System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting personal information; means for storing the input personal information; means for inputting the stored personal information to an AI model and generating an insurance plan; means for displaying the generated insurance plan; means for selecting and applying for the insurance plan; and means for confirming whether the applicant is an employee and applying a discount if the applicant is the employee.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, it is important to select an insurance plan that best suits an individual's lifestyle and financial situation, but this process is often complex and time-consuming. Furthermore, conventional insurance proposal systems often require manual input of personal information and plan generation, resulting in inefficiencies. Furthermore, even if a company has a system that offers special discounts to employees, this is not automatically reflected, creating a time-consuming process. This invention aims to solve the above problems by providing a system that utilizes AI to quickly and appropriately propose insurance plans tailored to individual circumstances. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means:

[0006] A means for entering personal information;

[0007] A means for storing the personal information entered;

[0008] A means for inputting the stored personal information into an AI model to generate an insurance plan;

[0009] a means for displaying the generated insurance plan;

[0010] How to select and apply for an insurance plan;

[0011] A way to verify employee status and apply discounts if they are employees

[0012] By providing a system that includes this, users can quickly find the insurance plan that best suits them, and the application of employee discounts will also be processed automatically. Furthermore, by including a means to generate asset formation plans, the company will be able to offer more comprehensive proposals at launch.

[0013] "Personal information" refers to information that can be used to identify a specific individual, such as name, age, gender, occupation, and family composition.

[0014] "Input means" refers to the interface through which a user provides personal information to the system.

[0015] "Storage means" refers to a mechanism for recording the entered personal information in a database or other storage system.

[0016] An "AI model" is an artificial intelligence model that uses machine learning algorithms to analyze data and generate optimal insurance plans.

[0017] The "means of generation" refers to the process or mechanism for creating insurance plans using AI models.

[0018] The "display means" is an interface for visually presenting the generated insurance plan to the user.

[0019] The "means of selection and application" is a mechanism by which a user can select an appropriate insurance plan from the proposed plans and apply for it.

[0020] The "means for applying the discount" is the process or mechanism for applying a specific discount rate after employee verification.

[0021] An "asset formation plan" is a proposed plan for planning the management and growth of assets based on the user's financial situation.

[0022] The term "system" refers to an integrated collection of computer programs and hardware including the above means. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] The system of the present invention automates the process in which a user inputs personal information and then proposes an appropriate insurance plan based on that information. A specific embodiment of the system will be described below.

[0045] The system consists of three main components: the user's device, the server that processes the data, and the AI ​​model. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[0046] User data input processing

[0047] User:

[0048] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[0049] Device:

[0050] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[0051] server:

[0052] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[0053] Insurance plan proposal processing

[0054] server:

[0055] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[0056] Device:

[0057] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[0058] Employee benefit discount processing

[0059] User:

[0060] The user selects an insurance plan and clicks the apply button.

[0061] Device:

[0062] The terminal transmits the selected plan information to the server.

[0063] server:

[0064] The server searches the database for employee information based on the user ID, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user.

[0065] Additional proposals for asset formation plans

[0066] server:

[0067] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[0068] Specific examples

[0069] 1. Enter your personal information

[0070] User: "I'm a 35-year-old male engineer with a wife and two children."

[0071] Device: "Personal and family information will be sent to the server."

[0072] Server: "Data received, thank you."

[0073] 2. Insurance plan proposals

[0074] Server: "Based on the user's data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0075] Device: "The following insurance plans are suitable."

[0076] 3. Employee discount processing

[0077] User: "Apply for car insurance."

[0078] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[0079] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees, and providing a more comprehensive offering by adding asset formation plans.

[0080] The processing flow will be explained below.

[0081] Step 1: Displaying the user data entry form

[0082] Server: When the insurance app is launched, it sends the HTML of a form for entering personal and family information to the device, allowing the user to enter the required data.

[0083] Step 2: Enter user data

[0084] User: Enters name, age, gender, occupation, family composition, and desired insurance conditions, and presses the submit button, which provides the necessary information to the system.

[0085] Step 3: Send input data

[0086] Terminal: The entered data is sent to the server in JSON format, encrypted using the secure HTTPS protocol.

[0087] Step 4: Save user data

[0088] Server: Stores the received data in a database, which also includes security measures such as SQL injection.

[0089] Step 5: Invoke the AI ​​model

[0090] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the data.

[0091] Step 6: Generate your insurance plan

[0092] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[0093] Step 7: Distributing the insurance plan

[0094] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[0095] Step 8: View your plan

[0096] Terminal: The received insurance plan is displayed on the screen, formatted for easy visual understanding by the user.

[0097] Step 9: Choose a plan and sign up

[0098] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[0099] Step 10: Submit Selected Data

[0100] Device: Sends the selected plan information to the server. It is sent in JSON format again.

[0101] Step 11: Employee Verification and Discount Processing

[0102] Server: Searches for employee information in the database based on the user ID, applies discounts if applicable, and calculates the final discounted price.

[0103] Step 12: Final price notification

[0104] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[0105] Step 13: Propose an asset formation plan

[0106] Server: Asset formation data is input into the AI ​​model in the same way as insurance plans, and appropriate proposals are generated. This data is also sent to the device.

[0107] Step 14: View your wealth plan

[0108] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[0109] Example 1

[0110] 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."

[0111] Conventional insurance plan proposal systems lack the functionality to automatically propose the most suitable plan based on the user's personal information. Also, the application of employee benefit discounts is often done manually, reducing user convenience. Another issue is the lack of a system that comprehensively proposes not only insurance plans but also asset formation plans for users.

[0112] 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.

[0113] In this invention, the server includes a means for inputting personal information, a means for storing the input personal information, and a means for inputting the stored personal information into a generative AI model to generate an insurance plan. This makes it possible to automatically propose optimal insurance plans and asset formation plans based on the user's personal information. Furthermore, it is possible to verify whether the user is an employee based on individual identification information and automatically apply discounts, improving user convenience.

[0114] "Personal information" refers to information such as a user's name, age, gender, occupation, and family composition.

[0115] "Retention" means saving the input data in a storage such as a database.

[0116] A "generative AI model" refers to an artificial intelligence model that is trained using machine learning algorithms and generates appropriate insurance plans or asset formation plans based on input data.

[0117] "Insurance Plans" refers to detailed plans such as life insurance and car insurance that are proposed based on the user's personal information.

[0118] "Asset formation plan" refers to a plan that includes suggestions regarding the user's asset management.

[0119] "Terminal" refers to a computer device used by a user, such as a smartphone or PC.

[0120] "Server" refers to a computer system that receives and processes data sent from a user's terminal.

[0121] "Individual identification information" refers to information that uniquely identifies a user and is primarily used to verify employee information.

[0122] "Discount" refers to a reduction in the price applied to insurance premiums.

[0123] The system of the present invention automatically proposes appropriate insurance plans and asset formation plans based on personal information entered by the user. The hardware and software required to implement this system, as well as the data processing method, are described in detail below.

[0124] User data input processing

[0125] User:

[0126] Users launch an insurance app on their smartphone or PC and enter their personal information (name, age, gender, occupation, family composition, etc.). For example, they might enter, "I am a 35-year-old man, an engineer, and have a wife and two children."

[0127] Data transmission process

[0128] Device:

[0129] The device, which can be a smartphone or PC, encodes the input data into JSON format and sends it to the server using the secure HTTPS protocol.

[0130] Data storage and preprocessing

[0131] server:

[0132] The server stores the received user data in a database. The stored data undergoes preprocessing to be input into the generative AI model. This preprocessing includes data normalization and cleaning. The server used is a common cloud service (e.g., AWS (registered trademark), Google (registered trademark) Cloud).

[0133] Insurance plan generation process

[0134] server:

[0135] The server inputs the preprocessed data into a generative AI model to generate the optimal insurance plan for the user. This generative AI model is based on a pre-trained machine learning algorithm. The model uses machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0136] Insurance plan display processing

[0137] Device:

[0138] The generated insurance plan is sent from the server to the user's device and displayed within the app. The user can choose from multiple insurance plans. The display format uses an easy-to-understand graphical user interface (GUI).

[0139] Employee benefit discount processing

[0140] User:

[0141] The user selects one of the insurance plans offered and clicks the apply button. For example, select "Apply for car insurance."

