system

Abstract

Provide a system. A means for collecting basic information and lifestyle information of a user; A means of analyzing collected data and predicting future risks, A means for generating an optimal insurance plan for a user based on the predicted risk; a means for notifying the user of the generated insurance plan; a means for accepting insurance-related questions and generating answers using natural language processing; A means of automating the insurance enrollment process; A means for automating claims processing and after-sales service after insurance is purchased; A system including:

Description

[Technical Field]

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

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

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

[0004] The types and contents of insurance are complex, making it difficult for users to select the most suitable insurance. As a result, users are unable to make an appropriate selection when purchasing insurance, and the application process takes a great deal of time and effort. Furthermore, there is also the issue of insufficient support after purchasing insurance, with claims handling and after-sales service not being provided quickly and efficiently. This results in low user satisfaction and a situation in which the usefulness of insurance is not fully realized. The purpose of this invention is to solve these problems and provide a system that allows users to easily select the most suitable insurance plan, complete the application process simply, and receive high-quality after-sales service. [Means for solving the problem]

[0005] The present invention is a system including the following means.

[0006] The system includes a means for collecting a user's basic information and lifestyle information, a means for analyzing the collected data and predicting future risks, a means for generating an optimal insurance plan for the user based on the predicted risks, a means for notifying the user of the generated insurance plan, a means for accepting questions about insurance and generating answers using natural language processing, a means for automating the insurance application process, and a means for automating claims processing and after-sales service after insurance is applied. This allows the user to make appropriate risk predictions based on the basic information and lifestyle information entered, and to be offered an optimal insurance plan based on the results. Furthermore, since insurance-related questions are resolved using natural language processing and the insurance application process is automated, the effort and time required are significantly reduced. These means allow for prompt and efficient claims processing and after-sales service even after insurance is applied, improving user satisfaction.

[0007] "Basic User Information" means data that personally identifies you and constitutes your basic background information, such as your age, gender, occupation, and contact information.

[0008] "Lifestyle information" refers to information related to a user's daily life and behavior, such as past risks, behavioral patterns, and past insurance history.

[0009] "Means of collection" are the processes and technologies used to obtain the required data from users, such as through online forms, surveys, sensors, etc.

[0010] "Means of analysis" refers to techniques for statistically or dynamically analyzing collected data using generative AI or data analysis algorithms.

[0011] The "means for predicting future risks" is an algorithm that allows the generative AI to assess and predict the user's future risks based on past data and current behavioral patterns.

[0012] The "means for generating insurance plans" refers to algorithms and systems that construct optimal combinations of insurance products for users based on predicted risks.

[0013] "Means of notification" refers to the interface or communication method for providing the generated insurance plan and related information to the user.

[0014] "A means of accepting questions and generating answers using natural language processing" is an AI technology that receives questions from users in text format, analyzes them, and automatically generates appropriate responses.

[0015] A "means for automating insurance enrollment procedures" is a system that automatically creates, submits, and authenticates the necessary documents based on the insurance plan selected by the user.

[0016] "Means for automating claims processing and after-sales service" refers to AI and automated systems that efficiently process and follow up on claims submitted by users after they have taken out insurance. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention provides a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[0039] User registration and data collection

[0040] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[0041] Data analysis and risk prediction

[0042] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[0043] Insurance plan proposals

[0044] The generative AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[0045] Insurance Questions and Answers

[0046] The user has a question about the proposed plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing, generates an answer such as, "This plan includes injury compensation up to 1 million yen," and sends it to the device via the server. The device then displays the answer to the user.

[0047] Insurance enrollment procedures

[0048] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0049] Complaint handling and after-sales service

[0050] If the user needs to submit a claim after taking out insurance, they enter the details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the claim content and determines the appropriate processing procedure, and the server works with the insurance company's system to handle the claim. The server notifies the user and the insurance company of the progress of the claim, and the device displays the progress to the user in real time.

[0051] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly in the area of ​​insurance selection and processing.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[0055] Step 2:

[0056] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[0057] Step 3:

[0058] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[0059] Step 4:

[0060] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[0061] Step 5:

[0062] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[0063] Step 6:

[0064] The generation AI generates the optimal insurance plan based on the predicted risks. The server imports the generated insurance plan data and sends a proposal notification to the user.

[0065] Step 7:

[0066] The device displays a proposal notification to the user and provides a screen where the user can view detailed insurance plan information. The user can visually compare multiple proposals.

[0067] Step 8:

[0068] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[0069] Step 9:

[0070] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer, which is then returned to the server.

[0071] Step 10:

[0072] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[0073] Step 11:

[0074] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[0075] Step 12:

[0076] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[0077] Step 13:

[0078] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[0079] Step 14:

[0080] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[0081] Step 15:

[0082] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[0083] Step 16:

[0084] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure.

[0085] Step 17:

[0086] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[0087] Step 18:

[0088] The terminal displays the progress and results of the complaint handling to the user in real time.

[0089] Example 1

[0090] 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."

[0091] Conventional insurance systems make it difficult for users to understand their own risks and select the most suitable insurance plan. They also face issues with responding to questions about individual insurance plans, enrolling in insurance, and processing claims, all of which are time-consuming. There is a need to streamline these processes and reduce the burden on users.

[0092] 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.

[0093] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers through natural language processing using a generative AI model, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for the user to visually compare insurance plans. This enables the user to efficiently select an insurance plan, respond to questions, apply for insurance, and even handle claims.

[0094] "Basic User Information" refers to basic information about you, such as your name, age, gender, occupation, and contact information.

[0095] "Lifestyle information" refers to information about a user's lifestyle, such as the user's past risks, behavioral patterns, and past insurance history.

[0096] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze data and process natural language to automate various tasks.

[0097] "Natural language processing" refers to the technology of processing and understanding human language using computers, and includes the process of generating answers to questions using generative AI models.

[0098] "Insurance Plan" refers to a plan that specifically describes the type and content of insurance that a User will subscribe to, and includes a proposal for the most appropriate insurance in response to predicted risks.

[0099] "Claims handling" refers to the process of appropriately responding to claims and problems related to insurance that arise from users after they have taken out insurance.

[0100] "After-sales service" refers to the provision of services and support to users after they have taken out insurance, and includes general user care, including handling claims.

[0101] "Visual comparison tools" refers to features that provide a visual interface that allows users to easily understand and compare insurance plans and other options.

[0102] This invention is a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[0103] User registration and data collection examples

[0104] Users access the service using a web browser or a dedicated app, and enter their name, age, gender, occupation, and contact information on the new registration page. The device collects the entered basic information in a form and sends it to the server. The server stores the received information in a database such as MySQL (registered trademark). The server then displays a questionnaire form on the user's device to collect lifestyle information. The user answers the questionnaire, and the device sends the response data to the server. The server stores this information in a database.

[0105] Examples of data analysis and risk prediction

[0106] The server provides the user's basic information and lifestyle information to a data analysis engine (e.g., TENSORFLOW®), which uses this information to generate a generative AI model that predicts future risks. The generative AI model considers past risks, behavioral patterns, and insurance history, and applies a predictive algorithm to generate a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to have a high risk of sports-related injuries.

[0107] Example of an insurance plan proposal

[0108] The generative AI model takes predicted risks into account and generates the optimal insurance plan. Specifically, it automatically generates a plan that includes injury compensation for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[0109] Insurance Questions and Answers Example

[0110] The user has a question about the proposed insurance plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." The server then sends the answer to the device, which displays it to the user.

[0111] An example of insurance enrollment procedures

[0112] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for any additional information required (e.g., health check results, past medical history), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0113] Examples of complaint handling and after-sales service

[0114] If a user wishes to submit a claim after purchasing insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model, which analyzes the claim content and determines the appropriate processing steps. The server works in conjunction with the insurance company's system to handle the claim and notifies the user's device of the progress of the claim in real time.

[0115] Prompt Sentence Examples

[0116] Insurance plan question examples:

[0117] User: How much coverage does this insurance plan provide?

[0118] Generative AI model: This plan includes injury compensation up to 1 million yen.

[0119] Example of handling a complaint:

[0120] User: I want to file a claim, how do I do that?

[0121] Generative AI Model: Please fill in the details in the claim submission form and submit it.

[0122] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly by streamlining insurance selection and procedures.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The user accesses the service and enters the required information on the new registration page.

[0126] Specific operation: The user accesses the service using a web browser or a dedicated app and enters basic information such as name, age, gender, occupation, and contact information.

[0127] Input: User basic information

[0128] Output: The basic information entered is sent from the terminal to the server.

[0129] Step 2:

[0130] The terminal prompts the user to enter basic information into a form and presses the submit button.

[0131] What happens: When the user clicks the submit button, an HTTP POST request is sent to the server via the HTML form.

[0132] Input: Basic information input data

[0133] Output: Sent to the server as an HTTP POST request

[0134] Step 3:

[0135] The server stores the submitted information in a database and then displays a questionnaire to gather the user's lifestyle information.

[0136] Specific operation: The server stores the received basic information in a database such as MySQL, and then sends the survey form to the user's device.

[0137] Input: Received basic information, questionnaire form template

[0138] Output: Basic information is saved in the database and the survey form is displayed on the user's device.

[0139] Step 4:

[0140] The user answers the questionnaire, and the terminal transmits the answer data to the server.

[0141] Specific operation: The user enters the necessary information into the questionnaire form and presses the send button, which sends the response data from the terminal to the server.

[0142] Input: Survey response data

[0143] Output: Answer data is sent from the device to the server

[0144] Step 5:

[0145] The server receives this information and stores it in a database.

[0146] Specific operation: The server stores the received survey response data in a database.

[0147] Input: Survey response data

[0148] Output: Response data is saved in the database

[0149] Step 6:

[0150] The server provides the user's basic information and lifestyle information to the data analysis engine, and the generative AI uses this information to predict future risks.

[0151] How it works: The server retrieves user data from the database and passes it to a data analysis engine such as TensorFlow. A generative AI model uses this data to predict risk.

[0152] Input: User's basic information and lifestyle information

[0153] Output: Risk assessment results

[0154] Step 7:

[0155] Generative AI generates insurance plans tailored to specific risks.

[0156] What it does: It applies predictive algorithms to generate personalized insurance plans, such as plans that include injury coverage for sports enthusiasts.

[0157] Input: Risk assessment results

[0158] Output: Generated insurance plan

[0159] Step 8:

[0160] The server notifies the user of the generated insurance plan.

[0161] Specific behavior: The generated insurance plan is sent to the user as JSON data.

[0162] Input: Generated insurance plan data

[0163] Output: Insurance plan details are sent to the user's device

[0164] Step 9:

[0165] The device will display plan details to the user and allow them to compare them visually.

[0166] Specific operation: The device receives insurance plan information and displays it on the screen using HTML and CSS. It displays it in a table format so that the user can compare multiple plans.

[0167] Input: Insurance plan details

[0168] Output: A list of insurance plans displayed to the user

[0169] Step 10:

[0170] The user enters a question about the insurance plan.

[0171] What happens: A user types a question into a terminal, such as "How much does this insurance plan cover?"

[0172] Input: User question

[0173] Output: The entered question data is sent to the server.

[0174] Step 11:

[0175] The device sends the question to the server, which passes it on to the generating AI.

[0176] Specific operation: A question sent from a device reaches the server, which then passes the question to the generative AI model.

[0177] Input: User question data

[0178] Output: Question data is sent to the generation AI

[0179] Step 12:

[0180] The generation AI generates an answer and sends it to the device via the server.

[0181] Specific operation: Using natural language processing, the generative AI generates an appropriate answer and sends it to the device via the server.

[0182] Input: User question

[0183] Output: The generated answer is displayed in the terminal.

[0184] Step 13:

[0185] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button on the terminal.

[0186] Specific operation: The user clicks the subscription button on the terminal and sends the intention to subscribe to the server.

[0187] Input: User's intention to join

[0188] Output: Willingness to join data is sent to the server

[0189] Step 14:

[0190] The server generates an input form for additional information and sends it to the user's terminal.

[0191] Specific operation: The server generates an input form for the required additional information (e.g., health check results, past medical history) and displays it on the user's device.

[0192] Input: Intention to join data

[0193] Output: A form for entering additional information is displayed on the user's device.

[0194] Step 15:

[0195] The user enters additional information, which the terminal sends to the server.

[0196] Specific operation: The user enters additional information and the device sends the data to the server.

[0197] Input: Additional Information

[0198] Output: Additional information data is sent to the server

[0199] Step 16:

[0200] The server checks all input data and automatically connects to the insurance company's system.

[0201] Specific operation: The server verifies the input data and sends it to the insurance company's system via API.

[0202] Input: All input data

[0203] Output: Data is linked to the insurance company's system

[0204] Step 17:

[0205] A subscription completion notice is generated and sent to the user's terminal.

[0206] Specific operation: The server generates a notification that the subscription procedure has been completed and sends it to the user's device.

[0207] Input: Confirmation of enrollment completion from insurance company

[0208] Output: A notification of successful enrollment is displayed on the user's device.

[0209] Step 18:

[0210] The user submits a claim and the terminal transmits the claim information to the server.

[0211] Specific operation: The user enters detailed information into the complaint submission form and clicks the submit button. The terminal sends the complaint information to the server.

[0212] Input: Claim information

[0213] Output: Claim information is sent to the server

[0214] Step 19:

[0215] The server sends the claim information to the generation AI.

[0216] Specific operation: The server sends the complaint information to the generative AI model, which analyzes the content and determines the appropriate processing procedure.

[0217] Input: Claim information

[0218] Output: Analysis results and recommended processing steps

[0219] Step 20:

[0220] The server will work in conjunction with the insurance company's system to respond to the situation.

[0221] Specific operation: The server connects the analysis results of the generated AI and recommended processing procedures to the insurance company's system.

[0222] Input: Analysis results and recommended processing steps

[0223] Output: Processing steps linked to insurance company

[0224] Step 21:

[0225] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[0226] Specific operation: The server periodically checks the progress of the response and notifies the user and the insurance company. The terminal displays the received progress information to the user in real time.

[0227] Input: Response progress

[0228] Output: Progress is displayed in real time on the user's device.

[0229] (Application example 1)

[0230] 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."

[0231] Currently, the insurance industry requires a great deal of time and effort because the process of proposing insurance plans tailored to users' needs, procedures, and after-sales service is not fully automated. Furthermore, security risk predictions and countermeasures are carried out separately, making comprehensive risk management difficult. In particular, cybersecurity risks are not adequately addressed, often preventing users from taking appropriate measures.

[0232] 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.

[0233] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for collecting and analyzing basic information and behavioral data of the user and predicting cybersecurity risks, means for generating an optimal security measures plan based on the predicted cybersecurity risks, and means for automatically updating the user's device settings with the generated security measures plan. This allows the user to efficiently select insurance, apply for insurance, and even handle claims in a consistent manner, while also enabling comprehensive measures against cybersecurity risks.

[0234] "User's basic information and lifestyle information" refers to personal information about the user and data related to their daily behavioral patterns.

[0235] "Future risks" are predictions of problems and dangers that users may face in the future.

[0236] "Insurance Plan" means the set of insurance terms, conditions and coverages offered to a User for a particular risk.

[0237] "Natural language processing" is a technology that allows machines to understand natural human language and generate appropriate responses.

[0238] "Behavioral data" refers to data related to a user's daily activities, including, for example, internet usage history and movement history.

[0239] "Cybersecurity risk" refers to risks such as unauthorized access, information leaks, and system failures via the Internet or digital devices.

[0240] A "security plan" is a set of measures or actions to be taken against cybersecurity risks, including settings and how to use tools.

[0241] This invention relates to a system that collects basic information and lifestyle information of users, analyzes this data to predict future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service. Furthermore, this system also predicts cybersecurity risks and generates optimal security countermeasure plans, which are automatically reflected in the user's device settings.

[0242] 1. System Overview

[0243] The system consists of the following main components:

[0244] How we collect your basic and lifestyle information:

[0245] To achieve this, an interface is provided that allows users to use their smartphones or other devices to enter and submit the necessary information on a new registration page.

[0246] Ways to predict future risks:

[0247] Based on the data collected from users, the server uses a generative AI model to predict various risks.

[0248] To generate the best insurance plan:

[0249] Taking into account predicted risks, generative AI automatically generates the optimal insurance plan for the user.

[0250] Informing you of your insurance plan:

[0251] The generated plan information is sent from the server to the user's terminal, where detailed information is displayed.

[0252] Natural language processing answer generation methods:

[0253] When a user enters a question, the server uses generative AI to generate an answer corresponding to the question and sends it to the device.

[0254] Ways to automate the insurance enrollment process:

[0255] When a user selects the insurance plan they wish to subscribe to, the system automatically checks all necessary information and contacts the insurance company.

[0256] Automating claims handling and after-sales service:

[0257] When you submit a complaint, we collect and analyze that information and generate appropriate response procedures to process it.

[0258] Cybersecurity risk prediction tools:

[0259] Collect user behavior data and predict cybersecurity risks using generative AI models.

[0260] How to generate a security action plan:

[0261] Generate optimal countermeasure plans to address predicted cybersecurity risks.

[0262] How to automatically apply security measures:

[0263] The generated security plan is automatically reflected in the user's device settings.

[0264] 2. Working Example

[0265] 2.1 Hardware and software used

[0266] Smartphone:

[0267] Use an iOS or ANDROID device.

[0268] server:

[0269] Use Flask to manage all data collection, analytics, notifications, and procedures cross-platform.

[0270] Generative AI models:

[0271] Build using scikit-learn or TensorFlow / Keras.

[0272] 2.2 Example of operation

[0273] User Registration and Data Collection:

[0274] Users access the service and enter their name, age, gender, occupation, and contact information on the new registration page, then answer a questionnaire about lifestyle information such as past risks, behavioral patterns, and insurance needs.

[0275] Risk Prediction:

[0276] The server passes the collected data to a generative AI model that predicts future risks specific to the user.

[0277] Insurance plan suggestions:

[0278] Based on the risk assessment results, the most suitable insurance plan is generated and notified to the user.

[0279] Security Measures:

[0280] Cybersecurity risks are predicted, optimal countermeasure plans are generated, and automatically applied to users' devices.

[0281] 2.3 Prompt Sentence Examples

[0282] Example prompt sentence:

[0283] User information: Name = Yamada Taro, Age = 30, Gender = Male, Occupation = IT engineer, Past risks = None, Behavior pattern = Frequent online shopping, Need for insurance = High

[0284] By using these system components, users can consistently and efficiently carry out insurance-related procedures and cybersecurity measures, enabling comprehensive risk management.

[0285] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0286] Step 1:

[0287] The user accesses the new registration page using a terminal, enters basic information (name, age, gender, occupation, contact details), and presses the submit button. The entered data is sent from the terminal to the server and stored in the database.

[0288] Input: User's basic information (name, age, gender, occupation, contact information)

[0289] Processing: The user enters information and sends the data from the device to the server

[0290] Output: Basic information saved in the database

[0291] Step 2:

[0292] The server displays a questionnaire to the user to collect lifestyle information (past risks, behavioral patterns, need for insurance). The user answers the questionnaire, and the device sends the response data to the server. The server stores the response data in a database.

