it contributes to improving workers' skills.

[1574] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1575] Step 1:

[1576] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. The input is the user's login information, and the output is the authentication result and profile information.

[1577] Step 2:

[1578] The user selects a conversation partner on the terminal screen. The terminal sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the terminal. The input is the user's selection of a conversation partner, and the output is the conversation scenario.

[1579] Step 3:

[1580] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and makes initial input in text or voice. The input voice is recognized by the terminal and converted into text data. The converted text data is sent to the server. Input is the user's voice input, and output is text data.

[1581] Step 4:

[1582] The server analyzes the received text data. This analysis includes speech recognition, sentiment analysis, and text analysis. The server then generates an appropriate response based on the analysis results and sends it to the terminal. The input is the user's text data, and the output is the generated response.

[1583] Step 5:

[1584] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion. Finally, the server stores this feedback in the user's learning profile. The input is the response from the server, and the output is the evaluation and feedback.

[1585] example

[1586] For example, if a factory worker instructs the robot they are interacting with by saying, "Please begin work," the server's generated AI model will produce a response saying, "I will begin work. Please tell me the details of the steps," send it to the terminal, and play it back aloud. The user then continues to give instructions such as, "Okay, please start by attaching part A." This dialogue continues through the system, training the worker to communicate smoothly with the robot.

[1587] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1588] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, primarily targeting individuals with developmental disabilities or those who struggle with communication. The system incorporates an emotion engine that recognizes and responds to user emotions in real time, thereby achieving even more realistic dialogue. This system is implemented in the following manner.

[1589] Program processing and explanation

[1590] 1. Initial Setup and User Authentication

[1591] The user starts up the device and enters their username and password on the login screen. The device sends this information to the server. The server compares the received user information with its database and performs authentication. If authentication is successful, the server sends the user's profile information to the device.

[1592] 2. Selection of a dialogue scenario

[1593] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.) on the device screen. The device sends this selection to the server. The server retrieves an appropriate conversation scenario from its database based on the selected conversation partner and sends it to the device.

[1594] 3. Start the dialogue simulation.

[1595] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[1596] 4. Input Analysis and Emotion Recognition

[1597] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (e.g., joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[1598] 5. Response generation and display

[1599] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state, and an appropriate response is taken that is empathetic to the user's feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[1600] 6. Display of responses and feedback

[1601] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. This evaluation includes conversational fluency, frequency of appropriate responses, and emotional expression, and is a comprehensive assessment to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[1602] Example: Dialogue simulation with a friend

[1603] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1604] Terminal: "Hello! How are you doing today?"

[1605] User: "I took a test at school." (input)

[1606] The terminal sends this input to the server.

[1607] 2. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[1608] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[1609] The server sends the generated response to the terminal.

[1610] 3. The terminal displays the received response to the user.

[1611] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[1612] The user enters the following information:

[1613] The terminal sends the next input to the server again.

[1614] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. In particular, by utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, thereby realizing more effective communication learning.

[1615] The following describes the processing flow.

[1616] Step 1:

[1617] The user starts up the device and enters their username and password on the login screen.

[1618] Step 2:

[1619] The terminal sends the entered username and password to the server.

[1620] Step 3:

[1621] The server compares the received user information with the database and performs authentication. If authentication is successful, the server retrieves the user's profile information. If authentication fails, the server sends an error message to the terminal.

[1622] Step 4:

[1623] The server sends the user's profile information to the device.

[1624] Step 5:

[1625] The device displays a screen for the user to select a conversation partner.

[1626] Step 6:

[1627] The user selects a conversation partner (e.g., parent, sibling, teacher, friend, etc.).

[1628] Step 7:

[1629] The device sends the user's selections to the server.

[1630] Step 8:

[1631] The server retrieves an appropriate dialogue scenario from the database based on the selected dialogue partner.

[1632] Step 9:

[1633] The server sends the acquired dialogue scenario to the terminal.

[1634] Step 10:

[1635] The device displays the conversation scenario it received to the user.