[0142] Device:

[0143] The selected insurance plan information is sent to the server.

[0144] server:

[0145] The server searches the database for employee information based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user's terminal. For example, the information notified may be "Automobile insurance (after discount): 79,200 yen."

[0146] Additional proposals for asset formation plans

[0147] server:

[0148] Data on wealth planning is also collected and fed into a generative AI model, similar to insurance plans, to generate appropriate proposals, which are then sent to the device and displayed to the user.

[0149] Examples and prompts

[0150] As a concrete example, the case where a user inputs his / her personal information is shown below.

[0151] User: "I'm a 35-year-old male engineer with a wife and two children."

[0152] Device: "Personal and family information will be sent to the server."

[0153] Server: "Data received, thank you."

[0154] Examples of insurance plans that are offered include:

[0155] Server: "Based on the user data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0156] Device: "The following insurance plans are suitable."

[0157] Examples of employee discounts include:

[0158] User: "I'm applying for car insurance."

[0159] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[0160] The system of the present invention allows users to receive proposals for optimal insurance plans and asset formation plans, and in particular, it greatly improves convenience by automatically applying discounts to company employees.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Step 1: User data input process

[0163] User:

[0164] The user launches an insurance app from their smartphone or PC and enters their personal information (name, age, gender, occupation, family composition, etc.). The data is entered manually into the app's form fields. Specifically, the user enters "Name: Taro Tanaka, Age: 35, Gender: Male, Occupation: Engineer, Family Composition: Wife, 2 Children." The input data is converted to JSON format.

[0165] Input: User's personal and family information

[0166] Output: User data encoded in JSON format

[0167] Step 2: Data transmission process

[0168] Device:

[0169] The device encodes the entered user data and sends it to the server using the secure HTTPS protocol. Specifically, when the user clicks the "Send" button, the data is converted to JSON format within the app and sent to the server as an HTTPS request.

[0170] Input: User data encoded in JSON format

[0171] Output: HTTPS request sent to the server

[0172] Step 3: Data storage and preprocessing

[0173] server:

[0174] The server stores the received user data in a database. After storing it, it performs preprocessing to prepare it for input to the AI ​​model. This preprocessing includes normalizing and cleaning the data. Specifically, the server executes an SQL query to insert the data into the database, and then normalizes it using a Python script.

[0175] Input: HTTPS request sent to the server

[0176] Output: User data stored in a database, preprocessed data

[0177] Step 4: Insurance plan generation process

[0178] server:

[0179] The server then inputs the preprocessed data into a generative AI model to generate an insurance plan. This generative AI model uses pre-trained machine learning algorithms, such as TensorFlow and PyTorch. The generated insurance plan includes the optimal plan based on the user's profile.

[0180] Input: Preprocessed data

[0181] Output: Generated insurance plan

[0182] Step 5: Insurance plan display process

[0183] Device:

[0184] The generated insurance plan is sent from the server to the user's device and displayed within the app. Specifically, the device parses the JSON response from the server and uses it to display data in a graphical user interface (GUI). The user can choose from multiple insurance plans.

[0185] Input: Generated insurance plan (JSON format)

[0186] Output: App screen showing insurance plans

[0187] Step 6: Process employee benefit discounts

[0188] User:

[0189] The user selects the desired insurance plan and clicks the "Apply" button. Specifically, the user performs an operation such as "apply for car insurance."

[0190] Input: The insurance plan selected by the user

[0191] Output: Application request

[0192] Device:

[0193] The selected insurance plan information is sent to the server. Specifically, the device encodes the user's selection information again into JSON format and sends it to the server as an HTTPS request.

[0194] Input: User-selected insurance plan (JSON format)

[0195] Output: The application request sent to the server

[0196] server:

[0197] The server searches for employee information from a database based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is again notified to the user's terminal. For example, it sends information such as "Automobile insurance (after discount): 79,200 yen." Specifically, the server executes an SQL query to obtain employee information and calculates the discount.

[0198] Input: User application information, employee information in the database

[0199] Output: Final premium with discount applied

[0200] Step 7: Additional proposals for asset formation plans

[0201] server:

[0202] Data on asset formation plans is also collected and input into a generative AI model, just like insurance plans, to generate appropriate proposals. The generated asset formation plans are sent to the device and displayed to the user. Specifically, the server inputs the asset formation data into a Python AI model and sends the generated results in JSON format to the device.

[0203] Input: Asset formation data

[0204] Output: Generated wealth plan (JSON format)

[0205] (Application example 1)

[0206] 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."

[0207] Conventional food delivery services did not adequately provide personalized suggestions that took into account users' individual preferences, health information, allergies, etc. This meant that they were unable to recommend the optimal food or health plans that users truly needed, potentially resulting in lower satisfaction. Furthermore, applying discounts based on specific conditions had to be done manually, which was inefficient.

[0208] 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.

[0209] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model to generate a personalized food plan, a means for displaying the generated food plan, a means for selecting and applying for a food plan, and a means for checking specific conditions and applying discounts based on the conditions. This makes it possible to automatically propose optimal food plans and health plans according to the individual needs of the user and efficiently apply discounts.

[0210] "Personal Information" is information that can individually identify a user, including information about the user's age, gender, dietary preferences, allergy information, and health information.

[0211] "Means for saving" refers to the function of saving input data in storage such as a database.

[0212] "AI Model" is an artificial intelligence model that uses machine learning algorithms to generate optimal food and health plans based on a user's profile and preferences.

[0213] A "food plan" is a meal plan suggested based on a user's dietary preferences and health information.

[0214] A "means for displaying" is a digital component that displays the generated plan on a terminal in a form that can be viewed by a user.

[0215] "Conditions" are specific criteria or requirements that a User must meet in order to receive special services or discounts.

[0216] A "means for applying discounts" is a function that automatically applies discounts, such as price reductions, when a user meets certain conditions.

[0217] A "health plan" is a plan that suggests optimal exercise and diet based on the user's health goals and situation.

[0218] A "terminal" is a device such as a smartphone, computer, or tablet that allows a user to enter data or view plans.

[0219] A "server" is a computer system with the computational resources to receive and store data submitted by users, input it into an AI model, and generate a plan.

[0220] A "system" is an integrated platform in which multiple components work together.

[0221] The system of the present invention allows a user to input personal information and then proposes optimal food and health plans based on that information. A specific embodiment of the system will be described below.

[0222] User data input processing

[0223] User:

[0224] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. The application provides a form for users to enter their personal information (name, age, gender, allergy information, health information).

[0225] Device:

[0226] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[0227] server:

[0228] The server stores the received user data in a database, while also preprocessing the data and formatting it as input data for the AI ​​model.

[0229] Food plan proposal processing

[0230] server:

[0231] The server then inputs the formatted user data into an AI model to generate a meal plan, which uses pre-trained machine learning algorithms to suggest optimal meal plans based on individual user preferences and health information.

[0232] Device:

[0233] The generated food plan is sent to the user's device and displayed within the app, where the user can choose the most appropriate one from multiple suggested food plans.

[0234] Additional health plan proposals

[0235] server:

[0236] Similarly, the health plan is generated based on user data and fed into an AI model to generate optimal recommendations, which are then sent to the user's device along with the food plan.

[0237] Discount Processing

[0238] User:

[0239] The user selects a food plan and clicks the sign up button.

[0240] server:

[0241] The server checks the database for specific conditions based on the user ID. If the conditions are met, it applies a certain discount (for example, 10%). The final price after the discount is applied is notified to the user.

[0242] Specific examples

[0243] Enter your personal information

[0244] User: "I'm a 30-year-old man who loves sushi but is allergic to peanuts. I'm on a diet and my daily calorie goal is 2000 kcal."

[0245] Device: "Personal and health information will be sent to the server."

[0246] Server: "Data received, thank you."

[0247] Food plan suggestions

[0248] Server: "Based on your data, we'll suggest the following food plans: low-carb set lunch, low-calorie set dinner."

[0249] Device: "The following food plan is suitable for you."

[0250] Health plan proposals

[0251] Server: "At the same time, we'll suggest the following health plan: exercise twice a week, and record your daily walking."

[0252] Discount Processing

[0253] User: "Order a low-calorie dinner set."