[0293] Input: User's lifestyle information (past risks, behavioral patterns, insurance needs)

[0294] Processing: The user answers the survey and sends the data from the device to the server.

[0295] Output: Lifestyle information saved in database

[0296] Step 3:

[0297] The server passes basic and lifestyle information to a generative AI model to predict future risks. The generative AI model performs risk assessment based on past data and behavioral patterns and generates a risk score.

[0298] Input: Basic information and lifestyle information stored in the database

[0299] Processing: Generative AI models analyze data and predict future risks

[0300] Output: Risk score per user

[0301] Step 4:

[0302] The server generates an optimal insurance plan using a generative AI model based on the predicted risks. The generated plan information is sent from the server to the device and displayed visually to the user.

[0303] Input: Risk Score

[0304] Processing: Generative AI model generates optimal insurance plan

[0305] Output: Insurance plan information is sent to the user's device

[0306] Step 5:

[0307] The user enters a question about the proposed insurance plan, and the device sends the question to the server, which uses generative AI to process natural language and generate an appropriate answer, which is then sent to the device.

[0308] Input: User question

[0309] Processing: The generative AI model processes natural language and generates an answer

[0310] Output: The answer is displayed on the terminal.

[0311] Step 6:

[0312] The user indicates their intention to sign up for an insurance plan and clicks the sign-up button on their device. The server receives this, generates a form for the user to enter additional information, and displays it on the user's device. The user enters the required information, which the device sends to the server. The server verifies the data and automatically contacts the insurance company.

[0313] Input: Intention to join and additional information

[0314] Processing: The server automatically sends the data to the insurance company

[0315] Output: A notification of successful enrollment is displayed on the user's device.

[0316] Step 7:

[0317] When a user files a claim after taking out insurance, they input the claim details and send them from their device to the server, which then analyzes the claim using a generative AI model, determines the appropriate response procedure, and responds in cooperation with the insurance company's system.

[0318] Input: User's claim details

[0319] Processing: The generative AI model analyzes the complaint and generates a response procedure.

[0320] Output: Progress notifications are displayed on the user's device

[0321] Step 8:

[0322] The server collects user behavior data and uses generative AI models to predict cybersecurity risks. Based on the predicted risks, it generates an optimal security countermeasure plan and automatically applies it to the user's device settings.

[0323] Input: User behavior data

[0324] Processing: Generative AI models predict cybersecurity risks and generate countermeasure plans

[0325] Output: The countermeasure plan is automatically reflected in the user's device settings.

[0326] 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.

[0327] This invention improves the user experience by combining a system that collects and analyzes a user's basic and lifestyle information, predicts future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service with an emotion engine that recognizes the user's emotions.

[0328] User registration and data collection

[0329] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[0330] Data analysis and risk prediction

[0331] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[0332] Incorporating an emotion engine

[0333] The emotion engine recognizes the user's emotions and reflects them when proposing insurance plans and answering questions. For example, by collecting emotional data when a user participates in a sporting event and analyzing it, the generative AI can add coverage to the proposed plan that not only addresses the risk of injury during sports, but also compensates for the user's mental peace of mind.

[0334] Insurance plan proposals

[0335] The generation AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The server then imports the generated insurance plan data and sends a proposal notification to the user. The device displays detailed insurance plan information to the user, allowing the user to visually compare multiple proposals.

[0336] Insurance Questions and Answers

[0337] A user has a question about a proposed plan and types a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." Furthermore, the emotion engine analyzes the emotions associated with the user's question and provides supplementary information to increase reassurance. The server sends the answer to the device, which displays it to the user.

[0338] Insurance enrollment procedures

[0339] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0340] Complaint handling and after-sales service

[0341] If a user wishes to submit a claim after taking out insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the content of the claim and determines the appropriate processing procedure, and the server works in conjunction with the insurance company's system to handle the claim. Furthermore, an emotion engine analyzes the user's emotions at the time of submitting the claim and derives the appropriate response method. The server notifies the user and the insurance company of the response progress, and the device displays the progress to the user in real time.

[0342] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. In particular, by incorporating an emotion engine, it is possible to provide services that are in tune with users' emotions, which is expected to improve satisfaction.

[0343] The processing flow will be explained below.

[0344] Step 1:

[0345] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[0346] Step 2:

[0347] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[0348] Step 3:

[0349] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[0350] Step 4:

[0351] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[0352] Step 5:

[0353] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[0354] Step 6:

[0355] Generative AI generates optimal insurance plans based on predicted risks. For example, it automatically generates plans that include injury compensation specifically for sports enthusiasts. The emotion engine analyzes the user's emotions and reflects them in the risk assessment results.

[0356] Step 7:

[0357] The server retrieves the generated insurance plan data and sends a proposal notification to the user. The device displays the proposal notification to the user and provides a screen where detailed insurance plan information can be viewed. The user visually compares multiple proposals.

[0358] Step 8:

[0359] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[0360] Step 9:

[0361] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer. The emotion engine analyzes the user's emotions and provides supplementary information to increase reassurance. The answer is returned to the server.

[0362] Step 10:

[0363] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[0364] Step 11:

[0365] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[0366] Step 12:

[0367] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[0368] Step 13:

[0369] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[0370] Step 14:

[0371] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[0372] Step 15:

[0373] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[0374] Step 16:

[0375] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure. The emotion engine analyzes the user's emotions and optimizes the response method.

[0376] Step 17:

[0377] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[0378] Step 18:

[0379] The device displays the progress and results of the complaint handling to the user in real time. The emotion engine appropriately analyzes the user's emotional changes and provides feedback as the response results.

[0380] Example 2

[0381] 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."

[0382] In modern insurance services, it takes a great deal of time and effort for users to select the most suitable insurance plan, complete the enrollment process, and then smoothly handle claims and after-sales service after enrollment. Furthermore, there is a lack of services that take user feelings into consideration, and it is necessary to improve the quality of the user experience. The purpose of this invention is to solve these issues and provide users with efficient and personalized insurance services.

[0383] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for adjusting insurance proposals and answers based on the user's emotional state, including an emotion engine that recognizes and analyzes the user's emotional state. This enables the user to efficiently and individually select an insurance plan, apply for insurance, and handle claims, and further realizes the provision of emotionally sensitive services.

[0384] "Basic information" refers to personal data such as the user's name, age, gender, occupation, and contact details.

[0385] "Lifestyle information" refers to data related to a user's lifestyle and behavioral history, such as past risks, behavioral patterns, and insurance enrollment history.

[0386] The "data analysis engine" is a software system that analyzes collected basic and lifestyle information and predicts future risks.

[0387] "Generative AI" is an artificial intelligence model that predicts future risks and generates optimal insurance plans for users based on those predictions.

[0388] An "emotion engine" is a software system that recognizes and analyzes a user's emotional state and has the ability to adjust insurance proposals and responses based on that emotional state.

[0389] "Insurance Plan" means a proposal containing the specific insurance coverage and terms offered to a User.

[0390] "Notification" refers to the transmission of information from the server to the user, and is done via email, in-app notifications, etc.

[0391] "Natural language processing" is a technology for processing collected data and user questions in natural language to understand and generate responses.

[0392] "Automation" refers to the autonomous processing of processes by systems or machines, rather than by manual means.

[0393] "Complaint handling" refers to the overall process of providing solutions to and responding to user complaints and dissatisfaction.

[0394] "After-sales service" refers to additional support and services provided after insurance is purchased.

[0395] This invention is a system for efficiently selecting insurance plans, enrolling procedures, and handling claims. The system consists of three main components: a server, a terminal, and a user. This system collects basic information and lifestyle information from users, and then uses a generative AI model to propose optimal insurance plans based on that information. Furthermore, it uses an emotion engine to provide services based on the user's emotional state.

[0396] User registration and data collection

[0397] The user accesses the new registration page using a device. The device displays a basic information form (name, age, gender, occupation, contact information), and the user enters the information. After entering the information, the device sends this basic information to the server, which stores the received information in a database. Next, the server generates a questionnaire to collect lifestyle information and displays it on the device. The user answers the questionnaire, and the device sends the response data to the server. The server also stores this information in a database.

[0398] Data analysis and risk prediction

[0399] The server provides the user's basic information and lifestyle information to a data analysis engine. The generative AI model uses this information to predict future risks. A predictive algorithm based on past risks, behavioral patterns, and insurance history is used to perform a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to be at high risk of sports-related injuries.

[0400] Incorporating an emotion engine

[0401] The emotion engine recognizes the user's emotional state and reflects it when proposing insurance plans and answering questions. For example, if emotional data is collected when a user participates in a sporting event, the emotion engine analyzes that data and the generative AI model adds coverage to the proposed plan that not only considers injury risk but also compensates for mental peace of mind.

[0402] Insurance plan proposals

[0403] The generative AI model takes into account the predicted risks and generates an optimal insurance plan. For example, it automatically generates a plan that includes injury coverage specifically for sports enthusiasts. The server imports the generated insurance plan data and sends a notification to the user. The device displays detailed insurance plan information to the user, allowing the user to visually compare multiple proposals.

[0404] Insurance Questions and Answers

[0405] A user may have a question about a proposed plan, such as, "How much does this insurance plan cover?" The device sends the question to a server, which passes it on to a generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." Furthermore, an emotion engine analyzes the emotions associated with the user's question and provides supplementary information to increase reassurance.

[0406] Insurance enrollment procedures

[0407] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0408] Complaint handling and after-sales service

[0409] If a user wishes to submit a claim after taking out insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model. The generative AI model analyzes the content of the claim and determines the appropriate processing procedure. The server connects with the insurance company's system to handle the claim. The emotion engine analyzes the user's emotions at the time of submitting the claim and derives the appropriate response method. The server notifies the user of the response progress, and the device displays the progress to the user in real time.

[0410] For example, a user might input, "What insurance plan do you offer for injuries sustained while participating in a sporting event?" The generative AI model responds, "This insurance plan includes compensation of up to 1 million yen for injuries sustained while participating in a sporting event," and the emotion engine adds a supplementary explanation, "In addition, counseling services by specialist doctors are also provided so that you can enjoy sports with peace of mind."

[0411] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0412] Step 1: Start user registration

[0413] The user accesses the new registration page using a device, and a form for entering name, age, gender, occupation, and contact information is displayed on the device.

[0414] (Input) The user inputs basic information such as name "Yamada Taro", age "30", gender "male", occupation "company employee", and contact information "example@example.com".

[0415] (Output) The terminal sends this information to the server.

[0416] Step 2: Receive and store basic information

[0417] The server receives the basic information sent from the terminal.

[0418] (Input) Basic user information sent from the device

[0419] (Output) The received information is saved in the database. Specifically, it is recorded in the database as "ID: 12345, Name: Yamada Taro, Age: 30, Gender: Male, Occupation: Company employee, Contact: example@example.com".

[0420] Step 3: Displaying a questionnaire to collect lifestyle information

[0421] The server generates a questionnaire for collecting lifestyle information and displays it on the user's terminal.

[0422] (Input) User registration ID

[0423] (Output) A questionnaire form will be displayed on the device, including questions such as "past health risks," "behavioral patterns," and "current need for insurance."

[0424] Step 4: Enter your survey answers

[0425] The user answers the displayed questionnaire. Specifically, the user answers "Yes" to the question "Do you exercise?" and "Have you ever been in a serious accident?"

[0426] (Input) Questionnaire responses as user lifestyle information

[0427] (Output) The terminal sends the response data to the server.

[0428] Step 5: Receive and store lifestyle information

[0429] The server receives the user's questionnaire response data and stores it in a database.

[0430] (Input) Survey response data sent from the device

[0431] (Output) This will be recorded in the database as "ID: 12345, Exercise: Yes, Serious Accident: No, Insurance Need: High."

[0432] Step 6: Perform data analysis

[0433] The server provides the user's basic information and lifestyle information to the data analysis engine.

[0434] (Input) Basic information and lifestyle information

[0435] (Output) The data analysis engine uses this information to predict future risks, and the generative AI model outputs an assessment such as "Sports event participation frequency: High, injury risk: High."

[0436] Step 7: Collect and analyze emotion data

[0437] The emotion engine collects and analyzes the user's emotion data.

[0438] (Input) Data related to emotions, such as the user's facial expression and tone of voice

[0439] (Output) The emotion engine analyzes the user's current emotional state and outputs analysis results such as "feeling relieved" or "feeling anxious."

[0440] Step 8: Generate your insurance plan

[0441] The generative AI model generates the optimal insurance plan based on the results of risk assessment and sentiment analysis.

[0442] (Input) Risk assessment results and emotion analysis results

[0443] (Output) Insurance plan data is generated. For example, it is created as an "injury compensation plan for sports enthusiasts."

[0444] Step 9: Insurance Plan Notification

[0445] The server retrieves the generated insurance plan data and sends a proposal notification to the user.

[0446] (Input) Generated insurance plan data

[0447] (Output) The user is notified with the "Injury Compensation Plan for Sports Enthusiasts."

[0448] Step 10: View plan details

[0449] The device displays detailed insurance plan information to the user and allows them to visually compare multiple offers.

[0450] (Input) Plan notification from the server

[0451] (Output) Details of the "Injury Compensation Plan" and "Additional Mental Support Plan" will be displayed on the terminal.

[0452] Step 11: Enter and submit your question

[0453] The user has a question about the proposed plan and types in a question: "How much does this insurance plan cover?" and clicks the submit button.

[0454] (Input) Question content

[0455] (Output) The terminal sends the query data to the server.

[0456] Step 12: Analyze question data and generate answers

[0457] The server passes the question data to the generative AI model, which analyzes the question through natural language processing and generates an answer.

[0458] (Input) User question data

[0459] (Output) The generative AI model generates an answer such as, "This plan includes injury compensation up to 1 million yen."

[0460] Step 13: Providing answers and reflecting

[0461] The server receives the generated answer and passes it to the emotion engine, which analyzes the emotion associated with the user's question and adds supplementary information to the answer.

[0462] (Input) Generated answers and user emotion data

[0463] (Output) An answer is generated that adds supplementary information such as, "In addition, counseling services by specialist doctors will be provided so that you can enjoy sports with peace of mind."

[0464] Step 14: Notification of Response

[0465] The server sends a response including supplemental information to the terminal.

[0466] (Input) Answer data including the analysis results of the emotion engine

[0467] (Output) The answer is displayed to the user on the terminal.

[0468] Step 15: Indicate your intention to take out insurance

[0469] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button.

[0470] (Input) Click the Join button

[0471] (Output) The server is notified of the intention to join.

[0472] Step 16: Enter additional information

[0473] The server generates an input form for the required additional information (health check results, past medical history, etc.) and displays it on the user's terminal.

[0474] (Input) Notice of intention to join

[0475] (Output) A form for entering health checkup results, etc., is displayed to the user.

[0476] Step 17: Submit and confirm additional information

[0477] The user inputs the necessary health checkup results and medical history, and the device sends it to the server. The server checks all the input data and automatically connects it to the insurance company's system.

[0478] (Input) Additional information sent from the terminal

[0479] (Output) The data is linked to the insurance company.

[0480] Step 18: Notification of enrollment completion

[0481] The server generates a notification of completion of the subscription procedure and sends it to the user's terminal.

[0482] (Input) Notification of completion of linking to the insurance company's system

[0483] (Output) A confirmation notice of the completion of the subscription will be displayed on the user's device.

[0484] Step 19: Enter your claim submission

[0485] The user fills in the details in the complaint submission form and clicks the submit button.

[0486] (Input) Complaint details

[0487] (Output) The terminal sends the complaint information to the server.

[0488] Step 20: Analyze complaint information and proceed with response

[0489] The server sends the claim information to the generative AI model, which analyzes the claim content and determines the appropriate processing procedure. The server then works with the insurance company to proceed with the response.

[0490] (Input) Claim information

[0491] (Output) Specific response procedures are generated and shared with the insurance company.

[0492] Step 21: Sentiment analysis and appropriate response

[0493] The emotion engine analyzes the user's emotions when submitting a complaint and determines the appropriate response method.

[0494] (Input) Emotion data at the time of complaint submission

[0495] (Output) A response method based on the emotion is provided to the server.

[0496] Step 22: Notification of progress

[0497] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[0498] (Input) Response progress information

[0499] (Output) Progress is displayed in real time on the user's device.

[0500] This system allows users to smoothly select insurance plans, apply for insurance, and handle claims. In particular, by incorporating an emotion engine, it is possible to provide services that are sensitive to the user's emotions, which is expected to improve user satisfaction.

[0501] (Application example 2)

[0502] 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."

[0503] Conventional insurance systems were able to predict risks and propose insurance plans based on a user's basic information and lifestyle information. However, they were unable to take into account the user's emotions and psychological state, and the proposed insurance plans did not necessarily increase user satisfaction. This has led to a demand for improved user experience and services that are more sensitive to emotions. Furthermore, understanding the user's emotional state and providing a sense of security and stress reduction has been a challenge for electronic payment services.

[0504] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0505] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for recognizing and analyzing the user's emotions, means for reflecting the analyzed emotion data in insurance plan proposals and question answers, and means for analyzing the user's emotional state using an emotion engine in an electronic payment service to provide a sense of security and stress reduction. This enables the proposal of insurance plans that take the user's emotions into consideration and an electronic payment service that provides a sense of security.

[0506] "Basic User Information" means personal identifying information such as your name, age, gender, occupation, and contact information.

[0507] "Lifestyle information" refers to information about a user's lifestyle habits, such as past risks, behavioral patterns, and past insurance enrollment history.

[0508] "Analyzing data" means analyzing collected basic information and lifestyle information of users using statistical methods and algorithms.

[0509] "Predicting future risks" means predicting dangers and problems that users may face in the future based on analyzed data.

[0510] "Generating an insurance plan" means constructing optimal insurance contract terms for a user based on predicted risks.

[0511] "Inform insurance plan" means to communicate details of the generated insurance plan to the user.

[0512] "Natural language processing" is a technology that enables computers to understand and generate natural language.

[0513] "Automating the insurance enrollment process" means mechanically carrying out a series of processes for users to enroll in insurance.

[0514] "Claims Handling" means handling complaints or claims submitted by Users regarding their insurance.

[0515] "Automating after-sales service" means mechanically providing the services and support provided after insurance is purchased.

[0516] "Emotion recognition" means identifying a user's emotional state from facial expressions, voice, text, etc.

[0517] "Analyzing emotional data" means performing a more detailed analysis based on the recognized emotional information.

[0518] "Electronic payment service" means a service for making and receiving payments using digital technology.

[0519] An "emotion engine" is software or a system that analyzes a user's emotional state and generates a response based on that.

[0520] "Providing a sense of security" means enabling users to use the service with peace of mind.

[0521] "Providing stress relief" means reducing the mental burden on users while using the service.

[0522] The present invention improves the user experience by adding a function to recognize user emotions to a system that collects and analyzes basic information and lifestyle information of a user to predict risks and propose appropriate insurance plans. The embodiments of the present invention are described in detail below.

