System

Abstract

A system is provided.SOLUTION: A system comprising: generation algorithm means; user characteristics database means; and conversation interface means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The lack of realistic communication is a problem in language learning. In particular, it is difficult to improve fluency and natural conversation skills, and there is a lack of deepening intercultural understanding. Furthermore, the lack of an experience that simulates a romantic relationship with a native speaker hinders the improvement of more practical language skills. To solve this problem, it is necessary to provide a simulation environment that allows users to learn in an enjoyable way. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. First, a generation algorithm means is used to simulate realistic conversations based on the user's characteristics. Then, a user characteristic database means is used to record and manage the user's preferences and characteristics and customize the content of conversations and activities. Furthermore, a conversation interface means is used to enable everyday conversations, and a variety of interactions are provided by including a dating simulation means, text and voice chat means, and video calling means. These allow users to continuously engage in natural conversational flow and practical language learning that deepens intercultural understanding.

[0006] A "generative algorithm means" is a program or process for dynamically generating appropriate conversations and replies based on user input data.

[0007] "User characteristic database means" is a database system for recording and managing a user's personal preferences, characteristics, and past interaction history.

[0008] A "conversational interface means" is an interface that allows a user and an AI to communicate through text, voice, or video.

[0009] A "dating simulation tool" is a system or process that allows a user and an AI to interact through a virtual dating situation.

[0010] "Text and voice chat means" refers to a chat system that allows users and AI to communicate in real time via text and voice.

[0011] "Video calling means" is a system that allows users and AI to communicate visually in real time via video. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0034] The system of the present invention is for users to improve their language skills through natural conversation, and specifically includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[0035] System configuration

[0036] 1. Generative algorithm means:

[0037] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[0038] 2. User characteristic database means:

[0039] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[0040] 3. Conversational Interface Means:

[0041] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[0042] 4. Dating simulation methods:

[0043] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[0044] 5. Text and voice chat methods:

[0045] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[0046] 6. Video calling methods:

[0047] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[0048] How it works

[0049] Initialization and loading user settings

[0050] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database and initializes the virtual lover AI object based on this information.

[0051] Generating everyday conversations

[0052] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. The user initiates and responds to conversations through their device, and this input data is sent to the server. The server generates an appropriate response based on this user input and returns it to the user's device.

[0053] Dating Simulation

[0054] The user selects the dating simulation mode on the device, and the server generates a virtual dating spot using the virtual lover AI, allowing the user and the AI ​​to enjoy natural conversation.

[0055] Text and voice chat

[0056] Users can chat via text or voice on their devices, and the server generates instant responses based on the user's input, allowing for real-time communication.

[0057] Video calling

[0058] When a user wants to make a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and starts the video call session.

[0059] As a concrete example, consider the following scenario:

[0060] Example: Everyday conversation scenario

[0061] User: "I saw a movie today."

[0062] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[0063] In this way, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[0067] Step 2:

[0068] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[0069] Step 3:

[0070] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[0071] Step 4:

[0072] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[0073] Step 5:

[0074] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[0075] Step 6:

[0076] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[0077] Step 7:

[0078] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[0079] Step 8:

[0080] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[0081] Step 9:

[0082] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[0083] Step 10:

[0084] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[0085] Step 11:

[0086] A user sends a request to start a text or voice chat on a device, which then sends the request to the server.

[0087] Step 12:

[0088] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[0089] Step 13:

[0090] The user requests to start a video call on the device, which then sends the request to the server.

[0091] Step 14:

[0092] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[0093] Step 15:

[0094] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] Conventional language learning systems have limited interaction with users, making it difficult to provide practical conversation practice tailored to specific situations. Furthermore, they often struggle to support real-time communication and customize the system to suit individual users. This poses a challenge in efficiently improving users' language skills.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means. This allows for customized conversation practice tailored to each individual user through real-time interaction with the user. Furthermore, practical conversation practice suited to a variety of situations is provided, efficiently supporting the improvement of the user's language skills.

[0100] The "generation algorithm means" is a means that implements an algorithm for generating natural conversations and responses based on user input data.

[0101] The "user characteristic database means" is a means for providing a database for recording and managing personal information and past interaction history of users.

[0102] A "conversational interface means" is a means for providing an interface for a user and a system to communicate through text, voice, video, etc.

[0103] The "date simulation means" is a means for generating a virtual date spot and providing a simulation in which the user and the virtual lover AI can enjoy a natural conversation.

[0104] "Text and voice chat means" refers to means that provides an interface for users to conduct real-time text chats and voice chats with the system.

[0105] A "video calling means" is a means for providing an interface for a user to make a video call with the system.

[0106] A "server" is a computer system that has the ability to read user configuration information, generate responses using natural language processing, and manage and control interactions with users through various interfaces.

[0107] A "terminal" is a device used by a user that provides an interface for interactions with the system, such as text chat, voice chat, and video calls.

[0108] "Virtual Lover AI" is an artificial intelligence designed to conduct natural conversations based on the user's settings and input data.

[0109] The following describes a specific system configuration and operation for implementing the present invention, which includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[0110] System Overview

[0111] The system aims to help users improve their language skills through natural conversation. The server manages real-time interactions with users using a generative AI model and generates responses tailored to each individual user from a database of user characteristics. The device provides users with a communication interface via text, voice, and video. In addition, a virtual lover AI uses a dating simulation tool to realize conversations in realistic situations.

[0112] Hardware and software used

[0113] Hardware

[0114] Server: Use general-purpose server equipment with high-performance processors and large storage capacity.

[0115] Device: A device available to a user, such as a smartphone, tablet, or PC.

[0116] software

[0117] Generative AI models: AI models that utilize natural language processing techniques (e.g., GPT-3, BERT, etc.).

[0118] Database Management System: A DBMS (e.g., MySQL, PostgreSQL, etc.) for managing the user characteristics database.

[0119] Communication interface: Send and receive data using an API that uses HTTP / HTTPS.

[0120] Detailed explanation of the system configuration

[0121] Generative Algorithm Means

[0122] The server operates a generative AI model to generate appropriate conversations and replies based on user input. The generative AI model provides natural responses in real time based on user utterances. The model is trained using machine learning algorithms to handle a variety of language patterns.

[0123] User characteristic database means

[0124] The server uses a user characteristic database to manage each user's personal information, preferences, and past interaction history, thereby enabling it to provide personalized responses to each individual user.

[0125] Conversational Interface Means

[0126] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[0127] Dating simulation tools

[0128] The server generates a virtual dating situation, where the user and the AI ​​interact. In this simulation, the user can choose a virtual date spot and enjoy natural conversation with the AI. During the simulation, the server dynamically changes the scenario based on the user's choices and responses.

[0129] Text and voice chat options

[0130] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language skills in real time and potentially improving their overall language ability.

[0131] Video calling means

[0132] The device provides an interface for users to have video calls with the AI, allowing for more practical conversation practice. The server processes the video data in real time and generates appropriate responses.

[0133] Examples of concrete examples and prompts

[0134] Example: Everyday conversation scenario

[0135] User: "I saw a movie today."

[0136] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[0137] Example prompt for a generative AI model:

[0138] 1. "Generate an AI response when a user states that they have seen a movie."

[0139] 2. "A user types, 'I saw a movie today.' How would the AI ​​respond?"

[0140] In this way, users can interact with their virtual AI lover through the system and improve their language skills while enjoying natural conversations and practical activities.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Program processing flow

[0143] Step 1:

[0144] Loading User Settings

[0145] When a user first accesses the system, the server reads the user's setting information from the user characteristics database. This information includes personal information such as the user's name, favorite food, and favorite movie. Based on this information, the virtual lover AI object is initialized. The input is the user ID, and the output is the user's setting information.

[0146] Input: User ID

[0147] Output: Personal information such as name, favorite food, favorite movie, etc.

[0148] Specific operation: The server retrieves information using a DB query such as "SELECT FROM user_preferences WHERE user_id = '12345'".

[0149] Step 2:

[0150] Generating everyday conversations

[0151] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. When the user starts a conversation using the device and enters a response or question, the data is sent to the server. The server generates a response using natural language processing (NLP) and sends it back to the user's device.

[0152] Input: User text input

[0153] Output: Response from virtual lover AI

[0154] What it does: The server sends a message to the user saying, "Good morning, what's your plan today?" The user replies, "I'm working today," and the server uses its NLP engine to generate a response: "Good luck, let me know if anything interesting comes up!"

[0155] Step 3:

[0156] Starting a dating simulation

[0157] The user selects the dating simulation mode on their device. The server uses a virtual lover AI to generate a virtual dating spot and provides a scenario in which the user and the AI ​​can enjoy natural conversation. The server dynamically changes the scenario based on the user's selection and real-time conversation.

[0158] Input: Select a date spot

[0159] Output: Hypothetical dating situations and conversations

[0160] Specific operation: When the user selects "Dating Simulation," the server presents date spots such as "Park," "Cafe," "Movie Theater," etc. When the user selects "Cafe," the server starts a conversation with a "Virtual Cafe" background, asking, "Today, we're relaxing at the cafe. What would you like to drink?"

[0161] Step 4:

[0162] Text and voice chat

[0163] Users can chat by text or voice on their devices, and the server generates responses based on the user's input in real time, enabling smooth communication.

[0164] Input: Text or voice input

[0165] Output: Real-time response

[0166] What it does: When a user sends a text chat request like "Hello, what are you doing?", the server responds with "Hello, I'm just enjoying my new coffee!". For voice chat, the server also generates a real-time voice response to the user's voice input.

[0167] Step 5:

[0168] Making a video call

[0169] When a user requests a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and initiates the video call session.

[0170] Input: Video call request

[0171] Output: Video call session

[0172] How it works: When a user presses the video call button, the device sends a request to the server. The server then starts the video processing engine, and the virtual lover AI responds via video call, saying, "Hello, I'm glad to see you!" During the video call, the server processes the video and audio so that the AI ​​can respond to the user's questions in real time.

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] Conventional virtual store user experiences have had the problem of being unable to fully respond to individual user characteristics and preferences and only being able to provide limited information. Furthermore, interaction with the user was limited, preventing natural conversation and question-answering. This made it difficult to stimulate users' purchasing motivation.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a virtual assistant means, and a product guide and recommendation means, which enable natural conversation and recommendations based on the user's individual characteristics and preferences.

[0178] The "generation algorithm means" is a technology for generating appropriate conversations and replies based on input data from the user, and uses natural language processing technology.

[0179] The "user characteristic database means" is a database for recording and managing the preferences and characteristics of users and their past interaction history.

[0180] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, and video.

[0181] The "virtual assistant means" is a means for providing a virtual assistant to guide and recommend products through natural conversation with a user.

[0182] "Product information and recommendation means" is a means of presenting appropriate product information and recommended products based on the user's interests and past purchase history.

[0183] A "date simulation means" is a means for generating a virtual date situation, allowing the user to choose a virtual date spot and enjoy natural conversation with AI.

[0184] "Text and voice chat means" means a means for users to practice language in real time through text and voice chat.

[0185] "Video call means" is a means for users to practice more practical conversations with AI through video calls.

[0186] "Sales promotion means" refers to means for providing users with the latest campaign information and sale notices.

[0187] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0188] System configuration

[0189] 1. Generative algorithm means:

[0190] The server generates appropriate conversations and replies based on the input data from the user, using natural language processing technology and the NLTK library as NLP technology.

[0191] 2. User characteristic database means:

[0192] The server manages a database that records user preferences, characteristics, and past interaction history, allowing for customized conversations and recommendations for each user.

[0193] 3. Conversational Interface Means:

[0194] The terminal provides an interface for users and systems to communicate through text, voice, and video, including a chat box, voice input, and a user interface for video calling.

[0195] 4. Virtual Assistant Tools:

[0196] The server provides product guidance and recommendations through a virtual assistant based on the user's preferences and past data.

[0197] 5. Product Information and Recommendations:

[0198] The server provides appropriate product information based on the user's input and presents recommended products based on the user's interests and past purchase history.

[0199] 6. Sales promotion methods:

[0200] The server provides users with the latest campaign information and sale announcements within the virtual store.

[0201] How it works

[0202] Initialization and loading user settings

[0203] The server reads the user's configuration information (e.g., name, favorite product categories, past purchase history) from the database and initializes a virtual assistant customized for each user based on this information.

[0204] Generating everyday conversations and product information

[0205] The server conducts daily conversations and product information with the user through the virtual assistant. In response to questions and requests from the user, it generates appropriate responses and recommendations and sends them to the device.

[0206] Providing sales promotions and campaign information

[0207] The server provides users with the latest campaign information and sales announcements through a virtual assistant, thereby increasing users' motivation to make purchases.

[0208] Hardware / Software Used

[0209] Hardware used: Smartphone, smart glasses, or head-mounted display, through which the user interacts with the system.

[0210] Software used: Python, natural language processing library (NLTK), speech recognition library (Google Speech Recognition API), video calling library (WebRTC). These software processes data and enables communication between the server and the device.

[0211] Adding specific examples

[0212] Scenario: Product Introduction

[0213] User: "What are the features of this product?"

[0214] Virtual Assistant: "This product has a modern design and is waterproof, making it especially suitable for outdoor use."

[0215] Example prompts for generative AI models

[0216] Based on user characteristic data, generate natural and appropriate responses to the following inputs:

[0217] User Input: "What are your recommended products?"

[0218] User characteristics: {'name': 'User', 'recommended_product': 'Latest smartphone model', 'past_interactions': [...]}

[0219] Example response: "Here's what you need: a new smartphone model with a high-resolution camera and long battery life."

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] The server reads the user's configuration information from the user characteristics database.

[0223] Input: User ID or authentication information

[0224] Processing: Database query

[0225] Output: User preferences, purchase history, and characteristic data

[0226] Specific operation: The server queries the database using the user's ID and obtains information such as the user's preferences and past purchase history.

[0227] Step 2:

[0228] The server initializes the virtual assistant based on the acquired user setting information.

[0229] Input: User settings information (preferences, purchase history, characteristic data)

[0230] Action: Customizing your virtual assistant

[0231] Output: A customized virtual assistant instance

[0232] Specific operation: Based on the acquired user setting information, the server generates a virtual assistant dedicated to the user and initializes it.

[0233] Step 3:

[0234] The terminal receives input from the user and sends it to the server.

[0235] Input: User voice commands or text input

[0236] Processing: speech recognition, text processing

[0237] Output: Processed text data

[0238] Specific operation: The terminal converts the user's voice into text and sends the text data to the server.

[0239] Step 4:

[0240] The server processes the received user input data and generates an appropriate response.

[0241] Input: User input data (text format)

[0242] Processing: generative algorithms, natural language processing

[0243] Output: The appropriate response message

[0244] What it does: The server uses NLP techniques to parse the user's input and generate an appropriate response. This process uses the NLTK library.

[0245] Step 5:

[0246] The server generates a response message and sends it to the terminal.

[0247] Input: The generated response message

[0248] Action: Send message

[0249] Output: Response message displayed on the terminal

[0250] Specific operation: The server sends the generated response message to the terminal, and the terminal displays it to the user.

[0251] Step 6:

[0252] The terminal displays a response message to the user and continues the interaction with the user.

[0253] Input: Response message sent by the server

[0254] Processing: Display message, update interface

[0255] Output: The response message that is displayed to the user

[0256] Specific operation: The terminal displays the response message received from the server on the screen and updates the interface to allow the user to continue further operations.

[0257] Step 7:

[0258] Users receive product information and recommendations through conversations with virtual assistants.

[0259] Input: Any further questions or instructions from the user

[0260] Processing: Continuing the dialogue, product information and recommendations

[0261] Output: Detailed product information and recommended products

[0262] How it works: The virtual assistant provides detailed product information and recommendations based on the user's interests and past data. The system processes this information in real time and displays it to the user.

[0263] Step 8:

[0264] The server provides sales promotion and campaign information to the user.

[0265] Input: Latest campaign information and sales information

[0266] Processing: Organizing and distributing information

[0267] Output: Promotional and campaign information provided to the user

[0268] How it works: The server compiles information about current campaigns and sales and delivers it to users through the virtual assistant, allowing users to obtain the latest information and increasing their motivation to make purchases.

[0269] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0270] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0271] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, and an emotion engine.

[0272] System configuration

[0273] 1. Generative algorithm means:

[0274] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[0275] 2. User characteristic database means:

[0276] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[0277] 3. Conversational Interface Means:

[0278] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[0279] 4. Dating simulation methods:

[0280] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[0281] 5. Text and voice chat methods:

[0282] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[0283] 6. Video calling methods:

[0284] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[0285] 7. Emotion Engine:

[0286] The server includes an emotion engine that analyzes emotions based on the user's text and voice input, recognizes the user's emotional state, and adjusts the content and tone of the conversation appropriately.

[0287] How it works

[0288] Initialization and loading user settings

[0289] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database. Based on this information, it initializes the virtual lover AI object. The AI ​​object reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[0290] Generating everyday conversations

[0291] The server uses a generation algorithm to generate a daily greeting message and sends it to the user's device. The device displays this message, allowing the user to start a conversation. When the user inputs a conversation start or response through the device, the device sends this to the server. The server uses a virtual lover AI to generate an appropriate response to the user's input and returns it to the user's device.

[0292] Dating Simulation

[0293] The user selects the dating simulation mode on the device. The device sends this request to the server, which then generates a virtual dating situation. The user and the AI ​​can enjoy natural conversation at a virtual dating spot.

[0294] Text and voice chat

[0295] The user sends a request for text or voice chat on their device. The device sends this to the server, and the server generates a real-time response using the virtual lover AI. The generated response is sent to the user's device, and the user practices everyday conversation.