[0254] Server: "Checking... You meet the requirements, so we'll give you a 10% discount. Your final price is 1800 yen."

[0255] Example prompt for a generative AI model:

[0256] "A 30-year-old man loves sushi and has a peanut allergy. He is on a calorie restriction diet, with a daily goal of 2000 kcal. Can you suggest a weekly meal plan that would be optimal for him?"

[0257] In this way, the system of the present invention streamlines the process of selecting a personalized food plan for users, improves convenience by automatically applying discounts when certain conditions are met, and enables more comprehensive offerings by adding health plans.

[0258] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0259] Step 1:

[0260] The user launches a dedicated application using a device such as a smartphone, tablet, or PC. The application provides the user with a form to enter personal information (name, age, gender, allergy information, health information). This input information becomes the data to be sent to the server in the next step.

[0261] Input: Personal and health information you enter.

[0262] Output: User data encoded in JSON format.

[0263] Step 2:

[0264] The terminal encodes the data entered by the user in JSON format and sends it to the server using the secure HTTPS protocol. Once the data has been sent, the server receives it in the next step.

[0265] Input: User data in JSON format.

[0266] Output: Notification of completion of transmission to the server.

[0267] Step 3:

[0268] The server stores the user data received from the device in a database. At the same time, it preprocesses the data and formats it as input data for the AI ​​model. This process converts the user data into a format that can be used by the AI ​​model.

[0269] Input: User data in JSON format sent to the server.

[0270] Output: User data stored in a database, formatted data for the AI ​​model.

[0271] Step 4:

[0272] The server inputs the formatted user data into an AI model to generate a food plan. The AI ​​model uses pre-trained machine learning algorithms to generate an optimal food plan based on the individual user's preferences and health information. This step results in several candidate food plans.

[0273] Input: User data formatted for the AI ​​model.

[0274] Output: The generated food plan.

[0275] Step 5:

[0276] The generated food plan is sent from the server to the user's device and displayed in the app. The user selects one of the proposed food plans. This selection data is used in the next step.

[0277] Input: The generated food plan.

[0278] Output: The food plan displayed on the user's device.

[0279] Step 6:

[0280] The user selects the most suitable food plan from the displayed options and clicks the "Apply" button. This selection information is sent from the device to the server.

[0281] Input: The food plan selected by the user.

[0282] Output: Data sent to the server (selected food plan).

[0283] Step 7:

[0284] The server checks the database for specific conditions (e.g., special offers or discounts) based on the user ID. If these conditions are met, it applies a certain discount (e.g., 10%). The final price with the discount applied is notified to the user.

[0285] Input: User selection data and discount conditions.

[0286] Output: Final price after applying discounts.

[0287] Step 8:

[0288] The server also generates a health plan and proposes it alongside the food plan, analyzing the user's health information based on an AI model and providing optimal exercise plans and dietary advice.

[0289] Input: User data required to generate a health plan.

[0290] Output: The generated health plan.

[0291] These are the processing steps of the system that realizes this application example. At each step, the specific operations performed by the server, terminal, and user, as well as their inputs and outputs, are clearly shown. This allows us to realize a system that can provide optimal food and health plans to users and efficiently apply discounts based on specific conditions.

[0292] 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.

[0293] The system of the present invention automates the process of suggesting appropriate insurance plans based on the user's personal information. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, allowing it to tailor the proposals to suit the user's emotional state.

[0294] The system consists of four main components: the user's device, the server that processes the data, the AI ​​model, and the emotion engine. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[0295] User data input processing

[0296] User:

[0297] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[0298] Device:

[0299] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[0300] server:

[0301] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[0302] Insurance plan proposal processing

[0303] server:

[0304] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[0305] Device:

[0306] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[0307] Employee benefit discount processing

[0308] User:

[0309] The user selects an insurance plan and clicks the apply button.

[0310] Device:

[0311] The terminal transmits the selected plan information to the server.

[0312] server:

[0313] The server searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[0314] Additional proposals for asset formation plans

[0315] server:

[0316] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[0317] Emotion recognition by emotion engine

[0318] User:

[0319] While a user is using the insurance app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition.

[0320] Device:

[0321] The emotion engine analyzes the emotion data and sends it to the server. The emotion data is encoded in JSON format and sent.

[0322] server:

[0323] The server inputs the received emotional data into an AI model and adjusts the contents of insurance and asset formation plans based on the user's emotional state. For example, if the user is feeling anxious, it will prioritize insurance plans with low risks.

[0324] Specific examples

[0325] 1. Enter your personal information

[0326] User: "I'm a 35-year-old male engineer with a wife and two children."

[0327] Device: "Personal and family information will be sent to the server."

[0328] Server: "Data received, thank you."

[0329] 2. Insurance plan proposals

[0330] Server: "Based on the user's data, we will recommend life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0331] Device: "The following insurance plans are suitable."

[0332] 3. Employee discount processing

[0333] User: "Apply for car insurance."

[0334] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[0335] 4. Emotional Engine Adjustment

[0336] User: Shows "anxious facial expression" while using the app.

[0337] Emotion engine: Recognizing "anxiety."

[0338] Server: "Show insurance plans with low risk (e.g., life insurance exclusion plans)."

[0339] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees. The addition of asset-building plans also enables more comprehensive proposals. Furthermore, by incorporating an emotion engine, the system can tailor insurance plans and proposals based on the user's emotional state, providing a more personalized service to users.

[0340] The processing flow will be explained below.

[0341] Step 1: Displaying the user data entry form

[0342] Server: When the insurance app is launched, it sends an HTML form to the device for entering personal and family information. This form includes fields such as name, age, gender, occupation, and family composition.

[0343] Step 2: Enter user data

[0344] User: Enters name, age, gender, occupation, family composition, etc. into the specified form and presses the submit button. This provides the necessary information to the system.

[0345] Step 3: Send input data

[0346] Terminal: The entered data is sent to the server in JSON format. The data is encrypted and sent securely using the HTTPS protocol.

[0347] Step 4: Save user data

[0348] Server: The received data is saved in a database. Security measures such as SQL injection are implemented when saving the data in the database.

[0349] Step 5: Invoke the AI ​​model

[0350] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the user's situation and desired conditions.

[0351] Step 6: Generate your insurance plan

[0352] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[0353] Step 7: Distributing the insurance plan

[0354] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[0355] Step 8: View your plan

[0356] Device: The insurance plan you received will be displayed on the screen, visually showing the plan details and features.

[0357] Step 9: Choose a plan and sign up

[0358] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[0359] Step 10: Submit Selected Data

[0360] Device: Sends the selected plan information to the server. The data is sent in JSON format again.

[0361] Step 11: Employee Verification and Discount Processing

[0362] Server: Searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[0363] Step 12: Final price notification

[0364] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[0365] Step 13: Propose an asset formation plan

[0366] Server: Asset formation data, along with the insurance plan, is input into the AI ​​model to generate appropriate proposals. The generated asset formation plan is sent to the user's device along with the insurance plan.

[0367] Step 14: View your wealth plan

[0368] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[0369] Step 15: Working with the Emotional Engine

[0370] User: Emotions are detected through tone of voice and facial expressions while using the app.

[0371] On the device: The detected emotions are analyzed by the emotion engine and the results are sent to the server in JSON format.

[0372] Step 16: Processing Emotion Data

[0373] Server: Analyzes the received emotional data and adjusts the proposed insurance and asset building plans based on the user's emotional state. If the user feels anxious, the plan will be customized to suggest a low-risk plan.

[0374] Step 17: Deliver the adjusted plan

[0375] Server: The adjusted insurance plan or asset formation plan is sent to the device in JSON format. The adjustment results are reflected in real time.

[0376] Step 18: View the adjusted plan

[0377] Device: The adjusted insurance plan or asset formation plan is redisplayed on the screen, allowing the user to see the optimal plan based on their emotional state.

[0378] Example 2

[0379] 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."

