System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an input from a user; means for transmitting the user's input to a server; means for generating a response by a generation AI based on the user's input at the server; and means for transmitting the generated response back to the user.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] One-on-one meetings are an important means of management and feedback, but there are limits to their effectiveness when they involve humans. Specifically, there is a high possibility that the supervisor or person providing the feedback will talk biasedly about work or evaluations, which can reduce the quality of the conversation. In addition, because it is conducted by humans, there are time and timing constraints. Furthermore, maintaining high-quality support on an ongoing basis incurs high costs. As such, one-on-one meetings conducted by humans have many limitations and problems, and it is necessary to improve the quality and effectiveness of the conversation. [Means for solving the problem]

[0005] To solve these problems, the present invention provides a system for a one-on-one service using a generation AI. First, a means for accepting input from a user is provided, followed by a means for transmitting the user's input to a server. Next, the server uses a means for the generation AI to generate a response based on the user's input, and further includes a means for returning the generated response to the user. The system also provides a means for the generation AI to store and analyze the user's past conversation data, and includes a means for the generation AI to optimize responses based on the conversation data, thereby improving the quality of the dialogue. Furthermore, the system provides a secure and reliable system by adding a means for the server to perform user authentication, perform session management, and record the session-managed data. This invention enables inexpensive and effective one-on-one meetings that are not bound by time or timing.

[0006] "User" refers to an individual who uses the system, inputs information, and engages in dialogue in order to receive one-on-one services using generative AI.

[0007] "Input" refers to information such as text or voice that the user provides to the system, which forms the basis for analysis and response by the generative AI.

[0008] "Server" refers to a computer system that receives input from a user, analyzes and responds using a generative AI, and returns the generated response to the user.

[0009] "Generative AI" refers to a program or algorithm that uses artificial intelligence techniques to analyze user input and create an appropriate response.

[0010] A "response" is the information or feedback that a generative AI generates and provides based on user input.

[0011] "Conversational data" refers to records of the dialogue between the user and the generating AI, which is used for subsequent analysis and response generation.

[0012] "User authentication" is the process of verifying a user's identity when they access a system and granting them authorized access rights.

[0013] "Session management" refers to the technical procedures for managing a series of interactions while a user is using a system and providing appropriate responses.

[0014] A "token" is a unique identifier issued as part of user authentication and used to identify and authorize a user during a session.

[0015] "Storage means" refers to a data storage system that stores user input and the generative AI's responses to it for later reference and analysis. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention relates to a system for providing a one-on-one service using a generation AI. This system is realized by the following configuration and operation.

[0038] composition

[0039] The system of the present invention mainly comprises the following components:

[0040] 1. Terminal

[0041] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0042] Includes a chat box and voice input functionality for accepting input.

[0043] 2. Server

[0044] It is a centralized system that receives input data and invokes generative AI to generate a response.

[0045] It performs user authentication, session management, and saves conversation data.

[0046] 3. Generation AI

[0047] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[0048] Program processing

[0049] The program processing in this system is explained below:

[0050] 1. User Login and Authentication

[0051] The user enters their ID and password on the terminal and presses the login button.

[0052] The terminal transmits the entered authentication information to the server.

[0053] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[0054] 2. User Input and Submission

[0055] The user enters a message in the chat box and presses the send button.

[0056] The terminal sends the message and the session token to the server.

[0057] 3. Server Response Generation Process

[0058] The server receives the sent message and session token, verifies the validity of the session, and then requests analysis from the generating AI.

[0059] The generation AI analyzes the message content and generates an appropriate response, which is returned to the server.

[0060] 4. Returning and Displaying Responses

[0061] The server sends the response received from the generation AI to the terminal.

[0062] The terminal displays the response to the user in a chat box.

[0063] Specific examples

[0064] User Login

[0065] The user enters the ID "user123" and password "password123" and presses the login button.

[0066] The terminal sends this information to the server.

[0067] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[0068] The device stores the session token and uses it for subsequent requests.

[0069] Start a conversation

[0070] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0071] The terminal sends the input content and the session token to the server.

[0072] The server asks the generation AI to analyze the message, and the generation AI generates a response saying, "Please tell us specifically which part is not working."

[0073] The server sends the generated response to the terminal, which displays the response in a chat box.

[0074] Continuing the dialogue

[0075] The user enters "I never meet work deadlines" and presses the send button again.

[0076] The terminal retransmits the message and the session token.

[0077] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[0078] The server sends the response to the terminal, which displays the response.

[0079] In this way, a 1-on-1 system using generative AI can provide an environment in which users can freely interact, overcoming time and cost constraints. In addition, the analytical capabilities of generative AI can provide appropriate and useful feedback to users.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[0083] Step 2:

[0084] The terminal sends an authentication request including the user ID and password to the server.

[0085] Step 3:

[0086] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[0087] Step 4:

[0088] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[0089] Step 5:

[0090] The terminal stores the session token received from the server and starts the user session.

[0091] Step 6:

[0092] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0093] Step 7:

[0094] The terminal sends a request to the server containing the entered message and the stored session token.

[0095] Step 8:

[0096] The server extracts the message and session token from the received request and verifies the validity of the session.

[0097] Step 9:

[0098] The server sends the message to the generation AI, asking it to analyze it and generate a response.

[0099] Step 10:

[0100] The generative AI analyzes the received message, "Work hasn't been going well lately," and generates an appropriate response: "Tell me specifically what's not going well."

[0101] Step 11:

[0102] The generation AI sends the generated response back to the server.

[0103] Step 12:

[0104] The server sends the response received from the generation AI to the terminal.

[0105] Step 13:

[0106] The terminal receives the response from the server and displays it in the chat box.

[0107] Step 14:

[0108] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[0109] Step 15:

[0110] The device sends a request to the server again, including the new message and the session token.

[0111] Step 16:

[0112] The server receives the new request and revalidates the message and session token.

[0113] Step 17:

[0114] The server generates a new message and asks the AI ​​to analyze it again, sending a message saying, "I always miss work deadlines."

[0115] Step 18:

[0116] The generative AI analyzes the message "I never meet work deadlines" and generates the response "Have you ever thought about why that is?"

[0117] Step 19:

[0118] The generating AI sends the new response back to the server.

[0119] Step 20:

[0120] The server sends the new response received from the generating AI to the terminal.

[0121] Step 21:

[0122] The terminal receives the new response from the server and displays it in the chat box.

[0123] Example 1

[0124] 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."

[0125] Conventional one-on-one dialogue services have issues such as insufficient security for user authentication and natural dialogue, and difficulty in optimizing responses based on the user's past dialogue records. There is also a need for improvements in session management and response generation speed. The present invention aims to solve these issues and provide a higher quality and safer one-on-one dialogue service.

[0126] 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.

[0127] In this invention, the server includes a means for performing user authentication and issuing a unique session token, a means for generating a response based on the user's input using a generation artificial intelligence, and a means for verifying the validity of the session and returning the generated response to the user. This enables secure and efficient user authentication and session management, and realizes the generation of natural and accurate responses and their prompt return.

[0128] "User authentication" is the process of verifying the authenticity of a user when they access a system.

[0129] A "session token" is a unique identifier issued to a user who has been successfully authenticated, and ensures the continuation of the session.

[0130] "Generative AI" is an algorithm that uses machine learning to generate human-like text and enable dialogue with users.

[0131] A "central processing unit" is the main component for processing input data from a user and generating a response.

[0132] A "user interface" is an interface that includes a visual display and an operation panel that allows a user to interact with a system.

[0133] "Session management" is the procedure for tracking a user's session and maintaining session validity.

[0134] The present invention relates to a system that provides one-on-one dialogue services using a generative AI. This system accepts input from a user, and the generative AI generates a response via a central processing unit, which is then returned to the user. This allows the user to enjoy a natural dialogue experience.

[0135] System configuration

[0136] The system mainly consists of the following components:

[0137] 1. Terminal

[0138] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0139] Includes a chat box and voice input functionality for accepting input.

[0140] 2. Server (Central Processing Unit)

[0141] A centralized system receives user input data and calls a generative AI to generate a response.

[0142] It performs user authentication, session management, and saves conversation data.

[0143] 3. Generative Artificial Intelligence

[0144] It is a program or algorithm (e.g., a deep learning model) that is built into the server and analyzes user input and generates an appropriate response.

[0145] Program processing

[0146] 1. User Login and Authentication

[0147] The user enters their ID and password on the terminal and presses the login button. For example, they enter the ID "user123" and the password "password123."

[0148] The device sends this authentication information to the server as an HTTP POST request.

[0149] The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it issues a unique session token (e.g., "sess-token-abc123") and returns it to the terminal.

[0150] The device stores the session token in local storage and uses it for subsequent requests.

[0151] 2. User Input and Submission

[0152] The user types a message in the chat box and presses the send button. For example, the user might type, "Work hasn't been going well lately."

[0153] The device sends the message and the session token to the server as an HTTP POST request.

[0154] 3. Server Response Generation Process

[0155] The server receives the message and the session token and verifies the validity of the session.

[0156] If the session is valid, the message content is sent to the generating AI and requested for analysis.

[0157] The generative AI analyzes the message content and generates an appropriate response, such as "Please tell me specifically what is not working."

[0158] The generation AI sends the generated response back to the server.

[0159] 4. Returning and Displaying Responses

[0160] The server sends the generated response to the terminal.

[0161] The terminal displays a response to the user in a chat box, for example, "Please tell me specifically what is not working."

[0162] 5. Continuing the dialogue

[0163] The user again enters the message and sends it. For example, the user enters "I always miss my work deadlines."

[0164] The terminal retransmits the message and the session token.

[0165] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[0166] The server sends the response to the terminal, which displays the response.

[0167] Specific examples

[0168] Prompt Sentence Examples

[0169] 1. Example of a user login and authentication prompt:

[0170] The user enters the ID "user123" and password "password123" and presses the login button. The server receives this and issues a session token.

[0171] 2. Example of a prompt for user input and submission:

[0172] The user types "Work hasn't been going well lately" and presses the send button. The server receives this message and asks the generation AI to analyze it.

[0173] 3. Examples of prompts to continue the dialogue:

[0174] The user enters "I always miss work deadlines" and presses the submit button again. The AI ​​should generate an appropriate response.

[0175] In this way, the system provides users with an environment in which they can freely interact, overcoming time and cost constraints.The analytical capabilities of the generative AI model provide appropriate and useful feedback to users.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] The user enters their ID and password and clicks the login button.

[0179] Input: User ID (e.g., "user123"), Password (e.g., "password123")

[0180] Operation: The user enters their ID and password into the login form and presses the login button.

[0181] Output: The device gets the login information.

[0182] Step 2:

[0183] The terminal sends the entered authentication information (ID and password) to the server.

[0184] Input: User ID, Password

[0185] How it works: The device sends an HTTP POST request to the server's authentication endpoint.

[0186] Output: The server receives the authentication information.

[0187] Step 3:

[0188] The server compares the received authentication information with a database and authenticates the user.

[0189] Input: Authentication information (ID, password)

[0190] How it works: The server performs a database query and processes the authentication information.

[0191] Output: Authentication result (success or failure), session token (if successful)

[0192] Step 4:

[0193] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[0194] Input: Authentication success information

[0195] How it works: The server generates a session token and sends it back to the device as an HTTP response.

[0196] Output: Session token (e.g. "sess-token-abc123")

[0197] Step 5:

[0198] The device stores the session token and uses it for subsequent requests.

[0199] Input: Session token

[0200] How it works: The device saves the session token in local storage.

[0201] Output: The saved session token

[0202] Step 6:

[0203] The user enters a message in the chat box and presses the send button.

[0204] Input: User message (e.g. "Work hasn't been going well lately")

[0205] Action: The user types a message in the chat box and presses the send button.

[0206] Output: The terminal gets the message.

[0207] Step 7:

[0208] The terminal sends the message and the session token to the server.

[0209] Input: message, session token

[0210] How it works: The device sends an HTTP POST request to the server containing a message and a session token.

[0211] Output: The server receives the message and the session token.

[0212] Step 8:

[0213] The server receives the message and the session token and verifies the validity of the session.

[0214] Input: message, session token

[0215] How it works: The server checks the session token against its database and verifies the session expiration time.

[0216] Output: Session validation result

[0217] Step 9:

[0218] If the session is valid, the server sends the message content to the generating AI and requests analysis.

[0219] Input: message, session token

[0220] How it works: The server sends a message to the spawned AI's API.

[0221] Output: Request for response analysis by generative AI

[0222] Step 10:

[0223] The generation AI analyzes the message content and generates an appropriate response.

[0224] Input: User message

[0225] How it works: Generative AI uses deep learning models to generate text.

[0226] Output: The generated response (e.g., "What exactly is going wrong?")

[0227] Step 11:

[0228] The generation AI sends the generated response back to the server.

[0229] Input: The generated response

[0230] Behavior: The generation AI sends the generated response back to the server as an HTTP response.

[0231] Output: The server receives the response.

[0232] Step 12:

[0233] The server sends the generated response to the terminal.

[0234] Input: The generated response

[0235] How it works: The server sends the response to the device as an HTTP response.

[0236] Output: The terminal receives the response.

[0237] Step 13:

[0238] The terminal displays the response to the user in a chat box.

[0239] Input: The generated response

[0240] BEHAVIOR: The device updates the HTML in the chat box to show the response.

[0241] Output: The response is made visible to the user.

[0242] (Application example 1)

[0243] 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."

[0244] Current electronic payment services face the challenge of providing fast and accurate support when users check their credit card statements or troubleshoot issues. In addition, user authentication and session management are complex, resulting in a poor user experience. To address these challenges, an effective one-on-one dialogue system using generative AI is needed.

[0245] 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.

[0246] In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for a generation AI to generate a response based on the user input in the server, means for returning the generated response to the user, means for performing login and authentication, means for issuing a session token and confirming session validity, and means for analyzing chat messages and generating responses using a generation AI model, thereby enabling users to receive prompt and accurate support.

[0247] "Means for accepting input from the user" refers to an interface that allows the user to input information, such as a chat box or a voice input function.

[0248] The "means for transmitting user input to a server" is a mechanism for transmitting input information to a server via a network.

[0249] "Means for generative AI to generate responses" refers to a system that uses artificial intelligence to analyze input information and generate appropriate responses.

[0250] "Means for returning the generated response to the user" refers to a communication means for returning the response generated by the generation AI back to the user's terminal.

[0251] "Means for login and authentication" refers to the mechanism by which a user enters an ID and password to access the system and authenticates them.

[0252] The "means for issuing a session token and verifying the validity of the session" is a function for issuing a unique session token to an authenticated user and verifying the validity of the token.

[0253] "Means for analyzing chat messages and generating responses using a generative AI model" refers to a system that uses a generative AI model to analyze users' chat messages and automatically generate appropriate responses in response to them.

[0254] The "means for storing and analyzing past conversation data" is a mechanism for storing a history of past conversations with a user and analyzing it to optimize future responses.

[0255] "Means for keeping a history of chat messages and maintaining the context of a continuous conversation" refers to a function that saves the content of past conversations with a user and continues the dialogue while understanding the context of the current conversation.

[0256] The "means for implementing user authentication and session management" is a mechanism for authenticating a user and appropriately managing the user's session.

[0257] "Means for verifying authentication information against a database" refers to a procedure for comparing the authentication information entered by a user with an existing database to determine whether it matches.

[0258] This invention relates to a system that uses generative AI to provide one-on-one user support for electronic payment services. The system uses a smartphone to streamline the process of users checking their credit card statements and receiving troubleshooting support.

[0259] System Configuration

[0260] The system of the present invention consists of the following main components:

[0261] 1. Terminal

[0262] A device such as a smartphone that is operated by a user.

[0263] Accepts input from users through a chat box or voice input function.

[0264] 2. Server

[0265] It is a centralized system that receives user input data and invokes generative AI models to generate responses.

[0266] It performs functions such as login and authentication, session management, and saving conversation history.

[0267] 3. Generative AI Models

[0268] It is a program or algorithm built into the server that analyzes the user's message and generates an appropriate response.

[0269] Program processing overview

[0270] 1. The user logs in and authenticates on their smartphone

[0271] The user enters their ID and password and presses the login button.

[0272] The terminal sends the input information to the server, which checks the authentication information against a database.

[0273] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[0274] 2. The user types something into the chat box

[0275] The user enters the question or inquiry into the chat box and presses the send button.

[0276] The terminal sends the input message together with the session token to the server.

[0277] 3. The server requests the AI ​​to generate a response.

[0278] The server checks the received message and session token and requests the generating AI model to analyze it.

[0279] The generative AI model analyzes the message content and generates an appropriate response.

[0280] The generated response is sent back to the server.

[0281] 4. View the generated response

[0282] The server sends the response from the generated AI to the terminal, which displays it in the chat box.

[0283] Hardware and software used

[0284] Hardware:

[0285] Smartphone: Used for displaying user input and responses.

[0286] Server: A computer that performs centralized data processing.

[0287] software:

[0288] Flask: Used as the server web framework.

[0289] transformers library: Used to process generative AI models.

[0290] Examples of concrete examples and prompts

[0291] User login example

[0292] User Input:

[0293] Enter "User ID: user123, Password: password123" and press the login button.

[0294] Expected response:

[0295] "Session token: sess-token-abc123" is returned.

[0296] Conversation starter examples

[0297] User Input:

[0298] I would like to check my credit card statement, where can I find it?

[0299] Expected response:

[0300] To view your usage details, select the "Details" tab from the app menu.

[0301] In this way, the system of the present invention is designed to ensure users receive prompt and appropriate support for electronic payment services, and the use of generative AI models can provide highly accurate responses and improve the user experience.

[0302] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0303] Step 1:

[0304] The user displays the login screen on their smartphone, enters their ID and password, and presses the login button. The device sends the entered authentication information (user ID and password) to the server. The server compares the received user ID and password with the information in its database, and if they match, generates a unique session token and sends it back to the device. The device stores this session token.

[0305] Input: User ID, Password

[0306] Output: Session token

[0307] Step 2:

[0308] The user enters a question or consultation (message) into the chat box and presses the send button. The device sends the entered message and session token to the server. The server then verifies the validity of the received message and session token.