[0296] Video calling

[0297] When a user wishes to make a video call, the device sends a video call request to the server. The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. The video call begins, and real-time visual communication takes place between the user and the AI.

[0298] emotion recognition

[0299] The server uses an emotion engine to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired." As a result, the virtual lover AI generates a response such as "Today was tough. Take a good rest." In this way, an appropriate conversation is held based on the user's emotions.

[0300] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover. Furthermore, the use of an emotion engine enables responses that are sensitive to the user's emotions, providing a more fulfilling learning experience.

[0301] As a specific example, we cite the "emotion recognition scenario during a video call."

[0302] Example: Emotion recognition scenario during a video call

[0303] When a user says "I'm having so much fun today!" during a video call, the emotion engine recognizes the word "fun." The virtual lover AI responds with "That was great! What happened?" and continues the conversation. In this way, users can improve their language skills through natural conversation in real time.

[0304] The processing flow will be explained below.

[0305] Step 1:

[0306] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[0307] Step 2:

[0308] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[0309] Step 3:

[0310] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[0311] Step 4:

[0312] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[0313] Step 5:

[0314] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[0315] Step 6:

[0316] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[0317] Step 7:

[0318] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[0319] Step 8:

[0320] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[0321] Step 9:

[0322] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[0323] Step 10:

[0324] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[0325] Step 11:

[0326] A user sends a request to start a text or voice chat on their device, which then sends it to the server.

[0327] Step 12:

[0328] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[0329] Step 13:

[0330] The user requests to start a video call on the device, which then sends the request to the server.

[0331] Step 14:

[0332] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[0333] Step 15:

[0334] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[0335] Step 16:

[0336] The server passes the user's input (text and voice) to the emotion engine for emotion analysis. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired."

[0337] Step 17:

[0338] Based on the results of the emotion analysis, the server will generate an appropriate response from the virtual lover AI, such as "Today was tough. Take a good rest."

[0339] Step 18:

[0340] The server sends a response message generated based on the emotion recognition to the user's terminal.

[0341] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual lover AI. Furthermore, the use of an emotion engine enables responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[0342] Example 2

[0343] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0344] Conventional language learning systems have difficulty in providing natural conversations and responses that take the user's emotions into account, resulting in low immersion and practicality. Furthermore, they have limited ability to provide customized interactions based on the user's preferences and characteristics, resulting in inconsistent learning outcomes. Furthermore, communication is limited to simple text or voice, resulting in insufficient practical conversation practice.

[0345] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0346] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, and an emotion analysis means. This allows for natural and customized interactions with the user by recognizing the user's emotions and generating responses accordingly. Furthermore, by further including a dating simulation means, a text and voice chat means, a video call means, and an emotion-based response generation means, the server can improve its language skills while engaging in practical conversation practice in a variety of formats.

[0347] "Generation algorithm means" refers to a processing method for generating appropriate conversations and responses based on input from a user, and refers to an algorithm that uses natural language processing techniques to simulate actual conversations.

[0348] The "user characteristic database means" refers to a database system that records and manages a user's personal preferences and characteristics, and past interaction history.

[0349] "Conversational interface means" refers to interface means that allow users and AI to communicate through text, voice, and video, and includes user interfaces for chat boxes, voice input, and video calls.

[0350] "Emotion analysis means" refers to a technical means for analyzing emotions from a user's text and voice input and recognizing their emotional state.

[0351] "Dating simulation means" refers to a simulation means in which a server generates a virtual dating situation and a user can enjoy interacting with an AI.

[0352] "Text and voice chat means" refers to the interface means through which a user can engage in text chat and voice chat with an AI.

[0353] "Video call means" refers to an interface means that allows a user to make a video call with AI, enabling real-time visual communication.

[0354] "Emotion-based response generation means" refers to a technical means for generating appropriate conversations and responses based on the user's emotional state recognized by the emotion analysis means.

[0355] MODE FOR CARRYING OUT THE INVENTION

[0356] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0357] System Overview

[0358] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, an emotion analysis means, and an emotion-based response generation means.

[0359] Hardware and software used

[0360] The system uses the following hardware and software:

[0361] Server: Includes a high-performance database server, a natural language processing server (NLP server), and a sentiment analysis engine.

[0362] Terminal: A device such as a PC, smartphone, or tablet that accepts user operations.

[0363] software:

[0364] Natural language processing algorithms (e.g., leveraging machine learning frameworks such as TensorFlow and PyTorch)

[0365] Database management systems (e.g., MySQL, PostgreSQL)

[0366] Sentiment analysis engines (e.g. IBM Watson or Microsoft Azure emotion recognition APIs)

[0367] System details

[0368] 1. Initialization and loading of user configuration information:

[0369] The server reads the user's personal information (e.g., name, favorite food, favorite movie) from the user characteristic database means, and initializes a virtual lover AI object according to the user's characteristics.

[0370] 2. Everyday conversation generation:

[0371] The server generates a daily greeting message using a generation algorithm and sends it to the terminal. For example, it generates a message such as "Good morning, how is your day going today?" When the user starts a conversation, the server analyzes the user's input and generates an appropriate response. For example, if the user inputs "Good morning, what should we do after work today?", the server will respond with "How about going to the movies?"

[0372] 3. Dating Simulation:

[0373] When a user selects the dating simulation mode on their device, the server generates a virtual dating situation. For example, if a user requests, "I want to go to a cafe," the server generates a conversation such as, "I've arrived at the cafe. What kind of drink do you like?"

[0374] 4. Text and Voice Chat:

[0375] When a user selects text chat or voice chat mode, the server generates a real-time response. For example, if a user asks "What's the latest movie?" in voice chat, the server generates a response such as "Inception is the hot topic these days. Have you seen it?"

[0376] 5. Video Calls:

[0377] When a user wants to make a video call, the terminal sends a video call request to the server. The server generates a video call initialization message and sends it to the terminal. For example, it generates a message saying, "Are you ready for video call?"

[0378] 6. Emotion recognition:

[0379] The server uses emotion analysis to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the server recognizes the emotion "tired" and generates a response such as "Today was tough. Take a good rest."

[0380] Examples and prompts

[0381] For example, the following prompts can be fed into a generative AI model to generate a response:

[0382] Prompt: "Generate an AI response when a user says, 'Today is so much fun!' during a video call."

[0383] Prompt: "Generate an AI response when a user asks in text chat, 'How was the movie yesterday?'"

[0384] This allows users to improve their language skills while enjoying natural conversations with a virtual AI lover. The system generates responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[0385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0386] Step 1: Loading user configuration information

[0387] Specific operation: The server reads the user's personal information (eg, name, favorite food, favorite movie) using the user characteristic database means.

[0388] Input: User ID

[0389] Data processing: Execute a database query to obtain personal information corresponding to the user ID.

[0390] Output: User's personal information (e.g. name, favorite food, favorite movie)

[0391] Step 2: Initializing the Virtual Lover AI Object

[0392] Specific operation: The server initializes the virtual lover AI object based on the loaded user information. This initialization is performed using a generation algorithm.

[0393] Input: User's personal information

[0394] Data processing: Customize the conversation style and topic selection of the virtual lover AI based on user information.

[0395] Output: Initialized virtual lover AI object

[0396] Step 3: Generate a daily greeting message

[0397] Specific operation: The server uses a generation algorithm to generate a daily greeting message suitable for the user and transmits it to the terminal.

[0398] Input: User characteristics, date

[0399] Data processing: A natural language processing algorithm is used to generate a daily greeting message.

[0400] Output: Daily greeting message

[0401] Step 4: Initiating and responding to conversations

[0402] Specific operation: The user inputs a response to the greeting message through the terminal, and the terminal sends it to the server. The server uses a generation algorithm to generate an appropriate reply and sends it to the terminal.

[0403] Input: User's response to greeting message

[0404] Data processing: Using natural language processing algorithms, we generate replies that are tailored to the user's responses.

[0405] Output: Reply message from server to device

[0406] Step 5: Choose your dating simulation mode

[0407] Specific operation: The user selects the dating simulation mode on the device, and the device sends the request to the server.

[0408] Input: Dating simulation mode selection request

[0409] Data processing: Analyzes the request and starts the process of generating a virtual dating situation.

[0410] Output: Date simulation start confirmation message

[0411] Step 6: Generate a virtual dating situation

[0412] Specific operation: The server generates a virtual date spot in response to a user's request and prepares a natural conversation scenario.

[0413] Input: Confirmation message for starting the dating simulation, user's date location selection

[0414] Data processing: Generate virtual dating spots and prepare natural conversation scenarios.

[0415] Output: A hypothetical dating scenario and a starting message

[0416] Step 7: Start text and voice chat

[0417] Specific operation: The user selects text chat or voice chat mode, and the device sends the request to the server.

[0418] Input: Chat mode selection request

[0419] Data processing: Parsing the request and initiating the process of generating a response in real time.

[0420] Output: Chat mode start confirmation message

[0421] Step 8: Generate real-time responses

[0422] Specific operation: The server generates appropriate real-time responses to the user's chat input and sends them to the device.

[0423] Input: User chat input

[0424] Data processing: Using natural language processing algorithms, we generate responses that fit the user's input.

[0425] Output: Real-time response message from the server to the terminal

[0426] Step 9: Send a video call request

[0427] Specific operation: When a user wants to make a video call, the terminal sends a video call request to the server.

[0428] Input: Video call request

[0429] Data processing: Parse the request and start the video call initialization process.

[0430] Output: Video call initialization message

[0431] Step 10: Initialize the video call

[0432] Specific operation: The server uses the virtual lover AI to generate an initialization message for the video call and sends it to the device. Real-time visual communication takes place between the user and the AI.

[0433] Input: Video call initialization message

[0434] Data processing: Set up the video call environment and generate an initialization message.

[0435] Output: Video call start message

[0436] Step 11: Perform sentiment analysis

[0437] Specific operation: The server uses the emotion analysis means to analyze emotions from the user's text and voice input.

[0438] Input: User text or voice input

[0439] Data processing: Analyze emotions using a sentiment analysis engine.

[0440] Output: User's emotional state

[0441] Step 12: Generate an emotion-based response

[0442] Specific operation: The server generates an appropriate response based on the results of the emotion analysis and sends it to the device.

[0443] Input: User's emotional state

[0444] Data processing: Using natural language processing algorithms to generate emotional responses.

[0445] Output: Emotion-based response message

[0446] (Application example 2)

[0447] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0448] Conventional food delivery services lack personalized recommendations and order management based on user emotions and characteristics, resulting in a lack of user satisfaction.

[0449] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0450] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, an order history database means, and an emotion recognition means, thereby enabling personalized recommendations based on the user's characteristics and emotions.

[0451] The "generation algorithm means" is an algorithm for generating appropriate conversations and replies based on input data from the user.

[0452] The "user characteristic database means" is a database for recording and managing the user's personal preferences and characteristics, and past interaction history.

[0453] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, or video.

[0454] The "order history database means" is a database for recording and managing the user's past order history.

[0455] The "emotion recognition means" is an engine that analyzes emotions from the user's text or voice input.

[0456] The "dating simulation means" is a means for generating a virtual dating situation and for the user and the system to interact with each other.

[0457] "Text and voice chat means" refers to the interface through which a user engages in text and voice chat with the system.

[0458] A "video calling means" is an interface through which a user can make a video call with the system.

[0459] "Personalized recommendation means" is a means for recommending appropriate menus and options based on the user's current mood and past data.

[0460] The "order confirmation means" is a means for confirming the user's final order, storing it, and transmitting it to an external service.

[0461] As an embodiment of the present invention, the configuration and operation of a specific system are described below. This system provides emotion recognition and personalized recommendations to enable users to order food delivery through natural conversation and to enhance the experience.

[0462] System configuration

[0463] 1. Server Configuration

[0464] Generative Algorithm: The server is equipped with an algorithm to generate natural-sounding conversations and replies based on input data from the user. Specifically, it uses a generative AI model.

[0465] User characteristic database means: A database that records and manages the user's personal preferences, characteristics, and past interaction history.

[0466] Order history database means: A database that records and manages the user's past order history.

[0467] Emotion recognizer: An engine that analyzes emotions from user text and voice input. TensorFlow can be used for this.

[0468] 2. Terminal Configuration

[0469] Conversational interface means: An interface that allows users and servers to communicate through text, voice, or video. This includes smartphone applications.

[0470] Personalized recommendation tools: These tools recommend appropriate menus and options based on the user's current mood and past data.

[0471] Order confirmation means: A means for confirming the user's final order, storing it, and transmitting it to an external service.

[0472] How it works

[0473] Initialization and loading user settings

[0474] The server reads the user's preferences (e.g., name, favorite foods, past orders) from a database and uses this information to generate customized conversations and recommendations for the user.

[0475] Everyday conversation generation and emotion recognition

[0476] When a user inputs a request such as "I'm a little tired today, so I'd like to eat something refreshing" through the terminal, the terminal sends this request to the server. The server uses emotion recognition means to analyze the input text and recognizes the emotion "tired." As a result, the generation algorithm means generates an appropriate response such as "How about a salad that's good for when you're tired?" and presents it to the user.

[0477] Interactive recommendation service

[0478] If the user selects "I'll take that" based on the presented recommendation, the server updates the order history database to record the selection and, if necessary, generates a follow-up question (e.g., "Would you like a choice of dressing?").

[0479] Final order confirmation and confirmation

[0480] When the user inputs "This confirms the order," the terminal sends this to the server, and the server confirms the final order using the order confirmation means. The order details are saved in the order history database means and are also transmitted to an external delivery service.

[0481] Actual examples and prompts

[0482] Specific examples

[0483] The user types in the app, "I'm feeling a little tired today, what's your recommended menu?"

[0484] Prompt statement

[0485] plaintext

[0486] You are a helpful assistant specialized in suggesting food delivery options based on user's emotions and preferences.

[0487] User: "I'm a little tired today, what do you recommend for my meal?"

[0488] Assistant: "You seem tired today. How about a refreshing salad? This salad especially comes with a refreshing special dressing. Do you need anything else?"

[0489] In this way, a personalized response is generated according to the user's emotional state, improving the user experience.

[0490] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0491] Step 1:

[0492] The user opens the application on their smartphone and enters text into the input interface, such as "I'm a little tired today, what do you recommend for my meal?" This text data is sent from the device to the server.

[0493] Input: User-entered text data

[0494] Output: User-entered data sent to the server

[0495] Step 2:

[0496] The server passes the received user input data to the emotion recognition unit, which uses a TensorFlow model to analyze the text data and recognize the user's emotional state (in this case, "fatigue").

[0497] Input: User-entered text data

[0498] Data processing: Pass text data to the emotion recognition model and analyze it

[0499] Output: Emotion recognition result ("tired")

[0500] Step 3:

[0501] Based on the emotion recognition results, the server passes the data to a generative algorithm means, which uses a generative AI model (e.g., GPT-3) to generate a personalized recommendation, in this case, the response: "What salad would you recommend when you're feeling tired?"

[0502] Input: Emotion recognition result ("tired")

[0503] Data processing: Passing emotion recognition results to a generative AI model to generate a response

[0504] Output: The generated response

[0505] Step 4:

[0506] The server generates a response and sends it back to the user's device. The user sees a message on their device asking, "How about a salad for when you're feeling tired?"

[0507] Input: The generated response

[0508] Output: A response message displayed on the user's terminal

[0509] Step 5:

[0510] The user selects "I'll take that," and the terminal transmits this selection data to the server.

[0511] Input: User selected data

[0512] Output: Selection data sent to the server

[0513] Step 6:

[0514] The server stores the received selection data in the order history database means, and in some cases generates an additional question (e.g., "Would you like to choose a type of dressing?") and sends it to the user terminal.

[0515] Input: User selected data

[0516] Data processing: storing in a database and generating additional questions

[0517] Output: Database updates and additional questions generated

[0518] Step 7:

[0519] The user enters "This confirms the order," and the terminal sends this to the server.

[0520] Input: User-entered data for "Order Confirmation"

[0521] Output: "Order Confirmation" data sent to the server

[0522] Step 8:

[0523] The server uses the order confirmation means to confirm the final order. The order details are again saved in the order history database means and transmitted to an external delivery service as needed. The server also uses the generation algorithm means to generate a thank you message to the user (e.g., "Your order has been accepted. Thank you!") and transmits it to the user terminal.

[0524] Input: User-entered data for "Order Confirmation"

[0525] Data processing: updating the database, transmitting to external services, generating thank you messages

[0526] Output: Confirmed order and a thank you message to the user

[0527] In this way, the entire process is carried out seamlessly, providing a personalized food delivery experience that responds to the user's emotional state.

[0528] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0529] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0530] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0531] [Second embodiment]

[0532] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0533] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0534] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0535] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0536] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0537] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0538] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0539] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0540] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0541] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0542] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0543] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0544] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0545] The system of the present invention is for users to improve their language skills through natural conversation, and specifically includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[0546] System configuration

[0547] 1. Generative algorithm means:

[0548] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[0549] 2. User characteristic database means:

[0550] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[0551] 3. Conversational Interface Means:

[0552] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[0553] 4. Dating simulation methods:

[0554] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[0555] 5. Text and voice chat methods:

[0556] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[0557] 6. Video calling methods:

[0558] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[0559] How it works

[0560] Initialization and loading user settings

[0561] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database and initializes the virtual lover AI object based on this information.

[0562] Generating everyday conversations

[0563] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. The user initiates and responds to conversations through their device, and this input data is sent to the server. The server generates an appropriate response based on this user input and returns it to the user's device.