[1636] Step 11:

[1637] The user follows the displayed prompts and makes their initial input via text or voice.

[1638] Step 12:

[1639] The terminal sends user input to the server.

[1640] Step 13:

[1641] The server receives user input data and analyzes it using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and performs speech recognition, sentiment analysis, and text analysis.

[1642] Step 14:

[1643] The server generates an appropriate response based on the analysis results. The generated response reflects the user's emotional state.

[1644] Step 15:

[1645] The server sends the generated response to the terminal.

[1646] Step 16:

[1647] The device displays or plays aloud the response it receives to the user.

[1648] Step 17:

[1649] The user enters the information again.

[1650] Step 18:

[1651] The terminal sends the user's next input back to the server.

[1652] Step 19:

[1653] Repeat the process from Step 13 to Step 18 until the dialogue is complete.

[1654] Step 20:

[1655] The server evaluates the user's overall performance after the interaction ends. Evaluation criteria include conversational fluency, frequency of appropriate responses, and expression of emotion.

[1656] Step 21:

[1657] The server saves the evaluation results and feedback to the user's learning profile.

[1658] Step 22:

[1659] The device displays the evaluation results and feedback to the user.

[1660] (Example 2)

[1661] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1662] In modern society, there is a need for educational systems that allow people with developmental disabilities and those who struggle with communication to improve their communication skills at their own pace without undue pressure. However, conventional educational systems have struggled to recognize users' emotions in real time and generate appropriate responses accordingly. As a result, they have been unable to realize dialogue that is in line with the user's emotional state, making effective communication learning difficult. To solve these problems, a system utilizing an emotion recognition engine and multimodal artificial intelligence is necessary.

[1663] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing user input, means for identifying the user's emotional state using an emotion recognition engine, and means for generating an appropriate response based on the analysis results. This enables real-time dialogue that is in line with the user's emotional state.

[1664] "Logging in" is the process by which a user enters the necessary credentials to access a system and undergoes authentication.

[1665] "Profile information" refers to a collection of data that includes personal information about the user, past activity history, learning progress, and more.

[1666] A "dialogue partner" refers to a virtual or real entity with which the user engages in dialogue, and includes parents, siblings, teachers, friends, and others.

[1667] A "dialogue scenario" is a script that includes prompts and example responses to anticipate the flow of a conversation based on a specific situation.

[1668] "Multimodal artificial intelligence" is an artificial intelligence technology that has the ability to process and analyze multiple data formats, such as text, audio, and images.

[1669] An "emotion recognition engine" is a software component that identifies emotional states (such as joy, sadness, anger, or anxiety) from user input.

[1670] A "response" is a text or audio response generated by the system in response to user input.

[1671] "Feedback" is the process of providing evaluations and advice regarding user behavior and performance.

[1672] "Dialogue data" refers to a collection of data that contains all information related to the dialogue that took place between the user and the system.

[1673] This invention is an educational system designed to help users improve their communication skills through a natural, interactive format, and is particularly aimed at individuals with developmental disabilities or those who have difficulty communicating. This system achieves more realistic dialogue by incorporating an emotion engine that recognizes and responds to the user's emotions in real time. Specific embodiments of this system are described below.

[1674] First, the user starts up the device and enters their username and password on the login screen. This information is sent from the device to the server using a secure communication method (e.g., HTTPS). The server compares the received user information with its internal database and performs authentication. If authentication is successful, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[1675] Next, the user selects a conversation partner on the device screen. These partners include parents, siblings, teachers, and friends, and the scenario changes accordingly. The user's selection information is sent to the server, which retrieves an appropriate conversation scenario from its database based on the selected partner and sends it to the device.

[1676] The received dialogue scenario is displayed on the terminal. The user follows the displayed prompts and makes the initial input in text or voice. In the case of voice input, the terminal uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[1677] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The emotion engine has functions such as speech recognition, text analysis, and sentiment analysis to identify the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.). Microsoft Azure Cognitive Services, etc., can be used.

