System

Abstract

A system is provided.SOLUTION: A system for providing automobile insurance AI to a user using a generative AI model, comprising: means for receiving user-entered information; means for storing the user-entered information in a database; means for generating answers to user questions using the generative education model; means for displaying the generated answers to the user; means for tracking user progress and providing interactive lessons; and means for matching with insurance experts.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] For car owners with little knowledge about car insurance and young drivers purchasing car insurance for the first time, understanding the complex insurance process and choosing the most suitable insurance can be a difficult challenge. In particular, there is a lack of effective means to provide the basic knowledge necessary to avoid the risks of overinsurance, underinsurance, and paying high premiums. For this reason, there is a need for a platform that can provide quick and personalized insurance education, help users choose the right insurance, and respond appropriately when problems arise. [Means for solving the problem]

[0005] To address the above-mentioned challenges, the present invention provides a system for providing users with education about auto insurance using a generative AI model. The system includes a means for receiving information entered by a user and storing it in a database, a means for using a generative AI model to generate and display answers to the user's questions, a means for tracking the user's progress and providing interactive lessons, and a means for matching the user with an insurance expert. The system also includes a means for the user to input information required for an auto insurance quote and for the generative AI model to generate an explanation of the insurance policy terms based on the quote, and a means for receiving complaint information from the user and matching the user with an appropriate expert. This allows users to gain comprehensive and practical knowledge about auto insurance and provides necessary support and information in a timely manner.

[0006] A "generative AI model" is an artificial intelligence model that performs natural language processing based on user input and automatically generates answers and explanations.

[0007] "User" refers to an individual or organization that uses this system to receive education and services related to automobile insurance.

[0008] A "database" is a collection of data that systematically stores information received from users and allows it to be quickly searched and retrieved when needed.

[0009] An "interactive lesson" is educational content in which users actively participate and the content changes depending on their reactions and progress.

[0010] "Insurance professionals" refer to people who have advanced knowledge and experience in insurance and who can provide appropriate advice and support to users, such as insurance agents and insurance investigators.

[0011] "Progress tracking means" is the part of the system that records and analyzes a user's learning progress and activity.

[0012] "Claim Information" refers to the set of details you provide when reporting an accident or damage.

[0013] "Matching" refers to the process of recommending and providing the most suitable insurance professionals and services based on the user's needs and circumstances.

[0014] "Estimate" refers to the calculation results and information required to present insurance premiums and contract details to users.

[0015] "Explanation of terms and conditions" refers to an explanation of the clauses and conditions of the insurance contract in language that is easy for users to understand. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system that provides education on automobile insurance using a generative AI model. Specific embodiments and program processing are described below.

[0038] User registration and profile creation

[0039] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The terminal receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, the user's profile is created.

[0040] Basic knowledge education

[0041] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[0042] Interactive lessons

[0043] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device, which displays the lesson content to the user and tracks their progress.

[0044] Insurance quotes and policy explanations

[0045] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and primary use). The device sends the information to the server, which runs an insurance quote algorithm and generates a quote and a brief explanation of the policy terms. The device displays these results to the user.

[0046] Claims handling and expert matching

[0047] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. The server then sends the progress of the claim and the expert's contact information to the device, which displays it to the user.

[0048] Specific examples

[0049] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[0050] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[0051] 2. The terminal sends the entered information to the server.

[0052] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[0053] 4. The server sends the progress and contact information of the expert to the device.

[0054] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[0055] The system is designed to provide users with effective education and support regarding car insurance, and to quickly address any insurance-related questions or problems.

[0056] The processing flow will be explained below.

[0057] User registration and profile creation

[0058] Step 1: Enter your user information

[0059] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[0060] Step 2: Send input data

[0061] Terminal: Sends the information entered by the user to the server.

[0062] Step 3: Save to database

[0063] Server: Validates the received information and stores it in the database if there are no problems.

[0064] Step 4: Notification of registration results

[0065] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[0066] Terminal: Receives messages from the server and displays them to the user.

[0067] Basic knowledge education

[0068] Step 1: Educational Content Request

[0069] User: Makes a request: "I want to know the basics of car insurance."

[0070] Step 2: Submit the request

[0071] Terminal: Sends user requests to the server.

[0072] Step 3: Invoke the AI ​​model

[0073] Server: Inputs topics into a generative AI model and generates relevant explanations.

[0074] Step 4: Returning content

[0075] Server: Prepares the generated description and sends it to the device.

[0076] Terminal: Display the received description to the user.

[0077] Interactive lessons

[0078] Step 1: Choose a lesson

[0079] User: Select from available lessons (e.g., accident procedures).

[0080] Step 2: Submit a lesson request

[0081] Terminal: Sends a request for the selected lesson to the server.

[0082] Step 3: Prepare the lesson content

[0083] Server: Selects the corresponding lesson content and sets up initial data to track user progress.

[0084] Step 4: Deliver the lesson

[0085] Server: Sends the selected lesson content to the device.

[0086] Device: Presents lesson content to the user and tracks the user's progress.

[0087] Insurance quotes and policy explanations

[0088] Step 1: Request a quote

[0089] User: Enter any additional information required for insurance quote (e.g. mileage, primary use).

[0090] Step 2: Send data

[0091] Terminal: Sends any additional information entered by the user to the server.

[0092] Step 3: Calculate estimates and generate explanations

[0093] Server: Analyzes the data, runs insurance quoting algorithms to generate quotes, and uses generative AI models to create policy summaries and explanations.

[0094] Step 4: Returning the results

[0095] Server: Sends the estimate results and explanations to the device.

[0096] Terminal: Displays results and explanations to the user.

[0097] Claims handling and expert matching

[0098] Step 1: Enter your claim information

[0099] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[0100] Step 2: Submit your claim data

[0101] Terminal: Sends the complaint information to the server.

[0102] Step 3: Claims processing and expert matching

[0103] Server: Review the claim details, select appropriate experts (insurance adjusters, lawyers, etc.), and set the initial steps in claim processing.

[0104] Step 4: Progress notification and expert information

[0105] Server: Sends claim progress and expert contact information to the device.

[0106] Terminal: Displays claim progress and expert information to the user.

[0107] Example 1

[0108] 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."

[0109] Conventional automobile insurance systems require a lot of time and effort for users to obtain the necessary information, and are difficult for users without specialized knowledge to understand. Furthermore, when an accident or damage occurs, the claim procedures and contact with experts take time, requiring a rapid response. Therefore, there is a need for a system that allows users to efficiently and accurately obtain information about automobile insurance and process claims.

[0110] 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.

[0111] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using the generative AI model, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for matching with insurance experts, and means for tracking the progress of claims and providing the expert's contact information to the user, thereby enabling users to efficiently and accurately obtain information about car insurance and quickly process claims and connect with experts.

[0112] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to automatically generate text or answers based on specific prompts.

[0113] "User" refers to anyone who uses the system to obtain information about or report a claim regarding auto insurance.

[0114] "Terminal" refers to a device, such as a computer or smartphone, that a user uses to enter information and view displayed results.

[0115] "Server" refers to a central processing unit that provides functions such as receiving, processing, and storing user input and generating answers using generative AI models.

[0116] "Database" refers to an electronic information management system for storing information entered by users and responses generated by users.

[0117] "Interactive Lesson" refers to a lesson format that provides educational content that changes dynamically based on the user's progress.

[0118] An "insurance professional" refers to a person with specialized knowledge of insurance, such as an insurance investigator or an employee of an insurance agency.

[0119] "Claim Progress" refers to the status of the insurance claim submitted by the User.

[0120] "Matching" refers to the process of identifying professionals and services that meet a user's needs and connecting them with the right people.

[0121] MODE FOR CARRYING OUT THE INVENTION

[0122] The present invention is a system for providing education on automobile insurance using a generative AI model. A specific embodiment of this system is described below.

[0123] System Configuration

[0124] The system mainly consists of a server, a terminal, and a user. The server acts as a central processing unit, receiving, storing, and processing data, and generating answers using the generative AI model. The terminal is a device used by the user, such as a computer or smartphone, that receives input from the user and displays output from the server. Users use the system to obtain information about car insurance and report claims.

[0125] Hardware and Software

[0126] The server is equipped with a high-performance processor (e.g., Intel Xeon processor) and large amounts of memory and storage (e.g., SSD) to run the generative AI model. A relational database management system such as MySQL or PostgreSQL is used as the database. OpenAI's GPT-3 and other models are used as generative AI models.

[0127] The device must have a commonly accessible web browser (e.g., Google Chrome or Mozilla Firefox) installed and be able to connect to the Internet. An interface is provided to receive user input and display responses from the server.

[0128] User registration and profile creation

[0129] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is received by the device and sent to the server, which validates the received information to ensure it is accurate. The verified information is then stored in a database, and a user profile is created.

[0130] Basic knowledge education

[0131] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the explanation to the user.

[0132] As a concrete example, if a user inputs the prompt "What are the basic types of car insurance?", the server would use a generative AI model to generate the answer "The basic types of car insurance include bodily injury liability insurance, property damage liability insurance, vehicle insurance, and personal injury insurance."

[0133] Interactive lessons

[0134] When a user selects an interactive lesson, the device sends the request to the server. The server prepares the appropriate lesson content and sends it to the device. The device displays the lesson content to the user and tracks their progress. Along the way, the user answers questions and quizzes to promote effective learning.

[0135] Insurance quotes and policy explanations

[0136] When a user requests an insurance quote, they are asked for additional information (e.g., annual mileage, purpose of use, etc.). The device sends the information entered by the user to the server, which then runs an insurance quote algorithm to generate a quote. Furthermore, a generative AI model is used to generate an explanation of the policy terms. The generated quote and explanation of the policy terms are then displayed to the user via the device.

[0137] For example, if a user enters a prompt such as "I would like to know the insurance cost if I drive 10,000 kilometers per year," the server runs an insurance estimation algorithm to generate an estimate such as "The annual insurance cost will be approximately 50,000 yen," while also providing a brief explanation of the terms and conditions.

[0138] Claims handling and expert matching

[0139] When a user files a claim, they need to enter information about the accident or damage. The device sends this information to the server, which analyzes the claim. Based on the analysis results, the server matches the most suitable expert and provides the user with their contact information. This information is displayed to the user on the device, allowing the user to contact the expert directly.

[0140] As a specific example, when a user reports a claim that they have been involved in an accident, the device sends the information input by the user (e.g., date, time, location, and situation) to the server, and the server analyzes this information and provides a matching result such as "Please contact Insurance Investigator A" along with contact information.

[0141] The system is designed to enable users to receive a variety of car insurance-related services quickly and efficiently.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The user accesses the system and enters their name, age, and vehicle information (model, year, etc.).

[0145] Specifically, you fill in the required information in the input form and press the send button. This input becomes the initial data for the system.

[0146] Step 2:

[0147] The device receives the user's input information.

[0148] The entered information is temporarily stored on the device, and this data is later sent to the server, where it is organized in a suitable format (such as JSON or XML).

[0149] Step 3:

[0150] The terminal transmits the received information to the server.

[0151] The device collects the information entered by the user into a data packet and sends an HTTP request to the server, which transfers the input data.

[0152] Step 4:

[0153] Validate the information received by the server.

[0154] The server parses the transmitted data packet and checks the correct format and content of each field, for example, name format (characters only), age range (numeric check), and vehicle information validity.

[0155] Step 5:

[0156] The server saves the successfully validated information in the database.

[0157] Once validated, the data is inserted into the database via the database manager, which creates a user profile.

[0158] Step 6:

[0159] A user makes a request to "learn the basics of car insurance."

[0160] The user enters text into the search bar on the interface and hits submit, which triggers the following request:

[0161] Step 7:

[0162] The device sends a request to the server.

[0163] The terminal assembles the requests entered by the user into data packets and forwards them to the server.

[0164] Step 8:

[0165] The server uses the generative AI model to generate relevant explanations.

[0166] The server analyzes the request and inputs the appropriate prompt sentence into the generative AI model, which then generates text based on the input prompt and returns the result to the server.

[0167] Step 9:

[0168] The server returns the generated description to the terminal.

[0169] The explanatory text obtained from the generative AI model is compiled into a data packet and sent to the terminal.

[0170] Step 10:

[0171] The device displays the generated description to the user.

[0172] The terminal parses the received data packets and displays the text on the user interface.

[0173] Step 11:

[0174] The user selects an interactive lesson.

[0175] Users select an interactive lesson item from the education menu and press the start button.

[0176] Step 12:

[0177] The device sends a request to the server.

[0178] The device packages the user's request into data packets and forwards them to the server.

[0179] Step 13:

[0180] The server prepares the appropriate lesson content.

[0181] The server parses the request and generates or retrieves the interactive lesson content (e.g., videos, quizzes, text).

[0182] Step 14:

[0183] The server sends the lesson content to the device.

[0184] The server assembles the prepared content into data packets and transfers them to the terminal.

[0185] Step 15:

[0186] The device displays the lesson content to the user and tracks their progress.

[0187] The device tracks the user's progress and displays the lesson on the screen, and each time the user takes a step, progress data is sent to the server.

[0188] Step 16:

[0189] A user requests an insurance quote.

[0190] The user enters the required information (e.g., annual mileage, purpose of use) into the quote request form and presses the submit button.

[0191] Step 17:

[0192] The device sends the user's input information to the server.

[0193] The terminal assembles the information from the user into data packets and sends them to the server.

[0194] Step 18:

[0195] The server runs the insurance quoting algorithm.

[0196] The server runs its internal insurance quoting algorithms on the received information to generate a quote, using appropriate APIs in accordance with the present invention.

[0197] Step 19:

[0198] The server uses the generative AI model to generate explanations of the insurance policy terms.

[0199] Based on the estimate results, the server inputs prompts into the generative AI model to generate a brief explanation of the terms and conditions.

[0200] Step 20:

[0201] The server sends the estimate results and an explanation of the terms and conditions to the terminal.

[0202] The server compiles the estimate results and explanations of the terms and conditions into a data packet and sends it to the terminal.

[0203] Step 21:

[0204] The terminal displays the estimate results and an explanation of the terms and conditions to the user.

[0205] The terminal analyzes the received data and displays the estimate results and an explanation of the terms and conditions on the user interface.

[0206] Step 22:

[0207] The user submits a claim.

[0208] Users enter information about the accident and damage and press the claim submission button.

[0209] Step 23:

[0210] The terminal transmits the input information to the server.

[0211] The terminal compiles the complaint information from the user into a data packet and sends it to the server.

[0212] Step 24:

[0213] The server parses the claim.

[0214] The server analyzes the received claim information and identifies the necessary action, including the severity of the accident and the type of insurance action required.

[0215] Step 25:

[0216] The server matches you with the right expert.

[0217] The server identifies and matches the most suitable expert (e.g., insurance investigator) based on the content of the claim.

[0218] Step 26:

[0219] The server sends the contact information of the expert to the terminal.

[0220] The server compiles the matched expert's contact information and the progress of the claim into a data packet and sends it to the device.

[0221] Step 27:

[0222] The device displays the expert's contact information and the claim's progress to the user.

[0223] The terminal analyzes the received data and displays the expert's contact information and the claim progress on a user interface.

[0224] In this way, users can efficiently and accurately obtain information about their car insurance, allowing for quick claims processing and contact with experts.

[0225] (Application example 1)

[0226] 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."

[0227] Education about automobile insurance is generally complex and difficult to understand, making it difficult for many users to obtain accurate information. Furthermore, traditional education methods lack interactivity and individuality, making it difficult to deepen users' understanding. Furthermore, it is not easy to provide detailed information instantly in physical stores due to time and resource constraints. This can prevent users from obtaining the necessary information immediately, leading to delays in choosing the right insurance and responding in the event of an accident.

[0228] 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.

[0229] In this invention, the server includes a means for receiving information entered by a user, a means for storing the information entered by the user in a data storage device, and a means for generating answers to the user's questions using a generative AI model. This allows the generative AI model to provide explanations so that the user can learn basic knowledge about automobile insurance. The server also includes a means for the user to take detailed interactive lessons, thereby enhancing the effectiveness of education. Furthermore, the server includes a means for the user to receive explanations of policy terms based on insurance quotes, enabling detailed information to be provided instantly even in physical stores. This allows users to instantly obtain the information they need, enabling them to quickly select appropriate insurance and respond in the event of an accident.

[0230] A "generative AI model" is an advanced artificial intelligence algorithm that generates answers in natural language based on user input.

[0231] "User" refers to an individual or corporation that uses the system to obtain information about automobile insurance and carry out various procedures.

[0232] "Information" refers to all data provided by users to the system, including data about their vehicle, questions, and personal information.

[0233] A "data storage device" is a digital storage device that stores information entered by a user and allows it to be searched or referenced later.

[0234] An "insurance expert" is a professional person or organization that has specialized knowledge about automobile insurance and provides appropriate advice and support to users.

[0235] "Matching" is the process of selecting an appropriate insurance professional based on the user's needs and enabling them to communicate and consult with each other.

[0236] A "physical store" is a physical location that customers can visit in person, where they can receive information and support about insurance.

[0237] "Basic knowledge" refers to the basic information and concepts users need to understand insurance, and in this system this is provided by a generative AI model.

[0238] An "interactive lesson" is an educational program that allows users to actively participate and track their learning progress.

[0239] A "quote" is a presentation of estimated insurance premiums based on the insurance coverage required by the user.

[0240] A "policy" is a document that details all the terms and conditions of an insurance contract.

[0241] This invention is a system that uses a generative AI model to provide education about automobile insurance in a brick-and-mortar store and deepen customer understanding. This system is mainly composed of a server, a terminal, and a user. Specific embodiments of the system are described below.

[0242] Hardware and Software Configuration

[0243] Hardware

[0244] Smartphone: A device used by users that functions as an input and display device.

[0245] Server: A computing device that stores generative AI models and databases and executes various processes.

[0246] software

[0247] Flask: A web framework that runs on the server and is responsible for the user interface and API processing.

[0248] OpenAI API: Perform natural language processing using generative AI models (e.g., text-davinci-003).

[0249] Program processing

[0250] User registration and profile creation

[0251] 1. Terminal: User enters name, age, and vehicle information.

[0252] 2. Server: Receives, validates, and stores data in a data repository.

[0253] 3. Example: A user enters their name, age, and vehicle information into a smartphone app and sends it to the server, which validates and stores the data.

[0254] Basic knowledge education

[0255] 1. User: Sends a request from the terminal to learn basic knowledge about car insurance.

[0256] 2. Server: Uses the generative AI model to generate an answer and send it back to the device.

[0257] 3. Example: Send a request such as "Please briefly explain what car insurance is." The generative AI model generates a basic explanation and sends it back to the device.

[0258] Interactive Lessons

[0259] 1. User: Selects an interactive lesson and sends a request from the device to the server.

[0260] 2. Server: Prepares the appropriate lesson content and sends it to the device, which displays the lesson to the user and tracks their progress.

[0261] 3. Example: A user selects a lesson on "How to choose car insurance," and the device sends the request to the server. The server generates the lesson content and sends it to the device.

[0262] Insurance quotes and policy explanations

[0263] 1. User: Request a quote and enter any additional information required.

[0264] 2. Server: Runs the insurance quote algorithm, generates quote results and a brief explanation of the policy terms, and sends them back to the terminal for display.

[0265] 3. Example: Send a request saying "Please tell me the estimate if the annual mileage is 10,000 km." The server calculates the estimate and returns the result along with an explanation of the terms and conditions.

[0266] Claims handling and expert matching

[0267] 1. User: Enters information about the accident or damage and sends it from the terminal to the server.

[0268] 2. Server: Analyzes the claim details and matches it with the appropriate insurance expert. The progress status and the expert's contact information are returned to the terminal and displayed.

[0269] 3. For example, you enter information such as the date, time, location, and circumstances of the accident, and the server matches it with an appropriate insurance investigator and returns their contact information and progress status.

[0270] Prompt Sentence Examples

[0271] "Please briefly explain what car insurance is."

[0272] This system allows customers to receive immediate and detailed education about car insurance at physical stores, and by gaining a deeper understanding, they will be able to choose the right insurance and respond quickly.