[0564] Dating Simulation

[0565] The user selects the dating simulation mode on the device, and the server generates a virtual dating spot using the virtual lover AI, allowing the user and the AI ​​to enjoy natural conversation.

[0566] Text and voice chat

[0567] Users can chat via text or voice on their devices, and the server generates instant responses based on the user's input, allowing for real-time communication.

[0568] Video calling

[0569] When a user wants to make a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and starts the video call session.

[0570] As a concrete example, consider the following scenario:

[0571] Example: Everyday conversation scenario

[0572] User: "I saw a movie today."

[0573] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[0574] In this way, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[0578] Step 2:

[0579] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[0580] Step 3:

[0581] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[0582] Step 4:

[0583] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[0584] Step 5:

[0585] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[0586] Step 6:

[0587] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[0588] Step 7:

[0589] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[0590] Step 8:

[0591] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[0592] Step 9:

[0593] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[0594] Step 10:

[0595] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[0596] Step 11:

[0597] A user sends a request to start a text or voice chat on a device, which then sends the request to the server.

[0598] Step 12:

[0599] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[0600] Step 13:

[0601] The user requests to start a video call on the device, which then sends the request to the server.

[0602] Step 14:

[0603] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[0604] Step 15:

[0605] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[0606] Example 1

[0607] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0608] Conventional language learning systems have limited interaction with users, making it difficult to provide practical conversation practice tailored to specific situations. Furthermore, they often struggle to support real-time communication and customize the system to suit individual users. This poses a challenge in efficiently improving users' language skills.

[0609] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0610] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means. This allows for customized conversation practice tailored to each individual user through real-time interaction with the user. Furthermore, practical conversation practice suited to a variety of situations is provided, efficiently supporting the improvement of the user's language skills.

[0611] The "generation algorithm means" is a means that implements an algorithm for generating natural conversations and responses based on user input data.

[0612] The "user characteristic database means" is a means for providing a database for recording and managing personal information and past interaction history of users.

[0613] A "conversational interface means" is a means for providing an interface for a user and a system to communicate through text, voice, video, etc.

[0614] The "date simulation means" is a means for generating a virtual date spot and providing a simulation in which the user and the virtual lover AI can enjoy a natural conversation.

[0615] "Text and voice chat means" refers to means that provides an interface for users to conduct real-time text chats and voice chats with the system.

[0616] A "video calling means" is a means for providing an interface for a user to make a video call with the system.

[0617] A "server" is a computer system that has the ability to read user configuration information, generate responses using natural language processing, and manage and control interactions with users through various interfaces.

[0618] A "terminal" is a device used by a user that provides an interface for interactions with the system, such as text chat, voice chat, and video calls.

[0619] "Virtual Lover AI" is an artificial intelligence designed to conduct natural conversations based on the user's settings and input data.

[0620] The following describes a specific system configuration and operation for implementing the present invention, which includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[0621] System Overview

[0622] The system aims to help users improve their language skills through natural conversation. The server manages real-time interactions with users using a generative AI model and generates responses tailored to each individual user from a database of user characteristics. The device provides users with a communication interface via text, voice, and video. In addition, a virtual lover AI uses a dating simulation tool to realize conversations in realistic situations.

[0623] Hardware and software used

[0624] Hardware

[0625] Server: Use general-purpose server equipment with high-performance processors and large storage capacity.

[0626] Device: A device available to a user, such as a smartphone, tablet, or PC.

[0627] software

[0628] Generative AI models: AI models that utilize natural language processing techniques (e.g., GPT-3, BERT, etc.).

[0629] Database Management System: A DBMS (e.g., MySQL, PostgreSQL, etc.) for managing the user characteristics database.

[0630] Communication interface: Send and receive data using an API that uses HTTP / HTTPS.

[0631] Detailed explanation of the system configuration

[0632] Generative Algorithm Means

[0633] The server operates a generative AI model to generate appropriate conversations and replies based on user input. The generative AI model provides natural responses in real time based on user utterances. The model is trained using machine learning algorithms to handle a variety of language patterns.

[0634] User characteristic database means

[0635] The server uses a user characteristic database to manage each user's personal information, preferences, and past interaction history, thereby enabling it to provide personalized responses to each individual user.

[0636] Conversational Interface Means

[0637] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[0638] Dating simulation tools

[0639] The server generates a virtual dating situation, where the user and the AI ​​interact. In this simulation, the user can choose a virtual date spot and enjoy natural conversation with the AI. During the simulation, the server dynamically changes the scenario based on the user's choices and responses.

[0640] Text and voice chat options

[0641] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language skills in real time and potentially improving their overall language ability.

[0642] Video calling means

[0643] The device provides an interface for users to have video calls with the AI, allowing for more practical conversation practice. The server processes the video data in real time and generates appropriate responses.

[0644] Examples of concrete examples and prompts

[0645] Example: Everyday conversation scenario

[0646] User: "I saw a movie today."

[0647] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[0648] Example prompt for a generative AI model:

[0649] 1. "Generate an AI response when a user states that they have seen a movie."

[0650] 2. "A user types, 'I saw a movie today.' How would the AI ​​respond?"

[0651] In this way, users can interact with their virtual AI lover through the system and improve their language skills while enjoying natural conversations and practical activities.

[0652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0653] Program processing flow

[0654] Step 1:

[0655] Loading User Settings

[0656] When a user first accesses the system, the server reads the user's setting information from the user characteristics database. This information includes personal information such as the user's name, favorite food, and favorite movie. Based on this information, the virtual lover AI object is initialized. The input is the user ID, and the output is the user's setting information.

[0657] Input: User ID

[0658] Output: Personal information such as name, favorite food, favorite movie, etc.

[0659] Specific operation: The server retrieves information using a DB query such as "SELECT FROM user_preferences WHERE user_id = '12345'".

[0660] Step 2:

[0661] Generating everyday conversations

[0662] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. When the user starts a conversation using the device and enters a response or question, the data is sent to the server. The server generates a response using natural language processing (NLP) and sends it back to the user's device.

[0663] Input: User text input

[0664] Output: Response from virtual lover AI

[0665] What it does: The server sends a message to the user saying, "Good morning, what's your plan today?" The user replies, "I'm working today," and the server uses its NLP engine to generate a response: "Good luck, let me know if anything interesting comes up!"

[0666] Step 3:

[0667] Starting a dating simulation

[0668] The user selects the dating simulation mode on their device. The server uses a virtual lover AI to generate a virtual dating spot and provides a scenario in which the user and the AI ​​can enjoy natural conversation. The server dynamically changes the scenario based on the user's selection and real-time conversation.

[0669] Input: Select a date spot

[0670] Output: Hypothetical dating situations and conversations

[0671] Specific operation: When the user selects "Dating Simulation," the server presents date spots such as "Park," "Cafe," "Movie Theater," etc. When the user selects "Cafe," the server starts a conversation with a "Virtual Cafe" background, asking, "Today, we're relaxing at the cafe. What would you like to drink?"

[0672] Step 4:

[0673] Text and voice chat

[0674] Users can chat by text or voice on their devices, and the server generates responses based on the user's input in real time, enabling smooth communication.

[0675] Input: Text or voice input

[0676] Output: Real-time response

[0677] What it does: When a user sends a text chat request like "Hello, what are you doing?", the server responds with "Hello, I'm just enjoying my new coffee!". For voice chat, the server also generates a real-time voice response to the user's voice input.

[0678] Step 5:

[0679] Making a video call

[0680] When a user requests a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and initiates the video call session.

[0681] Input: Video call request

[0682] Output: Video call session

[0683] How it works: When a user presses the video call button, the device sends a request to the server. The server then starts the video processing engine, and the virtual lover AI responds via video call, saying, "Hello, I'm glad to see you!" During the video call, the server processes the video and audio so that the AI ​​can respond to the user's questions in real time.

[0684] (Application example 1)

[0685] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] Conventional virtual store user experiences have had the problem of being unable to fully respond to individual user characteristics and preferences and only being able to provide limited information. Furthermore, interaction with the user was limited, preventing natural conversation and question-answering. This made it difficult to stimulate users' purchasing motivation.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0688] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a virtual assistant means, and a product guide and recommendation means, which enable natural conversation and recommendations based on the user's individual characteristics and preferences.

[0689] The "generation algorithm means" is a technology for generating appropriate conversations and replies based on input data from the user, and uses natural language processing technology.

[0690] The "user characteristic database means" is a database for recording and managing the preferences and characteristics of users and their past interaction history.

[0691] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, and video.

[0692] The "virtual assistant means" is a means for providing a virtual assistant to guide and recommend products through natural conversation with a user.

[0693] "Product information and recommendation means" is a means of presenting appropriate product information and recommended products based on the user's interests and past purchase history.

[0694] A "date simulation means" is a means for generating a virtual date situation, allowing the user to choose a virtual date spot and enjoy natural conversation with AI.

[0695] "Text and voice chat means" means a means for users to practice language in real time through text and voice chat.

[0696] "Video call means" is a means for users to practice more practical conversations with AI through video calls.

[0697] "Sales promotion means" refers to means for providing users with the latest campaign information and sale notices.

[0698] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0699] System configuration

[0700] 1. Generative algorithm means:

[0701] The server generates appropriate conversations and replies based on the input data from the user, using natural language processing technology and the NLTK library as NLP technology.

[0702] 2. User characteristic database means:

[0703] The server manages a database that records user preferences, characteristics, and past interaction history, allowing for customized conversations and recommendations for each user.

[0704] 3. Conversational Interface Means:

[0705] The terminal provides an interface for users and systems to communicate through text, voice, and video, including a chat box, voice input, and a user interface for video calling.

[0706] 4. Virtual Assistant Tools:

[0707] The server provides product guidance and recommendations through a virtual assistant based on the user's preferences and past data.

[0708] 5. Product Information and Recommendations:

[0709] The server provides appropriate product information based on the user's input and presents recommended products based on the user's interests and past purchase history.

[0710] 6. Sales promotion methods:

[0711] The server provides users with the latest campaign information and sale announcements within the virtual store.

[0712] How it works

[0713] Initialization and loading user settings

[0714] The server reads the user's configuration information (e.g., name, favorite product categories, past purchase history) from the database and initializes a virtual assistant customized for each user based on this information.

[0715] Generating everyday conversations and product information

[0716] The server conducts daily conversations and product information with the user through the virtual assistant. In response to questions and requests from the user, it generates appropriate responses and recommendations and sends them to the device.

[0717] Providing sales promotions and campaign information

[0718] The server provides users with the latest campaign information and sales announcements through a virtual assistant, thereby increasing users' motivation to make purchases.

[0719] Hardware / Software Used

[0720] Hardware used: Smartphone, smart glasses, or head-mounted display, through which the user interacts with the system.

[0721] Software used: Python, natural language processing library (NLTK), speech recognition library (Google Speech Recognition API), video calling library (WebRTC). These software processes data and enables communication between the server and the device.

[0722] Adding specific examples

[0723] Scenario: Product Introduction

[0724] User: "What are the features of this product?"

[0725] Virtual Assistant: "This product has a modern design and is waterproof, making it especially suitable for outdoor use."

[0726] Example prompts for generative AI models

[0727] Based on user characteristic data, generate natural and appropriate responses to the following inputs:

[0728] User Input: "What are your recommended products?"

[0729] User characteristics: {'name': 'User', 'recommended_product': 'Latest smartphone model', 'past_interactions': [...]}

[0730] Example response: "Here's what you need: a new smartphone model with a high-resolution camera and long battery life."

[0731] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0732] Step 1:

[0733] The server reads the user's configuration information from the user characteristics database.

[0734] Input: User ID or authentication information

[0735] Processing: Database query

[0736] Output: User preferences, purchase history, and characteristic data

[0737] Specific operation: The server queries the database using the user's ID and obtains information such as the user's preferences and past purchase history.

[0738] Step 2:

[0739] The server initializes the virtual assistant based on the acquired user setting information.

[0740] Input: User settings information (preferences, purchase history, characteristic data)

[0741] Action: Customizing your virtual assistant

[0742] Output: A customized virtual assistant instance

[0743] Specific operation: Based on the acquired user setting information, the server generates a virtual assistant dedicated to the user and initializes it.

[0744] Step 3:

[0745] The terminal receives input from the user and sends it to the server.

[0746] Input: User voice commands or text input

[0747] Processing: speech recognition, text processing

[0748] Output: Processed text data

[0749] Specific operation: The terminal converts the user's voice into text and sends the text data to the server.

[0750] Step 4:

[0751] The server processes the received user input data and generates an appropriate response.

[0752] Input: User input data (text format)

[0753] Processing: generative algorithms, natural language processing

[0754] Output: The appropriate response message

[0755] What it does: The server uses NLP techniques to parse the user's input and generate an appropriate response. This process uses the NLTK library.

[0756] Step 5:

[0757] The server generates a response message and sends it to the terminal.

[0758] Input: The generated response message

[0759] Action: Send message

[0760] Output: Response message displayed on the terminal

[0761] Specific operation: The server sends the generated response message to the terminal, and the terminal displays it to the user.

[0762] Step 6:

[0763] The terminal displays a response message to the user and continues the interaction with the user.

[0764] Input: Response message sent by the server

[0765] Processing: Display message, update interface

[0766] Output: The response message that is displayed to the user

[0767] Specific operation: The terminal displays the response message received from the server on the screen and updates the interface to allow the user to continue further operations.

[0768] Step 7:

[0769] Users receive product information and recommendations through conversations with virtual assistants.

[0770] Input: Any further questions or instructions from the user

[0771] Processing: Continuing the dialogue, product information and recommendations

[0772] Output: Detailed product information and recommended products

[0773] How it works: The virtual assistant provides detailed product information and recommendations based on the user's interests and past data. The system processes this information in real time and displays it to the user.

[0774] Step 8:

[0775] The server provides sales promotion and campaign information to the user.

[0776] Input: Latest campaign information and sales information

[0777] Processing: Organizing and distributing information

[0778] Output: Promotional and campaign information provided to the user

[0779] How it works: The server compiles information about current campaigns and sales and delivers it to users through the virtual assistant, allowing users to obtain the latest information and increasing their motivation to make purchases.

[0780] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0781] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0782] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, and an emotion engine.

[0783] System configuration

[0784] 1. Generative algorithm means:

[0785] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[0786] 2. User characteristic database means:

[0787] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[0788] 3. Conversational Interface Means:

[0789] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[0790] 4. Dating simulation methods:

[0791] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[0792] 5. Text and voice chat methods:

[0793] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[0794] 6. Video calling methods:

[0795] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[0796] 7. Emotion Engine:

[0797] The server includes an emotion engine that analyzes emotions based on the user's text and voice input, recognizes the user's emotional state, and adjusts the content and tone of the conversation appropriately.

[0798] How it works

[0799] Initialization and loading user settings

[0800] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database. Based on this information, it initializes the virtual lover AI object. The AI ​​object reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[0801] Generating everyday conversations

[0802] The server uses a generation algorithm to generate a daily greeting message and sends it to the user's device. The device displays this message, allowing the user to start a conversation. When the user inputs a conversation start or response through the device, the device sends this to the server. The server uses a virtual lover AI to generate an appropriate response to the user's input and returns it to the user's device.

[0803] Dating Simulation

[0804] The user selects the dating simulation mode on the device. The device sends this request to the server, which then generates a virtual dating situation. The user and the AI ​​can enjoy natural conversation at a virtual dating spot.

[0805] Text and voice chat

[0806] The user sends a request for text or voice chat on their device. The device sends this to the server, and the server generates a real-time response using the virtual lover AI. The generated response is sent to the user's device, and the user practices everyday conversation.

[0807] Video calling

[0808] When a user wishes to make a video call, the device sends a video call request to the server. The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. The video call begins, and real-time visual communication takes place between the user and the AI.

[0809] emotion recognition

[0810] The server uses an emotion engine to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired." As a result, the virtual lover AI generates a response such as "Today was tough. Take a good rest." In this way, an appropriate conversation is held based on the user's emotions.

[0811] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover. Furthermore, the use of an emotion engine enables responses that are sensitive to the user's emotions, providing a more fulfilling learning experience.

[0812] As a specific example, we cite the "emotion recognition scenario during a video call."

[0813] Example: Emotion recognition scenario during a video call

[0814] When a user says "I'm having so much fun today!" during a video call, the emotion engine recognizes the word "fun." The virtual lover AI responds with "That was great! What happened?" and continues the conversation. In this way, users can improve their language skills through natural conversation in real time.

[0815] The processing flow will be explained below.

[0816] Step 1:

[0817] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[0818] Step 2:

[0819] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[0820] Step 3:

[0821] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[0822] Step 4:

[0823] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[0824] Step 5:

[0825] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[0826] Step 6:

[0827] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[0828] Step 7:

[0829] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[0830] Step 8:

[0831] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[0832] Step 9:

[0833] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[0834] Step 10:

[0835] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[0836] Step 11:

[0837] A user sends a request to start a text or voice chat on their device, which then sends it to the server.

[0838] Step 12:

[0839] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[0840] Step 13:

[0841] The user requests to start a video call on the device, which then sends the request to the server.

[0842] Step 14:

[0843] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[0844] Step 15:

[0845] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[0846] Step 16:

[0847] The server passes the user's input (text and voice) to the emotion engine for emotion analysis. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired."

[0848] Step 17:

[0849] Based on the results of the emotion analysis, the server will generate an appropriate response from the virtual lover AI, such as "Today was tough. Take a good rest."

[0850] Step 18:

[0851] The server sends a response message generated based on the emotion recognition to the user's terminal.