[1678] Based on the analysis results, the server generates an appropriate response. Using generative AI models such as OpenAI's GPT-3, it generates text or audio responses that reflect the user's emotional state. The generated response is sent from the server to the terminal and displayed or played aloud to the user.

[1679] Finally, once the user's interaction concludes, the server evaluates the overall performance and provides feedback. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression, and is used as a comprehensive assessment to help improve the user's communication skills. The evaluation results are also saved in the user's learning profile.

[1680] Below is a concrete example of a conversation simulation with a friend.

[1681] 1. The user selects a conversation scenario with a "friend." The device then displays a prompt similar to the following:

[1682] Terminal: "Hello! How are you doing today?"

[1683] User: "I took a test at school." (input)

[1684] 2. The terminal sends this input to the server.

[1685] 3. The server analyzes the user's input using an emotion engine and multimodal AI, and recognizes that the user is feeling "tension."

[1686] Server (response generation): "That must have been tough. But if you did your best, I'm sure it'll be alright. I'm looking forward to the results."

[1687] The server sends the generated response to the terminal.

[1688] 4. The terminal displays the received response to the user.

[1689] Terminal: "That must have been tough. But if you gave it your all, I'm sure everything will be alright. I'm looking forward to seeing the results."

[1690] The user then enters the next command, and the terminal sends the input back to the server.

[1691] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[1692] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1693] Step 1:

[1694] The user starts up the device and enters their username and password on the login screen. This entered information, which includes the user's credentials, is sent from the device to the server.

[1695] Step 2:

[1696] The device sends the username and password to the server. HTTPS communication is used for transmission to ensure security.

[1697] Step 3:

[1698] The server compares the received user information with the information in the database and performs authentication. The database stores the username and hashed password. If authentication is successful, it generates an authentication result and retrieves the user's profile information from the database.

[1699] Step 4:

[1700] Upon successful authentication, the server sends the user's profile information to the device. This profile information includes past conversation history and learning progress.

[1701] Step 5:

[1702] The user selects a conversation partner on the device screen. This partner may include parents, siblings, teachers, or friends. This selection becomes the input data.

[1703] Step 6:

[1704] The terminal sends information about the selected conversation partner to the server. The transmitted data includes the identification information of the conversation partner selected by the user.

[1705] Step 7:

[1706] The server retrieves an appropriate dialogue scenario from the database based on the information of the selected dialogue partner. This scenario includes situation-specific prompts and possible response examples. The retrieved scenario data is generated and sent to the terminal.

[1707] Step 8:

[1708] The terminal displays the received dialogue scenario to the user. The first prompt appears on the screen. This prompt becomes the input data.

[1709] Step 9:

[1710] The user follows the displayed prompts and makes their initial input in text or voice. In the case of voice input, the device uses its built-in microphone to acquire voice data and sends it to the server. Similarly, in the case of text input, the input data is also sent to the server.

[1711] Step 10:

[1712] The terminal sends user input data to the server. The transmitted data includes user input.

[1713] Step 11:

[1714] The server analyzes the received user input data using an emotion engine and multimodal artificial intelligence. The analysis includes speech recognition, text analysis, and sentiment analysis. As a result of the analysis, the server identifies the user's emotional state (e.g., joy, sadness, anger, anxiety, etc.).

[1715] Step 12:

[1716] The server generates an appropriate response based on the analysis results. Using a generative AI model (e.g., GPT-3), it generates text and audio responses that reflect the user's emotional state. The generated responses become response data.

[1717] Step 13:

[1718] The server sends the generated response data to the terminal. The transmitted data contains the appropriate response.

[1719] Step 14:

[1720] The terminal displays or plays the received response to the user. The displayed response or played audio becomes the display data.

[1721] Step 15:

[1722] The user reviews the displayed response and then provides the next input. This loop continues until the interaction is complete.

