system

Abstract

Provide a system. The method includes: generating individual user profiles and providing a customizable conversational agent based on each profile; means for receiving a message from a user and generating a response by referring to a database of past conversations; a means for creating task-specific agents to process task-related information; A system including:

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] Existing generative AI is specialized in sentence generation, making it difficult to communicate with users in a friendly and affectionate manner. Furthermore, applying it to business operations requires individual development, which is time-consuming and costly. Furthermore, it has been difficult to provide an AI agent that can be customized for each user. Our goal is to solve these problems by providing a generative AI with individual personalities and knowledge, thereby improving business efficiency and enabling friendly dialogue. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for generating individual user profiles and providing customizable conversational agents based on each profile, a means for receiving messages from users and generating responses by referring to a database of past conversations, and a means for creating agents specialized for specific tasks and processing task-related information.The system also solves the aforementioned problems by further including a means for giving the conversational agents individual personalities and knowledge and reflecting user customization, a conversation database for saving the user's conversation history and reflecting it in future conversations, a means for monitoring the agent's usage status and displaying advertisements and charging fees, and a means for obtaining additional information from external information sources and reflecting it in the responses.

[0006] "User profile" means a data set that centrally collects and stores information about an individual user and is used to provide customized services.

[0007] A "dialogue agent" is an artificial intelligence system that communicates with a user in natural language and generates responses.

[0008] A "message" is text data sent from a user to a dialogue agent.

[0009] A "conversation database" is a database that stores the past conversation history between a user and a conversation agent and uses it in future conversations.

[0010] A "task-specific agent" is a conversational agent optimized for a specific task.

[0011] "Customization" refers to the adjustment and configuration of systems and services based on a user's individual requirements and preferences.

[0012] A "response" is an appropriate reply that a conversational agent generates to a user's message.

[0013] "Usage monitoring" is the process of collecting and analyzing the usage of a dialogue agent in real time.

[0014] "Displaying an advertisement" refers to the act of showing highly relevant advertising content to a user.

[0015] "Billing" is the process of informing a user of the amount due for the service provided.

[0016] An "external information source" is an external database or API that the conversational agent accesses to gather the necessary information. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the ability to receive messages from users and generate responses by referencing a database of past conversations. It also allows users to create agents specialized for specific tasks and process task-related information.

[0039] Initial Setup

[0040] First-time user registration

[0041] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[0042] Customization features

[0043] Customizing conversational agents

[0044] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[0045] Interactive processing

[0046] Conversation initiation and response generation

[0047] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0048] Specific examples

[0049] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message and references past conversation history and a schedule database. As a result, it generates a response such as, "I have a meeting tomorrow at 2 p.m." and sends it to the terminal. The terminal then displays this and provides it to the user.

[0050] Business-specific functions

[0051] Setting up specialized agents

[0052] Users enter the information and skill sets required for a specific task through a settings screen. The device sends this information to the server, which then fine-tunes a task-specific AI model based on the received information. The generated task-specific profile is sent to the user's device, where it can be viewed and used by the user.

[0053] Specific examples

[0054] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[0055] Monetization and Platform Operations

[0056] Monitoring usage and displaying advertisements

[0057] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs.

[0058] Specific examples

[0059] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[0060] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[0061] The processing flow will be explained below.

[0062] Initial Setup

[0063] User Registration

[0064] Step 1:

[0065] User: Enters name, email address, and password into the sign-up form.

[0066] Step 2:

[0067] Terminal: Sends the entered information to the server.

[0068] Step 3:

[0069] Server: Stores the received user information in a database and generates a new user profile.

[0070] Step 4:

[0071] Server: Generates the data for the initial setup screen and sends it to the user's device.

[0072] Customization features

[0073] Customizing conversational agents

[0074] Step 1:

[0075] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[0076] Step 2:

[0077] Device: Sends customization information to the server.

[0078] Step 3:

[0079] Server: Updates the AI ​​model profile based on the received customization information.

[0080] Step 4:

[0081] Server: Loads the required datasets and generates the customized profile.

[0082] Step 5:

[0083] Server: Sends customized profile data to the user's device.

[0084] Step 6:

[0085] On the device: The received customization profile is displayed to the user for confirmation.

[0086] Interactive processing

[0087] Conversation initiation and response generation

[0088] Step 1:

[0089] User: Type a message in the chat window.

[0090] Step 2:

[0091] Terminal: Sends the entered message to the server.

[0092] Step 3:

[0093] Server: Analyzes the received message.

[0094] Step 4:

[0095] Server: Refer to the conversation database and check past conversation history.

[0096] Step 5:

[0097] Server: Uses AI models to generate appropriate responses.

[0098] Step 6:

[0099] Server: Stores the response message in the conversation database.

[0100] Step 7:

[0101] Server: Sends the generated response message to the user's terminal.

[0102] Step 8:

[0103] Terminal: Displays the received response message to the user.

[0104] Business-specific functions

[0105] Setting up specialized agents

[0106] Step 1:

[0107] Users: Enter the information and skill sets required for a specific job through a setup screen.

[0108] Step 2:

[0109] Terminal: Sends business information to the server.

[0110] Step 3:

[0111] Server: Loads the necessary datasets and APIs based on the received business information.

[0112] Step 4:

[0113] Server: Fine-tunes AI models for specific tasks.

[0114] Step 5:

[0115] Server: Generates a business-specific profile and sends it to the user's device.

[0116] Step 6:

[0117] Terminal: The received business-specific profile is displayed to the user and made available for use.

[0118] Monetization and Platform Operations

[0119] Monitoring usage and displaying advertisements

[0120] Step 1:

[0121] Server: Monitors and logs agent usage in real time.

[0122] Step 2:

[0123] Server: Displays advertisements based on user usage patterns.

[0124] Billing and Feedback

[0125] Step 3:

[0126] Server: Generates billing and plan update information based on API usage and service fee logs.

[0127] Step 4:

[0128] Server: Calculates usage fees at the end of the month and issues invoices to users.

[0129] Step 5:

[0130] Server: Collecting user feedback to help improve our services.

[0131] This trend will enable the provision of user-friendly conversational agents and business efficiency agents, and the operation and monetization of services.

[0132] Example 1

[0133] 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."

[0134] Conventional conversational agents have limited functionality, such as the ability to generate individual profiles for each user and the ability to flexibly generate responses according to user customization. Furthermore, there is a lack of systems that allow agents to be configured to specialize in specific tasks, making them insufficient in addressing business efficiency. Furthermore, there are no integrated functions available, such as real-time monitoring based on usage, advertising display, or billing. These issues need to be resolved.

[0135] 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.

[0136] In this invention, the server includes means for generating individual profiles based on information input by users from their terminals and providing customizable conversational agents based on each profile, means for updating the customized conversational agent profile and loading necessary data sets, and means for receiving messages from users and analyzing and generating responses by referring to a database of past conversations. This makes it possible to generate customized responses based on each user's individual profile, and to achieve integrated configuration of agents specialized for specific tasks, real-time usage status monitoring, advertisement display, and fee billing.

[0137] An "individual profile" is a collection of individually customizable user information that is generated based on each user's input information.

[0138] A "customizable conversation agent" is a conversation support program that allows users to customize their appearance, personality, abilities, etc. based on their individual profiles.

[0139] The "past conversation database" is a database for storing the history of past conversations with a user and for use in future conversations.

[0140] A "means for generating a response" is an algorithm or system used to analyze a message from a user and generate an appropriate response.

[0141] The "means for processing business-related information" is a function that collects and analyzes information related to a specific business, and enables an agent to provide business support based on that information.

[0142] An "AI model profile" is a collection of information that defines the settings and characteristics of the artificial intelligence model used by the conversational agent.

[0143] "Means for loading datasets" refers to the functionality for loading data required for customization and response generation into the system.

[0144] "Fine-tuning" is a technique for adjusting an AI model to further adapt it to a specific purpose and improve its performance.

[0145] "Real-time monitoring" is the process of instantly observing and collecting data on system operation and user usage.

[0146] "Displaying advertisements" is a function that displays appropriate advertisements on the user's terminal based on the user's usage.

[0147] The "means for generating a bill" is a function that calculates the usage fee and issues a bill based on the data of the user's use of the system.

[0148] The present invention provides a system for generating individual user profiles and providing customizable conversational agents based on each profile. The system includes a server for generating profiles based on user input information and storing the profiles in a database, and multiple means for generating responses in real time through user interaction.

[0149] Hardware and Software Configuration

[0150] This system consists of the following main hardware and software:

[0151] Server: A computer equipped with a database management system (DBMS), natural language processing (NLP) libraries, and an AI model (e.g., GPT-3 (registered trademark)).

[0152] Device: A personal computer (PC), smartphone, or tablet operated by a user.

[0153] Network: The internet connection that connects the user's device to the server.

[0154] First-time user registration

[0155] When a new user uses the system, they must register for the first time. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends the information to the server. The server receives this information, generates an individual user profile, and stores it in a database. The server then generates data for the initial setup screen, which is sent to the user's device and displayed.

[0156] Customizing conversational agents

[0157] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. This creates a customized conversational agent, and the server sends this profile to the user's device.

[0158] Conversation initiation and response generation

[0159] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and checks past conversation history by referring to a conversation database. It then uses an AI model to generate an appropriate response, and if necessary, obtains additional information from external sources and incorporates it into the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0160] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message, references past conversation history and a schedule database, and generates a response such as, "I have a meeting tomorrow at 2 p.m." This response is sent to the terminal and displayed to the user.

[0161] Setting up specialized agents

[0162] The user enters the information and skill set required for a specific task through a configuration screen. The device sends this information to the server, which then fine-tunes the task-specific AI model based on the received information. The fine-tuned profile is then generated by the server and sent to the user's device. This allows the user to see the task-specific agent in a usable state.

[0163] For example, when a user sets up an agent specialized in sales activities, the user inputs the sales-related skill set and necessary data. The server generates a sales-specialized agent based on this and provides support such as customer management and performance analysis.

[0164] Monetization and Platform Operations

[0165] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs. At the end of the month, the server aggregates user usage, calculates usage fees, and issues invoices. It also collects user feedback to help improve the service.

[0166] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1: User Registration

[0169] Processing flow

[0170] 1. The user enters their name, email address, and password into a sign-up form on their device.

[0171] 2. The device sends this input information to the server.

[0172] Specific operations and inputs and outputs

[0173] When a user opens a browser, enters their name, email address, and password into the sign-up form, and presses the submit button, the data is sent from the device to the server. The entered information (name, email address, password) is passed to the server, which receives it and creates a new user profile. The created profile is saved in a database. The server then creates initial setup screen data and sends it to the device. This causes the initial setup screen to be displayed on the device.

[0174] Step 2: Customizing the conversation agent

[0175] Processing flow

[0176] 1. The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[0177] 2. The device sends this customization information to the server.

[0178] Specific operations and inputs and outputs

[0179] The user sets the agent's appearance (e.g., blue eyes), personality (e.g., kind), and abilities (e.g., good at giving business advice) on the initial setup screen. Then, by pressing the send button, the input information is sent from the device to the server. The server updates the AI ​​model profile based on the received customization information, loads the necessary datasets, and generates a customized agent profile. This is then sent to the device, allowing the user to view the customized agent.

[0180] Step 3: Conversation initiation and response generation

[0181] Processing flow

[0182] 1. The user inputs a message to the conversation agent.

[0183] 2. The device sends the message to the server.

[0184] 3. The server analyzes the received message and references the conversation database.

[0185] 4. The server generates an appropriate response.

[0186] Specific operations and inputs and outputs

[0187] When a user types "What are your plans for tomorrow?" into their device and sends it, the message is sent to the server. The server analyzes the received message using a natural language processing library, references past conversation history from a conversation database, and uses the acquired information to generate an appropriate response using an AI model. For example, by referencing past conversation history and a schedule database, the AI ​​model generates a response such as "I have a meeting tomorrow at 2 p.m." This response message is saved in the conversation database, sent to the device, and displayed to the user.

[0188] Step 4: Setting up specialized agents

[0189] Processing flow

[0190] 1. The user enters the information and skill set required for the job on the settings screen.

[0191] 2. The device sends the information to the server.

[0192] 3. The server fine-tunes the AI ​​model based on the information received.

[0193] Specific operations and inputs and outputs

[0194] For example, when a user sets up an agent specialized in sales activities, they enter the "sales-related skill set" and "required data" into the settings screen. The device then sends the input information to the server. The server then fine-tunes the AI ​​model based on the received information and generates a sales-specialized agent profile. The generated profile is then sent to the device and can be viewed by the user. This agent can then assist with customer management, sales performance analysis, and more.

[0195] Step 5: Monitoring usage and displaying advertisements

[0196] Processing flow

[0197] 1. The server monitors agent usage in real time.

[0198] 2. The server displays advertisements based on usage logs.

[0199] 3. The server generates billing and plan updates.

[0200] Specific operations and inputs and outputs

[0201] The server monitors agent usage in real time using an existing log management system, recording which functions users use and to what extent. At the end of the month, the server compiles statistics based on the usage logs, calculates usage fees, and generates invoices. Furthermore, the advertising display system uses this usage data to display appropriate advertisements to users. User feedback is also collected and used to improve the service.

[0202] (Application example 1)

[0203] 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."

[0204] Conventional conversational agents can respond based on the user's individual conversation history and preferences, but it is difficult to make optimal suggestions for a specific content distribution service that match the user's preferences. Furthermore, it is insufficient to provide a more personalized experience by giving the conversational agent individual personality and knowledge and reflecting the user's customization. As a result, users are unable to smoothly enjoy content that matches their preferences, which does not lead to improved satisfaction.

[0205] 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.

[0206] In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for creating agents specialized for specific tasks and processing task-related information, means for generating and responding to customized prompts based on the user's content preferences, and means for recommending optimal content based on the generated prompts. This enables personalized content recommendations based on the user's preferences, significantly improving user satisfaction.

[0207] A "user profile" is a database that includes each user's individual information, preferences, past interaction history, and so on.

[0208] A "conversational agent" is an artificial intelligence system that provides information through conversation with a user and generates the required responses.

[0209] "Means for receiving messages" refers to the interface or communication protocol for transferring input from the user to the server.

[0210] A "conversation database" is a data storage that stores the contents of past conversations with users and uses them to generate future responses.

[0211] A "task-specific agent" is a conversational agent that is optimized for a specific task or function.

[0212] "Content preferences" refer to the genres and characteristics of content that a user particularly likes.

[0213] "Prompt generation" is the process of generating appropriate questions or instructions based on the user's preferences and queries.

[0214] The "means for providing a response" refers to the function of creating an appropriate response based on the generated prompt and providing it to the user.

[0215] "Means for recommending content" refers to a function that selects optimal content based on the user's preferences and prompts and suggests it to the user.

[0216] The present invention is a system that recommends optimal content based on a user's content preferences using a conversational agent customized based on an individual user profile. This system is realized using a server, a terminal, and a generative AI model.

[0217] Initial Setup

[0218] First-time user registration

[0219] When a user first uses the system, they register. The user enters their name, email address, password, and content preferences into a sign-up form on their device, which then sends the information to the server. The server generates a user profile based on the received information and stores it in a database. The server then generates data for the initial setup screen and displays it on the user's device.

[0220] Customization features

[0221] Customizing conversational agents

[0222] The user selects and inputs the appearance, personality, and abilities of the conversational agent on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the profile of the generative AI model and loads the necessary datasets. Once the customized profile is generated, the server sends it to the user's device for confirmation.

[0223] Interactive processing

[0224] Conversation initiation and response generation

[0225] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and references the conversation database to check past conversation history and the user's content preferences. It then uses a generative AI model to generate an appropriate response. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0226] Content recommendation function

[0227] Content recommendation implementation

[0228] When a user enters a query for specific content, the server analyzes the message and generates prompts based on the user's preferences. For example, if a user's profile includes information such as "I like action movies," the following prompts will be generated in response to the query "What movies do you recommend?"

[0229] Example prompt:

[0230] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[0231] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[0232] Hardware and software used

[0233] Hardware

[0234] Server: Cloud server or on-premise server (AWS (registered trademark), Google (registered trademark), Azure (registered trademark), etc.)

[0235] Device: User devices such as smartphones, tablets, and PCs

[0236] software

[0237] Django: a web framework

[0238] Transformers: Generative AI model implementation (Huggingface's Transformers model)

[0239] GPT-2: Generative Model

[0240] This allows the personalized conversation agent to recommend optimal content based on the user's preferences.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] First-time user registration

[0244] A user enters their name, email address, password, and content preferences into a sign-up form on their device and clicks the Register button. The input data (name, email address, password, and content preferences) is sent from the device to the server. The server receives this data, generates a user profile, and stores it in a database. This profile includes the user's interaction history and preferences. Next, the server generates data for an initial setup screen, sends it to the user's device, and displays it.

[0245] Input: User's name, email address, password, content preferences

[0246] Output: Generated user profile, initial setup screen data

[0247] Step 2:

[0248] Customizing conversational agents

[0249] The user selects the appearance, personality, and abilities of the conversational agent from an initial setup screen, and the device sends the customization information to the server. The server receives this customization information, updates the profile of the generative AI model, and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device, displaying a confirmation screen.

[0250] Input: Customization information for the appearance, personality, and abilities of the conversational agent

[0251] Output: A customized conversational agent profile

[0252] Step 3:

[0253] Conversation initiation and response generation

[0254] The user inputs a message into the conversation agent, and the device sends the message to the server. The server analyzes the received message and references the conversation database to confirm the user's past conversation history and content preferences. The server uses a generative AI model to generate an appropriate response and stores it in the conversation database. The generated response message is sent to the device and displayed to the user.

[0255] Input: Message from the user

[0256] Output: The generated response message

[0257] Step 4:

[0258] Content recommendation implementation

[0259] The user enters a query for specific content, which the device sends to the server. The server analyzes the message and generates a prompt based on the user's preferences. For example, if the user says "I like action movies," the following prompt might be generated:

[0260] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[0261] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[0262] Input: User query

[0263] Output: Content recommendations

[0264] Step 5:

[0265] Storing conversation history and preference data

[0266] The generated responses and content recommendations are stored in a conversation database, improving the accuracy of future conversations and content recommendations. User profiles are also periodically updated to accumulate preference data.

[0267] Input: Generated responses, content recommendation history

[0268] Output: Updated conversation database and user profile

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

[0270] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the functions of receiving messages from users, generating responses by referencing a database of past conversations, and creating agents specialized for specific tasks and processing task-related information. The present invention also incorporates an emotion engine that can recognize user emotions and generate responses based on those emotions.

[0271] Initial Setup

[0272] First-time user registration

[0273] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[0274] Customization features

[0275] Customizing conversational agents

[0276] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[0277] Interactive processing

[0278] Conversation initiation and response generation

[0279] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0280] Use of emotion engine

[0281] The present invention incorporates an emotion engine that recognizes emotions from the user's message. The emotion engine analyzes messages received by the server and identifies the user's emotion. Based on this emotion information, the response message is further customized. The emotion information is stored in a conversation database and reflected in future conversations.

[0282] Specific examples

[0283] For example, if a user inputs "Today was a tough day," the emotion engine recognizes the emotion "tough." Based on this emotion information, the server generates a response such as "Thank you for your hard work. Let me know if there's anything I can help you with," and sends it to the user. The device then displays this and provides it to the user.