[0852] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual lover AI. Furthermore, the use of an emotion engine enables responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[0853] Example 2

[0854] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0855] Conventional language learning systems have difficulty in providing natural conversations and responses that take the user's emotions into account, resulting in low immersion and practicality. Furthermore, they have limited ability to provide customized interactions based on the user's preferences and characteristics, resulting in inconsistent learning outcomes. Furthermore, communication is limited to simple text or voice, resulting in insufficient practical conversation practice.

[0856] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0857] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, and an emotion analysis means. This allows for natural and customized interactions with the user by recognizing the user's emotions and generating responses accordingly. Furthermore, by further including a dating simulation means, a text and voice chat means, a video call means, and an emotion-based response generation means, the server can improve its language skills while engaging in practical conversation practice in a variety of formats.

[0858] "Generation algorithm means" refers to a processing method for generating appropriate conversations and responses based on input from a user, and refers to an algorithm that uses natural language processing techniques to simulate actual conversations.

[0859] The "user characteristic database means" refers to a database system that records and manages a user's personal preferences and characteristics, and past interaction history.

[0860] "Conversational interface means" refers to interface means that allow users and AI to communicate through text, voice, and video, and includes user interfaces for chat boxes, voice input, and video calls.

[0861] "Emotion analysis means" refers to a technical means for analyzing emotions from a user's text and voice input and recognizing their emotional state.

[0862] "Dating simulation means" refers to a simulation means in which a server generates a virtual dating situation and a user can enjoy interacting with an AI.

[0863] "Text and voice chat means" refers to the interface means through which a user can engage in text chat and voice chat with an AI.

[0864] "Video call means" refers to an interface means that allows a user to make a video call with AI, enabling real-time visual communication.

[0865] "Emotion-based response generation means" refers to a technical means for generating appropriate conversations and responses based on the user's emotional state recognized by the emotion analysis means.

[0866] MODE FOR CARRYING OUT THE INVENTION

[0867] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[0868] System Overview

[0869] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, an emotion analysis means, and an emotion-based response generation means.

[0870] Hardware and software used

[0871] The system uses the following hardware and software:

[0872] Server: Includes a high-performance database server, a natural language processing server (NLP server), and a sentiment analysis engine.

[0873] Terminal: A device such as a PC, smartphone, or tablet that accepts user operations.

[0874] software:

[0875] Natural language processing algorithms (e.g., leveraging machine learning frameworks such as TensorFlow and PyTorch)

[0876] Database management systems (e.g., MySQL, PostgreSQL)

[0877] Sentiment analysis engines (e.g. IBM Watson or Microsoft Azure emotion recognition APIs)

[0878] System details

[0879] 1. Initialization and loading of user configuration information:

[0880] The server reads the user's personal information (e.g., name, favorite food, favorite movie) from the user characteristic database means, and initializes a virtual lover AI object according to the user's characteristics.

[0881] 2. Everyday conversation generation:

[0882] The server generates a daily greeting message using a generation algorithm and sends it to the terminal. For example, it generates a message such as "Good morning, how is your day going today?" When the user starts a conversation, the server analyzes the user's input and generates an appropriate response. For example, if the user inputs "Good morning, what should we do after work today?", the server will respond with "How about going to the movies?"

[0883] 3. Dating Simulation:

[0884] When a user selects the dating simulation mode on their device, the server generates a virtual dating situation. For example, if a user requests, "I want to go to a cafe," the server generates a conversation such as, "I've arrived at the cafe. What kind of drink do you like?"

[0885] 4. Text and Voice Chat:

[0886] When a user selects text chat or voice chat mode, the server generates a real-time response. For example, if a user asks "What's the latest movie?" in voice chat, the server generates a response such as "Inception is the hot topic these days. Have you seen it?"

[0887] 5. Video Calls:

[0888] When a user wants to make a video call, the terminal sends a video call request to the server. The server generates a video call initialization message and sends it to the terminal. For example, it generates a message saying, "Are you ready for video call?"

[0889] 6. Emotion recognition:

[0890] The server uses emotion analysis to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the server recognizes the emotion "tired" and generates a response such as "Today was tough. Take a good rest."

[0891] Examples and prompts

[0892] For example, the following prompts can be fed into a generative AI model to generate a response:

[0893] Prompt: "Generate an AI response when a user says, 'Today is so much fun!' during a video call."

[0894] Prompt: "Generate an AI response when a user asks in text chat, 'How was the movie yesterday?'"

[0895] This allows users to improve their language skills while enjoying natural conversations with a virtual AI lover. The system generates responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[0896] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0897] Step 1: Loading user configuration information

[0898] Specific operation: The server reads the user's personal information (eg, name, favorite food, favorite movie) using the user characteristic database means.

[0899] Input: User ID

[0900] Data processing: Execute a database query to obtain personal information corresponding to the user ID.

[0901] Output: User's personal information (e.g. name, favorite food, favorite movie)

[0902] Step 2: Initializing the Virtual Lover AI Object

[0903] Specific operation: The server initializes the virtual lover AI object based on the loaded user information. This initialization is performed using a generation algorithm.

[0904] Input: User's personal information

[0905] Data processing: Customize the conversation style and topic selection of the virtual lover AI based on user information.

[0906] Output: Initialized virtual lover AI object

[0907] Step 3: Generate a daily greeting message

[0908] Specific operation: The server uses a generation algorithm to generate a daily greeting message suitable for the user and transmits it to the terminal.

[0909] Input: User characteristics, date

[0910] Data processing: A natural language processing algorithm is used to generate a daily greeting message.

[0911] Output: Daily greeting message

[0912] Step 4: Initiating and responding to conversations

[0913] Specific operation: The user inputs a response to the greeting message through the terminal, and the terminal sends it to the server. The server uses a generation algorithm to generate an appropriate reply and sends it to the terminal.

[0914] Input: User's response to greeting message

[0915] Data processing: Using natural language processing algorithms, we generate replies that are tailored to the user's responses.

[0916] Output: Reply message from server to device

[0917] Step 5: Choose your dating simulation mode

[0918] Specific operation: The user selects the dating simulation mode on the device, and the device sends the request to the server.

[0919] Input: Dating simulation mode selection request

[0920] Data processing: Analyzes the request and starts the process of generating a virtual dating situation.

[0921] Output: Date simulation start confirmation message

[0922] Step 6: Generate a virtual dating situation

[0923] Specific operation: The server generates a virtual date spot in response to a user's request and prepares a natural conversation scenario.

[0924] Input: Confirmation message for starting the dating simulation, user's date location selection

[0925] Data processing: Generate virtual dating spots and prepare natural conversation scenarios.

[0926] Output: A hypothetical dating scenario and a starting message

[0927] Step 7: Start text and voice chat

[0928] Specific operation: The user selects text chat or voice chat mode, and the device sends the request to the server.

[0929] Input: Chat mode selection request

[0930] Data processing: Parsing the request and initiating the process of generating a response in real time.

[0931] Output: Chat mode start confirmation message

[0932] Step 8: Generate real-time responses

[0933] Specific operation: The server generates appropriate real-time responses to the user's chat input and sends them to the device.

[0934] Input: User chat input

[0935] Data processing: Using natural language processing algorithms, we generate responses that fit the user's input.

[0936] Output: Real-time response message from the server to the terminal

[0937] Step 9: Send a video call request

[0938] Specific operation: When a user wants to make a video call, the terminal sends a video call request to the server.

[0939] Input: Video call request

[0940] Data processing: Parse the request and start the video call initialization process.

[0941] Output: Video call initialization message

[0942] Step 10: Initialize the video call

[0943] Specific operation: The server uses the virtual lover AI to generate an initialization message for the video call and sends it to the device. Real-time visual communication takes place between the user and the AI.

[0944] Input: Video call initialization message

[0945] Data processing: Set up the video call environment and generate an initialization message.

[0946] Output: Video call start message

[0947] Step 11: Perform sentiment analysis

[0948] Specific operation: The server uses the emotion analysis means to analyze emotions from the user's text and voice input.

[0949] Input: User text or voice input

[0950] Data processing: Analyze emotions using a sentiment analysis engine.

[0951] Output: User's emotional state

[0952] Step 12: Generate an emotion-based response

[0953] Specific operation: The server generates an appropriate response based on the results of the emotion analysis and sends it to the device.

[0954] Input: User's emotional state

[0955] Data processing: Using natural language processing algorithms to generate emotional responses.

[0956] Output: Emotion-based response message

[0957] (Application example 2)

[0958] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0959] Conventional food delivery services lack personalized recommendations and order management based on user emotions and characteristics, resulting in a lack of user satisfaction.

[0960] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0961] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, an order history database means, and an emotion recognition means, thereby enabling personalized recommendations based on the user's characteristics and emotions.

[0962] The "generation algorithm means" is an algorithm for generating appropriate conversations and replies based on input data from the user.

[0963] The "user characteristic database means" is a database for recording and managing the user's personal preferences and characteristics, and past interaction history.

[0964] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, or video.

[0965] The "order history database means" is a database for recording and managing the user's past order history.

[0966] The "emotion recognition means" is an engine that analyzes emotions from the user's text or voice input.

[0967] The "dating simulation means" is a means for generating a virtual dating situation and for the user and the system to interact with each other.

[0968] "Text and voice chat means" refers to the interface through which a user engages in text and voice chat with the system.

[0969] A "video calling means" is an interface through which a user can make a video call with the system.

[0970] "Personalized recommendation means" is a means for recommending appropriate menus and options based on the user's current mood and past data.

[0971] The "order confirmation means" is a means for confirming the user's final order, storing it, and transmitting it to an external service.

[0972] As an embodiment of the present invention, the configuration and operation of a specific system are described below. This system provides emotion recognition and personalized recommendations to enable users to order food delivery through natural conversation and to enhance the experience.

[0973] System configuration

[0974] 1. Server Configuration

[0975] Generative Algorithm: The server is equipped with an algorithm to generate natural-sounding conversations and replies based on input data from the user. Specifically, it uses a generative AI model.

[0976] User characteristic database means: A database that records and manages the user's personal preferences, characteristics, and past interaction history.

[0977] Order history database means: A database that records and manages the user's past order history.

[0978] Emotion recognizer: An engine that analyzes emotions from user text and voice input. TensorFlow can be used for this.

[0979] 2. Terminal Configuration

[0980] Conversational interface means: An interface that allows users and servers to communicate through text, voice, or video. This includes smartphone applications.

[0981] Personalized recommendation tools: These tools recommend appropriate menus and options based on the user's current mood and past data.

[0982] Order confirmation means: A means for confirming the user's final order, storing it, and transmitting it to an external service.

[0983] How it works

[0984] Initialization and loading user settings

[0985] The server reads the user's preferences (e.g., name, favorite foods, past orders) from a database and uses this information to generate customized conversations and recommendations for the user.

[0986] Everyday conversation generation and emotion recognition

[0987] When a user inputs a request such as "I'm a little tired today, so I'd like to eat something refreshing" through the terminal, the terminal sends this request to the server. The server uses emotion recognition means to analyze the input text and recognizes the emotion "tired." As a result, the generation algorithm means generates an appropriate response such as "How about a salad that's good for when you're tired?" and presents it to the user.

[0988] Interactive recommendation service

[0989] If the user selects "I'll take that" based on the presented recommendation, the server updates the order history database to record the selection and, if necessary, generates a follow-up question (e.g., "Would you like a choice of dressing?").

[0990] Final order confirmation and confirmation

[0991] When the user inputs "This confirms the order," the terminal sends this to the server, and the server confirms the final order using the order confirmation means. The order details are saved in the order history database means and are also transmitted to an external delivery service.

[0992] Actual examples and prompts

[0993] Specific examples

[0994] The user types in the app, "I'm feeling a little tired today, what's your recommended menu?"

[0995] Prompt statement

[0996] plaintext

[0997] You are a helpful assistant specialized in suggesting food delivery options based on user's emotions and preferences.

[0998] User: "I'm a little tired today, what do you recommend for my meal?"

[0999] Assistant: "You seem tired today. How about a refreshing salad? This salad especially comes with a refreshing special dressing. Do you need anything else?"

[1000] In this way, a personalized response is generated according to the user's emotional state, improving the user experience.

[1001] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1002] Step 1:

[1003] The user opens the application on their smartphone and enters text into the input interface, such as "I'm a little tired today, what do you recommend for my meal?" This text data is sent from the device to the server.

[1004] Input: User-entered text data

[1005] Output: User-entered data sent to the server

[1006] Step 2:

[1007] The server passes the received user input data to the emotion recognition unit, which uses a TensorFlow model to analyze the text data and recognize the user's emotional state (in this case, "fatigue").

[1008] Input: User-entered text data

[1009] Data processing: Pass text data to the emotion recognition model and analyze it

[1010] Output: Emotion recognition result ("tired")

[1011] Step 3:

[1012] Based on the emotion recognition results, the server passes the data to a generative algorithm means, which uses a generative AI model (e.g., GPT-3) to generate a personalized recommendation, in this case, the response: "What salad would you recommend when you're feeling tired?"

[1013] Input: Emotion recognition result ("tired")

[1014] Data processing: Passing emotion recognition results to a generative AI model to generate a response

[1015] Output: The generated response

[1016] Step 4:

[1017] The server generates a response and sends it back to the user's device. The user sees a message on their device asking, "How about a salad for when you're feeling tired?"

[1018] Input: The generated response

[1019] Output: A response message displayed on the user's terminal

[1020] Step 5:

[1021] The user selects "I'll take that," and the terminal transmits this selection data to the server.

[1022] Input: User selected data

[1023] Output: Selection data sent to the server

[1024] Step 6:

[1025] The server stores the received selection data in the order history database means, and in some cases generates an additional question (e.g., "Would you like to choose a type of dressing?") and sends it to the user terminal.

[1026] Input: User selected data

[1027] Data processing: storing in a database and generating additional questions

[1028] Output: Database updates and additional questions generated

[1029] Step 7:

[1030] The user enters "This confirms the order," and the terminal sends this to the server.

[1031] Input: User-entered data for "Order Confirmation"

[1032] Output: "Order Confirmation" data sent to the server

[1033] Step 8:

[1034] The server uses the order confirmation means to confirm the final order. The order details are again saved in the order history database means and transmitted to an external delivery service as needed. The server also uses the generation algorithm means to generate a thank you message to the user (e.g., "Your order has been accepted. Thank you!") and transmits it to the user terminal.

[1035] Input: User-entered data for "Order Confirmation"

[1036] Data processing: updating the database, transmitting to external services, generating thank you messages

[1037] Output: Confirmed order and a thank you message to the user

[1038] In this way, the entire process is carried out seamlessly, providing a personalized food delivery experience that responds to the user's emotional state.

[1039] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1040] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1041] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1042] [Third embodiment]

[1043] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1044] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1045] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1046] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1047] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1048] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1049] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1050] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1051] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1052] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1053] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1054] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1055] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1056] The system of the present invention is for users to improve their language skills through natural conversation, and specifically includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[1057] System configuration

[1058] 1. Generative algorithm means:

[1059] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[1060] 2. User characteristic database means:

[1061] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[1062] 3. Conversational Interface Means:

[1063] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[1064] 4. Dating simulation methods:

[1065] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[1066] 5. Text and voice chat methods:

[1067] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[1068] 6. Video calling methods:

[1069] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[1070] How it works

[1071] Initialization and loading user settings

[1072] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database and initializes the virtual lover AI object based on this information.

[1073] Generating everyday conversations

[1074] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. The user initiates and responds to conversations through their device, and this input data is sent to the server. The server generates an appropriate response based on this user input and returns it to the user's device.

[1075] Dating Simulation

[1076] The user selects the dating simulation mode on the device, and the server generates a virtual dating spot using the virtual lover AI, allowing the user and the AI ​​to enjoy natural conversation.

[1077] Text and voice chat

[1078] Users can chat via text or voice on their devices, and the server generates instant responses based on the user's input, allowing for real-time communication.

[1079] Video calling

[1080] When a user wants to make a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and starts the video call session.

[1081] As a concrete example, consider the following scenario:

[1082] Example: Everyday conversation scenario

[1083] User: "I saw a movie today."

[1084] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[1085] In this way, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover.

[1086] The processing flow will be explained below.

[1087] Step 1:

[1088] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[1089] Step 2:

[1090] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[1091] Step 3:

[1092] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[1093] Step 4:

[1094] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[1095] Step 5:

[1096] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[1097] Step 6:

[1098] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[1099] Step 7:

[1100] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[1101] Step 8:

[1102] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[1103] Step 9:

[1104] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[1105] Step 10:

[1106] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[1107] Step 11:

[1108] A user sends a request to start a text or voice chat on a device, which then sends the request to the server.

[1109] Step 12:

[1110] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[1111] Step 13:

[1112] The user requests to start a video call on the device, which then sends the request to the server.

[1113] Step 14:

[1114] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[1115] Step 15:

[1116] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[1117] Example 1

[1118] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1119] Conventional language learning systems have limited interaction with users, making it difficult to provide practical conversation practice tailored to specific situations. Furthermore, they often struggle to support real-time communication and customize the system to suit individual users. This poses a challenge in efficiently improving users' language skills.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1121] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means. This allows for customized conversation practice tailored to each individual user through real-time interaction with the user. Furthermore, practical conversation practice suited to a variety of situations is provided, efficiently supporting the improvement of the user's language skills.

[1122] The "generation algorithm means" is a means that implements an algorithm for generating natural conversations and responses based on user input data.

[1123] The "user characteristic database means" is a means for providing a database for recording and managing personal information and past interaction history of users.

[1124] A "conversational interface means" is a means for providing an interface for a user and a system to communicate through text, voice, video, etc.