[0523] Hardware and software used

[0524] Hardware

[0525] Smartphone (iOS, Android)

[0526] Server (database, analysis engine)

[0527] software

[0528] Database management system (MySQL, PostgreSQL, etc.)

[0529] Generative AI models (such as OpenAI® GPT-3®)

[0530] Sentiment analysis engine (Emotion AI, Affectiva, etc.)

[0531] Mobile app development framework (React Native, Flutter (registered trademark), etc.)

[0532] Program processing overview

[0533] User registration and data collection

[0534] When a user accesses the service and enters the required information on the new registration page, the server collects the user's basic information (name, age, gender, occupation, contact details), then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.), and stores the user's response data in a database.

[0535] Data analysis and risk prediction

[0536] The server provides the collected user basic information and lifestyle information to the analysis engine, which then uses generative AI to predict future risks. During this process, analysis is performed based on past risks, behavioral patterns, and past insurance history. As a specific example of a specific user, a user who frequently participates in sporting events is predicted to have a high risk of sports-related injuries.

[0537] Incorporating an emotion engine

[0538] The emotion analysis engine recognizes the user's emotions and reflects that emotional data when proposing insurance plans and answering questions. For example, the AI ​​can collect emotional data when a user participates in a sporting event and use that data to add coverage that not only addresses injury risk but also provides the user with peace of mind.

[0539] Insurance plan proposals

[0540] The AI ​​then takes into account the predicted risks and generates the optimal insurance plan for the user. The generated insurance plan is stored on a server and sent to the user's device. The user can then compare multiple insurance plans on their smartphone screen and select the most suitable one.

[0541] Insurance Questions and Answers

[0542] When a user has a question about the proposed insurance plan and asks, "How much does this insurance plan cover?", the device sends the question to the server, which then passes it on to the generation AI. The generation AI uses natural language processing to understand the question and generates an answer such as, "This plan includes injury compensation up to 1 million yen."

[0543] Insurance enrollment procedures

[0544] When the user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device, the server generates a form for entering the necessary additional information (such as health check results and past medical history) and automatically connects to the insurance company's system based on the information entered by the user.

[0545] Complaint handling and after-sales service

[0546] If a user wishes to file a claim after purchasing insurance, they enter details into a form on their device and click the submit button. The server receives the claim information, and the generation AI analyzes it to determine the appropriate processing procedure. Furthermore, the sentiment analysis engine analyzes the emotions expressed at the time of claim submission and derives the appropriate response method.

[0547] Specific examples

[0548] Prompt Sentence Examples

[0549] "Please tell me your name and age."

[0550] "Please tell us how you feel about your current insurance."

[0551] "How much coverage does this insurance plan provide?"

[0552] These prompts are used to gather necessary information through user interaction and perform sentiment analysis.

[0553] By using the above method, the present invention makes it possible to propose insurance plans that take into account the user's feelings and to provide an electronic payment service that provides a sense of security.

[0554] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0555] Step 1:

[0556] The user accesses the new registration page and enters the required information (name, age, gender, occupation, contact information).

[0557] Input: User's personal information

[0558] Data processing: Convert the entered personal information into JSON format and send it to the server.

[0559] Output: Basic information data in JSON format

[0560] Specific behavior: The device receives user input and sends it to the server.

[0561] Step 2:

[0562] The server stores the received basic information in a database, and then displays a questionnaire form to the user to collect lifestyle information.

[0563] Input: Basic information data in JSON format

[0564] Data operations: write operations to the database

[0565] Output: Questionnaire form for collecting lifestyle information

[0566] Specific operation: The server writes the data to the database and sends the questionnaire form to the terminal.

[0567] Step 3:

[0568] Users fill out a questionnaire form and enter lifestyle information.

[0569] Input: Lifestyle information (past risks, behavioral patterns, insurance needs, etc.)

[0570] Data processing: The input lifestyle information is converted into JSON format and sent to the server.

[0571] Output: Lifestyle information data in JSON format

[0572] Specific behavior: The device receives user input and sends it to the server.

[0573] Step 4:

[0574] The server stores the received lifestyle information in a database and provides it to a data analysis engine.

[0575] Input: Lifestyle information data in JSON format

[0576] Data calculation: Write operations to the database and provide data to the analysis engine

[0577] Output: Risk assessment by analytical engine

[0578] Specific operation: The server writes data to the database and provides the data to the analysis engine.

[0579] Step 5:

[0580] Generative AI models are used to predict future risks and generate insurance plans.

[0581] Input: Basic and lifestyle information

[0582] Data Computation: Generative AI models for risk prediction and insurance plan generation

[0583] Output: Proposed insurance plan

[0584] How it works: The analytical engine analyzes the data using generative AI models to generate risk assessments and insurance plans.

[0585] Step 6:

[0586] The server notifies the user of the generated insurance plan.

[0587] Input: Proposed insurance plan

[0588] Data processing: generating notification messages

[0589] Output: A notification message to the user

[0590] Specific operation: The server converts the insurance plan into a notification message and sends it to the device.

[0591] Step 7:

[0592] The user enters a question about their insurance plan.

[0593] Input: User question

[0594] Data processing: Send the question to a natural language processing engine

[0595] Output: Analysis result of question content

[0596] Specific operation: The device receives the user's question and sends it to the server.

[0597] Step 8:

[0598] The server uses a generative AI model to generate answers to questions and provides them to the user, and also uses an emotion engine to reflect the user's emotional data.

[0599] Input: User question and emotion data

[0600] Data calculation: Analysis and answer generation using generative AI models and emotion engines

[0601] Output: Answers to questions and additional information based on sentiment

[0602] Specific operation: The server uses the generative AI model and emotion engine to answer the question and send it to the device.

[0603] Step 9:

[0604] The user indicates their intention to subscribe to an insurance plan, and the server collects any additional information required and contacts the insurance company.

[0605] Input: User's intention to join and additional information (health check results, past medical history, etc.)

[0606] Data processing: collecting additional information and sending the data to the insurance company

[0607] Output: Joining completion notification

[0608] Specific operation: The terminal receives the user's input, the server connects the data to the insurance company, and sends a notification of enrollment completion.

[0609] Step 10:

[0610] When a user files a claim after taking out insurance, the server uses the generated AI model to analyze the content of the claim and notify the insurance company of the appropriate response procedures.

[0611] Input: Complaint information and emotion data

[0612] Data processing: Analyzing complaints and determining appropriate response procedures

[0613] Output: Notification of response procedures to insurance company and user

[0614] Specific operation: The server analyzes the claim using the generative AI model and emotion engine, coordinates response procedures with the insurance company, and notifies the user of progress.

[0615] 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.

[0616] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0617] 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.

[0618] [Second embodiment]

[0619] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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."

[0631] This invention provides a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[0632] User registration and data collection

[0633] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[0634] Data analysis and risk prediction

[0635] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[0636] Insurance plan proposals

[0637] The generative AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[0638] Insurance Questions and Answers

[0639] The user has a question about the proposed plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing, generates an answer such as, "This plan includes injury compensation up to 1 million yen," and sends it to the device via the server. The device then displays the answer to the user.

[0640] Insurance enrollment procedures

[0641] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0642] Complaint handling and after-sales service

[0643] If the user needs to submit a claim after taking out insurance, they enter the details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the claim content and determines the appropriate processing procedure, and the server works with the insurance company's system to handle the claim. The server notifies the user and the insurance company of the progress of the claim, and the device displays the progress to the user in real time.

[0644] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly in the area of ​​insurance selection and processing.

[0645] The processing flow will be explained below.

[0646] Step 1:

[0647] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[0648] Step 2:

[0649] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[0650] Step 3:

[0651] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[0652] Step 4:

[0653] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[0654] Step 5:

[0655] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[0656] Step 6:

[0657] The generation AI generates the optimal insurance plan based on the predicted risks. The server imports the generated insurance plan data and sends a proposal notification to the user.

[0658] Step 7:

[0659] The device displays a proposal notification to the user and provides a screen where the user can view detailed insurance plan information. The user can visually compare multiple proposals.

[0660] Step 8:

[0661] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[0662] Step 9:

[0663] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer, which is then returned to the server.

[0664] Step 10:

[0665] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[0666] Step 11:

[0667] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[0668] Step 12:

[0669] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[0670] Step 13:

[0671] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[0672] Step 14:

[0673] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[0674] Step 15:

[0675] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[0676] Step 16:

[0677] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure.

[0678] Step 17:

[0679] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[0680] Step 18:

[0681] The terminal displays the progress and results of the complaint handling to the user in real time.

[0682] Example 1

[0683] 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."

[0684] Conventional insurance systems make it difficult for users to understand their own risks and select the most suitable insurance plan. They also face issues with responding to questions about individual insurance plans, enrolling in insurance, and processing claims, all of which are time-consuming. There is a need to streamline these processes and reduce the burden on users.

[0685] 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.

[0686] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers through natural language processing using a generative AI model, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for the user to visually compare insurance plans. This enables the user to efficiently select an insurance plan, respond to questions, apply for insurance, and even handle claims.

[0687] "Basic User Information" refers to basic information about you, such as your name, age, gender, occupation, and contact information.

[0688] "Lifestyle information" refers to information about a user's lifestyle, such as the user's past risks, behavioral patterns, and past insurance history.

[0689] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze data and process natural language to automate various tasks.

[0690] "Natural language processing" refers to the technology of processing and understanding human language using computers, and includes the process of generating answers to questions using generative AI models.

[0691] "Insurance Plan" refers to a plan that specifically describes the type and content of insurance that a User will subscribe to, and includes a proposal for the most appropriate insurance in response to predicted risks.

[0692] "Claims handling" refers to the process of appropriately responding to claims and problems related to insurance that arise from users after they have taken out insurance.

[0693] "After-sales service" refers to the provision of services and support to users after they have taken out insurance, and includes general user care, including handling claims.

[0694] "Visual comparison tools" refers to features that provide a visual interface that allows users to easily understand and compare insurance plans and other options.

[0695] This invention is a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[0696] User registration and data collection examples

[0697] Users access the service using a web browser or a dedicated app, and enter their name, age, gender, occupation, and contact information on the new registration page. The device collects the entered basic information in a form and sends it to the server. The server stores the received information in a database such as MySQL. The server then displays a questionnaire form on the user's device to collect lifestyle information. The user answers the questionnaire, and the device sends the response data to the server. The server stores this information in a database.

[0698] Examples of data analysis and risk prediction

[0699] The server provides the user's basic information and lifestyle information to a data analysis engine (e.g., TensorFlow), which then uses this information to generate a generative AI model that predicts future risks. The generative AI model considers past risks, behavioral patterns, and insurance history, and applies a predictive algorithm to generate a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to be at high risk of sports-related injuries.

[0700] Example of an insurance plan proposal

[0701] The generative AI model takes predicted risks into account and generates the optimal insurance plan. Specifically, it automatically generates a plan that includes injury compensation for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[0702] Insurance Questions and Answers Example

[0703] The user has a question about the proposed insurance plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." The server then sends the answer to the device, which displays it to the user.

[0704] An example of insurance enrollment procedures

[0705] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for any additional information required (e.g., health check results, past medical history), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0706] Examples of complaint handling and after-sales service

[0707] If a user wishes to submit a claim after purchasing insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model, which analyzes the claim content and determines the appropriate processing steps. The server works in conjunction with the insurance company's system to handle the claim and notifies the user's device of the progress of the claim in real time.

[0708] Prompt Sentence Examples

[0709] Insurance plan question examples:

[0710] User: How much coverage does this insurance plan provide?

[0711] Generative AI model: This plan includes injury compensation up to 1 million yen.

[0712] Example of handling a complaint:

[0713] User: I want to file a claim, how do I do that?

[0714] Generative AI Model: Please fill in the details in the claim submission form and submit it.

[0715] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly by streamlining insurance selection and procedures.

[0716] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0717] Step 1:

[0718] The user accesses the service and enters the required information on the new registration page.

[0719] Specific operation: The user accesses the service using a web browser or a dedicated app and enters basic information such as name, age, gender, occupation, and contact information.

[0720] Input: User basic information

[0721] Output: The basic information entered is sent from the terminal to the server.

[0722] Step 2:

[0723] The terminal prompts the user to enter basic information into a form and presses the submit button.

[0724] What happens: When the user clicks the submit button, an HTTP POST request is sent to the server via the HTML form.

[0725] Input: Basic information input data

[0726] Output: Sent to the server as an HTTP POST request

[0727] Step 3:

[0728] The server stores the submitted information in a database and then displays a questionnaire to gather the user's lifestyle information.

[0729] Specific operation: The server stores the received basic information in a database such as MySQL, and then sends the survey form to the user's device.

[0730] Input: Received basic information, questionnaire form template

[0731] Output: Basic information is saved in the database and the survey form is displayed on the user's device.

[0732] Step 4:

[0733] The user answers the questionnaire, and the terminal transmits the answer data to the server.

[0734] Specific operation: The user enters the necessary information into the questionnaire form and presses the send button, which sends the response data from the terminal to the server.

[0735] Input: Survey response data

[0736] Output: Answer data is sent from the device to the server

[0737] Step 5:

[0738] The server receives this information and stores it in a database.

[0739] Specific operation: The server stores the received survey response data in a database.

[0740] Input: Survey response data

[0741] Output: Response data is saved in the database

[0742] Step 6:

[0743] The server provides the user's basic information and lifestyle information to the data analysis engine, and the generative AI uses this information to predict future risks.

[0744] How it works: The server retrieves user data from the database and passes it to a data analysis engine such as TensorFlow. A generative AI model uses this data to predict risk.

[0745] Input: User's basic information and lifestyle information

[0746] Output: Risk assessment results

[0747] Step 7:

[0748] Generative AI generates insurance plans tailored to specific risks.

[0749] What it does: It applies predictive algorithms to generate personalized insurance plans, such as plans that include injury coverage for sports enthusiasts.

[0750] Input: Risk assessment results

[0751] Output: Generated insurance plan

[0752] Step 8:

[0753] The server notifies the user of the generated insurance plan.

[0754] Specific behavior: The generated insurance plan is sent to the user as JSON data.

[0755] Input: Generated insurance plan data

[0756] Output: Insurance plan details are sent to the user's device

[0757] Step 9:

[0758] The device will display plan details to the user and allow them to compare them visually.

[0759] Specific operation: The device receives insurance plan information and displays it on the screen using HTML and CSS. It displays it in a table format so that the user can compare multiple plans.

[0760] Input: Insurance plan details

[0761] Output: A list of insurance plans displayed to the user

[0762] Step 10:

[0763] The user enters a question about the insurance plan.

[0764] What happens: A user types a question into a terminal, such as "How much does this insurance plan cover?"

[0765] Input: User question

[0766] Output: The entered question data is sent to the server.

[0767] Step 11:

[0768] The device sends the question to the server, which passes it on to the generating AI.

[0769] Specific operation: A question sent from a device reaches the server, which then passes the question to the generative AI model.

[0770] Input: User question data

[0771] Output: Question data is sent to the generation AI

[0772] Step 12:

[0773] The generation AI generates an answer and sends it to the device via the server.

[0774] Specific operation: Using natural language processing, the generative AI generates an appropriate answer and sends it to the device via the server.

[0775] Input: User question

[0776] Output: The generated answer is displayed in the terminal.

[0777] Step 13:

[0778] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button on the terminal.

[0779] Specific operation: The user clicks the subscription button on the terminal and sends the intention to subscribe to the server.

[0780] Input: User's intention to join

[0781] Output: Willingness to join data is sent to the server

[0782] Step 14:

[0783] The server generates an input form for additional information and sends it to the user's terminal.

[0784] Specific operation: The server generates an input form for the required additional information (e.g., health check results, past medical history) and displays it on the user's device.

[0785] Input: Intention to join data

[0786] Output: A form for entering additional information is displayed on the user's device.

[0787] Step 15:

[0788] The user enters additional information, which the terminal sends to the server.

[0789] Specific operation: The user enters additional information and the device sends the data to the server.

[0790] Input: Additional Information

[0791] Output: Additional information data is sent to the server

[0792] Step 16:

[0793] The server checks all input data and automatically connects to the insurance company's system.

[0794] Specific operation: The server verifies the input data and sends it to the insurance company's system via API.

[0795] Input: All input data

[0796] Output: Data is linked to the insurance company's system

[0797] Step 17:

[0798] A subscription completion notice is generated and sent to the user's terminal.

[0799] Specific operation: The server generates a notification that the subscription procedure has been completed and sends it to the user's device.

[0800] Input: Confirmation of enrollment completion from insurance company

[0801] Output: A notification of successful enrollment is displayed on the user's device.

[0802] Step 18:

[0803] The user submits a claim and the terminal transmits the claim information to the server.

[0804] Specific operation: The user enters detailed information into the complaint submission form and clicks the submit button. The terminal sends the complaint information to the server.

[0805] Input: Claim information

[0806] Output: Claim information is sent to the server

[0807] Step 19:

[0808] The server sends the claim information to the generation AI.

[0809] Specific operation: The server sends the complaint information to the generative AI model, which analyzes the content and determines the appropriate processing procedure.

[0810] Input: Claim information

[0811] Output: Analysis results and recommended processing steps

[0812] Step 20:

[0813] The server will work in conjunction with the insurance company's system to respond to the situation.

[0814] Specific operation: The server connects the analysis results of the generated AI and recommended processing procedures to the insurance company's system.

[0815] Input: Analysis results and recommended processing steps

[0816] Output: Processing steps linked to insurance company

[0817] Step 21:

[0818] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[0819] Specific operation: The server periodically checks the progress of the response and notifies the user and the insurance company. The terminal displays the received progress information to the user in real time.

[0820] Input: Response progress

[0821] Output: Progress is displayed in real time on the user's device.

[0822] (Application example 1)

[0823] 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."

[0824] Currently, the insurance industry requires a great deal of time and effort because the process of proposing insurance plans tailored to users' needs, procedures, and after-sales service is not fully automated. Furthermore, security risk predictions and countermeasures are carried out separately, making comprehensive risk management difficult. In particular, cybersecurity risks are not adequately addressed, often preventing users from taking appropriate measures.

[0825] 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.

[0826] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for collecting and analyzing basic information and behavioral data of the user and predicting cybersecurity risks, means for generating an optimal security measures plan based on the predicted cybersecurity risks, and means for automatically updating the user's device settings with the generated security measures plan. This allows the user to efficiently select insurance, apply for insurance, and even handle claims in a consistent manner, while also enabling comprehensive measures against cybersecurity risks.

[0827] "User's basic information and lifestyle information" refers to personal information about the user and data related to their daily behavioral patterns.

[0828] "Future risks" are predictions of problems and dangers that users may face in the future.

[0829] "Insurance Plan" means the set of insurance terms, conditions and coverages offered to a User for a particular risk.

[0830] "Natural language processing" is a technology that allows machines to understand natural human language and generate appropriate responses.