[0284] Business-specific functions

[0285] Setting up specialized agents

[0286] Users enter the information and skill sets required for a specific task through a configuration screen. The device sends this information to the server, which then loads the necessary datasets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use.

[0287] Specific examples

[0288] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[0289] Monetization and Platform Operations

[0290] Monitoring usage and displaying advertisements

[0291] The server monitors and logs agent usage in real time, displays ads appropriately based on usage patterns, and generates billing and plan updates based on API usage and service fee logs.

[0292] Specific examples

[0293] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[0294] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[0295] The processing flow will be explained below.

[0296] Initial Setup

[0297] User Registration

[0298] Step 1:

[0299] User: Enters name, email address, and password into a sign-up form.

[0300] Step 2:

[0301] Terminal: Sends the entered information to the server.

[0302] Step 3:

[0303] Server: Stores the received user information in a database and generates a new user profile.

[0304] Step 4:

[0305] Server: Generates the data for the initial setup screen and sends it to the user's device.

[0306] Customization features

[0307] Customizing conversational agents

[0308] Step 1:

[0309] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[0310] Step 2:

[0311] Device: Sends customization information to the server.

[0312] Step 3:

[0313] Server: Updates the AI ​​model profile based on the received customization information.

[0314] Step 4:

[0315] Server: Loads the required datasets and generates the customized profile.

[0316] Step 5:

[0317] Server: Sends customized profile data to the user's device.

[0318] Step 6:

[0319] On the device: The received customization profile is displayed to the user for confirmation.

[0320] Interactive processing

[0321] Conversation initiation and response generation

[0322] Step 1:

[0323] User: Type a message in the chat window.

[0324] Step 2:

[0325] Terminal: Sends the entered message to the server.

[0326] Step 3:

[0327] Server: Analyzes the received message.

[0328] Step 4:

[0329] Server: Refer to the conversation database and check past conversation history.

[0330] Step 5:

[0331] Server: Uses AI models to generate appropriate responses.

[0332] Step 6:

[0333] Server: Stores the response message in the conversation database.

[0334] Step 7:

[0335] Server: Sends the generated response message to the user's terminal.

[0336] Step 8:

[0337] Terminal: Displays the received response message to the user.

[0338] Use of emotion engine

[0339] Step 1:

[0340] User: Type and send a message.

[0341] Step 2:

[0342] Terminal: Sends the entered message to the server.

[0343] Step 3:

[0344] Server: Analyzes the received message using the emotion engine.

[0345] Step 4:

[0346] Server: The emotion engine recognizes the user's emotional state.

[0347] Step 5:

[0348] Server: Further customize the response message based on the emotional information.

[0349] Step 6:

[0350] Server: Stores the recognized emotion information in a conversation database.

[0351] Step 7:

[0352] Server: Sends a customized response message to the user's device.

[0353] Step 8:

[0354] Terminal: Displays the received response message to the user.

[0355] Business-specific functions

[0356] Setting up specialized agents

[0357] Step 1:

[0358] Users: Enter the information and skill sets required for a specific job through a setup screen.

[0359] Step 2:

[0360] Terminal: Sends business information to the server.

[0361] Step 3:

[0362] Server: Loads the necessary datasets and APIs based on the received business information.

[0363] Step 4:

[0364] Server: Fine-tunes AI models for specific tasks.

[0365] Step 5:

[0366] Server: Generates a business-specific profile and sends it to the user's device.

[0367] Step 6:

[0368] Terminal: The received business-specific profile is displayed to the user and made available for use.

[0369] Monetization and Platform Operations

[0370] Monitoring usage and displaying advertisements

[0371] Step 1:

[0372] Server: Monitors and logs agent usage in real time.

[0373] Step 2:

[0374] Server: Displays advertisements based on user usage patterns.

[0375] Billing and Feedback

[0376] Step 3:

[0377] Server: Generates billing and plan update information based on API usage and service fee logs.

[0378] Step 4:

[0379] Server: Calculates usage fees at the end of the month and issues invoices to users.

[0380] Step 5:

[0381] Server: Collecting user feedback to help improve our services.

[0382] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[0383] Example 2

[0384] 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."

[0385] Conventional conversational agent systems have difficulty fully recognizing a user's unique profile and emotions to generate customized responses. They also lack the functionality to generate agents specialized for specific tasks and efficiently process task-related information. This has prevented them from fully improving the user experience and streamlining operations.

[0386] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable interactive agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past interactions, means for creating agents specialized for specific tasks and processing task-related information, means for identifying the user's emotions using an emotion engine and generating responses based on the emotions, and means for monitoring the usage status of the interactive agents and recording them in a log. This enables flexible response generation based on individual profiles and emotions, efficiency improvement of specific tasks, and appropriate management of usage status.

[0387] A "user profile" is information about an individual user that the system collects and stores, including the user's name, contact information, preferences, and behavioral history.

[0388] A "customizable conversational agent" is a conversational agent that is optimized to meet the needs of a user by adjusting its appearance, personality, abilities, etc. based on the user's profile information.

[0389] A "message" is information in text or speech format that a user inputs to a dialogue agent.

[0390] The "dialogue database" is a database that contains past dialogue history and stores and manages cumulative interactions with users.

[0391] An "agent specialized for a specific task" is a conversational agent that has the knowledge and skill set required for a specific task and is designed to efficiently support that task.

[0392] An "emotion engine" is an algorithm or program that analyzes emotions from a user's input message and identifies those emotions.

[0393] A "response" is a reply message that a dialogue agent generates in response to a user's message.

[0394] "Usage status" refers to information about how users use the system, such as how often and how they use the dialogue agent.

[0395] A "log" is a series of activity histories or event records that are recorded while a system is running.

[0396] The present invention is a system for generating an individual user profile and providing a customizable conversational agent. A specific embodiment of this system is described below.

[0397] Initial Setup

[0398] First-time user registration

[0399] When a user uses the system for the first time, they enter information such as their name, email address, and password into a sign-up form and send it from their device to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen and sends it to the user's device to display it. For example, a user enters "Yamada Taro," "taro.yamada@example.com," and "password 123," and sends it to the server.

[0400] Customization features

[0401] Customizing conversational agents

[0402] The user selects and inputs the appearance, personality, and abilities of the conversational agent "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. A customized profile is generated, and the server sends it to the user's device for confirmation. Specifically, settings such as "friendly," "approachable," and "advanced search functions" are possible.

[0403] Interactive processing

[0404] Conversation initiation and response generation

[0405] When a user inputs a message into the dialogue agent, the device sends the message to the server. The server first analyzes the received message and references the dialogue database. It checks past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the dialogue database and sent to the device for display to the user.

[0406] Use of emotion engine

[0407] The emotion engine analyzes the user's message and identifies the user's emotion. The server uses this emotion information to further customize the response message. The emotion information is stored in the dialogue database and reflected in future dialogues. For example, if the user types, "Today was a tough day," the emotion engine recognizes the emotion "tough" and generates a response based on that, such as, "Thank you for your hard work. Let me know if there's anything I can do to help you."

[0408] Business-specific functions

[0409] Setting up specialized agents

[0410] The user enters the information and skill set required for a specific task through a settings screen. The device sends that information to the server, which then loads the necessary data sets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use. For example, when setting up an agent specializing in sales activities, the user enters the sales-related skill set and necessary data.

[0411] Monetization and Platform Operations

[0412] Monitoring usage and displaying advertisements

[0413] The server monitors agent usage in real time and records it in a log. It displays appropriate advertisements based on usage patterns, and generates billing and plan update information based on API usage and service fee logs. Specifically, the server aggregates user usage at the end of the month, calculates usage fees, generates an invoice, and sends it to the user.

[0414] This allows for the provision of user-friendly conversational agents and business efficiency agents, enabling service operation and monetization. Furthermore, by utilizing an emotion engine, it is possible to generate more detailed and empathetic responses, improving the user experience.

[0415] Example prompt sentence:

[0416] "Set your conversational agent's appearance as friendly, personality as approachable, and abilities as advanced search capabilities."

[0417] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0418] System program processing flow (specific explanation)

[0419] Initial Setup

[0420] Step 1:

[0421] Input: A user enters their name, email address, and password into a sign-up form.

[0422] Operation:

[0423] The user fills out a form on the device and provides information such as "Yamada Taro," "taro.yamada@example.com," and "password123."

[0424] Output: The device sends the input information to the server.

[0425] Step 2:

[0426] Input: User sign-up information received from the device.

[0427] Operation:

[0428] The server analyzes the received information, generates a new user profile, and stores it in the database. For example, the user is registered with a user ID of "001."

[0429] Output: The server generates a user profile and stores it in the database.

[0430] Step 3:

[0431] Input: User profile data on the server.

[0432] Operation:

[0433] The server generates data for the initial setting screen and sends it to the terminal.

[0434] Output: The terminal displays the initial setup screen to the user.

[0435] Customization features

[0436] Step 4:

[0437] Input: The user enters agent customization information on the initial setup screen.

[0438] Operation:

[0439] The user selects the appearance (e.g., friendly), personality (e.g., approachable), and capabilities (e.g., advanced search functionality) of "myGPT."

[0440] Output: The device sends this customization information to the server.

[0441] Step 5:

[0442] Input: Customization information received from the device.

[0443] Operation:

[0444] The server updates the AI ​​model profile based on the received information, loading the necessary datasets and generating a customized profile.

[0445] Output: A customized profile is generated.

[0446] Step 6:

[0447] Input: Your customized profile.

[0448] Operation:

[0449] The server transmits the generated customization profile to the terminal.

[0450] Output: The device displays the customized profile to the user and asks for confirmation.

[0451] Interactive processing

[0452] Step 7:

[0453] Input: The user types a message into the conversation agent.

[0454] Operation:

[0455] The user types a message, such as "What's the weather like today?"

[0456] Output: The device sends the input message to the server.

[0457] Step 8:

[0458] Input: User's message received from the terminal.

[0459] Operation:

[0460] The server analyzes the received message and determines the intent of the message.

[0461] Output: The analysis results are obtained.

[0462] Step 9:

[0463] Input: The parsed result of the message.

[0464] Operation:

[0465] The server refers to the dialogue database to check the relevant past conversation history.

[0466] Output: The relevant data is retrieved.

[0467] Step 10:

[0468] Input: Conversation database and analysis results.

[0469] Operation:

[0470] The server uses AI models to generate appropriate responses, and may obtain additional information from external sources (such as a weather API).

[0471] Output: A response message is generated.

[0472] Step 11:

[0473] Input: The generated response message.

[0474] Operation:

[0475] The server stores the response message in a dialogue database and sends it to the terminal.

[0476] Output: The terminal displays the response message to the user.

[0477] Use of emotion engine

[0478] Step 12:

[0479] Input: The user's message sent from the terminal.

[0480] Operation:

[0481] The server uses an emotion engine to analyze the message and identify the emotion.

[0482] Output: Emotional information is obtained.

[0483] Step 13:

[0484] Input: Emotion information.

[0485] Operation:

[0486] The server generates a response message based on the emotion.

[0487] Output: A customized response message is generated.

[0488] Step 14:

[0489] Input: The generated emotion-based response message.

[0490] Operation:

[0491] The server stores the emotion-based responses in a dialogue database and transmits them to the terminal.

[0492] Output: The device displays a response message that takes emotion into account.

[0493] Business-specific functions

[0494] Step 15:

[0495] Input: The user inputs the necessary information for the business-specific agent.

[0496] Operation:

[0497] The user enters the skill sets and data required for sales activities on the settings screen.

[0498] Output: The device sends the information to the server.

[0499] Step 16:

[0500] Input: Business-specific information received from the terminal.

[0501] Operation:

[0502] Based on the information received, the server loads the necessary datasets and APIs.

[0503] Output: The required data is loaded.

[0504] Step 17:

[0505] Input: Business specific information and loaded dataset.

[0506] Operation:

[0507] The server fine-tunes the AI ​​model to correspond to a specific task and generates a task-specific profile.

[0508] Output: A job-specific profile is generated.

[0509] Step 18:

[0510] Input: The generated job-specific profile.

[0511] Operation:

[0512] The server transmits the generated business-specific profile to the user's terminal.

[0513] Output: The device displays the business-specific profile and makes it available for use.

[0514] Monetization and Platform Operations

[0515] Step 19:

[0516] Input: Usage data.

[0517] Operation:

[0518] The server monitors agent usage in real time and records it in a log.

[0519] Output: Usage log data is generated.

[0520] Step 20:

[0521] Input: Monitored usage data.

[0522] Operation:

[0523] The server displays appropriate ads based on usage patterns. The ads are displayed on the device.

[0524] Output: The ad is displayed on the user's screen.

[0525] Step 21:

[0526] Input: Log data regarding API usage and service fees.

[0527] Operation:

[0528] The server generates billing and plan update information, calculates the usage fee, and issues an invoice at the end of the month.

[0529] Output: A usage invoice is generated and sent to the user.

[0530] These specific processing steps allow each function to operate smoothly, providing a friendly and efficient interactive agent service to the user.

[0531] (Application example 2)

[0532] 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."

[0533] In order to provide friendly yet effective assistance to users in autonomous vehicles, customization based on the user's emotions and individual needs is required. However, existing systems are unable to recognize the user's emotions and generate responses based on them, resulting in insufficient personalized navigation, entertainment, or emergency response.

[0534] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for recognizing user emotions and generating responses based on the emotions, means for creating agents specialized for specific tasks and processing task-related information, and means for being installed in autonomous vehicles to assist users with navigation, vehicle operation, entertainment, and emergency response. This makes it possible to significantly improve the user experience in autonomous vehicles.

[0535] A "user profile" is a collection of information about an individual user, including the user's name, contact information, preferences, and past interaction history.

[0536] A "dialogue agent" is a system that uses artificial intelligence to engage in dialogue with users, enabling the exchange of messages through natural language processing.

[0537] An "emotion engine" is a technology that analyzes emotions from a user's text or voice and generates a response based on those emotions.

[0538] "Navigation" is a function that provides route guidance and direction to reach a destination, and presents an appropriate route based on map information and current location data.

[0539] "Vehicle operation" refers to functions related to driving an autonomous vehicle, including speed control, lane changes, and parking.

[0540] "Entertainment" refers to content and services that provide users with enjoyment and entertainment, and includes music, movies, games, and the like.

[0541] "Emergency response" is a function that provides appropriate measures and support when an emergency such as an accident or illness occurs.

[0542] A "conversation database" is a database that stores past conversation history and uses it for future conversations.

[0543] A "task-specific agent" is a conversational agent designed to handle a specific task or business, and has specific skills and knowledge.

[0544] The present invention relates to a customizable dialogue agent system for use in autonomous vehicles. The following describes in detail a system for implementing this application.

[0545] Initial Setup

[0546] First-time user registration

[0547] When a user first uses the system, they create a user profile. They enter information such as their name, preferences, and contact details using the vehicle's infotainment system. Based on this, the server creates a user profile and stores it in a database.

[0548] Customizing conversational agents

[0549] Users can customize the appearance, personality, and capabilities of their conversational agent through the infotainment system's settings screen. The server receives this customization information and updates the AI ​​model's profile, generating a conversational agent optimized for the user.

[0550] Interactive processing

[0551] Conversation initiation and response generation

[0552] When a user types a message into the conversation agent, the infotainment system sends it to a server. The server first analyzes the message and checks past conversation history against a conversation database. It then uses an AI model to generate an appropriate response, incorporating additional information from external sources as needed.

[0553] Use of emotion engine

[0554] The emotion engine analyzes emotions from the user's message and generates a response based on that emotion. For example, if a user types, "I had a rough day today," the emotion engine recognizes the emotion "rough" and generates a response such as, "Thank you for your hard work. Let me know if there's anything I can do to help you." This information is stored in the conversation database and reflected in future interactions.

[0555] Application example in autonomous vehicles

[0556] The system is installed inside autonomous vehicles and provides the following functions:

[0557] Navigation assistance: When a user inputs their destination, the conversational agent guides them to the optimal route. For example, in response to a request such as "I want to go to a nearby restaurant," the agent generates a response such as "There is a certain restaurant one kilometer away. Do you want to go there?"

[0558] Vehicle operation assistance: The conversational agent assists with vehicle operation in response to user instructions. For example, if the user says, "Set the air conditioner temperature to 23 degrees," the agent will respond, "The air conditioner temperature has been set to 23 degrees," and actually perform the operation.

[0559] Entertainment recommendations: Based on the user's preferences, the app suggests entertainment such as music and movies. For example, if the user says, "I want to listen to classical music," the app will start playing classical music.

[0560] Emergency response: When an emergency occurs, the conversational agent will respond appropriately. For example, if you say "I'm not feeling well," it will generate a response such as "Would you like to contact a medical institution?"

[0561] Hardware and software used

[0562] Hardware: Autonomous vehicle infotainment systems, displays, speakers, sensors, etc.

[0563] Software: OpenAI (registered trademark) API, emotion recognition API, database system

[0564] Prompt Sentence Examples

[0565] Below are examples of prompt sentences that are generated based on messages from users.

[0566] User: Today was a rough day

[0567] User emotion: Sad

[0568] Generate a response for the in-car assistant:

[0569] This prompt allows OpenAI's text generation model to generate an appropriate response, thus enabling the implementation of a conversational agent system that provides a user-friendly experience within a self-driving vehicle.

[0570] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0571] Step 1:

[0572] The user enters profile information for the conversational agent. Specifically, the user enters information such as name, preferences, and contact details through the infotainment system and sends it from the device to the server. This data is received by the server and stored in a database as a user profile.

[0573] Input: User information (name, preferences, contact details, etc.)

[0574] Output: Generate and save user profile

[0575] Step 2:

[0576] The user customizes the conversational agent. The user selects the agent's appearance, personality, and abilities from the infotainment system's settings screen, and this information is sent from the device to the server. The server updates the AI ​​model profile based on the received information and generates a customized agent for the user.

[0577] Input: Customization information for the conversational agent (appearance, personality, abilities)

[0578] Output: A customized conversational agent

[0579] Step 3:

[0580] A user inputs a message to the dialogue agent through the infotainment system, and the message is sent from the terminal to the server. The server receives the message.

[0581] Input: Message from the user

[0582] Output: Message received by server

[0583] Step 4:

[0584] The server analyzes the message and performs emotion recognition. The server uses an emotion engine to analyze the user's message and identify the emotion. For example, it can recognize the emotion "sad" from the message "Today was a tough day."

[0585] Input: User message

[0586] Output: Identified emotion information

[0587] Step 5:

[0588] The server generates a prompt based on the user message and the identified emotional information, and provides it to OpenAI's generative AI model.

[0589] Input: User message and emotion information

[0590] Output: Generated prompt statement

[0591] Example: Prompt sentence example

[0592] User: Today was a rough day

[0593] User emotion: Sad

[0594] Generate a response for the in-car assistant:

[0595] Step 6:

[0596] Response generation by the generative AI model. The server sends the generated prompt to OpenAI's API, and the AI ​​model generates an appropriate response. For example, a response such as "Thank you for your hard work. Let me know if there's anything I can help you with."

[0597] Input: prompt statement

[0598] Output: The generated response message

[0599] Step 7:

[0600] The server sends a response message to the infotainment system, which then sends the generated response message to the terminal and displays it to the user.