[1125] The "date simulation means" is a means for generating a virtual date spot and providing a simulation in which the user and the virtual lover AI can enjoy a natural conversation.

[1126] "Text and voice chat means" refers to means that provides an interface for users to conduct real-time text chats and voice chats with the system.

[1127] A "video calling means" is a means for providing an interface for a user to make a video call with the system.

[1128] A "server" is a computer system that has the ability to read user configuration information, generate responses using natural language processing, and manage and control interactions with users through various interfaces.

[1129] A "terminal" is a device used by a user that provides an interface for interactions with the system, such as text chat, voice chat, and video calls.

[1130] "Virtual Lover AI" is an artificial intelligence designed to conduct natural conversations based on the user's settings and input data.

[1131] The following describes a specific system configuration and operation for implementing the present invention, which includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[1132] System Overview

[1133] The system aims to help users improve their language skills through natural conversation. The server manages real-time interactions with users using a generative AI model and generates responses tailored to each individual user from a database of user characteristics. The device provides users with a communication interface via text, voice, and video. In addition, a virtual lover AI uses a dating simulation tool to realize conversations in realistic situations.

[1134] Hardware and software used

[1135] Hardware

[1136] Server: Use general-purpose server equipment with high-performance processors and large storage capacity.

[1137] Device: A device available to a user, such as a smartphone, tablet, or PC.

[1138] software

[1139] Generative AI models: AI models that utilize natural language processing techniques (e.g., GPT-3, BERT, etc.).

[1140] Database Management System: A DBMS (e.g., MySQL, PostgreSQL, etc.) for managing the user characteristics database.

[1141] Communication interface: Send and receive data using an API that uses HTTP / HTTPS.

[1142] Detailed explanation of the system configuration

[1143] Generative Algorithm Means

[1144] The server operates a generative AI model to generate appropriate conversations and replies based on user input. The generative AI model provides natural responses in real time based on user utterances. The model is trained using machine learning algorithms to handle a variety of language patterns.

[1145] User characteristic database means

[1146] The server uses a user characteristic database to manage each user's personal information, preferences, and past interaction history, thereby enabling it to provide personalized responses to each individual user.

[1147] Conversational Interface Means

[1148] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[1149] Dating simulation tools

[1150] The server generates a virtual dating situation, where the user and the AI ​​interact. In this simulation, the user can choose a virtual date spot and enjoy natural conversation with the AI. During the simulation, the server dynamically changes the scenario based on the user's choices and responses.

[1151] Text and voice chat options

[1152] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language skills in real time and potentially improving their overall language ability.

[1153] Video calling means

[1154] The device provides an interface for users to have video calls with the AI, allowing for more practical conversation practice. The server processes the video data in real time and generates appropriate responses.

[1155] Examples of concrete examples and prompts

[1156] Example: Everyday conversation scenario

[1157] User: "I saw a movie today."

[1158] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[1159] Example prompt for a generative AI model:

[1160] 1. "Generate an AI response when a user states that they have seen a movie."

[1161] 2. "A user types, 'I saw a movie today.' How would the AI ​​respond?"

[1162] In this way, users can interact with their virtual AI lover through the system and improve their language skills while enjoying natural conversations and practical activities.

[1163] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1164] Program processing flow

[1165] Step 1:

[1166] Loading User Settings

[1167] When a user first accesses the system, the server reads the user's setting information from the user characteristics database. This information includes personal information such as the user's name, favorite food, and favorite movie. Based on this information, the virtual lover AI object is initialized. The input is the user ID, and the output is the user's setting information.

[1168] Input: User ID

[1169] Output: Personal information such as name, favorite food, favorite movie, etc.

[1170] Specific operation: The server retrieves information using a DB query such as "SELECT FROM user_preferences WHERE user_id = '12345'".

[1171] Step 2:

[1172] Generating everyday conversations

[1173] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. When the user starts a conversation using the device and enters a response or question, the data is sent to the server. The server generates a response using natural language processing (NLP) and sends it back to the user's device.

[1174] Input: User text input

[1175] Output: Response from virtual lover AI

[1176] What it does: The server sends a message to the user saying, "Good morning, what's your plan today?" The user replies, "I'm working today," and the server uses its NLP engine to generate a response: "Good luck, let me know if anything interesting comes up!"

[1177] Step 3:

[1178] Starting a dating simulation

[1179] The user selects the dating simulation mode on their device. The server uses a virtual lover AI to generate a virtual dating spot and provides a scenario in which the user and the AI ​​can enjoy natural conversation. The server dynamically changes the scenario based on the user's selection and real-time conversation.

[1180] Input: Select a date spot

[1181] Output: Hypothetical dating situations and conversations

[1182] Specific operation: When the user selects "Dating Simulation," the server presents date spots such as "Park," "Cafe," "Movie Theater," etc. When the user selects "Cafe," the server starts a conversation with a "Virtual Cafe" background, asking, "Today, we're relaxing at the cafe. What would you like to drink?"

[1183] Step 4:

[1184] Text and voice chat

[1185] Users can chat by text or voice on their devices, and the server generates responses based on the user's input in real time, enabling smooth communication.

[1186] Input: Text or voice input

[1187] Output: Real-time response

[1188] What it does: When a user sends a text chat request like "Hello, what are you doing?", the server responds with "Hello, I'm just enjoying my new coffee!". For voice chat, the server also generates a real-time voice response to the user's voice input.

[1189] Step 5:

[1190] Making a video call

[1191] When a user requests a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and initiates the video call session.

[1192] Input: Video call request

[1193] Output: Video call session

[1194] How it works: When a user presses the video call button, the device sends a request to the server. The server then starts the video processing engine, and the virtual lover AI responds via video call, saying, "Hello, I'm glad to see you!" During the video call, the server processes the video and audio so that the AI ​​can respond to the user's questions in real time.

[1195] (Application example 1)

[1196] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1197] Conventional virtual store user experiences have had the problem of being unable to fully respond to individual user characteristics and preferences and only being able to provide limited information. Furthermore, interaction with the user was limited, preventing natural conversation and question-answering. This made it difficult to stimulate users' purchasing motivation.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1199] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a virtual assistant means, and a product guide and recommendation means, which enable natural conversation and recommendations based on the user's individual characteristics and preferences.

[1200] The "generation algorithm means" is a technology for generating appropriate conversations and replies based on input data from the user, and uses natural language processing technology.

[1201] The "user characteristic database means" is a database for recording and managing the preferences and characteristics of users and their past interaction history.

[1202] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, and video.

[1203] The "virtual assistant means" is a means for providing a virtual assistant to guide and recommend products through natural conversation with a user.

[1204] "Product information and recommendation means" is a means of presenting appropriate product information and recommended products based on the user's interests and past purchase history.

[1205] A "date simulation means" is a means for generating a virtual date situation, allowing the user to choose a virtual date spot and enjoy natural conversation with AI.

[1206] "Text and voice chat means" means a means for users to practice language in real time through text and voice chat.

[1207] "Video call means" is a means for users to practice more practical conversations with AI through video calls.

[1208] "Sales promotion means" refers to means for providing users with the latest campaign information and sale notices.

[1209] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1210] System configuration

[1211] 1. Generative algorithm means:

[1212] The server generates appropriate conversations and replies based on the input data from the user, using natural language processing technology and the NLTK library as NLP technology.

[1213] 2. User characteristic database means:

[1214] The server manages a database that records user preferences, characteristics, and past interaction history, allowing for customized conversations and recommendations for each user.

[1215] 3. Conversational Interface Means:

[1216] The terminal provides an interface for users and systems to communicate through text, voice, and video, including a chat box, voice input, and a user interface for video calling.

[1217] 4. Virtual Assistant Tools:

[1218] The server provides product guidance and recommendations through a virtual assistant based on the user's preferences and past data.

[1219] 5. Product Information and Recommendations:

[1220] The server provides appropriate product information based on the user's input and presents recommended products based on the user's interests and past purchase history.

[1221] 6. Sales promotion methods:

[1222] The server provides users with the latest campaign information and sale announcements within the virtual store.

[1223] How it works

[1224] Initialization and loading user settings

[1225] The server reads the user's configuration information (e.g., name, favorite product categories, past purchase history) from the database and initializes a virtual assistant customized for each user based on this information.

[1226] Generating everyday conversations and product information

[1227] The server conducts daily conversations and product information with the user through the virtual assistant. In response to questions and requests from the user, it generates appropriate responses and recommendations and sends them to the device.

[1228] Providing sales promotions and campaign information

[1229] The server provides users with the latest campaign information and sales announcements through a virtual assistant, thereby increasing users' motivation to make purchases.

[1230] Hardware / Software Used

[1231] Hardware used: Smartphone, smart glasses, or head-mounted display, through which the user interacts with the system.

[1232] Software used: Python, natural language processing library (NLTK), speech recognition library (Google Speech Recognition API), video calling library (WebRTC). These software processes data and enables communication between the server and the device.

[1233] Adding specific examples

[1234] Scenario: Product Introduction

[1235] User: "What are the features of this product?"

[1236] Virtual Assistant: "This product has a modern design and is waterproof, making it especially suitable for outdoor use."

[1237] Example prompts for generative AI models

[1238] Based on user characteristic data, generate natural and appropriate responses to the following inputs:

[1239] User Input: "What are your recommended products?"

[1240] User characteristics: {'name': 'User', 'recommended_product': 'Latest smartphone model', 'past_interactions': [...]}

[1241] Example response: "Here's what you need: a new smartphone model with a high-resolution camera and long battery life."

[1242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1243] Step 1:

[1244] The server reads the user's configuration information from the user characteristics database.

[1245] Input: User ID or authentication information

[1246] Processing: Database query

[1247] Output: User preferences, purchase history, and characteristic data

[1248] Specific operation: The server queries the database using the user's ID and obtains information such as the user's preferences and past purchase history.

[1249] Step 2:

[1250] The server initializes the virtual assistant based on the acquired user setting information.

[1251] Input: User settings information (preferences, purchase history, characteristic data)

[1252] Action: Customizing your virtual assistant

[1253] Output: A customized virtual assistant instance

[1254] Specific operation: Based on the acquired user setting information, the server generates a virtual assistant dedicated to the user and initializes it.

[1255] Step 3:

[1256] The terminal receives input from the user and sends it to the server.

[1257] Input: User voice commands or text input

[1258] Processing: speech recognition, text processing

[1259] Output: Processed text data

[1260] Specific operation: The terminal converts the user's voice into text and sends the text data to the server.

[1261] Step 4:

[1262] The server processes the received user input data and generates an appropriate response.

[1263] Input: User input data (text format)

[1264] Processing: generative algorithms, natural language processing

[1265] Output: The appropriate response message

[1266] What it does: The server uses NLP techniques to parse the user's input and generate an appropriate response. This process uses the NLTK library.

[1267] Step 5:

[1268] The server generates a response message and sends it to the terminal.

[1269] Input: The generated response message

[1270] Action: Send message

[1271] Output: Response message displayed on the terminal

[1272] Specific operation: The server sends the generated response message to the terminal, and the terminal displays it to the user.

[1273] Step 6:

[1274] The terminal displays a response message to the user and continues the interaction with the user.

[1275] Input: Response message sent by the server

[1276] Processing: Display message, update interface

[1277] Output: The response message that is displayed to the user

[1278] Specific operation: The terminal displays the response message received from the server on the screen and updates the interface to allow the user to continue further operations.

[1279] Step 7:

[1280] Users receive product information and recommendations through conversations with virtual assistants.

[1281] Input: Any further questions or instructions from the user

[1282] Processing: Continuing the dialogue, product information and recommendations

[1283] Output: Detailed product information and recommended products

[1284] How it works: The virtual assistant provides detailed product information and recommendations based on the user's interests and past data. The system processes this information in real time and displays it to the user.

[1285] Step 8:

[1286] The server provides sales promotion and campaign information to the user.

[1287] Input: Latest campaign information and sales information

[1288] Processing: Organizing and distributing information

[1289] Output: Promotional and campaign information provided to the user

[1290] How it works: The server compiles information about current campaigns and sales and delivers it to users through the virtual assistant, allowing users to obtain the latest information and increasing their motivation to make purchases.

[1291] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1292] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1293] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, and an emotion engine.

[1294] System configuration

[1295] 1. Generative algorithm means:

[1296] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[1297] 2. User characteristic database means:

[1298] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[1299] 3. Conversational Interface Means:

[1300] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[1301] 4. Dating simulation methods:

[1302] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[1303] 5. Text and voice chat methods:

[1304] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[1305] 6. Video calling methods:

[1306] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[1307] 7. Emotion Engine:

[1308] The server includes an emotion engine that analyzes emotions based on the user's text and voice input, recognizes the user's emotional state, and adjusts the content and tone of the conversation appropriately.

[1309] How it works

[1310] Initialization and loading user settings

[1311] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database. Based on this information, it initializes the virtual lover AI object. The AI ​​object reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[1312] Generating everyday conversations

[1313] The server uses a generation algorithm to generate a daily greeting message and sends it to the user's device. The device displays this message, allowing the user to start a conversation. When the user inputs a conversation start or response through the device, the device sends this to the server. The server uses a virtual lover AI to generate an appropriate response to the user's input and returns it to the user's device.

[1314] Dating Simulation

[1315] The user selects the dating simulation mode on the device. The device sends this request to the server, which then generates a virtual dating situation. The user and the AI ​​can enjoy natural conversation at a virtual dating spot.

[1316] Text and voice chat

[1317] The user sends a request for text or voice chat on their device. The device sends this to the server, and the server generates a real-time response using the virtual lover AI. The generated response is sent to the user's device, and the user practices everyday conversation.

[1318] Video calling

[1319] When a user wishes to make a video call, the device sends a video call request to the server. The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. The video call begins, and real-time visual communication takes place between the user and the AI.

[1320] emotion recognition

[1321] The server uses an emotion engine to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired." As a result, the virtual lover AI generates a response such as "Today was tough. Take a good rest." In this way, an appropriate conversation is held based on the user's emotions.

[1322] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover. Furthermore, the use of an emotion engine enables responses that are sensitive to the user's emotions, providing a more fulfilling learning experience.

[1323] As a specific example, we cite the "emotion recognition scenario during a video call."

[1324] Example: Emotion recognition scenario during a video call

[1325] When a user says "I'm having so much fun today!" during a video call, the emotion engine recognizes the word "fun." The virtual lover AI responds with "That was great! What happened?" and continues the conversation. In this way, users can improve their language skills through natural conversation in real time.

[1326] The processing flow will be explained below.

[1327] Step 1:

[1328] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[1329] Step 2:

[1330] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[1331] Step 3:

[1332] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[1333] Step 4:

[1334] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[1335] Step 5:

[1336] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[1337] Step 6:

[1338] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[1339] Step 7:

[1340] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[1341] Step 8:

[1342] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[1343] Step 9:

[1344] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[1345] Step 10:

[1346] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[1347] Step 11:

[1348] A user sends a request to start a text or voice chat on their device, which then sends it to the server.

[1349] Step 12:

[1350] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[1351] Step 13:

[1352] The user requests to start a video call on the device, which then sends the request to the server.

[1353] Step 14:

[1354] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[1355] Step 15:

[1356] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[1357] Step 16:

[1358] The server passes the user's input (text and voice) to the emotion engine for emotion analysis. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired."

[1359] Step 17:

[1360] Based on the results of the emotion analysis, the server will generate an appropriate response from the virtual lover AI, such as "Today was tough. Take a good rest."

[1361] Step 18:

[1362] The server sends a response message generated based on the emotion recognition to the user's terminal.

[1363] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual lover AI. Furthermore, the use of an emotion engine enables responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[1364] Example 2

[1365] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1366] Conventional language learning systems have difficulty in providing natural conversations and responses that take the user's emotions into account, resulting in low immersion and practicality. Furthermore, they have limited ability to provide customized interactions based on the user's preferences and characteristics, resulting in inconsistent learning outcomes. Furthermore, communication is limited to simple text or voice, resulting in insufficient practical conversation practice.

[1367] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1368] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, and an emotion analysis means. This allows for natural and customized interactions with the user by recognizing the user's emotions and generating responses accordingly. Furthermore, by further including a dating simulation means, a text and voice chat means, a video call means, and an emotion-based response generation means, the server can improve its language skills while engaging in practical conversation practice in a variety of formats.

[1369] "Generation algorithm means" refers to a processing method for generating appropriate conversations and responses based on input from a user, and refers to an algorithm that uses natural language processing techniques to simulate actual conversations.

[1370] The "user characteristic database means" refers to a database system that records and manages a user's personal preferences and characteristics, and past interaction history.

[1371] "Conversational interface means" refers to interface means that allow users and AI to communicate through text, voice, and video, and includes user interfaces for chat boxes, voice input, and video calls.

[1372] "Emotion analysis means" refers to a technical means for analyzing emotions from a user's text and voice input and recognizing their emotional state.

[1373] "Dating simulation means" refers to a simulation means in which a server generates a virtual dating situation and a user can enjoy interacting with an AI.

[1374] "Text and voice chat means" refers to the interface means through which a user can engage in text chat and voice chat with an AI.

[1375] "Video call means" refers to an interface means that allows a user to make a video call with AI, enabling real-time visual communication.

[1376] "Emotion-based response generation means" refers to a technical means for generating appropriate conversations and responses based on the user's emotional state recognized by the emotion analysis means.

[1377] MODE FOR CARRYING OUT THE INVENTION

[1378] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1379] System Overview

[1380] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, an emotion analysis means, and an emotion-based response generation means.

[1381] Hardware and software used

[1382] The system uses the following hardware and software:

[1383] Server: Includes a high-performance database server, a natural language processing server (NLP server), and a sentiment analysis engine.