[0831] "Behavioral data" refers to data related to a user's daily activities, including, for example, internet usage history and movement history.

[0832] "Cybersecurity risk" refers to risks such as unauthorized access, information leaks, and system failures via the Internet or digital devices.

[0833] A "security plan" is a set of measures or actions to be taken against cybersecurity risks, including settings and how to use tools.

[0834] This invention relates to a system that collects basic information and lifestyle information of users, analyzes this data to predict future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service. Furthermore, this system also predicts cybersecurity risks and generates optimal security countermeasure plans, which are automatically reflected in the user's device settings.

[0835] 1. System Overview

[0836] The system consists of the following main components:

[0837] How we collect your basic and lifestyle information:

[0838] To achieve this, an interface is provided that allows users to use their smartphones or other devices to enter and submit the necessary information on a new registration page.

[0839] Ways to predict future risks:

[0840] Based on the data collected from users, the server uses a generative AI model to predict various risks.

[0841] To generate the best insurance plan:

[0842] Taking into account predicted risks, generative AI automatically generates the optimal insurance plan for the user.

[0843] Informing you of your insurance plan:

[0844] The generated plan information is sent from the server to the user's terminal, where detailed information is displayed.

[0845] Natural language processing answer generation methods:

[0846] When a user enters a question, the server uses generative AI to generate an answer corresponding to the question and sends it to the device.

[0847] Ways to automate the insurance enrollment process:

[0848] When a user selects the insurance plan they wish to subscribe to, the system automatically checks all necessary information and contacts the insurance company.

[0849] Automating claims handling and after-sales service:

[0850] When you submit a complaint, we collect and analyze that information and generate appropriate response procedures to process it.

[0851] Cybersecurity risk prediction tools:

[0852] Collect user behavior data and predict cybersecurity risks using generative AI models.

[0853] How to generate a security action plan:

[0854] Generate optimal countermeasure plans to address predicted cybersecurity risks.

[0855] How to automatically apply security measures:

[0856] The generated security plan is automatically reflected in the user's device settings.

[0857] 2. Working Example

[0858] 2.1 Hardware and software used

[0859] Smartphone:

[0860] Use an iOS or Android device.

[0861] server:

[0862] Use Flask to manage all data collection, analytics, notifications, and procedures cross-platform.

[0863] Generative AI models:

[0864] Build using scikit-learn or TensorFlow / Keras.

[0865] 2.2 Example of operation

[0866] User Registration and Data Collection:

[0867] Users access the service and enter their name, age, gender, occupation, and contact information on the new registration page, then answer a questionnaire about lifestyle information such as past risks, behavioral patterns, and insurance needs.

[0868] Risk Prediction:

[0869] The server passes the collected data to a generative AI model that predicts future risks specific to the user.

[0870] Insurance plan suggestions:

[0871] Based on the risk assessment results, the most suitable insurance plan is generated and notified to the user.

[0872] Security Measures:

[0873] Cybersecurity risks are predicted, optimal countermeasure plans are generated, and automatically applied to users' devices.

[0874] 2.3 Prompt Sentence Examples

[0875] Example prompt sentence:

[0876] User information: Name = Yamada Taro, Age = 30, Gender = Male, Occupation = IT engineer, Past risks = None, Behavior pattern = Frequent online shopping, Need for insurance = High

[0877] By using these system components, users can consistently and efficiently carry out insurance-related procedures and cybersecurity measures, enabling comprehensive risk management.

[0878] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0879] Step 1:

[0880] The user accesses the new registration page using a terminal, enters basic information (name, age, gender, occupation, contact details), and presses the submit button. The entered data is sent from the terminal to the server and stored in the database.

[0881] Input: User's basic information (name, age, gender, occupation, contact information)

[0882] Processing: The user enters information and sends the data from the device to the server

[0883] Output: Basic information saved in the database

[0884] Step 2:

[0885] The server displays a questionnaire to the user to collect lifestyle information (past risks, behavioral patterns, need for insurance). The user answers the questionnaire, and the device sends the response data to the server. The server stores the response data in a database.

[0886] Input: User's lifestyle information (past risks, behavioral patterns, insurance needs)

[0887] Processing: The user answers the survey and sends the data from the device to the server.

[0888] Output: Lifestyle information saved in database

[0889] Step 3:

[0890] The server passes basic and lifestyle information to a generative AI model to predict future risks. The generative AI model performs risk assessment based on past data and behavioral patterns and generates a risk score.

[0891] Input: Basic information and lifestyle information stored in the database

[0892] Processing: Generative AI models analyze data and predict future risks

[0893] Output: Risk score per user

[0894] Step 4:

[0895] The server generates an optimal insurance plan using a generative AI model based on the predicted risks. The generated plan information is sent from the server to the device and displayed visually to the user.

[0896] Input: Risk Score

[0897] Processing: Generative AI model generates optimal insurance plan

[0898] Output: Insurance plan information is sent to the user's device

[0899] Step 5:

[0900] The user enters a question about the proposed insurance plan, and the device sends the question to the server, which uses generative AI to process natural language and generate an appropriate answer, which is then sent to the device.

[0901] Input: User question

[0902] Processing: The generative AI model processes natural language and generates an answer

[0903] Output: The answer is displayed on the terminal.

[0904] Step 6:

[0905] The user indicates their intention to sign up for an insurance plan and clicks the sign-up button on their device. The server receives this, generates a form for the user to enter additional information, and displays it on the user's device. The user enters the required information, which the device sends to the server. The server verifies the data and automatically contacts the insurance company.

[0906] Input: Intention to join and additional information

[0907] Processing: The server automatically sends the data to the insurance company

[0908] Output: A notification of successful enrollment is displayed on the user's device.

[0909] Step 7:

[0910] When a user files a claim after taking out insurance, they input the claim details and send them from their device to the server, which then analyzes the claim using a generative AI model, determines the appropriate response procedure, and responds in cooperation with the insurance company's system.

[0911] Input: User's claim details

[0912] Processing: The generative AI model analyzes the complaint and generates a response procedure.

[0913] Output: Progress notifications are displayed on the user's device

[0914] Step 8:

[0915] The server collects user behavior data and uses generative AI models to predict cybersecurity risks. Based on the predicted risks, it generates an optimal security countermeasure plan and automatically applies it to the user's device settings.

[0916] Input: User behavior data

[0917] Processing: Generative AI models predict cybersecurity risks and generate countermeasure plans

[0918] Output: The countermeasure plan is automatically reflected in the user's device settings.

[0919] 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.

[0920] This invention improves the user experience by combining a system that collects and analyzes a user's basic and lifestyle information, predicts future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service with an emotion engine that recognizes the user's emotions.

[0921] User registration and data collection

[0922] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[0923] Data analysis and risk prediction

[0924] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[0925] Incorporating an emotion engine

[0926] The emotion engine recognizes the user's emotions and reflects them when proposing insurance plans and answering questions. For example, by collecting emotional data when a user participates in a sporting event and analyzing it, the generative AI can add coverage to the proposed plan that not only addresses the risk of injury during sports, but also compensates for the user's mental peace of mind.

[0927] Insurance plan proposals

[0928] The generation AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The server then imports the generated insurance plan data and sends a proposal notification to the user. The device displays detailed insurance plan information to the user, allowing the user to visually compare multiple proposals.

[0929] Insurance Questions and Answers

[0930] A user has a question about a proposed plan and types a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." Furthermore, the emotion engine analyzes the emotions associated with the user's question and provides supplementary information to increase reassurance. The server sends the answer to the device, which displays it to the user.

[0931] Insurance enrollment procedures

[0932] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[0933] Complaint handling and after-sales service

[0934] If a user wishes to submit a claim after taking out insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the content of the claim and determines the appropriate processing procedure, and the server works in conjunction with the insurance company's system to handle the claim. Furthermore, an emotion engine analyzes the user's emotions at the time of submitting the claim and derives the appropriate response method. The server notifies the user and the insurance company of the response progress, and the device displays the progress to the user in real time.

[0935] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. In particular, by incorporating an emotion engine, it is possible to provide services that are in tune with users' emotions, which is expected to improve satisfaction.

[0936] The processing flow will be explained below.

[0937] Step 1:

[0938] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[0939] Step 2:

[0940] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[0941] Step 3:

[0942] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[0943] Step 4:

[0944] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[0945] Step 5:

[0946] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[0947] Step 6:

[0948] Generative AI generates optimal insurance plans based on predicted risks. For example, it automatically generates plans that include injury compensation specifically for sports enthusiasts. The emotion engine analyzes the user's emotions and reflects them in the risk assessment results.

[0949] Step 7:

[0950] The server retrieves the generated insurance plan data and sends a proposal notification to the user. The device displays the proposal notification to the user and provides a screen where detailed insurance plan information can be viewed. The user visually compares multiple proposals.

[0951] Step 8:

[0952] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[0953] Step 9:

[0954] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer. The emotion engine analyzes the user's emotions and provides supplementary information to increase reassurance. The answer is returned to the server.

[0955] Step 10:

[0956] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[0957] Step 11:

[0958] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[0959] Step 12:

[0960] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[0961] Step 13:

[0962] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[0963] Step 14:

[0964] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[0965] Step 15:

[0966] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[0967] Step 16:

[0968] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure. The emotion engine analyzes the user's emotions and optimizes the response method.

[0969] Step 17:

[0970] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[0971] Step 18:

[0972] The device displays the progress and results of the complaint handling to the user in real time. The emotion engine appropriately analyzes the user's emotional changes and provides feedback as the response results.

[0973] Example 2

[0974] 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."

[0975] In modern insurance services, it takes a great deal of time and effort for users to select the most suitable insurance plan, complete the enrollment process, and then smoothly handle claims and after-sales service after enrollment. Furthermore, there is a lack of services that take user feelings into consideration, and it is necessary to improve the quality of the user experience. The purpose of this invention is to solve these issues and provide users with efficient and personalized insurance services.

[0976] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for adjusting insurance proposals and answers based on the user's emotional state, including an emotion engine that recognizes and analyzes the user's emotional state. This enables the user to efficiently and individually select an insurance plan, apply for insurance, and handle claims, and further realizes the provision of emotionally sensitive services.

[0977] "Basic information" refers to personal data such as the user's name, age, gender, occupation, and contact details.

[0978] "Lifestyle information" refers to data related to a user's lifestyle and behavioral history, such as past risks, behavioral patterns, and insurance enrollment history.

[0979] The "data analysis engine" is a software system that analyzes collected basic and lifestyle information and predicts future risks.

[0980] "Generative AI" is an artificial intelligence model that predicts future risks and generates optimal insurance plans for users based on those predictions.

[0981] An "emotion engine" is a software system that recognizes and analyzes a user's emotional state and has the ability to adjust insurance proposals and responses based on that emotional state.

[0982] "Insurance Plan" means a proposal containing the specific insurance coverage and terms offered to a User.

[0983] "Notification" refers to the transmission of information from the server to the user, and is done via email, in-app notifications, etc.

[0984] "Natural language processing" is a technology for processing collected data and user questions in natural language to understand and generate responses.

[0985] "Automation" refers to the autonomous processing of processes by systems or machines, rather than by manual means.

[0986] "Complaint handling" refers to the overall process of providing solutions to and responding to user complaints and dissatisfaction.

[0987] "After-sales service" refers to additional support and services provided after insurance is purchased.

[0988] This invention is a system for efficiently selecting insurance plans, enrolling procedures, and handling claims. The system consists of three main components: a server, a terminal, and a user. This system collects basic information and lifestyle information from users, and then uses a generative AI model to propose optimal insurance plans based on that information. Furthermore, it uses an emotion engine to provide services based on the user's emotional state.

[0989] User registration and data collection

[0990] The user accesses the new registration page using a device. The device displays a basic information form (name, age, gender, occupation, contact information), and the user enters the information. After entering the information, the device sends this basic information to the server, which stores the received information in a database. Next, the server generates a questionnaire to collect lifestyle information and displays it on the device. The user answers the questionnaire, and the device sends the response data to the server. The server also stores this information in a database.

[0991] Data analysis and risk prediction

[0992] The server provides the user's basic information and lifestyle information to a data analysis engine. The generative AI model uses this information to predict future risks. A predictive algorithm based on past risks, behavioral patterns, and insurance history is used to perform a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to be at high risk of sports-related injuries.

[0993] Incorporating an emotion engine

[0994] The emotion engine recognizes the user's emotional state and reflects it when proposing insurance plans and answering questions. For example, if emotional data is collected when a user participates in a sporting event, the emotion engine analyzes that data and the generative AI model adds coverage to the proposed plan that not only considers injury risk but also compensates for mental peace of mind.

[0995] Insurance plan proposals

[0996] The generative AI model takes into account the predicted risks and generates an optimal insurance plan. For example, it automatically generates a plan that includes injury coverage specifically for sports enthusiasts. The server imports the generated insurance plan data and sends a notification to the user. The device displays detailed insurance plan information to the user, allowing the user to visually compare multiple proposals.

[0997] Insurance Questions and Answers

[0998] A user may have a question about a proposed plan, such as, "How much does this insurance plan cover?" The device sends the question to a server, which passes it on to a generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." Furthermore, an emotion engine analyzes the emotions associated with the user's question and provides supplementary information to increase reassurance.

[0999] Insurance enrollment procedures

[1000] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1001] Complaint handling and after-sales service

[1002] If a user wishes to submit a claim after taking out insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model. The generative AI model analyzes the content of the claim and determines the appropriate processing procedure. The server connects with the insurance company's system to handle the claim. The emotion engine analyzes the user's emotions at the time of submitting the claim and derives the appropriate response method. The server notifies the user of the response progress, and the device displays the progress to the user in real time.

[1003] For example, a user might input, "What insurance plan do you offer for injuries sustained while participating in a sporting event?" The generative AI model responds, "This insurance plan includes compensation of up to 1 million yen for injuries sustained while participating in a sporting event," and the emotion engine adds a supplementary explanation, "In addition, counseling services by specialist doctors are also provided so that you can enjoy sports with peace of mind."

[1004] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1005] Step 1: Start user registration

[1006] The user accesses the new registration page using a device, and a form for entering name, age, gender, occupation, and contact information is displayed on the device.

[1007] (Input) The user inputs basic information such as name "Yamada Taro", age "30", gender "male", occupation "company employee", and contact information "example@example.com".

[1008] (Output) The terminal sends this information to the server.

[1009] Step 2: Receive and store basic information

[1010] The server receives the basic information sent from the terminal.

[1011] (Input) Basic user information sent from the device

[1012] (Output) The received information is saved in the database. Specifically, it is recorded in the database as "ID: 12345, Name: Yamada Taro, Age: 30, Gender: Male, Occupation: Company employee, Contact: example@example.com".

[1013] Step 3: Displaying a questionnaire to collect lifestyle information

[1014] The server generates a questionnaire for collecting lifestyle information and displays it on the user's terminal.

[1015] (Input) User registration ID

[1016] (Output) A questionnaire form will be displayed on the device, including questions such as "past health risks," "behavioral patterns," and "current need for insurance."

[1017] Step 4: Enter your survey answers

[1018] The user answers the displayed questionnaire. Specifically, the user answers "Yes" to the question "Do you exercise?" and "Have you ever been in a serious accident?"

[1019] (Input) Questionnaire responses as user lifestyle information

[1020] (Output) The terminal sends the response data to the server.

[1021] Step 5: Receive and store lifestyle information

[1022] The server receives the user's questionnaire response data and stores it in a database.

[1023] (Input) Survey response data sent from the device

[1024] (Output) This will be recorded in the database as "ID: 12345, Exercise: Yes, Serious Accident: No, Insurance Need: High."

[1025] Step 6: Perform data analysis

[1026] The server provides the user's basic information and lifestyle information to the data analysis engine.

[1027] (Input) Basic information and lifestyle information

[1028] (Output) The data analysis engine uses this information to predict future risks, and the generative AI model outputs an assessment such as "Sports event participation frequency: High, injury risk: High."

[1029] Step 7: Collect and analyze emotion data

[1030] The emotion engine collects and analyzes the user's emotion data.

[1031] (Input) Data related to emotions, such as the user's facial expression and tone of voice

[1032] (Output) The emotion engine analyzes the user's current emotional state and outputs analysis results such as "feeling relieved" or "feeling anxious."

[1033] Step 8: Generate your insurance plan

[1034] The generative AI model generates the optimal insurance plan based on the results of risk assessment and sentiment analysis.

[1035] (Input) Risk assessment results and emotion analysis results

[1036] (Output) Insurance plan data is generated. For example, it is created as an "injury compensation plan for sports enthusiasts."

[1037] Step 9: Insurance Plan Notification

[1038] The server retrieves the generated insurance plan data and sends a proposal notification to the user.

[1039] (Input) Generated insurance plan data

[1040] (Output) The user is notified with the "Injury Compensation Plan for Sports Enthusiasts."

[1041] Step 10: View plan details

[1042] The device displays detailed insurance plan information to the user and allows them to visually compare multiple offers.

[1043] (Input) Plan notification from the server

[1044] (Output) Details of the "Injury Compensation Plan" and "Additional Mental Support Plan" will be displayed on the terminal.

[1045] Step 11: Enter and submit your question

[1046] The user has a question about the proposed plan and types in a question: "How much does this insurance plan cover?" and clicks the submit button.

[1047] (Input) Question content

[1048] (Output) The terminal sends the query data to the server.

[1049] Step 12: Analyze question data and generate answers

[1050] The server passes the question data to the generative AI model, which analyzes the question through natural language processing and generates an answer.

[1051] (Input) User question data

[1052] (Output) The generative AI model generates an answer such as, "This plan includes injury compensation up to 1 million yen."

[1053] Step 13: Providing answers and reflecting

[1054] The server receives the generated answer and passes it to the emotion engine, which analyzes the emotion associated with the user's question and adds supplementary information to the answer.

[1055] (Input) Generated answers and user emotion data

[1056] (Output) An answer is generated that adds supplementary information such as, "In addition, counseling services by specialist doctors will be provided so that you can enjoy sports with peace of mind."

[1057] Step 14: Notification of Response

[1058] The server sends a response including supplemental information to the terminal.

[1059] (Input) Answer data including the analysis results of the emotion engine

[1060] (Output) The answer is displayed to the user on the terminal.

[1061] Step 15: Indicate your intention to take out insurance

[1062] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button.

[1063] (Input) Click the Join button

[1064] (Output) The server is notified of the intention to join.

[1065] Step 16: Enter additional information

[1066] The server generates an input form for the required additional information (health check results, past medical history, etc.) and displays it on the user's terminal.

[1067] (Input) Notice of intention to join

[1068] (Output) A form for entering health checkup results, etc., is displayed to the user.

[1069] Step 17: Submit and confirm additional information

[1070] The user inputs the necessary health checkup results and medical history, and the device sends it to the server. The server checks all the input data and automatically connects it to the insurance company's system.

[1071] (Input) Additional information sent from the terminal

[1072] (Output) The data is linked to the insurance company.

[1073] Step 18: Notification of enrollment completion

[1074] The server generates a notification of completion of the subscription procedure and sends it to the user's terminal.