[0601] Input: The generated response message

[0602] Output: The response message that is displayed to the user

[0603] Step 8:

[0604] Storing conversation information: The server stores conversation history in a database as part of the user profile, which can then be used to inform future conversations.

[0605] Input: User message and generated response

[0606] Output: Dialogue history stored in a database

[0607] The above steps provide customizable interaction assistance for the user in an autonomous vehicle.

[0608] 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.

[0609] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0610] 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.

[0611] [Second embodiment]

[0612] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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."

[0624] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the ability to receive messages from users and generate responses by referencing a database of past conversations. It also allows users to create agents specialized for specific tasks and process task-related information.

[0625] Initial Setup

[0626] First-time user registration

[0627] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[0628] Customization features

[0629] Customizing conversational agents

[0630] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[0631] Interactive processing

[0632] Conversation initiation and response generation

[0633] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0634] Specific examples

[0635] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message and references past conversation history and a schedule database. As a result, it generates a response such as, "I have a meeting tomorrow at 2 p.m." and sends it to the terminal. The terminal then displays this and provides it to the user.

[0636] Business-specific functions

[0637] Setting up specialized agents

[0638] Users enter the information and skill sets required for a specific task through a settings screen. The device sends this information to the server, which then fine-tunes a task-specific AI model based on the received information. The generated task-specific profile is sent to the user's device, where it can be viewed and used by the user.

[0639] Specific examples

[0640] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[0641] Monetization and Platform Operations

[0642] Monitoring usage and displaying advertisements

[0643] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs.

[0644] Specific examples

[0645] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[0646] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[0647] The processing flow will be explained below.

[0648] Initial Setup

[0649] User Registration

[0650] Step 1:

[0651] User: Enters name, email address, and password into the sign-up form.

[0652] Step 2:

[0653] Terminal: Sends the entered information to the server.

[0654] Step 3:

[0655] Server: Stores the received user information in a database and generates a new user profile.

[0656] Step 4:

[0657] Server: Generates the data for the initial setup screen and sends it to the user's device.

[0658] Customization features

[0659] Customizing conversational agents

[0660] Step 1:

[0661] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[0662] Step 2:

[0663] Device: Sends customization information to the server.

[0664] Step 3:

[0665] Server: Updates the AI ​​model profile based on the received customization information.

[0666] Step 4:

[0667] Server: Loads the required datasets and generates the customized profile.

[0668] Step 5:

[0669] Server: Sends customized profile data to the user's device.

[0670] Step 6:

[0671] On the device: The received customization profile is displayed to the user for confirmation.

[0672] Interactive processing

[0673] Conversation initiation and response generation

[0674] Step 1:

[0675] User: Type a message in the chat window.

[0676] Step 2:

[0677] Terminal: Sends the entered message to the server.

[0678] Step 3:

[0679] Server: Analyzes the received message.

[0680] Step 4:

[0681] Server: Refer to the conversation database and check past conversation history.

[0682] Step 5:

[0683] Server: Uses AI models to generate appropriate responses.

[0684] Step 6:

[0685] Server: Stores the response message in the conversation database.

[0686] Step 7:

[0687] Server: Sends the generated response message to the user's terminal.

[0688] Step 8:

[0689] Terminal: Displays the received response message to the user.

[0690] Business-specific functions

[0691] Setting up specialized agents

[0692] Step 1:

[0693] Users: Enter the information and skill sets required for a specific job through a setup screen.

[0694] Step 2:

[0695] Terminal: Sends business information to the server.

[0696] Step 3:

[0697] Server: Loads the necessary datasets and APIs based on the received business information.

[0698] Step 4:

[0699] Server: Fine-tunes AI models for specific tasks.

[0700] Step 5:

[0701] Server: Generates a business-specific profile and sends it to the user's device.

[0702] Step 6:

[0703] Terminal: The received business-specific profile is displayed to the user and made available for use.

[0704] Monetization and Platform Operations

[0705] Monitoring usage and displaying advertisements

[0706] Step 1:

[0707] Server: Monitors and logs agent usage in real time.

[0708] Step 2:

[0709] Server: Displays advertisements based on user usage patterns.

[0710] Billing and Feedback

[0711] Step 3:

[0712] Server: Generates billing and plan update information based on API usage and service fee logs.

[0713] Step 4:

[0714] Server: Calculates usage fees at the end of the month and issues invoices to users.

[0715] Step 5:

[0716] Server: Collecting user feedback to help improve our services.

[0717] This trend will enable the provision of user-friendly conversational agents and business efficiency agents, and the operation and monetization of services.

[0718] Example 1

[0719] 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."

[0720] Conventional conversational agents have limited functionality, such as the ability to generate individual profiles for each user and the ability to flexibly generate responses according to user customization. Furthermore, there is a lack of systems that allow agents to be configured to specialize in specific tasks, making them insufficient in addressing business efficiency. Furthermore, there are no integrated functions available, such as real-time monitoring based on usage, advertising display, or billing. These issues need to be resolved.

[0721] 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.

[0722] In this invention, the server includes means for generating individual profiles based on information input by users from their terminals and providing customizable conversational agents based on each profile, means for updating the customized conversational agent profile and loading necessary data sets, and means for receiving messages from users and analyzing and generating responses by referring to a database of past conversations. This makes it possible to generate customized responses based on each user's individual profile, and to achieve integrated configuration of agents specialized for specific tasks, real-time usage status monitoring, advertisement display, and fee billing.

[0723] An "individual profile" is a collection of individually customizable user information that is generated based on each user's input information.

[0724] A "customizable conversation agent" is a conversation support program that allows users to customize their appearance, personality, abilities, etc. based on their individual profiles.

[0725] The "past conversation database" is a database for storing the history of past conversations with a user and for use in future conversations.

[0726] A "means for generating a response" is an algorithm or system used to analyze a message from a user and generate an appropriate response.

[0727] The "means for processing business-related information" is a function that collects and analyzes information related to a specific business, and enables an agent to provide business support based on that information.

[0728] An "AI model profile" is a collection of information that defines the settings and characteristics of the artificial intelligence model used by the conversational agent.

[0729] "Means for loading datasets" refers to the functionality for loading data required for customization and response generation into the system.

[0730] "Fine-tuning" is a technique for adjusting an AI model to further adapt it to a specific purpose and improve its performance.

[0731] "Real-time monitoring" is the process of instantly observing and collecting data on system operation and user usage.

[0732] "Displaying advertisements" is a function that displays appropriate advertisements on the user's terminal based on the user's usage.

[0733] The "means for generating a bill" is a function that calculates the usage fee and issues a bill based on the data of the user's use of the system.

[0734] The present invention provides a system for generating individual user profiles and providing customizable conversational agents based on each profile. The system includes a server for generating profiles based on user input information and storing the profiles in a database, and multiple means for generating responses in real time through user interaction.

[0735] Hardware and Software Configuration

[0736] This system consists of the following main hardware and software:

[0737] Server: A computer equipped with a database management system (DBMS), natural language processing (NLP) libraries, and an AI model (e.g., GPT-3).

[0738] Device: A personal computer (PC), smartphone, or tablet operated by a user.

[0739] Network: The internet connection that connects the user's device to the server.

[0740] First-time user registration

[0741] When a new user uses the system, they must register for the first time. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends the information to the server. The server receives this information, generates an individual user profile, and stores it in a database. The server then generates data for the initial setup screen, which is sent to the user's device and displayed.

[0742] Customizing conversational agents

[0743] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. This creates a customized conversational agent, and the server sends this profile to the user's device.

[0744] Conversation initiation and response generation

[0745] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and checks past conversation history by referring to a conversation database. It then uses an AI model to generate an appropriate response, and if necessary, obtains additional information from external sources and incorporates it into the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0746] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message, references past conversation history and a schedule database, and generates a response such as, "I have a meeting tomorrow at 2 p.m." This response is sent to the terminal and displayed to the user.

[0747] Setting up specialized agents

[0748] The user enters the information and skill set required for a specific task through a configuration screen. The device sends this information to the server, which then fine-tunes the task-specific AI model based on the received information. The fine-tuned profile is then generated by the server and sent to the user's device. This allows the user to see the task-specific agent in a usable state.

[0749] For example, when a user sets up an agent specialized in sales activities, the user inputs the sales-related skill set and necessary data. The server generates a sales-specialized agent based on this and provides support such as customer management and performance analysis.

[0750] Monetization and Platform Operations

[0751] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs. At the end of the month, the server aggregates user usage, calculates usage fees, and issues invoices. It also collects user feedback to help improve the service.

[0752] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[0753] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0754] Step 1: User Registration

[0755] Processing flow

[0756] 1. The user enters their name, email address, and password into a sign-up form on their device.

[0757] 2. The device sends this input information to the server.

[0758] Specific operations and inputs and outputs

[0759] When a user opens a browser, enters their name, email address, and password into the sign-up form, and presses the submit button, the data is sent from the device to the server. The entered information (name, email address, password) is passed to the server, which receives it and creates a new user profile. The created profile is saved in a database. The server then creates initial setup screen data and sends it to the device. This causes the initial setup screen to be displayed on the device.

[0760] Step 2: Customizing the conversation agent

[0761] Processing flow

[0762] 1. The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[0763] 2. The device sends this customization information to the server.

[0764] Specific operations and inputs and outputs

[0765] The user sets the agent's appearance (e.g., blue eyes), personality (e.g., kind), and abilities (e.g., good at giving business advice) on the initial setup screen. Then, by pressing the send button, the input information is sent from the device to the server. The server updates the AI ​​model profile based on the received customization information, loads the necessary datasets, and generates a customized agent profile. This is then sent to the device, allowing the user to view the customized agent.

[0766] Step 3: Conversation initiation and response generation

[0767] Processing flow

[0768] 1. The user inputs a message to the conversation agent.

[0769] 2. The device sends the message to the server.

[0770] 3. The server analyzes the received message and references the conversation database.

[0771] 4. The server generates an appropriate response.

[0772] Specific operations and inputs and outputs

[0773] When a user types "What are your plans for tomorrow?" into their device and sends it, the message is sent to the server. The server analyzes the received message using a natural language processing library, references past conversation history from a conversation database, and uses the acquired information to generate an appropriate response using an AI model. For example, by referencing past conversation history and a schedule database, the AI ​​model generates a response such as "I have a meeting tomorrow at 2 p.m." This response message is saved in the conversation database, sent to the device, and displayed to the user.

[0774] Step 4: Setting up specialized agents

[0775] Processing flow

[0776] 1. The user enters the information and skill set required for the job on the settings screen.

[0777] 2. The device sends the information to the server.

[0778] 3. The server fine-tunes the AI ​​model based on the information received.

[0779] Specific operations and inputs and outputs

[0780] For example, when a user sets up an agent specialized in sales activities, they enter the "sales-related skill set" and "required data" into the settings screen. The device then sends the input information to the server. The server then fine-tunes the AI ​​model based on the received information and generates a sales-specialized agent profile. The generated profile is then sent to the device and can be viewed by the user. This agent can then assist with customer management, sales performance analysis, and more.

[0781] Step 5: Monitoring usage and displaying advertisements

[0782] Processing flow

[0783] 1. The server monitors agent usage in real time.

[0784] 2. The server displays advertisements based on usage logs.

[0785] 3. The server generates billing and plan updates.

[0786] Specific operations and inputs and outputs

[0787] The server monitors agent usage in real time using an existing log management system, recording which functions users use and to what extent. At the end of the month, the server compiles statistics based on the usage logs, calculates usage fees, and generates invoices. Furthermore, the advertising display system uses this usage data to display appropriate advertisements to users. User feedback is also collected and used to improve the service.

[0788] (Application example 1)

[0789] 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."

[0790] Conventional conversational agents can respond based on the user's individual conversation history and preferences, but it is difficult to make optimal suggestions for a specific content distribution service that match the user's preferences. Furthermore, it is insufficient to provide a more personalized experience by giving the conversational agent individual personality and knowledge and reflecting the user's customization. As a result, users are unable to smoothly enjoy content that matches their preferences, which does not lead to improved satisfaction.

[0791] 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.

[0792] In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for creating agents specialized for specific tasks and processing task-related information, means for generating and responding to customized prompts based on the user's content preferences, and means for recommending optimal content based on the generated prompts. This enables personalized content recommendations based on the user's preferences, significantly improving user satisfaction.

[0793] A "user profile" is a database that includes each user's individual information, preferences, past interaction history, and so on.

[0794] A "conversational agent" is an artificial intelligence system that provides information through conversation with a user and generates the required responses.

[0795] "Means for receiving messages" refers to the interface or communication protocol for transferring input from the user to the server.

[0796] A "conversation database" is a data storage that stores the contents of past conversations with users and uses them to generate future responses.

[0797] A "task-specific agent" is a conversational agent that is optimized for a specific task or function.

[0798] "Content preferences" refer to the genres and characteristics of content that a user particularly likes.

[0799] "Prompt generation" is the process of generating appropriate questions or instructions based on the user's preferences and queries.

[0800] The "means for providing a response" refers to the function of creating an appropriate response based on the generated prompt and providing it to the user.

[0801] "Means for recommending content" refers to a function that selects optimal content based on the user's preferences and prompts and suggests it to the user.

[0802] The present invention is a system that recommends optimal content based on a user's content preferences using a conversational agent customized based on an individual user profile. This system is realized using a server, a terminal, and a generative AI model.

[0803] Initial Setup

[0804] First-time user registration

[0805] When a user first uses the system, they register. The user enters their name, email address, password, and content preferences into a sign-up form on their device, which then sends the information to the server. The server generates a user profile based on the received information and stores it in a database. The server then generates data for the initial setup screen and displays it on the user's device.

[0806] Customization features

[0807] Customizing conversational agents

[0808] The user selects and inputs the appearance, personality, and abilities of the conversational agent on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the profile of the generative AI model and loads the necessary datasets. Once the customized profile is generated, the server sends it to the user's device for confirmation.

[0809] Interactive processing

[0810] Conversation initiation and response generation

[0811] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and references the conversation database to check past conversation history and the user's content preferences. It then uses a generative AI model to generate an appropriate response. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0812] Content recommendation function

[0813] Content recommendation implementation

[0814] When a user enters a query for specific content, the server analyzes the message and generates prompts based on the user's preferences. For example, if a user's profile includes information such as "I like action movies," the following prompts will be generated in response to the query "What movies do you recommend?"

[0815] Example prompt:

[0816] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[0817] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[0818] Hardware and software used

[0819] Hardware

[0820] Server: Cloud server or on-premise server (AWS, Google Cloud, Azure, etc.)

[0821] Device: User devices such as smartphones, tablets, and PCs

[0822] software

[0823] Django: a web framework

[0824] Transformers: Generative AI model implementation (Huggingface's Transformers model)

[0825] GPT-2: Generative Model

[0826] This allows the personalized conversation agent to recommend optimal content based on the user's preferences.

[0827] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0828] Step 1:

[0829] First-time user registration

[0830] A user enters their name, email address, password, and content preferences into a sign-up form on their device and clicks the Register button. The input data (name, email address, password, and content preferences) is sent from the device to the server. The server receives this data, generates a user profile, and stores it in a database. This profile includes the user's interaction history and preferences. Next, the server generates data for an initial setup screen, sends it to the user's device, and displays it.

[0831] Input: User's name, email address, password, content preferences

[0832] Output: Generated user profile, initial setup screen data

[0833] Step 2:

[0834] Customizing conversational agents

[0835] The user selects the appearance, personality, and abilities of the conversational agent from an initial setup screen, and the device sends the customization information to the server. The server receives this customization information, updates the profile of the generative AI model, and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device, displaying a confirmation screen.

[0836] Input: Customization information for the appearance, personality, and abilities of the conversational agent

[0837] Output: A customized conversational agent profile

[0838] Step 3:

[0839] Conversation initiation and response generation

[0840] The user inputs a message into the conversation agent, and the device sends the message to the server. The server analyzes the received message and references the conversation database to confirm the user's past conversation history and content preferences. The server uses a generative AI model to generate an appropriate response and stores it in the conversation database. The generated response message is sent to the device and displayed to the user.

[0841] Input: Message from the user

[0842] Output: The generated response message

[0843] Step 4:

[0844] Content recommendation implementation

[0845] The user enters a query for specific content, which the device sends to the server. The server analyzes the message and generates a prompt based on the user's preferences. For example, if the user says "I like action movies," the following prompt might be generated:

[0846] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[0847] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[0848] Input: User query

[0849] Output: Content recommendations

[0850] Step 5:

[0851] Storing conversation history and preference data

[0852] The generated responses and content recommendations are stored in a conversation database, improving the accuracy of future conversations and content recommendations. User profiles are also periodically updated to accumulate preference data.

[0853] Input: Generated responses, content recommendation history

[0854] Output: Updated conversation database and user profile

[0855] 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.

[0856] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the functions of receiving messages from users, generating responses by referencing a database of past conversations, and creating agents specialized for specific tasks and processing task-related information. The present invention also incorporates an emotion engine that can recognize user emotions and generate responses based on those emotions.

[0857] Initial Setup

[0858] First-time user registration

[0859] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[0860] Customization features

[0861] Customizing conversational agents

[0862] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[0863] Interactive processing

[0864] Conversation initiation and response generation

[0865] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[0866] Use of emotion engine

[0867] The present invention incorporates an emotion engine that recognizes emotions from the user's message. The emotion engine analyzes messages received by the server and identifies the user's emotion. Based on this emotion information, the response message is further customized. The emotion information is stored in a conversation database and reflected in future conversations.

[0868] Specific examples

[0869] For example, if a user inputs "Today was a tough day," the emotion engine recognizes the emotion "tough." Based on this emotion information, the server generates a response such as "Thank you for your hard work. Let me know if there's anything I can help you with," and sends it to the user. The device then displays this and provides it to the user.

[0870] Business-specific functions

[0871] Setting up specialized agents

[0872] Users enter the information and skill sets required for a specific task through a configuration screen. The device sends this information to the server, which then loads the necessary datasets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use.

[0873] Specific examples

[0874] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[0875] Monetization and Platform Operations

[0876] Monitoring usage and displaying advertisements

[0877] The server monitors and logs agent usage in real time, displays ads appropriately based on usage patterns, and generates billing and plan updates based on API usage and service fee logs.

[0878] Specific examples

[0879] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[0880] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[0881] The processing flow will be explained below.

[0882] Initial Setup

[0883] User Registration

[0884] Step 1:

[0885] User: Enters name, email address, and password into a sign-up form.

[0886] Step 2:

[0887] Terminal: Sends the entered information to the server.

[0888] Step 3:

[0889] Server: Stores the received user information in a database and generates a new user profile.

[0890] Step 4:

[0891] Server: Generates the data for the initial setup screen and sends it to the user's device.

[0892] Customization features

[0893] Customizing conversational agents

[0894] Step 1:

[0895] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[0896] Step 2:

[0897] Device: Sends customization information to the server.

[0898] Step 3:

[0899] Server: Updates the AI ​​model profile based on the received customization information.

[0900] Step 4:

[0901] Server: Loads the required datasets and generates the customized profile.

[0902] Step 5:

[0903] Server: Sends customized profile data to the user's device.

[0904] Step 6:

[0905] On the device: The received customization profile is displayed to the user for confirmation.

[0906] Interactive processing

[0907] Conversation initiation and response generation

[0908] Step 1:

[0909] User: Type a message in the chat window.

[0910] Step 2:

[0911] Terminal: Sends the entered message to the server.