[1384] Terminal: A device such as a PC, smartphone, or tablet that accepts user operations.

[1385] software:

[1386] Natural language processing algorithms (e.g., leveraging machine learning frameworks such as TensorFlow and PyTorch)

[1387] Database management systems (e.g., MySQL, PostgreSQL)

[1388] Sentiment analysis engines (e.g. IBM Watson or Microsoft Azure emotion recognition APIs)

[1389] System details

[1390] 1. Initialization and loading of user configuration information:

[1391] The server reads the user's personal information (e.g., name, favorite food, favorite movie) from the user characteristic database means, and initializes a virtual lover AI object according to the user's characteristics.

[1392] 2. Everyday conversation generation:

[1393] The server generates a daily greeting message using a generation algorithm and sends it to the terminal. For example, it generates a message such as "Good morning, how is your day going today?" When the user starts a conversation, the server analyzes the user's input and generates an appropriate response. For example, if the user inputs "Good morning, what should we do after work today?", the server will respond with "How about going to the movies?"

[1394] 3. Dating Simulation:

[1395] When a user selects the dating simulation mode on their device, the server generates a virtual dating situation. For example, if a user requests, "I want to go to a cafe," the server generates a conversation such as, "I've arrived at the cafe. What kind of drink do you like?"

[1396] 4. Text and Voice Chat:

[1397] When a user selects text chat or voice chat mode, the server generates a real-time response. For example, if a user asks "What's the latest movie?" in voice chat, the server generates a response such as "Inception is the hot topic these days. Have you seen it?"

[1398] 5. Video Calls:

[1399] When a user wants to make a video call, the terminal sends a video call request to the server. The server generates a video call initialization message and sends it to the terminal. For example, it generates a message saying, "Are you ready for video call?"

[1400] 6. Emotion recognition:

[1401] The server uses emotion analysis to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the server recognizes the emotion "tired" and generates a response such as "Today was tough. Take a good rest."

[1402] Examples and prompts

[1403] For example, the following prompts can be fed into a generative AI model to generate a response:

[1404] Prompt: "Generate an AI response when a user says, 'Today is so much fun!' during a video call."

[1405] Prompt: "Generate an AI response when a user asks in text chat, 'How was the movie yesterday?'"

[1406] This allows users to improve their language skills while enjoying natural conversations with a virtual AI lover. The system generates responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Step 1: Loading user configuration information

[1409] Specific operation: The server reads the user's personal information (eg, name, favorite food, favorite movie) using the user characteristic database means.

[1410] Input: User ID

[1411] Data processing: Execute a database query to obtain personal information corresponding to the user ID.

[1412] Output: User's personal information (e.g. name, favorite food, favorite movie)

[1413] Step 2: Initializing the Virtual Lover AI Object

[1414] Specific operation: The server initializes the virtual lover AI object based on the loaded user information. This initialization is performed using a generation algorithm.

[1415] Input: User's personal information

[1416] Data processing: Customize the conversation style and topic selection of the virtual lover AI based on user information.

[1417] Output: Initialized virtual lover AI object

[1418] Step 3: Generate a daily greeting message

[1419] Specific operation: The server uses a generation algorithm to generate a daily greeting message suitable for the user and transmits it to the terminal.

[1420] Input: User characteristics, date

[1421] Data processing: A natural language processing algorithm is used to generate a daily greeting message.

[1422] Output: Daily greeting message

[1423] Step 4: Initiating and responding to conversations

[1424] Specific operation: The user inputs a response to the greeting message through the terminal, and the terminal sends it to the server. The server uses a generation algorithm to generate an appropriate reply and sends it to the terminal.

[1425] Input: User's response to greeting message

[1426] Data processing: Using natural language processing algorithms, we generate replies that are tailored to the user's responses.

[1427] Output: Reply message from server to device

[1428] Step 5: Choose your dating simulation mode

[1429] Specific operation: The user selects the dating simulation mode on the device, and the device sends the request to the server.

[1430] Input: Dating simulation mode selection request

[1431] Data processing: Analyzes the request and starts the process of generating a virtual dating situation.

[1432] Output: Date simulation start confirmation message

[1433] Step 6: Generate a virtual dating situation

[1434] Specific operation: The server generates a virtual date spot in response to a user's request and prepares a natural conversation scenario.

[1435] Input: Confirmation message for starting the dating simulation, user's date location selection

[1436] Data processing: Generate virtual dating spots and prepare natural conversation scenarios.

[1437] Output: A hypothetical dating scenario and a starting message

[1438] Step 7: Start text and voice chat

[1439] Specific operation: The user selects text chat or voice chat mode, and the device sends the request to the server.

[1440] Input: Chat mode selection request

[1441] Data processing: Parsing the request and initiating the process of generating a response in real time.

[1442] Output: Chat mode start confirmation message

[1443] Step 8: Generate real-time responses

[1444] Specific operation: The server generates appropriate real-time responses to the user's chat input and sends them to the device.

[1445] Input: User chat input

[1446] Data processing: Using natural language processing algorithms, we generate responses that fit the user's input.

[1447] Output: Real-time response message from the server to the terminal

[1448] Step 9: Send a video call request

[1449] Specific operation: When a user wants to make a video call, the terminal sends a video call request to the server.

[1450] Input: Video call request

[1451] Data processing: Parse the request and start the video call initialization process.

[1452] Output: Video call initialization message

[1453] Step 10: Initialize the video call

[1454] Specific operation: The server uses the virtual lover AI to generate an initialization message for the video call and sends it to the device. Real-time visual communication takes place between the user and the AI.

[1455] Input: Video call initialization message

[1456] Data processing: Set up the video call environment and generate an initialization message.

[1457] Output: Video call start message

[1458] Step 11: Perform sentiment analysis

[1459] Specific operation: The server uses the emotion analysis means to analyze emotions from the user's text and voice input.

[1460] Input: User text or voice input

[1461] Data processing: Analyze emotions using a sentiment analysis engine.

[1462] Output: User's emotional state

[1463] Step 12: Generate an emotion-based response

[1464] Specific operation: The server generates an appropriate response based on the results of the emotion analysis and sends it to the device.

[1465] Input: User's emotional state

[1466] Data processing: Using natural language processing algorithms to generate emotional responses.

[1467] Output: Emotion-based response message

[1468] (Application example 2)

[1469] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1470] Conventional food delivery services lack personalized recommendations and order management based on user emotions and characteristics, resulting in a lack of user satisfaction.

[1471] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1472] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, an order history database means, and an emotion recognition means, thereby enabling personalized recommendations based on the user's characteristics and emotions.

[1473] The "generation algorithm means" is an algorithm for generating appropriate conversations and replies based on input data from the user.

[1474] The "user characteristic database means" is a database for recording and managing the user's personal preferences and characteristics, and past interaction history.

[1475] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, or video.

[1476] The "order history database means" is a database for recording and managing the user's past order history.

[1477] The "emotion recognition means" is an engine that analyzes emotions from the user's text or voice input.

[1478] The "dating simulation means" is a means for generating a virtual dating situation and for the user and the system to interact with each other.

[1479] "Text and voice chat means" refers to the interface through which a user engages in text and voice chat with the system.

[1480] A "video calling means" is an interface through which a user can make a video call with the system.

[1481] "Personalized recommendation means" is a means for recommending appropriate menus and options based on the user's current mood and past data.

[1482] The "order confirmation means" is a means for confirming the user's final order, storing it, and transmitting it to an external service.

[1483] As an embodiment of the present invention, the configuration and operation of a specific system are described below. This system provides emotion recognition and personalized recommendations to enable users to order food delivery through natural conversation and to enhance the experience.

[1484] System configuration

[1485] 1. Server Configuration

[1486] Generative Algorithm: The server is equipped with an algorithm to generate natural-sounding conversations and replies based on input data from the user. Specifically, it uses a generative AI model.

[1487] User characteristic database means: A database that records and manages the user's personal preferences, characteristics, and past interaction history.

[1488] Order history database means: A database that records and manages the user's past order history.

[1489] Emotion recognizer: An engine that analyzes emotions from user text and voice input. TensorFlow can be used for this.

[1490] 2. Terminal Configuration

[1491] Conversational interface means: An interface that allows users and servers to communicate through text, voice, or video. This includes smartphone applications.

[1492] Personalized recommendation tools: These tools recommend appropriate menus and options based on the user's current mood and past data.

[1493] Order confirmation means: A means for confirming the user's final order, storing it, and transmitting it to an external service.

[1494] How it works

[1495] Initialization and loading user settings

[1496] The server reads the user's preferences (e.g., name, favorite foods, past orders) from a database and uses this information to generate customized conversations and recommendations for the user.

[1497] Everyday conversation generation and emotion recognition

[1498] When a user inputs a request such as "I'm a little tired today, so I'd like to eat something refreshing" through the terminal, the terminal sends this request to the server. The server uses emotion recognition means to analyze the input text and recognizes the emotion "tired." As a result, the generation algorithm means generates an appropriate response such as "How about a salad that's good for when you're tired?" and presents it to the user.

[1499] Interactive recommendation service

[1500] If the user selects "I'll take that" based on the presented recommendation, the server updates the order history database to record the selection and, if necessary, generates a follow-up question (e.g., "Would you like a choice of dressing?").

[1501] Final order confirmation and confirmation

[1502] When the user inputs "This confirms the order," the terminal sends this to the server, and the server confirms the final order using the order confirmation means. The order details are saved in the order history database means and are also transmitted to an external delivery service.

[1503] Actual examples and prompts

[1504] Specific examples

[1505] The user types in the app, "I'm feeling a little tired today, what's your recommended menu?"

[1506] Prompt statement

[1507] plaintext

[1508] You are a helpful assistant specialized in suggesting food delivery options based on user's emotions and preferences.

[1509] User: "I'm a little tired today, what do you recommend for my meal?"

[1510] Assistant: "You seem tired today. How about a refreshing salad? This salad especially comes with a refreshing special dressing. Do you need anything else?"

[1511] In this way, a personalized response is generated according to the user's emotional state, improving the user experience.

[1512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1513] Step 1:

[1514] The user opens the application on their smartphone and enters text into the input interface, such as "I'm a little tired today, what do you recommend for my meal?" This text data is sent from the device to the server.

[1515] Input: User-entered text data

[1516] Output: User-entered data sent to the server

[1517] Step 2:

[1518] The server passes the received user input data to the emotion recognition unit, which uses a TensorFlow model to analyze the text data and recognize the user's emotional state (in this case, "fatigue").

[1519] Input: User-entered text data

[1520] Data processing: Pass text data to the emotion recognition model and analyze it

[1521] Output: Emotion recognition result ("tired")

[1522] Step 3:

[1523] Based on the emotion recognition results, the server passes the data to a generative algorithm means, which uses a generative AI model (e.g., GPT-3) to generate a personalized recommendation, in this case, the response: "What salad would you recommend when you're feeling tired?"

[1524] Input: Emotion recognition result ("tired")

[1525] Data processing: Passing emotion recognition results to a generative AI model to generate a response

[1526] Output: The generated response

[1527] Step 4:

[1528] The server generates a response and sends it back to the user's device. The user sees a message on their device asking, "How about a salad for when you're feeling tired?"

[1529] Input: The generated response

[1530] Output: A response message displayed on the user's terminal

[1531] Step 5:

[1532] The user selects "I'll take that," and the terminal transmits this selection data to the server.

[1533] Input: User selected data

[1534] Output: Selection data sent to the server

[1535] Step 6:

[1536] The server stores the received selection data in the order history database means, and in some cases generates an additional question (e.g., "Would you like to choose a type of dressing?") and sends it to the user terminal.

[1537] Input: User selected data

[1538] Data processing: storing in a database and generating additional questions

[1539] Output: Database updates and additional questions generated

[1540] Step 7:

[1541] The user enters "This confirms the order," and the terminal sends this to the server.

[1542] Input: User-entered data for "Order Confirmation"

[1543] Output: "Order Confirmation" data sent to the server

[1544] Step 8:

[1545] The server uses the order confirmation means to confirm the final order. The order details are again saved in the order history database means and transmitted to an external delivery service as needed. The server also uses the generation algorithm means to generate a thank you message to the user (e.g., "Your order has been accepted. Thank you!") and transmits it to the user terminal.

[1546] Input: User-entered data for "Order Confirmation"

[1547] Data processing: updating the database, transmitting to external services, generating thank you messages

[1548] Output: Confirmed order and a thank you message to the user

[1549] In this way, the entire process is carried out seamlessly, providing a personalized food delivery experience that responds to the user's emotional state.

[1550] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1551] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1552] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1553] [Fourth embodiment]

[1554] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1555] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1556] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1557] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1558] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1559] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1560] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1561] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1562] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1563] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1564] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1565] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1566] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1567] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1568] The system of the present invention is for users to improve their language skills through natural conversation, and specifically includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[1569] System configuration

[1570] 1. Generative algorithm means:

[1571] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[1572] 2. User characteristic database means:

[1573] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[1574] 3. Conversational Interface Means:

[1575] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[1576] 4. Dating simulation methods:

[1577] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[1578] 5. Text and voice chat methods:

[1579] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[1580] 6. Video calling methods:

[1581] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[1582] How it works

[1583] Initialization and loading user settings

[1584] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database and initializes the virtual lover AI object based on this information.

[1585] Generating everyday conversations

[1586] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. The user initiates and responds to conversations through their device, and this input data is sent to the server. The server generates an appropriate response based on this user input and returns it to the user's device.

[1587] Dating Simulation

[1588] The user selects the dating simulation mode on the device, and the server generates a virtual dating spot using the virtual lover AI, allowing the user and the AI ​​to enjoy natural conversation.

[1589] Text and voice chat

[1590] Users can chat via text or voice on their devices, and the server generates instant responses based on the user's input, allowing for real-time communication.

[1591] Video calling

[1592] When a user wants to make a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and starts the video call session.

[1593] As a concrete example, consider the following scenario:

[1594] Example: Everyday conversation scenario

[1595] User: "I saw a movie today."

[1596] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[1597] In this way, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover.

[1598] The processing flow will be explained below.

[1599] Step 1:

[1600] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[1601] Step 2:

[1602] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[1603] Step 3:

[1604] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[1605] Step 4:

[1606] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[1607] Step 5:

[1608] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[1609] Step 6:

[1610] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[1611] Step 7:

[1612] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[1613] Step 8:

[1614] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[1615] Step 9:

[1616] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[1617] Step 10:

[1618] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[1619] Step 11:

[1620] A user sends a request to start a text or voice chat on a device, which then sends the request to the server.

[1621] Step 12:

[1622] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[1623] Step 13:

[1624] The user requests to start a video call on the device, which then sends the request to the server.

[1625] Step 14:

[1626] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[1627] Step 15:

[1628] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[1629] Example 1

[1630] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1631] Conventional language learning systems have limited interaction with users, making it difficult to provide practical conversation practice tailored to specific situations. Furthermore, they often struggle to support real-time communication and customize the system to suit individual users. This poses a challenge in efficiently improving users' language skills.

[1632] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1633] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means. This allows for customized conversation practice tailored to each individual user through real-time interaction with the user. Furthermore, practical conversation practice suited to a variety of situations is provided, efficiently supporting the improvement of the user's language skills.

[1634] The "generation algorithm means" is a means that implements an algorithm for generating natural conversations and responses based on user input data.

[1635] The "user characteristic database means" is a means for providing a database for recording and managing personal information and past interaction history of users.

[1636] A "conversational interface means" is a means for providing an interface for a user and a system to communicate through text, voice, video, etc.

[1637] The "date simulation means" is a means for generating a virtual date spot and providing a simulation in which the user and the virtual lover AI can enjoy a natural conversation.

[1638] "Text and voice chat means" refers to means that provides an interface for users to conduct real-time text chats and voice chats with the system.

[1639] A "video calling means" is a means for providing an interface for a user to make a video call with the system.

[1640] A "server" is a computer system that has the ability to read user configuration information, generate responses using natural language processing, and manage and control interactions with users through various interfaces.

[1641] A "terminal" is a device used by a user that provides an interface for interactions with the system, such as text chat, voice chat, and video calls.

[1642] "Virtual Lover AI" is an artificial intelligence designed to conduct natural conversations based on the user's settings and input data.

[1643] The following describes a specific system configuration and operation for implementing the present invention, which includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, and a video call means.

[1644] System Overview

[1645] The system aims to help users improve their language skills through natural conversation. The server manages real-time interactions with users using a generative AI model and generates responses tailored to each individual user from a database of user characteristics. The device provides users with a communication interface via text, voice, and video. In addition, a virtual lover AI uses a dating simulation tool to realize conversations in realistic situations.

[1646] Hardware and software used

[1647] Hardware

[1648] Server: Use general-purpose server equipment with high-performance processors and large storage capacity.

[1649] Device: A device available to a user, such as a smartphone, tablet, or PC.

[1650] software

[1651] Generative AI models: AI models that utilize natural language processing techniques (e.g., GPT-3, BERT, etc.).

[1652] Database Management System: A DBMS (e.g., MySQL, PostgreSQL, etc.) for managing the user characteristics database.

[1653] Communication interface: Send and receive data using an API that uses HTTP / HTTPS.

[1654] Detailed explanation of the system configuration

[1655] Generative Algorithm Means

[1656] The server operates a generative AI model to generate appropriate conversations and replies based on user input. The generative AI model provides natural responses in real time based on user utterances. The model is trained using machine learning algorithms to handle a variety of language patterns.