[1723] Step 16:

[1724] Once the interaction ends, the server evaluates the user's overall performance. This evaluation includes aspects such as conversational fluency, frequency of appropriate responses, and emotional expression. The evaluation results are generated and stored in the user's learning profile. The evaluation data reflects the user's progress.

[1725] In this way, the system of the present invention provides an environment for users to learn and improve their communication skills through realistic dialogue. By utilizing an emotion engine, it becomes possible to respond in accordance with the user's emotions, resulting in more effective communication learning.

[1726] (Application Example 2)

[1727] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1728] In modern society, individuals with developmental disabilities or those who struggle with communication often face difficulties in engaging in smooth conversations in their daily lives and work. This is particularly true in virtual environments where real-time interaction with others presents challenges in recognizing emotions and generating appropriate responses. Therefore, there is a need for support systems that enable users to more effectively improve their communication skills.

[1729] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1730] In this invention, the server includes means for the user to log in with initial settings, means for acquiring the user's profile information, means for the user to select a conversation partner, means for the server to generate a conversation scenario based on the conversation partner, means for displaying the conversation scenario on the user's terminal, means for sending user input to the server, means for the server to analyze user input using multimodal AI, means for the server to generate an appropriate response based on the analysis results, means for sending the response to the user's terminal, means for displaying or playing the response on the user's terminal, means for simulating the user searching for, asking questions about, and purchasing products while interacting with others in a virtual environment, means for recognizing the user's emotions and generating and displaying a corresponding response in real time, and means for saving and evaluating the user's conversation data. This makes it possible for users to effectively improve their communication skills through realistic conversations even in a virtual environment.

[1731] "Users" refer to individuals who use this system with the aim of improving their communication skills.

[1732] "Initial login process" refers to the process of entering a username and password and performing authentication necessary for a user to begin using the system.

[1733] "Means of obtaining profile information" refers to the process of obtaining a user's personal information, past conversation history, etc., from the server.

[1734] "Means of selecting a dialogue partner" refers to a function that allows users to choose who they will interact with within the system (e.g., parents, siblings, teachers, friends, virtual customers, virtual shop assistants).

[1735] "Means for generating dialogue scenarios" refers to the process by which the server generates and configures an appropriate dialogue scenario based on the selected dialogue partner.

[1736] "Means for displaying dialogue scenarios" refers to a function that presents the generated dialogue scenario to the user's terminal either visually or audibly.

[1737] "Means for sending user input to the server" refers to a function that sends the content of a conversation entered by the user in text or voice to the server.

[1738] "Methods for analyzing user input using multimodal AI" refers to the process of understanding user input using AI technology that analyzes multiple data formats, such as speech and text.

[1739] "Means for generating appropriate responses" refers to the process of generating appropriate responses to user input based on analysis results.

[1740] "Means for displaying or playing a response" refers to a function that displays the generated response on the user's device or plays it back as audio.

[1741] "Means of conducting dialogue simulations within a virtual environment" refers to the process by which users perform actions such as searching for products, asking questions, and making purchases while simulating dialogues with others within a virtual environment.

[1742] "Means for recognizing emotions and generating responses in real time" refers to a function that recognizes the user's emotional state in real time and generates a response that corresponds to those emotions.

[1743] "Means for saving and evaluating dialogue data" refers to a process of saving a user's dialogue history and later evaluating the user's communication skills based on that data.

[1744] This invention is an educational system designed to help users improve their communication skills through interactive learning. This system is particularly intended for individuals with developmental disabilities or those who have difficulty communicating. Specific embodiments of this system are described below.

[1745] Hardware and software to be used

[1746] Hardware:

[1747] smartphone

[1748] Head-mounted display (HMD)

[1749] software:

[1750] Python programming language

[1751] Speech recognition API

[1752] Emotion recognition API

[1753] Multimodal AI (for analyzing multiple data formats)

[1754] System Configuration

[1755] This system includes the following components.

[1756] 1. Initial setup and user authentication methods:

[1757] To begin using the system, a user first starts up their device and enters their username and password on the login screen. The device sends this information to the server, which then authenticates the user by comparing the received user information with its database. If authentication is successful, the server sends the user's profile information to the device.