[0380] Conventional insurance plan recommendation systems have the following problems. First, the process of properly inputting the user's personal information and family composition information and then proposing the most suitable insurance plan based on that information is done manually, which is inefficient. Second, the lack of a function to automatically apply discount benefits to company employees leads to an inconsistent user experience. Third, recommendations are made without taking the user's emotional state into consideration, which may result in the recommendation not being the most suitable for the user. Furthermore, there is a need to provide a more comprehensive service by proposing asset formation plans in addition to insurance plans.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0382] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model and generating an insurance plan, a means for displaying the generated insurance plan, a means for selecting and applying for an insurance plan, a means for verifying whether the user is an employee and applying a discount if so, a means for recognizing the user's emotions and adjusting the proposal content based on the emotions, and a means for generating an asset formation plan. This allows the user to simply input their personal information and receive a proposal for the optimal insurance plan or asset formation plan, and further enables personalized proposals based on the user's emotional state. Furthermore, discounts are automatically applied to company employees, improving convenience.

[0383] "Personal information" refers to information such as the user's name, age, gender, occupation, and family composition and age.

[0384] "Saving" means storing input data in a storage device such as a database.

[0385] An "AI model" is a program that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on user data.

[0386] An "insurance plan" is a proposal for an insurance product, such as life insurance or auto insurance, offered to a user.

[0387] "Display" refers to visually showing the generated insurance plan on the user's terminal.

[0388] "Selection" refers to the act of a user choosing and deciding on the most suitable insurance plan from multiple options.

[0389] "Applying" means going through the process of officially enrolling in the insurance plan selected by the user.

[0390] An "employee" is an individual who belongs to a particular company and is entitled to the company's benefits and discounts.

[0391] A "discount" is a reduction of a certain amount or percentage from the original insurance premium based on certain criteria.

[0392] "Emotion recognition" refers to determining a user's current emotional state using technologies such as voice tone and facial recognition.

[0393] "Adjustment" refers to changing or modifying the contents of an insurance or wealth plan in response to a perceived emotional state.

[0394] An "asset formation plan" is a proposal for asset management and investment tailored to the user's financial goals and situation.

[0395] The system of the present invention allows users to input their personal information and, based on that information, provides the function of proposing optimal insurance plans. It also applies special discounts to corporate employees and can adjust the proposals based on the user's emotional state. The system is broadly composed of four components: the user's device, a server, an AI model, and an emotion engine. It also provides comprehensive services to users by proposing additional asset formation plans.

[0396] User data input processing

[0397] Users launch the insurance app on their smartphone, PC, or other device and enter their name, age, gender, occupation, and family information. This information is encoded into JSON format by the device and sent to the server via the secure HTTPS protocol.

[0398] Specific examples

[0399] "I'm a 35-year-old man, an engineer by profession, with a wife and two children."

[0400] Data transmission and storage

[0401] The device sends the input data to the server, which stores the received user data in a database, preprocesses the data, and formats it as input data for the AI ​​model.

[0402] Insurance plan proposal processing

[0403] The server inputs the formatted user data into an AI model to generate the optimal insurance plan. The AI ​​model uses pre-trained machine learning algorithms to suggest the insurance plan that best suits the user's profile. The generated insurance plan is sent to the user's device and displayed within the app.

[0404] Specific examples

[0405] "We offer life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0406] Employee benefit discount processing

[0407] When a user selects an insurance plan and applies, the device sends the selection information to the server, which searches the database for employee information based on the user ID and applies discounts if applicable. The final premium amount is then notified to the user.

[0408] Specific examples

[0409] "Checking... Employee discount will be applied. Final price is 79,200 yen."

[0410] Additional proposals for asset formation plans

[0411] The server also inputs data about the asset formation plan into the AI ​​model to generate optimal proposals, which are then sent to the user's device along with the insurance plan.

[0412] Emotion recognition by emotion engine

[0413] While a user is using the app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition. The analysis results are encoded in JSON format and sent to the server. The server then uses an AI model to adjust the suggestions based on the received emotional data and provide a plan that matches the user's emotional state.

[0414] Specific examples

[0415] If the user shows an "anxious expression," the emotion engine recognizes it and the server displays a low-risk insurance plan (e.g., a life insurance exception coverage plan).

[0416] In this way, the system of the present invention helps users efficiently select insurance plans, automatically applying discounts to corporate employees in particular. It also offers additional asset formation plans, providing more comprehensive insurance and asset management solutions. Furthermore, the emotion engine provides personalized services based on the user's emotional state.

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

[0418] Step 1: User Data Input

[0419] Input: The user enters personal information.

[0420] Specific operations: Users launch the app on their smartphone or PC and enter their name, age, gender, occupation, and family information into a dedicated form.

[0421] Output: The personal information entered is saved on the device.

[0422] Step 2: Send data

[0423] Input: Personal information entered by the user.

[0424] What happens: The device encodes the personal information entered by the user into JSON format.

[0425] Data processing: The encoded data is sent to the server using the HTTPS protocol.

[0426] Output: The server receives the encoded data sent by the device.

[0427] Step 3: Save data

[0428] Input: The encoded data sent.

[0429] Specific operation: The server analyzes the received data and stores it in a database.

[0430] Data calculation: Preprocessing data stored in the database and preparing it as input data for the AI ​​model.

[0431] Output: The formatted data is ready.

[0432] Step 4: Generate your insurance plan

[0433] Input: Formatted user data.

[0434] Specific operation: The server inputs the formatted data into the AI ​​model.

[0435] Data Computation: The AI ​​model uses pre-trained machine learning algorithms to generate optimal insurance plans.

[0436] Output: The generated insurance plan is saved on the server.

[0437] Step 5: View your insurance plan

[0438] Input: The generated insurance plan.

[0439] Specific operation: The server sends the generated insurance plan to the user's device.

[0440] Output: The device displays the insurance plan that was sent. The user can view the insurance plan in the app.

[0441] Step 6: Select and apply for an insurance plan

[0442] Input: The insurance plan selected by the user.

[0443] Specific behavior: The user selects the most suitable insurance plan from the displayed options and clicks the apply button.

[0444] Output: The selection is saved to the device.

[0445] Step 7: Submit your selections

[0446] Input: Information about the insurance plan selected by the user.

[0447] Specific operation: The device sends the selected plan information to the server.

[0448] Output: The server receives the selection information sent by the device.

[0449] Step 8: Apply employee perks discounts

[0450] Input: Received selection information and user ID.

[0451] Specific operation: The server searches the database for employee information based on the user ID.

[0452] Data calculation: Apply discounts, if applicable, to calculate the final premium.

[0453] Output: The final insurance premium is calculated and notified to the user.

[0454] Step 9: Additional proposals for asset formation plans

[0455] Input: User data and insurance plan.

[0456] Specific operation: The server inputs data about the asset formation plan into the AI ​​model.

[0457] Data calculation: Generate optimal asset formation plans.

[0458] Output: The generated asset formation plan is sent to the user's terminal and displayed.

[0459] Step 10: Emotion Recognition with the Emotion Engine

[0460] Input: User's voice tone and facial expression data.

[0461] How it works: While the user is using the app, the emotion engine performs real-time voice tone and facial recognition.

[0462] Data processing: Analyze the emotion data and encode it into JSON format.

[0463] Output: The analyzed emotion data is sent to the server.

[0464] Step 11: Adjust content based on emotional state

[0465] Input: Parsed emotion data.

[0466] Specific operation: The server inputs the received emotion data into the AI ​​model.

[0467] Data Computation: Adjusting insurance and wealth plans based on emotional state.

[0468] Output: The adjusted insurance plan or asset formation plan is sent to the user's device and displayed.

[0469] (Application example 2)

[0470] 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."

[0471] Conventional insurance plan recommendation systems can present optimal insurance plans based on a user's personal information, but do not take into account the user's emotional state. This creates the problem of making it difficult for users to select an appropriate insurance plan when they are feeling anxious or worried. Furthermore, asset formation plans are proposed solely based on personal information, and the contents of these plans are not adjusted to fit the user's emotional state. This results in lower user satisfaction and the possibility of users being unable to select the optimal plan.

[0472] 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.

[0473] In this invention, the server includes a means for recognizing the emotional state of the user and adjusting the contents of the insurance plan based on that emotional state, a means for generating an asset formation plan and adjusting the contents based on the emotional state of the user, and a means for inputting personal information from a terminal and transmitting it to the server, thereby making it possible to propose the optimal insurance plan and asset formation plan according to the emotional state of the user.

[0474] "Personal information" refers to information that identifies a user, such as the user's name, age, gender, occupation, and family composition.