[0309] Input: message, session token

[0310] Output: Message confirmation, session token validity check

[0311] Step 3:

[0312] After the message and session token are validated, the server sends the message to the generative AI model, which analyzes the message content and generates an appropriate response. This analysis includes tokenizing, generating, and decoding the text. The generated response is then sent back to the server.

[0313] Input: Message

[0314] Output: The generated response

[0315] Step 4:

[0316] The server receives the response from the generative AI model and sends it to the device, which then displays the received response to the user in a chat box.

[0317] Input: Generated response

[0318] Output: The response to display to the user

[0319] Step 5:

[0320] If the conversation continues, the user again enters a message in the chat box and presses the send button. The device again sends the message and session token to the server. This process is repeated as long as the user continues to ask questions or ask questions.

[0321] Input: New message, session token

[0322] Output: A new response from the generative AI model

[0323] These steps allow users to receive prompt and accurate assistance. By leveraging generative AI models and session management capabilities, the system is able to support continuous conversations while preserving context.

[0324] 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.

[0325] This invention relates to a system that combines a one-on-one service using generative AI with an emotion engine that recognizes the user's emotions. This system can improve the quality of interactions by understanding the user's emotional state and providing more appropriate and personalized responses.

[0326] composition

[0327] The system of the present invention mainly comprises the following components:

[0328] 1. Terminal

[0329] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0330] Includes a chat box and voice input functionality for accepting input.

[0331] 2. Server

[0332] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[0333] It performs user authentication, session management, and saves conversation data.

[0334] 3. Generation AI

[0335] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[0336] 4. Emotion Engine

[0337] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[0338] Program processing

[0339] The program processing in this system is explained below:

[0340] 1. User Login and Authentication

[0341] The user enters their ID and password on the terminal and presses the login button.

[0342] The terminal transmits the entered authentication information to the server.

[0343] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[0344] 2. User Input and Submission

[0345] The user enters a message in the chat box and presses the send button.

[0346] The terminal sends the message and the session token to the server.

[0347] 3. Emotion Recognition and Response Generation Process on the Server

[0348] The server extracts the received message and session token and verifies the validity of the session.

[0349] Next, a message is sent to the emotion engine to analyze the user's emotions.

[0350] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[0351] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[0352] 4. Generative AI response generation and optimization

[0353] The generative AI generates a response based on the emotion recognition results and user input. For example, if sadness is detected, an encouraging message is generated.

[0354] Responses are optimized by taking into account previous conversation data and emotional patterns.

[0355] 5. Returning and Displaying Responses

[0356] The server sends the generated response to the terminal.

[0357] The terminal displays the response to the user in a chat box.

[0358] Specific examples

[0359] User Login

[0360] The user enters the ID "user123" and password "password123" and presses the login button.

[0361] The terminal sends this information to the server.

[0362] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[0363] The device stores the session token and uses it for subsequent requests.

[0364] Conversation initiation and emotion recognition

[0365] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0366] The terminal sends the input content and the session token to the server.

[0367] The server sends a message to the emotion engine, requesting emotion analysis.

[0368] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[0369] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[0370] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[0371] The server sends the generated response to the terminal, which displays the response in a chat box.

[0372] Continuing the dialogue and using emotions

[0373] The user enters "I never meet work deadlines" and presses the send button again.

[0374] The terminal sends the new message and the session token to the server.

[0375] The server sends the message to the emotion engine, which analyzes the emotion again.

[0376] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[0377] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[0378] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[0379] The server sends the generated response to the terminal, which displays the response.

[0380] In this way, the present invention, combined with an emotion recognition engine, can understand the user's emotional state and provide more personalized responses, improving the quality of the interaction and the usefulness of the feedback the user receives.

[0381] The processing flow will be explained below.

[0382] Step 1:

[0383] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[0384] Step 2:

[0385] The terminal sends an authentication request including the user ID and password to the server.

[0386] Step 3:

[0387] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[0388] Step 4:

[0389] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[0390] Step 5:

[0391] The terminal stores the session token received from the server and starts the user session.

[0392] Step 6:

[0393] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0394] Step 7:

[0395] The terminal sends a request to the server containing the entered message and the stored session token.

[0396] Step 8:

[0397] The server extracts the message and session token from the received request and verifies the validity of the session.

[0398] Step 9:

[0399] The server sends the message to the emotion engine and requests emotion analysis.

[0400] Step 10:

[0401] The emotion engine recognizes the user's emotions from the message "Work hasn't been going well lately" and analyzes the user's sadness as a result.

[0402] Step 11:

[0403] The emotion engine sends the recognized emotion results back to the server.

[0404] Step 12:

[0405] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[0406] Step 13:

[0407] Based on the emotion recognition results and user input, the generative AI generates a response: "Please tell me specifically which part is not working."

[0408] Step 14:

[0409] The generation AI sends the generated response back to the server.

[0410] Step 15:

[0411] The server sends the response received from the generation AI to the terminal.

[0412] Step 16:

[0413] The terminal receives the response from the server and displays it in the chat box.

[0414] Step 17:

[0415] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[0416] Step 18:

[0417] The device sends a request to the server again, including the new message and the session token.

[0418] Step 19:

[0419] The server receives the new request and revalidates the message and session token.

[0420] Step 20:

[0421] The server sends the new message to the emotion engine and requests emotion analysis again.

[0422] Step 21:

[0423] The emotion engine analyzes the user's feelings of stress from the message "I never meet work deadlines."

[0424] Step 22:

[0425] The emotion engine sends the recognized emotion results back to the server.

[0426] Step 23:

[0427] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[0428] Step 24:

[0429] The generative AI generates the response "Have you ever thought about the cause of that?" based on the emotion recognition results and user input.

[0430] Step 25:

[0431] The generation AI sends the generated response back to the server.

[0432] Step 26:

[0433] The server sends the response received from the generation AI to the terminal.

[0434] Step 27:

[0435] The terminal receives the response from the server and displays it in the chat box.

[0436] Example 2

[0437] 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."

[0438] Conventional dialogue systems have difficulty accurately recognizing a user's emotions and generating personalized responses based on them, which leads to problems such as a decrease in the quality of dialogue with the user and an inability to provide the appropriate feedback the user desires.

[0439] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for an emotion engine in the server to recognize an emotion based on the user input, means for a generation AI to generate a response based on the recognized emotion, and means for returning the generated response to the user. This makes it possible to accurately recognize the user's emotion and provide a personalized response.

[0440] "Means for accepting input from the user" refers to the functionality for accepting text or voice input on the device used by the user (smartphone, PC, tablet, etc.).

[0441] The "means for transmitting the user's input to the server" refers to a communication means for transmitting the data input by the user to the server via the Internet.

[0442] "Means for the emotion engine in the server to recognize emotions based on user input" refers to algorithms and programs for analyzing user input data and identifying the user's emotional state from that data.

[0443] "Means for the generative AI to generate a response based on the recognized emotion" refers to generative AI algorithms and programs for creating appropriate conversational responses based on the user's emotion recognized by the emotion engine.

[0444] "Means for returning the generated response to the user" refers to communication means and display means for sending the response message generated by the generation AI to the user's device and displaying it.

[0445] The means for performing "user" authentication and session management refers to the algorithms and programs for verifying the user's ID and password when they log in and for managing the user's session state.

[0446] "Means for recording session-managed data" refers to a storage device such as a database for temporarily or long-term recording and storage of user authentication information and session-related data.

[0447] This invention is a system that combines generative AI and an emotion engine to recognize user emotions in one-on-one services and provide more appropriate and personalized responses, improving the quality of interactions and increasing the usefulness of the feedback users seek.

[0448] System configuration

[0449] The system of the present invention mainly consists of the following components:

[0450] 1. Terminal

[0451] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0452] Includes a chat box and voice input functionality for accepting input.

[0453] Web interfaces often use HTML / JavaScript.

[0454] 2. Server

[0455] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[0456] It performs user authentication, session management, and saves conversation data.

[0457] It is often implemented using Node.js or Python.

[0458] 3. Generation AI

[0459] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[0460] For example, you can use OpenAI's API.

[0461] 4. Emotion Engine

[0462] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[0463] For example, you can use Microsoft Azure's emotion recognition API.

[0464] Program processing

[0465] In this system, a series of processes are carried out: user input is accepted, the emotion engine analyzes the emotion, and then the generative AI generates a response based on that.

[0466] 1. User Login and Authentication

[0467] The user enters their ID and password on the terminal and presses the login button.

[0468] The terminal transmits the entered authentication information to the server.

[0469] The server compares the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[0470] If authentication is successful, a unique session token is issued and returned to the terminal.

[0471] 2. User Input and Submission

[0472] The user enters a message in the chat box and presses the send button.

[0473] The terminal sends the message and the session token to the server.

[0474] 3. Emotion Recognition and Response Generation Process on the Server

[0475] The server sends a message to the emotion engine, requesting it to analyze the user's emotions.

[0476] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[0477] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[0478] 4. Generative AI response generation and optimization

[0479] The generative AI generates a response based on emotion recognition results and user input.

[0480] For example, generate a response like "Tell me specifically what's not working."

[0481] Responses are optimized by taking into account previous conversation data and emotional patterns.

[0482] 5. Returning and Displaying Responses

[0483] The server sends the generated response to the terminal.

[0484] The terminal displays the response to the user in a chat box.

[0485] Specific examples

[0486] User Login

[0487] The user enters the ID "user123" and password "password123" and presses the login button.

[0488] The terminal sends this information to the server.

[0489] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[0490] The device stores the session token and uses it for subsequent requests.

[0491] Conversation initiation and emotion recognition

[0492] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0493] The terminal sends the input content and the session token to the server.

[0494] The server sends a message to the emotion engine, requesting emotion analysis.

[0495] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[0496] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[0497] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[0498] The server sends the generated response to the terminal, which displays the response in a chat box.

[0499] Continuing the dialogue and using emotions

[0500] The user enters "I never meet work deadlines" and presses the send button again.

[0501] The terminal sends the new message and the session token to the server.

[0502] The server sends the message to the emotion engine, which analyzes the emotion again.

[0503] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[0504] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[0505] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[0506] The server sends the generated response to the terminal, which displays the response.

[0507] As a result, by combining an emotion recognition engine, the present invention can accurately grasp the user's emotional state and provide more personalized responses, thereby improving the quality of the interaction and increasing the usefulness of the feedback the user receives.

[0508] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0509] Step 1: Login and Authentication

[0510] Input: The user enters their ID and password on the terminal and presses the login button.

[0511] Operation: The terminal sends the entered authentication information to the server, which then collates the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[0512] Output: If authentication is successful, a unique session token is generated and sent back to the device, which stores it.

[0513] Step 2: User Input and Submission

[0514] Input: The user types a message in the chat box and presses the send button.

[0515] Operation: The device sends the input and the session token to the server.

[0516] Output: The terminal sends a data packet containing the message and the session token, which is received by the server.

[0517] Step 3: Message Reception and Session Verification

[0518] Input: The server receives the message and the session token.

[0519] What happens: The server validates the session token to see if it is a valid session.

[0520] Output: If the session is valid, prepare to send the message to the emotion engine. If the session is invalid, generate an error message.

[0521] Step 4: Performing Emotion Recognition

[0522] Input: The server sends a valid session message to the emotion engine.

[0523] How it works: An emotion engine (e.g., Microsoft Azure's Emotion Recognition API) analyzes the message content and recognizes the user's emotion. Analysis is performed using natural language processing algorithms.

[0524] Output: The emotion engine returns the analysis results, including an emotion label (e.g., "sad") and an emotion score, to the server.

[0525] Step 5: Processing the emotion recognition results

[0526] Input: The server receives the emotion recognition results sent from the emotion engine.

[0527] Operation: The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response. The server provides the user's input message and the emotion result to the generation AI as a prompt.

[0528] Output: The generation AI generates an appropriate response based on the prompt and sends it back to the server.

[0529] Step 6: Optimize the response

[0530] Input: The server receives the response sent by the generating AI.

[0531] How it works: The server takes into account past conversation data and emotional patterns to optimize responses.

[0532] Output: Generates an optimized response message.

[0533] Step 7: Return and display the response

[0534] Input: The server holds the optimized response message.

[0535] Operation: The server generates a response and sends it to the terminal.

[0536] Output: The terminal displays the received response message on the user interface and provides it to the user.

[0537] (Application example 2)

[0538] 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."

[0539] Existing one-on-one services have difficulty fully understanding users' emotions and intentions, resulting in mechanical responses and a lack of personalization. Furthermore, when users make online purchases, the suggestions and support they receive are often inappropriate. This leads to issues such as low user satisfaction and a lack of promotion of purchasing behavior.

[0540] 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.

[0541] In this invention, the server includes an emotion engine that recognizes the user's emotional state, a means for the emotion engine to analyze the user's input content, a means for a generation AI to personalize a response based on the analysis results of the emotion engine, and a means for returning the generated response to the user, thereby enabling personalized suggestions and responses according to the user's emotional state.

[0542] An "emotion engine" is a program or algorithm that analyzes user input and recognizes the user's emotional state.

[0543] "Generative AI" refers to programs and algorithms that generate responses based on user input and the analysis results of an emotion engine.

[0544] The "server" is a centralized system that receives user input, invokes the emotion engine and generative AI to generate responses, and manages the necessary data and generated responses.

[0545] "Personalization" means providing the most appropriate responses and suggestions based on each individual user's specific emotional state and past conversational data.

[0546] "User authentication" is the process of verifying a user's identity using a user ID and password, etc.

[0547] "Session management" is a method for managing a series of communications while a user is accessing a system and maintaining consistency.

[0548] This invention relates to a 1-on-1 service system that combines an emotion engine that recognizes users' emotions with generative AI. The system aims to improve the shopping experience in virtual stores by understanding the user's emotional state and providing more appropriate and personalized responses.

[0549] System Configuration

[0550] This system mainly consists of the following components:

[0551] 1. Terminal

[0552] The smart device used by the user (smart glasses, smartphone, tablet, etc.).

[0553] Includes an interface for accepting input (chat box and voice input function).

[0554] 2. Server

[0555] It is a centralized system that receives user input and calls on the emotion engine and generative AI to generate a response.

[0556] User authentication, session management, and conversation data storage (e.g., AWS, Google Cloud)

[0557] 3. Generation AI

[0558] It uses large-scale language models (e.g., GPT-4) to analyze user input and generate appropriate responses.

[0559] Generate personalized suggestions and feedback.

[0560] 4. Emotion Engine

[0561] Recognizes emotions from user input and provides the analysis results to generative AI (e.g., Affectiva, Microsoft Azure Emotion API).

[0562] Program processing overview

[0563] 1. User Login and Authentication

[0564] Users log in to the virtual store through smart glasses or smartphones.

[0565] The authentication information is sent from the device to the server, and identity verification is performed.

[0566] 2. Collecting Emotional Data

[0567] While the user is browsing products, facial and voice data is collected from the smart glasses' camera and microphone.

[0568] The collected data is sent to a server and analyzed by an emotion engine.

[0569] 3. Emotion Recognition and Product Recommendations

[0570] The emotion engine recognizes the user's emotional state and provides the results to the generative AI.

[0571] The generative AI generates appropriate product suggestions and feedback based on emotion recognition results and past conversation data.

[0572] 4. Feedback and Support

[0573] The generated suggestions and feedback are sent back to the terminal via the server and displayed to the user in real time.

[0574] The system provides support as needed according to changes in the user's emotions.

[0575] Specific examples

[0576] Prompt Sentence Examples

[0577] If a user inputs "I'm looking for a new smartphone," and the emotion recognition result is "The user is a little confused," the input prompt will be as follows:

[0578] Emotion recognition result: "The user seems a little confused."

[0579] Previous conversation data: "User is looking for a new smartphone."

[0580] Input prompt: "The user is slightly confused. Use this information to generate a purchasing recommendation for a new smartphone."

[0581] Based on these prompts, the generative AI will provide the user with appropriate product suggestions, taking into account the user's previous inquiries and current emotional state, allowing for a more personalized response.

[0582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0583] Step 1: User Login and Authentication

[0584] Users log in to the virtual store through smart glasses or a smartphone. On the terminal, the user enters their ID and password and presses the login button. The entered authentication information is sent to the server, which then compares it with a database to authenticate the user. If authentication is successful, the server generates a unique session token and sends it back to the terminal.

[0585] Input: User ID, Password

[0586] Output: Session token (e.g. "sess-token-abc123")

[0587] Step 2: Collecting emotion data

[0588] When a user starts browsing the virtual store, the camera and microphone in the smart glasses record the user's facial and voice data. This data is transmitted in real time from the device to the server. The server then sends the received facial and voice data to the emotion engine for emotion analysis.

[0589] Input: User's facial expression data, voice data

[0590] Output: Emotion recognition result (e.g. "The user is slightly confused.")

[0591] Step 3: Emotion recognition and product recommendations

[0592] The server sends the emotion recognition results received from the emotion engine to the generation AI, which then requests the generation AI to generate appropriate product suggestions based on the user's past purchasing history and current interests. The generation AI then combines the emotion recognition results with past data to suggest optimal products and promotions.

[0593] Input: Emotion recognition results, past conversation data

[0594] Output: Product suggestion message (e.g. "The user is a little confused. Use this information to generate a suggestion for a new smartphone.")

[0595] Step 4: Feedback and support

[0596] The server receives the generated suggestion message from the AI ​​and sends it back to the device, which displays it to the user in real time. If the user has further questions or needs assistance based on the suggestion, they can send new input from the device to the server and the process will be repeated.

[0597] Input: Product suggestion message

[0598] Output: A message to display to the user (e.g., "Here's the new smartphone model that's best for you.")

[0599] Through the above process, the system is able to provide personalized suggestions and responses in real time that are tailored to the user's emotional state.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] [Second embodiment]

[0604] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0605] 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.

[0606] 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).

[0607] 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.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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."

[0616] The present invention relates to a system for providing a one-on-one service using a generation AI. This system is realized by the following configuration and operation.