[1075] (Input) Notification of completion of linking to the insurance company's system

[1076] (Output) A confirmation notice of the completion of the subscription will be displayed on the user's device.

[1077] Step 19: Enter your claim submission

[1078] The user fills in the details in the complaint submission form and clicks the submit button.

[1079] (Input) Complaint details

[1080] (Output) The terminal sends the complaint information to the server.

[1081] Step 20: Analyze complaint information and proceed with response

[1082] The server sends the claim information to the generative AI model, which analyzes the claim content and determines the appropriate processing procedure. The server then works with the insurance company to proceed with the response.

[1083] (Input) Claim information

[1084] (Output) Specific response procedures are generated and shared with the insurance company.

[1085] Step 21: Sentiment analysis and appropriate response

[1086] The emotion engine analyzes the user's emotions when submitting a complaint and determines the appropriate response method.

[1087] (Input) Emotion data at the time of complaint submission

[1088] (Output) A response method based on the emotion is provided to the server.

[1089] Step 22: Notification of progress

[1090] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[1091] (Input) Response progress information

[1092] (Output) Progress is displayed in real time on the user's device.

[1093] This system allows users to smoothly select insurance plans, apply for insurance, and handle claims. In particular, by incorporating an emotion engine, it is possible to provide services that are sensitive to the user's emotions, which is expected to improve user satisfaction.

[1094] (Application example 2)

[1095] 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."

[1096] Conventional insurance systems were able to predict risks and propose insurance plans based on a user's basic information and lifestyle information. However, they were unable to take into account the user's emotions and psychological state, and the proposed insurance plans did not necessarily increase user satisfaction. This has led to a demand for improved user experience and services that are more sensitive to emotions. Furthermore, understanding the user's emotional state and providing a sense of security and stress reduction has been a challenge for electronic payment services.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1098] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for recognizing and analyzing the user's emotions, means for reflecting the analyzed emotion data in insurance plan proposals and question answers, and means for analyzing the user's emotional state using an emotion engine in an electronic payment service to provide a sense of security and stress reduction. This enables the proposal of insurance plans that take the user's emotions into consideration and an electronic payment service that provides a sense of security.

[1099] "Basic User Information" means personal identifying information such as your name, age, gender, occupation, and contact information.

[1100] "Lifestyle information" refers to information about a user's lifestyle habits, such as past risks, behavioral patterns, and past insurance enrollment history.

[1101] "Analyzing data" means analyzing collected basic information and lifestyle information of users using statistical methods and algorithms.

[1102] "Predicting future risks" means predicting dangers and problems that users may face in the future based on analyzed data.

[1103] "Generating an insurance plan" means constructing optimal insurance contract terms for a user based on predicted risks.

[1104] "Inform insurance plan" means to communicate details of the generated insurance plan to the user.

[1105] "Natural language processing" is a technology that enables computers to understand and generate natural language.

[1106] "Automating the insurance enrollment process" means mechanically carrying out a series of processes for users to enroll in insurance.

[1107] "Claims Handling" means handling complaints or claims submitted by Users regarding their insurance.

[1108] "Automating after-sales service" means mechanically providing the services and support provided after insurance is purchased.

[1109] "Emotion recognition" means identifying a user's emotional state from facial expressions, voice, text, etc.

[1110] "Analyzing emotional data" means performing a more detailed analysis based on the recognized emotional information.

[1111] "Electronic payment service" means a service for making and receiving payments using digital technology.

[1112] An "emotion engine" is software or a system that analyzes a user's emotional state and generates a response based on that.

[1113] "Providing a sense of security" means enabling users to use the service with peace of mind.

[1114] "Providing stress relief" means reducing the mental burden on users while using the service.

[1115] The present invention improves the user experience by adding a function to recognize user emotions to a system that collects and analyzes basic information and lifestyle information of a user to predict risks and propose appropriate insurance plans. The embodiments of the present invention are described in detail below.

[1116] Hardware and software used

[1117] Hardware

[1118] Smartphone (iOS, Android)

[1119] Server (database, analysis engine)

[1120] software

[1121] Database management system (MySQL, PostgreSQL, etc.)

[1122] Generative AI models (e.g., OpenAI GPT-3)

[1123] Sentiment analysis engine (Emotion AI, Affectiva, etc.)

[1124] Mobile app development frameworks (React Native, Flutter, etc.)

[1125] Program processing overview

[1126] User registration and data collection

[1127] When a user accesses the service and enters the required information on the new registration page, the server collects the user's basic information (name, age, gender, occupation, contact details), then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.), and stores the user's response data in a database.

[1128] Data analysis and risk prediction

[1129] The server provides the collected user basic information and lifestyle information to the analysis engine, which then uses generative AI to predict future risks. During this process, analysis is performed based on past risks, behavioral patterns, and past insurance history. As a specific example of a specific user, a user who frequently participates in sporting events is predicted to have a high risk of sports-related injuries.

[1130] Incorporating an emotion engine

[1131] The emotion analysis engine recognizes the user's emotions and reflects that emotional data when proposing insurance plans and answering questions. For example, the AI ​​can collect emotional data when a user participates in a sporting event and use that data to add coverage that not only addresses injury risk but also provides the user with peace of mind.

[1132] Insurance plan proposals

[1133] The AI ​​then takes into account the predicted risks and generates the optimal insurance plan for the user. The generated insurance plan is stored on a server and sent to the user's device. The user can then compare multiple insurance plans on their smartphone screen and select the most suitable one.

[1134] Insurance Questions and Answers

[1135] When a user has a question about the proposed insurance plan and asks, "How much does this insurance plan cover?", the device sends the question to the server, which then passes it on to the generation AI. The generation AI uses natural language processing to understand the question and generates an answer such as, "This plan includes injury compensation up to 1 million yen."

[1136] Insurance enrollment procedures

[1137] When the user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device, the server generates a form for entering the necessary additional information (such as health check results and past medical history) and automatically connects to the insurance company's system based on the information entered by the user.

[1138] Complaint handling and after-sales service

[1139] If a user wishes to file a claim after purchasing insurance, they enter details into a form on their device and click the submit button. The server receives the claim information, and the generation AI analyzes it to determine the appropriate processing procedure. Furthermore, the sentiment analysis engine analyzes the emotions expressed at the time of claim submission and derives the appropriate response method.

[1140] Specific examples

[1141] Prompt Sentence Examples

[1142] "Please tell me your name and age."

[1143] "Please tell us how you feel about your current insurance."

[1144] "How much coverage does this insurance plan provide?"

[1145] These prompts are used to gather necessary information through user interaction and perform sentiment analysis.

[1146] By using the above method, the present invention makes it possible to propose insurance plans that take into account the user's feelings and to provide an electronic payment service that provides a sense of security.

[1147] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1148] Step 1:

[1149] The user accesses the new registration page and enters the required information (name, age, gender, occupation, contact information).

[1150] Input: User's personal information

[1151] Data processing: Convert the entered personal information into JSON format and send it to the server.

[1152] Output: Basic information data in JSON format

[1153] Specific behavior: The device receives user input and sends it to the server.

[1154] Step 2:

[1155] The server stores the received basic information in a database, and then displays a questionnaire form to the user to collect lifestyle information.

[1156] Input: Basic information data in JSON format

[1157] Data operations: write operations to the database

[1158] Output: Questionnaire form for collecting lifestyle information

[1159] Specific operation: The server writes the data to the database and sends the questionnaire form to the terminal.

[1160] Step 3:

[1161] Users fill out a questionnaire form and enter lifestyle information.

[1162] Input: Lifestyle information (past risks, behavioral patterns, insurance needs, etc.)

[1163] Data processing: The input lifestyle information is converted into JSON format and sent to the server.

[1164] Output: Lifestyle information data in JSON format

[1165] Specific behavior: The device receives user input and sends it to the server.

[1166] Step 4:

[1167] The server stores the received lifestyle information in a database and provides it to a data analysis engine.

[1168] Input: Lifestyle information data in JSON format

[1169] Data calculation: Write operations to the database and provide data to the analysis engine

[1170] Output: Risk assessment by analytical engine

[1171] Specific operation: The server writes data to the database and provides the data to the analysis engine.

[1172] Step 5:

[1173] Generative AI models are used to predict future risks and generate insurance plans.

[1174] Input: Basic and lifestyle information

[1175] Data Computation: Generative AI models for risk prediction and insurance plan generation

[1176] Output: Proposed insurance plan

[1177] How it works: The analytical engine analyzes the data using generative AI models to generate risk assessments and insurance plans.

[1178] Step 6:

[1179] The server notifies the user of the generated insurance plan.

[1180] Input: Proposed insurance plan

[1181] Data processing: generating notification messages

[1182] Output: A notification message to the user

[1183] Specific operation: The server converts the insurance plan into a notification message and sends it to the device.

[1184] Step 7:

[1185] The user enters a question about their insurance plan.

[1186] Input: User question

[1187] Data processing: Send the question to a natural language processing engine

[1188] Output: Analysis result of question content

[1189] Specific operation: The device receives the user's question and sends it to the server.

[1190] Step 8:

[1191] The server uses a generative AI model to generate answers to questions and provides them to the user, and also uses an emotion engine to reflect the user's emotional data.

[1192] Input: User question and emotion data

[1193] Data calculation: Analysis and answer generation using generative AI models and emotion engines

[1194] Output: Answers to questions and additional information based on sentiment

[1195] Specific operation: The server uses the generative AI model and emotion engine to answer the question and send it to the device.

[1196] Step 9:

[1197] The user indicates their intention to subscribe to an insurance plan, and the server collects any additional information required and contacts the insurance company.

[1198] Input: User's intention to join and additional information (health check results, past medical history, etc.)

[1199] Data processing: collecting additional information and sending the data to the insurance company

[1200] Output: Joining completion notification

[1201] Specific operation: The terminal receives the user's input, the server connects the data to the insurance company, and sends a notification of enrollment completion.

[1202] Step 10:

[1203] When a user files a claim after taking out insurance, the server uses the generated AI model to analyze the content of the claim and notify the insurance company of the appropriate response procedures.

[1204] Input: Complaint information and emotion data

[1205] Data processing: Analyzing complaints and determining appropriate response procedures

[1206] Output: Notification of response procedures to insurance company and user

[1207] Specific operation: The server analyzes the claim using the generative AI model and emotion engine, coordinates response procedures with the insurance company, and notifies the user of progress.

[1208] 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.

[1209] 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.

[1210] 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.

[1211] [Third embodiment]

[1212] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1213] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1214] 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).

[1215] 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.

[1216] 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.

[1217] 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).

[1218] 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.

[1219] 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.

[1220] 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.

[1221] 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.

[1222] 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.

[1223] 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."

[1224] This invention provides a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[1225] User registration and data collection

[1226] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[1227] Data analysis and risk prediction

[1228] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[1229] Insurance plan proposals

[1230] The generative AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[1231] Insurance Questions and Answers

[1232] The user has a question about the proposed plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing, generates an answer such as, "This plan includes injury compensation up to 1 million yen," and sends it to the device via the server. The device then displays the answer to the user.

[1233] Insurance enrollment procedures

[1234] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1235] Complaint handling and after-sales service

[1236] If the user needs to submit a claim after taking out insurance, they enter the details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the claim content and determines the appropriate processing procedure, and the server works with the insurance company's system to handle the claim. The server notifies the user and the insurance company of the progress of the claim, and the device displays the progress to the user in real time.

[1237] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly in the area of ​​insurance selection and processing.

[1238] The processing flow will be explained below.

[1239] Step 1:

[1240] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[1241] Step 2:

[1242] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[1243] Step 3:

[1244] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[1245] Step 4:

[1246] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[1247] Step 5:

[1248] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[1249] Step 6:

[1250] The generation AI generates the optimal insurance plan based on the predicted risks. The server imports the generated insurance plan data and sends a proposal notification to the user.

[1251] Step 7:

[1252] The device displays a proposal notification to the user and provides a screen where the user can view detailed insurance plan information. The user can visually compare multiple proposals.

[1253] Step 8:

[1254] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[1255] Step 9:

[1256] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer, which is then returned to the server.

[1257] Step 10:

[1258] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[1259] Step 11:

[1260] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[1261] Step 12:

[1262] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[1263] Step 13:

[1264] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[1265] Step 14:

[1266] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[1267] Step 15:

[1268] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[1269] Step 16:

[1270] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure.

[1271] Step 17:

[1272] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[1273] Step 18:

[1274] The terminal displays the progress and results of the complaint handling to the user in real time.

[1275] Example 1

[1276] 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."

[1277] Conventional insurance systems make it difficult for users to understand their own risks and select the most suitable insurance plan. They also face issues with responding to questions about individual insurance plans, enrolling in insurance, and processing claims, all of which are time-consuming. There is a need to streamline these processes and reduce the burden on users.

[1278] 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.

[1279] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers through natural language processing using a generative AI model, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for the user to visually compare insurance plans. This enables the user to efficiently select an insurance plan, respond to questions, apply for insurance, and even handle claims.

[1280] "Basic User Information" refers to basic information about you, such as your name, age, gender, occupation, and contact information.

[1281] "Lifestyle information" refers to information about a user's lifestyle, such as the user's past risks, behavioral patterns, and past insurance history.

[1282] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze data and process natural language to automate various tasks.

[1283] "Natural language processing" refers to the technology of processing and understanding human language using computers, and includes the process of generating answers to questions using generative AI models.

[1284] "Insurance Plan" refers to a plan that specifically describes the type and content of insurance that a User will subscribe to, and includes a proposal for the most appropriate insurance in response to predicted risks.

[1285] "Claims handling" refers to the process of appropriately responding to claims and problems related to insurance that arise from users after they have taken out insurance.

[1286] "After-sales service" refers to the provision of services and support to users after they have taken out insurance, and includes general user care, including handling claims.

[1287] "Visual comparison tools" refers to features that provide a visual interface that allows users to easily understand and compare insurance plans and other options.

[1288] This invention is a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[1289] User registration and data collection examples

[1290] Users access the service using a web browser or a dedicated app, and enter their name, age, gender, occupation, and contact information on the new registration page. The device collects the entered basic information in a form and sends it to the server. The server stores the received information in a database such as MySQL. The server then displays a questionnaire form on the user's device to collect lifestyle information. The user answers the questionnaire, and the device sends the response data to the server. The server stores this information in a database.

[1291] Examples of data analysis and risk prediction

[1292] The server provides the user's basic information and lifestyle information to a data analysis engine (e.g., TensorFlow), which then uses this information to generate a generative AI model that predicts future risks. The generative AI model considers past risks, behavioral patterns, and insurance history, and applies a predictive algorithm to generate a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to be at high risk of sports-related injuries.

[1293] Example of an insurance plan proposal

[1294] The generative AI model takes predicted risks into account and generates the optimal insurance plan. Specifically, it automatically generates a plan that includes injury compensation for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[1295] Insurance Questions and Answers Example

[1296] The user has a question about the proposed insurance plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." The server then sends the answer to the device, which displays it to the user.

[1297] An example of insurance enrollment procedures

[1298] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for any additional information required (e.g., health check results, past medical history), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1299] Examples of complaint handling and after-sales service

[1300] If a user wishes to submit a claim after purchasing insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model, which analyzes the claim content and determines the appropriate processing steps. The server works in conjunction with the insurance company's system to handle the claim and notifies the user's device of the progress of the claim in real time.

[1301] Prompt Sentence Examples

[1302] Insurance plan question examples:

[1303] User: How much coverage does this insurance plan provide?

[1304] Generative AI model: This plan includes injury compensation up to 1 million yen.

[1305] Example of handling a complaint:

[1306] User: I want to file a claim, how do I do that?

[1307] Generative AI Model: Please fill in the details in the claim submission form and submit it.

[1308] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly by streamlining insurance selection and procedures.

[1309] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1310] Step 1:

[1311] The user accesses the service and enters the required information on the new registration page.

[1312] Specific operation: The user accesses the service using a web browser or a dedicated app and enters basic information such as name, age, gender, occupation, and contact information.

[1313] Input: User basic information

[1314] Output: The basic information entered is sent from the terminal to the server.

[1315] Step 2:

[1316] The terminal prompts the user to enter basic information into a form and presses the submit button.

[1317] What happens: When the user clicks the submit button, an HTTP POST request is sent to the server via the HTML form.

[1318] Input: Basic information input data

[1319] Output: Sent to the server as an HTTP POST request

[1320] Step 3:

[1321] The server stores the submitted information in a database and then displays a questionnaire to gather the user's lifestyle information.

[1322] Specific operation: The server stores the received basic information in a database such as MySQL, and then sends the survey form to the user's device.

[1323] Input: Received basic information, questionnaire form template

[1324] Output: Basic information is saved in the database and the survey form is displayed on the user's device.

[1325] Step 4:

[1326] The user answers the questionnaire, and the terminal transmits the answer data to the server.

[1327] Specific operation: The user enters the necessary information into the questionnaire form and presses the send button, which sends the response data from the terminal to the server.

[1328] Input: Survey response data

[1329] Output: Answer data is sent from the device to the server

[1330] Step 5:

[1331] The server receives this information and stores it in a database.

[1332] Specific operation: The server stores the received survey response data in a database.

[1333] Input: Survey response data

[1334] Output: Response data is saved in the database

[1335] Step 6:

[1336] The server provides the user's basic information and lifestyle information to the data analysis engine, and the generative AI uses this information to predict future risks.

[1337] How it works: The server retrieves user data from the database and passes it to a data analysis engine such as TensorFlow. A generative AI model uses this data to predict risk.

[1338] Input: User's basic information and lifestyle information

[1339] Output: Risk assessment results

[1340] Step 7:

[1341] Generative AI generates insurance plans tailored to specific risks.

[1342] What it does: It applies predictive algorithms to generate personalized insurance plans, such as plans that include injury coverage for sports enthusiasts.

[1343] Input: Risk assessment results

[1344] Output: Generated insurance plan

[1345] Step 8:

[1346] The server notifies the user of the generated insurance plan.

[1347] Specific behavior: The generated insurance plan is sent to the user as JSON data.

[1348] Input: Generated insurance plan data

[1349] Output: Insurance plan details are sent to the user's device

[1350] Step 9:

[1351] The device will display plan details to the user and allow them to compare them visually.

[1352] Specific operation: The device receives insurance plan information and displays it on the screen using HTML and CSS. It displays it in a table format so that the user can compare multiple plans.

[1353] Input: Insurance plan details

[1354] Output: A list of insurance plans displayed to the user

[1355] Step 10:

[1356] The user enters a question about the insurance plan.

[1357] What happens: A user types a question into a terminal, such as "How much does this insurance plan cover?"

[1358] Input: User question

[1359] Output: The entered question data is sent to the server.

[1360] Step 11:

[1361] The device sends the question to the server, which passes it on to the generating AI.

[1362] Specific operation: A question sent from a device reaches the server, which then passes the question to the generative AI model.