[0475] An "AI model" is a model that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on input data.

[0476] An "insurance plan" is a combination of various insurance products and their terms and conditions offered by an insurance company and proposed to a user.

[0477] "Emotional state" refers to the psychological state of the user recognized through voice tone and facial expression analysis, and refers to emotions such as anxiety, joy, and sadness.

[0478] An "asset formation plan" includes investment and savings plans to increase a user's assets, and is proposed together with an insurance plan.

[0479] A "terminal" is an electronic device used by a user, such as a smartphone or PC, that is used to input personal information and recognize emotional states.

[0480] "Server" means a central computer system that processes data submitted by users and generates insurance and wealth plans.

[0481] The present invention relates to a system that automates the process by which a user selects, adjusts, and enrolls in an insurance or wealth plan.

[0482] Specific methods for carrying out the present invention will now be described.

[0483] System configuration

[0484] User's device

[0485] The user's device is an electronic device such as a smartphone or PC. The device has the following functions:

[0486] It provides a form for entering personal information, allowing users to enter personal information such as name, age, gender, occupation, and family composition.

[0487] It uses the smartphone's camera and microphone to recognize the user's emotional state and collect that data.

[0488] The device encodes the collected data in JSON format and sends it to the server using the secure HTTPS protocol.

[0489] server

[0490] The server processes the data and has the following functions:

[0491] Receive personal information and emotional data sent by users and store it in a database.

[0492] The pre-processed data is input into an AI model to generate optimal insurance and asset formation plans.

[0493] An emotion recognition engine is used to adjust the generated plan content based on the user's emotional state.

[0494] The optimal plan is sent to the device and presented to the user.

[0495] Hardware and Software

[0496] Specific examples of hardware and software used include:

[0497] Hardware: Smartphone (with camera and microphone), PC, server (cloud server is also acceptable)

[0498] Software: Flask (web framework), TensorFlow (machine learning library), OpenCV (image processing library), DeepFace (emotion recognition library)

[0499] Processing flow

[0500] Enter your personal information

[0501] User: Launches the application and fills in the personal information form.

[0502] On the device: The personal information entered is encoded in JSON format and sent to the server using the HTTPS protocol.

[0503] Generate an insurance plan

[0504] Server: Stores the received personal information in a database and inputs it into an AI model to generate an appropriate insurance plan.

[0505] Server: Sends the generated insurance plan to the device and displays it in the app.

[0506] Recognition of emotional states

[0507] User: Emotion recognition is activated while using the application, using the camera or microphone to collect your emotional state.

[0508] Emotion engine (terminal): Sends recognized emotion data to the server and analyzes the emotional state.

[0509] Emotion-Based Adjustment

[0510] Server: Adjusts the content of the plan generated by the AI ​​model based on emotional data and personal information.

[0511] Server: Sends the adjusted plan to the device and displays it to the user.

[0512] Specific examples

[0513] Prompt Sentence Examples

[0514] The prompt that users see when entering their personal information:

[0515] Please enter your name, age, gender, occupation, family composition, and ages of your family members.

[0516] For emotion recognition, the prompt displayed to the user is:

[0517] To recognize your emotional state while using the app, please face the camera and be in a stable position.

[0518] This system allows users to easily select the most suitable insurance and asset formation plan based on their emotional state and personal information. In particular, even if they are feeling anxious or worried, the system will suggest an appropriate plan based on their emotions, which will increase user satisfaction.

[0519] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0520] Step 1:

[0521] User: Launches the application on their smartphone or PC and enters their name, age, gender, occupation, family composition, and ages of family members into the personal information input form. This information is entered into the application as personal information.

[0522] Step 2:

[0523] On the device, the personal information entered is encoded in JSON format and sent to the server using the secure HTTPS protocol. The input data is divided into fields (name, age, gender, occupation, and family information) and properly formatted.

[0524] Step 3:

[0525] Server: Stores the received personal information in a database, mapping each field to the appropriate table and preprocessing the data. For example, age is treated as numeric data, while gender and occupation are converted to predefined categorical data.

[0526] Step 4:

[0527] Server: The preprocessed personal information is input into the AI ​​model to generate the optimal insurance plan for the user profile. The AI ​​model uses machine learning algorithms to compare the input data with past data and select the optimal insurance plan. For example, it generates the optimal life insurance and car insurance plan for a 30-year-old engineer with a family.

[0528] Step 5:

[0529] Server: The generated insurance plan is sent to the device and displayed in the application. The sent format is JSON, and includes information such as the insurance type, plan details, and premium.

[0530] Step 6:

[0531] User: While using the application, the user faces the camera to recognize their emotional state, using facial recognition and voice tone analysis.

[0532] Step 7:

[0533] Device: The smartphone's camera and microphone are used to recognize the user's emotional state and capture the data. The analysis results are processed in real time through the emotion engine and sent to the server in JSON format.

[0534] Step 8:

[0535] Server: Analyzes the received emotional state data and adjusts the insurance plan content. For example, if the user is feeling anxious, the content is changed to prioritize insurance plans with low risks.

[0536] Step 9:

[0537] Server: The adjusted insurance plan is sent back to the device and displayed in the application, receiving feedback based on the user's emotional state to make more relevant recommendations.

[0538] Step 10:

[0539] User: Selects the desired plan from the insurance plans presented and applies. This action sends the selected plan information to the server.

[0540] Step 11:

[0541] Server: Checks the user database and applies any benefits, such as employee discounts, that apply to the selected insurance plan. The final calculated premium is calculated and notified to the user.

[0542] Step 12:

[0543] Server: The server connects the finalized insurance plan information to the insurance company's system, allowing the user to officially enroll in the insurance.

[0544] Thus, this system allows users to easily select the optimal insurance plan and asset formation plan based on their emotional state and personal information, and provides appropriate plans even when they are feeling particularly anxious or worried, thereby improving user satisfaction.

[0545] 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.

[0546] 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.

[0547] 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.

[0548] [Second embodiment]

[0549] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0550] 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.

[0551] 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).

[0552] 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.

[0553] 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.

[0554] 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).

[0555] 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. 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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."

[0561] The system of the present invention automates the process in which a user inputs personal information and then proposes an appropriate insurance plan based on that information. A specific embodiment of the system will be described below.

[0562] The system consists of three main components: the user's device, the server that processes the data, and the AI ​​model. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[0563] User data input processing

[0564] User:

[0565] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[0566] Device:

[0567] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[0568] server:

[0569] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[0570] Insurance plan proposal processing

[0571] server:

[0572] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[0573] Device:

[0574] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[0575] Employee benefit discount processing

[0576] User:

[0577] The user selects an insurance plan and clicks the apply button.

[0578] Device:

[0579] The terminal transmits the selected plan information to the server.

[0580] server:

[0581] The server searches the database for employee information based on the user ID, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user.

[0582] Additional proposals for asset formation plans

[0583] server:

[0584] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[0585] Specific examples

[0586] 1. Enter your personal information

[0587] User: "I'm a 35-year-old male engineer with a wife and two children."

[0588] Device: "Personal and family information will be sent to the server."

[0589] Server: "Data received, thank you."

[0590] 2. Insurance plan proposals

[0591] Server: "Based on the user's data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0592] Device: "The following insurance plans are suitable."

[0593] 3. Employee discount processing

[0594] User: "Apply for car insurance."

[0595] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[0596] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees, and providing a more comprehensive offering by adding asset formation plans.

[0597] The processing flow will be explained below.

[0598] Step 1: Displaying the user data entry form

[0599] Server: When the insurance app is launched, it sends the HTML of a form for entering personal and family information to the device, allowing the user to enter the required data.

[0600] Step 2: Enter user data

[0601] User: Enters name, age, gender, occupation, family composition, and desired insurance conditions, and presses the submit button, which provides the necessary information to the system.

[0602] Step 3: Send input data

[0603] Terminal: The entered data is sent to the server in JSON format, encrypted using the secure HTTPS protocol.

[0604] Step 4: Save user data

[0605] Server: Stores the received data in a database, which also includes security measures such as SQL injection.

[0606] Step 5: Invoke the AI ​​model

[0607] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the data.

[0608] Step 6: Generate your insurance plan

[0609] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[0610] Step 7: Distributing the insurance plan

[0611] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[0612] Step 8: View your plan

[0613] Terminal: The received insurance plan is displayed on the screen, formatted for easy visual understanding by the user.