[0617] composition

[0618] The system of the present invention mainly comprises the following components:

[0619] 1. Terminal

[0620] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0621] Includes a chat box and voice input functionality for accepting input.

[0622] 2. Server

[0623] It is a centralized system that receives input data and invokes generative AI to generate a response.

[0624] It performs user authentication, session management, and saves conversation data.

[0625] 3. Generation AI

[0626] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[0627] Program processing

[0628] The program processing in this system is explained below:

[0629] 1. User Login and Authentication

[0630] The user enters their ID and password on the terminal and presses the login button.

[0631] The terminal transmits the entered authentication information to the server.

[0632] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[0633] 2. User Input and Submission

[0634] The user enters a message in the chat box and presses the send button.

[0635] The terminal sends the message and the session token to the server.

[0636] 3. Server Response Generation Process

[0637] The server receives the sent message and session token, verifies the validity of the session, and then requests analysis from the generating AI.

[0638] The generation AI analyzes the message content and generates an appropriate response, which is returned to the server.

[0639] 4. Returning and Displaying Responses

[0640] The server sends the response received from the generation AI to the terminal.

[0641] The terminal displays the response to the user in a chat box.

[0642] Specific examples

[0643] User Login

[0644] The user enters the ID "user123" and password "password123" and presses the login button.

[0645] The terminal sends this information to the server.

[0646] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[0647] The device stores the session token and uses it for subsequent requests.

[0648] Start a conversation

[0649] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0650] The terminal sends the input content and the session token to the server.

[0651] The server asks the generation AI to analyze the message, and the generation AI generates a response saying, "Please tell us specifically which part is not working."

[0652] The server sends the generated response to the terminal, which displays the response in a chat box.

[0653] Continuing the dialogue

[0654] The user enters "I never meet work deadlines" and presses the send button again.

[0655] The terminal retransmits the message and the session token.

[0656] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[0657] The server sends the response to the terminal, which displays the response.

[0658] In this way, a 1-on-1 system using generative AI can provide an environment in which users can freely interact, overcoming time and cost constraints. In addition, the analytical capabilities of generative AI can provide appropriate and useful feedback to users.

[0659] The processing flow will be explained below.

[0660] Step 1:

[0661] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[0662] Step 2:

[0663] The terminal sends an authentication request including the user ID and password to the server.

[0664] Step 3:

[0665] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[0666] Step 4:

[0667] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[0668] Step 5:

[0669] The terminal stores the session token received from the server and starts the user session.

[0670] Step 6:

[0671] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0672] Step 7:

[0673] The terminal sends a request to the server containing the entered message and the stored session token.

[0674] Step 8:

[0675] The server extracts the message and session token from the received request and verifies the validity of the session.

[0676] Step 9:

[0677] The server sends the message to the generation AI, asking it to analyze it and generate a response.

[0678] Step 10:

[0679] The generative AI analyzes the received message, "Work hasn't been going well lately," and generates an appropriate response: "Tell me specifically what's not going well."

[0680] Step 11:

[0681] The generation AI sends the generated response back to the server.

[0682] Step 12:

[0683] The server sends the response received from the generation AI to the terminal.

[0684] Step 13:

[0685] The terminal receives the response from the server and displays it in the chat box.

[0686] Step 14:

[0687] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[0688] Step 15:

[0689] The device sends a request to the server again, including the new message and the session token.

[0690] Step 16:

[0691] The server receives the new request and revalidates the message and session token.

[0692] Step 17:

[0693] The server generates a new message and asks the AI ​​to analyze it again, sending a message saying, "I always miss work deadlines."

[0694] Step 18:

[0695] The generative AI analyzes the message "I never meet work deadlines" and generates the response "Have you ever thought about why that is?"

[0696] Step 19:

[0697] The generating AI sends the new response back to the server.

[0698] Step 20:

[0699] The server sends the new response received from the generating AI to the terminal.

[0700] Step 21:

[0701] The terminal receives the new response from the server and displays it in the chat box.

[0702] Example 1

[0703] 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."

[0704] Conventional one-on-one dialogue services have issues such as insufficient security for user authentication and natural dialogue, and difficulty in optimizing responses based on the user's past dialogue records. There is also a need for improvements in session management and response generation speed. The present invention aims to solve these issues and provide a higher quality and safer one-on-one dialogue service.

[0705] 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.

[0706] In this invention, the server includes a means for performing user authentication and issuing a unique session token, a means for generating a response based on the user's input using a generation artificial intelligence, and a means for verifying the validity of the session and returning the generated response to the user. This enables secure and efficient user authentication and session management, and realizes the generation of natural and accurate responses and their prompt return.

[0707] "User authentication" is the process of verifying the authenticity of a user when they access a system.

[0708] A "session token" is a unique identifier issued to a user who has been successfully authenticated, and ensures the continuation of the session.

[0709] "Generative AI" is an algorithm that uses machine learning to generate human-like text and enable dialogue with users.

[0710] A "central processing unit" is the main component for processing input data from a user and generating a response.

[0711] A "user interface" is an interface that includes a visual display and an operation panel that allows a user to interact with a system.

[0712] "Session management" is the procedure for tracking a user's session and maintaining session validity.

[0713] The present invention relates to a system that provides one-on-one dialogue services using a generative AI. This system accepts input from a user, and the generative AI generates a response via a central processing unit, which is then returned to the user. This allows the user to enjoy a natural dialogue experience.

[0714] System configuration

[0715] The system mainly consists of the following components:

[0716] 1. Terminal

[0717] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0718] Includes a chat box and voice input functionality for accepting input.

[0719] 2. Server (Central Processing Unit)

[0720] A centralized system receives user input data and calls a generative AI to generate a response.

[0721] It performs user authentication, session management, and saves conversation data.

[0722] 3. Generative Artificial Intelligence

[0723] It is a program or algorithm (e.g., a deep learning model) that is built into the server and analyzes user input and generates an appropriate response.

[0724] Program processing

[0725] 1. User Login and Authentication

[0726] The user enters their ID and password on the terminal and presses the login button. For example, they enter the ID "user123" and the password "password123."

[0727] The device sends this authentication information to the server as an HTTP POST request.

[0728] The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it issues a unique session token (e.g., "sess-token-abc123") and returns it to the terminal.

[0729] The device stores the session token in local storage and uses it for subsequent requests.

[0730] 2. User Input and Submission

[0731] The user types a message in the chat box and presses the send button. For example, the user might type, "Work hasn't been going well lately."

[0732] The device sends the message and the session token to the server as an HTTP POST request.

[0733] 3. Server Response Generation Process

[0734] The server receives the message and the session token and verifies the validity of the session.

[0735] If the session is valid, the message content is sent to the generating AI and requested for analysis.

[0736] The generative AI analyzes the message content and generates an appropriate response, such as "Please tell me specifically what is not working."

[0737] The generation AI sends the generated response back to the server.

[0738] 4. Returning and Displaying Responses

[0739] The server sends the generated response to the terminal.

[0740] The terminal displays a response to the user in a chat box, for example, "Please tell me specifically what is not working."

[0741] 5. Continuing the dialogue

[0742] The user again enters the message and sends it. For example, the user enters "I always miss my work deadlines."

[0743] The terminal retransmits the message and the session token.

[0744] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[0745] The server sends the response to the terminal, which displays the response.

[0746] Specific examples

[0747] Prompt Sentence Examples

[0748] 1. Example of a user login and authentication prompt:

[0749] The user enters the ID "user123" and password "password123" and presses the login button. The server receives this and issues a session token.

[0750] 2. Example of a prompt for user input and submission:

[0751] The user types "Work hasn't been going well lately" and presses the send button. The server receives this message and asks the generation AI to analyze it.

[0752] 3. Examples of prompts to continue the dialogue:

[0753] The user enters "I always miss work deadlines" and presses the submit button again. The AI ​​should generate an appropriate response.

[0754] In this way, the system provides users with an environment in which they can freely interact, overcoming time and cost constraints.The analytical capabilities of the generative AI model provide appropriate and useful feedback to users.

[0755] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0756] Step 1:

[0757] The user enters their ID and password and clicks the login button.

[0758] Input: User ID (e.g., "user123"), Password (e.g., "password123")

[0759] Operation: The user enters their ID and password into the login form and presses the login button.

[0760] Output: The device gets the login information.

[0761] Step 2:

[0762] The terminal sends the entered authentication information (ID and password) to the server.

[0763] Input: User ID, Password

[0764] How it works: The device sends an HTTP POST request to the server's authentication endpoint.

[0765] Output: The server receives the authentication information.

[0766] Step 3:

[0767] The server compares the received authentication information with a database and authenticates the user.

[0768] Input: Authentication information (ID, password)

[0769] How it works: The server performs a database query and processes the authentication information.

[0770] Output: Authentication result (success or failure), session token (if successful)

[0771] Step 4:

[0772] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[0773] Input: Authentication success information

[0774] How it works: The server generates a session token and sends it back to the device as an HTTP response.

[0775] Output: Session token (e.g. "sess-token-abc123")

[0776] Step 5:

[0777] The device stores the session token and uses it for subsequent requests.

[0778] Input: Session token

[0779] How it works: The device saves the session token in local storage.

[0780] Output: The saved session token

[0781] Step 6:

[0782] The user enters a message in the chat box and presses the send button.

[0783] Input: User message (e.g. "Work hasn't been going well lately")

[0784] Action: The user types a message in the chat box and presses the send button.

[0785] Output: The terminal gets the message.

[0786] Step 7:

[0787] The terminal sends the message and the session token to the server.

[0788] Input: message, session token

[0789] How it works: The device sends an HTTP POST request to the server containing a message and a session token.

[0790] Output: The server receives the message and the session token.

[0791] Step 8:

[0792] The server receives the message and the session token and verifies the validity of the session.

[0793] Input: message, session token

[0794] How it works: The server checks the session token against its database and verifies the session expiration time.

[0795] Output: Session validation result

[0796] Step 9:

[0797] If the session is valid, the server sends the message content to the generating AI and requests analysis.

[0798] Input: message, session token

[0799] How it works: The server sends a message to the spawned AI's API.

[0800] Output: Request for response analysis by generative AI

[0801] Step 10:

[0802] The generation AI analyzes the message content and generates an appropriate response.

[0803] Input: User message

[0804] How it works: Generative AI uses deep learning models to generate text.

[0805] Output: The generated response (e.g., "What exactly is going wrong?")

[0806] Step 11:

[0807] The generation AI sends the generated response back to the server.

[0808] Input: The generated response

[0809] Behavior: The generation AI sends the generated response back to the server as an HTTP response.

[0810] Output: The server receives the response.

[0811] Step 12:

[0812] The server sends the generated response to the terminal.

[0813] Input: The generated response

[0814] How it works: The server sends the response to the device as an HTTP response.

[0815] Output: The terminal receives the response.

[0816] Step 13:

[0817] The terminal displays the response to the user in a chat box.

[0818] Input: The generated response

[0819] BEHAVIOR: The device updates the HTML in the chat box to show the response.

[0820] Output: The response is made visible to the user.

[0821] (Application example 1)

[0822] 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."

[0823] Current electronic payment services face the challenge of providing fast and accurate support when users check their credit card statements or troubleshoot issues. In addition, user authentication and session management are complex, resulting in a poor user experience. To address these challenges, an effective one-on-one dialogue system using generative AI is needed.

[0824] 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.

[0825] In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for a generation AI to generate a response based on the user input in the server, means for returning the generated response to the user, means for performing login and authentication, means for issuing a session token and confirming session validity, and means for analyzing chat messages and generating responses using a generation AI model, thereby enabling users to receive prompt and accurate support.

[0826] "Means for accepting input from the user" refers to an interface that allows the user to input information, such as a chat box or a voice input function.

[0827] The "means for transmitting user input to a server" is a mechanism for transmitting input information to a server via a network.

[0828] "Means for generative AI to generate responses" refers to a system that uses artificial intelligence to analyze input information and generate appropriate responses.

[0829] "Means for returning the generated response to the user" refers to a communication means for returning the response generated by the generation AI back to the user's terminal.

[0830] "Means for login and authentication" refers to the mechanism by which a user enters an ID and password to access the system and authenticates them.

[0831] The "means for issuing a session token and verifying the validity of the session" is a function for issuing a unique session token to an authenticated user and verifying the validity of the token.

[0832] "Means for analyzing chat messages and generating responses using a generative AI model" refers to a system that uses a generative AI model to analyze users' chat messages and automatically generate appropriate responses in response to them.

[0833] The "means for storing and analyzing past conversation data" is a mechanism for storing a history of past conversations with a user and analyzing it to optimize future responses.

[0834] "Means for keeping a history of chat messages and maintaining the context of a continuous conversation" refers to a function that saves the content of past conversations with a user and continues the dialogue while understanding the context of the current conversation.

[0835] The "means for implementing user authentication and session management" is a mechanism for authenticating a user and appropriately managing the user's session.

[0836] "Means for verifying authentication information against a database" refers to a procedure for comparing the authentication information entered by a user with an existing database to determine whether it matches.

[0837] This invention relates to a system that uses generative AI to provide one-on-one user support for electronic payment services. The system uses a smartphone to streamline the process of users checking their credit card statements and receiving troubleshooting support.

[0838] System Configuration

[0839] The system of the present invention consists of the following main components:

[0840] 1. Terminal

[0841] A device such as a smartphone that is operated by a user.

[0842] Accepts input from users through a chat box or voice input function.

[0843] 2. Server

[0844] It is a centralized system that receives user input data and invokes generative AI models to generate responses.

[0845] It performs functions such as login and authentication, session management, and saving conversation history.

[0846] 3. Generative AI Models

[0847] It is a program or algorithm built into the server that analyzes the user's message and generates an appropriate response.

[0848] Program processing overview

[0849] 1. The user logs in and authenticates on their smartphone

[0850] The user enters their ID and password and presses the login button.

[0851] The terminal sends the input information to the server, which checks the authentication information against a database.

[0852] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[0853] 2. The user types something into the chat box

[0854] The user enters the question or inquiry into the chat box and presses the send button.

[0855] The terminal sends the input message together with the session token to the server.

[0856] 3. The server requests the AI ​​to generate a response.

[0857] The server checks the received message and session token and requests the generating AI model to analyze it.

[0858] The generative AI model analyzes the message content and generates an appropriate response.

[0859] The generated response is sent back to the server.

[0860] 4. View the generated response

[0861] The server sends the response from the generated AI to the terminal, which displays it in the chat box.

[0862] Hardware and software used

[0863] Hardware:

[0864] Smartphone: Used for displaying user input and responses.

[0865] Server: A computer that performs centralized data processing.

[0866] software:

[0867] Flask: Used as the server web framework.

[0868] transformers library: Used to process generative AI models.

[0869] Examples of concrete examples and prompts

[0870] User login example

[0871] User Input:

[0872] Enter "User ID: user123, Password: password123" and press the login button.

[0873] Expected response:

[0874] "Session token: sess-token-abc123" is returned.

[0875] Conversation starter examples

[0876] User Input:

[0877] I would like to check my credit card statement, where can I find it?

[0878] Expected response:

[0879] To view your usage details, select the "Details" tab from the app menu.

[0880] In this way, the system of the present invention is designed to ensure users receive prompt and appropriate support for electronic payment services, and the use of generative AI models can provide highly accurate responses and improve the user experience.

[0881] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0882] Step 1:

[0883] The user displays the login screen on their smartphone, enters their ID and password, and presses the login button. The device sends the entered authentication information (user ID and password) to the server. The server compares the received user ID and password with the information in its database, and if they match, generates a unique session token and sends it back to the device. The device stores this session token.

[0884] Input: User ID, Password

[0885] Output: Session token

[0886] Step 2:

[0887] The user enters a question or consultation (message) into the chat box and presses the send button. The device sends the entered message and session token to the server. The server then verifies the validity of the received message and session token.

[0888] Input: message, session token

[0889] Output: Message confirmation, session token validity check

[0890] Step 3:

[0891] After the message and session token are validated, the server sends the message to the generative AI model, which analyzes the message content and generates an appropriate response. This analysis includes tokenizing, generating, and decoding the text. The generated response is then sent back to the server.

[0892] Input: Message

[0893] Output: The generated response

[0894] Step 4:

[0895] The server receives the response from the generative AI model and sends it to the device, which then displays the received response to the user in a chat box.

[0896] Input: Generated response

[0897] Output: The response to display to the user

[0898] Step 5:

[0899] If the conversation continues, the user again enters a message in the chat box and presses the send button. The device again sends the message and session token to the server. This process is repeated as long as the user continues to ask questions or ask questions.

[0900] Input: New message, session token

[0901] Output: A new response from the generative AI model

[0902] These steps allow users to receive prompt and accurate assistance. By leveraging generative AI models and session management capabilities, the system is able to support continuous conversations while preserving context.

[0903] 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.

[0904] This invention relates to a system that combines a one-on-one service using generative AI with an emotion engine that recognizes the user's emotions. This system can improve the quality of interactions by understanding the user's emotional state and providing more appropriate and personalized responses.

[0905] composition

[0906] The system of the present invention mainly comprises the following components:

[0907] 1. Terminal

[0908] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[0909] Includes a chat box and voice input functionality for accepting input.

[0910] 2. Server

[0911] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[0912] It performs user authentication, session management, and saves conversation data.

[0913] 3. Generation AI

[0914] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[0915] 4. Emotion Engine

[0916] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[0917] Program processing

[0918] The program processing in this system is explained below:

[0919] 1. User Login and Authentication

[0920] The user enters their ID and password on the terminal and presses the login button.

[0921] The terminal transmits the entered authentication information to the server.

[0922] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[0923] 2. User Input and Submission

[0924] The user enters a message in the chat box and presses the send button.

[0925] The terminal sends the message and the session token to the server.

[0926] 3. Emotion Recognition and Response Generation Process on the Server

[0927] The server extracts the received message and session token and verifies the validity of the session.

[0928] Next, a message is sent to the emotion engine to analyze the user's emotions.

[0929] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[0930] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[0931] 4. Generative AI response generation and optimization

[0932] The generative AI generates a response based on the emotion recognition results and user input. For example, if sadness is detected, an encouraging message is generated.