[1363] Input: User question data

[1364] Output: Question data is sent to the generation AI

[1365] Step 12:

[1366] The generation AI generates an answer and sends it to the device via the server.

[1367] Specific operation: Using natural language processing, the generative AI generates an appropriate answer and sends it to the device via the server.

[1368] Input: User question

[1369] Output: The generated answer is displayed in the terminal.

[1370] Step 13:

[1371] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button on the terminal.

[1372] Specific operation: The user clicks the subscription button on the terminal and sends the intention to subscribe to the server.

[1373] Input: User's intention to join

[1374] Output: Willingness to join data is sent to the server

[1375] Step 14:

[1376] The server generates an input form for additional information and sends it to the user's terminal.

[1377] Specific operation: The server generates an input form for the required additional information (e.g., health check results, past medical history) and displays it on the user's device.

[1378] Input: Intention to join data

[1379] Output: A form for entering additional information is displayed on the user's device.

[1380] Step 15:

[1381] The user enters additional information, which the terminal sends to the server.

[1382] Specific operation: The user enters additional information and the device sends the data to the server.

[1383] Input: Additional Information

[1384] Output: Additional information data is sent to the server

[1385] Step 16:

[1386] The server checks all input data and automatically connects to the insurance company's system.

[1387] Specific operation: The server verifies the input data and sends it to the insurance company's system via API.

[1388] Input: All input data

[1389] Output: Data is linked to the insurance company's system

[1390] Step 17:

[1391] A subscription completion notice is generated and sent to the user's terminal.

[1392] Specific operation: The server generates a notification that the subscription procedure has been completed and sends it to the user's device.

[1393] Input: Confirmation of enrollment completion from insurance company

[1394] Output: A notification of successful enrollment is displayed on the user's device.

[1395] Step 18:

[1396] The user submits a claim and the terminal transmits the claim information to the server.

[1397] Specific operation: The user enters detailed information into the complaint submission form and clicks the submit button. The terminal sends the complaint information to the server.

[1398] Input: Claim information

[1399] Output: Claim information is sent to the server

[1400] Step 19:

[1401] The server sends the claim information to the generation AI.

[1402] Specific operation: The server sends the complaint information to the generative AI model, which analyzes the content and determines the appropriate processing procedure.

[1403] Input: Claim information

[1404] Output: Analysis results and recommended processing steps

[1405] Step 20:

[1406] The server will work in conjunction with the insurance company's system to respond to the situation.

[1407] Specific operation: The server connects the analysis results of the generated AI and recommended processing procedures to the insurance company's system.

[1408] Input: Analysis results and recommended processing steps

[1409] Output: Processing steps linked to insurance company

[1410] Step 21:

[1411] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[1412] Specific operation: The server periodically checks the progress of the response and notifies the user and the insurance company. The terminal displays the received progress information to the user in real time.

[1413] Input: Response progress

[1414] Output: Progress is displayed in real time on the user's device.

[1415] (Application example 1)

[1416] 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."

[1417] Currently, the insurance industry requires a great deal of time and effort because the process of proposing insurance plans tailored to users' needs, procedures, and after-sales service is not fully automated. Furthermore, security risk predictions and countermeasures are carried out separately, making comprehensive risk management difficult. In particular, cybersecurity risks are not adequately addressed, often preventing users from taking appropriate measures.

[1418] 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.

[1419] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for collecting and analyzing basic information and behavioral data of the user and predicting cybersecurity risks, means for generating an optimal security measures plan based on the predicted cybersecurity risks, and means for automatically updating the user's device settings with the generated security measures plan. This allows the user to efficiently select insurance, apply for insurance, and even handle claims in a consistent manner, while also enabling comprehensive measures against cybersecurity risks.

[1420] "User's basic information and lifestyle information" refers to personal information about the user and data related to their daily behavioral patterns.

[1421] "Future risks" are predictions of problems and dangers that users may face in the future.

[1422] "Insurance Plan" means the set of insurance terms, conditions and coverages offered to a User for a particular risk.

[1423] "Natural language processing" is a technology that allows machines to understand natural human language and generate appropriate responses.

[1424] "Behavioral data" refers to data related to a user's daily activities, including, for example, internet usage history and movement history.

[1425] "Cybersecurity risk" refers to risks such as unauthorized access, information leaks, and system failures via the Internet or digital devices.

[1426] A "security plan" is a set of measures or actions to be taken against cybersecurity risks, including settings and how to use tools.

[1427] This invention relates to a system that collects basic information and lifestyle information of users, analyzes this data to predict future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service. Furthermore, this system also predicts cybersecurity risks and generates optimal security countermeasure plans, which are automatically reflected in the user's device settings.

[1428] 1. System Overview

[1429] The system consists of the following main components:

[1430] How we collect your basic and lifestyle information:

[1431] To achieve this, an interface is provided that allows users to use their smartphones or other devices to enter and submit the necessary information on a new registration page.

[1432] Ways to predict future risks:

[1433] Based on the data collected from users, the server uses a generative AI model to predict various risks.

[1434] To generate the best insurance plan:

[1435] Taking into account predicted risks, generative AI automatically generates the optimal insurance plan for the user.

[1436] Informing you of your insurance plan:

[1437] The generated plan information is sent from the server to the user's terminal, where detailed information is displayed.

[1438] Natural language processing answer generation methods:

[1439] When a user enters a question, the server uses generative AI to generate an answer corresponding to the question and sends it to the device.

[1440] Ways to automate the insurance enrollment process:

[1441] When a user selects the insurance plan they wish to subscribe to, the system automatically checks all necessary information and contacts the insurance company.

[1442] Automating claims handling and after-sales service:

[1443] When you submit a complaint, we collect and analyze that information and generate appropriate response procedures to process it.

[1444] Cybersecurity risk prediction tools:

[1445] Collect user behavior data and predict cybersecurity risks using generative AI models.

[1446] How to generate a security action plan:

[1447] Generate optimal countermeasure plans to address predicted cybersecurity risks.

[1448] How to automatically apply security measures:

[1449] The generated security plan is automatically reflected in the user's device settings.

[1450] 2. Working Example

[1451] 2.1 Hardware and software used

[1452] Smartphone:

[1453] Use an iOS or Android device.

[1454] server:

[1455] Use Flask to manage all data collection, analytics, notifications, and procedures cross-platform.

[1456] Generative AI models:

[1457] Build using scikit-learn or TensorFlow / Keras.

[1458] 2.2 Example of operation

[1459] User Registration and Data Collection:

[1460] Users access the service and enter their name, age, gender, occupation, and contact information on the new registration page, then answer a questionnaire about lifestyle information such as past risks, behavioral patterns, and insurance needs.

[1461] Risk Prediction:

[1462] The server passes the collected data to a generative AI model that predicts future risks specific to the user.

[1463] Insurance plan suggestions:

[1464] Based on the risk assessment results, the most suitable insurance plan is generated and notified to the user.

[1465] Security Measures:

[1466] Cybersecurity risks are predicted, optimal countermeasure plans are generated, and automatically applied to users' devices.

[1467] 2.3 Prompt Sentence Examples

[1468] Example prompt sentence:

[1469] User information: Name = Yamada Taro, Age = 30, Gender = Male, Occupation = IT engineer, Past risks = None, Behavior pattern = Frequent online shopping, Need for insurance = High

[1470] By using these system components, users can consistently and efficiently carry out insurance-related procedures and cybersecurity measures, enabling comprehensive risk management.

[1471] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1472] Step 1:

[1473] The user accesses the new registration page using a terminal, enters basic information (name, age, gender, occupation, contact details), and presses the submit button. The entered data is sent from the terminal to the server and stored in the database.

[1474] Input: User's basic information (name, age, gender, occupation, contact information)

[1475] Processing: The user enters information and sends the data from the device to the server

[1476] Output: Basic information saved in the database

[1477] Step 2:

[1478] The server displays a questionnaire to the user to collect lifestyle information (past risks, behavioral patterns, need for insurance). The user answers the questionnaire, and the device sends the response data to the server. The server stores the response data in a database.

[1479] Input: User's lifestyle information (past risks, behavioral patterns, insurance needs)

[1480] Processing: The user answers the survey and sends the data from the device to the server.

[1481] Output: Lifestyle information saved in database

[1482] Step 3:

[1483] The server passes basic and lifestyle information to a generative AI model to predict future risks. The generative AI model performs risk assessment based on past data and behavioral patterns and generates a risk score.

[1484] Input: Basic information and lifestyle information stored in the database

[1485] Processing: Generative AI models analyze data and predict future risks

[1486] Output: Risk score per user

[1487] Step 4:

[1488] The server generates an optimal insurance plan using a generative AI model based on the predicted risks. The generated plan information is sent from the server to the device and displayed visually to the user.

[1489] Input: Risk Score

[1490] Processing: Generative AI model generates optimal insurance plan

[1491] Output: Insurance plan information is sent to the user's device

[1492] Step 5:

[1493] The user enters a question about the proposed insurance plan, and the device sends the question to the server, which uses generative AI to process natural language and generate an appropriate answer, which is then sent to the device.

[1494] Input: User question

[1495] Processing: The generative AI model processes natural language and generates an answer

[1496] Output: The answer is displayed on the terminal.

[1497] Step 6:

[1498] The user indicates their intention to sign up for an insurance plan and clicks the sign-up button on their device. The server receives this, generates a form for the user to enter additional information, and displays it on the user's device. The user enters the required information, which the device sends to the server. The server verifies the data and automatically contacts the insurance company.

[1499] Input: Intention to join and additional information

[1500] Processing: The server automatically sends the data to the insurance company

[1501] Output: A notification of successful enrollment is displayed on the user's device.

[1502] Step 7:

[1503] When a user files a claim after taking out insurance, they input the claim details and send them from their device to the server, which then analyzes the claim using a generative AI model, determines the appropriate response procedure, and responds in cooperation with the insurance company's system.

[1504] Input: User's claim details

[1505] Processing: The generative AI model analyzes the complaint and generates a response procedure.

[1506] Output: Progress notifications are displayed on the user's device

[1507] Step 8:

[1508] The server collects user behavior data and uses generative AI models to predict cybersecurity risks. Based on the predicted risks, it generates an optimal security countermeasure plan and automatically applies it to the user's device settings.

[1509] Input: User behavior data

[1510] Processing: Generative AI models predict cybersecurity risks and generate countermeasure plans

[1511] Output: The countermeasure plan is automatically reflected in the user's device settings.

[1512] 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.

[1513] This invention improves the user experience by combining a system that collects and analyzes a user's basic and lifestyle information, predicts future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service with an emotion engine that recognizes the user's emotions.

[1514] User registration and data collection

[1515] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[1516] Data analysis and risk prediction

[1517] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[1518] Incorporating an emotion engine

[1519] The emotion engine recognizes the user's emotions and reflects them when proposing insurance plans and answering questions. For example, by collecting emotional data when a user participates in a sporting event and analyzing it, the generative AI can add coverage to the proposed plan that not only addresses the risk of injury during sports, but also compensates for the user's mental peace of mind.

[1520] Insurance plan proposals

[1521] The generation AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The server then imports the generated insurance plan data and sends a proposal notification to the user. The device displays detailed insurance plan information to the user, allowing the user to visually compare multiple proposals.

[1522] Insurance Questions and Answers

[1523] A user has a question about a proposed plan and types a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." Furthermore, the emotion engine analyzes the emotions associated with the user's question and provides supplementary information to increase reassurance. The server sends the answer to the device, which displays it to the user.

[1524] Insurance enrollment procedures

[1525] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1526] Complaint handling and after-sales service

[1527] If a user wishes to submit a claim after taking out insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the content of the claim and determines the appropriate processing procedure, and the server works in conjunction with the insurance company's system to handle the claim. Furthermore, an emotion engine analyzes the user's emotions at the time of submitting the claim and derives the appropriate response method. The server notifies the user and the insurance company of the response progress, and the device displays the progress to the user in real time.

[1528] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. In particular, by incorporating an emotion engine, it is possible to provide services that are in tune with users' emotions, which is expected to improve satisfaction.

[1529] The processing flow will be explained below.

[1530] Step 1:

[1531] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[1532] Step 2:

[1533] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[1534] Step 3:

[1535] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[1536] Step 4:

[1537] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[1538] Step 5:

[1539] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[1540] Step 6:

[1541] Generative AI generates optimal insurance plans based on predicted risks. For example, it automatically generates plans that include injury compensation specifically for sports enthusiasts. The emotion engine analyzes the user's emotions and reflects them in the risk assessment results.

[1542] Step 7:

[1543] The server retrieves the generated insurance plan data and sends a proposal notification to the user. The device displays the proposal notification to the user and provides a screen where detailed insurance plan information can be viewed. The user visually compares multiple proposals.

[1544] Step 8:

[1545] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[1546] Step 9:

[1547] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer. The emotion engine analyzes the user's emotions and provides supplementary information to increase reassurance. The answer is returned to the server.

[1548] Step 10:

[1549] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[1550] Step 11:

[1551] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[1552] Step 12:

[1553] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[1554] Step 13:

[1555] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[1556] Step 14:

[1557] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[1558] Step 15:

[1559] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[1560] Step 16:

[1561] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure. The emotion engine analyzes the user's emotions and optimizes the response method.

[1562] Step 17:

[1563] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[1564] Step 18:

[1565] The device displays the progress and results of the complaint handling to the user in real time. The emotion engine appropriately analyzes the user's emotional changes and provides feedback as the response results.

[1566] Example 2

[1567] 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."

[1568] In modern insurance services, it takes a great deal of time and effort for users to select the most suitable insurance plan, complete the enrollment process, and then smoothly handle claims and after-sales service after enrollment. Furthermore, there is a lack of services that take user feelings into consideration, and it is necessary to improve the quality of the user experience. The purpose of this invention is to solve these issues and provide users with efficient and personalized insurance services.

[1569] The specification processing by the specification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for adjusting insurance proposals and answers based on the user's emotional state, including an emotion engine that recognizes and analyzes the user's emotional state. This enables the user to efficiently and individually select an insurance plan, apply for insurance, and handle claims, and further realizes the provision of emotionally sensitive services.

[1570] "Basic information" refers to personal data such as the user's name, age, gender, occupation, and contact details.

[1571] "Lifestyle information" refers to data related to a user's lifestyle and behavioral history, such as past risks, behavioral patterns, and insurance enrollment history.

[1572] The "data analysis engine" is a software system that analyzes collected basic and lifestyle information and predicts future risks.

[1573] "Generative AI" is an artificial intelligence model that predicts future risks and generates optimal insurance plans for users based on those predictions.

[1574] An "emotion engine" is a software system that recognizes and analyzes a user's emotional state and has the ability to adjust insurance proposals and responses based on that emotional state.

[1575] "Insurance Plan" means a proposal containing the specific insurance coverage and terms offered to a User.

[1576] "Notification" refers to the transmission of information from the server to the user, and is done via email, in-app notifications, etc.

[1577] "Natural language processing" is a technology for processing collected data and user questions in natural language to understand and generate responses.

[1578] "Automation" refers to the autonomous processing of processes by systems or machines, rather than by manual means.

[1579] "Complaint handling" refers to the overall process of providing solutions to and responding to user complaints and dissatisfaction.

[1580] "After-sales service" refers to additional support and services provided after insurance is purchased.

[1581] This invention is a system for efficiently selecting insurance plans, enrolling procedures, and handling claims. The system consists of three main components: a server, a terminal, and a user. This system collects basic information and lifestyle information from users, and then uses a generative AI model to propose optimal insurance plans based on that information. Furthermore, it uses an emotion engine to provide services based on the user's emotional state.

[1582] User registration and data collection

[1583] The user accesses the new registration page using a device. The device displays a basic information form (name, age, gender, occupation, contact information), and the user enters the information. After entering the information, the device sends this basic information to the server, which stores the received information in a database. Next, the server generates a questionnaire to collect lifestyle information and displays it on the device. The user answers the questionnaire, and the device sends the response data to the server. The server also stores this information in a database.

[1584] Data analysis and risk prediction

[1585] The server provides the user's basic information and lifestyle information to a data analysis engine. The generative AI model uses this information to predict future risks. A predictive algorithm based on past risks, behavioral patterns, and insurance history is used to perform a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to be at high risk of sports-related injuries.

[1586] Incorporating an emotion engine

[1587] The emotion engine recognizes the user's emotional state and reflects it when proposing insurance plans and answering questions. For example, if emotional data is collected when a user participates in a sporting event, the emotion engine analyzes that data and the generative AI model adds coverage to the proposed plan that not only considers injury risk but also compensates for mental peace of mind.

[1588] Insurance plan proposals

[1589] The generative AI model takes into account the predicted risks and generates an optimal insurance plan. For example, it automatically generates a plan that includes injury coverage specifically for sports enthusiasts. The server imports the generated insurance plan data and sends a notification to the user. The device displays detailed insurance plan information to the user, allowing the user to visually compare multiple proposals.

[1590] Insurance Questions and Answers

[1591] A user may have a question about a proposed plan, such as, "How much does this insurance plan cover?" The device sends the question to a server, which passes it on to a generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." Furthermore, an emotion engine analyzes the emotions associated with the user's question and provides supplementary information to increase reassurance.

[1592] Insurance enrollment procedures

[1593] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1594] Complaint handling and after-sales service

[1595] If a user wishes to submit a claim after taking out insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model. The generative AI model analyzes the content of the claim and determines the appropriate processing procedure. The server connects with the insurance company's system to handle the claim. The emotion engine analyzes the user's emotions at the time of submitting the claim and derives the appropriate response method. The server notifies the user of the response progress, and the device displays the progress to the user in real time.

[1596] For example, a user might input, "What insurance plan do you offer for injuries sustained while participating in a sporting event?" The generative AI model responds, "This insurance plan includes compensation of up to 1 million yen for injuries sustained while participating in a sporting event," and the emotion engine adds a supplementary explanation, "In addition, counseling services by specialist doctors are also provided so that you can enjoy sports with peace of mind."

[1597] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1598] Step 1: Start user registration

[1599] The user accesses the new registration page using a device, and a form for entering name, age, gender, occupation, and contact information is displayed on the device.

[1600] (Input) The user inputs basic information such as name "Yamada Taro", age "30", gender "male", occupation "company employee", and contact information "example@example.com".

[1601] (Output) The terminal sends this information to the server.

[1602] Step 2: Receive and store basic information

[1603] The server receives the basic information sent from the terminal.

[1604] (Input) Basic user information sent from the device

[1605] (Output) The received information is saved in the database. Specifically, it is recorded in the database as "ID: 12345, Name: Yamada Taro, Age: 30, Gender: Male, Occupation: Company employee, Contact: example@example.com".

[1606] Step 3: Displaying a questionnaire to collect lifestyle information

[1607] The server generates a questionnaire for collecting lifestyle information and displays it on the user's terminal.

[1608] (Input) User registration ID

[1609] (Output) A questionnaire form will be displayed on the device, including questions such as "past health risks," "behavioral patterns," and "current need for insurance."