[0273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0274] Step 1:

[0275] User registration and profile creation

[0276] Input: The user enters personal information such as name, age, and vehicle information into the device.

[0277] Specific operation: The terminal receives the input information and sends it to the server.

[0278] Data processing: The server validates the received information and stores it in the database.

[0279] Output: A profile for the user is created.

[0280] Step 2:

[0281] Basic Knowledge Education Request

[0282] Input: The user sends a request from the terminal to the server saying, "I want to know the basics of car insurance."

[0283] Specific operation: The server sends a prompt sentence to the generative AI model.

[0284] Data processing: The generative AI model generates an answer based on the input prompt.

[0285] Output: The generated answer is sent from the server to the terminal and displayed to the user.

[0286] Step 3:

[0287] Select an Interactive Lesson

[0288] Input: The user selects an interactive lesson and sends the request from the device to the server.

[0289] Specific Actions: The server prepares the appropriate lesson content.

[0290] Data processing: Lesson content is generated and user progress data is initialized.

[0291] Output: The lesson content is sent to the device and the user's progress is displayed on the device.

[0292] Step 4:

[0293] Insurance quote request

[0294] Input: The user enters additional information such as mileage and primary use, and sends a request for an insurance quote from the device to the server.

[0295] Specific operation: The server sends the received information to the estimation algorithm.

[0296] Data Processing: A quoting algorithm calculates an insurance quote based on the information entered.

[0297] Output: The calculated estimate results and explanation of the terms and conditions are sent to the terminal and displayed to the user.

[0298] Step 5:

[0299] Claims handling request

[0300] Input: The user inputs detailed information about the accident (date, time, location, and circumstances) and sends the claim from the terminal to the server.

[0301] Specific operation: The server analyzes the claim details and matches the claim with the appropriate insurance professional.

[0302] Data processing: Contact information of the experts and progress of the claim are generated.

[0303] Output: The progress of the claim and the contact information of the expert are sent to the terminal and displayed to the user.

[0304] This concludes the detailed explanation of the process flow and each step of the system program that realizes this application example. This system allows users to easily obtain detailed information about automobile insurance, enabling them to select the appropriate insurance and process claims quickly.

[0305] 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.

[0306] The present invention is a system for providing education on automobile insurance using a generative AI model and an emotion engine. Specific embodiments and program processing of the system are described below.

[0307] User registration and profile creation

[0308] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The device receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, a user profile is created.

[0309] Basic knowledge education

[0310] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses the generative AI model and emotion engine to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[0311] Utilizing the Emotion Engine

[0312] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data (e.g., camera footage and audio analysis) is collected and the user's emotional state is determined. The server receives and analyzes this emotional data and generates appropriate answers and educational content according to the user's psychological state.

[0313] Interactive lessons

[0314] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device. The device then displays the lesson content to the user and tracks their progress, again using an emotion engine to provide feedback based on the user's emotional state.

[0315] Insurance quotes and policy explanations

[0316] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and main use). The device sends the information the user has entered to the server, which runs an insurance quote algorithm to generate a quote and a brief explanation of the policy. This process also uses an emotion engine to adjust the explanation based on the user's emotional state. The device then displays these results to the user.

[0317] Claims handling and expert matching

[0318] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. Again, an emotion engine is used here to provide a response that reflects the user's emotional state. The server then sends the progress of the claim and the expert's contact information to the device, which displays this to the user.

[0319] Specific examples

[0320] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[0321] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[0322] 2. The terminal sends the entered information to the server.

[0323] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[0324] 4. The server sends the progress and contact information of the expert to the device.

[0325] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[0326] During this process, the emotion engine monitors the user's emotional state in real time and provides support accordingly if stress levels rise. For example, if the system determines that the user is emotionally upset, it will quickly refer them to professional support and provide advice on how to stay calm.

[0327] The system allows users to gain comprehensive and practical knowledge about car insurance, providing them with the necessary assistance and information in a timely manner, as well as providing them with appropriate emotional support.

[0328] The processing flow will be explained below.

[0329] User registration and profile creation

[0330] Step 1: Enter your user information

[0331] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[0332] Step 2: Send input data

[0333] Terminal: Sends the information entered by the user to the server.

[0334] Step 3: Save to database

[0335] Server: Validates the received information and stores it in the database if there are no problems.

[0336] Step 4: Notification of registration results

[0337] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[0338] Terminal: Receives messages from the server and displays them to the user.

[0339] Basic knowledge education

[0340] Step 1: Educational Content Request

[0341] User: Makes a request: "I want to know the basics of car insurance."

[0342] Step 2: Submit the request

[0343] Terminal: Sends user requests to the server.

[0344] Step 3: Invoke the AI ​​model

[0345] Server: Inputs topics into a generative AI model and generates relevant explanations.

[0346] Step 4: Returning content

[0347] Server: Prepares the generated description and sends it to the device.

[0348] Terminal: Display the received description to the user.

[0349] Coordination using emotion engine

[0350] Step 1: Collecting sentiment data

[0351] Terminal: Collects the user's camera footage and audio data and sends it to the emotion engine.

[0352] Step 2: Analyze emotional state

[0353] Emotion engine: Analyzes collected data to determine the user's emotional state (stress, excitement, calm, etc.).

[0354] Step 3: Adjust the description

[0355] Server: Based on feedback from the emotion engine, the explanations generated by the generative AI model are adjusted according to the user's emotional state.

[0356] Terminal: Display the adjusted description to the user.

[0357] Interactive lessons

[0358] Step 1: Choose a lesson

[0359] User: Select from available lessons (e.g., accident procedures).

[0360] Step 2: Submit a lesson request

[0361] Terminal: Sends a request for the selected lesson to the server.

[0362] Step 3: Prepare the lesson content

[0363] Server: Selects appropriate lesson content and sets initial data.

[0364] Step 4: Deliver the lesson

[0365] Server: Sends lesson content to devices.

[0366] Device: Presents lesson content to users and tracks progress.

[0367] Step 5: Provide emotional feedback

[0368] On the device: Uses an emotion engine to provide feedback based on the user's emotional state.

[0369] Insurance quotes and policy explanations

[0370] Step 1: Request a quote

[0371] User: Enter any additional information required for insurance quote (mileage, primary use, etc.).

[0372] Step 2: Send data

[0373] Terminal: Sends any additional information entered by the user to the server.

[0374] Step 3: Calculate estimates and generate explanations

[0375] Server: Runs insurance quoting algorithms and generates quote results, and also uses generative AI models to create policy summaries and commentary.

[0376] Step 4: Emotional Adjustment

[0377] Server: Adjusts the estimation results and commentary based on the user's emotional state.

[0378] Step 5: Returning the results

[0379] Server: Sends the adjusted estimate results and explanations to the device.

[0380] Terminal: Displays results and explanations to the user.

[0381] Claims handling and expert matching

[0382] Step 1: Enter your claim information

[0383] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[0384] Step 2: Submit your claim data

[0385] Terminal: Sends the complaint information to the server.

[0386] Step 3: Claims processing and expert matching

[0387] Server: Analyzes the claims, selects appropriate experts, and sets initial steps.

[0388] Step 4: Emotional feedback and response

[0389] Server: Uses an emotion engine to consider appropriate responses based on the user's emotional state.

[0390] Step 5: Progress notification and expert information

[0391] Server: Sends claim progress and expert contact information to the device.

[0392] Terminal: Displays claim progress and expert information to the user.

[0393] Example 2

[0394] 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."

[0395] When providing education and support regarding automobile insurance, it is difficult to provide users with the information they need in a timely and appropriate manner. In particular, flexible responses that take into account the user's emotional state and efficient matching with experts are required. Furthermore, it is important that the educational content be appropriately adjusted according to the user's progress.

[0396] 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.

[0397] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using a generative AI model and an emotion engine, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for monitoring and analyzing the user's emotional state in real time, means for providing appropriate feedback according to the user's psychological state using the emotion engine, and means for matching with insurance experts. This enables flexible and appropriate automobile insurance education and support according to the user's emotions and progress.

[0398] "User" means any individual or entity that uses the System to receive services such as automobile insurance education, quotes, and claims processing.

[0399] "Terminal" means a computing device through which a user enters information and receives and displays information from a server.

[0400] A "server" is a computer system that processes information received from users and generates appropriate answers and feedback using generative AI models and emotion engines.

[0401] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions or requests.

[0402] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and uses that information to provide appropriate feedback and answers.

[0403] "Database" refers to a system for storing data such as information received from users, generated responses, and educational content.

[0404] "Interactive lessons" are educational content that dynamically adjusts based on the user's progress and emotional state.

[0405] An "insurance expert" is an individual or institution with detailed knowledge and experience regarding automobile insurance who can provide specific advice and support to users.

[0406] This invention is a system that uses a generative AI model and an emotion engine to provide education and support regarding car insurance. This system provides users with the information they need in a timely and appropriate manner, and provides flexible responses that take into account the user's emotional state.

[0407] User registration and profile creation

[0408] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is sent to the server via the user's device. The server validates this information and, if there are no problems, stores it in the database. This creates a user profile.

[0409] Basic knowledge education

[0410] When a user makes a request such as "I want to know the basics of car insurance," the request is sent from the device to the server, which uses a generative AI model and an emotion engine to generate a relevant explanation and sends it back to the device, which then displays it to the user.

[0411] For example, some prompts for a generative AI model might include:

[0412] "What is car insurance?"

[0413] Utilizing the Emotion Engine

[0414] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data is collected through camera footage and audio analysis and sent to a server. The server then uses the emotion engine to analyze the emotional data and generate appropriate answers and educational content based on the user's psychological state.

[0415] Interactive lessons

[0416] When a user selects an interactive lesson, the request is sent from the device to the server. The server prepares the appropriate lesson content and sends it back to the device. The device displays this content and tracks the user's progress. An emotion engine can be used to provide feedback based on the user's emotional state.

[0417] Insurance quotes and policy explanations

[0418] When a user requests an insurance quote, they are asked to enter additional information (such as mileage and main use). The information entered by the user is sent to the server via the device. The server then runs an insurance quote algorithm to generate a quote and a brief explanation of the policy terms. This process also utilizes an emotion engine to provide an explanation based on the user's emotional state.

[0419] Claims handling and expert matching

[0420] When a user files a claim, they enter details of the accident and damage into the device. The device sends this information to the server, which analyzes the claim. The server then matches the claim with an appropriate insurance expert and sends the progress report and the expert's contact information to the device. The device displays this information to the user and provides an interface for direct contact with the expert. The device can monitor the user's emotional state in real time using an emotion engine and provide support as needed.

[0421] This system will enable users to obtain comprehensive and practical knowledge about car insurance, provide necessary assistance and information in a timely manner, and also provide appropriate emotional support, allowing users to proceed with car insurance procedures with greater peace of mind.

[0422] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0423] Step 1:

[0424] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.) This information is called input data. The device receives this information and sends the input data to the server.

[0425] Step 2:

[0426] The server validates the received user information. Specifically, it checks whether the data is in the correct format (for example, whether the name is blank, whether the age is a number, etc.). This is the data calculation step, and the validated information becomes the output data. If the information passes validation, the server saves it in the database. This save is the output result.

[0427] Step 3:

[0428] The user makes a request such as "I want to know the basics of car insurance." This request is the input data. The device sends this request to the server.

[0429] Step 4:

[0430] The server uses a generative AI model and an emotion engine to generate an explanation based on the request. Specifically, the generative AI model generates an answer based on a prompt (e.g., "What is car insurance?"), and the emotion engine analyzes the user's psychological state and adjusts the answer. This process is a data calculation, and the generated explanation becomes the output data. The server then sends the generated explanation back to the device.

[0431] Step 5:

[0432] The terminal displays the generated explanation received from the server to the user, which is the output to the user.

[0433] Step 6:

[0434] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. This emotion data is input data. Data is collected through camera footage and audio analysis and sent to a server.

[0435] Step 7:

[0436] The server analyzes the emotional data using an emotion engine. This analysis process is a data calculation, and the user's emotional state is the output data. As a result, depending on the user's emotional state, the server generates appropriate educational content and feedback and sends it to the device.

[0437] Step 8:

[0438] The user selects an interactive lesson. This selection becomes the input data. The device sends the request to the server.

[0439] Step 9:

[0440] The server prepares interactive lesson content, which is a data generation process, and the prepared lesson content is the output data. The server then sends the lesson content to the device.

[0441] Step 10:

[0442] The device displays the received lesson content to the user and tracks the user's progress. This progress data becomes input data, and the device sends the information to the server.

[0443] Step 11:

[0444] The server integrates the user's progress information with the data analyzed by the emotion engine to generate optimal feedback for the user. This is the data fusion and generation step, and the integrated feedback becomes the output data. The server then sends the feedback to the device.

[0445] Step 12:

[0446] When a user requests an insurance quote, they enter additional information (mileage, main use, etc.). This information becomes input data. The device sends the information entered by the user to the server.

[0447] Step 13:

[0448] The server runs an insurance quote algorithm and generates quote results and brief explanations of the policy terms. This process is a data computation, with the quote results and explanations being the output data. It also utilizes an emotion engine to provide explanations tailored to the user's emotional state. This tailored explanation is also output data.

[0449] Step 14:

[0450] The server sends the generated estimate and adjusted commentary to the terminal, which displays it to the user. The displayed information is the final output.

[0451] Step 15:

[0452] When a user files a claim, they input detailed information about the accident or damage. This information is the input data. The device sends the information to the server.

[0453] Step 16:

[0454] The server analyzes the claim details and matches them with the appropriate insurance experts. This analysis and matching process is a data calculation, and the expert information is the output data. The server sends the progress status and the expert contact information to the terminal.

[0455] Step 17:

[0456] The terminal displays the progress and contact information of the expert to the user, and provides an interface for direct contact with the expert. This display and interface are the final output.

[0457] Step 18:

[0458] The emotion engine monitors the user's emotional state in real time and provides support as needed. The support provided by the emotion engine is output data, and appropriate support is provided to the user.

[0459] (Application example 2)

[0460] 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."

[0461] While conventional insurance education systems aim to improve users' knowledge, they do not adequately consider the stress and anxiety users feel during the learning process. As a result, the education is not fully effective, and users often abandon their studies midway. Furthermore, there are also situations where psychological support is lacking in the event of an accident or insurance claim. Given this background, there is a need for a system that can provide appropriate information and support according to the user's emotional state.

[0462] 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 receiving information input by the user, means for generating an answer to the user's question, means for displaying the generated answer to the user, means for activating an emotion engine for recognizing and analyzing the user's emotional state, and means for generating educational content based on the emotion engine in accordance with the user's emotional state. This makes it possible to provide appropriate information and support in accordance with the user's emotional state.

[0463] A "generative AI model" is an artificial intelligence model that generates appropriate answers and information in response to user questions and requests.

[0464] An "emotion engine" is a software engine for recognizing and analyzing a user's emotional state.

[0465] "Means for receiving information entered by the user" refers to the means by which the terminal or server collects and processes information provided by the user.

[0466] "Means for generating answers to user questions" refers to means for generating appropriate answers to user questions using a generative AI model.

[0467] "Means for displaying the generated answer to the user" refers to a means for presenting the answer generated by the generative AI model to the user in an easy-to-read format.

[0468] "Means for tracking user progress and providing interactive lessons" means means for tracking a user's learning progress and providing lessons in an interactive format.

[0469] "Means for matching with insurance experts" refers to means for selecting an insurance expert suitable for the user and providing that information.

[0470] The "means for generating educational content according to the emotional state of the user" is a means for generating educational content that is optimal for the emotional state of the user based on the analysis results of the emotion engine.

[0471] The "means for inputting information necessary for insurance quotes" refers to the means by which a user inputs various information necessary for insurance quotes.

[0472] "Means for generating explanations of insurance policy terms and conditions using a generative AI model" refers to means for generating easy-to-understand explanations of insurance policy terms and conditions using a generative AI model.

[0473] The following describes an embodiment of the present invention. The system uses a generative AI model and an emotion engine to provide users with insurance education. The system comprises the following specific steps:

[0474] User registration and profile creation

[0475] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. This information is sent from the user's device to the server, where it is validated and stored in a database. At this stage, a user profile is created.

[0476] Basic knowledge education

[0477] When a user makes a request such as "I want to know the basics of insurance," the generative AI model generates an appropriate explanation. The generated content is sent to the user's device and displayed. During this process, the emotion engine monitors the user's emotional state and provides appropriate feedback.

[0478] Utilizing the Emotion Engine

[0479] While a user is using educational content, the emotion engine collects and analyzes emotional data (camera footage, audio analysis, etc.). Based on the analysis results, the server generates content appropriate to the user's psychological state and provides it to the user.

[0480] Interactive lessons

[0481] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the user's device. Progress is tracked and an emotion engine provides feedback based on the user's emotional state.

[0482] Insurance quote and explanation of contract details

[0483] When a user requests an insurance quote, the server prompts them to enter additional necessary information (such as the type of insurance and key terms and conditions). The generative AI model is used to generate an insurance quote and explanation of the policy terms and conditions, which are then provided to the user. The emotion engine monitors the user's emotional state during this process and provides appropriate assistance.

[0484] Claims handling and expert matching

[0485] When a user submits an insurance claim, they enter information about the accident and damage. This information is sent to the server, which starts the claim process. An appropriate expert is matched and the progress and expert contact information are provided to the user. The emotion engine monitors the user's emotional state and provides a quick response in case of increased stress.

[0486] Hardware and software used

[0487] Hardware: Smartphone (iOS or Android)

[0488] Software: Insurance education support app, generative AI models (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva SDK)

[0489] Examples and prompts

[0490] As a concrete example, if a 30-year-old male user requests basic insurance knowledge, the following prompt is sent to the generative AI model:

[0491] Example prompt sentence:

[0492] "Generate an easy-to-understand explanation for a 30-year-old male user about basic insurance knowledge for him. If the emotion engine determines that the user is confused, regenerate a simplified explanation."

[0493] In this way, the system can provide appropriate education according to the user's emotional state, improving the user experience.

[0494] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0495] Step 1: Register and create a profile

[0496] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. The terminal collects this information and sends it to the server, which validates it and stores it in a database if there are no problems. The input to this process is the user's personal and insurance-related information, and the output is a user profile stored in the database.

[0497] Step 2: Request Foundational Knowledge Training

[0498] The user inputs a request such as "I want to learn the basics of insurance." The device sends this request to the server. The server receives the request and sends a prompt to the generative AI model to generate appropriate educational content. The input of this process is the user's request, and the output is the generated educational content.

[0499] Step 3: Activating and monitoring the emotion engine

[0500] While the user is using educational content, the device collects camera footage and audio data. The emotion engine analyzes this data and determines the user's emotional state. The input to this process is the collected emotion data, and the output is the determination of the user's emotional state. The server receives this determination result and uses it as feedback for the next step.

[0501] Step 4: Regenerate content based on emotions

[0502] Based on the emotion engine's judgment, the server sends a prompt to the generative AI model again to generate new educational content that corresponds to the user's emotional state. For example, if the user is confused, it generates a more understandable and simplified explanation. The input to this process is the emotion engine's judgment, and the output is educational content that corresponds to the user's emotional state.

[0503] Step 5: Deliver interactive lessons

[0504] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the device, tracking the user's progress and providing feedback based on data obtained by the emotion engine. The input to this process is the user's progress data and emotional state data, and the output is the tailored lesson content.

[0505] Step 6: Insurance quote and contract explanation

[0506] When a user requests an insurance quote, the device sends it to the server. The server requests additional information (e.g., type of insurance and terms) and generates a quote using a generative AI model based on the information entered by the user. The input to this process is the insurance information entered by the user, and the output is the generated quote and a description of the policy.

[0507] Step 7: Claims processing and expert matching

[0508] When a user enters a claim about an accident or damage, the device sends the information to the server. The server analyzes the claim and matches it with an appropriate expert. It then provides the user with progress status and the expert's contact information. The input to this process is the claim details, and the output is the expert's contact information and the claim processing progress.

[0509] In this way, the user, device, and server work together at each step to create a system that provides optimal insurance education and support according to the user's emotional state.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 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.