[0912] Step 3:

[0913] Server: Analyzes the received message.

[0914] Step 4:

[0915] Server: Refer to the conversation database and check past conversation history.

[0916] Step 5:

[0917] Server: Uses AI models to generate appropriate responses.

[0918] Step 6:

[0919] Server: Stores the response message in the conversation database.

[0920] Step 7:

[0921] Server: Sends the generated response message to the user's terminal.

[0922] Step 8:

[0923] Terminal: Displays the received response message to the user.

[0924] Use of emotion engine

[0925] Step 1:

[0926] User: Type and send a message.

[0927] Step 2:

[0928] Terminal: Sends the entered message to the server.

[0929] Step 3:

[0930] Server: Analyzes the received message using the emotion engine.

[0931] Step 4:

[0932] Server: The emotion engine recognizes the user's emotional state.

[0933] Step 5:

[0934] Server: Further customize the response message based on the emotional information.

[0935] Step 6:

[0936] Server: Stores the recognized emotion information in a conversation database.

[0937] Step 7:

[0938] Server: Sends a customized response message to the user's device.

[0939] Step 8:

[0940] Terminal: Displays the received response message to the user.

[0941] Business-specific functions

[0942] Setting up specialized agents

[0943] Step 1:

[0944] Users: Enter the information and skill sets required for a specific job through a setup screen.

[0945] Step 2:

[0946] Terminal: Sends business information to the server.

[0947] Step 3:

[0948] Server: Loads the necessary datasets and APIs based on the received business information.

[0949] Step 4:

[0950] Server: Fine-tunes AI models for specific tasks.

[0951] Step 5:

[0952] Server: Generates a business-specific profile and sends it to the user's device.

[0953] Step 6:

[0954] Terminal: The received business-specific profile is displayed to the user and made available for use.

[0955] Monetization and Platform Operations

[0956] Monitoring usage and displaying advertisements

[0957] Step 1:

[0958] Server: Monitors and logs agent usage in real time.

[0959] Step 2:

[0960] Server: Displays advertisements based on user usage patterns.

[0961] Billing and Feedback

[0962] Step 3:

[0963] Server: Generates billing and plan update information based on API usage and service fee logs.

[0964] Step 4:

[0965] Server: Calculates usage fees at the end of the month and issues invoices to users.

[0966] Step 5:

[0967] Server: Collecting user feedback to help improve our services.

[0968] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[0969] Example 2

[0970] 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."

[0971] Conventional conversational agent systems have difficulty fully recognizing a user's unique profile and emotions to generate customized responses. They also lack the functionality to generate agents specialized for specific tasks and efficiently process task-related information. This has prevented them from fully improving the user experience and streamlining operations.

[0972] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable interactive agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past interactions, means for creating agents specialized for specific tasks and processing task-related information, means for identifying the user's emotions using an emotion engine and generating responses based on the emotions, and means for monitoring the usage status of the interactive agents and recording them in a log. This enables flexible response generation based on individual profiles and emotions, efficiency improvement of specific tasks, and appropriate management of usage status.

[0973] A "user profile" is information about an individual user that the system collects and stores, including the user's name, contact information, preferences, and behavioral history.

[0974] A "customizable conversational agent" is a conversational agent that is optimized to meet the needs of a user by adjusting its appearance, personality, abilities, etc. based on the user's profile information.

[0975] A "message" is information in text or speech format that a user inputs to a dialogue agent.

[0976] The "dialogue database" is a database that contains past dialogue history and stores and manages cumulative interactions with users.

[0977] An "agent specialized for a specific task" is a conversational agent that has the knowledge and skill set required for a specific task and is designed to efficiently support that task.

[0978] An "emotion engine" is an algorithm or program that analyzes emotions from a user's input message and identifies those emotions.

[0979] A "response" is a reply message that a dialogue agent generates in response to a user's message.

[0980] "Usage status" refers to information about how users use the system, such as how often and how they use the dialogue agent.

[0981] A "log" is a series of activity histories or event records that are recorded while a system is running.

[0982] The present invention is a system for generating an individual user profile and providing a customizable conversational agent. A specific embodiment of this system is described below.

[0983] Initial Setup

[0984] First-time user registration

[0985] When a user uses the system for the first time, they enter information such as their name, email address, and password into a sign-up form and send it from their device to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen and sends it to the user's device to display it. For example, a user enters "Yamada Taro," "taro.yamada@example.com," and "password 123," and sends it to the server.

[0986] Customization features

[0987] Customizing conversational agents

[0988] The user selects and inputs the appearance, personality, and abilities of the conversational agent "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. A customized profile is generated, and the server sends it to the user's device for confirmation. Specifically, settings such as "friendly," "approachable," and "advanced search functions" are possible.

[0989] Interactive processing

[0990] Conversation initiation and response generation

[0991] When a user inputs a message into the dialogue agent, the device sends the message to the server. The server first analyzes the received message and references the dialogue database. It checks past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the dialogue database and sent to the device for display to the user.

[0992] Use of emotion engine

[0993] The emotion engine analyzes the user's message and identifies the user's emotion. The server uses this emotion information to further customize the response message. The emotion information is stored in the dialogue database and reflected in future dialogues. For example, if the user types, "Today was a tough day," the emotion engine recognizes the emotion "tough" and generates a response based on that, such as, "Thank you for your hard work. Let me know if there's anything I can do to help you."

[0994] Business-specific functions

[0995] Setting up specialized agents

[0996] The user enters the information and skill set required for a specific task through a settings screen. The device sends that information to the server, which then loads the necessary data sets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use. For example, when setting up an agent specializing in sales activities, the user enters the sales-related skill set and necessary data.

[0997] Monetization and Platform Operations

[0998] Monitoring usage and displaying advertisements

[0999] The server monitors agent usage in real time and records it in a log. It displays appropriate advertisements based on usage patterns, and generates billing and plan update information based on API usage and service fee logs. Specifically, the server aggregates user usage at the end of the month, calculates usage fees, generates an invoice, and sends it to the user.

[1000] This allows for the provision of user-friendly conversational agents and business efficiency agents, enabling service operation and monetization. Furthermore, by utilizing an emotion engine, it is possible to generate more detailed and empathetic responses, improving the user experience.

[1001] Example prompt sentence:

[1002] "Set your conversational agent's appearance as friendly, personality as approachable, and abilities as advanced search capabilities."

[1003] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1004] System program processing flow (specific explanation)

[1005] Initial Setup

[1006] Step 1:

[1007] Input: A user enters their name, email address, and password into a sign-up form.

[1008] Operation:

[1009] The user fills out a form on the device and provides information such as "Yamada Taro," "taro.yamada@example.com," and "password123."

[1010] Output: The device sends the input information to the server.

[1011] Step 2:

[1012] Input: User sign-up information received from the device.

[1013] Operation:

[1014] The server analyzes the received information, generates a new user profile, and stores it in the database. For example, the user is registered with a user ID of "001."

[1015] Output: The server generates a user profile and stores it in the database.

[1016] Step 3:

[1017] Input: User profile data on the server.

[1018] Operation:

[1019] The server generates data for the initial setting screen and sends it to the terminal.

[1020] Output: The terminal displays the initial setup screen to the user.

[1021] Customization features

[1022] Step 4:

[1023] Input: The user enters agent customization information on the initial setup screen.

[1024] Operation:

[1025] The user selects the appearance (e.g., friendly), personality (e.g., approachable), and capabilities (e.g., advanced search functionality) of "myGPT."

[1026] Output: The device sends this customization information to the server.

[1027] Step 5:

[1028] Input: Customization information received from the device.

[1029] Operation:

[1030] The server updates the AI ​​model profile based on the received information, loading the necessary datasets and generating a customized profile.

[1031] Output: A customized profile is generated.

[1032] Step 6:

[1033] Input: Your customized profile.

[1034] Operation:

[1035] The server transmits the generated customization profile to the terminal.

[1036] Output: The device displays the customized profile to the user and asks for confirmation.

[1037] Interactive processing

[1038] Step 7:

[1039] Input: The user types a message into the conversation agent.

[1040] Operation:

[1041] The user types a message, such as "What's the weather like today?"

[1042] Output: The device sends the input message to the server.

[1043] Step 8:

[1044] Input: User's message received from the terminal.

[1045] Operation:

[1046] The server analyzes the received message and determines the intent of the message.

[1047] Output: The analysis results are obtained.

[1048] Step 9:

[1049] Input: The parsed result of the message.

[1050] Operation:

[1051] The server refers to the dialogue database to check the relevant past conversation history.

[1052] Output: The relevant data is retrieved.

[1053] Step 10:

[1054] Input: Conversation database and analysis results.

[1055] Operation:

[1056] The server uses AI models to generate appropriate responses, and may obtain additional information from external sources (such as a weather API).

[1057] Output: A response message is generated.

[1058] Step 11:

[1059] Input: The generated response message.

[1060] Operation:

[1061] The server stores the response message in a dialogue database and sends it to the terminal.

[1062] Output: The terminal displays the response message to the user.

[1063] Use of emotion engine

[1064] Step 12:

[1065] Input: The user's message sent from the terminal.

[1066] Operation:

[1067] The server uses an emotion engine to analyze the message and identify the emotion.

[1068] Output: Emotional information is obtained.

[1069] Step 13:

[1070] Input: Emotion information.

[1071] Operation:

[1072] The server generates a response message based on the emotion.

[1073] Output: A customized response message is generated.

[1074] Step 14:

[1075] Input: The generated emotion-based response message.

[1076] Operation:

[1077] The server stores the emotion-based responses in a dialogue database and transmits them to the terminal.

[1078] Output: The device displays a response message that takes emotion into account.

[1079] Business-specific functions

[1080] Step 15:

[1081] Input: The user inputs the necessary information for the business-specific agent.

[1082] Operation:

[1083] The user enters the skill sets and data required for sales activities on the settings screen.

[1084] Output: The device sends the information to the server.

[1085] Step 16:

[1086] Input: Business-specific information received from the terminal.

[1087] Operation:

[1088] Based on the information received, the server loads the necessary datasets and APIs.

[1089] Output: The required data is loaded.

[1090] Step 17:

[1091] Input: Business specific information and loaded dataset.

[1092] Operation:

[1093] The server fine-tunes the AI ​​model to correspond to a specific task and generates a task-specific profile.

[1094] Output: A job-specific profile is generated.

[1095] Step 18:

[1096] Input: The generated job-specific profile.

[1097] Operation:

[1098] The server transmits the generated business-specific profile to the user's terminal.

[1099] Output: The device displays the business-specific profile and makes it available for use.

[1100] Monetization and Platform Operations

[1101] Step 19:

[1102] Input: Usage data.

[1103] Operation:

[1104] The server monitors agent usage in real time and records it in a log.

[1105] Output: Usage log data is generated.

[1106] Step 20:

[1107] Input: Monitored usage data.

[1108] Operation:

[1109] The server displays appropriate ads based on usage patterns. The ads are displayed on the device.

[1110] Output: The ad is displayed on the user's screen.

[1111] Step 21:

[1112] Input: Log data regarding API usage and service fees.

[1113] Operation:

[1114] The server generates billing and plan update information, calculates the usage fee, and issues an invoice at the end of the month.

[1115] Output: A usage invoice is generated and sent to the user.

[1116] These specific processing steps allow each function to operate smoothly, providing a friendly and efficient interactive agent service to the user.

[1117] (Application example 2)

[1118] 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."

[1119] In order to provide friendly yet effective assistance to users in autonomous vehicles, customization based on the user's emotions and individual needs is required. However, existing systems are unable to recognize the user's emotions and generate responses based on them, resulting in insufficient personalized navigation, entertainment, or emergency response.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for recognizing user emotions and generating responses based on the emotions, means for creating agents specialized for specific tasks and processing task-related information, and means for being installed in autonomous vehicles to assist users with navigation, vehicle operation, entertainment, and emergency response. This makes it possible to significantly improve the user experience in autonomous vehicles.

[1121] A "user profile" is a collection of information about an individual user, including the user's name, contact information, preferences, and past interaction history.

[1122] A "dialogue agent" is a system that uses artificial intelligence to engage in dialogue with users, enabling the exchange of messages through natural language processing.

[1123] An "emotion engine" is a technology that analyzes emotions from a user's text or voice and generates a response based on those emotions.

[1124] "Navigation" is a function that provides route guidance and direction to reach a destination, and presents an appropriate route based on map information and current location data.

[1125] "Vehicle operation" refers to functions related to driving an autonomous vehicle, including speed control, lane changes, and parking.

[1126] "Entertainment" refers to content and services that provide users with enjoyment and entertainment, and includes music, movies, games, and the like.

[1127] "Emergency response" is a function that provides appropriate measures and support when an emergency such as an accident or illness occurs.

[1128] A "conversation database" is a database that stores past conversation history and uses it for future conversations.

[1129] A "task-specific agent" is a conversational agent designed to handle a specific task or business, and has specific skills and knowledge.

[1130] The present invention relates to a customizable dialogue agent system for use in autonomous vehicles. The following describes in detail a system for implementing this application.

[1131] Initial Setup

[1132] First-time user registration

[1133] When a user first uses the system, they create a user profile. They enter information such as their name, preferences, and contact details using the vehicle's infotainment system. Based on this, the server creates a user profile and stores it in a database.

[1134] Customizing conversational agents

[1135] Users can customize the appearance, personality, and capabilities of their conversational agent through the infotainment system's settings screen. The server receives this customization information and updates the AI ​​model's profile, generating a conversational agent optimized for the user.

[1136] Interactive processing

[1137] Conversation initiation and response generation

[1138] When a user types a message into the conversation agent, the infotainment system sends it to a server. The server first analyzes the message and checks past conversation history against a conversation database. It then uses an AI model to generate an appropriate response, incorporating additional information from external sources as needed.

[1139] Use of emotion engine

[1140] The emotion engine analyzes emotions from the user's message and generates a response based on that emotion. For example, if a user types, "I had a rough day today," the emotion engine recognizes the emotion "rough" and generates a response such as, "Thank you for your hard work. Let me know if there's anything I can do to help you." This information is stored in the conversation database and reflected in future interactions.

[1141] Application example in autonomous vehicles

[1142] The system is installed inside autonomous vehicles and provides the following functions:

[1143] Navigation assistance: When a user inputs their destination, the conversational agent guides them to the optimal route. For example, in response to a request such as "I want to go to a nearby restaurant," the agent generates a response such as "There is a certain restaurant one kilometer away. Do you want to go there?"

[1144] Vehicle operation assistance: The conversational agent assists with vehicle operation in response to user instructions. For example, if the user says, "Set the air conditioner temperature to 23 degrees," the agent will respond, "The air conditioner temperature has been set to 23 degrees," and actually perform the operation.

[1145] Entertainment recommendations: Based on the user's preferences, the app suggests entertainment such as music and movies. For example, if the user says, "I want to listen to classical music," the app will start playing classical music.

[1146] Emergency response: When an emergency occurs, the conversational agent will respond appropriately. For example, if you say "I'm not feeling well," it will generate a response such as "Would you like to contact a medical institution?"

[1147] Hardware and software used

[1148] Hardware: Autonomous vehicle infotainment systems, displays, speakers, sensors, etc.

[1149] Software: OpenAI API, emotion recognition API, database system

[1150] Prompt Sentence Examples

[1151] Below are examples of prompt sentences that are generated based on messages from users.

[1152] User: Today was a rough day

[1153] User emotion: Sad

[1154] Generate a response for the in-car assistant:

[1155] This prompt allows OpenAI's text generation model to generate an appropriate response, thus enabling the implementation of a conversational agent system that provides a user-friendly experience within a self-driving vehicle.

[1156] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1157] Step 1:

[1158] The user enters profile information for the conversational agent. Specifically, the user enters information such as name, preferences, and contact details through the infotainment system and sends it from the device to the server. This data is received by the server and stored in a database as a user profile.

[1159] Input: User information (name, preferences, contact details, etc.)

[1160] Output: Generate and save user profile

[1161] Step 2:

[1162] The user customizes the conversational agent. The user selects the agent's appearance, personality, and abilities from the infotainment system's settings screen, and this information is sent from the device to the server. The server updates the AI ​​model profile based on the received information and generates a customized agent for the user.

[1163] Input: Customization information for the conversational agent (appearance, personality, abilities)

[1164] Output: A customized conversational agent

[1165] Step 3:

[1166] A user inputs a message to the dialogue agent through the infotainment system, and the message is sent from the terminal to the server. The server receives the message.

[1167] Input: Message from the user

[1168] Output: Message received by server

[1169] Step 4:

[1170] The server analyzes the message and performs emotion recognition. The server uses an emotion engine to analyze the user's message and identify the emotion. For example, it can recognize the emotion "sad" from the message "Today was a tough day."

[1171] Input: User message

[1172] Output: Identified emotion information

[1173] Step 5:

[1174] The server generates a prompt based on the user message and the identified emotional information, and provides it to OpenAI's generative AI model.

[1175] Input: User message and emotion information

[1176] Output: Generated prompt statement

[1177] Example: Prompt sentence example

[1178] User: Today was a rough day

[1179] User emotion: Sad

[1180] Generate a response for the in-car assistant:

[1181] Step 6:

[1182] Response generation by the generative AI model. The server sends the generated prompt to OpenAI's API, and the AI ​​model generates an appropriate response. For example, a response such as "Thank you for your hard work. Let me know if there's anything I can help you with."

[1183] Input: prompt statement

[1184] Output: The generated response message

[1185] Step 7:

[1186] The server sends a response message to the infotainment system, which then sends the generated response message to the terminal and displays it to the user.

[1187] Input: The generated response message

[1188] Output: The response message that is displayed to the user

[1189] Step 8:

[1190] Storing conversation information: The server stores conversation history in a database as part of the user profile, which can then be used to inform future conversations.

[1191] Input: User message and generated response

[1192] Output: Dialogue history stored in a database

[1193] The above steps provide customizable interaction assistance for the user in an autonomous vehicle.

[1194] 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.

[1195] 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.

[1196] 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.

[1197] [Third embodiment]

[1198] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1199] 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.

[1200] 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).

[1201] 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.

[1202] 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.

[1203] 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).

[1204] 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.

[1205] 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.

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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."

[1210] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the ability to receive messages from users and generate responses by referencing a database of past conversations. It also allows users to create agents specialized for specific tasks and process task-related information.

[1211] Initial Setup

[1212] First-time user registration

[1213] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[1214] Customization features

[1215] Customizing conversational agents

[1216] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[1217] Interactive processing

[1218] Conversation initiation and response generation

[1219] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1220] Specific examples

[1221] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message and references past conversation history and a schedule database. As a result, it generates a response such as, "I have a meeting tomorrow at 2 p.m." and sends it to the terminal. The terminal then displays this and provides it to the user.

[1222] Business-specific functions

[1223] Setting up specialized agents

[1224] Users enter the information and skill sets required for a specific task through a settings screen. The device sends this information to the server, which then fine-tunes a task-specific AI model based on the received information. The generated task-specific profile is sent to the user's device, where it can be viewed and used by the user.

[1225] Specific examples

[1226] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[1227] Monetization and Platform Operations

[1228] Monitoring usage and displaying advertisements

[1229] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs.

[1230] Specific examples

[1231] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[1232] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[1233] The processing flow will be explained below.