[1610] Step 4: Enter your survey answers

[1611] The user answers the displayed questionnaire. Specifically, the user answers "Yes" to the question "Do you exercise?" and "Have you ever been in a serious accident?"

[1612] (Input) Questionnaire responses as user lifestyle information

[1613] (Output) The terminal sends the response data to the server.

[1614] Step 5: Receive and store lifestyle information

[1615] The server receives the user's questionnaire response data and stores it in a database.

[1616] (Input) Survey response data sent from the device

[1617] (Output) This will be recorded in the database as "ID: 12345, Exercise: Yes, Serious Accident: No, Insurance Need: High."

[1618] Step 6: Perform data analysis

[1619] The server provides the user's basic information and lifestyle information to the data analysis engine.

[1620] (Input) Basic information and lifestyle information

[1621] (Output) The data analysis engine uses this information to predict future risks, and the generative AI model outputs an assessment such as "Sports event participation frequency: High, injury risk: High."

[1622] Step 7: Collect and analyze emotion data

[1623] The emotion engine collects and analyzes the user's emotion data.

[1624] (Input) Data related to emotions, such as the user's facial expression and tone of voice

[1625] (Output) The emotion engine analyzes the user's current emotional state and outputs analysis results such as "feeling relieved" or "feeling anxious."

[1626] Step 8: Generate your insurance plan

[1627] The generative AI model generates the optimal insurance plan based on the results of risk assessment and sentiment analysis.

[1628] (Input) Risk assessment results and emotion analysis results

[1629] (Output) Insurance plan data is generated. For example, it is created as an "injury compensation plan for sports enthusiasts."

[1630] Step 9: Insurance Plan Notification

[1631] The server retrieves the generated insurance plan data and sends a proposal notification to the user.

[1632] (Input) Generated insurance plan data

[1633] (Output) The user is notified with the "Injury Compensation Plan for Sports Enthusiasts."

[1634] Step 10: View plan details

[1635] The device displays detailed insurance plan information to the user and allows them to visually compare multiple offers.

[1636] (Input) Plan notification from the server

[1637] (Output) Details of the "Injury Compensation Plan" and "Additional Mental Support Plan" will be displayed on the terminal.

[1638] Step 11: Enter and submit your question

[1639] The user has a question about the proposed plan and types in a question: "How much does this insurance plan cover?" and clicks the submit button.

[1640] (Input) Question content

[1641] (Output) The terminal sends the query data to the server.

[1642] Step 12: Analyze question data and generate answers

[1643] The server passes the question data to the generative AI model, which analyzes the question through natural language processing and generates an answer.

[1644] (Input) User question data

[1645] (Output) The generative AI model generates an answer such as, "This plan includes injury compensation up to 1 million yen."

[1646] Step 13: Providing answers and reflecting

[1647] The server receives the generated answer and passes it to the emotion engine, which analyzes the emotion associated with the user's question and adds supplementary information to the answer.

[1648] (Input) Generated answers and user emotion data

[1649] (Output) An answer is generated that adds supplementary information such as, "In addition, counseling services by specialist doctors will be provided so that you can enjoy sports with peace of mind."

[1650] Step 14: Notification of Response

[1651] The server sends a response including supplemental information to the terminal.

[1652] (Input) Answer data including the analysis results of the emotion engine

[1653] (Output) The answer is displayed to the user on the terminal.

[1654] Step 15: Indicate your intention to take out insurance

[1655] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button.

[1656] (Input) Click the Join button

[1657] (Output) The server is notified of the intention to join.

[1658] Step 16: Enter additional information

[1659] The server generates an input form for the required additional information (health check results, past medical history, etc.) and displays it on the user's terminal.

[1660] (Input) Notice of intention to join

[1661] (Output) A form for entering health checkup results, etc., is displayed to the user.

[1662] Step 17: Submit and confirm additional information

[1663] The user inputs the necessary health checkup results and medical history, and the device sends it to the server. The server checks all the input data and automatically connects it to the insurance company's system.

[1664] (Input) Additional information sent from the terminal

[1665] (Output) The data is linked to the insurance company.

[1666] Step 18: Notification of enrollment completion

[1667] The server generates a notification of completion of the subscription procedure and sends it to the user's terminal.

[1668] (Input) Notification of completion of linking to the insurance company's system

[1669] (Output) A confirmation notice of the completion of the subscription will be displayed on the user's device.

[1670] Step 19: Enter your claim submission

[1671] The user fills in the details in the complaint submission form and clicks the submit button.

[1672] (Input) Complaint details

[1673] (Output) The terminal sends the complaint information to the server.

[1674] Step 20: Analyze complaint information and proceed with response

[1675] The server sends the claim information to the generative AI model, which analyzes the claim content and determines the appropriate processing procedure. The server then works with the insurance company to proceed with the response.

[1676] (Input) Claim information

[1677] (Output) Specific response procedures are generated and shared with the insurance company.

[1678] Step 21: Sentiment analysis and appropriate response

[1679] The emotion engine analyzes the user's emotions when submitting a complaint and determines the appropriate response method.

[1680] (Input) Emotion data at the time of complaint submission

[1681] (Output) A response method based on the emotion is provided to the server.

[1682] Step 22: Notification of progress

[1683] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[1684] (Input) Response progress information

[1685] (Output) Progress is displayed in real time on the user's device.

[1686] This system allows users to smoothly select insurance plans, apply for insurance, and handle claims. In particular, by incorporating an emotion engine, it is possible to provide services that are sensitive to the user's emotions, which is expected to improve user satisfaction.

[1687] (Application example 2)

[1688] 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."

[1689] Conventional insurance systems were able to predict risks and propose insurance plans based on a user's basic information and lifestyle information. However, they were unable to take into account the user's emotions and psychological state, and the proposed insurance plans did not necessarily increase user satisfaction. This has led to a demand for improved user experience and services that are more sensitive to emotions. Furthermore, understanding the user's emotional state and providing a sense of security and stress reduction has been a challenge for electronic payment services.

[1690] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1691] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for recognizing and analyzing the user's emotions, means for reflecting the analyzed emotion data in insurance plan proposals and question answers, and means for analyzing the user's emotional state using an emotion engine in an electronic payment service to provide a sense of security and stress reduction. This enables the proposal of insurance plans that take the user's emotions into consideration and an electronic payment service that provides a sense of security.

[1692] "Basic User Information" means personal identifying information such as your name, age, gender, occupation, and contact information.

[1693] "Lifestyle information" refers to information about a user's lifestyle habits, such as past risks, behavioral patterns, and past insurance enrollment history.

[1694] "Analyzing data" means analyzing collected basic information and lifestyle information of users using statistical methods and algorithms.

[1695] "Predicting future risks" means predicting dangers and problems that users may face in the future based on analyzed data.

[1696] "Generating an insurance plan" means constructing optimal insurance contract terms for a user based on predicted risks.

[1697] "Inform insurance plan" means to communicate details of the generated insurance plan to the user.

[1698] "Natural language processing" is a technology that enables computers to understand and generate natural language.

[1699] "Automating the insurance enrollment process" means mechanically carrying out a series of processes for users to enroll in insurance.

[1700] "Claims Handling" means handling complaints or claims submitted by Users regarding their insurance.

[1701] "Automating after-sales service" means mechanically providing the services and support provided after insurance is purchased.

[1702] "Emotion recognition" means identifying a user's emotional state from facial expressions, voice, text, etc.

[1703] "Analyzing emotional data" means performing a more detailed analysis based on the recognized emotional information.

[1704] "Electronic payment service" means a service for making and receiving payments using digital technology.

[1705] An "emotion engine" is software or a system that analyzes a user's emotional state and generates a response based on that.

[1706] "Providing a sense of security" means enabling users to use the service with peace of mind.

[1707] "Providing stress relief" means reducing the mental burden on users while using the service.

[1708] The present invention improves the user experience by adding a function to recognize user emotions to a system that collects and analyzes basic information and lifestyle information of a user to predict risks and propose appropriate insurance plans. The embodiments of the present invention are described in detail below.

[1709] Hardware and software used

[1710] Hardware

[1711] Smartphone (iOS, Android)

[1712] Server (database, analysis engine)

[1713] software

[1714] Database management system (MySQL, PostgreSQL, etc.)

[1715] Generative AI models (e.g., OpenAI GPT-3)

[1716] Sentiment analysis engine (Emotion AI, Affectiva, etc.)

[1717] Mobile app development frameworks (React Native, Flutter, etc.)

[1718] Program processing overview

[1719] User registration and data collection

[1720] When a user accesses the service and enters the required information on the new registration page, the server collects the user's basic information (name, age, gender, occupation, contact details), then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.), and stores the user's response data in a database.

[1721] Data analysis and risk prediction

[1722] The server provides the collected user basic information and lifestyle information to the analysis engine, which then uses generative AI to predict future risks. During this process, analysis is performed based on past risks, behavioral patterns, and past insurance history. As a specific example of a specific user, a user who frequently participates in sporting events is predicted to have a high risk of sports-related injuries.

[1723] Incorporating an emotion engine

[1724] The emotion analysis engine recognizes the user's emotions and reflects that emotional data when proposing insurance plans and answering questions. For example, the AI ​​can collect emotional data when a user participates in a sporting event and use that data to add coverage that not only addresses injury risk but also provides the user with peace of mind.

[1725] Insurance plan proposals

[1726] The AI ​​then takes into account the predicted risks and generates the optimal insurance plan for the user. The generated insurance plan is stored on a server and sent to the user's device. The user can then compare multiple insurance plans on their smartphone screen and select the most suitable one.

[1727] Insurance Questions and Answers

[1728] When a user has a question about the proposed insurance plan and asks, "How much does this insurance plan cover?", the device sends the question to the server, which then passes it on to the generation AI. The generation AI uses natural language processing to understand the question and generates an answer such as, "This plan includes injury compensation up to 1 million yen."

[1729] Insurance enrollment procedures

[1730] When the user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device, the server generates a form for entering the necessary additional information (such as health check results and past medical history) and automatically connects to the insurance company's system based on the information entered by the user.

[1731] Complaint handling and after-sales service

[1732] If a user wishes to file a claim after purchasing insurance, they enter details into a form on their device and click the submit button. The server receives the claim information, and the generation AI analyzes it to determine the appropriate processing procedure. Furthermore, the sentiment analysis engine analyzes the emotions expressed at the time of claim submission and derives the appropriate response method.

[1733] Specific examples

[1734] Prompt Sentence Examples

[1735] "Please tell me your name and age."

[1736] "Please tell us how you feel about your current insurance."

[1737] "How much coverage does this insurance plan provide?"

[1738] These prompts are used to gather necessary information through user interaction and perform sentiment analysis.

[1739] By using the above method, the present invention makes it possible to propose insurance plans that take into account the user's feelings and to provide an electronic payment service that provides a sense of security.

[1740] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1741] Step 1:

[1742] The user accesses the new registration page and enters the required information (name, age, gender, occupation, contact information).

[1743] Input: User's personal information

[1744] Data processing: Convert the entered personal information into JSON format and send it to the server.

[1745] Output: Basic information data in JSON format

[1746] Specific behavior: The device receives user input and sends it to the server.

[1747] Step 2:

[1748] The server stores the received basic information in a database, and then displays a questionnaire form to the user to collect lifestyle information.

[1749] Input: Basic information data in JSON format

[1750] Data operations: write operations to the database

[1751] Output: Questionnaire form for collecting lifestyle information

[1752] Specific operation: The server writes the data to the database and sends the questionnaire form to the terminal.

[1753] Step 3:

[1754] Users fill out a questionnaire form and enter lifestyle information.

[1755] Input: Lifestyle information (past risks, behavioral patterns, insurance needs, etc.)

[1756] Data processing: The input lifestyle information is converted into JSON format and sent to the server.

[1757] Output: Lifestyle information data in JSON format

[1758] Specific behavior: The device receives user input and sends it to the server.

[1759] Step 4:

[1760] The server stores the received lifestyle information in a database and provides it to a data analysis engine.

[1761] Input: Lifestyle information data in JSON format

[1762] Data calculation: Write operations to the database and provide data to the analysis engine

[1763] Output: Risk assessment by analytical engine

[1764] Specific operation: The server writes data to the database and provides the data to the analysis engine.

[1765] Step 5:

[1766] Generative AI models are used to predict future risks and generate insurance plans.

[1767] Input: Basic and lifestyle information

[1768] Data Computation: Generative AI models for risk prediction and insurance plan generation

[1769] Output: Proposed insurance plan

[1770] How it works: The analytical engine analyzes the data using generative AI models to generate risk assessments and insurance plans.

[1771] Step 6:

[1772] The server notifies the user of the generated insurance plan.

[1773] Input: Proposed insurance plan

[1774] Data processing: generating notification messages

[1775] Output: A notification message to the user

[1776] Specific operation: The server converts the insurance plan into a notification message and sends it to the device.

[1777] Step 7:

[1778] The user enters a question about their insurance plan.

[1779] Input: User question

[1780] Data processing: Send the question to a natural language processing engine

[1781] Output: Analysis result of question content

[1782] Specific operation: The device receives the user's question and sends it to the server.

[1783] Step 8:

[1784] The server uses a generative AI model to generate answers to questions and provides them to the user, and also uses an emotion engine to reflect the user's emotional data.

[1785] Input: User question and emotion data

[1786] Data calculation: Analysis and answer generation using generative AI models and emotion engines

[1787] Output: Answers to questions and additional information based on sentiment

[1788] Specific operation: The server uses the generative AI model and emotion engine to answer the question and send it to the device.

[1789] Step 9:

[1790] The user indicates their intention to subscribe to an insurance plan, and the server collects any additional information required and contacts the insurance company.

[1791] Input: User's intention to join and additional information (health check results, past medical history, etc.)

[1792] Data processing: collecting additional information and sending the data to the insurance company

[1793] Output: Joining completion notification

[1794] Specific operation: The terminal receives the user's input, the server connects the data to the insurance company, and sends a notification of enrollment completion.

[1795] Step 10:

[1796] When a user files a claim after taking out insurance, the server uses the generated AI model to analyze the content of the claim and notify the insurance company of the appropriate response procedures.

[1797] Input: Complaint information and emotion data

[1798] Data processing: Analyzing complaints and determining appropriate response procedures

[1799] Output: Notification of response procedures to insurance company and user

[1800] Specific operation: The server analyzes the claim using the generative AI model and emotion engine, coordinates response procedures with the insurance company, and notifies the user of progress.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] [Fourth embodiment]

[1805] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1806] 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.

[1807] 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).

[1808] 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.

[1809] 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.

[1810] 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).

[1811] 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.

[1812] 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.

[1813] 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.

[1814] 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.

[1815] 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.

[1816] 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.

[1817] 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."

[1818] This invention provides a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[1819] User registration and data collection

[1820] The user accesses the service and enters the required information on the new registration page. The device prompts the user to enter basic information (name, age, sex, occupation, contact details) into a form, and when the user presses the submit button, this information is sent to the server. The server saves the submitted information in a database and then displays a questionnaire to collect the user's lifestyle information (past risks, behavioral patterns, insurance needs, etc.). The user answers the questionnaire, and the device sends the response data to the server. The server receives this information and stores it in a database.

[1821] Data analysis and risk prediction

[1822] The server provides the user's basic information and lifestyle information to a data analysis engine, which uses this information to generate AI that predicts future risks. The AI ​​then uses a predictive algorithm to conduct a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance history. If the AI ​​determines that a user who frequently participates in sporting events is at high risk of sports-related injuries, it will need an insurance plan that addresses this risk.

[1823] Insurance plan proposals

[1824] The generative AI takes predicted risks into account and generates the optimal insurance plan. For example, it automatically generates a plan that includes injury compensation specifically for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[1825] Insurance Questions and Answers

[1826] The user has a question about the proposed plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generation AI. The generation AI understands the question through natural language processing, generates an answer such as, "This plan includes injury compensation up to 1 million yen," and sends it to the device via the server. The device then displays the answer to the user.

[1827] Insurance enrollment procedures

[1828] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for the required additional information (health check results, past medical history, etc.), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1829] Complaint handling and after-sales service

[1830] If the user needs to submit a claim after taking out insurance, they enter the details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generation AI. The generation AI analyzes the claim content and determines the appropriate processing procedure, and the server works with the insurance company's system to handle the claim. The server notifies the user and the insurance company of the progress of the claim, and the device displays the progress to the user in real time.

[1831] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly in the area of ​​insurance selection and processing.

[1832] The processing flow will be explained below.

[1833] Step 1:

[1834] The user accesses the service and enters the necessary information on the new registration page. The device then sends the information to the server.

[1835] Step 2:

[1836] The server receives the user's basic information (name, age, gender, occupation, contact details) and saves it in the database. It generates a confirmation of the save completion and sends it to the device.

[1837] Step 3:

[1838] As a next step, the terminal displays a questionnaire form to the user to collect lifestyle information. The user answers the questionnaire and presses the send button to send the answer data to the server.

[1839] Step 4:

[1840] The server receives the survey response data, stores it in a database, and provides the stored data to a data analysis engine.

[1841] Step 5:

[1842] Generative AI predicts future risks based on the user's basic information and lifestyle information. It uses a predictive algorithm to perform a risk assessment specific to the user, taking into account past risks, behavioral patterns, and past insurance enrollment history.

[1843] Step 6:

[1844] The generation AI generates the optimal insurance plan based on the predicted risks. The server imports the generated insurance plan data and sends a proposal notification to the user.

[1845] Step 7:

[1846] The device displays a proposal notification to the user and provides a screen where the user can view detailed insurance plan information. The user can visually compare multiple proposals.

[1847] Step 8:

[1848] The user inputs a question about the insurance plan and submits the question. The device sends the question to the server.

[1849] Step 9:

[1850] The server sends the question data to the generation AI, which uses natural language processing to analyze the question and generate an appropriate answer, which is then returned to the server.

[1851] Step 10:

[1852] The server sends the answer from the generated AI to the user's device, which displays the answer to the user.

[1853] Step 11:

[1854] The user indicates their intention to subscribe to the proposed insurance plan and clicks the Subscribe button. The terminal sends the intention to subscribe to the server.

[1855] Step 12:

[1856] The server generates an additional information collection form and sends it to the user's terminal. The user enters additional information (health check results, past medical history, etc.) and presses the submit button.

[1857] Step 13:

[1858] The terminal sends the additional information to the server, which checks all the input data and, after confirming that there are no missing data, connects it to the insurance company's system.

[1859] Step 14:

[1860] The server generates a subscription completion notification and sends it to the user's terminal, which displays the subscription completion notification.

[1861] Step 15:

[1862] When a user files a claim after taking out insurance, he enters the details in the claim submission form and presses the submit button. The terminal sends the claim information to the server.

[1863] Step 16:

[1864] The server sends the complaint information to the generation AI, which analyzes the complaint content and determines the appropriate processing procedure.

[1865] Step 17:

[1866] The server manages the progress of claims handling according to the processing procedures generated by the AI, and notifies the insurance company and the user of the progress of the handling.

[1867] Step 18:

[1868] The terminal displays the progress and results of the complaint handling to the user in real time.