[0933] Responses are optimized by taking into account previous conversation data and emotional patterns.

[0934] 5. Returning and Displaying Responses

[0935] The server sends the generated response to the terminal.

[0936] The terminal displays the response to the user in a chat box.

[0937] Specific examples

[0938] User Login

[0939] The user enters the ID "user123" and password "password123" and presses the login button.

[0940] The terminal sends this information to the server.

[0941] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[0942] The device stores the session token and uses it for subsequent requests.

[0943] Conversation initiation and emotion recognition

[0944] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0945] The terminal sends the input content and the session token to the server.

[0946] The server sends a message to the emotion engine, requesting emotion analysis.

[0947] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[0948] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[0949] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[0950] The server sends the generated response to the terminal, which displays the response in a chat box.

[0951] Continuing the dialogue and using emotions

[0952] The user enters "I never meet work deadlines" and presses the send button again.

[0953] The terminal sends the new message and the session token to the server.

[0954] The server sends the message to the emotion engine, which analyzes the emotion again.

[0955] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[0956] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[0957] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[0958] The server sends the generated response to the terminal, which displays the response.

[0959] In this way, the present invention, combined with an emotion recognition engine, can understand the user's emotional state and provide more personalized responses, improving the quality of the interaction and the usefulness of the feedback the user receives.

[0960] The processing flow will be explained below.

[0961] Step 1:

[0962] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[0963] Step 2:

[0964] The terminal sends an authentication request including the user ID and password to the server.

[0965] Step 3:

[0966] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[0967] Step 4:

[0968] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[0969] Step 5:

[0970] The terminal stores the session token received from the server and starts the user session.

[0971] Step 6:

[0972] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[0973] Step 7:

[0974] The terminal sends a request to the server containing the entered message and the stored session token.

[0975] Step 8:

[0976] The server extracts the message and session token from the received request and verifies the validity of the session.

[0977] Step 9:

[0978] The server sends the message to the emotion engine and requests emotion analysis.

[0979] Step 10:

[0980] The emotion engine recognizes the user's emotions from the message "Work hasn't been going well lately" and analyzes the user's sadness as a result.

[0981] Step 11:

[0982] The emotion engine sends the recognized emotion results back to the server.

[0983] Step 12:

[0984] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[0985] Step 13:

[0986] Based on the emotion recognition results and user input, the generative AI generates a response: "Please tell me specifically which part is not working."

[0987] Step 14:

[0988] The generation AI sends the generated response back to the server.

[0989] Step 15:

[0990] The server sends the response received from the generation AI to the terminal.

[0991] Step 16:

[0992] The terminal receives the response from the server and displays it in the chat box.

[0993] Step 17:

[0994] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[0995] Step 18:

[0996] The device sends a request to the server again, including the new message and the session token.

[0997] Step 19:

[0998] The server receives the new request and revalidates the message and session token.

[0999] Step 20:

[1000] The server sends the new message to the emotion engine and requests emotion analysis again.

[1001] Step 21:

[1002] The emotion engine analyzes the user's feelings of stress from the message "I never meet work deadlines."

[1003] Step 22:

[1004] The emotion engine sends the recognized emotion results back to the server.

[1005] Step 23:

[1006] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[1007] Step 24:

[1008] The generative AI generates the response "Have you ever thought about the cause of that?" based on the emotion recognition results and user input.

[1009] Step 25:

[1010] The generation AI sends the generated response back to the server.

[1011] Step 26:

[1012] The server sends the response received from the generation AI to the terminal.

[1013] Step 27:

[1014] The terminal receives the response from the server and displays it in the chat box.

[1015] Example 2

[1016] 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."

[1017] Conventional dialogue systems have difficulty accurately recognizing a user's emotions and generating personalized responses based on them, which leads to problems such as a decrease in the quality of dialogue with the user and an inability to provide the appropriate feedback the user desires.

[1018] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for an emotion engine in the server to recognize an emotion based on the user input, means for a generation AI to generate a response based on the recognized emotion, and means for returning the generated response to the user. This makes it possible to accurately recognize the user's emotion and provide a personalized response.

[1019] "Means for accepting input from the user" refers to the functionality for accepting text or voice input on the device used by the user (smartphone, PC, tablet, etc.).

[1020] The "means for transmitting the user's input to the server" refers to a communication means for transmitting the data input by the user to the server via the Internet.

[1021] "Means for the emotion engine in the server to recognize emotions based on user input" refers to algorithms and programs for analyzing user input data and identifying the user's emotional state from that data.

[1022] "Means for the generative AI to generate a response based on the recognized emotion" refers to generative AI algorithms and programs for creating appropriate conversational responses based on the user's emotion recognized by the emotion engine.

[1023] "Means for returning the generated response to the user" refers to communication means and display means for sending the response message generated by the generation AI to the user's device and displaying it.

[1024] The means for performing "user" authentication and session management refers to the algorithms and programs for verifying the user's ID and password when they log in and for managing the user's session state.

[1025] "Means for recording session-managed data" refers to a storage device such as a database for temporarily or long-term recording and storage of user authentication information and session-related data.

[1026] This invention is a system that combines generative AI and an emotion engine to recognize user emotions in one-on-one services and provide more appropriate and personalized responses, improving the quality of interactions and increasing the usefulness of the feedback users seek.

[1027] System configuration

[1028] The system of the present invention mainly consists of the following components:

[1029] 1. Terminal

[1030] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1031] Includes a chat box and voice input functionality for accepting input.

[1032] Web interfaces often use HTML / JavaScript.

[1033] 2. Server

[1034] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[1035] It performs user authentication, session management, and saves conversation data.

[1036] It is often implemented using Node.js or Python.

[1037] 3. Generation AI

[1038] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[1039] For example, you can use OpenAI's API.

[1040] 4. Emotion Engine

[1041] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[1042] For example, you can use Microsoft Azure's emotion recognition API.

[1043] Program processing

[1044] In this system, a series of processes are carried out: user input is accepted, the emotion engine analyzes the emotion, and then the generative AI generates a response based on that.

[1045] 1. User Login and Authentication

[1046] The user enters their ID and password on the terminal and presses the login button.

[1047] The terminal transmits the entered authentication information to the server.

[1048] The server compares the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[1049] If authentication is successful, a unique session token is issued and returned to the terminal.

[1050] 2. User Input and Submission

[1051] The user enters a message in the chat box and presses the send button.

[1052] The terminal sends the message and the session token to the server.

[1053] 3. Emotion Recognition and Response Generation Process on the Server

[1054] The server sends a message to the emotion engine, requesting it to analyze the user's emotions.

[1055] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[1056] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[1057] 4. Generative AI response generation and optimization

[1058] The generative AI generates a response based on emotion recognition results and user input.

[1059] For example, generate a response like "Tell me specifically what's not working."

[1060] Responses are optimized by taking into account previous conversation data and emotional patterns.

[1061] 5. Returning and Displaying Responses

[1062] The server sends the generated response to the terminal.

[1063] The terminal displays the response to the user in a chat box.

[1064] Specific examples

[1065] User Login

[1066] The user enters the ID "user123" and password "password123" and presses the login button.

[1067] The terminal sends this information to the server.

[1068] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[1069] The device stores the session token and uses it for subsequent requests.

[1070] Conversation initiation and emotion recognition

[1071] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1072] The terminal sends the input content and the session token to the server.

[1073] The server sends a message to the emotion engine, requesting emotion analysis.

[1074] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[1075] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[1076] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[1077] The server sends the generated response to the terminal, which displays the response in a chat box.

[1078] Continuing the dialogue and using emotions

[1079] The user enters "I never meet work deadlines" and presses the send button again.

[1080] The terminal sends the new message and the session token to the server.

[1081] The server sends the message to the emotion engine, which analyzes the emotion again.

[1082] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[1083] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[1084] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[1085] The server sends the generated response to the terminal, which displays the response.

[1086] As a result, by combining an emotion recognition engine, the present invention can accurately grasp the user's emotional state and provide more personalized responses, thereby improving the quality of the interaction and increasing the usefulness of the feedback the user receives.

[1087] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1088] Step 1: Login and Authentication

[1089] Input: The user enters their ID and password on the terminal and presses the login button.

[1090] Operation: The terminal sends the entered authentication information to the server, which then collates the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[1091] Output: If authentication is successful, a unique session token is generated and sent back to the device, which stores it.

[1092] Step 2: User Input and Submission

[1093] Input: The user types a message in the chat box and presses the send button.

[1094] Operation: The device sends the input and the session token to the server.

[1095] Output: The terminal sends a data packet containing the message and the session token, which is received by the server.

[1096] Step 3: Message Reception and Session Verification

[1097] Input: The server receives the message and the session token.

[1098] What happens: The server validates the session token to see if it is a valid session.

[1099] Output: If the session is valid, prepare to send the message to the emotion engine. If the session is invalid, generate an error message.

[1100] Step 4: Performing Emotion Recognition

[1101] Input: The server sends a valid session message to the emotion engine.

[1102] How it works: An emotion engine (e.g., Microsoft Azure's Emotion Recognition API) analyzes the message content and recognizes the user's emotion. Analysis is performed using natural language processing algorithms.

[1103] Output: The emotion engine returns the analysis results, including an emotion label (e.g., "sad") and an emotion score, to the server.

[1104] Step 5: Processing the emotion recognition results

[1105] Input: The server receives the emotion recognition results sent from the emotion engine.

[1106] Operation: The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response. The server provides the user's input message and the emotion result to the generation AI as a prompt.

[1107] Output: The generation AI generates an appropriate response based on the prompt and sends it back to the server.

[1108] Step 6: Optimize the response

[1109] Input: The server receives the response sent by the generating AI.

[1110] How it works: The server takes into account past conversation data and emotional patterns to optimize responses.

[1111] Output: Generates an optimized response message.

[1112] Step 7: Return and display the response

[1113] Input: The server holds the optimized response message.

[1114] Operation: The server generates a response and sends it to the terminal.

[1115] Output: The terminal displays the received response message on the user interface and provides it to the user.

[1116] (Application example 2)

[1117] 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."

[1118] Existing one-on-one services have difficulty fully understanding users' emotions and intentions, resulting in mechanical responses and a lack of personalization. Furthermore, when users make online purchases, the suggestions and support they receive are often inappropriate. This leads to issues such as low user satisfaction and a lack of promotion of purchasing behavior.

[1119] 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.

[1120] In this invention, the server includes an emotion engine that recognizes the user's emotional state, a means for the emotion engine to analyze the user's input content, a means for a generation AI to personalize a response based on the analysis results of the emotion engine, and a means for returning the generated response to the user, thereby enabling personalized suggestions and responses according to the user's emotional state.

[1121] An "emotion engine" is a program or algorithm that analyzes user input and recognizes the user's emotional state.

[1122] "Generative AI" refers to programs and algorithms that generate responses based on user input and the analysis results of an emotion engine.

[1123] The "server" is a centralized system that receives user input, invokes the emotion engine and generative AI to generate responses, and manages the necessary data and generated responses.

[1124] "Personalization" means providing the most appropriate responses and suggestions based on each individual user's specific emotional state and past conversational data.

[1125] "User authentication" is the process of verifying a user's identity using a user ID and password, etc.

[1126] "Session management" is a method for managing a series of communications while a user is accessing a system and maintaining consistency.

[1127] This invention relates to a 1-on-1 service system that combines an emotion engine that recognizes users' emotions with generative AI. The system aims to improve the shopping experience in virtual stores by understanding the user's emotional state and providing more appropriate and personalized responses.

[1128] System Configuration

[1129] This system mainly consists of the following components:

[1130] 1. Terminal

[1131] The smart device used by the user (smart glasses, smartphone, tablet, etc.).

[1132] Includes an interface for accepting input (chat box and voice input function).

[1133] 2. Server

[1134] It is a centralized system that receives user input and calls on the emotion engine and generative AI to generate a response.

[1135] User authentication, session management, and conversation data storage (e.g., AWS, Google Cloud)

[1136] 3. Generation AI

[1137] It uses large-scale language models (e.g., GPT-4) to analyze user input and generate appropriate responses.

[1138] Generate personalized suggestions and feedback.

[1139] 4. Emotion Engine

[1140] Recognizes emotions from user input and provides the analysis results to generative AI (e.g., Affectiva, Microsoft Azure Emotion API).

[1141] Program processing overview

[1142] 1. User Login and Authentication

[1143] Users log in to the virtual store through smart glasses or smartphones.

[1144] The authentication information is sent from the device to the server, and identity verification is performed.

[1145] 2. Collecting Emotional Data

[1146] While the user is browsing products, facial and voice data is collected from the smart glasses' camera and microphone.

[1147] The collected data is sent to a server and analyzed by an emotion engine.

[1148] 3. Emotion Recognition and Product Recommendations

[1149] The emotion engine recognizes the user's emotional state and provides the results to the generative AI.

[1150] The generative AI generates appropriate product suggestions and feedback based on emotion recognition results and past conversation data.

[1151] 4. Feedback and Support

[1152] The generated suggestions and feedback are sent back to the terminal via the server and displayed to the user in real time.

[1153] The system provides support as needed according to changes in the user's emotions.

[1154] Specific examples

[1155] Prompt Sentence Examples

[1156] If a user inputs "I'm looking for a new smartphone," and the emotion recognition result is "The user is a little confused," the input prompt will be as follows:

[1157] Emotion recognition result: "The user seems a little confused."

[1158] Previous conversation data: "User is looking for a new smartphone."

[1159] Input prompt: "The user is slightly confused. Use this information to generate a purchasing recommendation for a new smartphone."

[1160] Based on these prompts, the generative AI will provide the user with appropriate product suggestions, taking into account the user's previous inquiries and current emotional state, allowing for a more personalized response.

[1161] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1162] Step 1: User Login and Authentication

[1163] Users log in to the virtual store through smart glasses or a smartphone. On the terminal, the user enters their ID and password and presses the login button. The entered authentication information is sent to the server, which then compares it with a database to authenticate the user. If authentication is successful, the server generates a unique session token and sends it back to the terminal.

[1164] Input: User ID, Password

[1165] Output: Session token (e.g. "sess-token-abc123")

[1166] Step 2: Collecting emotion data

[1167] When a user starts browsing the virtual store, the camera and microphone in the smart glasses record the user's facial and voice data. This data is transmitted in real time from the device to the server. The server then sends the received facial and voice data to the emotion engine for emotion analysis.

[1168] Input: User's facial expression data, voice data

[1169] Output: Emotion recognition result (e.g. "The user is slightly confused.")

[1170] Step 3: Emotion recognition and product recommendations

[1171] The server sends the emotion recognition results received from the emotion engine to the generation AI, which then requests the generation AI to generate appropriate product suggestions based on the user's past purchasing history and current interests. The generation AI then combines the emotion recognition results with past data to suggest optimal products and promotions.

[1172] Input: Emotion recognition results, past conversation data

[1173] Output: Product suggestion message (e.g. "The user is a little confused. Use this information to generate a suggestion for a new smartphone.")

[1174] Step 4: Feedback and support

[1175] The server receives the generated suggestion message from the AI ​​and sends it back to the device, which displays it to the user in real time. If the user has further questions or needs assistance based on the suggestion, they can send new input from the device to the server and the process will be repeated.

[1176] Input: Product suggestion message

[1177] Output: A message to display to the user (e.g., "Here's the new smartphone model that's best for you.")

[1178] Through the above process, the system is able to provide personalized suggestions and responses in real time that are tailored to the user's emotional state.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] [Third embodiment]

[1183] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1184] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1185] 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).

[1186] 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.

[1187] 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.

[1188] 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).

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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."

[1195] The present invention relates to a system for providing a one-on-one service using a generation AI. This system is realized by the following configuration and operation.

[1196] composition

[1197] The system of the present invention mainly comprises the following components:

[1198] 1. Terminal

[1199] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1200] Includes a chat box and voice input functionality for accepting input.

[1201] 2. Server

[1202] It is a centralized system that receives input data and invokes generative AI to generate a response.

[1203] It performs user authentication, session management, and saves conversation data.

[1204] 3. Generation AI

[1205] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[1206] Program processing

[1207] The program processing in this system is explained below:

[1208] 1. User Login and Authentication

[1209] The user enters their ID and password on the terminal and presses the login button.

[1210] The terminal transmits the entered authentication information to the server.

[1211] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[1212] 2. User Input and Submission

[1213] The user enters a message in the chat box and presses the send button.

[1214] The terminal sends the message and the session token to the server.

[1215] 3. Server Response Generation Process

[1216] The server receives the sent message and session token, verifies the validity of the session, and then requests analysis from the generating AI.

[1217] The generation AI analyzes the message content and generates an appropriate response, which is returned to the server.

[1218] 4. Returning and Displaying Responses

[1219] The server sends the response received from the generation AI to the terminal.

[1220] The terminal displays the response to the user in a chat box.

[1221] Specific examples

[1222] User Login

[1223] The user enters the ID "user123" and password "password123" and presses the login button.

[1224] The terminal sends this information to the server.

[1225] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[1226] The device stores the session token and uses it for subsequent requests.

[1227] Start a conversation

[1228] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1229] The terminal sends the input content and the session token to the server.

[1230] The server asks the generation AI to analyze the message, and the generation AI generates a response saying, "Please tell us specifically which part is not working."

[1231] The server sends the generated response to the terminal, which displays the response in a chat box.

[1232] Continuing the dialogue

[1233] The user enters "I never meet work deadlines" and presses the send button again.

[1234] The terminal retransmits the message and the session token.

[1235] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[1236] The server sends the response to the terminal, which displays the response.

[1237] In this way, a 1-on-1 system using generative AI can provide an environment in which users can freely interact, overcoming time and cost constraints. In addition, the analytical capabilities of generative AI can provide appropriate and useful feedback to users.

[1238] The processing flow will be explained below.

[1239] Step 1:

[1240] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[1241] Step 2:

[1242] The terminal sends an authentication request including the user ID and password to the server.

[1243] Step 3:

[1244] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[1245] Step 4:

[1246] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[1247] Step 5:

[1248] The terminal stores the session token received from the server and starts the user session.