[0516] 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).

[0517] 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.

[0518] 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.

[0519] 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).

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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."

[0526] The present invention is a system that provides education on automobile insurance using a generative AI model. Specific embodiments and program processing are described below.

[0527] User registration and profile creation

[0528] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The terminal receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, the user's profile is created.

[0529] Basic knowledge education

[0530] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[0531] Interactive lessons

[0532] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device, which displays the lesson content to the user and tracks their progress.

[0533] Insurance quotes and policy explanations

[0534] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and primary use). The device sends the information to the server, which runs an insurance quote algorithm and generates a quote and a brief explanation of the policy terms. The device displays these results to the user.

[0535] Claims handling and expert matching

[0536] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. The server then sends the progress of the claim and the expert's contact information to the device, which displays it to the user.

[0537] Specific examples

[0538] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[0539] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[0540] 2. The terminal sends the entered information to the server.

[0541] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[0542] 4. The server sends the progress and contact information of the expert to the device.

[0543] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[0544] The system is designed to provide users with effective education and support regarding car insurance, and to quickly address any insurance-related questions or problems.

[0545] The processing flow will be explained below.

[0546] User registration and profile creation

[0547] Step 1: Enter your user information

[0548] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[0549] Step 2: Send input data

[0550] Terminal: Sends the information entered by the user to the server.

[0551] Step 3: Save to database

[0552] Server: Validates the received information and stores it in the database if there are no problems.

[0553] Step 4: Notification of registration results

[0554] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[0555] Terminal: Receives messages from the server and displays them to the user.

[0556] Basic knowledge education

[0557] Step 1: Educational Content Request

[0558] User: Makes a request: "I want to know the basics of car insurance."

[0559] Step 2: Submit the request

[0560] Terminal: Sends user requests to the server.

[0561] Step 3: Invoke the AI ​​model

[0562] Server: Inputs topics into a generative AI model and generates relevant explanations.

[0563] Step 4: Returning content

[0564] Server: Prepares the generated description and sends it to the device.

[0565] Terminal: Display the received description to the user.

[0566] Interactive lessons

[0567] Step 1: Choose a lesson

[0568] User: Select from available lessons (e.g., accident procedures).

[0569] Step 2: Submit a lesson request

[0570] Terminal: Sends a request for the selected lesson to the server.

[0571] Step 3: Prepare the lesson content

[0572] Server: Selects the corresponding lesson content and sets up initial data to track user progress.

[0573] Step 4: Deliver the lesson

[0574] Server: Sends the selected lesson content to the device.

[0575] Device: Presents lesson content to the user and tracks the user's progress.

[0576] Insurance quotes and policy explanations

[0577] Step 1: Request a quote

[0578] User: Enter any additional information required for insurance quote (e.g. mileage, primary use).

[0579] Step 2: Send data

[0580] Terminal: Sends any additional information entered by the user to the server.

[0581] Step 3: Calculate estimates and generate explanations

[0582] Server: Analyzes the data, runs insurance quoting algorithms to generate quotes, and uses generative AI models to create policy summaries and explanations.

[0583] Step 4: Returning the results

[0584] Server: Sends the estimate results and explanations to the device.

[0585] Terminal: Displays results and explanations to the user.

[0586] Claims handling and expert matching

[0587] Step 1: Enter your claim information

[0588] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[0589] Step 2: Submit your claim data

[0590] Terminal: Sends the complaint information to the server.

[0591] Step 3: Claims processing and expert matching

[0592] Server: Review the claim details, select appropriate experts (insurance adjusters, lawyers, etc.), and set the initial steps in claim processing.

[0593] Step 4: Progress notification and expert information

[0594] Server: Sends claim progress and expert contact information to the device.

[0595] Terminal: Displays claim progress and expert information to the user.

[0596] Example 1

[0597] 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."

[0598] Conventional automobile insurance systems require a lot of time and effort for users to obtain the necessary information, and are difficult for users without specialized knowledge to understand. Furthermore, when an accident or damage occurs, the claim procedures and contact with experts take time, requiring a rapid response. Therefore, there is a need for a system that allows users to efficiently and accurately obtain information about automobile insurance and process claims.

[0599] 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.

[0600] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using the generative AI model, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for matching with insurance experts, and means for tracking the progress of claims and providing the expert's contact information to the user, thereby enabling users to efficiently and accurately obtain information about car insurance and quickly process claims and connect with experts.

[0601] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to automatically generate text or answers based on specific prompts.

[0602] "User" refers to anyone who uses the system to obtain information about or report a claim regarding auto insurance.

[0603] "Terminal" refers to a device, such as a computer or smartphone, that a user uses to enter information and view displayed results.

[0604] "Server" refers to a central processing unit that provides functions such as receiving, processing, and storing user input and generating answers using generative AI models.

[0605] "Database" refers to an electronic information management system for storing information entered by users and responses generated by users.

[0606] "Interactive Lesson" refers to a lesson format that provides educational content that changes dynamically based on the user's progress.

[0607] An "insurance professional" refers to a person with specialized knowledge of insurance, such as an insurance investigator or an employee of an insurance agency.

[0608] "Claim Progress" refers to the status of the insurance claim submitted by the User.

[0609] "Matching" refers to the process of identifying professionals and services that meet a user's needs and connecting them with the right people.

[0610] MODE FOR CARRYING OUT THE INVENTION

[0611] The present invention is a system for providing education on automobile insurance using a generative AI model. A specific embodiment of this system is described below.

[0612] System Configuration

[0613] The system mainly consists of a server, a terminal, and a user. The server acts as a central processing unit, receiving, storing, and processing data, and generating answers using the generative AI model. The terminal is a device used by the user, such as a computer or smartphone, that receives input from the user and displays output from the server. Users use the system to obtain information about car insurance and report claims.

[0614] Hardware and Software

[0615] The server is equipped with a high-performance processor (e.g., Intel Xeon processor) and large amounts of memory and storage (e.g., SSD) to run the generative AI model. A relational database management system such as MySQL or PostgreSQL is used as the database. OpenAI's GPT-3 and other models are used as generative AI models.

[0616] The device must have a commonly accessible web browser (e.g., Google Chrome or Mozilla Firefox) installed and be able to connect to the Internet. An interface is provided to receive user input and display responses from the server.

[0617] User registration and profile creation

[0618] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is received by the device and sent to the server, which validates the received information to ensure it is accurate. The verified information is then stored in a database, and a user profile is created.

[0619] Basic knowledge education

[0620] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the explanation to the user.

[0621] As a concrete example, if a user inputs the prompt "What are the basic types of car insurance?", the server would use a generative AI model to generate the answer "The basic types of car insurance include bodily injury liability insurance, property damage liability insurance, vehicle insurance, and personal injury insurance."

[0622] Interactive lessons

[0623] When a user selects an interactive lesson, the device sends the request to the server. The server prepares the appropriate lesson content and sends it to the device. The device displays the lesson content to the user and tracks their progress. Along the way, the user answers questions and quizzes to promote effective learning.

[0624] Insurance quotes and policy explanations

[0625] When a user requests an insurance quote, they are asked for additional information (e.g., annual mileage, purpose of use, etc.). The device sends the information entered by the user to the server, which then runs an insurance quote algorithm to generate a quote. Furthermore, a generative AI model is used to generate an explanation of the policy terms. The generated quote and explanation of the policy terms are then displayed to the user via the device.

[0626] For example, if a user enters a prompt such as "I would like to know the insurance cost if I drive 10,000 kilometers per year," the server runs an insurance estimation algorithm to generate an estimate such as "The annual insurance cost will be approximately 50,000 yen," while also providing a brief explanation of the terms and conditions.

[0627] Claims handling and expert matching

[0628] When a user files a claim, they need to enter information about the accident or damage. The device sends this information to the server, which analyzes the claim. Based on the analysis results, the server matches the most suitable expert and provides the user with their contact information. This information is displayed to the user on the device, allowing the user to contact the expert directly.

[0629] As a specific example, when a user reports a claim that they have been involved in an accident, the device sends the information input by the user (e.g., date, time, location, and situation) to the server, and the server analyzes this information and provides a matching result such as "Please contact Insurance Investigator A" along with contact information.

[0630] The system is designed to enable users to receive a variety of car insurance-related services quickly and efficiently.

[0631] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0632] Step 1:

[0633] The user accesses the system and enters their name, age, and vehicle information (model, year, etc.).

[0634] Specifically, you fill in the required information in the input form and press the send button. This input becomes the initial data for the system.

[0635] Step 2:

[0636] The device receives the user's input information.

[0637] The entered information is temporarily stored on the device, and this data is later sent to the server, where it is organized in a suitable format (such as JSON or XML).

[0638] Step 3:

[0639] The terminal transmits the received information to the server.

[0640] The device collects the information entered by the user into a data packet and sends an HTTP request to the server, which transfers the input data.

[0641] Step 4:

[0642] Validate the information received by the server.

[0643] The server parses the transmitted data packet and checks the correct format and content of each field, for example, name format (characters only), age range (numeric check), and vehicle information validity.

[0644] Step 5:

[0645] The server saves the successfully validated information in the database.

[0646] Once validated, the data is inserted into the database via the database manager, which creates a user profile.

[0647] Step 6:

[0648] A user makes a request to "learn the basics of car insurance."

[0649] The user enters text into the search bar on the interface and hits submit, which triggers the following request:

[0650] Step 7:

[0651] The device sends a request to the server.

[0652] The terminal assembles the requests entered by the user into data packets and forwards them to the server.

[0653] Step 8:

[0654] The server uses the generative AI model to generate relevant explanations.

[0655] The server analyzes the request and inputs the appropriate prompt sentence into the generative AI model, which then generates text based on the input prompt and returns the result to the server.

[0656] Step 9:

[0657] The server returns the generated description to the terminal.

[0658] The explanatory text obtained from the generative AI model is compiled into a data packet and sent to the terminal.

[0659] Step 10:

[0660] The device displays the generated description to the user.

[0661] The terminal parses the received data packets and displays the text on the user interface.

[0662] Step 11:

[0663] The user selects an interactive lesson.

[0664] Users select an interactive lesson item from the education menu and press the start button.

[0665] Step 12:

[0666] The device sends a request to the server.

[0667] The device packages the user's request into data packets and forwards them to the server.

[0668] Step 13:

[0669] The server prepares the appropriate lesson content.

[0670] The server parses the request and generates or retrieves the interactive lesson content (e.g., videos, quizzes, text).

[0671] Step 14:

[0672] The server sends the lesson content to the device.

[0673] The server assembles the prepared content into data packets and transfers them to the terminal.

[0674] Step 15:

[0675] The device displays the lesson content to the user and tracks their progress.

[0676] The device tracks the user's progress and displays the lesson on the screen, and each time the user takes a step, progress data is sent to the server.

[0677] Step 16:

[0678] A user requests an insurance quote.

[0679] The user enters the required information (e.g., annual mileage, purpose of use) into the quote request form and presses the submit button.

[0680] Step 17:

[0681] The device sends the user's input information to the server.

[0682] The terminal assembles the information from the user into data packets and sends them to the server.

[0683] Step 18:

[0684] The server runs the insurance quoting algorithm.

[0685] The server runs its internal insurance quoting algorithms on the received information to generate a quote, using appropriate APIs in accordance with the present invention.

[0686] Step 19:

[0687] The server uses the generative AI model to generate explanations of the insurance policy terms.

[0688] Based on the estimate results, the server inputs prompts into the generative AI model to generate a brief explanation of the terms and conditions.

[0689] Step 20:

[0690] The server sends the estimate results and an explanation of the terms and conditions to the terminal.

[0691] The server compiles the estimate results and explanations of the terms and conditions into a data packet and sends it to the terminal.

[0692] Step 21:

[0693] The terminal displays the estimate results and an explanation of the terms and conditions to the user.

[0694] The terminal analyzes the received data and displays the estimate results and an explanation of the terms and conditions on the user interface.

[0695] Step 22:

[0696] The user submits a claim.

[0697] Users enter information about the accident and damage and press the claim submission button.

[0698] Step 23:

[0699] The terminal transmits the input information to the server.

[0700] The terminal compiles the complaint information from the user into a data packet and sends it to the server.

[0701] Step 24:

[0702] The server parses the claim.

[0703] The server analyzes the received claim information and identifies the necessary action, including the severity of the accident and the type of insurance action required.

[0704] Step 25:

[0705] The server matches you with the right expert.

[0706] The server identifies and matches the most suitable expert (e.g., insurance investigator) based on the content of the claim.

[0707] Step 26:

[0708] The server sends the contact information of the expert to the terminal.

[0709] The server compiles the matched expert's contact information and the progress of the claim into a data packet and sends it to the device.

[0710] Step 27:

[0711] The device displays the expert's contact information and the claim's progress to the user.

[0712] The terminal analyzes the received data and displays the expert's contact information and the claim progress on a user interface.

[0713] In this way, users can efficiently and accurately obtain information about their car insurance, allowing for quick claims processing and contact with experts.

[0714] (Application example 1)

[0715] 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."

[0716] Education about automobile insurance is generally complex and difficult to understand, making it difficult for many users to obtain accurate information. Furthermore, traditional education methods lack interactivity and individuality, making it difficult to deepen users' understanding. Furthermore, it is not easy to provide detailed information instantly in physical stores due to time and resource constraints. This can prevent users from obtaining the necessary information immediately, leading to delays in choosing the right insurance and responding in the event of an accident.

[0717] 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.

[0718] In this invention, the server includes a means for receiving information entered by a user, a means for storing the information entered by the user in a data storage device, and a means for generating answers to the user's questions using a generative AI model. This allows the generative AI model to provide explanations so that the user can learn basic knowledge about automobile insurance. The server also includes a means for the user to take detailed interactive lessons, thereby enhancing the effectiveness of education. Furthermore, the server includes a means for the user to receive explanations of policy terms based on insurance quotes, enabling detailed information to be provided instantly even in physical stores. This allows users to instantly obtain the information they need, enabling them to quickly select appropriate insurance and respond in the event of an accident.

[0719] A "generative AI model" is an advanced artificial intelligence algorithm that generates answers in natural language based on user input.

[0720] "User" refers to an individual or corporation that uses the system to obtain information about automobile insurance and carry out various procedures.

[0721] "Information" refers to all data provided by users to the system, including data about their vehicle, questions, and personal information.

[0722] A "data storage device" is a digital storage device that stores information entered by a user and allows it to be searched or referenced later.

[0723] An "insurance expert" is a professional person or organization that has specialized knowledge about automobile insurance and provides appropriate advice and support to users.

[0724] "Matching" is the process of selecting an appropriate insurance professional based on the user's needs and enabling them to communicate and consult with each other.

[0725] A "physical store" is a physical location that customers can visit in person, where they can receive information and support about insurance.

[0726] "Basic knowledge" refers to the basic information and concepts users need to understand insurance, and in this system this is provided by a generative AI model.

[0727] An "interactive lesson" is an educational program that allows users to actively participate and track their learning progress.

[0728] A "quote" is a presentation of estimated insurance premiums based on the insurance coverage required by the user.

[0729] A "policy" is a document that details all the terms and conditions of an insurance contract.

[0730] This invention is a system that uses a generative AI model to provide education about automobile insurance in a brick-and-mortar store and deepen customer understanding. This system is mainly composed of a server, a terminal, and a user. Specific embodiments of the system are described below.

[0731] Hardware and Software Configuration

[0732] Hardware

[0733] Smartphone: A device used by users that functions as an input and display device.

[0734] Server: A computing device that stores generative AI models and databases and executes various processes.

[0735] software

[0736] Flask: A web framework that runs on the server and is responsible for the user interface and API processing.

[0737] OpenAI API: Perform natural language processing using generative AI models (e.g., text-davinci-003).

[0738] Program processing

[0739] User registration and profile creation

[0740] 1. Terminal: User enters name, age, and vehicle information.

[0741] 2. Server: Receives, validates, and stores data in a data repository.

[0742] 3. Example: A user enters their name, age, and vehicle information into a smartphone app and sends it to the server, which validates and stores the data.

[0743] Basic knowledge education

[0744] 1. User: Sends a request from the terminal to learn basic knowledge about car insurance.

[0745] 2. Server: Uses the generative AI model to generate an answer and send it back to the device.

[0746] 3. Example: Send a request such as "Please briefly explain what car insurance is." The generative AI model generates a basic explanation and sends it back to the device.

[0747] Interactive Lessons

[0748] 1. User: Selects an interactive lesson and sends a request from the device to the server.

[0749] 2. Server: Prepares the appropriate lesson content and sends it to the device, which displays the lesson to the user and tracks their progress.

[0750] 3. Example: A user selects a lesson on "How to choose car insurance," and the device sends the request to the server. The server generates the lesson content and sends it to the device.

[0751] Insurance quotes and policy explanations

[0752] 1. User: Request a quote and enter any additional information required.

[0753] 2. Server: Runs the insurance quote algorithm, generates quote results and a brief explanation of the policy terms, and sends them back to the terminal for display.

[0754] 3. Example: Send a request saying "Please tell me the estimate if the annual mileage is 10,000 km." The server calculates the estimate and returns the result along with an explanation of the terms and conditions.

[0755] Claims handling and expert matching

[0756] 1. User: Enters information about the accident or damage and sends it from the terminal to the server.

[0757] 2. Server: Analyzes the claim details and matches it with the appropriate insurance expert. The progress status and the expert's contact information are returned to the terminal and displayed.

[0758] 3. For example, you enter information such as the date, time, location, and circumstances of the accident, and the server matches it with an appropriate insurance investigator and returns their contact information and progress status.

[0759] Prompt Sentence Examples

[0760] "Please briefly explain what car insurance is."

[0761] This system allows customers to receive immediate and detailed education about car insurance at physical stores, and by gaining a deeper understanding, they will be able to choose the right insurance and respond quickly.

[0762] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0763] Step 1:

[0764] User registration and profile creation

[0765] Input: The user enters personal information such as name, age, and vehicle information into the device.

[0766] Specific operation: The terminal receives the input information and sends it to the server.

[0767] Data processing: The server validates the received information and stores it in the database.

[0768] Output: A profile for the user is created.

[0769] Step 2:

[0770] Basic Knowledge Education Request

[0771] Input: The user sends a request from the terminal to the server saying, "I want to know the basics of car insurance."

[0772] Specific operation: The server sends a prompt sentence to the generative AI model.

[0773] Data processing: The generative AI model generates an answer based on the input prompt.

[0774] Output: The generated answer is sent from the server to the terminal and displayed to the user.

[0775] Step 3:

[0776] Select an Interactive Lesson

[0777] Input: The user selects an interactive lesson and sends the request from the device to the server.

[0778] Specific Actions: The server prepares the appropriate lesson content.

[0779] Data processing: Lesson content is generated and user progress data is initialized.

[0780] Output: The lesson content is sent to the device and the user's progress is displayed on the device.

[0781] Step 4:

[0782] Insurance quote request

[0783] Input: The user enters additional information such as mileage and primary use, and sends a request for an insurance quote from the device to the server.

[0784] Specific operation: The server sends the received information to the estimation algorithm.

[0785] Data Processing: A quoting algorithm calculates an insurance quote based on the information entered.

[0786] Output: The calculated estimate results and explanation of the terms and conditions are sent to the terminal and displayed to the user.

[0787] Step 5:

[0788] Claims handling request

[0789] Input: The user inputs detailed information about the accident (date, time, location, and circumstances) and sends the claim from the terminal to the server.

[0790] Specific operation: The server analyzes the claim details and matches the claim with the appropriate insurance professional.

[0791] Data processing: Contact information of the experts and progress of the claim are generated.

[0792] Output: The progress of the claim and the contact information of the expert are sent to the terminal and displayed to the user.

[0793] This concludes the detailed explanation of the process flow and each step of the system program that realizes this application example. This system allows users to easily obtain detailed information about automobile insurance, enabling them to select the appropriate insurance and process claims quickly.

[0794] 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.

[0795] The present invention is a system for providing education on automobile insurance using a generative AI model and an emotion engine. Specific embodiments and program processing of the system are described below.

[0796] User registration and profile creation

[0797] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The device receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, a user profile is created.