[1869] Example 1

[1870] 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."

[1871] Conventional insurance systems make it difficult for users to understand their own risks and select the most suitable insurance plan. They also face issues with responding to questions about individual insurance plans, enrolling in insurance, and processing claims, all of which are time-consuming. There is a need to streamline these processes and reduce the burden on users.

[1872] 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.

[1873] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers through natural language processing using a generative AI model, means for automating insurance application procedures, means for automating claims processing and after-sales service after insurance application, and means for the user to visually compare insurance plans. This enables the user to efficiently select an insurance plan, respond to questions, apply for insurance, and even handle claims.

[1874] "Basic User Information" refers to basic information about you, such as your name, age, gender, occupation, and contact information.

[1875] "Lifestyle information" refers to information about a user's lifestyle, such as the user's past risks, behavioral patterns, and past insurance history.

[1876] A "generative AI model" refers to an algorithmic model that uses artificial intelligence to analyze data and process natural language to automate various tasks.

[1877] "Natural language processing" refers to the technology of processing and understanding human language using computers, and includes the process of generating answers to questions using generative AI models.

[1878] "Insurance Plan" refers to a plan that specifically describes the type and content of insurance that a User will subscribe to, and includes a proposal for the most appropriate insurance in response to predicted risks.

[1879] "Claims handling" refers to the process of appropriately responding to claims and problems related to insurance that arise from users after they have taken out insurance.

[1880] "After-sales service" refers to the provision of services and support to users after they have taken out insurance, and includes general user care, including handling claims.

[1881] "Visual comparison tools" refers to features that provide a visual interface that allows users to easily understand and compare insurance plans and other options.

[1882] This invention is a system that collects basic information and lifestyle information about users, analyzes this data to predict future risks, creates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service.

[1883] User registration and data collection examples

[1884] Users access the service using a web browser or a dedicated app, and enter their name, age, gender, occupation, and contact information on the new registration page. The device collects the entered basic information in a form and sends it to the server. The server stores the received information in a database such as MySQL. The server then displays a questionnaire form on the user's device to collect lifestyle information. The user answers the questionnaire, and the device sends the response data to the server. The server stores this information in a database.

[1885] Examples of data analysis and risk prediction

[1886] The server provides the user's basic information and lifestyle information to a data analysis engine (e.g., TensorFlow), which then uses this information to generate a generative AI model that predicts future risks. The generative AI model considers past risks, behavioral patterns, and insurance history, and applies a predictive algorithm to generate a risk assessment specific to the user. For example, a user who frequently participates in sporting events may be deemed to be at high risk of sports-related injuries.

[1887] Example of an insurance plan proposal

[1888] The generative AI model takes predicted risks into account and generates the optimal insurance plan. Specifically, it automatically generates a plan that includes injury compensation for sports enthusiasts. The generated insurance plan is notified to the user via the server, and detailed information is displayed on the device. The user can visually compare multiple proposals.

[1889] Insurance Questions and Answers Example

[1890] The user has a question about the proposed insurance plan and types in a question such as, "How much does this insurance plan cover?" The device sends the question to the server, which passes it on to the generative AI model. The generative AI model understands the question through natural language processing and generates an answer such as, "This plan includes injury compensation up to 1 million yen." The server then sends the answer to the device, which displays it to the user.

[1891] An example of insurance enrollment procedures

[1892] The user indicates their intention to sign up for the proposed insurance plan and clicks the sign-up button on their device. The server receives this, generates an input form for any additional information required (e.g., health check results, past medical history), and displays it on the user's device. The user enters the required information, and the device sends it to the server. The server verifies all input data, automatically connects to the insurance company's system, and generates a notice of enrollment completion, which is sent to the user's device.

[1893] Examples of complaint handling and after-sales service

[1894] If a user wishes to submit a claim after purchasing insurance, they enter details into the claim submission form on their device and click the submit button. The server receives the claim information and sends it to the generative AI model, which analyzes the claim content and determines the appropriate processing steps. The server works in conjunction with the insurance company's system to handle the claim and notifies the user's device of the progress of the claim in real time.

[1895] Prompt Sentence Examples

[1896] Insurance plan question examples:

[1897] User: How much coverage does this insurance plan provide?

[1898] Generative AI model: This plan includes injury compensation up to 1 million yen.

[1899] Example of handling a complaint:

[1900] User: I want to file a claim, how do I do that?

[1901] Generative AI Model: Please fill in the details in the claim submission form and submit it.

[1902] This system allows users to efficiently select insurance plans, apply for insurance, and even handle claims. The introduction of this system will dramatically improve user satisfaction, particularly by streamlining insurance selection and procedures.

[1903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1904] Step 1:

[1905] The user accesses the service and enters the required information on the new registration page.

[1906] Specific operation: The user accesses the service using a web browser or a dedicated app and enters basic information such as name, age, gender, occupation, and contact information.

[1907] Input: User basic information

[1908] Output: The basic information entered is sent from the terminal to the server.

[1909] Step 2:

[1910] The terminal prompts the user to enter basic information into a form and presses the submit button.

[1911] What happens: When the user clicks the submit button, an HTTP POST request is sent to the server via the HTML form.

[1912] Input: Basic information input data

[1913] Output: Sent to the server as an HTTP POST request

[1914] Step 3:

[1915] The server stores the submitted information in a database and then displays a questionnaire to gather the user's lifestyle information.

[1916] Specific operation: The server stores the received basic information in a database such as MySQL, and then sends the survey form to the user's device.

[1917] Input: Received basic information, questionnaire form template

[1918] Output: Basic information is saved in the database and the survey form is displayed on the user's device.

[1919] Step 4:

[1920] The user answers the questionnaire, and the terminal transmits the answer data to the server.

[1921] Specific operation: The user enters the necessary information into the questionnaire form and presses the send button, which sends the response data from the terminal to the server.

[1922] Input: Survey response data

[1923] Output: Answer data is sent from the device to the server

[1924] Step 5:

[1925] The server receives this information and stores it in a database.

[1926] Specific operation: The server stores the received survey response data in a database.

[1927] Input: Survey response data

[1928] Output: Response data is saved in the database

[1929] Step 6:

[1930] The server provides the user's basic information and lifestyle information to the data analysis engine, and the generative AI uses this information to predict future risks.

[1931] How it works: The server retrieves user data from the database and passes it to a data analysis engine such as TensorFlow. A generative AI model uses this data to predict risk.

[1932] Input: User's basic information and lifestyle information

[1933] Output: Risk assessment results

[1934] Step 7:

[1935] Generative AI generates insurance plans tailored to specific risks.

[1936] What it does: It applies predictive algorithms to generate personalized insurance plans, such as plans that include injury coverage for sports enthusiasts.

[1937] Input: Risk assessment results

[1938] Output: Generated insurance plan

[1939] Step 8:

[1940] The server notifies the user of the generated insurance plan.

[1941] Specific behavior: The generated insurance plan is sent to the user as JSON data.

[1942] Input: Generated insurance plan data

[1943] Output: Insurance plan details are sent to the user's device

[1944] Step 9:

[1945] The device will display plan details to the user and allow them to compare them visually.

[1946] Specific operation: The device receives insurance plan information and displays it on the screen using HTML and CSS. It displays it in a table format so that the user can compare multiple plans.

[1947] Input: Insurance plan details

[1948] Output: A list of insurance plans displayed to the user

[1949] Step 10:

[1950] The user enters a question about the insurance plan.

[1951] What happens: A user types a question into a terminal, such as "How much does this insurance plan cover?"

[1952] Input: User question

[1953] Output: The entered question data is sent to the server.

[1954] Step 11:

[1955] The device sends the question to the server, which passes it on to the generating AI.

[1956] Specific operation: A question sent from a device reaches the server, which then passes the question to the generative AI model.

[1957] Input: User question data

[1958] Output: Question data is sent to the generation AI

[1959] Step 12:

[1960] The generation AI generates an answer and sends it to the device via the server.

[1961] Specific operation: Using natural language processing, the generative AI generates an appropriate answer and sends it to the device via the server.

[1962] Input: User question

[1963] Output: The generated answer is displayed in the terminal.

[1964] Step 13:

[1965] The user indicates their intention to enroll in the proposed insurance plan and clicks the enrollment button on the terminal.

[1966] Specific operation: The user clicks the subscription button on the terminal and sends the intention to subscribe to the server.

[1967] Input: User's intention to join

[1968] Output: Willingness to join data is sent to the server

[1969] Step 14:

[1970] The server generates an input form for additional information and sends it to the user's terminal.

[1971] Specific operation: The server generates an input form for the required additional information (e.g., health check results, past medical history) and displays it on the user's device.

[1972] Input: Intention to join data

[1973] Output: A form for entering additional information is displayed on the user's device.

[1974] Step 15:

[1975] The user enters additional information, which the terminal sends to the server.

[1976] Specific operation: The user enters additional information and the device sends the data to the server.

[1977] Input: Additional Information

[1978] Output: Additional information data is sent to the server

[1979] Step 16:

[1980] The server checks all input data and automatically connects to the insurance company's system.

[1981] Specific operation: The server verifies the input data and sends it to the insurance company's system via API.

[1982] Input: All input data

[1983] Output: Data is linked to the insurance company's system

[1984] Step 17:

[1985] A subscription completion notice is generated and sent to the user's terminal.

[1986] Specific operation: The server generates a notification that the subscription procedure has been completed and sends it to the user's device.

[1987] Input: Confirmation of enrollment completion from insurance company

[1988] Output: A notification of successful enrollment is displayed on the user's device.

[1989] Step 18:

[1990] The user submits a claim and the terminal transmits the claim information to the server.

[1991] Specific operation: The user enters detailed information into the complaint submission form and clicks the submit button. The terminal sends the complaint information to the server.

[1992] Input: Claim information

[1993] Output: Claim information is sent to the server

[1994] Step 19:

[1995] The server sends the claim information to the generation AI.

[1996] Specific operation: The server sends the complaint information to the generative AI model, which analyzes the content and determines the appropriate processing procedure.

[1997] Input: Claim information

[1998] Output: Analysis results and recommended processing steps

[1999] Step 20:

[2000] The server will work in conjunction with the insurance company's system to respond to the situation.

[2001] Specific operation: The server connects the analysis results of the generated AI and recommended processing procedures to the insurance company's system.

[2002] Input: Analysis results and recommended processing steps

[2003] Output: Processing steps linked to insurance company

[2004] Step 21:

[2005] The server notifies the user and the insurance company of the progress of the treatment, and the terminal displays the progress to the user in real time.

[2006] Specific operation: The server periodically checks the progress of the response and notifies the user and the insurance company. The terminal displays the received progress information to the user in real time.

[2007] Input: Response progress

[2008] Output: Progress is displayed in real time on the user's device.

[2009] (Application example 1)

[2010] 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."

[2011] Currently, the insurance industry requires a great deal of time and effort because the process of proposing insurance plans tailored to users' needs, procedures, and after-sales service is not fully automated. Furthermore, security risk predictions and countermeasures are carried out separately, making comprehensive risk management difficult. In particular, cybersecurity risks are not adequately addressed, often preventing users from taking appropriate measures.

[2012] 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.

[2013] In this invention, the server includes means for collecting basic information and lifestyle information of a user, means for analyzing the collected data and predicting future risks, means for generating an optimal insurance plan for the user based on the predicted risks, means for notifying the user of the generated insurance plan, means for accepting questions about insurance and generating answers using natural language processing, means for automating the insurance application process, means for automating claims processing and after-sales service after insurance application, means for collecting and analyzing basic information and behavioral data of the user and predicting cybersecurity risks, means for generating an optimal security measures plan based on the predicted cybersecurity risks, and means for automatically updating the user's device settings with the generated security measures plan. This allows the user to efficiently select insurance, apply for insurance, and even handle claims in a consistent manner, while also enabling comprehensive measures against cybersecurity risks.

[2014] "User's basic information and lifestyle information" refers to personal information about the user and data related to their daily behavioral patterns.

[2015] "Future risks" are predictions of problems and dangers that users may face in the future.

[2016] "Insurance Plan" means the set of insurance terms, conditions and coverages offered to a User for a particular risk.

[2017] "Natural language processing" is a technology that allows machines to understand natural human language and generate appropriate responses.

[2018] "Behavioral data" refers to data related to a user's daily activities, including, for example, internet usage history and movement history.

[2019] "Cybersecurity risk" refers to risks such as unauthorized access, information leaks, and system failures via the Internet or digital devices.

[2020] A "security plan" is a set of measures or actions to be taken against cybersecurity risks, including settings and how to use tools.

[2021] This invention relates to a system that collects basic information and lifestyle information of users, analyzes this data to predict future risks, generates and proposes optimal insurance plans, and automates everything from insurance enrollment to after-sales service. Furthermore, this system also predicts cybersecurity risks and generates optimal security countermeasure plans, which are automatically reflected in the user's device settings.

[2022] 1. System Overview

[2023] The system consists of the following main components:

[2024] How we collect your basic and lifestyle information:

[2025] To achieve this, an interface is provided that allows users to use their smartphones or other devices to enter and submit the necessary information on a new registration page.

[2026] Ways to predict future risks:

[2027] Based on the data collected from users, the server uses a generative AI model to predict various risks.

[2028] To generate the best insurance plan:

[2029] Taking into account predicted risks, generative AI automatically generates the optimal insurance plan for the user.

[2030] Informing you of your insurance plan:

[2031] The generated plan information is sent from the server to the user's terminal, where detailed information is displayed.

[2032] Natural language processing answer generation methods:

[2033] When a user enters a question, the server uses generative AI to generate an answer corresponding to the question and sends it to the device.

[2034] Ways to automate the insurance enrollment process:

[2035] When a user selects the insurance plan they wish to subscribe to, the system automatically checks all necessary information and contacts the insurance company.

[2036] Automating claims handling and after-sales service:

[2037] When you submit a complaint, we collect and analyze that information and generate appropriate response procedures to process it.

[2038] Cybersecurity risk prediction tools:

[2039] Collect user behavior data and predict cybersecurity risks using generative AI models.

[2040] How to generate a security action plan:

[2041] Generate optimal countermeasure plans to address predicted cybersecurity risks.

[2042] How to automatically apply security measures:

[2043] The generated security plan is automatically reflected in the user's device settings.

[2044] 2. Working Example

[2045] 2.1 Hardware and software used

[2046] Smartphone:

[2047] Use an iOS or Android device.

[2048] server:

[2049] Use Flask to manage all data collection, analytics, notifications, and procedures cross-platform.

[2050] Generative AI models:

[2051] Build using scikit-learn or TensorFlow / Keras.

[2052] 2.2 Example of operation

[2053] User Registration and Data Collection:

[2054] Users access the service and enter their name, age, gender, occupation, and contact information on the new registration page, then answer a questionnaire about lifestyle information such as past risks, behavioral patterns, and insurance needs.

[2055] Risk Prediction:

[2056] The server passes the collected data to a generative AI model that predicts future risks specific to the user.

[2057] Insurance plan suggestions:

[2058] Based on the risk assessment results, the most suitable insurance plan is generated and notified to the user.

[2059] Security Measures:

[2060] Cybersecurity risks are predicted, optimal countermeasure plans are generated, and automatically applied to users' devices.

[2061] 2.3 Prompt Sentence Examples

[2062] Example prompt sentence:

[2063] User information: Name = Yamada Taro, Age = 30, Gender = Male, Occupation = IT engineer, Past risks = None, Behavior pattern = Frequent online shopping, Need for insurance = High

[2064] By using these system components, users can consistently and efficiently carry out insurance-related procedures and cybersecurity measures, enabling comprehensive risk management.

[2065] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2066] Step 1:

[2067] The user accesses the new registration page using a terminal, enters basic information (name, age, gender, occupation, contact details), and presses the submit button. The entered data is sent from the terminal to the server and stored in the database.

[2068] Input: User's basic information (name, age, gender, occupation, contact information)

[2069] Processing: The user enters information and sends the data from the device to the server

[2070] Output: Basic information saved in the database

[2071] Step 2:

[2072] The server displays a questionnaire to the user to collect lifestyle information (past risks, behavioral patterns, need for insurance). The user answers the questionnaire, and the device sends the response data to the server. The server stores the response data in a database.

[2073] Input: User's lifestyle information (past risks, behavioral patterns, insurance needs)

[2074] Processing: The user answers the survey and sends the data from the device to the server.

[2075] Output: Lifestyle information saved in database

[2076] Step 3:

[2077] The server passes basic and lifestyle information to a generative AI model to predict future risks. The generative AI model performs risk assessment based on past data and behavioral patterns and generates a risk score.

[2078] Input: Basic information and lifestyle information stored in the database

[2079] Processing: Generative AI models analyze data and predict future risks

[2080] Output: Risk score per user

[2081] Step 4:

[2082] The server generates an optimal insurance plan using a generative AI model based on the predicted risks. The generated plan information is sent from the server to the device and displayed visually to the user.

[2083] Input: Risk Score

[2084] Processing: Generative AI model generates optimal insurance plan

[2085] Output: Insurance plan information is sent to the user's device

[2086] Step 5:

[2087] The user enters a question about the proposed insurance plan, and the device sends the question to the server, which uses generative AI to process natural language and generate an appropriate answer, which is then sent to the device.

[2088] Input: User question

[2089] Processing: The generative AI model processes natural language and generates an answer

[2090] Output: The answer is displayed on the terminal.

[2091] Step 6:

[2092] The user indicates their intention to sign up for an insurance plan and clicks the sign-up button on their device. The server receives this, generates a form for the user to enter additional information, and displays it on the user's device. The user enters the required information, which the device sends to the server. The server verifies the data and automatically contacts the insurance company.

[2093] Input: Intention to join and additional information

[2094] Processing: The server automatically sends the data to the insurance company

[2095] Output: A notification of successful enrollment is displayed on the user's device.

[2096] Step 7:

[2097] When a user files a claim after taking out insurance, they input the claim details and send them from their device to the server, which then analyzes the claim using a generative AI model, determines the appropriate response procedure, and responds in cooperation with the insurance company's system.

[2098] Input: User's claim details

[2099] Processing: The generative AI model analyzes the complaint and generates a response procedure.

[2100] Output: Progress notifications are displayed on the user's device

[2101] Step 8:

[2102] The server collects user behavior data and uses generative AI models to predict cybersecurity risks. Based on the predicted risks, it generates an optimal security countermeasure plan and automatically applies it to the user's device settings.

[2103] Input: User b...

Claims

1. A means of collecting basic information and lifestyle information of users; A means of analyzing collected data and predicting future risks, A means for generating an optimal insurance plan for a user based on the predicted risk; a means for notifying the user of the generated insurance plan; a means for accepting insurance-related questions and generating answers using natural language processing; A means of automating the insurance enrollment process; A means for automating claims processing and after-sales service after insurance is purchased; A system including:

2. 2. The system according to claim 1, wherein the basic user information includes age, gender, occupation, and contact information.

3. 2. The system according to claim 1, wherein the lifestyle information includes past risks, behavioral patterns, and past insurance history.

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