[1249] Step 6:

[1250] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1251] Step 7:

[1252] The terminal sends a request to the server containing the entered message and the stored session token.

[1253] Step 8:

[1254] The server extracts the message and session token from the received request and verifies the validity of the session.

[1255] Step 9:

[1256] The server sends the message to the generation AI, asking it to analyze it and generate a response.

[1257] Step 10:

[1258] The generative AI analyzes the received message, "Work hasn't been going well lately," and generates an appropriate response: "Tell me specifically what's not going well."

[1259] Step 11:

[1260] The generation AI sends the generated response back to the server.

[1261] Step 12:

[1262] The server sends the response received from the generation AI to the terminal.

[1263] Step 13:

[1264] The terminal receives the response from the server and displays it in the chat box.

[1265] Step 14:

[1266] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[1267] Step 15:

[1268] The device sends a request to the server again, including the new message and the session token.

[1269] Step 16:

[1270] The server receives the new request and revalidates the message and session token.

[1271] Step 17:

[1272] The server generates a new message and asks the AI ​​to analyze it again, sending a message saying, "I always miss work deadlines."

[1273] Step 18:

[1274] The generative AI analyzes the message "I never meet work deadlines" and generates the response "Have you ever thought about why that is?"

[1275] Step 19:

[1276] The generating AI sends the new response back to the server.

[1277] Step 20:

[1278] The server sends the new response received from the generating AI to the terminal.

[1279] Step 21:

[1280] The terminal receives the new response from the server and displays it in the chat box.

[1281] Example 1

[1282] 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."

[1283] Conventional one-on-one dialogue services have issues such as insufficient security for user authentication and natural dialogue, and difficulty in optimizing responses based on the user's past dialogue records. There is also a need for improvements in session management and response generation speed. The present invention aims to solve these issues and provide a higher quality and safer one-on-one dialogue service.

[1284] 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.

[1285] In this invention, the server includes a means for performing user authentication and issuing a unique session token, a means for generating a response based on the user's input using a generation artificial intelligence, and a means for verifying the validity of the session and returning the generated response to the user. This enables secure and efficient user authentication and session management, and realizes the generation of natural and accurate responses and their prompt return.

[1286] "User authentication" is the process of verifying the authenticity of a user when they access a system.

[1287] A "session token" is a unique identifier issued to a user who has been successfully authenticated, and ensures the continuation of the session.

[1288] "Generative AI" is an algorithm that uses machine learning to generate human-like text and enable dialogue with users.

[1289] A "central processing unit" is the main component for processing input data from a user and generating a response.

[1290] A "user interface" is an interface that includes a visual display and an operation panel that allows a user to interact with a system.

[1291] "Session management" is the procedure for tracking a user's session and maintaining session validity.

[1292] The present invention relates to a system that provides one-on-one dialogue services using a generative AI. This system accepts input from a user, and the generative AI generates a response via a central processing unit, which is then returned to the user. This allows the user to enjoy a natural dialogue experience.

[1293] System configuration

[1294] The system mainly consists of the following components:

[1295] 1. Terminal

[1296] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1297] Includes a chat box and voice input functionality for accepting input.

[1298] 2. Server (Central Processing Unit)

[1299] A centralized system receives user input data and calls a generative AI to generate a response.

[1300] It performs user authentication, session management, and saves conversation data.

[1301] 3. Generative Artificial Intelligence

[1302] It is a program or algorithm (e.g., a deep learning model) that is built into the server and analyzes user input and generates an appropriate response.

[1303] Program processing

[1304] 1. User Login and Authentication

[1305] The user enters their ID and password on the terminal and presses the login button. For example, they enter the ID "user123" and the password "password123."

[1306] The device sends this authentication information to the server as an HTTP POST request.

[1307] The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it issues a unique session token (e.g., "sess-token-abc123") and returns it to the terminal.

[1308] The device stores the session token in local storage and uses it for subsequent requests.

[1309] 2. User Input and Submission

[1310] The user types a message in the chat box and presses the send button. For example, the user might type, "Work hasn't been going well lately."

[1311] The device sends the message and the session token to the server as an HTTP POST request.

[1312] 3. Server Response Generation Process

[1313] The server receives the message and the session token and verifies the validity of the session.

[1314] If the session is valid, the message content is sent to the generating AI and requested for analysis.

[1315] The generative AI analyzes the message content and generates an appropriate response, such as "Please tell me specifically what is not working."

[1316] The generation AI sends the generated response back to the server.

[1317] 4. Returning and Displaying Responses

[1318] The server sends the generated response to the terminal.

[1319] The terminal displays a response to the user in a chat box, for example, "Please tell me specifically what is not working."

[1320] 5. Continuing the dialogue

[1321] The user again enters the message and sends it. For example, the user enters "I always miss my work deadlines."

[1322] The terminal retransmits the message and the session token.

[1323] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[1324] The server sends the response to the terminal, which displays the response.

[1325] Specific examples

[1326] Prompt Sentence Examples

[1327] 1. Example of a user login and authentication prompt:

[1328] The user enters the ID "user123" and password "password123" and presses the login button. The server receives this and issues a session token.

[1329] 2. Example of a prompt for user input and submission:

[1330] The user types "Work hasn't been going well lately" and presses the send button. The server receives this message and asks the generation AI to analyze it.

[1331] 3. Examples of prompts to continue the dialogue:

[1332] The user enters "I always miss work deadlines" and presses the submit button again. The AI ​​should generate an appropriate response.

[1333] In this way, the system provides users with an environment in which they can freely interact, overcoming time and cost constraints.The analytical capabilities of the generative AI model provide appropriate and useful feedback to users.

[1334] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] The user enters their ID and password and clicks the login button.

[1337] Input: User ID (e.g., "user123"), Password (e.g., "password123")

[1338] Operation: The user enters their ID and password into the login form and presses the login button.

[1339] Output: The device gets the login information.

[1340] Step 2:

[1341] The terminal sends the entered authentication information (ID and password) to the server.

[1342] Input: User ID, Password

[1343] How it works: The device sends an HTTP POST request to the server's authentication endpoint.

[1344] Output: The server receives the authentication information.

[1345] Step 3:

[1346] The server compares the received authentication information with a database and authenticates the user.

[1347] Input: Authentication information (ID, password)

[1348] How it works: The server performs a database query and processes the authentication information.

[1349] Output: Authentication result (success or failure), session token (if successful)

[1350] Step 4:

[1351] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[1352] Input: Authentication success information

[1353] How it works: The server generates a session token and sends it back to the device as an HTTP response.

[1354] Output: Session token (e.g. "sess-token-abc123")

[1355] Step 5:

[1356] The device stores the session token and uses it for subsequent requests.

[1357] Input: Session token

[1358] How it works: The device saves the session token in local storage.

[1359] Output: The saved session token

[1360] Step 6:

[1361] The user enters a message in the chat box and presses the send button.

[1362] Input: User message (e.g. "Work hasn't been going well lately")

[1363] Action: The user types a message in the chat box and presses the send button.

[1364] Output: The terminal gets the message.

[1365] Step 7:

[1366] The terminal sends the message and the session token to the server.

[1367] Input: message, session token

[1368] How it works: The device sends an HTTP POST request to the server containing a message and a session token.

[1369] Output: The server receives the message and the session token.

[1370] Step 8:

[1371] The server receives the message and the session token and verifies the validity of the session.

[1372] Input: message, session token

[1373] How it works: The server checks the session token against its database and verifies the session expiration time.

[1374] Output: Session validation result

[1375] Step 9:

[1376] If the session is valid, the server sends the message content to the generating AI and requests analysis.

[1377] Input: message, session token

[1378] How it works: The server sends a message to the spawned AI's API.

[1379] Output: Request for response analysis by generative AI

[1380] Step 10:

[1381] The generation AI analyzes the message content and generates an appropriate response.

[1382] Input: User message

[1383] How it works: Generative AI uses deep learning models to generate text.

[1384] Output: The generated response (e.g., "What exactly is going wrong?")

[1385] Step 11:

[1386] The generation AI sends the generated response back to the server.

[1387] Input: The generated response

[1388] Behavior: The generation AI sends the generated response back to the server as an HTTP response.

[1389] Output: The server receives the response.

[1390] Step 12:

[1391] The server sends the generated response to the terminal.

[1392] Input: The generated response

[1393] How it works: The server sends the response to the device as an HTTP response.

[1394] Output: The terminal receives the response.

[1395] Step 13:

[1396] The terminal displays the response to the user in a chat box.

[1397] Input: The generated response

[1398] BEHAVIOR: The device updates the HTML in the chat box to show the response.

[1399] Output: The response is made visible to the user.

[1400] (Application example 1)

[1401] 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."

[1402] Current electronic payment services face the challenge of providing fast and accurate support when users check their credit card statements or troubleshoot issues. In addition, user authentication and session management are complex, resulting in a poor user experience. To address these challenges, an effective one-on-one dialogue system using generative AI is needed.

[1403] 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.

[1404] In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for a generation AI to generate a response based on the user input in the server, means for returning the generated response to the user, means for performing login and authentication, means for issuing a session token and confirming session validity, and means for analyzing chat messages and generating responses using a generation AI model, thereby enabling users to receive prompt and accurate support.

[1405] "Means for accepting input from the user" refers to an interface that allows the user to input information, such as a chat box or a voice input function.

[1406] The "means for transmitting user input to a server" is a mechanism for transmitting input information to a server via a network.

[1407] "Means for generative AI to generate responses" refers to a system that uses artificial intelligence to analyze input information and generate appropriate responses.

[1408] "Means for returning the generated response to the user" refers to a communication means for returning the response generated by the generation AI back to the user's terminal.

[1409] "Means for login and authentication" refers to the mechanism by which a user enters an ID and password to access the system and authenticates them.

[1410] The "means for issuing a session token and verifying the validity of the session" is a function for issuing a unique session token to an authenticated user and verifying the validity of the token.

[1411] "Means for analyzing chat messages and generating responses using a generative AI model" refers to a system that uses a generative AI model to analyze users' chat messages and automatically generate appropriate responses in response to them.

[1412] The "means for storing and analyzing past conversation data" is a mechanism for storing a history of past conversations with a user and analyzing it to optimize future responses.

[1413] "Means for keeping a history of chat messages and maintaining the context of a continuous conversation" refers to a function that saves the content of past conversations with a user and continues the dialogue while understanding the context of the current conversation.

[1414] The "means for implementing user authentication and session management" is a mechanism for authenticating a user and appropriately managing the user's session.

[1415] "Means for verifying authentication information against a database" refers to a procedure for comparing the authentication information entered by a user with an existing database to determine whether it matches.

[1416] This invention relates to a system that uses generative AI to provide one-on-one user support for electronic payment services. The system uses a smartphone to streamline the process of users checking their credit card statements and receiving troubleshooting support.

[1417] System Configuration

[1418] The system of the present invention consists of the following main components:

[1419] 1. Terminal

[1420] A device such as a smartphone that is operated by a user.

[1421] Accepts input from users through a chat box or voice input function.

[1422] 2. Server

[1423] It is a centralized system that receives user input data and invokes generative AI models to generate responses.

[1424] It performs functions such as login and authentication, session management, and saving conversation history.

[1425] 3. Generative AI Models

[1426] It is a program or algorithm built into the server that analyzes the user's message and generates an appropriate response.

[1427] Program processing overview

[1428] 1. The user logs in and authenticates on their smartphone

[1429] The user enters their ID and password and presses the login button.

[1430] The terminal sends the input information to the server, which checks the authentication information against a database.

[1431] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[1432] 2. The user types something into the chat box

[1433] The user enters the question or inquiry into the chat box and presses the send button.

[1434] The terminal sends the input message together with the session token to the server.

[1435] 3. The server requests the AI ​​to generate a response.

[1436] The server checks the received message and session token and requests the generating AI model to analyze it.

[1437] The generative AI model analyzes the message content and generates an appropriate response.

[1438] The generated response is sent back to the server.

[1439] 4. View the generated response

[1440] The server sends the response from the generated AI to the terminal, which displays it in the chat box.

[1441] Hardware and software used

[1442] Hardware:

[1443] Smartphone: Used for displaying user input and responses.

[1444] Server: A computer that performs centralized data processing.

[1445] software:

[1446] Flask: Used as the server web framework.

[1447] transformers library: Used to process generative AI models.

[1448] Examples of concrete examples and prompts

[1449] User login example

[1450] User Input:

[1451] Enter "User ID: user123, Password: password123" and press the login button.

[1452] Expected response:

[1453] "Session token: sess-token-abc123" is returned.

[1454] Conversation starter examples

[1455] User Input:

[1456] I would like to check my credit card statement, where can I find it?

[1457] Expected response:

[1458] To view your usage details, select the "Details" tab from the app menu.

[1459] In this way, the system of the present invention is designed to ensure users receive prompt and appropriate support for electronic payment services, and the use of generative AI models can provide highly accurate responses and improve the user experience.

[1460] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1461] Step 1:

[1462] The user displays the login screen on their smartphone, enters their ID and password, and presses the login button. The device sends the entered authentication information (user ID and password) to the server. The server compares the received user ID and password with the information in its database, and if they match, generates a unique session token and sends it back to the device. The device stores this session token.

[1463] Input: User ID, Password

[1464] Output: Session token

[1465] Step 2:

[1466] The user enters a question or consultation (message) into the chat box and presses the send button. The device sends the entered message and session token to the server. The server then verifies the validity of the received message and session token.

[1467] Input: message, session token

[1468] Output: Message confirmation, session token validity check

[1469] Step 3:

[1470] After the message and session token are validated, the server sends the message to the generative AI model, which analyzes the message content and generates an appropriate response. This analysis includes tokenizing, generating, and decoding the text. The generated response is then sent back to the server.

[1471] Input: Message

[1472] Output: The generated response

[1473] Step 4:

[1474] The server receives the response from the generative AI model and sends it to the device, which then displays the received response to the user in a chat box.

[1475] Input: Generated response

[1476] Output: The response to display to the user

[1477] Step 5:

[1478] If the conversation continues, the user again enters a message in the chat box and presses the send button. The device again sends the message and session token to the server. This process is repeated as long as the user continues to ask questions or ask questions.

[1479] Input: New message, session token

[1480] Output: A new response from the generative AI model

[1481] These steps allow users to receive prompt and accurate assistance. By leveraging generative AI models and session management capabilities, the system is able to support continuous conversations while preserving context.

[1482] 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.

[1483] This invention relates to a system that combines a one-on-one service using generative AI with an emotion engine that recognizes the user's emotions. This system can improve the quality of interactions by understanding the user's emotional state and providing more appropriate and personalized responses.

[1484] composition

[1485] The system of the present invention mainly comprises the following components:

[1486] 1. Terminal

[1487] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1488] Includes a chat box and voice input functionality for accepting input.

[1489] 2. Server

[1490] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[1491] It performs user authentication, session management, and saves conversation data.

[1492] 3. Generation AI

[1493] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[1494] 4. Emotion Engine

[1495] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[1496] Program processing

[1497] The program processing in this system is explained below:

[1498] 1. User Login and Authentication

[1499] The user enters their ID and password on the terminal and presses the login button.

[1500] The terminal transmits the entered authentication information to the server.

[1501] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[1502] 2. User Input and Submission

[1503] The user enters a message in the chat box and presses the send button.

[1504] The terminal sends the message and the session token to the server.

[1505] 3. Emotion Recognition and Response Generation Process on the Server

[1506] The server extracts the received message and session token and verifies the validity of the session.

[1507] Next, a message is sent to the emotion engine to analyze the user's emotions.

[1508] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[1509] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[1510] 4. Generative AI response generation and optimization

[1511] The generative AI generates a response based on the emotion recognition results and user input. For example, if sadness is detected, an encouraging message is generated.

[1512] Responses are optimized by taking into account previous conversation data and emotional patterns.

[1513] 5. Returning and Displaying Responses

[1514] The server sends the generated response to the terminal.

[1515] The terminal displays the response to the user in a chat box.

[1516] Specific examples

[1517] User Login

[1518] The user enters the ID "user123" and password "password123" and presses the login button.

[1519] The terminal sends this information to the server.

[1520] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[1521] The device stores the session token and uses it for subsequent requests.

[1522] Conversation initiation and emotion recognition

[1523] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1524] The terminal sends the input content and the session token to the server.

[1525] The server sends a message to the emotion engine, requesting emotion analysis.

[1526] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[1527] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[1528] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[1529] The server sends the generated response to the terminal, which displays the response in a chat box.

[1530] Continuing the dialogue and using emotions

[1531] The user enters "I never meet work deadlines" and presses the send button again.

[1532] The terminal sends the new message and the session token to the server.

[1533] The server sends the message to the emotion engine, which analyzes the emotion again.

[1534] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[1535] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[1536] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[1537] The server sends the generated response to the terminal, which displays the response.

[1538] In this way, the present invention, combined with an emotion recognition engine, can understand the user's emotional state and provide more personalized responses, improving the quality of the interaction and the usefulness of the feedback the user receives.

[1539] The processing flow will be explained below.

[1540] Step 1:

[1541] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[1542] Step 2:

[1543] The terminal sends an authentication request including the user ID and password to the server.

[1544] Step 3:

[1545] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[1546] Step 4:

[1547] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[1548] Step 5:

[1549] The terminal stores the session token received from the server and starts the user session.

[1550] Step 6:

[1551] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1552] Step 7:

[1553] The terminal sends a request to the server containing the entered message and the stored session token.

[1554] Step 8:

[1555] The server extracts the message and session token from the received request and verifies the validity of the session.

[1556] Step 9:

[1557] The server sends the message to the emotion engine and requests emotion analysis.

[1558] Step 10:

[1559] The emotion engine recognizes the user's emotions from the message "Work hasn't been going well lately" and analyzes the user's sadness as a result.

[1560] Step 11:

[1561] The emotion engine sends the recognized emotion results back to the server.

[1562] Step 12:

[1563] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[1564] Step 13:

[1565] Based on the emotion recognition results and user input, the generative AI generates a response: "Please tell me specifically which part is not working."

[1566] Step 14:

[1567] The generation AI sends the generated response back to the server.