[0798] Basic knowledge education

[0799] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses the generative AI model and emotion engine to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[0800] Utilizing the Emotion Engine

[0801] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data (e.g., camera footage and audio analysis) is collected and the user's emotional state is determined. The server receives and analyzes this emotional data and generates appropriate answers and educational content according to the user's psychological state.

[0802] Interactive lessons

[0803] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device. The device then displays the lesson content to the user and tracks their progress, again using an emotion engine to provide feedback based on the user's emotional state.

[0804] Insurance quotes and policy explanations

[0805] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and main use). The device sends the information the user has entered to the server, which runs an insurance quote algorithm to generate a quote and a brief explanation of the policy. This process also uses an emotion engine to adjust the explanation based on the user's emotional state. The device then displays these results to the user.

[0806] Claims handling and expert matching

[0807] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. Again, an emotion engine is used here to provide a response that reflects the user's emotional state. The server then sends the progress of the claim and the expert's contact information to the device, which displays this to the user.

[0808] Specific examples

[0809] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[0810] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[0811] 2. The terminal sends the entered information to the server.

[0812] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[0813] 4. The server sends the progress and contact information of the expert to the device.

[0814] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[0815] During this process, the emotion engine monitors the user's emotional state in real time and provides support accordingly if stress levels rise. For example, if the system determines that the user is emotionally upset, it will quickly refer them to professional support and provide advice on how to stay calm.

[0816] The system allows users to gain comprehensive and practical knowledge about car insurance, providing them with the necessary assistance and information in a timely manner, as well as providing them with appropriate emotional support.

[0817] The processing flow will be explained below.

[0818] User registration and profile creation

[0819] Step 1: Enter your user information

[0820] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[0821] Step 2: Send input data

[0822] Terminal: Sends the information entered by the user to the server.

[0823] Step 3: Save to database

[0824] Server: Validates the received information and stores it in the database if there are no problems.

[0825] Step 4: Notification of registration results

[0826] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[0827] Terminal: Receives messages from the server and displays them to the user.

[0828] Basic knowledge education

[0829] Step 1: Educational Content Request

[0830] User: Makes a request: "I want to know the basics of car insurance."

[0831] Step 2: Submit the request

[0832] Terminal: Sends user requests to the server.

[0833] Step 3: Invoke the AI ​​model

[0834] Server: Inputs topics into a generative AI model and generates relevant explanations.

[0835] Step 4: Returning content

[0836] Server: Prepares the generated description and sends it to the device.

[0837] Terminal: Display the received description to the user.

[0838] Coordination using emotion engine

[0839] Step 1: Collecting sentiment data

[0840] Terminal: Collects the user's camera footage and audio data and sends it to the emotion engine.

[0841] Step 2: Analyze emotional state

[0842] Emotion engine: Analyzes collected data to determine the user's emotional state (stress, excitement, calm, etc.).

[0843] Step 3: Adjust the description

[0844] Server: Based on feedback from the emotion engine, the explanations generated by the generative AI model are adjusted according to the user's emotional state.

[0845] Terminal: Display the adjusted description to the user.

[0846] Interactive lessons

[0847] Step 1: Choose a lesson

[0848] User: Select from available lessons (e.g., accident procedures).

[0849] Step 2: Submit a lesson request

[0850] Terminal: Sends a request for the selected lesson to the server.

[0851] Step 3: Prepare the lesson content

[0852] Server: Selects appropriate lesson content and sets initial data.

[0853] Step 4: Deliver the lesson

[0854] Server: Sends lesson content to devices.

[0855] Device: Presents lesson content to users and tracks progress.

[0856] Step 5: Provide emotional feedback

[0857] On the device: Uses an emotion engine to provide feedback based on the user's emotional state.

[0858] Insurance quotes and policy explanations

[0859] Step 1: Request a quote

[0860] User: Enter any additional information required for insurance quote (mileage, primary use, etc.).

[0861] Step 2: Send data

[0862] Terminal: Sends any additional information entered by the user to the server.

[0863] Step 3: Calculate estimates and generate explanations

[0864] Server: Runs insurance quoting algorithms and generates quote results, and also uses generative AI models to create policy summaries and commentary.

[0865] Step 4: Emotional Adjustment

[0866] Server: Adjusts the estimation results and commentary based on the user's emotional state.

[0867] Step 5: Returning the results

[0868] Server: Sends the adjusted estimate results and explanations to the device.

[0869] Terminal: Displays results and explanations to the user.

[0870] Claims handling and expert matching

[0871] Step 1: Enter your claim information

[0872] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[0873] Step 2: Submit your claim data

[0874] Terminal: Sends the complaint information to the server.

[0875] Step 3: Claims processing and expert matching

[0876] Server: Analyzes the claims, selects appropriate experts, and sets initial steps.

[0877] Step 4: Emotional feedback and response

[0878] Server: Uses an emotion engine to consider appropriate responses based on the user's emotional state.

[0879] Step 5: Progress notification and expert information

[0880] Server: Sends claim progress and expert contact information to the device.

[0881] Terminal: Displays claim progress and expert information to the user.

[0882] Example 2

[0883] 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."

[0884] When providing education and support regarding automobile insurance, it is difficult to provide users with the information they need in a timely and appropriate manner. In particular, flexible responses that take into account the user's emotional state and efficient matching with experts are required. Furthermore, it is important that the educational content be appropriately adjusted according to the user's progress.

[0885] 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.

[0886] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using a generative AI model and an emotion engine, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for monitoring and analyzing the user's emotional state in real time, means for providing appropriate feedback according to the user's psychological state using the emotion engine, and means for matching with insurance experts. This enables flexible and appropriate automobile insurance education and support according to the user's emotions and progress.

[0887] "User" means any individual or entity that uses the System to receive services such as automobile insurance education, quotes, and claims processing.

[0888] "Terminal" means a computing device through which a user enters information and receives and displays information from a server.

[0889] A "server" is a computer system that processes information received from users and generates appropriate answers and feedback using generative AI models and emotion engines.

[0890] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions or requests.

[0891] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and uses that information to provide appropriate feedback and answers.

[0892] "Database" refers to a system for storing data such as information received from users, generated responses, and educational content.

[0893] "Interactive lessons" are educational content that dynamically adjusts based on the user's progress and emotional state.

[0894] An "insurance expert" is an individual or institution with detailed knowledge and experience regarding automobile insurance who can provide specific advice and support to users.

[0895] This invention is a system that uses a generative AI model and an emotion engine to provide education and support regarding car insurance. This system provides users with the information they need in a timely and appropriate manner, and provides flexible responses that take into account the user's emotional state.

[0896] User registration and profile creation

[0897] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is sent to the server via the user's device. The server validates this information and, if there are no problems, stores it in the database. This creates a user profile.

[0898] Basic knowledge education

[0899] When a user makes a request such as "I want to know the basics of car insurance," the request is sent from the device to the server, which uses a generative AI model and an emotion engine to generate a relevant explanation and sends it back to the device, which then displays it to the user.

[0900] For example, some prompts for a generative AI model might include:

[0901] "What is car insurance?"

[0902] Utilizing the Emotion Engine

[0903] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data is collected through camera footage and audio analysis and sent to a server. The server then uses the emotion engine to analyze the emotional data and generate appropriate answers and educational content based on the user's psychological state.

[0904] Interactive lessons

[0905] When a user selects an interactive lesson, the request is sent from the device to the server. The server prepares the appropriate lesson content and sends it back to the device. The device displays this content and tracks the user's progress. An emotion engine can be used to provide feedback based on the user's emotional state.

[0906] Insurance quotes and policy explanations

[0907] When a user requests an insurance quote, they are asked to enter additional information (such as mileage and main use). The information entered by the user is sent to the server via the device. The server then runs an insurance quote algorithm to generate a quote and a brief explanation of the policy terms. This process also utilizes an emotion engine to provide an explanation based on the user's emotional state.

[0908] Claims handling and expert matching

[0909] When a user files a claim, they enter details of the accident and damage into the device. The device sends this information to the server, which analyzes the claim. The server then matches the claim with an appropriate insurance expert and sends the progress report and the expert's contact information to the device. The device displays this information to the user and provides an interface for direct contact with the expert. The device can monitor the user's emotional state in real time using an emotion engine and provide support as needed.

[0910] This system will enable users to obtain comprehensive and practical knowledge about car insurance, provide necessary assistance and information in a timely manner, and also provide appropriate emotional support, allowing users to proceed with car insurance procedures with greater peace of mind.

[0911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0912] Step 1:

[0913] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.) This information is called input data. The device receives this information and sends the input data to the server.

[0914] Step 2:

[0915] The server validates the received user information. Specifically, it checks whether the data is in the correct format (for example, whether the name is blank, whether the age is a number, etc.). This is the data calculation step, and the validated information becomes the output data. If the information passes validation, the server saves it in the database. This save is the output result.

[0916] Step 3:

[0917] The user makes a request such as "I want to know the basics of car insurance." This request is the input data. The device sends this request to the server.

[0918] Step 4:

[0919] The server uses a generative AI model and an emotion engine to generate an explanation based on the request. Specifically, the generative AI model generates an answer based on a prompt (e.g., "What is car insurance?"), and the emotion engine analyzes the user's psychological state and adjusts the answer. This process is a data calculation, and the generated explanation becomes the output data. The server then sends the generated explanation back to the device.

[0920] Step 5:

[0921] The terminal displays the generated explanation received from the server to the user, which is the output to the user.

[0922] Step 6:

[0923] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. This emotion data is input data. Data is collected through camera footage and audio analysis and sent to a server.

[0924] Step 7:

[0925] The server analyzes the emotional data using an emotion engine. This analysis process is a data calculation, and the user's emotional state is the output data. As a result, depending on the user's emotional state, the server generates appropriate educational content and feedback and sends it to the device.

[0926] Step 8:

[0927] The user selects an interactive lesson. This selection becomes the input data. The device sends the request to the server.

[0928] Step 9:

[0929] The server prepares interactive lesson content, which is a data generation process, and the prepared lesson content is the output data. The server then sends the lesson content to the device.

[0930] Step 10:

[0931] The device displays the received lesson content to the user and tracks the user's progress. This progress data becomes input data, and the device sends the information to the server.

[0932] Step 11:

[0933] The server integrates the user's progress information with the data analyzed by the emotion engine to generate optimal feedback for the user. This is the data fusion and generation step, and the integrated feedback becomes the output data. The server then sends the feedback to the device.

[0934] Step 12:

[0935] When a user requests an insurance quote, they enter additional information (mileage, main use, etc.). This information becomes input data. The device sends the information entered by the user to the server.

[0936] Step 13:

[0937] The server runs an insurance quote algorithm and generates quote results and brief explanations of the policy terms. This process is a data computation, with the quote results and explanations being the output data. It also utilizes an emotion engine to provide explanations tailored to the user's emotional state. This tailored explanation is also output data.

[0938] Step 14:

[0939] The server sends the generated estimate and adjusted commentary to the terminal, which displays it to the user. The displayed information is the final output.

[0940] Step 15:

[0941] When a user files a claim, they input detailed information about the accident or damage. This information is the input data. The device sends the information to the server.

[0942] Step 16:

[0943] The server analyzes the claim details and matches them with the appropriate insurance experts. This analysis and matching process is a data calculation, and the expert information is the output data. The server sends the progress status and the expert contact information to the terminal.

[0944] Step 17:

[0945] The terminal displays the progress and contact information of the expert to the user, and provides an interface for direct contact with the expert. This display and interface are the final output.

[0946] Step 18:

[0947] The emotion engine monitors the user's emotional state in real time and provides support as needed. The support provided by the emotion engine is output data, and appropriate support is provided to the user.

[0948] (Application example 2)

[0949] 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."

[0950] While conventional insurance education systems aim to improve users' knowledge, they do not adequately consider the stress and anxiety users feel during the learning process. As a result, the education is not fully effective, and users often abandon their studies midway. Furthermore, there are also situations where psychological support is lacking in the event of an accident or insurance claim. Given this background, there is a need for a system that can provide appropriate information and support according to the user's emotional state.

[0951] 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 receiving information input by the user, means for generating an answer to the user's question, means for displaying the generated answer to the user, means for activating an emotion engine for recognizing and analyzing the user's emotional state, and means for generating educational content based on the emotion engine in accordance with the user's emotional state. This makes it possible to provide appropriate information and support in accordance with the user's emotional state.

[0952] A "generative AI model" is an artificial intelligence model that generates appropriate answers and information in response to user questions and requests.

[0953] An "emotion engine" is a software engine for recognizing and analyzing a user's emotional state.

[0954] "Means for receiving information entered by the user" refers to the means by which the terminal or server collects and processes information provided by the user.

[0955] "Means for generating answers to user questions" refers to means for generating appropriate answers to user questions using a generative AI model.

[0956] "Means for displaying the generated answer to the user" refers to a means for presenting the answer generated by the generative AI model to the user in an easy-to-read format.

[0957] "Means for tracking user progress and providing interactive lessons" means means for tracking a user's learning progress and providing lessons in an interactive format.

[0958] "Means for matching with insurance experts" refers to means for selecting an insurance expert suitable for the user and providing that information.

[0959] The "means for generating educational content according to the emotional state of the user" is a means for generating educational content that is optimal for the emotional state of the user based on the analysis results of the emotion engine.

[0960] The "means for inputting information necessary for insurance quotes" refers to the means by which a user inputs various information necessary for insurance quotes.

[0961] "Means for generating explanations of insurance policy terms and conditions using a generative AI model" refers to means for generating easy-to-understand explanations of insurance policy terms and conditions using a generative AI model.

[0962] The following describes an embodiment of the present invention. The system uses a generative AI model and an emotion engine to provide users with insurance education. The system comprises the following specific steps:

[0963] User registration and profile creation

[0964] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. This information is sent from the user's device to the server, where it is validated and stored in a database. At this stage, a user profile is created.

[0965] Basic knowledge education

[0966] When a user makes a request such as "I want to know the basics of insurance," the generative AI model generates an appropriate explanation. The generated content is sent to the user's device and displayed. During this process, the emotion engine monitors the user's emotional state and provides appropriate feedback.

[0967] Utilizing the Emotion Engine

[0968] While a user is using educational content, the emotion engine collects and analyzes emotional data (camera footage, audio analysis, etc.). Based on the analysis results, the server generates content appropriate to the user's psychological state and provides it to the user.

[0969] Interactive lessons

[0970] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the user's device. Progress is tracked and an emotion engine provides feedback based on the user's emotional state.

[0971] Insurance quote and explanation of contract details

[0972] When a user requests an insurance quote, the server prompts them to enter additional necessary information (such as the type of insurance and key terms and conditions). The generative AI model is used to generate an insurance quote and explanation of the policy terms and conditions, which are then provided to the user. The emotion engine monitors the user's emotional state during this process and provides appropriate assistance.

[0973] Claims handling and expert matching

[0974] When a user submits an insurance claim, they enter information about the accident and damage. This information is sent to the server, which starts the claim process. An appropriate expert is matched and the progress and expert contact information are provided to the user. The emotion engine monitors the user's emotional state and provides a quick response in case of increased stress.

[0975] Hardware and software used

[0976] Hardware: Smartphone (iOS or Android)

[0977] Software: Insurance education support app, generative AI models (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva SDK)

[0978] Examples and prompts

[0979] As a concrete example, if a 30-year-old male user requests basic insurance knowledge, the following prompt is sent to the generative AI model:

[0980] Example prompt sentence:

[0981] "Generate an easy-to-understand explanation for a 30-year-old male user about basic insurance knowledge for him. If the emotion engine determines that the user is confused, regenerate a simplified explanation."

[0982] In this way, the system can provide appropriate education according to the user's emotional state, improving the user experience.

[0983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0984] Step 1: Register and create a profile

[0985] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. The terminal collects this information and sends it to the server, which validates it and stores it in a database if there are no problems. The input to this process is the user's personal and insurance-related information, and the output is a user profile stored in the database.

[0986] Step 2: Request Foundational Knowledge Training

[0987] The user inputs a request such as "I want to learn the basics of insurance." The device sends this request to the server. The server receives the request and sends a prompt to the generative AI model to generate appropriate educational content. The input of this process is the user's request, and the output is the generated educational content.

[0988] Step 3: Activating and monitoring the emotion engine

[0989] While the user is using educational content, the device collects camera footage and audio data. The emotion engine analyzes this data and determines the user's emotional state. The input to this process is the collected emotion data, and the output is the determination of the user's emotional state. The server receives this determination result and uses it as feedback for the next step.

[0990] Step 4: Regenerate content based on emotions

[0991] Based on the emotion engine's judgment, the server sends a prompt to the generative AI model again to generate new educational content that corresponds to the user's emotional state. For example, if the user is confused, it generates a more understandable and simplified explanation. The input to this process is the emotion engine's judgment, and the output is educational content that corresponds to the user's emotional state.

[0992] Step 5: Deliver interactive lessons

[0993] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the device, tracking the user's progress and providing feedback based on data obtained by the emotion engine. The input to this process is the user's progress data and emotional state data, and the output is the tailored lesson content.

[0994] Step 6: Insurance quote and contract explanation

[0995] When a user requests an insurance quote, the device sends it to the server. The server requests additional information (e.g., type of insurance and terms) and generates a quote using a generative AI model based on the information entered by the user. The input to this process is the insurance information entered by the user, and the output is the generated quote and a description of the policy.

[0996] Step 7: Claims processing and expert matching

[0997] When a user enters a claim about an accident or damage, the device sends the information to the server. The server analyzes the claim and matches it with an appropriate expert. It then provides the user with progress status and the expert's contact information. The input to this process is the claim details, and the output is the expert's contact information and the claim processing progress.

[0998] In this way, the user, device, and server work together at each step to create a system that provides optimal insurance education and support according to the user's emotional state.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] [Third embodiment]

[1003] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1004] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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).

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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."

[1015] The present invention is a system that provides education on automobile insurance using a generative AI model. Specific embodiments and program processing are described below.

[1016] User registration and profile creation

[1017] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The terminal receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, the user's profile is created.

[1018] Basic knowledge education

[1019] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[1020] Interactive lessons

[1021] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device, which displays the lesson content to the user and tracks their progress.

[1022] Insurance quotes and policy explanations

[1023] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and primary use). The device sends the information to the server, which runs an insurance quote algorithm and generates a quote and a brief explanation of the policy terms. The device displays these results to the user.

[1024] Claims handling and expert matching

[1025] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. The server then sends the progress of the claim and the expert's contact information to the device, which displays it to the user.

[1026] Specific examples

[1027] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[1028] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[1029] 2. The terminal sends the entered information to the server.

[1030] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[1031] 4. The server sends the progress and contact information of the expert to the device.

[1032] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[1033] The system is designed to provide users with effective education and support regarding car insurance, and to quickly address any insurance-related questions or problems.

[1034] The processing flow will be explained below.

[1035] User registration and profile creation

[1036] Step 1: Enter your user information

[1037] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[1038] Step 2: Send input data

[1039] Terminal: Sends the information entered by the user to the server.

[1040] Step 3: Save to database

[1041] Server: Validates the received information and stores it in the database if there are no problems.

[1042] Step 4: Notification of registration results

[1043] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[1044] Terminal: Receives messages from the server and displays them to the user.

[1045] Basic knowledge education

[1046] Step 1: Educational Content Request

[1047] User: Makes a request: "I want to know the basics of car insurance."

[1048] Step 2: Submit the request

[1049] Terminal: Sends user requests to the server.

[1050] Step 3: Invoke the AI ​​model

[1051] Server: Inputs topics into a generative AI model and generates relevant explanations.

[1052] Step 4: Returning content

[1053] Server: Prepares the generated description and sends it to the device.

[1054] Terminal: Display the received description to the user.

[1055] Interactive lessons

[1056] Step 1: Choose a lesson

[1057] User: Select from available lessons (e.g., accident procedures).

[1058] Step 2: Submit a lesson request

[1059] Terminal: Sends a request for the selected lesson to the server.

[1060] Step 3: Prepare the lesson content

[1061] Server: Selects the corresponding lesson content and sets up initial data to track user progress.