[0614] Step 9: Choose a plan and sign up

[0615] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[0616] Step 10: Submit Selected Data

[0617] Device: Sends the selected plan information to the server. It is sent in JSON format again.

[0618] Step 11: Employee Verification and Discount Processing

[0619] Server: Searches for employee information in the database based on the user ID, applies discounts if applicable, and calculates the final discounted price.

[0620] Step 12: Final price notification

[0621] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[0622] Step 13: Propose an asset formation plan

[0623] Server: Asset formation data is input into the AI ​​model in the same way as insurance plans, and appropriate proposals are generated. This data is also sent to the device.

[0624] Step 14: View your wealth plan

[0625] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[0626] Example 1

[0627] 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."

[0628] Conventional insurance plan proposal systems lack the functionality to automatically propose the most suitable plan based on the user's personal information. Also, the application of employee benefit discounts is often done manually, reducing user convenience. Another issue is the lack of a system that comprehensively proposes not only insurance plans but also asset formation plans for users.

[0629] 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.

[0630] In this invention, the server includes a means for inputting personal information, a means for storing the input personal information, and a means for inputting the stored personal information into a generative AI model to generate an insurance plan. This makes it possible to automatically propose optimal insurance plans and asset formation plans based on the user's personal information. Furthermore, it is possible to verify whether the user is an employee based on individual identification information and automatically apply discounts, improving user convenience.

[0631] "Personal information" refers to information such as a user's name, age, gender, occupation, and family composition.

[0632] "Retention" means saving the input data in a storage such as a database.

[0633] A "generative AI model" refers to an artificial intelligence model that is trained using machine learning algorithms and generates appropriate insurance plans or asset formation plans based on input data.

[0634] "Insurance Plans" refers to detailed plans such as life insurance and car insurance that are proposed based on the user's personal information.

[0635] "Asset formation plan" refers to a plan that includes suggestions regarding the user's asset management.

[0636] "Terminal" refers to a computer device used by a user, such as a smartphone or PC.

[0637] "Server" refers to a computer system that receives and processes data sent from a user's terminal.

[0638] "Individual identification information" refers to information that uniquely identifies a user and is primarily used to verify employee information.

[0639] "Discount" refers to a reduction in the price applied to insurance premiums.

[0640] The system of the present invention automatically proposes appropriate insurance plans and asset formation plans based on personal information entered by the user. The hardware and software required to implement this system, as well as the data processing method, are described in detail below.

[0641] User data input processing

[0642] User:

[0643] Users launch an insurance app on their smartphone or PC and enter their personal information (name, age, gender, occupation, family composition, etc.). For example, they might enter, "I am a 35-year-old man, an engineer, and have a wife and two children."

[0644] Data transmission process

[0645] Device:

[0646] The device, which can be a smartphone or PC, encodes the input data into JSON format and sends it to the server using the secure HTTPS protocol.

[0647] Data storage and preprocessing

[0648] server:

[0649] The server stores the received user data in a database. The stored data undergoes preprocessing to be input into the generative AI model. This preprocessing includes data normalization and cleaning. The server used is a common cloud service (e.g., AWS, Google Cloud).

[0650] Insurance plan generation process

[0651] server:

[0652] The server then inputs the preprocessed data into a generative AI model to generate the optimal insurance plan for the user. This generative AI model is based on a pre-trained machine learning algorithm and utilizes machine learning libraries such as TensorFlow and PyTorch.

[0653] Insurance plan display processing

[0654] Device:

[0655] The generated insurance plan is sent from the server to the user's device and displayed within the app. The user can choose from multiple insurance plans. The display format uses an easy-to-understand graphical user interface (GUI).

[0656] Employee benefit discount processing

[0657] User:

[0658] The user selects one of the insurance plans offered and clicks the apply button. For example, select "Apply for car insurance."

[0659] Device:

[0660] The selected insurance plan information is sent to the server.

[0661] server:

[0662] The server searches the database for employee information based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user's terminal. For example, the information notified may be "Automobile insurance (after discount): 79,200 yen."

[0663] Additional proposals for asset formation plans

[0664] server:

[0665] Data on wealth planning is also collected and fed into a generative AI model, similar to insurance plans, to generate appropriate proposals, which are then sent to the device and displayed to the user.

[0666] Examples and prompts

[0667] As a concrete example, the case where a user inputs his / her personal information is shown below.

[0668] User: "I'm a 35-year-old male engineer with a wife and two children."

[0669] Device: "Personal and family information will be sent to the server."

[0670] Server: "Data received, thank you."

[0671] Examples of insurance plans that are offered include:

[0672] Server: "Based on the user data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0673] Device: "The following insurance plans are suitable."

[0674] Examples of employee discounts include:

[0675] User: "I'm applying for car insurance."

[0676] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[0677] The system of the present invention allows users to receive proposals for optimal insurance plans and asset formation plans, and in particular, it greatly improves convenience by automatically applying discounts to company employees.

[0678] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0679] Step 1: User data input process

[0680] User:

[0681] The user launches an insurance app from their smartphone or PC and enters their personal information (name, age, gender, occupation, family composition, etc.). The data is entered manually into the app's form fields. Specifically, the user enters "Name: Taro Tanaka, Age: 35, Gender: Male, Occupation: Engineer, Family Composition: Wife, 2 Children." The input data is converted to JSON format.

[0682] Input: User's personal and family information

[0683] Output: User data encoded in JSON format

[0684] Step 2: Data transmission process

[0685] Device:

[0686] The device encodes the entered user data and sends it to the server using the secure HTTPS protocol. Specifically, when the user clicks the "Send" button, the data is converted to JSON format within the app and sent to the server as an HTTPS request.

[0687] Input: User data encoded in JSON format

[0688] Output: HTTPS request sent to the server

[0689] Step 3: Data storage and preprocessing

[0690] server:

[0691] The server stores the received user data in a database. After storing it, it performs preprocessing to prepare it for input to the AI ​​model. This preprocessing includes normalizing and cleaning the data. Specifically, the server executes an SQL query to insert the data into the database, and then normalizes it using a Python script.

[0692] Input: HTTPS request sent to the server

[0693] Output: User data stored in a database, preprocessed data

[0694] Step 4: Insurance plan generation process

[0695] server:

[0696] The server then inputs the preprocessed data into a generative AI model to generate an insurance plan. This generative AI model uses pre-trained machine learning algorithms, such as TensorFlow and PyTorch. The generated insurance plan includes the optimal plan based on the user's profile.

[0697] Input: Preprocessed data

[0698] Output: Generated insurance plan

[0699] Step 5: Insurance plan display process

[0700] Device:

[0701] The generated insurance plan is sent from the server to the user's device and displayed within the app. Specifically, the device parses the JSON response from the server and uses it to display data in a graphical user interface (GUI). The user can choose from multiple insurance plans.

[0702] Input: Generated insurance plan (JSON format)

[0703] Output: App screen showing insurance plans

[0704] Step 6: Process employee benefit discounts

[0705] User:

[0706] The user selects the desired insurance plan and clicks the "Apply" button. Specifically, the user performs an operation such as "apply for car insurance."

[0707] Input: The insurance plan selected by the user

[0708] Output: Application request

[0709] Device:

[0710] The selected insurance plan information is sent to the server. Specifically, the device encodes the user's selection information again into JSON format and sends it to the server as an HTTPS request.

[0711] Input: User-selected insurance plan (JSON format)

[0712] Output: The application request sent to the server

[0713] server:

[0714] The server searches for employee information from a database based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is again notified to the user's terminal. For example, it sends information such as "Automobile insurance (after discount): 79,200 yen." Specifically, the server executes an SQL query to obtain employee information and calculates the discount.

[0715] Input: User application information, employee information in the database

[0716] Output: Final premium with discount applied

[0717] Step 7: Additional proposals for asset formation plans

[0718] server:

[0719] Data on asset formation plans is also collected and input into a generative AI model, just like insurance plans, to generate appropriate proposals. The generated asset formation plans are sent to the device and displayed to the user. Specifically, the server inputs the asset formation data into a Python AI model and sends the generated results in JSON format to the device.