[1758] 2. Means of selecting a dialogue partner:

[1759] The user selects a conversation partner (such as a parent, sibling, teacher, friend, virtual customer, or virtual shop assistant) on the device screen. The device sends this selection to the server, which generates an appropriate conversation scenario based on the selected conversation partner and sends it back to the device.

[1760] 3. Start the dialogue simulation:

[1761] The terminal displays the received dialogue scenario to the user. The user follows the displayed prompts and provides initial input via text or voice. The terminal sends this input to the server.

[1762] 4. Input analysis and emotion recognition methods:

[1763] The server analyzes the received user input data using an emotion engine and multimodal AI. The emotion engine extracts emotional information from the user's input and identifies emotional states (joy, sadness, anger, anxiety, etc.). This analysis includes speech recognition, sentiment analysis, and text analysis.

[1764] 5. Response generation and display means:

[1765] The server generates an appropriate response based on the analysis results. This response reflects the user's emotional state, and an appropriate response is taken that is sensitive to those feelings. For example, if the user is feeling anxious, a response including words of encouragement will be generated. The server sends this response to the terminal.

[1766] 6. Display of responses and feedback:

[1767] The terminal displays or plays the received response to the user. The user confirms this response and provides the next input. This process is repeated until the dialogue ends. After the dialogue ends, the server evaluates the user's overall performance and provides feedback. The evaluation includes conversational fluency, frequency of appropriate responses, and expression of emotion, to support the improvement of the user's communication skills. The server saves the feedback results to the user's learning profile.

[1768] Example: Dialogue simulation in a virtual store

[1769] In a virtual store interaction simulation, users engage in activities such as searching for products, asking questions, and making purchases while interacting with store staff and other customers. Examples of prompts are shown below.

[1770] 1. Application:

[1771] "Hello, what are you looking for today?"

[1772] 2. User:

[1773] "I came to check out the new smartphone."

[1774] 3. Application: Based on the selected emotion ("anxiety")

[1775] "Having trouble choosing a smartphone? Don't worry, we'll guide you."

[1776] In this way, this system can effectively improve users' communication skills through realistic dialogue, even in a virtual environment.

[1777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1778] Step 1:

[1779] The user logs in with the initial settings.

[1780] Input: Username, Password

[1781] Data processing: The terminal sends this information to the server, and the server authenticates the user by comparing the received user information with the database.

[1782] Output: Authentication result, user profile information

[1783] Specific operation: The user enters their username and password on the device's login screen, and the device sends this information to the server. The server verifies this against the database, and if authentication is successful, sends the user's profile information to the device.

[1784] Step 2:

[1785] The user selects their conversation partner.

[1786] Input: Select your conversation partner (e.g., parent, sibling, teacher, friend, virtual customer, virtual shop assistant)

[1787] Data processing: The terminal sends information about the selected conversation partner to the server, and the server generates an appropriate conversation scenario based on the selection.

[1788] Output: Dialogue Scenario

[1789] Specific operation: The user selects a conversation partner on the device screen, and the device sends this information to the server. The server generates a corresponding conversation scenario and sends it to the device.

[1790] Step 3:

[1791] The dialogue simulation will now begin.

[1792] Input: Dialogue scenario, user's initial input (e.g., text, voice)

[1793] Data processing: The terminal displays a dialogue scenario to the user and sends the user's input to the server.

[1794] Output: User input data sent to the server

[1795] Specific operation: The terminal displays a dialogue scenario, and the user provides initial input via text or voice. The terminal sends this input to the server.

[1796] Step 4:

[1797] The server analyzes user input and recognizes emotions.

[1798] Input: User input data

[1799] Data processing: The server uses multimodal AI to analyze user input data and extracts emotional information using an emotion engine.

[1800] Output: Emotional state (e.g., joy, sadness, anger, anxiety)

[1801] Specific operation: The server analyzes the received user input data through speech recognition, sentiment analysis, and text analysis to identify the user's emotional state.