[1657] User characteristic database means

[1658] The server uses a user characteristic database to manage each user's personal information, preferences, and past interaction history, thereby enabling it to provide personalized responses to each individual user.

[1659] Conversational Interface Means

[1660] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[1661] Dating simulation tools

[1662] The server generates a virtual dating situation, where the user and the AI ​​interact. In this simulation, the user can choose a virtual date spot and enjoy natural conversation with the AI. During the simulation, the server dynamically changes the scenario based on the user's choices and responses.

[1663] Text and voice chat options

[1664] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language skills in real time and potentially improving their overall language ability.

[1665] Video calling means

[1666] The device provides an interface for users to have video calls with the AI, allowing for more practical conversation practice. The server processes the video data in real time and generates appropriate responses.

[1667] Examples of concrete examples and prompts

[1668] Example: Everyday conversation scenario

[1669] User: "I saw a movie today."

[1670] The server passes this user's input to the virtual lover AI, which then generates a response such as "That's interesting! What was the movie like?" The generated message is displayed on the user's device, allowing the user to continue the conversation.

[1671] Example prompt for a generative AI model:

[1672] 1. "Generate an AI response when a user states that they have seen a movie."

[1673] 2. "A user types, 'I saw a movie today.' How would the AI ​​respond?"

[1674] In this way, users can interact with their virtual AI lover through the system and improve their language skills while enjoying natural conversations and practical activities.

[1675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1676] Program processing flow

[1677] Step 1:

[1678] Loading User Settings

[1679] When a user first accesses the system, the server reads the user's setting information from the user characteristics database. This information includes personal information such as the user's name, favorite food, and favorite movie. Based on this information, the virtual lover AI object is initialized. The input is the user ID, and the output is the user's setting information.

[1680] Input: User ID

[1681] Output: Personal information such as name, favorite food, favorite movie, etc.

[1682] Specific operation: The server retrieves information using a DB query such as "SELECT FROM user_preferences WHERE user_id = '12345'".

[1683] Step 2:

[1684] Generating everyday conversations

[1685] The server generates a daily greeting message through the virtual lover AI and sends it to the user's device. When the user starts a conversation using the device and enters a response or question, the data is sent to the server. The server generates a response using natural language processing (NLP) and sends it back to the user's device.

[1686] Input: User text input

[1687] Output: Response from virtual lover AI

[1688] What it does: The server sends a message to the user saying, "Good morning, what's your plan today?" The user replies, "I'm working today," and the server uses its NLP engine to generate a response: "Good luck, let me know if anything interesting comes up!"

[1689] Step 3:

[1690] Starting a dating simulation

[1691] The user selects the dating simulation mode on their device. The server uses a virtual lover AI to generate a virtual dating spot and provides a scenario in which the user and the AI ​​can enjoy natural conversation. The server dynamically changes the scenario based on the user's selection and real-time conversation.

[1692] Input: Select a date spot

[1693] Output: Hypothetical dating situations and conversations

[1694] Specific operation: When the user selects "Dating Simulation," the server presents date spots such as "Park," "Cafe," "Movie Theater," etc. When the user selects "Cafe," the server starts a conversation with a "Virtual Cafe" background, asking, "Today, we're relaxing at the cafe. What would you like to drink?"

[1695] Step 4:

[1696] Text and voice chat

[1697] Users can chat by text or voice on their devices, and the server generates responses based on the user's input in real time, enabling smooth communication.

[1698] Input: Text or voice input

[1699] Output: Real-time response

[1700] What it does: When a user sends a text chat request like "Hello, what are you doing?", the server responds with "Hello, I'm just enjoying my new coffee!". For voice chat, the server also generates a real-time voice response to the user's voice input.

[1701] Step 5:

[1702] Making a video call

[1703] When a user requests a video call, the device sends a video call request to the server, which generates a response from the virtual lover AI and initiates the video call session.

[1704] Input: Video call request

[1705] Output: Video call session

[1706] How it works: When a user presses the video call button, the device sends a request to the server. The server then starts the video processing engine, and the virtual lover AI responds via video call, saying, "Hello, I'm glad to see you!" During the video call, the server processes the video and audio so that the AI ​​can respond to the user's questions in real time.

[1707] (Application example 1)

[1708] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1709] Conventional virtual store user experiences have had the problem of being unable to fully respond to individual user characteristics and preferences and only being able to provide limited information. Furthermore, interaction with the user was limited, preventing natural conversation and question-answering. This made it difficult to stimulate users' purchasing motivation.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1711] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, a virtual assistant means, and a product guide and recommendation means, which enable natural conversation and recommendations based on the user's individual characteristics and preferences.

[1712] The "generation algorithm means" is a technology for generating appropriate conversations and replies based on input data from the user, and uses natural language processing technology.

[1713] The "user characteristic database means" is a database for recording and managing the preferences and characteristics of users and their past interaction history.

[1714] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, and video.

[1715] The "virtual assistant means" is a means for providing a virtual assistant to guide and recommend products through natural conversation with a user.

[1716] "Product information and recommendation means" is a means of presenting appropriate product information and recommended products based on the user's interests and past purchase history.

[1717] A "date simulation means" is a means for generating a virtual date situation, allowing the user to choose a virtual date spot and enjoy natural conversation with AI.

[1718] "Text and voice chat means" means a means for users to practice language in real time through text and voice chat.

[1719] "Video call means" is a means for users to practice more practical conversations with AI through video calls.

[1720] "Sales promotion means" refers to means for providing users with the latest campaign information and sale notices.

[1721] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1722] System configuration

[1723] 1. Generative algorithm means:

[1724] The server generates appropriate conversations and replies based on the input data from the user, using natural language processing technology and the NLTK library as NLP technology.

[1725] 2. User characteristic database means:

[1726] The server manages a database that records user preferences, characteristics, and past interaction history, allowing for customized conversations and recommendations for each user.

[1727] 3. Conversational Interface Means:

[1728] The terminal provides an interface for users and systems to communicate through text, voice, and video, including a chat box, voice input, and a user interface for video calling.

[1729] 4. Virtual Assistant Tools:

[1730] The server provides product guidance and recommendations through a virtual assistant based on the user's preferences and past data.

[1731] 5. Product Information and Recommendations:

[1732] The server provides appropriate product information based on the user's input and presents recommended products based on the user's interests and past purchase history.

[1733] 6. Sales promotion methods:

[1734] The server provides users with the latest campaign information and sale announcements within the virtual store.

[1735] How it works

[1736] Initialization and loading user settings

[1737] The server reads the user's configuration information (e.g., name, favorite product categories, past purchase history) from the database and initializes a virtual assistant customized for each user based on this information.

[1738] Generating everyday conversations and product information

[1739] The server conducts daily conversations and product information with the user through the virtual assistant. In response to questions and requests from the user, it generates appropriate responses and recommendations and sends them to the device.

[1740] Providing sales promotions and campaign information

[1741] The server provides users with the latest campaign information and sales announcements through a virtual assistant, thereby increasing users' motivation to make purchases.

[1742] Hardware / Software Used

[1743] Hardware used: Smartphone, smart glasses, or head-mounted display, through which the user interacts with the system.

[1744] Software used: Python, natural language processing library (NLTK), speech recognition library (Google Speech Recognition API), video calling library (WebRTC). These software processes data and enables communication between the server and the device.

[1745] Adding specific examples

[1746] Scenario: Product Introduction

[1747] User: "What are the features of this product?"

[1748] Virtual Assistant: "This product has a modern design and is waterproof, making it especially suitable for outdoor use."

[1749] Example prompts for generative AI models

[1750] Based on user characteristic data, generate natural and appropriate responses to the following inputs:

[1751] User Input: "What are your recommended products?"

[1752] User characteristics: {'name': 'User', 'recommended_product': 'Latest smartphone model', 'past_interactions': [...]}

[1753] Example response: "Here's what you need: a new smartphone model with a high-resolution camera and long battery life."

[1754] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1755] Step 1:

[1756] The server reads the user's configuration information from the user characteristics database.

[1757] Input: User ID or authentication information

[1758] Processing: Database query

[1759] Output: User preferences, purchase history, and characteristic data

[1760] Specific operation: The server queries the database using the user's ID and obtains information such as the user's preferences and past purchase history.

[1761] Step 2:

[1762] The server initializes the virtual assistant based on the acquired user setting information.

[1763] Input: User settings information (preferences, purchase history, characteristic data)

[1764] Action: Customizing your virtual assistant

[1765] Output: A customized virtual assistant instance

[1766] Specific operation: Based on the acquired user setting information, the server generates a virtual assistant dedicated to the user and initializes it.

[1767] Step 3:

[1768] The terminal receives input from the user and sends it to the server.

[1769] Input: User voice commands or text input

[1770] Processing: speech recognition, text processing

[1771] Output: Processed text data

[1772] Specific operation: The terminal converts the user's voice into text and sends the text data to the server.

[1773] Step 4:

[1774] The server processes the received user input data and generates an appropriate response.

[1775] Input: User input data (text format)

[1776] Processing: generative algorithms, natural language processing

[1777] Output: The appropriate response message

[1778] What it does: The server uses NLP techniques to parse the user's input and generate an appropriate response. This process uses the NLTK library.

[1779] Step 5:

[1780] The server generates a response message and sends it to the terminal.

[1781] Input: The generated response message

[1782] Action: Send message

[1783] Output: Response message displayed on the terminal

[1784] Specific operation: The server sends the generated response message to the terminal, and the terminal displays it to the user.

[1785] Step 6:

[1786] The terminal displays a response message to the user and continues the interaction with the user.

[1787] Input: Response message sent by the server

[1788] Processing: Display message, update interface

[1789] Output: The response message that is displayed to the user

[1790] Specific operation: The terminal displays the response message received from the server on the screen and updates the interface to allow the user to continue further operations.

[1791] Step 7:

[1792] Users receive product information and recommendations through conversations with virtual assistants.

[1793] Input: Any further questions or instructions from the user

[1794] Processing: Continuing the dialogue, product information and recommendations

[1795] Output: Detailed product information and recommended products

[1796] How it works: The virtual assistant provides detailed product information and recommendations based on the user's interests and past data. The system processes this information in real time and displays it to the user.

[1797] Step 8:

[1798] The server provides sales promotion and campaign information to the user.

[1799] Input: Latest campaign information and sales information

[1800] Processing: Organizing and distributing information

[1801] Output: Promotional and campaign information provided to the user

[1802] How it works: The server compiles information about current campaigns and sales and delivers it to users through the virtual assistant, allowing users to obtain the latest information and increasing their motivation to make purchases.

[1803] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1804] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1805] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, and an emotion engine.

[1806] System configuration

[1807] 1. Generative algorithm means:

[1808] The server generates appropriate dialogue and replies based on user input, using natural language processing techniques and incorporating algorithms to simulate real-life conversations.

[1809] 2. User characteristic database means:

[1810] The server manages a database that records the user's personal preferences, characteristics, and past interaction history, allowing for customized conversations and simulations based on the user's characteristics.

[1811] 3. Conversational Interface Means:

[1812] The device provides an interface for the user and the AI ​​to communicate through text, voice, or video, including a chat box, voice input, and a user interface for video calling.

[1813] 4. Dating simulation methods:

[1814] The server generates a virtual date situation, where the user and the AI ​​interact. The user can choose a virtual date spot and enjoy natural conversation with the AI.

[1815] 5. Text and voice chat methods:

[1816] The device provides an interface for users to engage in text and voice chat with the AI, allowing them to practice their language in real time.

[1817] 6. Video calling methods:

[1818] The device provides an interface for users to make video calls with the AI, allowing for more practical conversation practice.

[1819] 7. Emotion Engine:

[1820] The server includes an emotion engine that analyzes emotions based on the user's text and voice input, recognizes the user's emotional state, and adjusts the content and tone of the conversation appropriately.

[1821] How it works

[1822] Initialization and loading user settings

[1823] The server reads the user's configuration information (e.g., name, favorite food, favorite movie) from the database. Based on this information, it initializes the virtual lover AI object. The AI ​​object reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[1824] Generating everyday conversations

[1825] The server uses a generation algorithm to generate a daily greeting message and sends it to the user's device. The device displays this message, allowing the user to start a conversation. When the user inputs a conversation start or response through the device, the device sends this to the server. The server uses a virtual lover AI to generate an appropriate response to the user's input and returns it to the user's device.

[1826] Dating Simulation

[1827] The user selects the dating simulation mode on the device. The device sends this request to the server, which then generates a virtual dating situation. The user and the AI ​​can enjoy natural conversation at a virtual dating spot.

[1828] Text and voice chat

[1829] The user sends a request for text or voice chat on their device. The device sends this to the server, and the server generates a real-time response using the virtual lover AI. The generated response is sent to the user's device, and the user practices everyday conversation.

[1830] Video calling

[1831] When a user wishes to make a video call, the device sends a video call request to the server. The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. The video call begins, and real-time visual communication takes place between the user and the AI.

[1832] emotion recognition

[1833] The server uses an emotion engine to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired." As a result, the virtual lover AI generates a response such as "Today was tough. Take a good rest." In this way, an appropriate conversation is held based on the user's emotions.

[1834] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual AI lover. Furthermore, the use of an emotion engine enables responses that are sensitive to the user's emotions, providing a more fulfilling learning experience.

[1835] As a specific example, we cite the "emotion recognition scenario during a video call."

[1836] Example: Emotion recognition scenario during a video call

[1837] When a user says "I'm having so much fun today!" during a video call, the emotion engine recognizes the word "fun." The virtual lover AI responds with "That was great! What happened?" and continues the conversation. In this way, users can improve their language skills through natural conversation in real time.

[1838] The processing flow will be explained below.

[1839] Step 1:

[1840] The server reads user setting information from the database, specifically, information such as the user's name, favorite food, favorite movie, etc., and uses it to initialize the virtual lover AI.

[1841] Step 2:

[1842] The server initializes the virtual lover AI object, which reflects the user's characteristics and is ready to generate customized conversations and responses for the user.

[1843] Step 3:

[1844] The server generates a daily greeting message using a generation algorithm, for example, randomly selecting a message such as "Good morning! Let's do our best today!"

[1845] Step 4:

[1846] The server generates a greeting message and sends it to the user's terminal, which displays it and allows the user to begin a conversation.

[1847] Step 5:

[1848] The user inputs the start of a conversation or a response through the terminal. For example, the user inputs "I saw a movie today."

[1849] Step 6:

[1850] The device sends the user's input to the server, which receives it and passes it on to the virtual lover AI.

[1851] Step 7:

[1852] The server uses the virtual lover AI to generate appropriate responses to user input, such as "That was interesting! What was the movie like?"

[1853] Step 8:

[1854] The server generates a response message and sends it to the user's terminal, which displays it and allows the user to continue the conversation.

[1855] Step 9:

[1856] The user selects the date simulation mode on the terminal, and the terminal sends this request to the server.

[1857] Step 10:

[1858] The server generates a virtual dating situation and starts a virtual date between the user and the AI. For example, the server selects a virtual date spot and generates a scenario for enjoying natural dating conversation.

[1859] Step 11:

[1860] A user sends a request to start a text or voice chat on their device, which then sends it to the server.

[1861] Step 12:

[1862] The server uses the virtual lover AI to generate real-time responses via text chat or voice chat and sends them to the user's device, allowing the user to practice everyday conversation.

[1863] Step 13:

[1864] The user requests to start a video call on the device, which then sends the request to the server.

[1865] Step 14:

[1866] The server generates a video call initialization message using the virtual lover AI and sends it to the user's device. For example, it sends a message saying, "Starting video call. Are you ready?"

[1867] Step 15:

[1868] A video call will be initiated between the user and the virtual lover AI, allowing for real-time visual communication, allowing the user to practice more practical conversations.

[1869] Step 16:

[1870] The server passes the user's input (text and voice) to the emotion engine for emotion analysis. For example, if the user inputs "I'm a little tired today," the emotion engine recognizes the emotion "tired."

[1871] Step 17:

[1872] Based on the results of the emotion analysis, the server will generate an appropriate response from the virtual lover AI, such as "Today was tough. Take a good rest."

[1873] Step 18:

[1874] The server sends a response message generated based on the emotion recognition to the user's terminal.

[1875] Through these steps, users can improve their language skills while enjoying natural conversations and practical activities through interactions with their virtual lover AI. Furthermore, the use of an emotion engine enables responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[1876] Example 2

[1877] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1878] Conventional language learning systems have difficulty in providing natural conversations and responses that take the user's emotions into account, resulting in low immersion and practicality. Furthermore, they have limited ability to provide customized interactions based on the user's preferences and characteristics, resulting in inconsistent learning outcomes. Furthermore, communication is limited to simple text or voice, resulting in insufficient practical conversation practice.

[1879] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1880] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, and an emotion analysis means. This allows for natural and customized interactions with the user by recognizing the user's emotions and generating responses accordingly. Furthermore, by further including a dating simulation means, a text and voice chat means, a video call means, and an emotion-based response generation means, the server can improve its language skills while engaging in practical conversation practice in a variety of formats.

[1881] "Generation algorithm means" refers to a processing method for generating appropriate conversations and responses based on input from a user, and refers to an algorithm that uses natural language processing techniques to simulate actual conversations.

[1882] The "user characteristic database means" refers to a database system that records and manages a user's personal preferences and characteristics, and past interaction history.

[1883] "Conversational interface means" refers to interface means that allow users and AI to communicate through text, voice, and video, and includes user interfaces for chat boxes, voice input, and video calls.

[1884] "Emotion analysis means" refers to a technical means for analyzing emotions from a user's text and voice input and recognizing their emotional state.

[1885] "Dating simulation means" refers to a simulation means in which a server generates a virtual dating situation and a user can enjoy interacting with an AI.