[1062] Step 4: Deliver the lesson

[1063] Server: Sends the selected lesson content to the device.

[1064] Device: Presents lesson content to the user and tracks the user's progress.

[1065] Insurance quotes and policy explanations

[1066] Step 1: Request a quote

[1067] User: Enter any additional information required for insurance quote (e.g. mileage, primary use).

[1068] Step 2: Send data

[1069] Terminal: Sends any additional information entered by the user to the server.

[1070] Step 3: Calculate estimates and generate explanations

[1071] Server: Analyzes the data, runs insurance quoting algorithms to generate quotes, and uses generative AI models to create policy summaries and explanations.

[1072] Step 4: Returning the results

[1073] Server: Sends the estimate results and explanations to the device.

[1074] Terminal: Displays results and explanations to the user.

[1075] Claims handling and expert matching

[1076] Step 1: Enter your claim information

[1077] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[1078] Step 2: Submit your claim data

[1079] Terminal: Sends the complaint information to the server.

[1080] Step 3: Claims processing and expert matching

[1081] Server: Review the claim details, select appropriate experts (insurance adjusters, lawyers, etc.), and set the initial steps in claim processing.

[1082] Step 4: Progress notification and expert information

[1083] Server: Sends claim progress and expert contact information to the device.

[1084] Terminal: Displays claim progress and expert information to the user.

[1085] Example 1

[1086] 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."

[1087] Conventional automobile insurance systems require a lot of time and effort for users to obtain the necessary information, and are difficult for users without specialized knowledge to understand. Furthermore, when an accident or damage occurs, the claim procedures and contact with experts take time, requiring a rapid response. Therefore, there is a need for a system that allows users to efficiently and accurately obtain information about automobile insurance and process claims.

[1088] 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.

[1089] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using the generative AI model, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for matching with insurance experts, and means for tracking the progress of claims and providing the expert's contact information to the user, thereby enabling users to efficiently and accurately obtain information about car insurance and quickly process claims and connect with experts.

[1090] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to automatically generate text or answers based on specific prompts.

[1091] "User" refers to anyone who uses the system to obtain information about or report a claim regarding auto insurance.

[1092] "Terminal" refers to a device, such as a computer or smartphone, that a user uses to enter information and view displayed results.

[1093] "Server" refers to a central processing unit that provides functions such as receiving, processing, and storing user input and generating answers using generative AI models.

[1094] "Database" refers to an electronic information management system for storing information entered by users and responses generated by users.

[1095] "Interactive Lesson" refers to a lesson format that provides educational content that changes dynamically based on the user's progress.

[1096] An "insurance professional" refers to a person with specialized knowledge of insurance, such as an insurance investigator or an employee of an insurance agency.

[1097] "Claim Progress" refers to the status of the insurance claim submitted by the User.

[1098] "Matching" refers to the process of identifying professionals and services that meet a user's needs and connecting them with the right people.

[1099] MODE FOR CARRYING OUT THE INVENTION

[1100] The present invention is a system for providing education on automobile insurance using a generative AI model. A specific embodiment of this system is described below.

[1101] System Configuration

[1102] The system mainly consists of a server, a terminal, and a user. The server acts as a central processing unit, receiving, storing, and processing data, and generating answers using the generative AI model. The terminal is a device used by the user, such as a computer or smartphone, that receives input from the user and displays output from the server. Users use the system to obtain information about car insurance and report claims.

[1103] Hardware and Software

[1104] The server is equipped with a high-performance processor (e.g., Intel Xeon processor) and large amounts of memory and storage (e.g., SSD) to run the generative AI model. A relational database management system such as MySQL or PostgreSQL is used as the database. OpenAI's GPT-3 and other models are used as generative AI models.

[1105] The device must have a commonly accessible web browser (e.g., Google Chrome or Mozilla Firefox) installed and be able to connect to the Internet. An interface is provided to receive user input and display responses from the server.

[1106] User registration and profile creation

[1107] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is received by the device and sent to the server, which validates the received information to ensure it is accurate. The verified information is then stored in a database, and a user profile is created.

[1108] Basic knowledge education

[1109] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the explanation to the user.

[1110] As a concrete example, if a user inputs the prompt "What are the basic types of car insurance?", the server would use a generative AI model to generate the answer "The basic types of car insurance include bodily injury liability insurance, property damage liability insurance, vehicle insurance, and personal injury insurance."

[1111] Interactive lessons

[1112] When a user selects an interactive lesson, the device sends the request to the server. The server prepares the appropriate lesson content and sends it to the device. The device displays the lesson content to the user and tracks their progress. Along the way, the user answers questions and quizzes to promote effective learning.

[1113] Insurance quotes and policy explanations

[1114] When a user requests an insurance quote, they are asked for additional information (e.g., annual mileage, purpose of use, etc.). The device sends the information entered by the user to the server, which then runs an insurance quote algorithm to generate a quote. Furthermore, a generative AI model is used to generate an explanation of the policy terms. The generated quote and explanation of the policy terms are then displayed to the user via the device.

[1115] For example, if a user enters a prompt such as "I would like to know the insurance cost if I drive 10,000 kilometers per year," the server runs an insurance estimation algorithm to generate an estimate such as "The annual insurance cost will be approximately 50,000 yen," while also providing a brief explanation of the terms and conditions.

[1116] Claims handling and expert matching

[1117] When a user files a claim, they need to enter information about the accident or damage. The device sends this information to the server, which analyzes the claim. Based on the analysis results, the server matches the most suitable expert and provides the user with their contact information. This information is displayed to the user on the device, allowing the user to contact the expert directly.

[1118] As a specific example, when a user reports a claim that they have been involved in an accident, the device sends the information input by the user (e.g., date, time, location, and situation) to the server, and the server analyzes this information and provides a matching result such as "Please contact Insurance Investigator A" along with contact information.

[1119] The system is designed to enable users to receive a variety of car insurance-related services quickly and efficiently.

[1120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1121] Step 1:

[1122] The user accesses the system and enters their name, age, and vehicle information (model, year, etc.).

[1123] Specifically, you fill in the required information in the input form and press the send button. This input becomes the initial data for the system.

[1124] Step 2:

[1125] The device receives the user's input information.

[1126] The entered information is temporarily stored on the device, and this data is later sent to the server, where it is organized in a suitable format (such as JSON or XML).

[1127] Step 3:

[1128] The terminal transmits the received information to the server.

[1129] The device collects the information entered by the user into a data packet and sends an HTTP request to the server, which transfers the input data.

[1130] Step 4:

[1131] Validate the information received by the server.

[1132] The server parses the transmitted data packet and checks the correct format and content of each field, for example, name format (characters only), age range (numeric check), and vehicle information validity.

[1133] Step 5:

[1134] The server saves the successfully validated information in the database.

[1135] Once validated, the data is inserted into the database via the database manager, which creates a user profile.

[1136] Step 6:

[1137] A user makes a request to "learn the basics of car insurance."

[1138] The user enters text into the search bar on the interface and hits submit, which triggers the following request:

[1139] Step 7:

[1140] The device sends a request to the server.

[1141] The terminal assembles the requests entered by the user into data packets and forwards them to the server.

[1142] Step 8:

[1143] The server uses the generative AI model to generate relevant explanations.

[1144] The server analyzes the request and inputs the appropriate prompt sentence into the generative AI model, which then generates text based on the input prompt and returns the result to the server.

[1145] Step 9:

[1146] The server returns the generated description to the terminal.

[1147] The explanatory text obtained from the generative AI model is compiled into a data packet and sent to the terminal.

[1148] Step 10:

[1149] The device displays the generated description to the user.

[1150] The terminal parses the received data packets and displays the text on the user interface.

[1151] Step 11:

[1152] The user selects an interactive lesson.

[1153] Users select an interactive lesson item from the education menu and press the start button.

[1154] Step 12:

[1155] The device sends a request to the server.

[1156] The device packages the user's request into data packets and forwards them to the server.

[1157] Step 13:

[1158] The server prepares the appropriate lesson content.

[1159] The server parses the request and generates or retrieves the interactive lesson content (e.g., videos, quizzes, text).

[1160] Step 14:

[1161] The server sends the lesson content to the device.

[1162] The server assembles the prepared content into data packets and transfers them to the terminal.

[1163] Step 15:

[1164] The device displays the lesson content to the user and tracks their progress.

[1165] The device tracks the user's progress and displays the lesson on the screen, and each time the user takes a step, progress data is sent to the server.

[1166] Step 16:

[1167] A user requests an insurance quote.

[1168] The user enters the required information (e.g., annual mileage, purpose of use) into the quote request form and presses the submit button.

[1169] Step 17:

[1170] The device sends the user's input information to the server.

[1171] The terminal assembles the information from the user into data packets and sends them to the server.

[1172] Step 18:

[1173] The server runs the insurance quoting algorithm.

[1174] The server runs its internal insurance quoting algorithms on the received information to generate a quote, using appropriate APIs in accordance with the present invention.

[1175] Step 19:

[1176] The server uses the generative AI model to generate explanations of the insurance policy terms.

[1177] Based on the estimate results, the server inputs prompts into the generative AI model to generate a brief explanation of the terms and conditions.

[1178] Step 20:

[1179] The server sends the estimate results and an explanation of the terms and conditions to the terminal.

[1180] The server compiles the estimate results and explanations of the terms and conditions into a data packet and sends it to the terminal.

[1181] Step 21:

[1182] The terminal displays the estimate results and an explanation of the terms and conditions to the user.

[1183] The terminal analyzes the received data and displays the estimate results and an explanation of the terms and conditions on the user interface.

[1184] Step 22:

[1185] The user submits a claim.

[1186] Users enter information about the accident and damage and press the claim submission button.

[1187] Step 23:

[1188] The terminal transmits the input information to the server.

[1189] The terminal compiles the complaint information from the user into a data packet and sends it to the server.

[1190] Step 24:

[1191] The server parses the claim.

[1192] The server analyzes the received claim information and identifies the necessary action, including the severity of the accident and the type of insurance action required.

[1193] Step 25:

[1194] The server matches you with the right expert.

[1195] The server identifies and matches the most suitable expert (e.g., insurance investigator) based on the content of the claim.

[1196] Step 26:

[1197] The server sends the contact information of the expert to the terminal.

[1198] The server compiles the matched expert's contact information and the progress of the claim into a data packet and sends it to the device.

[1199] Step 27:

[1200] The device displays the expert's contact information and the claim's progress to the user.

[1201] The terminal analyzes the received data and displays the expert's contact information and the claim progress on a user interface.

[1202] In this way, users can efficiently and accurately obtain information about their car insurance, allowing for quick claims processing and contact with experts.

[1203] (Application example 1)

[1204] 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."

[1205] Education about automobile insurance is generally complex and difficult to understand, making it difficult for many users to obtain accurate information. Furthermore, traditional education methods lack interactivity and individuality, making it difficult to deepen users' understanding. Furthermore, it is not easy to provide detailed information instantly in physical stores due to time and resource constraints. This can prevent users from obtaining the necessary information immediately, leading to delays in choosing the right insurance and responding in the event of an accident.

[1206] 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.

[1207] In this invention, the server includes a means for receiving information entered by a user, a means for storing the information entered by the user in a data storage device, and a means for generating answers to the user's questions using a generative AI model. This allows the generative AI model to provide explanations so that the user can learn basic knowledge about automobile insurance. The server also includes a means for the user to take detailed interactive lessons, thereby enhancing the effectiveness of education. Furthermore, the server includes a means for the user to receive explanations of policy terms based on insurance quotes, enabling detailed information to be provided instantly even in physical stores. This allows users to instantly obtain the information they need, enabling them to quickly select appropriate insurance and respond in the event of an accident.

[1208] A "generative AI model" is an advanced artificial intelligence algorithm that generates answers in natural language based on user input.

[1209] "User" refers to an individual or corporation that uses the system to obtain information about automobile insurance and carry out various procedures.

[1210] "Information" refers to all data provided by users to the system, including data about their vehicle, questions, and personal information.

[1211] A "data storage device" is a digital storage device that stores information entered by a user and allows it to be searched or referenced later.

[1212] An "insurance expert" is a professional person or organization that has specialized knowledge about automobile insurance and provides appropriate advice and support to users.

[1213] "Matching" is the process of selecting an appropriate insurance professional based on the user's needs and enabling them to communicate and consult with each other.

[1214] A "physical store" is a physical location that customers can visit in person, where they can receive information and support about insurance.

[1215] "Basic knowledge" refers to the basic information and concepts users need to understand insurance, and in this system this is provided by a generative AI model.

[1216] An "interactive lesson" is an educational program that allows users to actively participate and track their learning progress.

[1217] A "quote" is a presentation of estimated insurance premiums based on the insurance coverage required by the user.

[1218] A "policy" is a document that details all the terms and conditions of an insurance contract.

[1219] This invention is a system that uses a generative AI model to provide education about automobile insurance in a brick-and-mortar store and deepen customer understanding. This system is mainly composed of a server, a terminal, and a user. Specific embodiments of the system are described below.

[1220] Hardware and Software Configuration

[1221] Hardware

[1222] Smartphone: A device used by users that functions as an input and display device.

[1223] Server: A computing device that stores generative AI models and databases and executes various processes.

[1224] software

[1225] Flask: A web framework that runs on the server and is responsible for the user interface and API processing.

[1226] OpenAI API: Perform natural language processing using generative AI models (e.g., text-davinci-003).

[1227] Program processing

[1228] User registration and profile creation

[1229] 1. Terminal: User enters name, age, and vehicle information.

[1230] 2. Server: Receives, validates, and stores data in a data repository.

[1231] 3. Example: A user enters their name, age, and vehicle information into a smartphone app and sends it to the server, which validates and stores the data.

[1232] Basic knowledge education

[1233] 1. User: Sends a request from the terminal to learn basic knowledge about car insurance.

[1234] 2. Server: Uses the generative AI model to generate an answer and send it back to the device.

[1235] 3. Example: Send a request such as "Please briefly explain what car insurance is." The generative AI model generates a basic explanation and sends it back to the device.

[1236] Interactive Lessons

[1237] 1. User: Selects an interactive lesson and sends a request from the device to the server.

[1238] 2. Server: Prepares the appropriate lesson content and sends it to the device, which displays the lesson to the user and tracks their progress.

[1239] 3. Example: A user selects a lesson on "How to choose car insurance," and the device sends the request to the server. The server generates the lesson content and sends it to the device.

[1240] Insurance quotes and policy explanations

[1241] 1. User: Request a quote and enter any additional information required.

[1242] 2. Server: Runs the insurance quote algorithm, generates quote results and a brief explanation of the policy terms, and sends them back to the terminal for display.

[1243] 3. Example: Send a request saying "Please tell me the estimate if the annual mileage is 10,000 km." The server calculates the estimate and returns the result along with an explanation of the terms and conditions.

[1244] Claims handling and expert matching

[1245] 1. User: Enters information about the accident or damage and sends it from the terminal to the server.

[1246] 2. Server: Analyzes the claim details and matches it with the appropriate insurance expert. The progress status and the expert's contact information are returned to the terminal and displayed.

[1247] 3. For example, you enter information such as the date, time, location, and circumstances of the accident, and the server matches it with an appropriate insurance investigator and returns their contact information and progress status.

[1248] Prompt Sentence Examples

[1249] "Please briefly explain what car insurance is."

[1250] This system allows customers to receive immediate and detailed education about car insurance at physical stores, and by gaining a deeper understanding, they will be able to choose the right insurance and respond quickly.

[1251] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1252] Step 1:

[1253] User registration and profile creation

[1254] Input: The user enters personal information such as name, age, and vehicle information into the device.

[1255] Specific operation: The terminal receives the input information and sends it to the server.

[1256] Data processing: The server validates the received information and stores it in the database.

[1257] Output: A profile for the user is created.

[1258] Step 2:

[1259] Basic Knowledge Education Request

[1260] Input: The user sends a request from the terminal to the server saying, "I want to know the basics of car insurance."

[1261] Specific operation: The server sends a prompt sentence to the generative AI model.

[1262] Data processing: The generative AI model generates an answer based on the input prompt.

[1263] Output: The generated answer is sent from the server to the terminal and displayed to the user.

[1264] Step 3:

[1265] Select an Interactive Lesson

[1266] Input: The user selects an interactive lesson and sends the request from the device to the server.

[1267] Specific Actions: The server prepares the appropriate lesson content.

[1268] Data processing: Lesson content is generated and user progress data is initialized.

[1269] Output: The lesson content is sent to the device and the user's progress is displayed on the device.

[1270] Step 4:

[1271] Insurance quote request

[1272] Input: The user enters additional information such as mileage and primary use, and sends a request for an insurance quote from the device to the server.

[1273] Specific operation: The server sends the received information to the estimation algorithm.

[1274] Data Processing: A quoting algorithm calculates an insurance quote based on the information entered.

[1275] Output: The calculated estimate results and explanation of the terms and conditions are sent to the terminal and displayed to the user.

[1276] Step 5:

[1277] Claims handling request

[1278] Input: The user inputs detailed information about the accident (date, time, location, and circumstances) and sends the claim from the terminal to the server.

[1279] Specific operation: The server analyzes the claim details and matches the claim with the appropriate insurance professional.

[1280] Data processing: Contact information of the experts and progress of the claim are generated.

[1281] Output: The progress of the claim and the contact information of the expert are sent to the terminal and displayed to the user.

[1282] This concludes the detailed explanation of the process flow and each step of the system program that realizes this application example. This system allows users to easily obtain detailed information about automobile insurance, enabling them to select the appropriate insurance and process claims quickly.

[1283] 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.

[1284] The present invention is a system for providing education on automobile insurance using a generative AI model and an emotion engine. Specific embodiments and program processing of the system are described below.

[1285] User registration and profile creation

[1286] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The device receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, a user profile is created.

[1287] Basic knowledge education

[1288] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses the generative AI model and emotion engine to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[1289] Utilizing the Emotion Engine

[1290] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data (e.g., camera footage and audio analysis) is collected and the user's emotional state is determined. The server receives and analyzes this emotional data and generates appropriate answers and educational content according to the user's psychological state.

[1291] Interactive lessons

[1292] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device. The device then displays the lesson content to the user and tracks their progress, again using an emotion engine to provide feedback based on the user's emotional state.

[1293] Insurance quotes and policy explanations

[1294] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and main use). The device sends the information the user has entered to the server, which runs an insurance quote algorithm to generate a quote and a brief explanation of the policy. This process also uses an emotion engine to adjust the explanation based on the user's emotional state. The device then displays these results to the user.

[1295] Claims handling and expert matching

[1296] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. Again, an emotion engine is used here to provide a response that reflects the user's emotional state. The server then sends the progress of the claim and the expert's contact information to the device, which displays this to the user.

[1297] Specific examples

[1298] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[1299] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[1300] 2. The terminal sends the entered information to the server.

[1301] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[1302] 4. The server sends the progress and contact information of the expert to the device.

[1303] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[1304] During this process, the emotion engine monitors the user's emotional state in real time and provides support accordingly if stress levels rise. For example, if the system determines that the user is emotionally upset, it will quickly refer them to professional support and provide advice on how to stay calm.

[1305] The system allows users to gain comprehensive and practical knowledge about car insurance, providing them with the necessary assistance and information in a timely manner, as well as providing them with appropriate emotional support.

[1306] The processing flow will be explained below.

[1307] User registration and profile creation

[1308] Step 1: Enter your user information

[1309] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[1310] Step 2: Send input data

[1311] Terminal: Sends the information entered by the user to the server.

[1312] Step 3: Save to database

[1313] Server: Validates the received information and stores it in the database if there are no problems.

[1314] Step 4: Notification of registration results

[1315] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[1316] Terminal: Receives messages from the server and displays them to the user.

[1317] Basic knowledge education

[1318] Step 1: Educational Content Request

[1319] User: Makes a request: "I want to know the basics of car insurance."

[1320] Step 2: Submit the request

[1321] Terminal: Sends user requests to the server.

[1322] Step 3: Invoke the AI ​​model

[1323] Server: Inputs topics into a generative AI model and generates relevant explanations.