[1568] Step 15:

[1569] The server sends the response received from the generation AI to the terminal.

[1570] Step 16:

[1571] The terminal receives the response from the server and displays it in the chat box.

[1572] Step 17:

[1573] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[1574] Step 18:

[1575] The device sends a request to the server again, including the new message and the session token.

[1576] Step 19:

[1577] The server receives the new request and revalidates the message and session token.

[1578] Step 20:

[1579] The server sends the new message to the emotion engine and requests emotion analysis again.

[1580] Step 21:

[1581] The emotion engine analyzes the user's feelings of stress from the message "I never meet work deadlines."

[1582] Step 22:

[1583] The emotion engine sends the recognized emotion results back to the server.

[1584] Step 23:

[1585] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[1586] Step 24:

[1587] The generative AI generates the response "Have you ever thought about the cause of that?" based on the emotion recognition results and user input.

[1588] Step 25:

[1589] The generation AI sends the generated response back to the server.

[1590] Step 26:

[1591] The server sends the response received from the generation AI to the terminal.

[1592] Step 27:

[1593] The terminal receives the response from the server and displays it in the chat box.

[1594] Example 2

[1595] 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."

[1596] Conventional dialogue systems have difficulty accurately recognizing a user's emotions and generating personalized responses based on them, which leads to problems such as a decrease in the quality of dialogue with the user and an inability to provide the appropriate feedback the user desires.

[1597] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for an emotion engine in the server to recognize an emotion based on the user input, means for a generation AI to generate a response based on the recognized emotion, and means for returning the generated response to the user. This makes it possible to accurately recognize the user's emotion and provide a personalized response.

[1598] "Means for accepting input from the user" refers to the functionality for accepting text or voice input on the device used by the user (smartphone, PC, tablet, etc.).

[1599] The "means for transmitting the user's input to the server" refers to a communication means for transmitting the data input by the user to the server via the Internet.

[1600] "Means for the emotion engine in the server to recognize emotions based on user input" refers to algorithms and programs for analyzing user input data and identifying the user's emotional state from that data.

[1601] "Means for the generative AI to generate a response based on the recognized emotion" refers to generative AI algorithms and programs for creating appropriate conversational responses based on the user's emotion recognized by the emotion engine.

[1602] "Means for returning the generated response to the user" refers to communication means and display means for sending the response message generated by the generation AI to the user's device and displaying it.

[1603] The means for performing "user" authentication and session management refers to the algorithms and programs for verifying the user's ID and password when they log in and for managing the user's session state.

[1604] "Means for recording session-managed data" refers to a storage device such as a database for temporarily or long-term recording and storage of user authentication information and session-related data.

[1605] This invention is a system that combines generative AI and an emotion engine to recognize user emotions in one-on-one services and provide more appropriate and personalized responses, improving the quality of interactions and increasing the usefulness of the feedback users seek.

[1606] System configuration

[1607] The system of the present invention mainly consists of the following components:

[1608] 1. Terminal

[1609] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1610] Includes a chat box and voice input functionality for accepting input.

[1611] Web interfaces often use HTML / JavaScript.

[1612] 2. Server

[1613] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[1614] It performs user authentication, session management, and saves conversation data.

[1615] It is often implemented using Node.js or Python.

[1616] 3. Generation AI

[1617] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[1618] For example, you can use OpenAI's API.

[1619] 4. Emotion Engine

[1620] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[1621] For example, you can use Microsoft Azure's emotion recognition API.

[1622] Program processing

[1623] In this system, a series of processes are carried out: user input is accepted, the emotion engine analyzes the emotion, and then the generative AI generates a response based on that.

[1624] 1. User Login and Authentication

[1625] The user enters their ID and password on the terminal and presses the login button.

[1626] The terminal transmits the entered authentication information to the server.

[1627] The server compares the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[1628] If authentication is successful, a unique session token is issued and returned to the terminal.

[1629] 2. User Input and Submission

[1630] The user enters a message in the chat box and presses the send button.

[1631] The terminal sends the message and the session token to the server.

[1632] 3. Emotion Recognition and Response Generation Process on the Server

[1633] The server sends a message to the emotion engine, requesting it to analyze the user's emotions.

[1634] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[1635] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[1636] 4. Generative AI response generation and optimization

[1637] The generative AI generates a response based on emotion recognition results and user input.

[1638] For example, generate a response like "Tell me specifically what's not working."

[1639] Responses are optimized by taking into account previous conversation data and emotional patterns.

[1640] 5. Returning and Displaying Responses

[1641] The server sends the generated response to the terminal.

[1642] The terminal displays the response to the user in a chat box.

[1643] Specific examples

[1644] User Login

[1645] The user enters the ID "user123" and password "password123" and presses the login button.

[1646] The terminal sends this information to the server.

[1647] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[1648] The device stores the session token and uses it for subsequent requests.

[1649] Conversation initiation and emotion recognition

[1650] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1651] The terminal sends the input content and the session token to the server.

[1652] The server sends a message to the emotion engine, requesting emotion analysis.

[1653] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[1654] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[1655] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[1656] The server sends the generated response to the terminal, which displays the response in a chat box.

[1657] Continuing the dialogue and using emotions

[1658] The user enters "I never meet work deadlines" and presses the send button again.

[1659] The terminal sends the new message and the session token to the server.

[1660] The server sends the message to the emotion engine, which analyzes the emotion again.

[1661] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[1662] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[1663] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[1664] The server sends the generated response to the terminal, which displays the response.

[1665] As a result, by combining an emotion recognition engine, the present invention can accurately grasp the user's emotional state and provide more personalized responses, thereby improving the quality of the interaction and increasing the usefulness of the feedback the user receives.

[1666] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1667] Step 1: Login and Authentication

[1668] Input: The user enters their ID and password on the terminal and presses the login button.

[1669] Operation: The terminal sends the entered authentication information to the server, which then collates the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[1670] Output: If authentication is successful, a unique session token is generated and sent back to the device, which stores it.

[1671] Step 2: User Input and Submission

[1672] Input: The user types a message in the chat box and presses the send button.

[1673] Operation: The device sends the input and the session token to the server.

[1674] Output: The terminal sends a data packet containing the message and the session token, which is received by the server.

[1675] Step 3: Message Reception and Session Verification

[1676] Input: The server receives the message and the session token.

[1677] What happens: The server validates the session token to see if it is a valid session.

[1678] Output: If the session is valid, prepare to send the message to the emotion engine. If the session is invalid, generate an error message.

[1679] Step 4: Performing Emotion Recognition

[1680] Input: The server sends a valid session message to the emotion engine.

[1681] How it works: An emotion engine (e.g., Microsoft Azure's Emotion Recognition API) analyzes the message content and recognizes the user's emotion. Analysis is performed using natural language processing algorithms.

[1682] Output: The emotion engine returns the analysis results, including an emotion label (e.g., "sad") and an emotion score, to the server.

[1683] Step 5: Processing the emotion recognition results

[1684] Input: The server receives the emotion recognition results sent from the emotion engine.

[1685] Operation: The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response. The server provides the user's input message and the emotion result to the generation AI as a prompt.

[1686] Output: The generation AI generates an appropriate response based on the prompt and sends it back to the server.

[1687] Step 6: Optimize the response

[1688] Input: The server receives the response sent by the generating AI.

[1689] How it works: The server takes into account past conversation data and emotional patterns to optimize responses.

[1690] Output: Generates an optimized response message.

[1691] Step 7: Return and display the response

[1692] Input: The server holds the optimized response message.

[1693] Operation: The server generates a response and sends it to the terminal.

[1694] Output: The terminal displays the received response message on the user interface and provides it to the user.

[1695] (Application example 2)

[1696] 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."

[1697] Existing one-on-one services have difficulty fully understanding users' emotions and intentions, resulting in mechanical responses and a lack of personalization. Furthermore, when users make online purchases, the suggestions and support they receive are often inappropriate. This leads to issues such as low user satisfaction and a lack of promotion of purchasing behavior.

[1698] 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.

[1699] In this invention, the server includes an emotion engine that recognizes the user's emotional state, a means for the emotion engine to analyze the user's input content, a means for a generation AI to personalize a response based on the analysis results of the emotion engine, and a means for returning the generated response to the user, thereby enabling personalized suggestions and responses according to the user's emotional state.

[1700] An "emotion engine" is a program or algorithm that analyzes user input and recognizes the user's emotional state.

[1701] "Generative AI" refers to programs and algorithms that generate responses based on user input and the analysis results of an emotion engine.

[1702] The "server" is a centralized system that receives user input, invokes the emotion engine and generative AI to generate responses, and manages the necessary data and generated responses.

[1703] "Personalization" means providing the most appropriate responses and suggestions based on each individual user's specific emotional state and past conversational data.

[1704] "User authentication" is the process of verifying a user's identity using a user ID and password, etc.

[1705] "Session management" is a method for managing a series of communications while a user is accessing a system and maintaining consistency.

[1706] This invention relates to a 1-on-1 service system that combines an emotion engine that recognizes users' emotions with generative AI. The system aims to improve the shopping experience in virtual stores by understanding the user's emotional state and providing more appropriate and personalized responses.

[1707] System Configuration

[1708] This system mainly consists of the following components:

[1709] 1. Terminal

[1710] The smart device used by the user (smart glasses, smartphone, tablet, etc.).

[1711] Includes an interface for accepting input (chat box and voice input function).

[1712] 2. Server

[1713] It is a centralized system that receives user input and calls on the emotion engine and generative AI to generate a response.

[1714] User authentication, session management, and conversation data storage (e.g., AWS, Google Cloud)

[1715] 3. Generation AI

[1716] It uses large-scale language models (e.g., GPT-4) to analyze user input and generate appropriate responses.

[1717] Generate personalized suggestions and feedback.

[1718] 4. Emotion Engine

[1719] Recognizes emotions from user input and provides the analysis results to generative AI (e.g., Affectiva, Microsoft Azure Emotion API).

[1720] Program processing overview

[1721] 1. User Login and Authentication

[1722] Users log in to the virtual store through smart glasses or smartphones.

[1723] The authentication information is sent from the device to the server, and identity verification is performed.

[1724] 2. Collecting Emotional Data

[1725] While the user is browsing products, facial and voice data is collected from the smart glasses' camera and microphone.

[1726] The collected data is sent to a server and analyzed by an emotion engine.

[1727] 3. Emotion Recognition and Product Recommendations

[1728] The emotion engine recognizes the user's emotional state and provides the results to the generative AI.

[1729] The generative AI generates appropriate product suggestions and feedback based on emotion recognition results and past conversation data.

[1730] 4. Feedback and Support

[1731] The generated suggestions and feedback are sent back to the terminal via the server and displayed to the user in real time.

[1732] The system provides support as needed according to changes in the user's emotions.

[1733] Specific examples

[1734] Prompt Sentence Examples

[1735] If a user inputs "I'm looking for a new smartphone," and the emotion recognition result is "The user is a little confused," the input prompt will be as follows:

[1736] Emotion recognition result: "The user seems a little confused."

[1737] Previous conversation data: "User is looking for a new smartphone."

[1738] Input prompt: "The user is slightly confused. Use this information to generate a purchasing recommendation for a new smartphone."

[1739] Based on these prompts, the generative AI will provide the user with appropriate product suggestions, taking into account the user's previous inquiries and current emotional state, allowing for a more personalized response.

[1740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1741] Step 1: User Login and Authentication

[1742] Users log in to the virtual store through smart glasses or a smartphone. On the terminal, the user enters their ID and password and presses the login button. The entered authentication information is sent to the server, which then compares it with a database to authenticate the user. If authentication is successful, the server generates a unique session token and sends it back to the terminal.

[1743] Input: User ID, Password

[1744] Output: Session token (e.g. "sess-token-abc123")

[1745] Step 2: Collecting emotion data

[1746] When a user starts browsing the virtual store, the camera and microphone in the smart glasses record the user's facial and voice data. This data is transmitted in real time from the device to the server. The server then sends the received facial and voice data to the emotion engine for emotion analysis.

[1747] Input: User's facial expression data, voice data

[1748] Output: Emotion recognition result (e.g. "The user is slightly confused.")

[1749] Step 3: Emotion recognition and product recommendations

[1750] The server sends the emotion recognition results received from the emotion engine to the generation AI, which then requests the generation AI to generate appropriate product suggestions based on the user's past purchasing history and current interests. The generation AI then combines the emotion recognition results with past data to suggest optimal products and promotions.

[1751] Input: Emotion recognition results, past conversation data

[1752] Output: Product suggestion message (e.g. "The user is a little confused. Use this information to generate a suggestion for a new smartphone.")

[1753] Step 4: Feedback and support

[1754] The server receives the generated suggestion message from the AI ​​and sends it back to the device, which displays it to the user in real time. If the user has further questions or needs assistance based on the suggestion, they can send new input from the device to the server and the process will be repeated.

[1755] Input: Product suggestion message

[1756] Output: A message to display to the user (e.g., "Here's the new smartphone model that's best for you.")

[1757] Through the above process, the system is able to provide personalized suggestions and responses in real time that are tailored to the user's emotional state.

[1758] 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.

[1759] 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.

[1760] 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.

[1761] [Fourth embodiment]

[1762] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1763] 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.

[1764] 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).

[1765] 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.

[1766] 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.

[1767] 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).

[1768] 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.

[1769] 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.

[1770] 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.

[1771] 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.

[1772] 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.

[1773] 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.

[1774] 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."

[1775] The present invention relates to a system for providing a one-on-one service using a generation AI. This system is realized by the following configuration and operation.

[1776] composition

[1777] The system of the present invention mainly comprises the following components:

[1778] 1. Terminal

[1779] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1780] Includes a chat box and voice input functionality for accepting input.

[1781] 2. Server

[1782] It is a centralized system that receives input data and invokes generative AI to generate a response.

[1783] It performs user authentication, session management, and saves conversation data.

[1784] 3. Generation AI

[1785] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[1786] Program processing

[1787] The program processing in this system is explained below:

[1788] 1. User Login and Authentication

[1789] The user enters their ID and password on the terminal and presses the login button.

[1790] The terminal transmits the entered authentication information to the server.

[1791] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[1792] 2. User Input and Submission

[1793] The user enters a message in the chat box and presses the send button.

[1794] The terminal sends the message and the session token to the server.

[1795] 3. Server Response Generation Process

[1796] The server receives the sent message and session token, verifies the validity of the session, and then requests analysis from the generating AI.

[1797] The generation AI analyzes the message content and generates an appropriate response, which is returned to the server.

[1798] 4. Returning and Displaying Responses

[1799] The server sends the response received from the generation AI to the terminal.

[1800] The terminal displays the response to the user in a chat box.

[1801] Specific examples

[1802] User Login

[1803] The user enters the ID "user123" and password "password123" and presses the login button.

[1804] The terminal sends this information to the server.

[1805] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[1806] The device stores the session token and uses it for subsequent requests.

[1807] Start a conversation

[1808] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1809] The terminal sends the input content and the session token to the server.

[1810] The server asks the generation AI to analyze the message, and the generation AI generates a response saying, "Please tell us specifically which part is not working."

[1811] The server sends the generated response to the terminal, which displays the response in a chat box.

[1812] Continuing the dialogue

[1813] The user enters "I never meet work deadlines" and presses the send button again.

[1814] The terminal retransmits the message and the session token.

[1815] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[1816] The server sends the response to the terminal, which displays the response.

[1817] In this way, a 1-on-1 system using generative AI can provide an environment in which users can freely interact, overcoming time and cost constraints. In addition, the analytical capabilities of generative AI can provide appropriate and useful feedback to users.

[1818] The processing flow will be explained below.

[1819] Step 1:

[1820] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[1821] Step 2:

[1822] The terminal sends an authentication request including the user ID and password to the server.

[1823] Step 3:

[1824] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[1825] Step 4:

[1826] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[1827] Step 5:

[1828] The terminal stores the session token received from the server and starts the user session.

[1829] Step 6:

[1830] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[1831] Step 7:

[1832] The terminal sends a request to the server containing the entered message and the stored session token.

[1833] Step 8:

[1834] The server extracts the message and session token from the received request and verifies the validity of the session.

[1835] Step 9:

[1836] The server sends the message to the generation AI, asking it to analyze it and generate a response.

[1837] Step 10:

[1838] The generative AI analyzes the received message, "Work hasn't been going well lately," and generates an appropriate response: "Tell me specifically what's not going well."

[1839] Step 11:

[1840] The generation AI sends the generated response back to the server.

[1841] Step 12:

[1842] The server sends the response received from the generation AI to the terminal.

[1843] Step 13:

[1844] The terminal receives the response from the server and displays it in the chat box.

[1845] Step 14:

[1846] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[1847] Step 15:

[1848] The device sends a request to the server again, including the new message and the session token.

[1849] Step 16:

[1850] The server receives the new request and revalidates the message and session token.

[1851] Step 17:

[1852] The server generates a new message and asks the AI ​​to analyze it again, sending a message saying, "I always miss work deadlines."

[1853] Step 18:

[1854] The generative AI analyzes the message "I never meet work deadlines" and generates the response "Have you ever thought about why that is?"

[1855] Step 19:

[1856] The generating AI sends the new response back to the server.

[1857] Step 20:

[1858] The server sends the new response received from the generating AI to the terminal.

[1859] Step 21:

[1860] The terminal receives the new response from the server and displays it in the chat box.

[1861] Example 1

[1862] 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."

[1863] Conventional one-on-one dialogue services have issues such as insufficient security for user authentication and natural dialogue, and difficulty in optimizing responses based on the user's past dialogue records. There is also a need for improvements in session management and response generation speed. The present invention aims to solve these issues and provide a higher quality and safer one-on-one dialogue service.

[1864] 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.

[1865] In this invention, the server includes a means for performing user authentication and issuing a unique session token, a means for generating a response based on the user's input using a generation artificial intelligence, and a means for verifying the validity of the session and returning the generated response to the user. This enables secure and efficient user authentication and session management, and realizes the generation of natural and accurate responses and their prompt return.

[1866] "User authentication" is the process of verifying the authenticity of a user when they access a system.