[1802] Step 5:

[1803] The server generates an appropriate response.

[1804] Input: Emotional state, user input data

[1805] Data processing: Based on emotional state, the server generates an appropriate response.

[1806] Output: Generated response

[1807] Specific operation: The server generates an appropriate response that reflects the user's emotional state and sends it to the terminal. For example, if the user is feeling "anxious," the server will generate an encouraging response.

[1808] Step 6:

[1809] Display or play the response on the user's terminal.

[1810] Input: Generated response

[1811] Data processing: The terminal displays or plays the received response to the user.

[1812] Output: Displayed response, played audio

[1813] Specific actions: The terminal displays the response received from the server on the screen or plays it aloud. The user confirms this response and then makes the next input.

[1814] Step 7:

[1815] Save and evaluate user interaction data.

[1816] Input: User interaction history, simulation results

[1817] Data processing: The server saves the interaction history and evaluates the user's overall performance.

[1818] Output: User ratings, feedback

[1819] Specific operation: After the conversation ends, the server saves the conversation data, evaluates the fluency of the conversation, the frequency of appropriate responses, the expression of emotion, etc., and generates feedback. The feedback is saved in the user's learning profile.

[1820] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1821] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1822] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1823] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1824] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1825] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1826] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1827] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1828] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1829] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1830] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1831] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1832] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1833] 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.

[1834] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1835] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1836] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1837] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1838] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1839] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1840] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1841] The following is further disclosed regarding the embodiments described above.

[1842] (Claim 1)

[1843] The means by which users log in during initial setup,

[1844] Means for obtaining user profile information,

[1845] A means for the user to select a conversation partner,

[1846] A means by which the server generates a dialogue scenario based on the dialogue partner,

[1847] A means of displaying the dialogue scenario on the user's terminal,

[1848] A means of sending user input to the server,

[1849] A means by which the server analyzes user input using multimodal AI,

[1850] A means by which the server generates an appropriate response based on the analysis results,

[1851] A means of sending a response to the user's terminal,

[1852] Means for displaying or playing the response on the user's terminal,

[1853] A system that includes means for saving and evaluating user interaction data.

[1854] (Claim 2)

[1855] The system according to claim 1, further comprising means for providing feedback based on the user's progress.

[1856] (Claim 3)

[1857] The system according to claim 1, further comprising means for the user to select from multiple options, such as a parent, sibling, teacher, or friend, as a conversation partner.

[1858] "Example 1"

[1859] (Claim 1)

[1860] The means by which users log in during initial setup,

[1861] Means for obtaining user profile information,

[1862] A means for the user to select a conversation partner,

[1863] A means by which the server generates a dialogue scenario based on the dialogue partner,

[1864] A means of displaying the dialogue scenario on the user's terminal,

[1865] A means of sending user input to the server,

[1866] A means by which the server analyzes user input using multimodal AI,

[1867] A means by which the server generates an appropriate response based on the analysis results,

[1868] A means of sending a response to the user's terminal,

[1869] Means for displaying or playing the response on the user's terminal,

[1870] A means of saving and evaluating user interaction data,

[1871] A means of evaluating the user's overall performance after the interaction ends and providing feedback,

[1872] A system that includes means for saving feedback results to the user's learning profile.

[1873] (Claim 2)

[1874] The system according to claim 1, further comprising means for providing feedback based on the user's progress.

[1875] (Claim 3)

[1876] The system according to claim 1, further comprising means for the user to select from multiple options, such as a parent, sibling, teacher, or friend, as a conversation partner.