[1324] Step 4: Returning content

[1325] Server: Prepares the generated description and sends it to the device.

[1326] Terminal: Display the received description to the user.

[1327] Coordination using emotion engine

[1328] Step 1: Collecting sentiment data

[1329] Terminal: Collects the user's camera footage and audio data and sends it to the emotion engine.

[1330] Step 2: Analyze emotional state

[1331] Emotion engine: Analyzes collected data to determine the user's emotional state (stress, excitement, calm, etc.).

[1332] Step 3: Adjust the description

[1333] Server: Based on feedback from the emotion engine, the explanations generated by the generative AI model are adjusted according to the user's emotional state.

[1334] Terminal: Display the adjusted description to the user.

[1335] Interactive lessons

[1336] Step 1: Choose a lesson

[1337] User: Select from available lessons (e.g., accident procedures).

[1338] Step 2: Submit a lesson request

[1339] Terminal: Sends a request for the selected lesson to the server.

[1340] Step 3: Prepare the lesson content

[1341] Server: Selects appropriate lesson content and sets initial data.

[1342] Step 4: Deliver the lesson

[1343] Server: Sends lesson content to devices.

[1344] Device: Presents lesson content to users and tracks progress.

[1345] Step 5: Provide emotional feedback

[1346] On the device: Uses an emotion engine to provide feedback based on the user's emotional state.

[1347] Insurance quotes and policy explanations

[1348] Step 1: Request a quote

[1349] User: Enter any additional information required for insurance quote (mileage, primary use, etc.).

[1350] Step 2: Send data

[1351] Terminal: Sends any additional information entered by the user to the server.

[1352] Step 3: Calculate estimates and generate explanations

[1353] Server: Runs insurance quoting algorithms and generates quote results, and also uses generative AI models to create policy summaries and commentary.

[1354] Step 4: Emotional Adjustment

[1355] Server: Adjusts the estimation results and commentary based on the user's emotional state.

[1356] Step 5: Returning the results

[1357] Server: Sends the adjusted estimate results and explanations to the device.

[1358] Terminal: Displays results and explanations to the user.

[1359] Claims handling and expert matching

[1360] Step 1: Enter your claim information

[1361] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[1362] Step 2: Submit your claim data

[1363] Terminal: Sends the complaint information to the server.

[1364] Step 3: Claims processing and expert matching

[1365] Server: Analyzes the claims, selects appropriate experts, and sets initial steps.

[1366] Step 4: Emotional feedback and response

[1367] Server: Uses an emotion engine to consider appropriate responses based on the user's emotional state.

[1368] Step 5: Progress notification and expert information

[1369] Server: Sends claim progress and expert contact information to the device.

[1370] Terminal: Displays claim progress and expert information to the user.

[1371] Example 2

[1372] 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."

[1373] When providing education and support regarding automobile insurance, it is difficult to provide users with the information they need in a timely and appropriate manner. In particular, flexible responses that take into account the user's emotional state and efficient matching with experts are required. Furthermore, it is important that the educational content be appropriately adjusted according to the user's progress.

[1374] 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.

[1375] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using a generative AI model and an emotion engine, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for monitoring and analyzing the user's emotional state in real time, means for providing appropriate feedback according to the user's psychological state using the emotion engine, and means for matching with insurance experts. This enables flexible and appropriate automobile insurance education and support according to the user's emotions and progress.

[1376] "User" means any individual or entity that uses the System to receive services such as automobile insurance education, quotes, and claims processing.

[1377] "Terminal" means a computing device through which a user enters information and receives and displays information from a server.

[1378] A "server" is a computer system that processes information received from users and generates appropriate answers and feedback using generative AI models and emotion engines.

[1379] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions or requests.

[1380] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and uses that information to provide appropriate feedback and answers.

[1381] "Database" refers to a system for storing data such as information received from users, generated responses, and educational content.

[1382] "Interactive lessons" are educational content that dynamically adjusts based on the user's progress and emotional state.

[1383] An "insurance expert" is an individual or institution with detailed knowledge and experience regarding automobile insurance who can provide specific advice and support to users.

[1384] This invention is a system that uses a generative AI model and an emotion engine to provide education and support regarding car insurance. This system provides users with the information they need in a timely and appropriate manner, and provides flexible responses that take into account the user's emotional state.

[1385] User registration and profile creation

[1386] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is sent to the server via the user's device. The server validates this information and, if there are no problems, stores it in the database. This creates a user profile.

[1387] Basic knowledge education

[1388] When a user makes a request such as "I want to know the basics of car insurance," the request is sent from the device to the server, which uses a generative AI model and an emotion engine to generate a relevant explanation and sends it back to the device, which then displays it to the user.

[1389] For example, some prompts for a generative AI model might include:

[1390] "What is car insurance?"

[1391] Utilizing the Emotion Engine

[1392] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data is collected through camera footage and audio analysis and sent to a server. The server then uses the emotion engine to analyze the emotional data and generate appropriate answers and educational content based on the user's psychological state.

[1393] Interactive lessons

[1394] When a user selects an interactive lesson, the request is sent from the device to the server. The server prepares the appropriate lesson content and sends it back to the device. The device displays this content and tracks the user's progress. An emotion engine can be used to provide feedback based on the user's emotional state.

[1395] Insurance quotes and policy explanations

[1396] When a user requests an insurance quote, they are asked to enter additional information (such as mileage and main use). The information entered by the user is sent to the server via the device. The server then runs an insurance quote algorithm to generate a quote and a brief explanation of the policy terms. This process also utilizes an emotion engine to provide an explanation based on the user's emotional state.

[1397] Claims handling and expert matching

[1398] When a user files a claim, they enter details of the accident and damage into the device. The device sends this information to the server, which analyzes the claim. The server then matches the claim with an appropriate insurance expert and sends the progress report and the expert's contact information to the device. The device displays this information to the user and provides an interface for direct contact with the expert. The device can monitor the user's emotional state in real time using an emotion engine and provide support as needed.

[1399] This system will enable users to obtain comprehensive and practical knowledge about car insurance, provide necessary assistance and information in a timely manner, and also provide appropriate emotional support, allowing users to proceed with car insurance procedures with greater peace of mind.

[1400] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1401] Step 1:

[1402] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.) This information is called input data. The device receives this information and sends the input data to the server.

[1403] Step 2:

[1404] The server validates the received user information. Specifically, it checks whether the data is in the correct format (for example, whether the name is blank, whether the age is a number, etc.). This is the data calculation step, and the validated information becomes the output data. If the information passes validation, the server saves it in the database. This save is the output result.

[1405] Step 3:

[1406] The user makes a request such as "I want to know the basics of car insurance." This request is the input data. The device sends this request to the server.

[1407] Step 4:

[1408] The server uses a generative AI model and an emotion engine to generate an explanation based on the request. Specifically, the generative AI model generates an answer based on a prompt (e.g., "What is car insurance?"), and the emotion engine analyzes the user's psychological state and adjusts the answer. This process is a data calculation, and the generated explanation becomes the output data. The server then sends the generated explanation back to the device.

[1409] Step 5:

[1410] The terminal displays the generated explanation received from the server to the user, which is the output to the user.

[1411] Step 6:

[1412] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. This emotion data is input data. Data is collected through camera footage and audio analysis and sent to a server.

[1413] Step 7:

[1414] The server analyzes the emotional data using an emotion engine. This analysis process is a data calculation, and the user's emotional state is the output data. As a result, depending on the user's emotional state, the server generates appropriate educational content and feedback and sends it to the device.

[1415] Step 8:

[1416] The user selects an interactive lesson. This selection becomes the input data. The device sends the request to the server.

[1417] Step 9:

[1418] The server prepares interactive lesson content, which is a data generation process, and the prepared lesson content is the output data. The server then sends the lesson content to the device.

[1419] Step 10:

[1420] The device displays the received lesson content to the user and tracks the user's progress. This progress data becomes input data, and the device sends the information to the server.

[1421] Step 11:

[1422] The server integrates the user's progress information with the data analyzed by the emotion engine to generate optimal feedback for the user. This is the data fusion and generation step, and the integrated feedback becomes the output data. The server then sends the feedback to the device.

[1423] Step 12:

[1424] When a user requests an insurance quote, they enter additional information (mileage, main use, etc.). This information becomes input data. The device sends the information entered by the user to the server.

[1425] Step 13:

[1426] The server runs an insurance quote algorithm and generates quote results and brief explanations of the policy terms. This process is a data computation, with the quote results and explanations being the output data. It also utilizes an emotion engine to provide explanations tailored to the user's emotional state. This tailored explanation is also output data.

[1427] Step 14:

[1428] The server sends the generated estimate and adjusted commentary to the terminal, which displays it to the user. The displayed information is the final output.

[1429] Step 15:

[1430] When a user files a claim, they input detailed information about the accident or damage. This information is the input data. The device sends the information to the server.

[1431] Step 16:

[1432] The server analyzes the claim details and matches them with the appropriate insurance experts. This analysis and matching process is a data calculation, and the expert information is the output data. The server sends the progress status and the expert contact information to the terminal.

[1433] Step 17:

[1434] The terminal displays the progress and contact information of the expert to the user, and provides an interface for direct contact with the expert. This display and interface are the final output.

[1435] Step 18:

[1436] The emotion engine monitors the user's emotional state in real time and provides support as needed. The support provided by the emotion engine is output data, and appropriate support is provided to the user.

[1437] (Application example 2)

[1438] 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."

[1439] While conventional insurance education systems aim to improve users' knowledge, they do not adequately consider the stress and anxiety users feel during the learning process. As a result, the education is not fully effective, and users often abandon their studies midway. Furthermore, there are also situations where psychological support is lacking in the event of an accident or insurance claim. Given this background, there is a need for a system that can provide appropriate information and support according to the user's emotional state.

[1440] 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 receiving information input by the user, means for generating an answer to the user's question, means for displaying the generated answer to the user, means for activating an emotion engine for recognizing and analyzing the user's emotional state, and means for generating educational content based on the emotion engine in accordance with the user's emotional state. This makes it possible to provide appropriate information and support in accordance with the user's emotional state.

[1441] A "generative AI model" is an artificial intelligence model that generates appropriate answers and information in response to user questions and requests.

[1442] An "emotion engine" is a software engine for recognizing and analyzing a user's emotional state.

[1443] "Means for receiving information entered by the user" refers to the means by which the terminal or server collects and processes information provided by the user.

[1444] "Means for generating answers to user questions" refers to means for generating appropriate answers to user questions using a generative AI model.

[1445] "Means for displaying the generated answer to the user" refers to a means for presenting the answer generated by the generative AI model to the user in an easy-to-read format.

[1446] "Means for tracking user progress and providing interactive lessons" means means for tracking a user's learning progress and providing lessons in an interactive format.

[1447] "Means for matching with insurance experts" refers to means for selecting an insurance expert suitable for the user and providing that information.

[1448] The "means for generating educational content according to the emotional state of the user" is a means for generating educational content that is optimal for the emotional state of the user based on the analysis results of the emotion engine.

[1449] The "means for inputting information necessary for insurance quotes" refers to the means by which a user inputs various information necessary for insurance quotes.

[1450] "Means for generating explanations of insurance policy terms and conditions using a generative AI model" refers to means for generating easy-to-understand explanations of insurance policy terms and conditions using a generative AI model.

[1451] The following describes an embodiment of the present invention. The system uses a generative AI model and an emotion engine to provide users with insurance education. The system comprises the following specific steps:

[1452] User registration and profile creation

[1453] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. This information is sent from the user's device to the server, where it is validated and stored in a database. At this stage, a user profile is created.

[1454] Basic knowledge education

[1455] When a user makes a request such as "I want to know the basics of insurance," the generative AI model generates an appropriate explanation. The generated content is sent to the user's device and displayed. During this process, the emotion engine monitors the user's emotional state and provides appropriate feedback.

[1456] Utilizing the Emotion Engine

[1457] While a user is using educational content, the emotion engine collects and analyzes emotional data (camera footage, audio analysis, etc.). Based on the analysis results, the server generates content appropriate to the user's psychological state and provides it to the user.

[1458] Interactive lessons

[1459] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the user's device. Progress is tracked and an emotion engine provides feedback based on the user's emotional state.

[1460] Insurance quote and explanation of contract details

[1461] When a user requests an insurance quote, the server prompts them to enter additional necessary information (such as the type of insurance and key terms and conditions). The generative AI model is used to generate an insurance quote and explanation of the policy terms and conditions, which are then provided to the user. The emotion engine monitors the user's emotional state during this process and provides appropriate assistance.

[1462] Claims handling and expert matching

[1463] When a user submits an insurance claim, they enter information about the accident and damage. This information is sent to the server, which starts the claim process. An appropriate expert is matched and the progress and expert contact information are provided to the user. The emotion engine monitors the user's emotional state and provides a quick response in case of increased stress.

[1464] Hardware and software used

[1465] Hardware: Smartphone (iOS or Android)

[1466] Software: Insurance education support app, generative AI models (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva SDK)

[1467] Examples and prompts

[1468] As a concrete example, if a 30-year-old male user requests basic insurance knowledge, the following prompt is sent to the generative AI model:

[1469] Example prompt sentence:

[1470] "Generate an easy-to-understand explanation for a 30-year-old male user about basic insurance knowledge for him. If the emotion engine determines that the user is confused, regenerate a simplified explanation."

[1471] In this way, the system can provide appropriate education according to the user's emotional state, improving the user experience.

[1472] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1473] Step 1: Register and create a profile

[1474] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. The terminal collects this information and sends it to the server, which validates it and stores it in a database if there are no problems. The input to this process is the user's personal and insurance-related information, and the output is a user profile stored in the database.

[1475] Step 2: Request Foundational Knowledge Training

[1476] The user inputs a request such as "I want to learn the basics of insurance." The device sends this request to the server. The server receives the request and sends a prompt to the generative AI model to generate appropriate educational content. The input of this process is the user's request, and the output is the generated educational content.

[1477] Step 3: Activating and monitoring the emotion engine

[1478] While the user is using educational content, the device collects camera footage and audio data. The emotion engine analyzes this data and determines the user's emotional state. The input to this process is the collected emotion data, and the output is the determination of the user's emotional state. The server receives this determination result and uses it as feedback for the next step.

[1479] Step 4: Regenerate content based on emotions

[1480] Based on the emotion engine's judgment, the server sends a prompt to the generative AI model again to generate new educational content that corresponds to the user's emotional state. For example, if the user is confused, it generates a more understandable and simplified explanation. The input to this process is the emotion engine's judgment, and the output is educational content that corresponds to the user's emotional state.

[1481] Step 5: Deliver interactive lessons

[1482] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the device, tracking the user's progress and providing feedback based on data obtained by the emotion engine. The input to this process is the user's progress data and emotional state data, and the output is the tailored lesson content.

[1483] Step 6: Insurance quote and contract explanation

[1484] When a user requests an insurance quote, the device sends it to the server. The server requests additional information (e.g., type of insurance and terms) and generates a quote using a generative AI model based on the information entered by the user. The input to this process is the insurance information entered by the user, and the output is the generated quote and a description of the policy.

[1485] Step 7: Claims processing and expert matching

[1486] When a user enters a claim about an accident or damage, the device sends the information to the server. The server analyzes the claim and matches it with an appropriate expert. It then provides the user with progress status and the expert's contact information. The input to this process is the claim details, and the output is the expert's contact information and the claim processing progress.

[1487] In this way, the user, device, and server work together at each step to create a system that provides optimal insurance education and support according to the user's emotional state.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] [Fourth embodiment]

[1492] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1493] 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.

[1494] 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).

[1495] 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.

[1496] 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.

[1497] 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).

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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."

[1505] The present invention is a system that provides education on automobile insurance using a generative AI model. Specific embodiments and program processing are described below.

[1506] User registration and profile creation

[1507] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The terminal receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, the user's profile is created.

[1508] Basic knowledge education

[1509] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[1510] Interactive lessons

[1511] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device, which displays the lesson content to the user and tracks their progress.

[1512] Insurance quotes and policy explanations

[1513] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and primary use). The device sends the information to the server, which runs an insurance quote algorithm and generates a quote and a brief explanation of the policy terms. The device displays these results to the user.

[1514] Claims handling and expert matching

[1515] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. The server then sends the progress of the claim and the expert's contact information to the device, which displays it to the user.

[1516] Specific examples

[1517] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[1518] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[1519] 2. The terminal sends the entered information to the server.

[1520] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[1521] 4. The server sends the progress and contact information of the expert to the device.

[1522] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[1523] The system is designed to provide users with effective education and support regarding car insurance, and to quickly address any insurance-related questions or problems.

[1524] The processing flow will be explained below.

[1525] User registration and profile creation

[1526] Step 1: Enter your user information

[1527] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[1528] Step 2: Send input data

[1529] Terminal: Sends the information entered by the user to the server.

[1530] Step 3: Save to database

[1531] Server: Validates the received information and stores it in the database if there are no problems.

[1532] Step 4: Notification of registration results

[1533] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[1534] Terminal: Receives messages from the server and displays them to the user.

[1535] Basic knowledge education

[1536] Step 1: Educational Content Request

[1537] User: Makes a request: "I want to know the basics of car insurance."

[1538] Step 2: Submit the request

[1539] Terminal: Sends user requests to the server.

[1540] Step 3: Invoke the AI ​​model

[1541] Server: Inputs topics into a generative AI model and generates relevant explanations.

[1542] Step 4: Returning content

[1543] Server: Prepares the generated description and sends it to the device.

[1544] Terminal: Display the received description to the user.

[1545] Interactive lessons

[1546] Step 1: Choose a lesson

[1547] User: Select from available lessons (e.g., accident procedures).

[1548] Step 2: Submit a lesson request

[1549] Terminal: Sends a request for the selected lesson to the server.

[1550] Step 3: Prepare the lesson content

[1551] Server: Selects the corresponding lesson content and sets up initial data to track user progress.

[1552] Step 4: Deliver the lesson

[1553] Server: Sends the selected lesson content to the device.

[1554] Device: Presents lesson content to the user and tracks the user's progress.

[1555] Insurance quotes and policy explanations

[1556] Step 1: Request a quote

[1557] User: Enter any additional information required for insurance quote (e.g. mileage, primary use).

[1558] Step 2: Send data

[1559] Terminal: Sends any additional information entered by the user to the server.

[1560] Step 3: Calculate estimates and generate explanations

[1561] Server: Analyzes the data, runs insurance quoting algorithms to generate quotes, and uses generative AI models to create policy summaries and explanations.

[1562] Step 4: Returning the results

[1563] Server: Sends the estimate results and explanations to the device.

[1564] Terminal: Displays results and explanations to the user.

[1565] Claims handling and expert matching

[1566] Step 1: Enter your claim information

[1567] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[1568] Step 2: Submit your claim data

[1569] Terminal: Sends the complaint information to the server.

[1570] Step 3: Claims processing and expert matching

[1571] Server: Review the claim details, select appropriate experts (insurance adjusters, lawyers, etc.), and set the initial steps in claim processing.

[1572] Step 4: Progress notification and expert information

[1573] Server: Sends claim progress and expert contact information to the device.

[1574] Terminal: Displays claim progress and expert information to the user.

[1575] Example 1

[1576] 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."

[1577] Conventional automobile insurance systems require a lot of time and effort for users to obtain the necessary information, and are difficult for users without specialized knowledge to understand. Furthermore, when an accident or damage occurs, the claim procedures and contact with experts take time, requiring a rapid response. Therefore, there is a need for a system that allows users to efficiently and accurately obtain information about automobile insurance and process claims.

[1578] 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.

[1579] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using the generative AI model, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for matching with insurance experts, and means for tracking the progress of claims and providing the expert's contact information to the user, thereby enabling users to efficiently and accurately obtain information about car insurance and quickly process claims and connect with experts.

[1580] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques to automatically generate text or answers based on specific prompts.

[1581] "User" refers to anyone who uses the system to obtain information about or report a claim regarding auto insurance.

[1582] "Terminal" refers to a device, such as a computer or smartphone, that a user uses to enter information and view displayed results.

[1583] "Server" refers to a central processing unit that provides functions such as receiving, processing, and storing user input and generating answers using generative AI models.