[1234] Initial Setup

[1235] User Registration

[1236] Step 1:

[1237] User: Enters name, email address, and password into the sign-up form.

[1238] Step 2:

[1239] Terminal: Sends the entered information to the server.

[1240] Step 3:

[1241] Server: Stores the received user information in a database and generates a new user profile.

[1242] Step 4:

[1243] Server: Generates the data for the initial setup screen and sends it to the user's device.

[1244] Customization features

[1245] Customizing conversational agents

[1246] Step 1:

[1247] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[1248] Step 2:

[1249] Device: Sends customization information to the server.

[1250] Step 3:

[1251] Server: Updates the AI ​​model profile based on the received customization information.

[1252] Step 4:

[1253] Server: Loads the required datasets and generates the customized profile.

[1254] Step 5:

[1255] Server: Sends customized profile data to the user's device.

[1256] Step 6:

[1257] On the device: The received customization profile is displayed to the user for confirmation.

[1258] Interactive processing

[1259] Conversation initiation and response generation

[1260] Step 1:

[1261] User: Type a message in the chat window.

[1262] Step 2:

[1263] Terminal: Sends the entered message to the server.

[1264] Step 3:

[1265] Server: Analyzes the received message.

[1266] Step 4:

[1267] Server: Refer to the conversation database and check past conversation history.

[1268] Step 5:

[1269] Server: Uses AI models to generate appropriate responses.

[1270] Step 6:

[1271] Server: Stores the response message in the conversation database.

[1272] Step 7:

[1273] Server: Sends the generated response message to the user's terminal.

[1274] Step 8:

[1275] Terminal: Displays the received response message to the user.

[1276] Business-specific functions

[1277] Setting up specialized agents

[1278] Step 1:

[1279] Users: Enter the information and skill sets required for a specific job through a setup screen.

[1280] Step 2:

[1281] Terminal: Sends business information to the server.

[1282] Step 3:

[1283] Server: Loads the necessary datasets and APIs based on the received business information.

[1284] Step 4:

[1285] Server: Fine-tunes AI models for specific tasks.

[1286] Step 5:

[1287] Server: Generates a business-specific profile and sends it to the user's device.

[1288] Step 6:

[1289] Terminal: The received business-specific profile is displayed to the user and made available for use.

[1290] Monetization and Platform Operations

[1291] Monitoring usage and displaying advertisements

[1292] Step 1:

[1293] Server: Monitors and logs agent usage in real time.

[1294] Step 2:

[1295] Server: Displays advertisements based on user usage patterns.

[1296] Billing and Feedback

[1297] Step 3:

[1298] Server: Generates billing and plan update information based on API usage and service fee logs.

[1299] Step 4:

[1300] Server: Calculates usage fees at the end of the month and issues invoices to users.

[1301] Step 5:

[1302] Server: Collecting user feedback to help improve our services.

[1303] This trend will enable the provision of user-friendly conversational agents and business efficiency agents, and the operation and monetization of services.

[1304] Example 1

[1305] 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."

[1306] Conventional conversational agents have limited functionality, such as the ability to generate individual profiles for each user and the ability to flexibly generate responses according to user customization. Furthermore, there is a lack of systems that allow agents to be configured to specialize in specific tasks, making them insufficient in addressing business efficiency. Furthermore, there are no integrated functions available, such as real-time monitoring based on usage, advertising display, or billing. These issues need to be resolved.

[1307] 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.

[1308] In this invention, the server includes means for generating individual profiles based on information input by users from their terminals and providing customizable conversational agents based on each profile, means for updating the customized conversational agent profile and loading necessary data sets, and means for receiving messages from users and analyzing and generating responses by referring to a database of past conversations. This makes it possible to generate customized responses based on each user's individual profile, and to achieve integrated configuration of agents specialized for specific tasks, real-time usage status monitoring, advertisement display, and fee billing.

[1309] An "individual profile" is a collection of individually customizable user information that is generated based on each user's input information.

[1310] A "customizable conversation agent" is a conversation support program that allows users to customize their appearance, personality, abilities, etc. based on their individual profiles.

[1311] The "past conversation database" is a database for storing the history of past conversations with a user and for use in future conversations.

[1312] A "means for generating a response" is an algorithm or system used to analyze a message from a user and generate an appropriate response.

[1313] The "means for processing business-related information" is a function that collects and analyzes information related to a specific business, and enables an agent to provide business support based on that information.

[1314] An "AI model profile" is a collection of information that defines the settings and characteristics of the artificial intelligence model used by the conversational agent.

[1315] "Means for loading datasets" refers to the functionality for loading data required for customization and response generation into the system.

[1316] "Fine-tuning" is a technique for adjusting an AI model to further adapt it to a specific purpose and improve its performance.

[1317] "Real-time monitoring" is the process of instantly observing and collecting data on system operation and user usage.

[1318] "Displaying advertisements" is a function that displays appropriate advertisements on the user's terminal based on the user's usage.

[1319] The "means for generating a bill" is a function that calculates the usage fee and issues a bill based on the data of the user's use of the system.

[1320] The present invention provides a system for generating individual user profiles and providing customizable conversational agents based on each profile. The system includes a server for generating profiles based on user input information and storing the profiles in a database, and multiple means for generating responses in real time through user interaction.

[1321] Hardware and Software Configuration

[1322] This system consists of the following main hardware and software:

[1323] Server: A computer equipped with a database management system (DBMS), natural language processing (NLP) libraries, and an AI model (e.g., GPT-3).

[1324] Device: A personal computer (PC), smartphone, or tablet operated by a user.

[1325] Network: The internet connection that connects the user's device to the server.

[1326] First-time user registration

[1327] When a new user uses the system, they must register for the first time. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends the information to the server. The server receives this information, generates an individual user profile, and stores it in a database. The server then generates data for the initial setup screen, which is sent to the user's device and displayed.

[1328] Customizing conversational agents

[1329] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. This creates a customized conversational agent, and the server sends this profile to the user's device.

[1330] Conversation initiation and response generation

[1331] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and checks past conversation history by referring to a conversation database. It then uses an AI model to generate an appropriate response, and if necessary, obtains additional information from external sources and incorporates it into the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1332] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message, references past conversation history and a schedule database, and generates a response such as, "I have a meeting tomorrow at 2 p.m." This response is sent to the terminal and displayed to the user.

[1333] Setting up specialized agents

[1334] The user enters the information and skill set required for a specific task through a configuration screen. The device sends this information to the server, which then fine-tunes the task-specific AI model based on the received information. The fine-tuned profile is then generated by the server and sent to the user's device. This allows the user to see the task-specific agent in a usable state.

[1335] For example, when a user sets up an agent specialized in sales activities, the user inputs the sales-related skill set and necessary data. The server generates a sales-specialized agent based on this and provides support such as customer management and performance analysis.

[1336] Monetization and Platform Operations

[1337] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs. At the end of the month, the server aggregates user usage, calculates usage fees, and issues invoices. It also collects user feedback to help improve the service.

[1338] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[1339] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1340] Step 1: User Registration

[1341] Processing flow

[1342] 1. The user enters their name, email address, and password into a sign-up form on their device.

[1343] 2. The device sends this input information to the server.

[1344] Specific operations and inputs and outputs

[1345] When a user opens a browser, enters their name, email address, and password into the sign-up form, and presses the submit button, the data is sent from the device to the server. The entered information (name, email address, password) is passed to the server, which receives it and creates a new user profile. The created profile is saved in a database. The server then creates initial setup screen data and sends it to the device. This causes the initial setup screen to be displayed on the device.

[1346] Step 2: Customizing the conversation agent

[1347] Processing flow

[1348] 1. The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[1349] 2. The device sends this customization information to the server.

[1350] Specific operations and inputs and outputs

[1351] The user sets the agent's appearance (e.g., blue eyes), personality (e.g., kind), and abilities (e.g., good at giving business advice) on the initial setup screen. Then, by pressing the send button, the input information is sent from the device to the server. The server updates the AI ​​model profile based on the received customization information, loads the necessary datasets, and generates a customized agent profile. This is then sent to the device, allowing the user to view the customized agent.

[1352] Step 3: Conversation initiation and response generation

[1353] Processing flow

[1354] 1. The user inputs a message to the conversation agent.

[1355] 2. The device sends the message to the server.

[1356] 3. The server analyzes the received message and references the conversation database.

[1357] 4. The server generates an appropriate response.

[1358] Specific operations and inputs and outputs

[1359] When a user types "What are your plans for tomorrow?" into their device and sends it, the message is sent to the server. The server analyzes the received message using a natural language processing library, references past conversation history from a conversation database, and uses the acquired information to generate an appropriate response using an AI model. For example, by referencing past conversation history and a schedule database, the AI ​​model generates a response such as "I have a meeting tomorrow at 2 p.m." This response message is saved in the conversation database, sent to the device, and displayed to the user.

[1360] Step 4: Setting up specialized agents

[1361] Processing flow

[1362] 1. The user enters the information and skill set required for the job on the settings screen.

[1363] 2. The device sends the information to the server.

[1364] 3. The server fine-tunes the AI ​​model based on the information received.

[1365] Specific operations and inputs and outputs

[1366] For example, when a user sets up an agent specialized in sales activities, they enter the "sales-related skill set" and "required data" into the settings screen. The device then sends the input information to the server. The server then fine-tunes the AI ​​model based on the received information and generates a sales-specialized agent profile. The generated profile is then sent to the device and can be viewed by the user. This agent can then assist with customer management, sales performance analysis, and more.

[1367] Step 5: Monitoring usage and displaying advertisements

[1368] Processing flow

[1369] 1. The server monitors agent usage in real time.

[1370] 2. The server displays advertisements based on usage logs.

[1371] 3. The server generates billing and plan updates.

[1372] Specific operations and inputs and outputs

[1373] The server monitors agent usage in real time using an existing log management system, recording which functions users use and to what extent. At the end of the month, the server compiles statistics based on the usage logs, calculates usage fees, and generates invoices. Furthermore, the advertising display system uses this usage data to display appropriate advertisements to users. User feedback is also collected and used to improve the service.

[1374] (Application example 1)

[1375] 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."

[1376] Conventional conversational agents can respond based on the user's individual conversation history and preferences, but it is difficult to make optimal suggestions for a specific content distribution service that match the user's preferences. Furthermore, it is insufficient to provide a more personalized experience by giving the conversational agent individual personality and knowledge and reflecting the user's customization. As a result, users are unable to smoothly enjoy content that matches their preferences, which does not lead to improved satisfaction.

[1377] 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.

[1378] In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for creating agents specialized for specific tasks and processing task-related information, means for generating and responding to customized prompts based on the user's content preferences, and means for recommending optimal content based on the generated prompts. This enables personalized content recommendations based on the user's preferences, significantly improving user satisfaction.

[1379] A "user profile" is a database that includes each user's individual information, preferences, past interaction history, and so on.

[1380] A "conversational agent" is an artificial intelligence system that provides information through conversation with a user and generates the required responses.

[1381] "Means for receiving messages" refers to the interface or communication protocol for transferring input from the user to the server.

[1382] A "conversation database" is a data storage that stores the contents of past conversations with users and uses them to generate future responses.

[1383] A "task-specific agent" is a conversational agent that is optimized for a specific task or function.

[1384] "Content preferences" refer to the genres and characteristics of content that a user particularly likes.

[1385] "Prompt generation" is the process of generating appropriate questions or instructions based on the user's preferences and queries.

[1386] The "means for providing a response" refers to the function of creating an appropriate response based on the generated prompt and providing it to the user.

[1387] "Means for recommending content" refers to a function that selects optimal content based on the user's preferences and prompts and suggests it to the user.

[1388] The present invention is a system that recommends optimal content based on a user's content preferences using a conversational agent customized based on an individual user profile. This system is realized using a server, a terminal, and a generative AI model.

[1389] Initial Setup

[1390] First-time user registration

[1391] When a user first uses the system, they register. The user enters their name, email address, password, and content preferences into a sign-up form on their device, which then sends the information to the server. The server generates a user profile based on the received information and stores it in a database. The server then generates data for the initial setup screen and displays it on the user's device.

[1392] Customization features

[1393] Customizing conversational agents

[1394] The user selects and inputs the appearance, personality, and abilities of the conversational agent on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the profile of the generative AI model and loads the necessary datasets. Once the customized profile is generated, the server sends it to the user's device for confirmation.

[1395] Interactive processing

[1396] Conversation initiation and response generation

[1397] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and references the conversation database to check past conversation history and the user's content preferences. It then uses a generative AI model to generate an appropriate response. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1398] Content recommendation function

[1399] Content recommendation implementation

[1400] When a user enters a query for specific content, the server analyzes the message and generates prompts based on the user's preferences. For example, if a user's profile includes information such as "I like action movies," the following prompts will be generated in response to the query "What movies do you recommend?"

[1401] Example prompt:

[1402] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[1403] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[1404] Hardware and software used

[1405] Hardware

[1406] Server: Cloud server or on-premise server (AWS, Google Cloud, Azure, etc.)

[1407] Device: User devices such as smartphones, tablets, and PCs

[1408] software

[1409] Django: a web framework

[1410] Transformers: Generative AI model implementation (Huggingface's Transformers model)

[1411] GPT-2: Generative Model

[1412] This allows the personalized conversation agent to recommend optimal content based on the user's preferences.

[1413] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1414] Step 1:

[1415] First-time user registration

[1416] A user enters their name, email address, password, and content preferences into a sign-up form on their device and clicks the Register button. The input data (name, email address, password, and content preferences) is sent from the device to the server. The server receives this data, generates a user profile, and stores it in a database. This profile includes the user's interaction history and preferences. Next, the server generates data for an initial setup screen, sends it to the user's device, and displays it.

[1417] Input: User's name, email address, password, content preferences

[1418] Output: Generated user profile, initial setup screen data

[1419] Step 2:

[1420] Customizing conversational agents

[1421] The user selects the appearance, personality, and abilities of the conversational agent from an initial setup screen, and the device sends the customization information to the server. The server receives this customization information, updates the profile of the generative AI model, and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device, displaying a confirmation screen.

[1422] Input: Customization information for the appearance, personality, and abilities of the conversational agent

[1423] Output: A customized conversational agent profile

[1424] Step 3:

[1425] Conversation initiation and response generation

[1426] The user inputs a message into the conversation agent, and the device sends the message to the server. The server analyzes the received message and references the conversation database to confirm the user's past conversation history and content preferences. The server uses a generative AI model to generate an appropriate response and stores it in the conversation database. The generated response message is sent to the device and displayed to the user.

[1427] Input: Message from the user

[1428] Output: The generated response message

[1429] Step 4:

[1430] Content recommendation implementation

[1431] The user enters a query for specific content, which the device sends to the server. The server analyzes the message and generates a prompt based on the user's preferences. For example, if the user says "I like action movies," the following prompt might be generated:

[1432] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[1433] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[1434] Input: User query

[1435] Output: Content recommendations

[1436] Step 5:

[1437] Storing conversation history and preference data

[1438] The generated responses and content recommendations are stored in a conversation database, improving the accuracy of future conversations and content recommendations. User profiles are also periodically updated to accumulate preference data.

[1439] Input: Generated responses, content recommendation history

[1440] Output: Updated conversation database and user profile

[1441] 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.

[1442] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the functions of receiving messages from users, generating responses by referencing a database of past conversations, and creating agents specialized for specific tasks and processing task-related information. The present invention also incorporates an emotion engine that can recognize user emotions and generate responses based on those emotions.

[1443] Initial Setup

[1444] First-time user registration

[1445] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[1446] Customization features

[1447] Customizing conversational agents

[1448] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[1449] Interactive processing

[1450] Conversation initiation and response generation

[1451] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1452] Use of emotion engine

[1453] The present invention incorporates an emotion engine that recognizes emotions from the user's message. The emotion engine analyzes messages received by the server and identifies the user's emotion. Based on this emotion information, the response message is further customized. The emotion information is stored in a conversation database and reflected in future conversations.

[1454] Specific examples

[1455] For example, if a user inputs "Today was a tough day," the emotion engine recognizes the emotion "tough." Based on this emotion information, the server generates a response such as "Thank you for your hard work. Let me know if there's anything I can help you with," and sends it to the user. The device then displays this and provides it to the user.

[1456] Business-specific functions

[1457] Setting up specialized agents

[1458] Users enter the information and skill sets required for a specific task through a configuration screen. The device sends this information to the server, which then loads the necessary datasets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use.

[1459] Specific examples

[1460] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[1461] Monetization and Platform Operations

[1462] Monitoring usage and displaying advertisements

[1463] The server monitors and logs agent usage in real time, displays ads appropriately based on usage patterns, and generates billing and plan updates based on API usage and service fee logs.

[1464] Specific examples

[1465] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[1466] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[1467] The processing flow will be explained below.

[1468] Initial Setup

[1469] User Registration

[1470] Step 1:

[1471] User: Enters name, email address, and password into a sign-up form.

[1472] Step 2:

[1473] Terminal: Sends the entered information to the server.

[1474] Step 3:

[1475] Server: Stores the received user information in a database and generates a new user profile.

[1476] Step 4:

[1477] Server: Generates the data for the initial setup screen and sends it to the user's device.

[1478] Customization features

[1479] Customizing conversational agents

[1480] Step 1:

[1481] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[1482] Step 2:

[1483] Device: Sends customization information to the server.

[1484] Step 3:

[1485] Server: Updates the AI ​​model profile based on the received customization information.

[1486] Step 4:

[1487] Server: Loads the required datasets and generates the customized profile.

[1488] Step 5:

[1489] Server: Sends customized profile data to the user's device.

[1490] Step 6:

[1491] On the device: The received customization profile is displayed to the user for confirmation.

[1492] Interactive processing

[1493] Conversation initiation and response generation

[1494] Step 1:

[1495] User: Type a message in the chat window.

[1496] Step 2:

[1497] Terminal: Sends the entered message to the server.

[1498] Step 3:

[1499] Server: Analyzes the received message.

[1500] Step 4:

[1501] Server: Refer to the conversation database and check past conversation history.

[1502] Step 5:

[1503] Server: Uses AI models to generate appropriate responses.

[1504] Step 6:

[1505] Server: Stores the response message in the conversation database.

[1506] Step 7:

[1507] Server: Sends the generated response message to the user's terminal.

[1508] Step 8:

[1509] Terminal: Displays the received response message to the user.

[1510] Use of emotion engine

[1511] Step 1:

[1512] User: Type and send a message.

[1513] Step 2:

[1514] Terminal: Sends the entered message to the server.

[1515] Step 3:

[1516] Server: Analyzes the received message using the emotion engine.

[1517] Step 4:

[1518] Server: The emotion engine recognizes the user's emotional state.

[1519] Step 5:

[1520] Server: Further customize the response message based on the emotional information.

[1521] Step 6:

[1522] Server: Stores the recognized emotion information in a conversation database.

[1523] Step 7:

[1524] Server: Sends a customized response message to the user's device.

[1525] Step 8:

[1526] Terminal: Displays the received response message to the user.

[1527] Business-specific functions

[1528] Setting up specialized agents

[1529] Step 1:

[1530] Users: Enter the information and skill sets required for a specific job through a setup screen.

[1531] Step 2:

[1532] Terminal: Sends business information to the server.

[1533] Step 3:

[1534] Server: Loads the necessary datasets and APIs based on the received business information.

[1535] Step 4:

[1536] Server: Fine-tunes AI models for specific tasks.