[0720] Input: Asset formation data

[0721] Output: Generated wealth plan (JSON format)

[0722] (Application example 1)

[0723] 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."

[0724] Conventional food delivery services did not adequately provide personalized suggestions that took into account users' individual preferences, health information, allergies, etc. This meant that they were unable to recommend the optimal food or health plans that users truly needed, potentially resulting in lower satisfaction. Furthermore, applying discounts based on specific conditions had to be done manually, which was inefficient.

[0725] 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.

[0726] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model to generate a personalized food plan, a means for displaying the generated food plan, a means for selecting and applying for a food plan, and a means for checking specific conditions and applying discounts based on the conditions. This makes it possible to automatically propose optimal food plans and health plans according to the individual needs of the user and efficiently apply discounts.

[0727] "Personal Information" is information that can individually identify a user, including information about the user's age, gender, dietary preferences, allergy information, and health information.

[0728] "Means for saving" refers to the function of saving input data in storage such as a database.

[0729] "AI Model" is an artificial intelligence model that uses machine learning algorithms to generate optimal food and health plans based on a user's profile and preferences.

[0730] A "food plan" is a meal plan suggested based on a user's dietary preferences and health information.

[0731] A "means for displaying" is a digital component that displays the generated plan on a terminal in a form that can be viewed by a user.

[0732] "Conditions" are specific criteria or requirements that a User must meet in order to receive special services or discounts.

[0733] A "means for applying discounts" is a function that automatically applies discounts, such as price reductions, when a user meets certain conditions.

[0734] A "health plan" is a plan that suggests optimal exercise and diet based on the user's health goals and situation.

[0735] A "terminal" is a device such as a smartphone, computer, or tablet that allows a user to enter data or view plans.

[0736] A "server" is a computer system with the computational resources to receive and store data submitted by users, input it into an AI model, and generate a plan.

[0737] A "system" is an integrated platform in which multiple components work together.

[0738] The system of the present invention allows a user to input personal information and then proposes optimal food and health plans based on that information. A specific embodiment of the system will be described below.

[0739] User data input processing

[0740] User:

[0741] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. The application provides a form for users to enter their personal information (name, age, gender, allergy information, health information).

[0742] Device:

[0743] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[0744] server:

[0745] The server stores the received user data in a database, while also preprocessing the data and formatting it as input data for the AI ​​model.

[0746] Food plan proposal processing

[0747] server:

[0748] The server then inputs the formatted user data into an AI model to generate a meal plan, which uses pre-trained machine learning algorithms to suggest optimal meal plans based on individual user preferences and health information.

[0749] Device:

[0750] The generated food plan is sent to the user's device and displayed within the app, where the user can choose the most appropriate one from multiple suggested food plans.

[0751] Additional health plan proposals

[0752] server:

[0753] Similarly, the health plan is generated based on user data and fed into an AI model to generate optimal recommendations, which are then sent to the user's device along with the food plan.

[0754] Discount Processing

[0755] User:

[0756] The user selects a food plan and clicks the sign up button.

[0757] server:

[0758] The server checks the database for specific conditions based on the user ID. If the conditions are met, it applies a certain discount (for example, 10%). The final price after the discount is applied is notified to the user.

[0759] Specific examples

[0760] Enter your personal information

[0761] User: "I'm a 30-year-old man who loves sushi but is allergic to peanuts. I'm on a diet and my daily calorie goal is 2000 kcal."

[0762] Device: "Personal and health information will be sent to the server."

[0763] Server: "Data received, thank you."

[0764] Food plan suggestions

[0765] Server: "Based on your data, we'll suggest the following food plans: low-carb set lunch, low-calorie set dinner."

[0766] Device: "The following food plan is suitable for you."

[0767] Health plan proposals

[0768] Server: "At the same time, we'll suggest the following health plan: exercise twice a week, and record your daily walking."

[0769] Discount Processing

[0770] User: "Order a low-calorie dinner set."

[0771] Server: "Checking... You meet the requirements, so we'll give you a 10% discount. Your final price is 1800 yen."

[0772] Example prompt for a generative AI model:

[0773] "A 30-year-old man loves sushi and has a peanut allergy. He is on a calorie restriction diet, with a daily goal of 2000 kcal. Can you suggest a weekly meal plan that would be optimal for him?"

[0774] In this way, the system of the present invention streamlines the process of selecting a personalized food plan for users, improves convenience by automatically applying discounts when certain conditions are met, and enables more comprehensive offerings by adding health plans.

[0775] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0776] Step 1:

[0777] The user launches a dedicated application using a device such as a smartphone, tablet, or PC. The application provides the user with a form to enter personal information (name, age, gender, allergy information, health information). This input information becomes the data to be sent to the server in the next step.

[0778] Input: Personal and health information you enter.

[0779] Output: User data encoded in JSON format.

[0780] Step 2:

[0781] The terminal encodes the data entered by the user in JSON format and sends it to the server using the secure HTTPS protocol. Once the data has been sent, the server receives it in the next step.

[0782] Input: User data in JSON format.

[0783] Output: Notification of completion of transmission to the server.

[0784] Step 3:

[0785] The server stores the user data received from the device in a database. At the same time, it preprocesses the data and formats it as input data for the AI ​​model. This process converts the user data into a format that can be used by the AI ​​model.

[0786] Input: User data in JSON format sent to the server.

[0787] Output: User data stored in a database, formatted data for the AI ​​model.

[0788] Step 4:

[0789] The server inputs the formatted user data into an AI model to generate a food plan. The AI ​​model uses pre-trained machine learning algorithms to generate an optimal food plan based on the individual user's preferences and health information. This step results in several candidate food plans.

[0790] Input: User data formatted for the AI ​​model.

[0791] Output: The generated food plan.

[0792] Step 5:

[0793] The generated food plan is sent from the server to the user's device and displayed in the app. The user selects one of the proposed food plans. This selection data is used in the next step.

[0794] Input: The generated food plan.

[0795] Output: The food plan displayed on the user's device.

[0796] Step 6:

[0797] The user selects the most suitable food plan from the displayed options and clicks the "Apply" button. This selection information is sent from the device to the server.

[0798] Input: The food plan selected by the user.

[0799] Output: Data sent to the server (selected food plan).

[0800] Step 7:

[0801] The server checks the database for specific conditions (e.g., special offers or discounts) based on the user ID. If these conditions are met, it applies a certain discount (e.g., 10%). The final price with the discount applied is notified to the user.

[0802] Input: User selection data and discount conditions.

[0803] Output: Final price after applying discounts.

[0804] Step 8:

[0805] The server also generates a health plan and proposes it alongside the food plan, analyzing the user's health information based on an AI model and providing optimal exercise plans and dietary advice.

[0806] Input: User data required to generate a health plan.

[0807] Output: The generated health plan.

[0808] These are the processing steps of the system that realizes this application example. At each step, the specific operations performed by the server, terminal, and user, as well as their inputs and outputs, are clearly shown. This allows us to realize a system that can provide optimal food and health plans to users and efficiently apply discounts based on specific conditions.

[0809] 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.

[0810] The system of the present invention automates the process of suggesting appropriate insurance plans based on the user's personal information. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, allowing it to tailor the proposals to suit the user's emotional state.

[0811] The system consists of four main components: the user's device, the server that processes the data, the AI ​​model, and the emotion engine. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[0812] User data input processing

[0813] User:

[0814] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[0815] Device:

[0816] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[0817] server:

[0818] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[0819] Insurance plan proposal processing

[0820] server:

[0821] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[0822] Device:

[0823] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[0824] Employee benefit discount processing

[0825] User:

[0826] The user selects an insurance plan and clicks the apply button.

[0827] Device:

[0828] The terminal transmits the selected plan information to the server.

[0829] server:

[0830] The server searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[0831] Additional proposals for asset formation plans

[0832] server:

[0833] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[0834] Emotion recognition by emotion engine

[0835] User:

[0836] While a user is using the insurance app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition.

[0837] Device:

[0838] The emotion engine analyzes the emotion data and sends it to the server. The emotion data is encoded in JSON format and sent.

[0839] server:

[0840] The server inputs the received emotional data into an AI model and adjusts the contents of insurance and asset formation plans based on the user's emotional state. For example, if the user is feeling anxious, it will prioritize insurance plans with low risks.