[1584] "Database" refers to an electronic information management system for storing information entered by users and responses generated by users.

[1585] "Interactive Lesson" refers to a lesson format that provides educational content that changes dynamically based on the user's progress.

[1586] An "insurance professional" refers to a person with specialized knowledge of insurance, such as an insurance investigator or an employee of an insurance agency.

[1587] "Claim Progress" refers to the status of the insurance claim submitted by the User.

[1588] "Matching" refers to the process of identifying professionals and services that meet a user's needs and connecting them with the right people.

[1589] MODE FOR CARRYING OUT THE INVENTION

[1590] The present invention is a system for providing education on automobile insurance using a generative AI model. A specific embodiment of this system is described below.

[1591] System Configuration

[1592] The system mainly consists of a server, a terminal, and a user. The server acts as a central processing unit, receiving, storing, and processing data, and generating answers using the generative AI model. The terminal is a device used by the user, such as a computer or smartphone, that receives input from the user and displays output from the server. Users use the system to obtain information about car insurance and report claims.

[1593] Hardware and Software

[1594] The server is equipped with a high-performance processor (e.g., Intel Xeon processor) and large amounts of memory and storage (e.g., SSD) to run the generative AI model. A relational database management system such as MySQL or PostgreSQL is used as the database. OpenAI's GPT-3 and other models are used as generative AI models.

[1595] The device must have a commonly accessible web browser (e.g., Google Chrome or Mozilla Firefox) installed and be able to connect to the Internet. An interface is provided to receive user input and display responses from the server.

[1596] User registration and profile creation

[1597] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is received by the device and sent to the server, which validates the received information to ensure it is accurate. The verified information is then stored in a database, and a user profile is created.

[1598] Basic knowledge education

[1599] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses a generative AI model to generate a relevant explanation and sends it back to the device, which then displays the explanation to the user.

[1600] As a concrete example, if a user inputs the prompt "What are the basic types of car insurance?", the server would use a generative AI model to generate the answer "The basic types of car insurance include bodily injury liability insurance, property damage liability insurance, vehicle insurance, and personal injury insurance."

[1601] Interactive lessons

[1602] When a user selects an interactive lesson, the device sends the request to the server. The server prepares the appropriate lesson content and sends it to the device. The device displays the lesson content to the user and tracks their progress. Along the way, the user answers questions and quizzes to promote effective learning.

[1603] Insurance quotes and policy explanations

[1604] When a user requests an insurance quote, they are asked for additional information (e.g., annual mileage, purpose of use, etc.). The device sends the information entered by the user to the server, which then runs an insurance quote algorithm to generate a quote. Furthermore, a generative AI model is used to generate an explanation of the policy terms. The generated quote and explanation of the policy terms are then displayed to the user via the device.

[1605] For example, if a user enters a prompt such as "I would like to know the insurance cost if I drive 10,000 kilometers per year," the server runs an insurance estimation algorithm to generate an estimate such as "The annual insurance cost will be approximately 50,000 yen," while also providing a brief explanation of the terms and conditions.

[1606] Claims handling and expert matching

[1607] When a user files a claim, they need to enter information about the accident or damage. The device sends this information to the server, which analyzes the claim. Based on the analysis results, the server matches the most suitable expert and provides the user with their contact information. This information is displayed to the user on the device, allowing the user to contact the expert directly.

[1608] As a specific example, when a user reports a claim that they have been involved in an accident, the device sends the information input by the user (e.g., date, time, location, and situation) to the server, and the server analyzes this information and provides a matching result such as "Please contact Insurance Investigator A" along with contact information.

[1609] The system is designed to enable users to receive a variety of car insurance-related services quickly and efficiently.

[1610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1611] Step 1:

[1612] The user accesses the system and enters their name, age, and vehicle information (model, year, etc.).

[1613] Specifically, you fill in the required information in the input form and press the send button. This input becomes the initial data for the system.

[1614] Step 2:

[1615] The device receives the user's input information.

[1616] The entered information is temporarily stored on the device, and this data is later sent to the server, where it is organized in a suitable format (such as JSON or XML).

[1617] Step 3:

[1618] The terminal transmits the received information to the server.

[1619] The device collects the information entered by the user into a data packet and sends an HTTP request to the server, which transfers the input data.

[1620] Step 4:

[1621] Validate the information received by the server.

[1622] The server parses the transmitted data packet and checks the correct format and content of each field, for example, name format (characters only), age range (numeric check), and vehicle information validity.

[1623] Step 5:

[1624] The server saves the successfully validated information in the database.

[1625] Once validated, the data is inserted into the database via the database manager, which creates a user profile.

[1626] Step 6:

[1627] A user makes a request to "learn the basics of car insurance."

[1628] The user enters text into the search bar on the interface and hits submit, which triggers the following request:

[1629] Step 7:

[1630] The device sends a request to the server.

[1631] The terminal assembles the requests entered by the user into data packets and forwards them to the server.

[1632] Step 8:

[1633] The server uses the generative AI model to generate relevant explanations.

[1634] The server analyzes the request and inputs the appropriate prompt sentence into the generative AI model, which then generates text based on the input prompt and returns the result to the server.

[1635] Step 9:

[1636] The server returns the generated description to the terminal.

[1637] The explanatory text obtained from the generative AI model is compiled into a data packet and sent to the terminal.

[1638] Step 10:

[1639] The device displays the generated description to the user.

[1640] The terminal parses the received data packets and displays the text on the user interface.

[1641] Step 11:

[1642] The user selects an interactive lesson.

[1643] Users select an interactive lesson item from the education menu and press the start button.

[1644] Step 12:

[1645] The device sends a request to the server.

[1646] The device packages the user's request into data packets and forwards them to the server.

[1647] Step 13:

[1648] The server prepares the appropriate lesson content.

[1649] The server parses the request and generates or retrieves the interactive lesson content (e.g., videos, quizzes, text).

[1650] Step 14:

[1651] The server sends the lesson content to the device.

[1652] The server assembles the prepared content into data packets and transfers them to the terminal.

[1653] Step 15:

[1654] The device displays the lesson content to the user and tracks their progress.

[1655] The device tracks the user's progress and displays the lesson on the screen, and each time the user takes a step, progress data is sent to the server.

[1656] Step 16:

[1657] A user requests an insurance quote.

[1658] The user enters the required information (e.g., annual mileage, purpose of use) into the quote request form and presses the submit button.

[1659] Step 17:

[1660] The device sends the user's input information to the server.

[1661] The terminal assembles the information from the user into data packets and sends them to the server.

[1662] Step 18:

[1663] The server runs the insurance quoting algorithm.

[1664] The server runs its internal insurance quoting algorithms on the received information to generate a quote, using appropriate APIs in accordance with the present invention.

[1665] Step 19:

[1666] The server uses the generative AI model to generate explanations of the insurance policy terms.

[1667] Based on the estimate results, the server inputs prompts into the generative AI model to generate a brief explanation of the terms and conditions.

[1668] Step 20:

[1669] The server sends the estimate results and an explanation of the terms and conditions to the terminal.

[1670] The server compiles the estimate results and explanations of the terms and conditions into a data packet and sends it to the terminal.

[1671] Step 21:

[1672] The terminal displays the estimate results and an explanation of the terms and conditions to the user.

[1673] The terminal analyzes the received data and displays the estimate results and an explanation of the terms and conditions on the user interface.

[1674] Step 22:

[1675] The user submits a claim.

[1676] Users enter information about the accident and damage and press the claim submission button.

[1677] Step 23:

[1678] The terminal transmits the input information to the server.

[1679] The terminal compiles the complaint information from the user into a data packet and sends it to the server.

[1680] Step 24:

[1681] The server parses the claim.

[1682] The server analyzes the received claim information and identifies the necessary action, including the severity of the accident and the type of insurance action required.

[1683] Step 25:

[1684] The server matches you with the right expert.

[1685] The server identifies and matches the most suitable expert (e.g., insurance investigator) based on the content of the claim.

[1686] Step 26:

[1687] The server sends the contact information of the expert to the terminal.

[1688] The server compiles the matched expert's contact information and the progress of the claim into a data packet and sends it to the device.

[1689] Step 27:

[1690] The device displays the expert's contact information and the claim's progress to the user.

[1691] The terminal analyzes the received data and displays the expert's contact information and the claim progress on a user interface.

[1692] In this way, users can efficiently and accurately obtain information about their car insurance, allowing for quick claims processing and contact with experts.

[1693] (Application example 1)

[1694] 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."

[1695] Education about automobile insurance is generally complex and difficult to understand, making it difficult for many users to obtain accurate information. Furthermore, traditional education methods lack interactivity and individuality, making it difficult to deepen users' understanding. Furthermore, it is not easy to provide detailed information instantly in physical stores due to time and resource constraints. This can prevent users from obtaining the necessary information immediately, leading to delays in choosing the right insurance and responding in the event of an accident.

[1696] 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.

[1697] In this invention, the server includes a means for receiving information entered by a user, a means for storing the information entered by the user in a data storage device, and a means for generating answers to the user's questions using a generative AI model. This allows the generative AI model to provide explanations so that the user can learn basic knowledge about automobile insurance. The server also includes a means for the user to take detailed interactive lessons, thereby enhancing the effectiveness of education. Furthermore, the server includes a means for the user to receive explanations of policy terms based on insurance quotes, enabling detailed information to be provided instantly even in physical stores. This allows users to instantly obtain the information they need, enabling them to quickly select appropriate insurance and respond in the event of an accident.

[1698] A "generative AI model" is an advanced artificial intelligence algorithm that generates answers in natural language based on user input.

[1699] "User" refers to an individual or corporation that uses the system to obtain information about automobile insurance and carry out various procedures.

[1700] "Information" refers to all data provided by users to the system, including data about their vehicle, questions, and personal information.

[1701] A "data storage device" is a digital storage device that stores information entered by a user and allows it to be searched or referenced later.

[1702] An "insurance expert" is a professional person or organization that has specialized knowledge about automobile insurance and provides appropriate advice and support to users.

[1703] "Matching" is the process of selecting an appropriate insurance professional based on the user's needs and enabling them to communicate and consult with each other.

[1704] A "physical store" is a physical location that customers can visit in person, where they can receive information and support about insurance.

[1705] "Basic knowledge" refers to the basic information and concepts users need to understand insurance, and in this system this is provided by a generative AI model.

[1706] An "interactive lesson" is an educational program that allows users to actively participate and track their learning progress.

[1707] A "quote" is a presentation of estimated insurance premiums based on the insurance coverage required by the user.

[1708] A "policy" is a document that details all the terms and conditions of an insurance contract.

[1709] This invention is a system that uses a generative AI model to provide education about automobile insurance in a brick-and-mortar store and deepen customer understanding. This system is mainly composed of a server, a terminal, and a user. Specific embodiments of the system are described below.

[1710] Hardware and Software Configuration

[1711] Hardware

[1712] Smartphone: A device used by users that functions as an input and display device.

[1713] Server: A computing device that stores generative AI models and databases and executes various processes.

[1714] software

[1715] Flask: A web framework that runs on the server and is responsible for the user interface and API processing.

[1716] OpenAI API: Perform natural language processing using generative AI models (e.g., text-davinci-003).

[1717] Program processing

[1718] User registration and profile creation

[1719] 1. Terminal: User enters name, age, and vehicle information.

[1720] 2. Server: Receives, validates, and stores data in a data repository.

[1721] 3. Example: A user enters their name, age, and vehicle information into a smartphone app and sends it to the server, which validates and stores the data.

[1722] Basic knowledge education

[1723] 1. User: Sends a request from the terminal to learn basic knowledge about car insurance.

[1724] 2. Server: Uses the generative AI model to generate an answer and send it back to the device.

[1725] 3. Example: Send a request such as "Please briefly explain what car insurance is." The generative AI model generates a basic explanation and sends it back to the device.

[1726] Interactive Lessons

[1727] 1. User: Selects an interactive lesson and sends a request from the device to the server.

[1728] 2. Server: Prepares the appropriate lesson content and sends it to the device, which displays the lesson to the user and tracks their progress.

[1729] 3. Example: A user selects a lesson on "How to choose car insurance," and the device sends the request to the server. The server generates the lesson content and sends it to the device.

[1730] Insurance quotes and policy explanations

[1731] 1. User: Request a quote and enter any additional information required.

[1732] 2. Server: Runs the insurance quote algorithm, generates quote results and a brief explanation of the policy terms, and sends them back to the terminal for display.

[1733] 3. Example: Send a request saying "Please tell me the estimate if the annual mileage is 10,000 km." The server calculates the estimate and returns the result along with an explanation of the terms and conditions.

[1734] Claims handling and expert matching

[1735] 1. User: Enters information about the accident or damage and sends it from the terminal to the server.

[1736] 2. Server: Analyzes the claim details and matches it with the appropriate insurance expert. The progress status and the expert's contact information are returned to the terminal and displayed.

[1737] 3. For example, you enter information such as the date, time, location, and circumstances of the accident, and the server matches it with an appropriate insurance investigator and returns their contact information and progress status.

[1738] Prompt Sentence Examples

[1739] "Please briefly explain what car insurance is."

[1740] This system allows customers to receive immediate and detailed education about car insurance at physical stores, and by gaining a deeper understanding, they will be able to choose the right insurance and respond quickly.

[1741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1742] Step 1:

[1743] User registration and profile creation

[1744] Input: The user enters personal information such as name, age, and vehicle information into the device.

[1745] Specific operation: The terminal receives the input information and sends it to the server.

[1746] Data processing: The server validates the received information and stores it in the database.

[1747] Output: A profile for the user is created.

[1748] Step 2:

[1749] Basic Knowledge Education Request

[1750] Input: The user sends a request from the terminal to the server saying, "I want to know the basics of car insurance."

[1751] Specific operation: The server sends a prompt sentence to the generative AI model.

[1752] Data processing: The generative AI model generates an answer based on the input prompt.

[1753] Output: The generated answer is sent from the server to the terminal and displayed to the user.

[1754] Step 3:

[1755] Select an Interactive Lesson

[1756] Input: The user selects an interactive lesson and sends the request from the device to the server.

[1757] Specific Actions: The server prepares the appropriate lesson content.

[1758] Data processing: Lesson content is generated and user progress data is initialized.

[1759] Output: The lesson content is sent to the device and the user's progress is displayed on the device.

[1760] Step 4:

[1761] Insurance quote request

[1762] Input: The user enters additional information such as mileage and primary use, and sends a request for an insurance quote from the device to the server.

[1763] Specific operation: The server sends the received information to the estimation algorithm.

[1764] Data Processing: A quoting algorithm calculates an insurance quote based on the information entered.

[1765] Output: The calculated estimate results and explanation of the terms and conditions are sent to the terminal and displayed to the user.

[1766] Step 5:

[1767] Claims handling request

[1768] Input: The user inputs detailed information about the accident (date, time, location, and circumstances) and sends the claim from the terminal to the server.

[1769] Specific operation: The server analyzes the claim details and matches the claim with the appropriate insurance professional.

[1770] Data processing: Contact information of the experts and progress of the claim are generated.

[1771] Output: The progress of the claim and the contact information of the expert are sent to the terminal and displayed to the user.

[1772] This concludes the detailed explanation of the process flow and each step of the system program that realizes this application example. This system allows users to easily obtain detailed information about automobile insurance, enabling them to select the appropriate insurance and process claims quickly.

[1773] 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.

[1774] The present invention is a system for providing education on automobile insurance using a generative AI model and an emotion engine. Specific embodiments and program processing of the system are described below.

[1775] User registration and profile creation

[1776] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). The device receives this information and sends it to the server. The server validates the received information and, if there are no problems, stores it in a database. At this stage, a user profile is created.

[1777] Basic knowledge education

[1778] When a user makes a request such as "I want to know the basics of car insurance," the device sends the request to the server, which uses the generative AI model and emotion engine to generate a relevant explanation and sends it back to the device, which then displays the generated explanation to the user.

[1779] Utilizing the Emotion Engine

[1780] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data (e.g., camera footage and audio analysis) is collected and the user's emotional state is determined. The server receives and analyzes this emotional data and generates appropriate answers and educational content according to the user's psychological state.

[1781] Interactive lessons

[1782] When a user selects an interactive lesson, the device sends a request to the server, which prepares the appropriate lesson content and sends it to the device. The device then displays the lesson content to the user and tracks their progress, again using an emotion engine to provide feedback based on the user's emotional state.

[1783] Insurance quotes and policy explanations

[1784] When a user requests an insurance quote, they are prompted to enter additional information (such as mileage and main use). The device sends the information the user has entered to the server, which runs an insurance quote algorithm to generate a quote and a brief explanation of the policy. This process also uses an emotion engine to adjust the explanation based on the user's emotional state. The device then displays these results to the user.

[1785] Claims handling and expert matching

[1786] When a user wants to file a claim, they enter information about the accident or damage. The device sends this information to the server, which analyzes the claim and matches it with an appropriate expert. Again, an emotion engine is used here to provide a response that reflects the user's emotional state. The server then sends the progress of the claim and the expert's contact information to the device, which displays this to the user.

[1787] Specific examples

[1788] For example, if a user reports a complaint stating that they have been involved in an accident, the following steps are taken:

[1789] 1. The user enters detailed information about the accident (date, time, location, situation, etc.).

[1790] 2. The terminal sends the entered information to the server.

[1791] 3. The server analyzes the claim and matches it with an appropriate insurance professional (e.g., an insurance adjuster).

[1792] 4. The server sends the progress and contact information of the expert to the device.

[1793] 5. The device displays this information to the user and provides an interface for direct contact with an expert.

[1794] During this process, the emotion engine monitors the user's emotional state in real time and provides support accordingly if stress levels rise. For example, if the system determines that the user is emotionally upset, it will quickly refer them to professional support and provide advice on how to stay calm.

[1795] The system allows users to gain comprehensive and practical knowledge about car insurance, providing them with the necessary assistance and information in a timely manner, as well as providing them with appropriate emotional support.

[1796] The processing flow will be explained below.

[1797] User registration and profile creation

[1798] Step 1: Enter your user information

[1799] User: Enter your name, age, and vehicle information (model, year, etc.) into the input form.

[1800] Step 2: Send input data

[1801] Terminal: Sends the information entered by the user to the server.

[1802] Step 3: Save to database

[1803] Server: Validates the received information and stores it in the database if there are no problems.

[1804] Step 4: Notification of registration results

[1805] Server: Generates a success message and sends it to the device to inform the user of the results of the process.

[1806] Terminal: Receives messages from the server and displays them to the user.

[1807] Basic knowledge education

[1808] Step 1: Educational Content Request

[1809] User: Makes a request: "I want to know the basics of car insurance."

[1810] Step 2: Submit the request

[1811] Terminal: Sends user requests to the server.

[1812] Step 3: Invoke the AI ​​model

[1813] Server: Inputs topics into a generative AI model and generates relevant explanations.

[1814] Step 4: Returning content

[1815] Server: Prepares the generated description and sends it to the device.

[1816] Terminal: Display the received description to the user.

[1817] Coordination using emotion engine

[1818] Step 1: Collecting sentiment data

[1819] Terminal: Collects the user's camera footage and audio data and sends it to the emotion engine.

[1820] Step 2: Analyze emotional state

[1821] Emotion engine: Analyzes collected data to determine the user's emotional state (stress, excitement, calm, etc.).

[1822] Step 3: Adjust the description

[1823] Server: Based on feedback from the emotion engine, the explanations generated by the generative AI model are adjusted according to the user's emotional state.

[1824] Terminal: Display the adjusted description to the user.

[1825] Interactive lessons

[1826] Step 1: Choose a lesson

[1827] User: Select from available lessons (e.g., accident procedures).

[1828] Step 2: Submit a lesson request

[1829] Terminal: Sends a request for the selected lesson to the server.

[1830] Step 3: Prepare the lesson content

[1831] Server: Selects appropriate lesson content and sets initial data.

[1832] Step 4: Deliver the lesson

[1833] Server: Sends lesson content to devices.

[1834] Device: Presents lesson content to users and tracks progress.