[1537] Step 5:

[1538] Server: Generates a business-specific profile and sends it to the user's device.

[1539] Step 6:

[1540] Terminal: The received business-specific profile is displayed to the user and made available for use.

[1541] Monetization and Platform Operations

[1542] Monitoring usage and displaying advertisements

[1543] Step 1:

[1544] Server: Monitors and logs agent usage in real time.

[1545] Step 2:

[1546] Server: Displays advertisements based on user usage patterns.

[1547] Billing and Feedback

[1548] Step 3:

[1549] Server: Generates billing and plan update information based on API usage and service fee logs.

[1550] Step 4:

[1551] Server: Calculates usage fees at the end of the month and issues invoices to users.

[1552] Step 5:

[1553] Server: Collecting user feedback to help improve our services.

[1554] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[1555] Example 2

[1556] 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."

[1557] Conventional conversational agent systems have difficulty fully recognizing a user's unique profile and emotions to generate customized responses. They also lack the functionality to generate agents specialized for specific tasks and efficiently process task-related information. This has prevented them from fully improving the user experience and streamlining operations.

[1558] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable interactive agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past interactions, means for creating agents specialized for specific tasks and processing task-related information, means for identifying the user's emotions using an emotion engine and generating responses based on the emotions, and means for monitoring the usage status of the interactive agents and recording them in a log. This enables flexible response generation based on individual profiles and emotions, efficiency improvement of specific tasks, and appropriate management of usage status.

[1559] A "user profile" is information about an individual user that the system collects and stores, including the user's name, contact information, preferences, and behavioral history.

[1560] A "customizable conversational agent" is a conversational agent that is optimized to meet the needs of a user by adjusting its appearance, personality, abilities, etc. based on the user's profile information.

[1561] A "message" is information in text or speech format that a user inputs to a dialogue agent.

[1562] The "dialogue database" is a database that contains past dialogue history and stores and manages cumulative interactions with users.

[1563] An "agent specialized for a specific task" is a conversational agent that has the knowledge and skill set required for a specific task and is designed to efficiently support that task.

[1564] An "emotion engine" is an algorithm or program that analyzes emotions from a user's input message and identifies those emotions.

[1565] A "response" is a reply message that a dialogue agent generates in response to a user's message.

[1566] "Usage status" refers to information about how users use the system, such as how often and how they use the dialogue agent.

[1567] A "log" is a series of activity histories or event records that are recorded while a system is running.

[1568] The present invention is a system for generating an individual user profile and providing a customizable conversational agent. A specific embodiment of this system is described below.

[1569] Initial Setup

[1570] First-time user registration

[1571] When a user uses the system for the first time, they enter information such as their name, email address, and password into a sign-up form and send it from their device to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen and sends it to the user's device to display it. For example, a user enters "Yamada Taro," "taro.yamada@example.com," and "password 123," and sends it to the server.

[1572] Customization features

[1573] Customizing conversational agents

[1574] The user selects and inputs the appearance, personality, and abilities of the conversational agent "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. A customized profile is generated, and the server sends it to the user's device for confirmation. Specifically, settings such as "friendly," "approachable," and "advanced search functions" are possible.

[1575] Interactive processing

[1576] Conversation initiation and response generation

[1577] When a user inputs a message into the dialogue agent, the device sends the message to the server. The server first analyzes the received message and references the dialogue database. It checks past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the dialogue database and sent to the device for display to the user.

[1578] Use of emotion engine

[1579] The emotion engine analyzes the user's message and identifies the user's emotion. The server uses this emotion information to further customize the response message. The emotion information is stored in the dialogue database and reflected in future dialogues. For example, if the user types, "Today was a tough day," the emotion engine recognizes the emotion "tough" and generates a response based on that, such as, "Thank you for your hard work. Let me know if there's anything I can do to help you."

[1580] Business-specific functions

[1581] Setting up specialized agents

[1582] The user enters the information and skill set required for a specific task through a settings screen. The device sends that information to the server, which then loads the necessary data sets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use. For example, when setting up an agent specializing in sales activities, the user enters the sales-related skill set and necessary data.

[1583] Monetization and Platform Operations

[1584] Monitoring usage and displaying advertisements

[1585] The server monitors agent usage in real time and records it in a log. It displays appropriate advertisements based on usage patterns, and generates billing and plan update information based on API usage and service fee logs. Specifically, the server aggregates user usage at the end of the month, calculates usage fees, generates an invoice, and sends it to the user.

[1586] This allows for the provision of user-friendly conversational agents and business efficiency agents, enabling service operation and monetization. Furthermore, by utilizing an emotion engine, it is possible to generate more detailed and empathetic responses, improving the user experience.

[1587] Example prompt sentence:

[1588] "Set your conversational agent's appearance as friendly, personality as approachable, and abilities as advanced search capabilities."

[1589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1590] System program processing flow (specific explanation)

[1591] Initial Setup

[1592] Step 1:

[1593] Input: A user enters their name, email address, and password into a sign-up form.

[1594] Operation:

[1595] The user fills out a form on the device and provides information such as "Yamada Taro," "taro.yamada@example.com," and "password123."

[1596] Output: The device sends the input information to the server.

[1597] Step 2:

[1598] Input: User sign-up information received from the device.

[1599] Operation:

[1600] The server analyzes the received information, generates a new user profile, and stores it in the database. For example, the user is registered with a user ID of "001."

[1601] Output: The server generates a user profile and stores it in the database.

[1602] Step 3:

[1603] Input: User profile data on the server.

[1604] Operation:

[1605] The server generates data for the initial setting screen and sends it to the terminal.

[1606] Output: The terminal displays the initial setup screen to the user.

[1607] Customization features

[1608] Step 4:

[1609] Input: The user enters agent customization information on the initial setup screen.

[1610] Operation:

[1611] The user selects the appearance (e.g., friendly), personality (e.g., approachable), and capabilities (e.g., advanced search functionality) of "myGPT."

[1612] Output: The device sends this customization information to the server.

[1613] Step 5:

[1614] Input: Customization information received from the device.

[1615] Operation:

[1616] The server updates the AI ​​model profile based on the received information, loading the necessary datasets and generating a customized profile.

[1617] Output: A customized profile is generated.

[1618] Step 6:

[1619] Input: Your customized profile.

[1620] Operation:

[1621] The server transmits the generated customization profile to the terminal.

[1622] Output: The device displays the customized profile to the user and asks for confirmation.

[1623] Interactive processing

[1624] Step 7:

[1625] Input: The user types a message into the conversation agent.

[1626] Operation:

[1627] The user types a message, such as "What's the weather like today?"

[1628] Output: The device sends the input message to the server.

[1629] Step 8:

[1630] Input: User's message received from the terminal.

[1631] Operation:

[1632] The server analyzes the received message and determines the intent of the message.

[1633] Output: The analysis results are obtained.

[1634] Step 9:

[1635] Input: The parsed result of the message.

[1636] Operation:

[1637] The server refers to the dialogue database to check the relevant past conversation history.

[1638] Output: The relevant data is retrieved.

[1639] Step 10:

[1640] Input: Conversation database and analysis results.

[1641] Operation:

[1642] The server uses AI models to generate appropriate responses, and may obtain additional information from external sources (such as a weather API).

[1643] Output: A response message is generated.

[1644] Step 11:

[1645] Input: The generated response message.

[1646] Operation:

[1647] The server stores the response message in a dialogue database and sends it to the terminal.

[1648] Output: The terminal displays the response message to the user.

[1649] Use of emotion engine

[1650] Step 12:

[1651] Input: The user's message sent from the terminal.

[1652] Operation:

[1653] The server uses an emotion engine to analyze the message and identify the emotion.

[1654] Output: Emotional information is obtained.

[1655] Step 13:

[1656] Input: Emotion information.

[1657] Operation:

[1658] The server generates a response message based on the emotion.

[1659] Output: A customized response message is generated.

[1660] Step 14:

[1661] Input: The generated emotion-based response message.

[1662] Operation:

[1663] The server stores the emotion-based responses in a dialogue database and transmits them to the terminal.

[1664] Output: The device displays a response message that takes emotion into account.

[1665] Business-specific functions

[1666] Step 15:

[1667] Input: The user inputs the necessary information for the business-specific agent.

[1668] Operation:

[1669] The user enters the skill sets and data required for sales activities on the settings screen.

[1670] Output: The device sends the information to the server.

[1671] Step 16:

[1672] Input: Business-specific information received from the terminal.

[1673] Operation:

[1674] Based on the information received, the server loads the necessary datasets and APIs.

[1675] Output: The required data is loaded.

[1676] Step 17:

[1677] Input: Business specific information and loaded dataset.

[1678] Operation:

[1679] The server fine-tunes the AI ​​model to correspond to a specific task and generates a task-specific profile.

[1680] Output: A job-specific profile is generated.

[1681] Step 18:

[1682] Input: The generated job-specific profile.

[1683] Operation:

[1684] The server transmits the generated business-specific profile to the user's terminal.

[1685] Output: The device displays the business-specific profile and makes it available for use.

[1686] Monetization and Platform Operations

[1687] Step 19:

[1688] Input: Usage data.

[1689] Operation:

[1690] The server monitors agent usage in real time and records it in a log.

[1691] Output: Usage log data is generated.

[1692] Step 20:

[1693] Input: Monitored usage data.

[1694] Operation:

[1695] The server displays appropriate ads based on usage patterns. The ads are displayed on the device.

[1696] Output: The ad is displayed on the user's screen.

[1697] Step 21:

[1698] Input: Log data regarding API usage and service fees.

[1699] Operation:

[1700] The server generates billing and plan update information, calculates the usage fee, and issues an invoice at the end of the month.

[1701] Output: A usage invoice is generated and sent to the user.

[1702] These specific processing steps allow each function to operate smoothly, providing a friendly and efficient interactive agent service to the user.

[1703] (Application example 2)

[1704] 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."

[1705] In order to provide friendly yet effective assistance to users in autonomous vehicles, customization based on the user's emotions and individual needs is required. However, existing systems are unable to recognize the user's emotions and generate responses based on them, resulting in insufficient personalized navigation, entertainment, or emergency response.

[1706] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for recognizing user emotions and generating responses based on the emotions, means for creating agents specialized for specific tasks and processing task-related information, and means for being installed in autonomous vehicles to assist users with navigation, vehicle operation, entertainment, and emergency response. This makes it possible to significantly improve the user experience in autonomous vehicles.

[1707] A "user profile" is a collection of information about an individual user, including the user's name, contact information, preferences, and past interaction history.

[1708] A "dialogue agent" is a system that uses artificial intelligence to engage in dialogue with users, enabling the exchange of messages through natural language processing.

[1709] An "emotion engine" is a technology that analyzes emotions from a user's text or voice and generates a response based on those emotions.

[1710] "Navigation" is a function that provides route guidance and direction to reach a destination, and presents an appropriate route based on map information and current location data.

[1711] "Vehicle operation" refers to functions related to driving an autonomous vehicle, including speed control, lane changes, and parking.

[1712] "Entertainment" refers to content and services that provide users with enjoyment and entertainment, and includes music, movies, games, and the like.

[1713] "Emergency response" is a function that provides appropriate measures and support when an emergency such as an accident or illness occurs.

[1714] A "conversation database" is a database that stores past conversation history and uses it for future conversations.

[1715] A "task-specific agent" is a conversational agent designed to handle a specific task or business, and has specific skills and knowledge.

[1716] The present invention relates to a customizable dialogue agent system for use in autonomous vehicles. The following describes in detail a system for implementing this application.

[1717] Initial Setup

[1718] First-time user registration

[1719] When a user first uses the system, they create a user profile. They enter information such as their name, preferences, and contact details using the vehicle's infotainment system. Based on this, the server creates a user profile and stores it in a database.

[1720] Customizing conversational agents

[1721] Users can customize the appearance, personality, and capabilities of their conversational agent through the infotainment system's settings screen. The server receives this customization information and updates the AI ​​model's profile, generating a conversational agent optimized for the user.

[1722] Interactive processing

[1723] Conversation initiation and response generation

[1724] When a user types a message into the conversation agent, the infotainment system sends it to a server. The server first analyzes the message and checks past conversation history against a conversation database. It then uses an AI model to generate an appropriate response, incorporating additional information from external sources as needed.

[1725] Use of emotion engine

[1726] The emotion engine analyzes emotions from the user's message and generates a response based on that emotion. For example, if a user types, "I had a rough day today," the emotion engine recognizes the emotion "rough" and generates a response such as, "Thank you for your hard work. Let me know if there's anything I can do to help you." This information is stored in the conversation database and reflected in future interactions.

[1727] Application example in autonomous vehicles

[1728] The system is installed inside autonomous vehicles and provides the following functions:

[1729] Navigation assistance: When a user inputs their destination, the conversational agent guides them to the optimal route. For example, in response to a request such as "I want to go to a nearby restaurant," the agent generates a response such as "There is a certain restaurant one kilometer away. Do you want to go there?"

[1730] Vehicle operation assistance: The conversational agent assists with vehicle operation in response to user instructions. For example, if the user says, "Set the air conditioner temperature to 23 degrees," the agent will respond, "The air conditioner temperature has been set to 23 degrees," and actually perform the operation.

[1731] Entertainment recommendations: Based on the user's preferences, the app suggests entertainment such as music and movies. For example, if the user says, "I want to listen to classical music," the app will start playing classical music.

[1732] Emergency response: When an emergency occurs, the conversational agent will respond appropriately. For example, if you say "I'm not feeling well," it will generate a response such as "Would you like to contact a medical institution?"

[1733] Hardware and software used

[1734] Hardware: Autonomous vehicle infotainment systems, displays, speakers, sensors, etc.

[1735] Software: OpenAI API, emotion recognition API, database system

[1736] Prompt Sentence Examples

[1737] Below are examples of prompt sentences that are generated based on messages from users.

[1738] User: Today was a rough day

[1739] User emotion: Sad

[1740] Generate a response for the in-car assistant:

[1741] This prompt allows OpenAI's text generation model to generate an appropriate response, thus enabling the implementation of a conversational agent system that provides a user-friendly experience within a self-driving vehicle.

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] The user enters profile information for the conversational agent. Specifically, the user enters information such as name, preferences, and contact details through the infotainment system and sends it from the device to the server. This data is received by the server and stored in a database as a user profile.

[1745] Input: User information (name, preferences, contact details, etc.)

[1746] Output: Generate and save user profile

[1747] Step 2:

[1748] The user customizes the conversational agent. The user selects the agent's appearance, personality, and abilities from the infotainment system's settings screen, and this information is sent from the device to the server. The server updates the AI ​​model profile based on the received information and generates a customized agent for the user.

[1749] Input: Customization information for the conversational agent (appearance, personality, abilities)

[1750] Output: A customized conversational agent

[1751] Step 3:

[1752] A user inputs a message to the dialogue agent through the infotainment system, and the message is sent from the terminal to the server. The server receives the message.

[1753] Input: Message from the user

[1754] Output: Message received by server

[1755] Step 4:

[1756] The server analyzes the message and performs emotion recognition. The server uses an emotion engine to analyze the user's message and identify the emotion. For example, it can recognize the emotion "sad" from the message "Today was a tough day."

[1757] Input: User message

[1758] Output: Identified emotion information

[1759] Step 5:

[1760] The server generates a prompt based on the user message and the identified emotional information, and provides it to OpenAI's generative AI model.

[1761] Input: User message and emotion information

[1762] Output: Generated prompt statement

[1763] Example: Prompt sentence example

[1764] User: Today was a rough day

[1765] User emotion: Sad

[1766] Generate a response for the in-car assistant:

[1767] Step 6:

[1768] Response generation by the generative AI model. The server sends the generated prompt to OpenAI's API, and the AI ​​model generates an appropriate response. For example, a response such as "Thank you for your hard work. Let me know if there's anything I can help you with."

[1769] Input: prompt statement

[1770] Output: The generated response message

[1771] Step 7:

[1772] The server sends a response message to the infotainment system, which then sends the generated response message to the terminal and displays it to the user.

[1773] Input: The generated response message

[1774] Output: The response message that is displayed to the user

[1775] Step 8:

[1776] Storing conversation information: The server stores conversation history in a database as part of the user profile, which can then be used to inform future conversations.

[1777] Input: User message and generated response

[1778] Output: Dialogue history stored in a database

[1779] The above steps provide customizable interaction assistance for the user in an autonomous vehicle.

[1780] 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.

[1781] 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.

[1782] 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.

[1783] [Fourth embodiment]

[1784] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1785] 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.

[1786] 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).

[1787] 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.

[1788] 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.

[1789] 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).

[1790] 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.

[1791] 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.

[1792] 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.

[1793] 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.

[1794] 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.

[1795] 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.

[1796] 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."

[1797] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the ability to receive messages from users and generate responses by referencing a database of past conversations. It also allows users to create agents specialized for specific tasks and process task-related information.

[1798] Initial Setup

[1799] First-time user registration

[1800] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[1801] Customization features

[1802] Customizing conversational agents

[1803] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[1804] Interactive processing

[1805] Conversation initiation and response generation

[1806] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1807] Specific examples

[1808] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message and references past conversation history and a schedule database. As a result, it generates a response such as, "I have a meeting tomorrow at 2 p.m." and sends it to the terminal. The terminal then displays this and provides it to the user.

[1809] Business-specific functions

[1810] Setting up specialized agents

[1811] Users enter the information and skill sets required for a specific task through a settings screen. The device sends this information to the server, which then fine-tunes a task-specific AI model based on the received information. The generated task-specific profile is sent to the user's device, where it can be viewed and used by the user.

[1812] Specific examples

[1813] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[1814] Monetization and Platform Operations

[1815] Monitoring usage and displaying advertisements

[1816] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs.

[1817] Specific examples

[1818] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[1819] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[1820] The processing flow will be explained below.

[1821] Initial Setup

[1822] User Registration

[1823] Step 1:

[1824] User: Enters name, email address, and password into the sign-up form.

[1825] Step 2:

[1826] Terminal: Sends the entered information to the server.

[1827] Step 3:

[1828] Server: Stores the received user information in a database and generates a new user profile.

[1829] Step 4:

[1830] Server: Generates the data for the initial setup screen and sends it to the user's device.

[1831] Customization features

[1832] Customizing conversational agents

[1833] Step 1:

[1834] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[1835] Step 2:

[1836] Device: Sends customization information to the server.

[1837] Step 3:

[1838] Server: Updates the AI ​​model profile based on the received customization information.

[1839] Step 4:

[1840] Server: Loads the required datasets and generates the customized profile.

[1841] Step 5:

[1842] Server: Sends customized profile data to the user's device.

[1843] Step 6:

[1844] On the device: The received customization profile is displayed to the user for confirmation.

[1845] Interactive processing

[1846] Conversation initiation and response generation

[1847] Step 1:

[1848] User: Type a message in the chat window.

[1849] Step 2:

[1850] Terminal: Sends the entered message to the server.

[1851] Step 3:

[1852] Server: Analyzes the received message.

[1853] Step 4:

[1854] Server: Refer to the conversation database and check past conversation history.

[1855] Step 5:

[1856] Server: Uses AI models to generate appropriate responses.

[1857] Step 6:

[1858] Server: Stores the response message in the conversation database.

[1859] Step 7:

[1860] Server: Sends the generated response message to the user's terminal.