[1867] A "session token" is a unique identifier issued to a user who has been successfully authenticated, and ensures the continuation of the session.

[1868] "Generative AI" is an algorithm that uses machine learning to generate human-like text and enable dialogue with users.

[1869] A "central processing unit" is the main component for processing input data from a user and generating a response.

[1870] A "user interface" is an interface that includes a visual display and an operation panel that allows a user to interact with a system.

[1871] "Session management" is the procedure for tracking a user's session and maintaining session validity.

[1872] The present invention relates to a system that provides one-on-one dialogue services using a generative AI. This system accepts input from a user, and the generative AI generates a response via a central processing unit, which is then returned to the user. This allows the user to enjoy a natural dialogue experience.

[1873] System configuration

[1874] The system mainly consists of the following components:

[1875] 1. Terminal

[1876] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[1877] Includes a chat box and voice input functionality for accepting input.

[1878] 2. Server (Central Processing Unit)

[1879] A centralized system receives user input data and calls a generative AI to generate a response.

[1880] It performs user authentication, session management, and saves conversation data.

[1881] 3. Generative Artificial Intelligence

[1882] It is a program or algorithm (e.g., a deep learning model) that is built into the server and analyzes user input and generates an appropriate response.

[1883] Program processing

[1884] 1. User Login and Authentication

[1885] The user enters their ID and password on the terminal and presses the login button. For example, they enter the ID "user123" and the password "password123."

[1886] The device sends this authentication information to the server as an HTTP POST request.

[1887] The server compares the received authentication information with its database and authenticates the user. If authentication is successful, it issues a unique session token (e.g., "sess-token-abc123") and returns it to the terminal.

[1888] The device stores the session token in local storage and uses it for subsequent requests.

[1889] 2. User Input and Submission

[1890] The user types a message in the chat box and presses the send button. For example, the user might type, "Work hasn't been going well lately."

[1891] The device sends the message and the session token to the server as an HTTP POST request.

[1892] 3. Server Response Generation Process

[1893] The server receives the message and the session token and verifies the validity of the session.

[1894] If the session is valid, the message content is sent to the generating AI and requested for analysis.

[1895] The generative AI analyzes the message content and generates an appropriate response, such as "Please tell me specifically what is not working."

[1896] The generation AI sends the generated response back to the server.

[1897] 4. Returning and Displaying Responses

[1898] The server sends the generated response to the terminal.

[1899] The terminal displays a response to the user in a chat box, for example, "Please tell me specifically what is not working."

[1900] 5. Continuing the dialogue

[1901] The user again enters the message and sends it. For example, the user enters "I always miss my work deadlines."

[1902] The terminal retransmits the message and the session token.

[1903] The server then asks the generation AI to analyze the new message again, and the generation AI generates a response saying, "Have you ever thought about the cause of that?"

[1904] The server sends the response to the terminal, which displays the response.

[1905] Specific examples

[1906] Prompt Sentence Examples

[1907] 1. Example of a user login and authentication prompt:

[1908] The user enters the ID "user123" and password "password123" and presses the login button. The server receives this and issues a session token.

[1909] 2. Example of a prompt for user input and submission:

[1910] The user types "Work hasn't been going well lately" and presses the send button. The server receives this message and asks the generation AI to analyze it.

[1911] 3. Examples of prompts to continue the dialogue:

[1912] The user enters "I always miss work deadlines" and presses the submit button again. The AI ​​should generate an appropriate response.

[1913] In this way, the system provides users with an environment in which they can freely interact, overcoming time and cost constraints.The analytical capabilities of the generative AI model provide appropriate and useful feedback to users.

[1914] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1915] Step 1:

[1916] The user enters their ID and password and clicks the login button.

[1917] Input: User ID (e.g., "user123"), Password (e.g., "password123")

[1918] Operation: The user enters their ID and password into the login form and presses the login button.

[1919] Output: The device gets the login information.

[1920] Step 2:

[1921] The terminal sends the entered authentication information (ID and password) to the server.

[1922] Input: User ID, Password

[1923] How it works: The device sends an HTTP POST request to the server's authentication endpoint.

[1924] Output: The server receives the authentication information.

[1925] Step 3:

[1926] The server compares the received authentication information with a database and authenticates the user.

[1927] Input: Authentication information (ID, password)

[1928] How it works: The server performs a database query and processes the authentication information.

[1929] Output: Authentication result (success or failure), session token (if successful)

[1930] Step 4:

[1931] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[1932] Input: Authentication success information

[1933] How it works: The server generates a session token and sends it back to the device as an HTTP response.

[1934] Output: Session token (e.g. "sess-token-abc123")

[1935] Step 5:

[1936] The device stores the session token and uses it for subsequent requests.

[1937] Input: Session token

[1938] How it works: The device saves the session token in local storage.

[1939] Output: The saved session token

[1940] Step 6:

[1941] The user enters a message in the chat box and presses the send button.

[1942] Input: User message (e.g. "Work hasn't been going well lately")

[1943] Action: The user types a message in the chat box and presses the send button.

[1944] Output: The terminal gets the message.

[1945] Step 7:

[1946] The terminal sends the message and the session token to the server.

[1947] Input: message, session token

[1948] How it works: The device sends an HTTP POST request to the server containing a message and a session token.

[1949] Output: The server receives the message and the session token.

[1950] Step 8:

[1951] The server receives the message and the session token and verifies the validity of the session.

[1952] Input: message, session token

[1953] How it works: The server checks the session token against its database and verifies the session expiration time.

[1954] Output: Session validation result

[1955] Step 9:

[1956] If the session is valid, the server sends the message content to the generating AI and requests analysis.

[1957] Input: message, session token

[1958] How it works: The server sends a message to the spawned AI's API.

[1959] Output: Request for response analysis by generative AI

[1960] Step 10:

[1961] The generation AI analyzes the message content and generates an appropriate response.

[1962] Input: User message

[1963] How it works: Generative AI uses deep learning models to generate text.

[1964] Output: The generated response (e.g., "What exactly is going wrong?")

[1965] Step 11:

[1966] The generation AI sends the generated response back to the server.

[1967] Input: The generated response

[1968] Behavior: The generation AI sends the generated response back to the server as an HTTP response.

[1969] Output: The server receives the response.

[1970] Step 12:

[1971] The server sends the generated response to the terminal.

[1972] Input: The generated response

[1973] How it works: The server sends the response to the device as an HTTP response.

[1974] Output: The terminal receives the response.

[1975] Step 13:

[1976] The terminal displays the response to the user in a chat box.

[1977] Input: The generated response

[1978] BEHAVIOR: The device updates the HTML in the chat box to show the response.

[1979] Output: The response is made visible to the user.

[1980] (Application example 1)

[1981] 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."

[1982] Current electronic payment services face the challenge of providing fast and accurate support when users check their credit card statements or troubleshoot issues. In addition, user authentication and session management are complex, resulting in a poor user experience. To address these challenges, an effective one-on-one dialogue system using generative AI is needed.

[1983] 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.

[1984] In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for a generation AI to generate a response based on the user input in the server, means for returning the generated response to the user, means for performing login and authentication, means for issuing a session token and confirming session validity, and means for analyzing chat messages and generating responses using a generation AI model, thereby enabling users to receive prompt and accurate support.

[1985] "Means for accepting input from the user" refers to an interface that allows the user to input information, such as a chat box or a voice input function.

[1986] The "means for transmitting user input to a server" is a mechanism for transmitting input information to a server via a network.

[1987] "Means for generative AI to generate responses" refers to a system that uses artificial intelligence to analyze input information and generate appropriate responses.

[1988] "Means for returning the generated response to the user" refers to a communication means for returning the response generated by the generation AI back to the user's terminal.

[1989] "Means for login and authentication" refers to the mechanism by which a user enters an ID and password to access the system and authenticates them.

[1990] The "means for issuing a session token and verifying the validity of the session" is a function for issuing a unique session token to an authenticated user and verifying the validity of the token.

[1991] "Means for analyzing chat messages and generating responses using a generative AI model" refers to a system that uses a generative AI model to analyze users' chat messages and automatically generate appropriate responses in response to them.

[1992] The "means for storing and analyzing past conversation data" is a mechanism for storing a history of past conversations with a user and analyzing it to optimize future responses.

[1993] "Means for keeping a history of chat messages and maintaining the context of a continuous conversation" refers to a function that saves the content of past conversations with a user and continues the dialogue while understanding the context of the current conversation.

[1994] The "means for implementing user authentication and session management" is a mechanism for authenticating a user and appropriately managing the user's session.

[1995] "Means for verifying authentication information against a database" refers to a procedure for comparing the authentication information entered by a user with an existing database to determine whether it matches.

[1996] This invention relates to a system that uses generative AI to provide one-on-one user support for electronic payment services. The system uses a smartphone to streamline the process of users checking their credit card statements and receiving troubleshooting support.

[1997] System Configuration

[1998] The system of the present invention consists of the following main components:

[1999] 1. Terminal

[2000] A device such as a smartphone that is operated by a user.

[2001] Accepts input from users through a chat box or voice input function.

[2002] 2. Server

[2003] It is a centralized system that receives user input data and invokes generative AI models to generate responses.

[2004] It performs functions such as login and authentication, session management, and saving conversation history.

[2005] 3. Generative AI Models

[2006] It is a program or algorithm built into the server that analyzes the user's message and generates an appropriate response.

[2007] Program processing overview

[2008] 1. The user logs in and authenticates on their smartphone

[2009] The user enters their ID and password and presses the login button.

[2010] The terminal sends the input information to the server, which checks the authentication information against a database.

[2011] If the authentication is successful, the server issues a unique session token and returns it to the terminal.

[2012] 2. The user types something into the chat box

[2013] The user enters the question or inquiry into the chat box and presses the send button.

[2014] The terminal sends the input message together with the session token to the server.

[2015] 3. The server requests the AI ​​to generate a response.

[2016] The server checks the received message and session token and requests the generating AI model to analyze it.

[2017] The generative AI model analyzes the message content and generates an appropriate response.

[2018] The generated response is sent back to the server.

[2019] 4. View the generated response

[2020] The server sends the response from the generated AI to the terminal, which displays it in the chat box.

[2021] Hardware and software used

[2022] Hardware:

[2023] Smartphone: Used for displaying user input and responses.

[2024] Server: A computer that performs centralized data processing.

[2025] software:

[2026] Flask: Used as the server web framework.

[2027] transformers library: Used to process generative AI models.

[2028] Examples of concrete examples and prompts

[2029] User login example

[2030] User Input:

[2031] Enter "User ID: user123, Password: password123" and press the login button.

[2032] Expected response:

[2033] "Session token: sess-token-abc123" is returned.

[2034] Conversation starter examples

[2035] User Input:

[2036] I would like to check my credit card statement, where can I find it?

[2037] Expected response:

[2038] To view your usage details, select the "Details" tab from the app menu.

[2039] In this way, the system of the present invention is designed to ensure users receive prompt and appropriate support for electronic payment services, and the use of generative AI models can provide highly accurate responses and improve the user experience.

[2040] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2041] Step 1:

[2042] The user displays the login screen on their smartphone, enters their ID and password, and presses the login button. The device sends the entered authentication information (user ID and password) to the server. The server compares the received user ID and password with the information in its database, and if they match, generates a unique session token and sends it back to the device. The device stores this session token.

[2043] Input: User ID, Password

[2044] Output: Session token

[2045] Step 2:

[2046] The user enters a question or consultation (message) into the chat box and presses the send button. The device sends the entered message and session token to the server. The server then verifies the validity of the received message and session token.

[2047] Input: message, session token

[2048] Output: Message confirmation, session token validity check

[2049] Step 3:

[2050] After the message and session token are validated, the server sends the message to the generative AI model, which analyzes the message content and generates an appropriate response. This analysis includes tokenizing, generating, and decoding the text. The generated response is then sent back to the server.

[2051] Input: Message

[2052] Output: The generated response

[2053] Step 4:

[2054] The server receives the response from the generative AI model and sends it to the device, which then displays the received response to the user in a chat box.

[2055] Input: Generated response

[2056] Output: The response to display to the user

[2057] Step 5:

[2058] If the conversation continues, the user again enters a message in the chat box and presses the send button. The device again sends the message and session token to the server. This process is repeated as long as the user continues to ask questions or ask questions.

[2059] Input: New message, session token

[2060] Output: A new response from the generative AI model

[2061] These steps allow users to receive prompt and accurate assistance. By leveraging generative AI models and session management capabilities, the system is able to support continuous conversations while preserving context.

[2062] 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.

[2063] This invention relates to a system that combines a one-on-one service using generative AI with an emotion engine that recognizes the user's emotions. This system can improve the quality of interactions by understanding the user's emotional state and providing more appropriate and personalized responses.

[2064] composition

[2065] The system of the present invention mainly comprises the following components:

[2066] 1. Terminal

[2067] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[2068] Includes a chat box and voice input functionality for accepting input.

[2069] 2. Server

[2070] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[2071] It performs user authentication, session management, and saves conversation data.

[2072] 3. Generation AI

[2073] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[2074] 4. Emotion Engine

[2075] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[2076] Program processing

[2077] The program processing in this system is explained below:

[2078] 1. User Login and Authentication

[2079] The user enters their ID and password on the terminal and presses the login button.

[2080] The terminal transmits the entered authentication information to the server.

[2081] The server compares the received authentication information with a database to authenticate the user. If authentication is successful, it issues a unique session token and returns it to the terminal.

[2082] 2. User Input and Submission

[2083] The user enters a message in the chat box and presses the send button.

[2084] The terminal sends the message and the session token to the server.

[2085] 3. Emotion Recognition and Response Generation Process on the Server

[2086] The server extracts the received message and session token and verifies the validity of the session.

[2087] Next, a message is sent to the emotion engine to analyze the user's emotions.

[2088] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[2089] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[2090] 4. Generative AI response generation and optimization

[2091] The generative AI generates a response based on the emotion recognition results and user input. For example, if sadness is detected, an encouraging message is generated.

[2092] Responses are optimized by taking into account previous conversation data and emotional patterns.

[2093] 5. Returning and Displaying Responses

[2094] The server sends the generated response to the terminal.

[2095] The terminal displays the response to the user in a chat box.

[2096] Specific examples

[2097] User Login

[2098] The user enters the ID "user123" and password "password123" and presses the login button.

[2099] The terminal sends this information to the server.

[2100] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[2101] The device stores the session token and uses it for subsequent requests.

[2102] Conversation initiation and emotion recognition

[2103] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[2104] The terminal sends the input content and the session token to the server.

[2105] The server sends a message to the emotion engine, requesting emotion analysis.

[2106] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[2107] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[2108] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[2109] The server sends the generated response to the terminal, which displays the response in a chat box.

[2110] Continuing the dialogue and using emotions

[2111] The user enters "I never meet work deadlines" and presses the send button again.

[2112] The terminal sends the new message and the session token to the server.

[2113] The server sends the message to the emotion engine, which analyzes the emotion again.

[2114] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[2115] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[2116] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[2117] The server sends the generated response to the terminal, which displays the response.

[2118] In this way, the present invention, combined with an emotion recognition engine, can understand the user's emotional state and provide more personalized responses, improving the quality of the interaction and the usefulness of the feedback the user receives.

[2119] The processing flow will be explained below.

[2120] Step 1:

[2121] The user opens the login screen on the device, enters the ID "user123" and password "password123", and presses the login button.

[2122] Step 2:

[2123] The terminal sends an authentication request including the user ID and password to the server.

[2124] Step 3:

[2125] The server analyzes the received authentication request and accesses the database to verify the ID and password.

[2126] Step 4:

[2127] If the authentication is successful, the server generates a unique session token "sess-token-abc123" and returns it to the terminal.

[2128] Step 5:

[2129] The terminal stores the session token received from the server and starts the user session.

[2130] Step 6:

[2131] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[2132] Step 7:

[2133] The terminal sends a request to the server containing the entered message and the stored session token.

[2134] Step 8:

[2135] The server extracts the message and session token from the received request and verifies the validity of the session.

[2136] Step 9:

[2137] The server sends the message to the emotion engine and requests emotion analysis.

[2138] Step 10:

[2139] The emotion engine recognizes the user's emotions from the message "Work hasn't been going well lately" and analyzes the user's sadness as a result.

[2140] Step 11:

[2141] The emotion engine sends the recognized emotion results back to the server.

[2142] Step 12:

[2143] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[2144] Step 13:

[2145] Based on the emotion recognition results and user input, the generative AI generates a response: "Please tell me specifically which part is not working."

[2146] Step 14:

[2147] The generation AI sends the generated response back to the server.

[2148] Step 15:

[2149] The server sends the response received from the generation AI to the terminal.

[2150] Step 16:

[2151] The terminal receives the response from the server and displays it in the chat box.

[2152] Step 17:

[2153] The user types "I never meet work deadlines" into the chat box and presses the send button again.

[2154] Step 18:

[2155] The device sends a request to the server again, including the new message and the session token.

[2156] Step 19:

[2157] The server receives the new request and revalidates the message and session token.

[2158] Step 20:

[2159] The server sends the new message to the emotion engine and requests emotion analysis again.

[2160] Step 21:

[2161] The emotion engine analyzes the user's feelings of stress from the message "I never meet work deadlines."

[2162] Step 22:

[2163] The emotion engine sends the recognized emotion results back to the server.

[2164] Step 23:

[2165] The server sends the emotion recognition results to the generation AI and asks it to generate an appropriate response based on the user's emotions.

[2166] Step 24:

[2167] The generative AI generates the response "Have you ever thought about the cause of that?" based on the emotion recognition results and user input.

[2168] Step 25:

[2169] The generation AI sends the generated response back to the server.

[2170] Step 26:

[2171] The server sends the response received from the generation AI to the terminal.

[2172] Step 27:

[2173] The terminal receives the response from the server and displays it in the chat box.

[2174] Example 2

[2175] 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."

[2176] Conventional dialogue systems have difficulty accurately recognizing a user's emotions and generating personalized responses based on them, which leads to problems such as a decrease in the quality of dialogue with the user and an inability to provide the appropriate feedback the user desires.