[1835] Step 5: Provide emotional feedback

[1836] On the device: Uses an emotion engine to provide feedback based on the user's emotional state.

[1837] Insurance quotes and policy explanations

[1838] Step 1: Request a quote

[1839] User: Enter any additional information required for insurance quote (mileage, primary use, etc.).

[1840] Step 2: Send data

[1841] Terminal: Sends any additional information entered by the user to the server.

[1842] Step 3: Calculate estimates and generate explanations

[1843] Server: Runs insurance quoting algorithms and generates quote results, and also uses generative AI models to create policy summaries and commentary.

[1844] Step 4: Emotional Adjustment

[1845] Server: Adjusts the estimation results and commentary based on the user's emotional state.

[1846] Step 5: Returning the results

[1847] Server: Sends the adjusted estimate results and explanations to the device.

[1848] Terminal: Displays results and explanations to the user.

[1849] Claims handling and expert matching

[1850] Step 1: Enter your claim information

[1851] User: Enter information about the accident or damage (date, time, location, situation, etc.).

[1852] Step 2: Submit your claim data

[1853] Terminal: Sends the complaint information to the server.

[1854] Step 3: Claims processing and expert matching

[1855] Server: Analyzes the claims, selects appropriate experts, and sets initial steps.

[1856] Step 4: Emotional feedback and response

[1857] Server: Uses an emotion engine to consider appropriate responses based on the user's emotional state.

[1858] Step 5: Progress notification and expert information

[1859] Server: Sends claim progress and expert contact information to the device.

[1860] Terminal: Displays claim progress and expert information to the user.

[1861] Example 2

[1862] 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."

[1863] When providing education and support regarding automobile insurance, it is difficult to provide users with the information they need in a timely and appropriate manner. In particular, flexible responses that take into account the user's emotional state and efficient matching with experts are required. Furthermore, it is important that the educational content be appropriately adjusted according to the user's progress.

[1864] 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.

[1865] In this invention, the server includes means for receiving information entered by a user, means for storing the information entered by the user in a database, means for generating answers to the user's questions using a generative AI model and an emotion engine, means for displaying the generated answers to the user, means for tracking the user's progress and providing interactive lessons, means for monitoring and analyzing the user's emotional state in real time, means for providing appropriate feedback according to the user's psychological state using the emotion engine, and means for matching with insurance experts. This enables flexible and appropriate automobile insurance education and support according to the user's emotions and progress.

[1866] "User" means any individual or entity that uses the System to receive services such as automobile insurance education, quotes, and claims processing.

[1867] "Terminal" means a computing device through which a user enters information and receives and displays information from a server.

[1868] A "server" is a computer system that processes information received from users and generates appropriate answers and feedback using generative AI models and emotion engines.

[1869] A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions or requests.

[1870] An "emotion engine" is a system that recognizes and analyzes a user's emotional state in real time and uses that information to provide appropriate feedback and answers.

[1871] "Database" refers to a system for storing data such as information received from users, generated responses, and educational content.

[1872] "Interactive lessons" are educational content that dynamically adjusts based on the user's progress and emotional state.

[1873] An "insurance expert" is an individual or institution with detailed knowledge and experience regarding automobile insurance who can provide specific advice and support to users.

[1874] This invention is a system that uses a generative AI model and an emotion engine to provide education and support regarding car insurance. This system provides users with the information they need in a timely and appropriate manner, and provides flexible responses that take into account the user's emotional state.

[1875] User registration and profile creation

[1876] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.). This information is sent to the server via the user's device. The server validates this information and, if there are no problems, stores it in the database. This creates a user profile.

[1877] Basic knowledge education

[1878] When a user makes a request such as "I want to know the basics of car insurance," the request is sent from the device to the server, which uses a generative AI model and an emotion engine to generate a relevant explanation and sends it back to the device, which then displays it to the user.

[1879] For example, some prompts for a generative AI model might include:

[1880] "What is car insurance?"

[1881] Utilizing the Emotion Engine

[1882] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. Emotional data is collected through camera footage and audio analysis and sent to a server. The server then uses the emotion engine to analyze the emotional data and generate appropriate answers and educational content based on the user's psychological state.

[1883] Interactive lessons

[1884] When a user selects an interactive lesson, the request is sent from the device to the server. The server prepares the appropriate lesson content and sends it back to the device. The device displays this content and tracks the user's progress. An emotion engine can be used to provide feedback based on the user's emotional state.

[1885] Insurance quotes and policy explanations

[1886] When a user requests an insurance quote, they are asked to enter additional information (such as mileage and main use). The information entered by the user is sent to the server via the device. The server then runs an insurance quote algorithm to generate a quote and a brief explanation of the policy terms. This process also utilizes an emotion engine to provide an explanation based on the user's emotional state.

[1887] Claims handling and expert matching

[1888] When a user files a claim, they enter details of the accident and damage into the device. The device sends this information to the server, which analyzes the claim. The server then matches the claim with an appropriate insurance expert and sends the progress report and the expert's contact information to the device. The device displays this information to the user and provides an interface for direct contact with the expert. The device can monitor the user's emotional state in real time using an emotion engine and provide support as needed.

[1889] This system will enable users to obtain comprehensive and practical knowledge about car insurance, provide necessary assistance and information in a timely manner, and also provide appropriate emotional support, allowing users to proceed with car insurance procedures with greater peace of mind.

[1890] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1891] Step 1:

[1892] When a user first accesses the system, they enter their name, age, and vehicle information (model, year, etc.) This information is called input data. The device receives this information and sends the input data to the server.

[1893] Step 2:

[1894] The server validates the received user information. Specifically, it checks whether the data is in the correct format (for example, whether the name is blank, whether the age is a number, etc.). This is the data calculation step, and the validated information becomes the output data. If the information passes validation, the server saves it in the database. This save is the output result.

[1895] Step 3:

[1896] The user makes a request such as "I want to know the basics of car insurance." This request is the input data. The device sends this request to the server.

[1897] Step 4:

[1898] The server uses a generative AI model and an emotion engine to generate an explanation based on the request. Specifically, the generative AI model generates an answer based on a prompt (e.g., "What is car insurance?"), and the emotion engine analyzes the user's psychological state and adjusts the answer. This process is a data calculation, and the generated explanation becomes the output data. The server then sends the generated explanation back to the device.

[1899] Step 5:

[1900] The terminal displays the generated explanation received from the server to the user, which is the output to the user.

[1901] Step 6:

[1902] When a user uses educational content, the device activates an emotion engine to recognize the user's emotions. This emotion data is input data. Data is collected through camera footage and audio analysis and sent to a server.

[1903] Step 7:

[1904] The server analyzes the emotional data using an emotion engine. This analysis process is a data calculation, and the user's emotional state is the output data. As a result, depending on the user's emotional state, the server generates appropriate educational content and feedback and sends it to the device.

[1905] Step 8:

[1906] The user selects an interactive lesson. This selection becomes the input data. The device sends the request to the server.

[1907] Step 9:

[1908] The server prepares interactive lesson content, which is a data generation process, and the prepared lesson content is the output data. The server then sends the lesson content to the device.

[1909] Step 10:

[1910] The device displays the received lesson content to the user and tracks the user's progress. This progress data becomes input data, and the device sends the information to the server.

[1911] Step 11:

[1912] The server integrates the user's progress information with the data analyzed by the emotion engine to generate optimal feedback for the user. This is the data fusion and generation step, and the integrated feedback becomes the output data. The server then sends the feedback to the device.

[1913] Step 12:

[1914] When a user requests an insurance quote, they enter additional information (mileage, main use, etc.). This information becomes input data. The device sends the information entered by the user to the server.

[1915] Step 13:

[1916] The server runs an insurance quote algorithm and generates quote results and brief explanations of the policy terms. This process is a data computation, with the quote results and explanations being the output data. It also utilizes an emotion engine to provide explanations tailored to the user's emotional state. This tailored explanation is also output data.

[1917] Step 14:

[1918] The server sends the generated estimate and adjusted commentary to the terminal, which displays it to the user. The displayed information is the final output.

[1919] Step 15:

[1920] When a user files a claim, they input detailed information about the accident or damage. This information is the input data. The device sends the information to the server.

[1921] Step 16:

[1922] The server analyzes the claim details and matches them with the appropriate insurance experts. This analysis and matching process is a data calculation, and the expert information is the output data. The server sends the progress status and the expert contact information to the terminal.

[1923] Step 17:

[1924] The terminal displays the progress and contact information of the expert to the user, and provides an interface for direct contact with the expert. This display and interface are the final output.

[1925] Step 18:

[1926] The emotion engine monitors the user's emotional state in real time and provides support as needed. The support provided by the emotion engine is output data, and appropriate support is provided to the user.

[1927] (Application example 2)

[1928] 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."

[1929] While conventional insurance education systems aim to improve users' knowledge, they do not adequately consider the stress and anxiety users feel during the learning process. As a result, the education is not fully effective, and users often abandon their studies midway. Furthermore, there are also situations where psychological support is lacking in the event of an accident or insurance claim. Given this background, there is a need for a system that can provide appropriate information and support according to the user's emotional state.

[1930] 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 receiving information input by the user, means for generating an answer to the user's question, means for displaying the generated answer to the user, means for activating an emotion engine for recognizing and analyzing the user's emotional state, and means for generating educational content based on the emotion engine in accordance with the user's emotional state. This makes it possible to provide appropriate information and support in accordance with the user's emotional state.

[1931] A "generative AI model" is an artificial intelligence model that generates appropriate answers and information in response to user questions and requests.

[1932] An "emotion engine" is a software engine for recognizing and analyzing a user's emotional state.

[1933] "Means for receiving information entered by the user" refers to the means by which the terminal or server collects and processes information provided by the user.

[1934] "Means for generating answers to user questions" refers to means for generating appropriate answers to user questions using a generative AI model.

[1935] "Means for displaying the generated answer to the user" refers to a means for presenting the answer generated by the generative AI model to the user in an easy-to-read format.

[1936] "Means for tracking user progress and providing interactive lessons" means means for tracking a user's learning progress and providing lessons in an interactive format.

[1937] "Means for matching with insurance experts" refers to means for selecting an insurance expert suitable for the user and providing that information.

[1938] The "means for generating educational content according to the emotional state of the user" is a means for generating educational content that is optimal for the emotional state of the user based on the analysis results of the emotion engine.

[1939] The "means for inputting information necessary for insurance quotes" refers to the means by which a user inputs various information necessary for insurance quotes.

[1940] "Means for generating explanations of insurance policy terms and conditions using a generative AI model" refers to means for generating easy-to-understand explanations of insurance policy terms and conditions using a generative AI model.

[1941] The following describes an embodiment of the present invention. The system uses a generative AI model and an emotion engine to provide users with insurance education. The system comprises the following specific steps:

[1942] User registration and profile creation

[1943] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. This information is sent from the user's device to the server, where it is validated and stored in a database. At this stage, a user profile is created.

[1944] Basic knowledge education

[1945] When a user makes a request such as "I want to know the basics of insurance," the generative AI model generates an appropriate explanation. The generated content is sent to the user's device and displayed. During this process, the emotion engine monitors the user's emotional state and provides appropriate feedback.

[1946] Utilizing the Emotion Engine

[1947] While a user is using educational content, the emotion engine collects and analyzes emotional data (camera footage, audio analysis, etc.). Based on the analysis results, the server generates content appropriate to the user's psychological state and provides it to the user.

[1948] Interactive lessons

[1949] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the user's device. Progress is tracked and an emotion engine provides feedback based on the user's emotional state.

[1950] Insurance quote and explanation of contract details

[1951] When a user requests an insurance quote, the server prompts them to enter additional necessary information (such as the type of insurance and key terms and conditions). The generative AI model is used to generate an insurance quote and explanation of the policy terms and conditions, which are then provided to the user. The emotion engine monitors the user's emotional state during this process and provides appropriate assistance.

[1952] Claims handling and expert matching

[1953] When a user submits an insurance claim, they enter information about the accident and damage. This information is sent to the server, which starts the claim process. An appropriate expert is matched and the progress and expert contact information are provided to the user. The emotion engine monitors the user's emotional state and provides a quick response in case of increased stress.

[1954] Hardware and software used

[1955] Hardware: Smartphone (iOS or Android)

[1956] Software: Insurance education support app, generative AI models (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva SDK)

[1957] Examples and prompts

[1958] As a concrete example, if a 30-year-old male user requests basic insurance knowledge, the following prompt is sent to the generative AI model:

[1959] Example prompt sentence:

[1960] "Generate an easy-to-understand explanation for a 30-year-old male user about basic insurance knowledge for him. If the emotion engine determines that the user is confused, regenerate a simplified explanation."

[1961] In this way, the system can provide appropriate education according to the user's emotional state, improving the user experience.

[1962] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1963] Step 1: Register and create a profile

[1964] When a user first accesses the system, they enter their name, date of birth, insurance information, etc. The terminal collects this information and sends it to the server, which validates it and stores it in a database if there are no problems. The input to this process is the user's personal and insurance-related information, and the output is a user profile stored in the database.

[1965] Step 2: Request Foundational Knowledge Training

[1966] The user inputs a request such as "I want to learn the basics of insurance." The device sends this request to the server. The server receives the request and sends a prompt to the generative AI model to generate appropriate educational content. The input of this process is the user's request, and the output is the generated educational content.

[1967] Step 3: Activating and monitoring the emotion engine

[1968] While the user is using educational content, the device collects camera footage and audio data. The emotion engine analyzes this data and determines the user's emotional state. The input to this process is the collected emotion data, and the output is the determination of the user's emotional state. The server receives this determination result and uses it as feedback for the next step.

[1969] Step 4: Regenerate content based on emotions

[1970] Based on the emotion engine's judgment, the server sends a prompt to the generative AI model again to generate new educational content that corresponds to the user's emotional state. For example, if the user is confused, it generates a more understandable and simplified explanation. The input to this process is the emotion engine's judgment, and the output is educational content that corresponds to the user's emotional state.

[1971] Step 5: Deliver interactive lessons

[1972] When a user selects an interactive lesson, the server prepares the appropriate lesson content and sends it to the device, tracking the user's progress and providing feedback based on data obtained by the emotion engine. The input to this process is the user's progress data and emotional state data, and the output is the tailored lesson content.

[1973] Step 6: Insurance quote and contract explanation

[1974] When a user requests an insurance quote, the device sends it to the server. The server requests additional information (e.g., type of insurance and terms) and generates a quote using a generative AI model based on the information entered by the user. The input to this process is the insurance information entered by the user, and the output is the generated quote and a description of the policy.

[1975] Step 7: Claims processing and expert matching

[1976] When a user enters a claim about an accident or damage, the device sends the information to the server. The server analyzes the claim and matches it with an appropriate expert. It then provides the user with progress status and the expert's contact information. The input to this process is the claim details, and the output is the expert's contact information and the claim processing progress.

[1977] In this way, the user, device, and server work together at each step to create a system that provides optimal insurance education and support according to the user's emotional state.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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).

[1985] 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.

[1986] 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."

[1987] 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.

[1988] 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).

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] 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.

[1995] 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.

[1996] 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.

[1997] 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.

[1998] 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.

[1999] The following is further disclosed regarding the above embodiment.

[2000] (Claim 1)

[2001] A system that uses a generative AI model to educate users about automobile insurance,

[2002] a means for receiving user-entered information;

[2003] means for storing the information entered by the user in a database;

[2004] means for generating answers to user questions using the generative AI model;

[2005] means for displaying the generated answers to a user;

[2006] a means of tracking users' progress and providing interactive lessons;

[2007] A means of matching with insurance professionals;

[2008] A system including:

[2009] (Claim 2)

[2010] 10. The system of claim 1, further comprising means for a user to input information required for an automobile insurance quote.

[2011] (Claim 3)

[2012] The system of claim 1, further comprising means for generating an explanation of the insurance policy terms using a generative AI model based on the estimate.

[2013] (Claim 4)

[2014] The system of claim 1, further comprising means for receiving complaint information from a user and matching the complaint information with an appropriate expert.

[2015] "Example 1"

[2016] (Claim 1)

[2017] A system that uses a generative AI model to educate users about automobile insurance,

[2018] a means for receiving user-entered information;

[2019] means for storing the information entered by the user in a database;

[2020] means for generating answers to user questions using the generative AI model;

[2021] means for displaying the generated answers to a user;

[2022] a means of tracking users' progress and providing interactive lessons;

[2023] A means of matching with insurance professionals;

[2024] a means to track the progress of claims and provide users with expert contact information;

[2025] A system including:

[2026] (Claim 2)

[2027] 10. The system of claim 1, further comprising means for a user to input information required for an automobile insurance quote.

[2028] (Claim 3)

[2029] The system of claim 1, further comprising means for generating an explanation of the insurance policy terms using a generative AI model based on the estimate.

[2030] "Application Example 1"

[2031] (Claim 1)

[2032] A system that uses a generative AI model to provide users with education about automobile insurance,

[2033] means for receiving information entered by a user;

[2034] means for storing the information input by the user in a data storage device;

[2035] means for generating answers to user questions using the generative AI model;

[2036] means for displaying the generated answer to a user;

[2037] a means of tracking your progress and providing interactive lessons;

[2038] A means of matching with insurance professionals;

[2039] A means for generative AI models to provide explanations to customers in physical stores so they can learn basic knowledge about car insurance, and

[2040] A way for customers to take in-depth interactive lessons on car insurance in-store, and

[2041] A means for users to receive explanations of the terms and conditions based on insurance quotes;

[2042] A system including:

[2043] (Claim 2)

[2044] 10. The system of claim 1, further comprising means for a user to input information required for an automobile insurance quote.

[2045] (Claim 3)

[2046] The system of claim 1, further comprising means for generating an explanation of the insurance policy terms using a generative AI model based on the estimate.

[2047] "Example 2: Combining Emotion Engines"

[2048] (Claim 1)

[2049] a means for receiving user-entered information;

[2050] means for storing the information entered by the user in a database;

[2051] a means for generating answers to user questions using a generative AI model and an emotion engine;

[2052] means for displaying the generated answers to a user;

[2053] a means of tracking users' progress and providing interactive lessons;

[2054] A means for monitoring and analyzing the user's emotional state in real time;

[2055] A means for providing appropriate feedback according to the user's psychological state using an emotion engine;

[2056] A means of matching with insurance professionals;

[2057] A system including:

[2058] (Claim 2)

[2059] 10. The system of claim 1, further comprising means for a user to input information required for an automobile insurance quote.

[2060] (Claim 3)

[2061] The system of claim 1, further comprising means for generating an explanation of the insurance policy terms using a generative AI model based on the estimate.

[2062] "Application example 2 when combining emotion engines"

[2063] (Claim 1)

[2064] A system that uses a generative AI model to provide insurance education to users,

[2065] a means for receiving user-entered information;

[2066] means for storing the information entered by the user in a database;

[2067] means for generating answers to user questions using the generative AI model;

[2068] means for displaying the generated answers to a user;

[2069] a means of tracking users' progress and providing interactive lessons;

[2070] A means of matching with insurance professionals;

[2071] means for activating an emotion engine for recognizing and analyzing the user's emotional state;

[2072] means for generating educational content according to the emotional state of the user based on the emotion engine;

[2073] A system including:

[2074] (Claim 2)

[2075] 10. The system of claim 1, further comprising means for a user to input information required for an insurance quote.

[2076] (Claim 3)

[2077] The system of claim 1, further comprising means for generating an explanation of the insurance policy terms using a generative AI model based on the estimate. [Explanation of symbols]

[2078] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system that uses a generative AI model to educate users about automobile insurance, a means for receiving user-entered information; means for storing the information entered by the user in a database; means for generating answers to user questions using the generative AI model; means for displaying the generated answers to a user; a means of tracking users' progress and providing interactive lessons; A means of matching with insurance professionals; A system including:

2. 10. The system of claim 1, further comprising means for a user to input information required for an automobile insurance quote.

3. The system of claim 1 further comprising means for generating an explanation of the insurance policy terms using a generative AI model based on the estimate.

4. The system of claim 1 further comprising means for receiving complaint information from a user and matching the complaint information with an appropriate expert.

Images