[1877] "Application Example 1"

[1878] (Claim 1)

[1879] The means by which users log in during initial setup,

[1880] Means for obtaining user profile information,

[1881] A means for the user to select a conversation partner,

[1882] A means by which the server generates a dialogue scenario based on the dialogue partner,

[1883] A means of displaying the dialogue scenario on the user's terminal,

[1884] A means of sending user input to the server,

[1885] A means by which the server analyzes user input using multimodal AI,

[1886] A means by which the server generates an appropriate response based on the analysis results,

[1887] A means of sending a response to the user's terminal,

[1888] Means for displaying or playing the response on the user's terminal,

[1889] A means of saving and evaluating user interaction data,

[1890] A means of providing dialogue simulations that enable factory workers to efficiently give work instructions and solve problems with robots,

[1891] A means for performing speech recognition and text conversion of user input in a simulated dialogue, and for playing back a response based on that conversion in voice,

[1892] A system that includes this.

[1893] (Claim 2)

[1894] The system according to claim 1, further comprising means for providing feedback based on the user's progress.

[1895] (Claim 3)

[1896] The system according to claim 1, further comprising means for the user to select from multiple options, such as a parent, sibling, teacher, friend, factory worker, or robot, as a conversation partner.

[1897] "Example 2 of combining an emotion engine"

[1898] (Claim 1)

[1899] The means by which users log in during initial setup,

[1900] Means for obtaining user profile information,

[1901] A means for the user to select a conversation partner,

[1902] A means by which the server obtains a dialogue scenario based on the dialogue partner,

[1903] A means of displaying the dialogue scenario on the user's terminal,

[1904] A means of sending user input to the server,

[1905] A means by which the server analyzes user input using multimodal artificial intelligence,

[1906] A means by which the server uses an emotion recognition engine to identify the user's emotional state,

[1907] A means by which the server generates an appropriate response based on the analysis results,

[1908] A means of sending a response to the user's terminal,

[1909] Means for displaying or playing the response on the user's terminal,

[1910] A system that includes means for saving and evaluating user interaction data.

[1911] (Claim 2)

[1912] The system according to claim 1, further comprising means for providing feedback based on the user's progress.

[1913] (Claim 3)

[1914] The system according to claim 1, further comprising means for the user to select from multiple options, such as a parent, sibling, teacher, or friend, as a conversation partner.

[1915] "Application example 2 when combining with an emotional engine"

[1916] (Claim 1)

[1917] The means by which users log in during initial setup,

[1918] Means for obtaining user profile information,

[1919] A means for the user to select a conversation partner,

[1920] A means by which the server generates a dialogue scenario based on the dialogue partner,

[1921] A means of displaying the dialogue scenario on the user's terminal,

[1922] A means of sending user input to the server,

[1923] A means by which the server analyzes user input using multimodal AI,

[1924] A means by which the server generates an appropriate response based on the analysis results,

[1925] A means of sending a response to the user's terminal,

[1926] Means for displaying or playing the response on the user's terminal,

[1927] A means of simulating the process of searching for, asking questions about, and purchasing products while interacting with others in a virtual environment,

[1928] A means of recognizing user emotions and generating and displaying corresponding responses in real time,

[1929] A system that includes means for saving and evaluating user interaction data.

[1930] (Claim 2)

[1931] The system according to claim 1, further comprising means for providing feedback based on the user's progress.

[1932] (Claim 3)

[1933] The system according to claim 1, further comprising means for the user to select from multiple options for a conversation partner, such as a parent, sibling, teacher, friend, and virtual customer or shop assistant. [Explanation of Symbols]

[1934] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The means by which users log in during initial setup, Means for obtaining user profile information, A means for the user to select a conversation partner, A means by which the server generates a dialogue scenario based on the dialogue partner, A means of displaying the dialogue scenario on the user's terminal, A means of sending user input to the server, A means by which the server analyzes user input using multimodal AI, A means by which the server generates an appropriate response based on the analysis results, A means of sending a response to the user's terminal, Means for displaying or playing the response on the user's terminal, A system that includes means for saving and evaluating user interaction data.

2. The system according to claim 1, further comprising means for providing feedback based on the user's progress.

3. The system according to claim 1, further comprising means for the user to select from multiple options such as a parent, sibling, teacher, or friend as a conversation partner.

Images