[2177] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for accepting input from a user, means for transmitting the user input to the server, means for an emotion engine in the server to recognize an emotion based on the user input, means for a generation AI to generate a response based on the recognized emotion, and means for returning the generated response to the user. This makes it possible to accurately recognize the user's emotion and provide a personalized response.

[2178] "Means for accepting input from the user" refers to the functionality for accepting text or voice input on the device used by the user (smartphone, PC, tablet, etc.).

[2179] The "means for transmitting the user's input to the server" refers to a communication means for transmitting the data input by the user to the server via the Internet.

[2180] "Means for the emotion engine in the server to recognize emotions based on user input" refers to algorithms and programs for analyzing user input data and identifying the user's emotional state from that data.

[2181] "Means for the generative AI to generate a response based on the recognized emotion" refers to generative AI algorithms and programs for creating appropriate conversational responses based on the user's emotion recognized by the emotion engine.

[2182] "Means for returning the generated response to the user" refers to communication means and display means for sending the response message generated by the generation AI to the user's device and displaying it.

[2183] The means for performing "user" authentication and session management refers to the algorithms and programs for verifying the user's ID and password when they log in and for managing the user's session state.

[2184] "Means for recording session-managed data" refers to a storage device such as a database for temporarily or long-term recording and storage of user authentication information and session-related data.

[2185] This invention is a system that combines generative AI and an emotion engine to recognize user emotions in one-on-one services and provide more appropriate and personalized responses, improving the quality of interactions and increasing the usefulness of the feedback users seek.

[2186] System configuration

[2187] The system of the present invention mainly consists of the following components:

[2188] 1. Terminal

[2189] A device used by a user (smartphone, PC, tablet, etc.) that provides the user interface.

[2190] Includes a chat box and voice input functionality for accepting input.

[2191] Web interfaces often use HTML / JavaScript.

[2192] 2. Server

[2193] It is a centralized system for receiving input data and invoking generative AI and emotion engines to generate responses.

[2194] It performs user authentication, session management, and saves conversation data.

[2195] It is often implemented using Node.js or Python.

[2196] 3. Generation AI

[2197] It is a program or algorithm built into the server that analyzes user input and generates an appropriate response.

[2198] For example, you can use OpenAI's API.

[2199] 4. Emotion Engine

[2200] These are programs and algorithms that recognize emotions from user input and provide the analysis results to the generative AI.

[2201] For example, you can use Microsoft Azure's emotion recognition API.

[2202] Program processing

[2203] In this system, a series of processes are carried out: user input is accepted, the emotion engine analyzes the emotion, and then the generative AI generates a response based on that.

[2204] 1. User Login and Authentication

[2205] The user enters their ID and password on the terminal and presses the login button.

[2206] The terminal transmits the entered authentication information to the server.

[2207] The server compares the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[2208] If authentication is successful, a unique session token is issued and returned to the terminal.

[2209] 2. User Input and Submission

[2210] The user enters a message in the chat box and presses the send button.

[2211] The terminal sends the message and the session token to the server.

[2212] 3. Emotion Recognition and Response Generation Process on the Server

[2213] The server sends a message to the emotion engine, requesting it to analyze the user's emotions.

[2214] The emotion engine recognizes the user's emotion from the message content and returns the result to the server.

[2215] The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response.

[2216] 4. Generative AI response generation and optimization

[2217] The generative AI generates a response based on emotion recognition results and user input.

[2218] For example, generate a response like "Tell me specifically what's not working."

[2219] Responses are optimized by taking into account previous conversation data and emotional patterns.

[2220] 5. Returning and Displaying Responses

[2221] The server sends the generated response to the terminal.

[2222] The terminal displays the response to the user in a chat box.

[2223] Specific examples

[2224] User Login

[2225] The user enters the ID "user123" and password "password123" and presses the login button.

[2226] The terminal sends this information to the server.

[2227] The server performs authentication, issues a session token "sess-token-abc123", and returns it to the terminal.

[2228] The device stores the session token and uses it for subsequent requests.

[2229] Conversation initiation and emotion recognition

[2230] The user types "Work hasn't been going well lately" into the chat box and presses the send button.

[2231] The terminal sends the input content and the session token to the server.

[2232] The server sends a message to the emotion engine, requesting emotion analysis.

[2233] The emotion engine recognizes the user's sad emotion from the message "Work hasn't been going well lately" and sends the result back to the server.

[2234] The server sends the emotion recognition results to the generation AI and asks it to generate a response saying, "Please tell us specifically what is not working."

[2235] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[2236] The server sends the generated response to the terminal, which displays the response in a chat box.

[2237] Continuing the dialogue and using emotions

[2238] The user enters "I never meet work deadlines" and presses the send button again.

[2239] The terminal sends the new message and the session token to the server.

[2240] The server sends the message to the emotion engine, which analyzes the emotion again.

[2241] The emotion engine recognizes that the user is feeling stressed from the message "I never meet work deadlines" and sends the result back to the server.

[2242] The server sends the emotion recognition results to the generation AI and asks it to generate a response such as, "Have you ever thought about the cause of that?"

[2243] The generation AI generates a response based on the emotion recognition results and sends it back to the server.

[2244] The server sends the generated response to the terminal, which displays the response.

[2245] As a result, by combining an emotion recognition engine, the present invention can accurately grasp the user's emotional state and provide more personalized responses, thereby improving the quality of the interaction and increasing the usefulness of the feedback the user receives.

[2246] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2247] Step 1: Login and Authentication

[2248] Input: The user enters their ID and password on the terminal and presses the login button.

[2249] Operation: The terminal sends the entered authentication information to the server, which then collates the received authentication information with a database (e.g., PostgreSQL) and authenticates the user.

[2250] Output: If authentication is successful, a unique session token is generated and sent back to the device, which stores it.

[2251] Step 2: User Input and Submission

[2252] Input: The user types a message in the chat box and presses the send button.

[2253] Operation: The device sends the input and the session token to the server.

[2254] Output: The terminal sends a data packet containing the message and the session token, which is received by the server.

[2255] Step 3: Message Reception and Session Verification

[2256] Input: The server receives the message and the session token.

[2257] What happens: The server validates the session token to see if it is a valid session.

[2258] Output: If the session is valid, prepare to send the message to the emotion engine. If the session is invalid, generate an error message.

[2259] Step 4: Performing Emotion Recognition

[2260] Input: The server sends a valid session message to the emotion engine.

[2261] How it works: An emotion engine (e.g., Microsoft Azure's Emotion Recognition API) analyzes the message content and recognizes the user's emotion. Analysis is performed using natural language processing algorithms.

[2262] Output: The emotion engine returns the analysis results, including an emotion label (e.g., "sad") and an emotion score, to the server.

[2263] Step 5: Processing the emotion recognition results

[2264] Input: The server receives the emotion recognition results sent from the emotion engine.

[2265] Operation: The server passes the emotion recognition results to the generation AI and asks it to generate an appropriate response. The server provides the user's input message and the emotion result to the generation AI as a prompt.

[2266] Output: The generation AI generates an appropriate response based on the prompt and sends it back to the server.

[2267] Step 6: Optimize the response

[2268] Input: The server receives the response sent by the generating AI.

[2269] How it works: The server takes into account past conversation data and emotional patterns to optimize responses.

[2270] Output: Generates an optimized response message.

[2271] Step 7: Return and display the response

[2272] Input: The server holds the optimized response message.

[2273] Operation: The server generates a response and sends it to the terminal.

[2274] Output: The terminal displays the received response message on the user interface and provides it to the user.

[2275] (Application example 2)

[2276] 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."

[2277] Existing one-on-one services have difficulty fully understanding users' emotions and intentions, resulting in mechanical responses and a lack of personalization. Furthermore, when users make online purchases, the suggestions and support they receive are often inappropriate. This leads to issues such as low user satisfaction and a lack of promotion of purchasing behavior.

[2278] 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.

[2279] In this invention, the server includes an emotion engine that recognizes the user's emotional state, a means for the emotion engine to analyze the user's input content, a means for a generation AI to personalize a response based on the analysis results of the emotion engine, and a means for returning the generated response to the user, thereby enabling personalized suggestions and responses according to the user's emotional state.

[2280] An "emotion engine" is a program or algorithm that analyzes user input and recognizes the user's emotional state.

[2281] "Generative AI" refers to programs and algorithms that generate responses based on user input and the analysis results of an emotion engine.

[2282] The "server" is a centralized system that receives user input, invokes the emotion engine and generative AI to generate responses, and manages the necessary data and generated responses.

[2283] "Personalization" means providing the most appropriate responses and suggestions based on each individual user's specific emotional state and past conversational data.

[2284] "User authentication" is the process of verifying a user's identity using a user ID and password, etc.

[2285] "Session management" is a method for managing a series of communications while a user is accessing a system and maintaining consistency.

[2286] This invention relates to a 1-on-1 service system that combines an emotion engine that recognizes users' emotions with generative AI. The system aims to improve the shopping experience in virtual stores by understanding the user's emotional state and providing more appropriate and personalized responses.

[2287] System Configuration

[2288] This system mainly consists of the following components:

[2289] 1. Terminal

[2290] The smart device used by the user (smart glasses, smartphone, tablet, etc.).

[2291] Includes an interface for accepting input (chat box and voice input function).

[2292] 2. Server

[2293] It is a centralized system that receives user input and calls on the emotion engine and generative AI to generate a response.

[2294] User authentication, session management, and conversation data storage (e.g., AWS, Google Cloud)

[2295] 3. Generation AI

[2296] It uses large-scale language models (e.g., GPT-4) to analyze user input and generate appropriate responses.

[2297] Generate personalized suggestions and feedback.

[2298] 4. Emotion Engine

[2299] Recognizes emotions from user input and provides the analysis results to generative AI (e.g., Affectiva, Microsoft Azure Emotion API).

[2300] Program processing overview

[2301] 1. User Login and Authentication

[2302] Users log in to the virtual store through smart glasses or smartphones.

[2303] The authentication information is sent from the device to the server, and identity verification is performed.

[2304] 2. Collecting Emotional Data

[2305] While the user is browsing products, facial and voice data is collected from the smart glasses' camera and microphone.

[2306] The collected data is sent to a server and analyzed by an emotion engine.

[2307] 3. Emotion Recognition and Product Recommendations

[2308] The emotion engine recognizes the user's emotional state and provides the results to the generative AI.

[2309] The generative AI generates appropriate product suggestions and feedback based on emotion recognition results and past conversation data.

[2310] 4. Feedback and Support

[2311] The generated suggestions and feedback are sent back to the terminal via the server and displayed to the user in real time.

[2312] The system provides support as needed according to changes in the user's emotions.

[2313] Specific examples

[2314] Prompt Sentence Examples

[2315] If a user inputs "I'm looking for a new smartphone," and the emotion recognition result is "The user is a little confused," the input prompt will be as follows:

[2316] Emotion recognition result: "The user seems a little confused."

[2317] Previous conversation data: "User is looking for a new smartphone."

[2318] Input prompt: "The user is slightly confused. Use this information to generate a purchasing recommendation for a new smartphone."

[2319] Based on these prompts, the generative AI will provide the user with appropriate product suggestions, taking into account the user's previous inquiries and current emotional state, allowing for a more personalized response.

[2320] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2321] Step 1: User Login and Authentication

[2322] Users log in to the virtual store through smart glasses or a smartphone. On the terminal, the user enters their ID and password and presses the login button. The entered authentication information is sent to the server, which then compares it with a database to authenticate the user. If authentication is successful, the server generates a unique session token and sends it back to the terminal.

[2323] Input: User ID, Password

[2324] Output: Session token (e.g. "sess-token-abc123")

[2325] Step 2: Collecting emotion data

[2326] When a user starts browsing the virtual store, the camera and microphone in the smart glasses record the user's facial and voice data. This data is transmitted in real time from the device to the server. The server then sends the received facial and voice data to the emotion engine for emotion analysis.

[2327] Input: User's facial expression data, voice data

[2328] Output: Emotion recognition result (e.g. "The user is slightly confused.")

[2329] Step 3: Emotion recognition and product recommendations

[2330] The server sends the emotion recognition results received from the emotion engine to the generation AI, which then requests the generation AI to generate appropriate product suggestions based on the user's past purchasing history and current interests. The generation AI then combines the emotion recognition results with past data to suggest optimal products and promotions.

[2331] Input: Emotion recognition results, past conversation data

[2332] Output: Product suggestion message (e.g. "The user is a little confused. Use this information to generate a suggestion for a new smartphone.")

[2333] Step 4: Feedback and support

[2334] The server receives the generated suggestion message from the AI ​​and sends it back to the device, which displays it to the user in real time. If the user has further questions or needs assistance based on the suggestion, they can send new input from the device to the server and the process will be repeated.

[2335] Input: Product suggestion message

[2336] Output: A message to display to the user (e.g., "Here's the new smartphone model that's best for you.")

[2337] Through the above process, the system is able to provide personalized suggestions and responses in real time that are tailored to the user's emotional state.

[2338] 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.

[2339] 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.

[2340] 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.

[2341] 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.

[2342] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2343] 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.

[2344] 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).

[2345] 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.

[2346] 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."

[2347] 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.

[2348] 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).

[2349] 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.

[2350] 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.

[2351] 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.

[2352] 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.

[2353] 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.

[2354] 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.

[2355] 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.

[2356] 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.

[2357] 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.

[2358] 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.

[2359] The following is further disclosed regarding the above embodiment.

[2360] (Claim 1)

[2361] means for accepting input from a user;

[2362] means for transmitting said user input to a server;

[2363] A means for generating a response by a generation AI based on a user's input in the server;

[2364] means for returning the generated response to a user;

[2365] A system including:

[2366] (Claim 2)

[2367] A means for the generation AI to store and analyze past conversation data of the user;

[2368] A means for generating an AI to optimize a response based on the conversation data;

[2369] The system of claim 1 further comprising:

[2370] (Claim 3)

[2371] means for the server to perform user authentication and session management;

[2372] means for recording the session-managed data;

[2373] The system of claim 1 further comprising:

[2374] "Example 1"

[2375] (Claim 1)

[2376] means for accepting input from a user;

[2377] means for transmitting said user input to a central processing unit;

[2378] means for generating a response by a generation artificial intelligence based on a user's input in the central processing unit;

[2379] means for returning the generated response to a user;

[2380] means for transmitting user authentication information to a central processing unit;

[2381] means for the central processing unit to authenticate the user and issue a unique session token;

[2382] a means for storing the issued session token;

[2383] A means for the generating artificial intelligence to analyze the message content and generate a response;

[2384] means for displaying the generated response in a user interface;

[2385] A system including:

[2386] (Claim 2)

[2387] A means for the generating artificial intelligence to store and analyze the user's past interaction data and optimize the generated response;

[2388] means for the central processing unit to verify the validity of the session;

[2389] The system of claim 1 further comprising:

[2390] (Claim 3)

[2391] means for the central processing unit to perform user authentication and session management;

[2392] means for recording the managed session data;

[2393] A means for the generative artificial intelligence to perform text generation using a deep learning model;

[2394] The system of claim 1 further comprising:

[2395] "Application Example 1"

[2396] (Claim 1)

[2397] means for accepting input from a user;

[2398] means for transmitting said user input to a server;

[2399] A means for generating a response by a generation AI based on a user's input in the server;

[2400] means for returning the generated response to a user;

[2401] A means of login and authentication;

[2402] A means of issuing session tokens and validating sessions;

[2403] means for analyzing chat messages and generating responses using a generative AI model;

[2404] A system including:

[2405] (Claim 2)

[2406] A means for the generation AI to store and analyze past conversation data of the user;

[2407] A means for optimizing a response by the generation AI based on the past conversation data;

[2408] means for maintaining a history of said chat messages and maintaining context in a continuous conversation;

[2409] The system of claim 1 further comprising:

[2410] (Claim 3)

[2411] means for the server to perform user authentication and session management;

[2412] means for recording the session-managed data;

[2413] a means for verifying the authentication information against a database;

[2414] The system of claim 1 further comprising:

[2415] "Example 2: Combining Emotion Engines"

[2416] (Claim 1)

[2417] means for accepting input from a user;

[2418] means for transmitting said user input to a server;

[2419] a means for an emotion engine in the server to recognize emotions based on user input;

[2420] A means for generating a response by a generation AI based on the recognized emotion;

[2421] means for returning the generated response to a user;

[2422] A system including:

[2423] (Claim 2)

[2424] A means for the generation AI to store and analyze past conversation data of the user;

[2425] A means for generating an AI to optimize a response based on the conversation data;

[2426] The system of claim 1 further comprising:

[2427] (Claim 3)

[2428] means for the server to perform user authentication and session management;

[2429] means for recording the session-managed data;

[2430] The system of claim 1 further comprising:

[2431] "Application example 2 when combining emotion engines"

[2432] (Claim 1)

[2433] means for accepting input from a user;

[2434] means for transmitting said user input to a server;

[2435] A means for generating a response by a generation AI based on a user's input in the server;

[2436] means for returning the generated response to a user;

[2437] an emotion engine for recognizing an emotional state of a user, said emotion engine being adapted to analyze input content of said user;

[2438] A means for the generation AI to personalize a response based on the analysis result of the emotion engine;

[2439] A system including:

[2440] (Claim 2)

[2441] A means for the generation AI to store and analyze past conversation data of the user;

[2442] A means for optimizing a response by a generation AI based on the analysis results of the conversation data and the emotion engine;

[2443] The system of claim 1 further comprising:

[2444] (Claim 3)

[2445] means for the server to perform user authentication and session management;

[2446] means for recording the session-managed data and emotion engine analysis results;

[2447] The system of claim 1 further comprising: [Explanation of symbols]

[2448] 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. means for accepting input from a user; means for transmitting said user input to a server; A means for generating a response by a generation AI based on a user's input in the server; means for returning the generated response to a user; A system including:

2. A means for the generation AI to store and analyze past conversation data of the user; A means for generating an AI to optimize a response based on the conversation data; The system of claim 1 further comprising:

3. means for the server to perform user authentication and session management; means for recording the session-managed data; The system of claim 1 further comprising:

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