[1861] Step 8:

[1862] Terminal: Displays the received response message to the user.

[1863] Business-specific functions

[1864] Setting up specialized agents

[1865] Step 1:

[1866] Users: Enter the information and skill sets required for a specific job through a setup screen.

[1867] Step 2:

[1868] Terminal: Sends business information to the server.

[1869] Step 3:

[1870] Server: Loads the necessary datasets and APIs based on the received business information.

[1871] Step 4:

[1872] Server: Fine-tunes AI models for specific tasks.

[1873] Step 5:

[1874] Server: Generates a business-specific profile and sends it to the user's device.

[1875] Step 6:

[1876] Terminal: The received business-specific profile is displayed to the user and made available for use.

[1877] Monetization and Platform Operations

[1878] Monitoring usage and displaying advertisements

[1879] Step 1:

[1880] Server: Monitors and logs agent usage in real time.

[1881] Step 2:

[1882] Server: Displays advertisements based on user usage patterns.

[1883] Billing and Feedback

[1884] Step 3:

[1885] Server: Generates billing and plan update information based on API usage and service fee logs.

[1886] Step 4:

[1887] Server: Calculates usage fees at the end of the month and issues invoices to users.

[1888] Step 5:

[1889] Server: Collecting user feedback to help improve our services.

[1890] This trend will enable the provision of user-friendly conversational agents and business efficiency agents, and the operation and monetization of services.

[1891] Example 1

[1892] 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."

[1893] Conventional conversational agents have limited functionality, such as the ability to generate individual profiles for each user and the ability to flexibly generate responses according to user customization. Furthermore, there is a lack of systems that allow agents to be configured to specialize in specific tasks, making them insufficient in addressing business efficiency. Furthermore, there are no integrated functions available, such as real-time monitoring based on usage, advertising display, or billing. These issues need to be resolved.

[1894] 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.

[1895] In this invention, the server includes means for generating individual profiles based on information input by users from their terminals and providing customizable conversational agents based on each profile, means for updating the customized conversational agent profile and loading necessary data sets, and means for receiving messages from users and analyzing and generating responses by referring to a database of past conversations. This makes it possible to generate customized responses based on each user's individual profile, and to achieve integrated configuration of agents specialized for specific tasks, real-time usage status monitoring, advertisement display, and fee billing.

[1896] An "individual profile" is a collection of individually customizable user information that is generated based on each user's input information.

[1897] A "customizable conversation agent" is a conversation support program that allows users to customize their appearance, personality, abilities, etc. based on their individual profiles.

[1898] The "past conversation database" is a database for storing the history of past conversations with a user and for use in future conversations.

[1899] A "means for generating a response" is an algorithm or system used to analyze a message from a user and generate an appropriate response.

[1900] The "means for processing business-related information" is a function that collects and analyzes information related to a specific business, and enables an agent to provide business support based on that information.

[1901] An "AI model profile" is a collection of information that defines the settings and characteristics of the artificial intelligence model used by the conversational agent.

[1902] "Means for loading datasets" refers to the functionality for loading data required for customization and response generation into the system.

[1903] "Fine-tuning" is a technique for adjusting an AI model to further adapt it to a specific purpose and improve its performance.

[1904] "Real-time monitoring" is the process of instantly observing and collecting data on system operation and user usage.

[1905] "Displaying advertisements" is a function that displays appropriate advertisements on the user's terminal based on the user's usage.

[1906] The "means for generating a bill" is a function that calculates the usage fee and issues a bill based on the data of the user's use of the system.

[1907] The present invention provides a system for generating individual user profiles and providing customizable conversational agents based on each profile. The system includes a server for generating profiles based on user input information and storing the profiles in a database, and multiple means for generating responses in real time through user interaction.

[1908] Hardware and Software Configuration

[1909] This system consists of the following main hardware and software:

[1910] Server: A computer equipped with a database management system (DBMS), natural language processing (NLP) libraries, and an AI model (e.g., GPT-3).

[1911] Device: A personal computer (PC), smartphone, or tablet operated by a user.

[1912] Network: The internet connection that connects the user's device to the server.

[1913] First-time user registration

[1914] When a new user uses the system, they must register for the first time. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends the information to the server. The server receives this information, generates an individual user profile, and stores it in a database. The server then generates data for the initial setup screen, which is sent to the user's device and displayed.

[1915] Customizing conversational agents

[1916] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. This creates a customized conversational agent, and the server sends this profile to the user's device.

[1917] Conversation initiation and response generation

[1918] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and checks past conversation history by referring to a conversation database. It then uses an AI model to generate an appropriate response, and if necessary, obtains additional information from external sources and incorporates it into the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1919] For example, if a user asks, "What are your plans for tomorrow?", the server analyzes this message, references past conversation history and a schedule database, and generates a response such as, "I have a meeting tomorrow at 2 p.m." This response is sent to the terminal and displayed to the user.

[1920] Setting up specialized agents

[1921] The user enters the information and skill set required for a specific task through a configuration screen. The device sends this information to the server, which then fine-tunes the task-specific AI model based on the received information. The fine-tuned profile is then generated by the server and sent to the user's device. This allows the user to see the task-specific agent in a usable state.

[1922] For example, when a user sets up an agent specialized in sales activities, the user inputs the sales-related skill set and necessary data. The server generates a sales-specialized agent based on this and provides support such as customer management and performance analysis.

[1923] Monetization and Platform Operations

[1924] The server monitors agent usage in real time, displays advertisements appropriately, and generates billing and plan update information based on usage logs. At the end of the month, the server aggregates user usage, calculates usage fees, and issues invoices. It also collects user feedback to help improve the service.

[1925] In this way, the present invention provides a multifunctional and flexible generative AI system that enables emotionally familiar dialogue with users and supports business efficiency.

[1926] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1927] Step 1: User Registration

[1928] Processing flow

[1929] 1. The user enters their name, email address, and password into a sign-up form on their device.

[1930] 2. The device sends this input information to the server.

[1931] Specific operations and inputs and outputs

[1932] When a user opens a browser, enters their name, email address, and password into the sign-up form, and presses the submit button, the data is sent from the device to the server. The entered information (name, email address, password) is passed to the server, which receives it and creates a new user profile. The created profile is saved in a database. The server then creates initial setup screen data and sends it to the device. This causes the initial setup screen to be displayed on the device.

[1933] Step 2: Customizing the conversation agent

[1934] Processing flow

[1935] 1. The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[1936] 2. The device sends this customization information to the server.

[1937] Specific operations and inputs and outputs

[1938] The user sets the agent's appearance (e.g., blue eyes), personality (e.g., kind), and abilities (e.g., good at giving business advice) on the initial setup screen. Then, by pressing the send button, the input information is sent from the device to the server. The server updates the AI ​​model profile based on the received customization information, loads the necessary datasets, and generates a customized agent profile. This is then sent to the device, allowing the user to view the customized agent.

[1939] Step 3: Conversation initiation and response generation

[1940] Processing flow

[1941] 1. The user inputs a message to the conversation agent.

[1942] 2. The device sends the message to the server.

[1943] 3. The server analyzes the received message and references the conversation database.

[1944] 4. The server generates an appropriate response.

[1945] Specific operations and inputs and outputs

[1946] When a user types "What are your plans for tomorrow?" into their device and sends it, the message is sent to the server. The server analyzes the received message using a natural language processing library, references past conversation history from a conversation database, and uses the acquired information to generate an appropriate response using an AI model. For example, by referencing past conversation history and a schedule database, the AI ​​model generates a response such as "I have a meeting tomorrow at 2 p.m." This response message is saved in the conversation database, sent to the device, and displayed to the user.

[1947] Step 4: Setting up specialized agents

[1948] Processing flow

[1949] 1. The user enters the information and skill set required for the job on the settings screen.

[1950] 2. The device sends the information to the server.

[1951] 3. The server fine-tunes the AI ​​model based on the information received.

[1952] Specific operations and inputs and outputs

[1953] For example, when a user sets up an agent specialized in sales activities, they enter the "sales-related skill set" and "required data" into the settings screen. The device then sends the input information to the server. The server then fine-tunes the AI ​​model based on the received information and generates a sales-specialized agent profile. The generated profile is then sent to the device and can be viewed by the user. This agent can then assist with customer management, sales performance analysis, and more.

[1954] Step 5: Monitoring usage and displaying advertisements

[1955] Processing flow

[1956] 1. The server monitors agent usage in real time.

[1957] 2. The server displays advertisements based on usage logs.

[1958] 3. The server generates billing and plan updates.

[1959] Specific operations and inputs and outputs

[1960] The server monitors agent usage in real time using an existing log management system, recording which functions users use and to what extent. At the end of the month, the server compiles statistics based on the usage logs, calculates usage fees, and generates invoices. Furthermore, the advertising display system uses this usage data to display appropriate advertisements to users. User feedback is also collected and used to improve the service.

[1961] (Application example 1)

[1962] 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."

[1963] Conventional conversational agents can respond based on the user's individual conversation history and preferences, but it is difficult to make optimal suggestions for a specific content distribution service that match the user's preferences. Furthermore, it is insufficient to provide a more personalized experience by giving the conversational agent individual personality and knowledge and reflecting the user's customization. As a result, users are unable to smoothly enjoy content that matches their preferences, which does not lead to improved satisfaction.

[1964] 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.

[1965] In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for creating agents specialized for specific tasks and processing task-related information, means for generating and responding to customized prompts based on the user's content preferences, and means for recommending optimal content based on the generated prompts. This enables personalized content recommendations based on the user's preferences, significantly improving user satisfaction.

[1966] A "user profile" is a database that includes each user's individual information, preferences, past interaction history, and so on.

[1967] A "conversational agent" is an artificial intelligence system that provides information through conversation with a user and generates the required responses.

[1968] "Means for receiving messages" refers to the interface or communication protocol for transferring input from the user to the server.

[1969] A "conversation database" is a data storage that stores the contents of past conversations with users and uses them to generate future responses.

[1970] A "task-specific agent" is a conversational agent that is optimized for a specific task or function.

[1971] "Content preferences" refer to the genres and characteristics of content that a user particularly likes.

[1972] "Prompt generation" is the process of generating appropriate questions or instructions based on the user's preferences and queries.

[1973] The "means for providing a response" refers to the function of creating an appropriate response based on the generated prompt and providing it to the user.

[1974] "Means for recommending content" refers to a function that selects optimal content based on the user's preferences and prompts and suggests it to the user.

[1975] The present invention is a system that recommends optimal content based on a user's content preferences using a conversational agent customized based on an individual user profile. This system is realized using a server, a terminal, and a generative AI model.

[1976] Initial Setup

[1977] First-time user registration

[1978] When a user first uses the system, they register. The user enters their name, email address, password, and content preferences into a sign-up form on their device, which then sends the information to the server. The server generates a user profile based on the received information and stores it in a database. The server then generates data for the initial setup screen and displays it on the user's device.

[1979] Customization features

[1980] Customizing conversational agents

[1981] The user selects and inputs the appearance, personality, and abilities of the conversational agent on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the profile of the generative AI model and loads the necessary datasets. Once the customized profile is generated, the server sends it to the user's device for confirmation.

[1982] Interactive processing

[1983] Conversation initiation and response generation

[1984] When a user inputs a message into the conversation agent, the device sends the message to the server. The server analyzes the received message and references the conversation database to check past conversation history and the user's content preferences. It then uses a generative AI model to generate an appropriate response. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[1985] Content recommendation function

[1986] Content recommendation implementation

[1987] When a user enters a query for specific content, the server analyzes the message and generates prompts based on the user's preferences. For example, if a user's profile includes information such as "I like action movies," the following prompts will be generated in response to the query "What movies do you recommend?"

[1988] Example prompt:

[1989] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[1990] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[1991] Hardware and software used

[1992] Hardware

[1993] Server: Cloud server or on-premise server (AWS, Google Cloud, Azure, etc.)

[1994] Device: User devices such as smartphones, tablets, and PCs

[1995] software

[1996] Django: a web framework

[1997] Transformers: Generative AI model implementation (Huggingface's Transformers model)

[1998] GPT-2: Generative Model

[1999] This allows the personalized conversation agent to recommend optimal content based on the user's preferences.

[2000] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2001] Step 1:

[2002] First-time user registration

[2003] A user enters their name, email address, password, and content preferences into a sign-up form on their device and clicks the Register button. The input data (name, email address, password, and content preferences) is sent from the device to the server. The server receives this data, generates a user profile, and stores it in a database. This profile includes the user's interaction history and preferences. Next, the server generates data for an initial setup screen, sends it to the user's device, and displays it.

[2004] Input: User's name, email address, password, content preferences

[2005] Output: Generated user profile, initial setup screen data

[2006] Step 2:

[2007] Customizing conversational agents

[2008] The user selects the appearance, personality, and abilities of the conversational agent from an initial setup screen, and the device sends the customization information to the server. The server receives this customization information, updates the profile of the generative AI model, and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device, displaying a confirmation screen.

[2009] Input: Customization information for the appearance, personality, and abilities of the conversational agent

[2010] Output: A customized conversational agent profile

[2011] Step 3:

[2012] Conversation initiation and response generation

[2013] The user inputs a message into the conversation agent, and the device sends the message to the server. The server analyzes the received message and references the conversation database to confirm the user's past conversation history and content preferences. The server uses a generative AI model to generate an appropriate response and stores it in the conversation database. The generated response message is sent to the device and displayed to the user.

[2014] Input: Message from the user

[2015] Output: The generated response message

[2016] Step 4:

[2017] Content recommendation implementation

[2018] The user enters a query for specific content, which the device sends to the server. The server analyzes the message and generates a prompt based on the user's preferences. For example, if the user says "I like action movies," the following prompt might be generated:

[2019] I like action movies, I like classical music: What movies would you recommend based on these preferences?

[2020] The server uses a generative AI model to generate a response based on the prompt, such as a movie recommendation like "Inception" or "The Dark Knight," which is then sent to the device and displayed to the user.

[2021] Input: User query

[2022] Output: Content recommendations

[2023] Step 5:

[2024] Storing conversation history and preference data

[2025] The generated responses and content recommendations are stored in a conversation database, improving the accuracy of future conversations and content recommendations. User profiles are also periodically updated to accumulate preference data.

[2026] Input: Generated responses, content recommendation history

[2027] Output: Updated conversation database and user profile

[2028] 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.

[2029] The present invention is a system that generates individual user profiles and provides customizable conversational agents based on each profile. This system has the functions of receiving messages from users, generating responses by referencing a database of past conversations, and creating agents specialized for specific tasks and processing task-related information. The present invention also incorporates an emotion engine that can recognize user emotions and generate responses based on those emotions.

[2030] Initial Setup

[2031] First-time user registration

[2032] When a new user starts using the system, they first register. The user enters information such as their name, email address, and password into a sign-up form on their device, which then sends it to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen, sends it to the user's device, and displays it to the user.

[2033] Customization features

[2034] Customizing conversational agents

[2035] The user selects and inputs the appearance, personality, and abilities of "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. The customized profile is generated, and the server sends it to the user's device for confirmation.

[2036] Interactive processing

[2037] Conversation initiation and response generation

[2038] When a user inputs a message into the conversation agent, the device sends the message to the server. The server first analyzes the received message, then references the conversation database to check past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the conversation database and sent to the device for display to the user.

[2039] Use of emotion engine

[2040] The present invention incorporates an emotion engine that recognizes emotions from the user's message. The emotion engine analyzes messages received by the server and identifies the user's emotion. Based on this emotion information, the response message is further customized. The emotion information is stored in a conversation database and reflected in future conversations.

[2041] Specific examples

[2042] For example, if a user inputs "Today was a tough day," the emotion engine recognizes the emotion "tough." Based on this emotion information, the server generates a response such as "Thank you for your hard work. Let me know if there's anything I can help you with," and sends it to the user. The device then displays this and provides it to the user.

[2043] Business-specific functions

[2044] Setting up specialized agents

[2045] Users enter the information and skill sets required for a specific task through a configuration screen. The device sends this information to the server, which then loads the necessary datasets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use.

[2046] Specific examples

[2047] For example, if a user wants to set up an agent specialized in sales activities, they input sales-related skill sets and necessary data. Based on this, the server generates a sales-specialized agent and supports sales performance analysis and customer management.

[2048] Monetization and Platform Operations

[2049] Monitoring usage and displaying advertisements

[2050] The server monitors and logs agent usage in real time, displays ads appropriately based on usage patterns, and generates billing and plan updates based on API usage and service fee logs.

[2051] Specific examples

[2052] At the end of the month, the server compiles user usage data, calculates usage fees, and issues bills. User feedback is collected and used to improve the service.

[2053] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[2054] The processing flow will be explained below.

[2055] Initial Setup

[2056] User Registration

[2057] Step 1:

[2058] User: Enters name, email address, and password into a sign-up form.

[2059] Step 2:

[2060] Terminal: Sends the entered information to the server.

[2061] Step 3:

[2062] Server: Stores the received user information in a database and generates a new user profile.

[2063] Step 4:

[2064] Server: Generates the data for the initial setup screen and sends it to the user's device.

[2065] Customization features

[2066] Customizing conversational agents

[2067] Step 1:

[2068] User: Select and enter the appearance, personality, and abilities of "myGPT" on the initial setup screen.

[2069] Step 2:

[2070] Device: Sends customization information to the server.

[2071] Step 3:

[2072] Server: Updates the AI ​​model profile based on the received customization information.

[2073] Step 4:

[2074] Server: Loads the required datasets and generates the customized profile.

[2075] Step 5:

[2076] Server: Sends customized profile data to the user's device.

[2077] Step 6:

[2078] On the device: The received customization profile is displayed to the user for confirmation.

[2079] Interactive processing

[2080] Conversation initiation and response generation

[2081] Step 1:

[2082] User: Type a message in the chat window.

[2083] Step 2:

[2084] Terminal: Sends the entered message to the server.

[2085] Step 3:

[2086] Server: Analyzes the received message.

[2087] Step 4:

[2088] Server: Refer to the conversation database and check past conversation history.

[2089] Step 5:

[2090] Server: Uses AI models to generate appropriate responses.

[2091] Step 6:

[2092] Server: Stores the response message in the conversation database.

[2093] Step 7:

[2094] Server: Sends the generated response message to the user's terminal.

[2095] Step 8:

[2096] Terminal: Displays the received response message to the user.

[2097] Use of emotion engine

[2098] Step 1:

[2099] User: Type and send a message.

[2100] Step 2:

[2101] Terminal: Sends the entered message to the server.

[2102] Step 3:

[2103] Server: Analyzes the received message using the emotion engine.

[2104] Step 4:

[2105] Server: The emotion engine recognizes the user's emotional state.

[2106] Step 5:

[2107] Server: Further customize the response message based on the emotional information.

[2108] Step 6:

[2109] Server: Stores the recognized emotion information in a conversation database.

[2110] Step 7:

[2111] Server: Sends a customized response message to the user's device.

[2112] Step 8:

[2113] Terminal: Displays the received response message to the user.

[2114] Business-specific functions

[2115] Setting up specialized agents

[2116] Step 1:

[2117] Users: Enter the information and skill sets required for a specific job through a setup screen.

[2118] Step 2:

[2119] Terminal: Sends business information to the server.

[2120] Step 3:

[2121] Server: Loads the necessary datasets and APIs based on the received business information.