[1886] "Text and voice chat means" refers to the interface means through which a user can engage in text chat and voice chat with an AI.

[1887] "Video call means" refers to an interface means that allows a user to make a video call with AI, enabling real-time visual communication.

[1888] "Emotion-based response generation means" refers to a technical means for generating appropriate conversations and responses based on the user's emotional state recognized by the emotion analysis means.

[1889] MODE FOR CARRYING OUT THE INVENTION

[1890] As an embodiment of the present invention, a specific system configuration and its operation will be described below.

[1891] System Overview

[1892] The system of the present invention allows users to improve their language skills through natural conversation and has the function of recognizing users' emotions. Specifically, the system includes a generation algorithm means, a user characteristic database means, a conversation interface means, a dating simulation means, a text and voice chat means, a video call means, an emotion analysis means, and an emotion-based response generation means.

[1893] Hardware and software used

[1894] The system uses the following hardware and software:

[1895] Server: Includes a high-performance database server, a natural language processing server (NLP server), and a sentiment analysis engine.

[1896] Terminal: A device such as a PC, smartphone, or tablet that accepts user operations.

[1897] software:

[1898] Natural language processing algorithms (e.g., leveraging machine learning frameworks such as TensorFlow and PyTorch)

[1899] Database management systems (e.g., MySQL, PostgreSQL)

[1900] Sentiment analysis engines (e.g. IBM Watson or Microsoft Azure emotion recognition APIs)

[1901] System details

[1902] 1. Initialization and loading of user configuration information:

[1903] The server reads the user's personal information (e.g., name, favorite food, favorite movie) from the user characteristic database means, and initializes a virtual lover AI object according to the user's characteristics.

[1904] 2. Everyday conversation generation:

[1905] The server generates a daily greeting message using a generation algorithm and sends it to the terminal. For example, it generates a message such as "Good morning, how is your day going today?" When the user starts a conversation, the server analyzes the user's input and generates an appropriate response. For example, if the user inputs "Good morning, what should we do after work today?", the server will respond with "How about going to the movies?"

[1906] 3. Dating Simulation:

[1907] When a user selects the dating simulation mode on their device, the server generates a virtual dating situation. For example, if a user requests, "I want to go to a cafe," the server generates a conversation such as, "I've arrived at the cafe. What kind of drink do you like?"

[1908] 4. Text and Voice Chat:

[1909] When a user selects text chat or voice chat mode, the server generates a real-time response. For example, if a user asks "What's the latest movie?" in voice chat, the server generates a response such as "Inception is the hot topic these days. Have you seen it?"

[1910] 5. Video Calls:

[1911] When a user wants to make a video call, the terminal sends a video call request to the server. The server generates a video call initialization message and sends it to the terminal. For example, it generates a message saying, "Are you ready for video call?"

[1912] 6. Emotion recognition:

[1913] The server uses emotion analysis to analyze emotions from the user's text and voice input. For example, if the user inputs "I'm a little tired today," the server recognizes the emotion "tired" and generates a response such as "Today was tough. Take a good rest."

[1914] Examples and prompts

[1915] For example, the following prompts can be fed into a generative AI model to generate a response:

[1916] Prompt: "Generate an AI response when a user says, 'Today is so much fun!' during a video call."

[1917] Prompt: "Generate an AI response when a user asks in text chat, 'How was the movie yesterday?'"

[1918] This allows users to improve their language skills while enjoying natural conversations with a virtual AI lover. The system generates responses that are in tune with the user's emotions, providing a more fulfilling learning experience.

[1919] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1920] Step 1: Loading user configuration information

[1921] Specific operation: The server reads the user's personal information (eg, name, favorite food, favorite movie) using the user characteristic database means.

[1922] Input: User ID

[1923] Data processing: Execute a database query to obtain personal information corresponding to the user ID.

[1924] Output: User's personal information (e.g. name, favorite food, favorite movie)

[1925] Step 2: Initializing the Virtual Lover AI Object

[1926] Specific operation: The server initializes the virtual lover AI object based on the loaded user information. This initialization is performed using a generation algorithm.

[1927] Input: User's personal information

[1928] Data processing: Customize the conversation style and topic selection of the virtual lover AI based on user information.

[1929] Output: Initialized virtual lover AI object

[1930] Step 3: Generate a daily greeting message

[1931] Specific operation: The server uses a generation algorithm to generate a daily greeting message suitable for the user and transmits it to the terminal.

[1932] Input: User characteristics, date

[1933] Data processing: A natural language processing algorithm is used to generate a daily greeting message.

[1934] Output: Daily greeting message

[1935] Step 4: Initiating and responding to conversations

[1936] Specific operation: The user inputs a response to the greeting message through the terminal, and the terminal sends it to the server. The server uses a generation algorithm to generate an appropriate reply and sends it to the terminal.

[1937] Input: User's response to greeting message

[1938] Data processing: Using natural language processing algorithms, we generate replies that are tailored to the user's responses.

[1939] Output: Reply message from server to device

[1940] Step 5: Choose your dating simulation mode

[1941] Specific operation: The user selects the dating simulation mode on the device, and the device sends the request to the server.

[1942] Input: Dating simulation mode selection request

[1943] Data processing: Analyzes the request and starts the process of generating a virtual dating situation.

[1944] Output: Date simulation start confirmation message

[1945] Step 6: Generate a virtual dating situation

[1946] Specific operation: The server generates a virtual date spot in response to a user's request and prepares a natural conversation scenario.

[1947] Input: Confirmation message for starting the dating simulation, user's date location selection

[1948] Data processing: Generate virtual dating spots and prepare natural conversation scenarios.

[1949] Output: A hypothetical dating scenario and a starting message

[1950] Step 7: Start text and voice chat

[1951] Specific operation: The user selects text chat or voice chat mode, and the device sends the request to the server.

[1952] Input: Chat mode selection request

[1953] Data processing: Parsing the request and initiating the process of generating a response in real time.

[1954] Output: Chat mode start confirmation message

[1955] Step 8: Generate real-time responses

[1956] Specific operation: The server generates appropriate real-time responses to the user's chat input and sends them to the device.

[1957] Input: User chat input

[1958] Data processing: Using natural language processing algorithms, we generate responses that fit the user's input.

[1959] Output: Real-time response message from the server to the terminal

[1960] Step 9: Send a video call request

[1961] Specific operation: When a user wants to make a video call, the terminal sends a video call request to the server.

[1962] Input: Video call request

[1963] Data processing: Parse the request and start the video call initialization process.

[1964] Output: Video call initialization message

[1965] Step 10: Initialize the video call

[1966] Specific operation: The server uses the virtual lover AI to generate an initialization message for the video call and sends it to the device. Real-time visual communication takes place between the user and the AI.

[1967] Input: Video call initialization message

[1968] Data processing: Set up the video call environment and generate an initialization message.

[1969] Output: Video call start message

[1970] Step 11: Perform sentiment analysis

[1971] Specific operation: The server uses the emotion analysis means to analyze emotions from the user's text and voice input.

[1972] Input: User text or voice input

[1973] Data processing: Analyze emotions using a sentiment analysis engine.

[1974] Output: User's emotional state

[1975] Step 12: Generate an emotion-based response

[1976] Specific operation: The server generates an appropriate response based on the results of the emotion analysis and sends it to the device.

[1977] Input: User's emotional state

[1978] Data processing: Using natural language processing algorithms to generate emotional responses.

[1979] Output: Emotion-based response message

[1980] (Application example 2)

[1981] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1982] Conventional food delivery services lack personalized recommendations and order management based on user emotions and characteristics, resulting in a lack of user satisfaction.

[1983] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1984] In this invention, the server includes a generation algorithm means, a user characteristic database means, a conversation interface means, an order history database means, and an emotion recognition means, thereby enabling personalized recommendations based on the user's characteristics and emotions.

[1985] The "generation algorithm means" is an algorithm for generating appropriate conversations and replies based on input data from the user.

[1986] The "user characteristic database means" is a database for recording and managing the user's personal preferences and characteristics, and past interaction history.

[1987] A "conversational interface means" is an interface that allows a user and a system to communicate through text, voice, or video.

[1988] The "order history database means" is a database for recording and managing the user's past order history.

[1989] The "emotion recognition means" is an engine that analyzes emotions from the user's text or voice input.

[1990] The "dating simulation means" is a means for generating a virtual dating situation and for the user and the system to interact with each other.

[1991] "Text and voice chat means" refers to the interface through which a user engages in text and voice chat with the system.

[1992] A "video calling means" is an interface through which a user can make a video call with the system.

[1993] "Personalized recommendation means" is a means for recommending appropriate menus and options based on the user's current mood and past data.

[1994] The "order confirmation means" is a means for confirming the user's final order, storing it, and transmitting it to an external service.

[1995] As an embodiment of the present invention, the configuration and operation of a specific system are described below. This system provides emotion recognition and personalized recommendations to enable users to order food delivery through natural conversation and to enhance the experience.

[1996] System configuration

[1997] 1. Server Configuration

[1998] Generative Algorithm: The server is equipped with an algorithm to generate natural-sounding conversations and replies based on input data from the user. Specifically, it uses a generative AI model.

[1999] User characteristic database means: A database that records and manages the user's personal preferences, characteristics, and past interaction history.

[2000] Order history database means: A database that records and manages the user's past order history.

[2001] Emotion recognizer: An engine that analyzes emotions from user text and voice input. TensorFlow can be used for this.

[2002] 2. Terminal Configuration

[2003] Conversational interface means: An interface that allows users and servers to communicate through text, voice, or video. This includes smartphone applications.

[2004] Personalized recommendation tools: These tools recommend appropriate menus and options based on the user's current mood and past data.

[2005] Order confirmation means: A means for confirming the user's final order, storing it, and transmitting it to an external service.

[2006] How it works

[2007] Initialization and loading user settings

[2008] The server reads the user's preferences (e.g., name, favorite foods, past orders) from a database and uses this information to generate customized conversations and recommendations for the user.

[2009] Everyday conversation generation and emotion recognition

[2010] When a user inputs a request such as "I'm a little tired today, so I'd like to eat something refreshing" through the terminal, the terminal sends this request to the server. The server uses emotion recognition means to analyze the input text and recognizes the emotion "tired." As a result, the generation algorithm means generates an appropriate response such as "How about a salad that's good for when you're tired?" and presents it to the user.

[2011] Interactive recommendation service

[2012] If the user selects "I'll take that" based on the presented recommendation, the server updates the order history database to record the selection and, if necessary, generates a follow-up question (e.g., "Would you like a choice of dressing?").

[2013] Final order confirmation and confirmation

[2014] When the user inputs "This confirms the order," the terminal sends this to the server, and the server confirms the final order using the order confirmation means. The order details are saved in the order history database means and are also transmitted to an external delivery service.

[2015] Actual examples and prompts

[2016] Specific examples

[2017] The user types in the app, "I'm feeling a little tired today, what's your recommended menu?"

[2018] Prompt statement

[2019] plaintext

[2020] You are a helpful assistant specialized in suggesting food delivery options based on user's emotions and preferences.

[2021] User: "I'm a little tired today, what do you recommend for my meal?"

[2022] Assistant: "You seem tired today. How about a refreshing salad? This salad especially comes with a refreshing special dressing. Do you need anything else?"

[2023] In this way, a personalized response is generated according to the user's emotional state, improving the user experience.

[2024] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2025] Step 1:

[2026] The user opens the application on their smartphone and enters text into the input interface, such as "I'm a little tired today, what do you recommend for my meal?" This text data is sent from the device to the server.

[2027] Input: User-entered text data

[2028] Output: User-entered data sent to the server

[2029] Step 2:

[2030] The server passes the received user input data to the emotion recognition unit, which uses a TensorFlow model to analyze the text data and recognize the user's emotional state (in this case, "fatigue").

[2031] Input: User-entered text data

[2032] Data processing: Pass text data to the emotion recognition model and analyze it

[2033] Output: Emotion recognition result ("tired")

[2034] Step 3:

[2035] Based on the emotion recognition results, the server passes the data to a generative algorithm means, which uses a generative AI model (e.g., GPT-3) to generate a personalized recommendation, in this case, the response: "What salad would you recommend when you're feeling tired?"

[2036] Input: Emotion recognition result ("tired")

[2037] Data processing: Passing emotion recognition results to a generative AI model to generate a response

[2038] Output: The generated response

[2039] Step 4:

[2040] The server generates a response and sends it back to the user's device. The user sees a message on their device asking, "How about a salad for when you're feeling tired?"

[2041] Input: The generated response

[2042] Output: A response message displayed on the user's terminal

[2043] Step 5:

[2044] The user selects "I'll take that," and the terminal transmits this selection data to the server.

[2045] Input: User selected data

[2046] Output: Selection data sent to the server

[2047] Step 6:

[2048] The server stores the received selection data in the order history database means, and in some cases generates an additional question (e.g., "Would you like to choose a type of dressing?") and sends it to the user terminal.

[2049] Input: User selected data

[2050] Data processing: storing in a database and generating additional questions

[2051] Output: Database updates and additional questions generated

[2052] Step 7:

[2053] The user enters "This confirms the order," and the terminal sends this to the server.

[2054] Input: User-entered data for "Order Confirmation"

[2055] Output: "Order Confirmation" data sent to the server

[2056] Step 8:

[2057] The server uses the order confirmation means to confirm the final order. The order details are again saved in the order history database means and transmitted to an external delivery service as needed. The server also uses the generation algorithm means to generate a thank you message to the user (e.g., "Your order has been accepted. Thank you!") and transmits it to the user terminal.

[2058] Input: User-entered data for "Order Confirmation"

[2059] Data processing: updating the database, transmitting to external services, generating thank you messages

[2060] Output: Confirmed order and a thank you message to the user

[2061] In this way, the entire process is carried out seamlessly, providing a personalized food delivery experience that responds to the user's emotional state.

[2062] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2063] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2064] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2065] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2066] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2067] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2068] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2069] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2070] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2071] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2072] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2073] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2074] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2075] 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.

[2076] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2077] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2078] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2079] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2080] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2081] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2082] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2083] The following is further disclosed regarding the above embodiment.

[2084] (Claim 1)

[2085] a generating algorithm means;

[2086] a user characteristic database means;

[2087] a conversational interface means;

[2088] A system including:

[2089] (Claim 2)

[2090] A dating simulation means;

[2091] text and voice chat means;

[2092] Video calling means;

[2093] The system of claim 1 further comprising:

[2094] (Claim 3)

[2095] 2. The system according to claim 1, wherein the user characteristic database means is a means for recording and managing user preferences and characteristics.

[2096] "Example 1"

[2097] (Claim 1)

[2098] a generating algorithm means;

[2099] a user characteristic database means;

[2100] a conversational interface means;

[2101] A dating simulation means;

[2102] text and voice chat means;

[2103] Video calling means;

[2104] A system including:

[2105] (Claim 2)

[2106] a means by which the server reads user preference information from a database;

[2107] A means for the server to generate responses in everyday conversation using natural language processing;

[2108] A means for the terminal to execute a dating simulation between the user and a virtual lover AI;

[2109] means for the server to generate a response in real time based on user input;

[2110] means for transmitting a video call request to a server;

[2111] The system of claim 1 further comprising:

[2112] (Claim 3)

[2113] 2. The system according to claim 1, wherein the user characteristic database means is a means for recording and managing personal information and past interaction history of users.

[2114] "Application Example 1"

[2115] (Claim 1)

[2116] a generating algorithm means;

[2117] a user characteristic database means;

[2118] a conversational interface means;

[2119] Virtual assistant means;

[2120] Product information and recommendations,

[2121] A system including:

[2122] (Claim 2)

[2123] A dating simulation means;

[2124] text and voice chat means;

[2125] Video calling means;

[2126] Sales promotion measures;

[2127] The system of claim 1 further comprising:

[2128] (Claim 3)

[2129] 2. The system according to claim 1, wherein the user characteristic database means is a means for recording and managing user preferences and characteristics.

[2130] "Example 2: Combining Emotion Engines"

[2131] (Claim 1)

[2132] a generating algorithm means;

[2133] a user characteristic database means;

[2134] a conversational interface means;

[2135] A sentiment analysis means;

[2136] A system including:

[2137] (Claim 2)

[2138] A dating simulation means;

[2139] text and voice chat means;

[2140] Video calling means;

[2141] emotion-based response generation means;

[2142] The system of claim 1 further comprising:

[2143] (Claim 3)

[2144] 2. The system according to claim 1, wherein the user characteristic database means is a means for recording and managing user preferences and characteristics.

[2145] "Application example 2 when combining emotion engines"

[2146] (Claim 1)

[2147] a generating algorithm means;

[2148] a user characteristic database means;

[2149] a conversational interface means;

[2150] an order history database means;

[2151] An emotion recognition means;

[2152] A system including:

[2153] (Claim 2)

[2154] A dating simulation means;

[2155] text and voice chat means;

[2156] Video calling means;

[2157] A personalized recommendation means;

[2158] An order confirmation means;

[2159] The system of claim 1 further comprising:

[2160] (Claim 3)

[2161] The user characteristic database means is a means for recording and managing the preferences and characteristics of the user;

[2162] 2. The system according to claim 1, wherein the order history database means is a means for recording and managing a user's past order history. [Explanation of symbols]

[2163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a generating algorithm means; a user characteristic database means; a conversational interface means; A system including:

2. A dating simulation means; text and voice chat means; Video calling means; The system of claim 1 further comprising:

3. 2. The system according to claim 1, wherein the user characteristic database means is a means for recording and managing user preferences and characteristics.

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