[2122] Step 4:

[2123] Server: Fine-tunes AI models for specific tasks.

[2124] Step 5:

[2125] Server: Generates a business-specific profile and sends it to the user's device.

[2126] Step 6:

[2127] Terminal: The received business-specific profile is displayed to the user and made available for use.

[2128] Monetization and Platform Operations

[2129] Monitoring usage and displaying advertisements

[2130] Step 1:

[2131] Server: Monitors and logs agent usage in real time.

[2132] Step 2:

[2133] Server: Displays advertisements based on user usage patterns.

[2134] Billing and Feedback

[2135] Step 3:

[2136] Server: Generates billing and plan update information based on API usage and service fee logs.

[2137] Step 4:

[2138] Server: Calculates usage fees at the end of the month and issues invoices to users.

[2139] Step 5:

[2140] Server: Collecting user feedback to help improve our services.

[2141] This trend will enable the provision of user-friendly conversational agents and business efficiency agents for the operation and monetization of services. The addition of an emotion engine will enable more detailed and empathetic responses, improving the user experience.

[2142] Example 2

[2143] 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."

[2144] Conventional conversational agent systems have difficulty fully recognizing a user's unique profile and emotions to generate customized responses. They also lack the functionality to generate agents specialized for specific tasks and efficiently process task-related information. This has prevented them from fully improving the user experience and streamlining operations.

[2145] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable interactive agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past interactions, means for creating agents specialized for specific tasks and processing task-related information, means for identifying the user's emotions using an emotion engine and generating responses based on the emotions, and means for monitoring the usage status of the interactive agents and recording them in a log. This enables flexible response generation based on individual profiles and emotions, efficiency improvement of specific tasks, and appropriate management of usage status.

[2146] A "user profile" is information about an individual user that the system collects and stores, including the user's name, contact information, preferences, and behavioral history.

[2147] A "customizable conversational agent" is a conversational agent that is optimized to meet the needs of a user by adjusting its appearance, personality, abilities, etc. based on the user's profile information.

[2148] A "message" is information in text or speech format that a user inputs to a dialogue agent.

[2149] The "dialogue database" is a database that contains past dialogue history and stores and manages cumulative interactions with users.

[2150] An "agent specialized for a specific task" is a conversational agent that has the knowledge and skill set required for a specific task and is designed to efficiently support that task.

[2151] An "emotion engine" is an algorithm or program that analyzes emotions from a user's input message and identifies those emotions.

[2152] A "response" is a reply message that a dialogue agent generates in response to a user's message.

[2153] "Usage status" refers to information about how users use the system, such as how often and how they use the dialogue agent.

[2154] A "log" is a series of activity histories or event records that are recorded while a system is running.

[2155] The present invention is a system for generating an individual user profile and providing a customizable conversational agent. A specific embodiment of this system is described below.

[2156] Initial Setup

[2157] First-time user registration

[2158] When a user uses the system for the first time, they enter information such as their name, email address, and password into a sign-up form and send it from their device to the server. The server receives this information, generates a user profile, and stores it in a database. The server then generates data for the initial setup screen and sends it to the user's device to display it. For example, a user enters "Yamada Taro," "taro.yamada@example.com," and "password 123," and sends it to the server.

[2159] Customization features

[2160] Customizing conversational agents

[2161] The user selects and inputs the appearance, personality, and abilities of the conversational agent "myGPT" on the initial setup screen. The device then sends this customization information to the server. Based on the received information, the server updates the AI ​​model profile and loads the necessary datasets. A customized profile is generated, and the server sends it to the user's device for confirmation. Specifically, settings such as "friendly," "approachable," and "advanced search functions" are possible.

[2162] Interactive processing

[2163] Conversation initiation and response generation

[2164] When a user inputs a message into the dialogue agent, the device sends the message to the server. The server first analyzes the received message and references the dialogue database. It checks past conversation history and generates an appropriate response using an AI model. If necessary, it obtains additional information from external sources and reflects it in the response. The generated response message is saved in the dialogue database and sent to the device for display to the user.

[2165] Use of emotion engine

[2166] The emotion engine analyzes the user's message and identifies the user's emotion. The server uses this emotion information to further customize the response message. The emotion information is stored in the dialogue database and reflected in future dialogues. For example, if the user types, "Today was a tough day," the emotion engine recognizes the emotion "tough" and generates a response based on that, such as, "Thank you for your hard work. Let me know if there's anything I can do to help you."

[2167] Business-specific functions

[2168] Setting up specialized agents

[2169] The user enters the information and skill set required for a specific task through a settings screen. The device sends that information to the server, which then loads the necessary data sets and APIs based on the received information. The server then fine-tunes the AI ​​model for the specific task and generates a task-specific profile. The server then sends this to the user's device, making it available for use. For example, when setting up an agent specializing in sales activities, the user enters the sales-related skill set and necessary data.

[2170] Monetization and Platform Operations

[2171] Monitoring usage and displaying advertisements

[2172] The server monitors agent usage in real time and records it in a log. It displays appropriate advertisements based on usage patterns, and generates billing and plan update information based on API usage and service fee logs. Specifically, the server aggregates user usage at the end of the month, calculates usage fees, generates an invoice, and sends it to the user.

[2173] This allows for the provision of user-friendly conversational agents and business efficiency agents, enabling service operation and monetization. Furthermore, by utilizing an emotion engine, it is possible to generate more detailed and empathetic responses, improving the user experience.

[2174] Example prompt sentence:

[2175] "Set your conversational agent's appearance as friendly, personality as approachable, and abilities as advanced search capabilities."

[2176] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2177] System program processing flow (specific explanation)

[2178] Initial Setup

[2179] Step 1:

[2180] Input: A user enters their name, email address, and password into a sign-up form.

[2181] Operation:

[2182] The user fills out a form on the device and provides information such as "Yamada Taro," "taro.yamada@example.com," and "password123."

[2183] Output: The device sends the input information to the server.

[2184] Step 2:

[2185] Input: User sign-up information received from the device.

[2186] Operation:

[2187] The server analyzes the received information, generates a new user profile, and stores it in the database. For example, the user is registered with a user ID of "001."

[2188] Output: The server generates a user profile and stores it in the database.

[2189] Step 3:

[2190] Input: User profile data on the server.

[2191] Operation:

[2192] The server generates data for the initial setting screen and sends it to the terminal.

[2193] Output: The terminal displays the initial setup screen to the user.

[2194] Customization features

[2195] Step 4:

[2196] Input: The user enters agent customization information on the initial setup screen.

[2197] Operation:

[2198] The user selects the appearance (e.g., friendly), personality (e.g., approachable), and capabilities (e.g., advanced search functionality) of "myGPT."

[2199] Output: The device sends this customization information to the server.

[2200] Step 5:

[2201] Input: Customization information received from the device.

[2202] Operation:

[2203] The server updates the AI ​​model profile based on the received information, loading the necessary datasets and generating a customized profile.

[2204] Output: A customized profile is generated.

[2205] Step 6:

[2206] Input: Your customized profile.

[2207] Operation:

[2208] The server transmits the generated customization profile to the terminal.

[2209] Output: The device displays the customized profile to the user and asks for confirmation.

[2210] Interactive processing

[2211] Step 7:

[2212] Input: The user types a message into the conversation agent.

[2213] Operation:

[2214] The user types a message, such as "What's the weather like today?"

[2215] Output: The device sends the input message to the server.

[2216] Step 8:

[2217] Input: User's message received from the terminal.

[2218] Operation:

[2219] The server analyzes the received message and determines the intent of the message.

[2220] Output: The analysis results are obtained.

[2221] Step 9:

[2222] Input: The parsed result of the message.

[2223] Operation:

[2224] The server refers to the dialogue database to check the relevant past conversation history.

[2225] Output: The relevant data is retrieved.

[2226] Step 10:

[2227] Input: Conversation database and analysis results.

[2228] Operation:

[2229] The server uses AI models to generate appropriate responses, and may obtain additional information from external sources (such as a weather API).

[2230] Output: A response message is generated.

[2231] Step 11:

[2232] Input: The generated response message.

[2233] Operation:

[2234] The server stores the response message in a dialogue database and sends it to the terminal.

[2235] Output: The terminal displays the response message to the user.

[2236] Use of emotion engine

[2237] Step 12:

[2238] Input: The user's message sent from the terminal.

[2239] Operation:

[2240] The server uses an emotion engine to analyze the message and identify the emotion.

[2241] Output: Emotional information is obtained.

[2242] Step 13:

[2243] Input: Emotion information.

[2244] Operation:

[2245] The server generates a response message based on the emotion.

[2246] Output: A customized response message is generated.

[2247] Step 14:

[2248] Input: The generated emotion-based response message.

[2249] Operation:

[2250] The server stores the emotion-based responses in a dialogue database and transmits them to the terminal.

[2251] Output: The device displays a response message that takes emotion into account.

[2252] Business-specific functions

[2253] Step 15:

[2254] Input: The user inputs the necessary information for the business-specific agent.

[2255] Operation:

[2256] The user enters the skill sets and data required for sales activities on the settings screen.

[2257] Output: The device sends the information to the server.

[2258] Step 16:

[2259] Input: Business-specific information received from the terminal.

[2260] Operation:

[2261] Based on the information received, the server loads the necessary datasets and APIs.

[2262] Output: The required data is loaded.

[2263] Step 17:

[2264] Input: Business specific information and loaded dataset.

[2265] Operation:

[2266] The server fine-tunes the AI ​​model to correspond to a specific task and generates a task-specific profile.

[2267] Output: A job-specific profile is generated.

[2268] Step 18:

[2269] Input: The generated job-specific profile.

[2270] Operation:

[2271] The server transmits the generated business-specific profile to the user's terminal.

[2272] Output: The device displays the business-specific profile and makes it available for use.

[2273] Monetization and Platform Operations

[2274] Step 19:

[2275] Input: Usage data.

[2276] Operation:

[2277] The server monitors agent usage in real time and records it in a log.

[2278] Output: Usage log data is generated.

[2279] Step 20:

[2280] Input: Monitored usage data.

[2281] Operation:

[2282] The server displays appropriate ads based on usage patterns. The ads are displayed on the device.

[2283] Output: The ad is displayed on the user's screen.

[2284] Step 21:

[2285] Input: Log data regarding API usage and service fees.

[2286] Operation:

[2287] The server generates billing and plan update information, calculates the usage fee, and issues an invoice at the end of the month.

[2288] Output: A usage invoice is generated and sent to the user.

[2289] These specific processing steps allow each function to operate smoothly, providing a friendly and efficient interactive agent service to the user.

[2290] (Application example 2)

[2291] 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."

[2292] In order to provide friendly yet effective assistance to users in autonomous vehicles, customization based on the user's emotions and individual needs is required. However, existing systems are unable to recognize the user's emotions and generate responses based on them, resulting in insufficient personalized navigation, entertainment, or emergency response.

[2293] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating individual user profiles and providing customizable conversation agents based on each profile, means for receiving messages from users and generating responses by referring to a database of past conversations, means for recognizing user emotions and generating responses based on the emotions, means for creating agents specialized for specific tasks and processing task-related information, and means for being installed in autonomous vehicles to assist users with navigation, vehicle operation, entertainment, and emergency response. This makes it possible to significantly improve the user experience in autonomous vehicles.

[2294] A "user profile" is a collection of information about an individual user, including the user's name, contact information, preferences, and past interaction history.

[2295] A "dialogue agent" is a system that uses artificial intelligence to engage in dialogue with users, enabling the exchange of messages through natural language processing.

[2296] An "emotion engine" is a technology that analyzes emotions from a user's text or voice and generates a response based on those emotions.

[2297] "Navigation" is a function that provides route guidance and direction to reach a destination, and presents an appropriate route based on map information and current location data.

[2298] "Vehicle operation" refers to functions related to driving an autonomous vehicle, including speed control, lane changes, and parking.

[2299] "Entertainment" refers to content and services that provide users with enjoyment and entertainment, and includes music, movies, games, and the like.

[2300] "Emergency response" is a function that provides appropriate measures and support when an emergency such as an accident or illness occurs.

[2301] A "conversation database" is a database that stores past conversation history and uses it for future conversations.

[2302] A "task-specific agent" is a conversational agent designed to handle a specific task or business, and has specific skills and knowledge.

[2303] The present invention relates to a customizable dialogue agent system for use in autonomous vehicles. The following describes in detail a system for implementing this application.

[2304] Initial Setup

[2305] First-time user registration

[2306] When a user first uses the system, they create a user profile. They enter information such as their name, preferences, and contact details using the vehicle's infotainment system. Based on this, the server creates a user profile and stores it in a database.

[2307] Customizing conversational agents

[2308] Users can customize the appearance, personality, and capabilities of their conversational agent through the infotainment system's settings screen. The server receives this customization information and updates the AI ​​model's profile, generating a conversational agent optimized for the user.

[2309] Interactive processing

[2310] Conversation initiation and response generation

[2311] When a user types a message into the conversation agent, the infotainment system sends it to a server. The server first analyzes the message and checks past conversation history against a conversation database. It then uses an AI model to generate an appropriate response, incorporating additional information from external sources as needed.

[2312] Use of emotion engine

[2313] The emotion engine analyzes emotions from the user's message and generates a response based on that emotion. For example, if a user types, "I had a rough day today," the emotion engine recognizes the emotion "rough" and generates a response such as, "Thank you for your hard work. Let me know if there's anything I can do to help you." This information is stored in the conversation database and reflected in future interactions.

[2314] Application example in autonomous vehicles

[2315] The system is installed inside autonomous vehicles and provides the following functions:

[2316] Navigation assistance: When a user inputs their destination, the conversational agent guides them to the optimal route. For example, in response to a request such as "I want to go to a nearby restaurant," the agent generates a response such as "There is a certain restaurant one kilometer away. Do you want to go there?"

[2317] Vehicle operation assistance: The conversational agent assists with vehicle operation in response to user instructions. For example, if the user says, "Set the air conditioner temperature to 23 degrees," the agent will respond, "The air conditioner temperature has been set to 23 degrees," and actually perform the operation.

[2318] Entertainment recommendations: Based on the user's preferences, the app suggests entertainment such as music and movies. For example, if the user says, "I want to listen to classical music," the app will start playing classical music.

[2319] Emergency response: When an emergency occurs, the conversational agent will respond appropriately. For example, if you say "I'm not feeling well," it will generate a response such as "Would you like to contact a medical institution?"

[2320] Hardware and software used

[2321] Hardware: Autonomous vehicle infotainment systems, displays, speakers, sensors, etc.

[2322] Software: OpenAI API, emotion recognition API, database system

[2323] Prompt Sentence Examples

[2324] Below are examples of prompt sentences that are generated based on messages from users.

[2325] User: Today was a rough day

[2326] User emotion: Sad

[2327] Generate a response for the in-car assistant:

[2328] This prompt allows OpenAI's text generation model to generate an appropriate response, thus enabling the implementation of a conversational agent system that provides a user-friendly experience within a self-driving vehicle.

[2329] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2330] Step 1:

[2331] The user enters profile information for the conversational agent. Specifically, the user enters information such as name, preferences, and contact details through the infotainment system and sends it from the device to the server. This data is received by the server and stored in a database as a user profile.

[2332] Input: User information (name, preferences, contact details, etc.)

[2333] Output: Generate and save user profile

[2334] Step 2:

[2335] The user customizes the conversational agent. The user selects the agent's appearance, personality, and abilities from the infotainment system's settings screen, and this information is sent from the device to the server. The server updates the AI ​​model profile based on the received information and generates a customized agent for the user.

[2336] Input: Customization information for the conversational agent (appearance, personality, abilities)

[2337] Output: A customized conversational agent

[2338] Step 3:

[2339] A user inputs a message to the dialogue agent through the infotainment system, and the message is sent from the terminal to the server. The server receives the message.

[2340] Input: Message from the user

[2341] Output: Message received by server

[2342] Step 4:

[2343] The server analyzes the message and performs emotion recognition. The server uses an emotion engine to analyze the user's message and identify the emotion. For example, it can recognize the emotion "sad" from the message "Today was a tough day."

[2344] Input: User message

[2345] Output: Identified emotion information

[2346] Step 5:

[2347] The server generates a prompt based on the user message and the identified emotional information, and provides it to OpenAI's generative AI model.

[2348] Input: User message and emotion information

[2349] Output: Generated prompt statement

[2350] Example: Prompt sentence example

[2351] User: Today was a rough day

[2352] User emotion: Sad

[2353] Generate a response for the in-car assistant:

[2354] Step 6:

[2355] Response generation by the generative AI model. The server sends the generated prompt to OpenAI's API, and the AI ​​model generates an appropriate response. For example, a response such as "Thank you for your hard work. Let me know if there's anything I can help you with."

[2356] Input: prompt statement

[2357] Output: The generated response message

[2358] Step 7:

[2359] The server sends a response message to the infotainment system, which then sends the generated response message to the terminal and displays it to the user.

[2360] Input: The generated response message

[2361] Output: The response message that is displayed to the user

[2362] Step 8:

[2363] Storing conversation information: The server stores conversation history in a database as part of the user profile, which can then be used to inform future conversations.

[2364] Input: User message and generated response

[2365] Output: Dialogue history stored in a database

[2366] The above steps provide customizable interaction assistance for the user in an autonomous vehicle.

[2367] 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.

[2368] 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.

[2369] 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.

[2370] 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.

[2371] 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.

[2372] 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.

[2373] 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).

[2374] 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.

[2375] 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."

[2376] 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.

[2377] 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).

[2378] 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.

[2379] 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.

[2380] 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.

[2381] 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.

[2382] 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.

[2383] 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.

[2384] 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.

[2385] 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.

[2386] 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.

[2387] 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.

[2388] The following is further disclosed regarding the above embodiment.

[2389] (Claim 1)

[2390] means for generating individual user profiles and providing customizable conversational agents based on each profile;

[2391] means for receiving a message from a user and generating a response by referring to a database of past conversations;

[2392] a means for creating task-specific agents to process task-related information;

[2393] A system including:

[2394] (Claim 2)

[2395] 10. The system of claim 1, further comprising means for providing the interactive agent with an individual personality and knowledge to reflect user customization.

[2396] (Claim 3)

[2397] 10. The system of claim 1, further comprising a conversation database for storing a user's conversation history and reflecting it in future conversations.

[2398] (Claim 4)

[2399] 2. The system according to claim 1, further comprising means for monitoring the usage of the agent, displaying advertisements and charging fees.

[2400] (Claim 5)

[2401] 10. The system of claim 1, further comprising means for obtaining additional information from an external information source and incorporating the information into the response content.

[2402] "Example 1"

[2403] (Claim 1)

[2404] means for generating individual profiles based on information input by a user from a terminal and providing a c...

Claims

1. means for generating individual user profiles and providing customizable conversational agents based on each profile; means for receiving a message from a user and generating a response by referring to a database of past conversations; a means for creating task-specific agents to process task-related information; A system including:

2. 10. The system of claim 1, further comprising means for providing the interactive agent with an individual personality and knowledge to reflect user customization.

3. 10. The system of claim 1, further comprising a conversation database for storing a user's conversation history and reflecting it in future conversations.

4. 2. The system according to claim 1, further comprising means for monitoring the use of the agent, displaying advertisements and charging fees.

5. 2. The system according to claim 1, further comprising means for acquiring additional information from an external information source and incorporating the information into the response content.

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