[0841] Specific examples

[0842] 1. Enter your personal information

[0843] User: "I'm a 35-year-old male engineer with a wife and two children."

[0844] Device: "Personal and family information will be sent to the server."

[0845] Server: "Data received, thank you."

[0846] 2. Insurance plan proposals

[0847] Server: "Based on the user's data, we will recommend life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0848] Device: "The following insurance plans are suitable."

[0849] 3. Employee discount processing

[0850] User: "Apply for car insurance."

[0851] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[0852] 4. Emotional Engine Adjustment

[0853] User: Shows "anxious facial expression" while using the app.

[0854] Emotion engine: Recognizing "anxiety."

[0855] Server: "Show insurance plans with low risk (e.g., life insurance exclusion plans)."

[0856] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees. The addition of asset-building plans also enables more comprehensive proposals. Furthermore, by incorporating an emotion engine, the system can tailor insurance plans and proposals based on the user's emotional state, providing a more personalized service to users.

[0857] The processing flow will be explained below.

[0858] Step 1: Displaying the user data entry form

[0859] Server: When the insurance app is launched, it sends an HTML form to the device for entering personal and family information. This form includes fields such as name, age, gender, occupation, and family composition.

[0860] Step 2: Enter user data

[0861] User: Enters name, age, gender, occupation, family composition, etc. into the specified form and presses the submit button. This provides the necessary information to the system.

[0862] Step 3: Send input data

[0863] Terminal: The entered data is sent to the server in JSON format. The data is encrypted and sent securely using the HTTPS protocol.

[0864] Step 4: Save user data

[0865] Server: The received data is saved in a database. Security measures such as SQL injection are implemented when saving the data in the database.

[0866] Step 5: Invoke the AI ​​model

[0867] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the user's situation and desired conditions.

[0868] Step 6: Generate your insurance plan

[0869] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[0870] Step 7: Distributing the insurance plan

[0871] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[0872] Step 8: View your plan

[0873] Device: The insurance plan you received will be displayed on the screen, visually showing the plan details and features.

[0874] Step 9: Choose a plan and sign up

[0875] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[0876] Step 10: Submit Selected Data

[0877] Device: Sends the selected plan information to the server. The data is sent in JSON format again.

[0878] Step 11: Employee Verification and Discount Processing

[0879] Server: Searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[0880] Step 12: Final price notification

[0881] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[0882] Step 13: Propose an asset formation plan

[0883] Server: Asset formation data, along with the insurance plan, is input into the AI ​​model to generate appropriate proposals. The generated asset formation plan is sent to the user's device along with the insurance plan.

[0884] Step 14: View your wealth plan

[0885] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[0886] Step 15: Working with the Emotional Engine

[0887] User: Emotions are detected through tone of voice and facial expressions while using the app.

[0888] On the device: The detected emotions are analyzed by the emotion engine and the results are sent to the server in JSON format.

[0889] Step 16: Processing Emotion Data

[0890] Server: Analyzes the received emotional data and adjusts the proposed insurance and asset building plans based on the user's emotional state. If the user feels anxious, the plan will be customized to suggest a low-risk plan.

[0891] Step 17: Deliver the adjusted plan

[0892] Server: The adjusted insurance plan or asset formation plan is sent to the device in JSON format. The adjustment results are reflected in real time.

[0893] Step 18: View the adjusted plan

[0894] Device: The adjusted insurance plan or asset formation plan is redisplayed on the screen, allowing the user to see the optimal plan based on their emotional state.

[0895] Example 2

[0896] 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."

[0897] Conventional insurance plan recommendation systems have the following problems. First, the process of properly inputting the user's personal information and family composition information and then proposing the most suitable insurance plan based on that information is done manually, which is inefficient. Second, the lack of a function to automatically apply discount benefits to company employees leads to an inconsistent user experience. Third, recommendations are made without taking the user's emotional state into consideration, which may result in the recommendation not being the most suitable for the user. Furthermore, there is a need to provide a more comprehensive service by proposing asset formation plans in addition to insurance plans.

[0898] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0899] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model and generating an insurance plan, a means for displaying the generated insurance plan, a means for selecting and applying for an insurance plan, a means for verifying whether the user is an employee and applying a discount if so, a means for recognizing the user's emotions and adjusting the proposal content based on the emotions, and a means for generating an asset formation plan. This allows the user to simply input their personal information and receive a proposal for the optimal insurance plan or asset formation plan, and further enables personalized proposals based on the user's emotional state. Furthermore, discounts are automatically applied to company employees, improving convenience.

[0900] "Personal information" refers to information such as the user's name, age, gender, occupation, and family composition and age.

[0901] "Saving" means storing input data in a storage device such as a database.

[0902] An "AI model" is a program that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on user data.

[0903] An "insurance plan" is a proposal for an insurance product, such as life insurance or auto insurance, offered to a user.

[0904] "Display" refers to visually showing the generated insurance plan on the user's terminal.

[0905] "Selection" refers to the act of a user choosing and deciding on the most suitable insurance plan from multiple options.

[0906] "Applying" means going through the process of officially enrolling in the insurance plan selected by the user.

[0907] An "employee" is an individual who belongs to a particular company and is entitled to the company's benefits and discounts.

[0908] A "discount" is a reduction of a certain amount or percentage from the original insurance premium based on certain criteria.

[0909] "Emotion recognition" refers to determining a user's current emotional state using technologies such as voice tone and facial recognition.

[0910] "Adjustment" refers to changing or modifying the contents of an insurance or wealth plan in response to a perceived emotional state.

[0911] An "asset formation plan" is a proposal for asset management and investment tailored to the user's financial goals and situation.

[0912] The system of the present invention allows users to input their personal information and, based on that information, provides the function of proposing optimal insurance plans. It also applies special discounts to corporate employees and can adjust the proposals based on the user's emotional state. The system is broadly composed of four components: the user's device, a server, an AI model, and an emotion engine. It also provides comprehensive services to users by proposing additional asset formation plans.

[0913] User data input processing

[0914] Users launch the insurance app on their smartphone, PC, or other device and enter their name, age, gender, occupation, and family information. This information is encoded into JSON format by the device and sent to the server via the secure HTTPS protocol.

[0915] Specific examples

[0916] "I'm a 35-year-old man, an engineer by profession, with a wife and two children."

[0917] Data transmission and storage

[0918] The device sends the input data to the server, which stores the received user data in a database, preprocesses the data, and formats it as input data for the AI ​​model.

[0919] Insurance plan proposal processing

[0920] The server inputs the formatted user data into an AI model to generate the optimal insurance plan. The AI ​​model uses pre-trained machine learning algorithms to suggest the insurance plan that best suits the user's profile. The generated insurance plan is sent to the user's device and displayed within the app.

[0921] Specific examples

[0922] "We offer life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[0923] Employee benefit discount processing

[0924] When a user selects an insurance plan and applies, the device sends the selection information to the server, which searches the database for employee information based on the user ID and applies discounts if applicable. The final premium amount is then notified to the user.

[0925] Specific examples

[0926] "Checking... Employee discount will be applied. Final price is 79,200 yen."

[0927] Additional proposals for asset formation plans

[0928] The server also inputs data about the asset formation plan into the AI ​​model to generate optimal proposals, which are then sent to the user's device along with the insurance plan.

[0929] Emotion recognition by emotion engine

[0930] While a user is using the app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition. The analysis results are encoded in JSON format and sent to the server. The server then uses an AI model to adjust the suggestions based on the received emotional data and provide a plan that matches the user's emotional state.

[0931] Specific examples

[0932] If the user shows an "anxious expression," the emotion engine recognizes it and the server displays a low-risk insurance plan (e.g., a life insurance exception coverage plan).

[0933] In this way, the system of the present invention helps users efficiently select insurance plans, automatically applying discounts to corporate employees in particular. It also offers additional asset formation plans, providing more comprehensive insurance and asset management solutions. Furthermore, the emotion engine provides personalized services based on the user's emotional state.

[0934] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0935] Step 1: User Data Input

[0936] Input: The user enters personal information.

[0937] Specific operations: Users launch the app on their smartphone or PC and enter their name, age, gender, occupation, and family information into a dedicated form.

[0938] Output: The personal information entered is saved on the device.

[0939] Step 2: Send data

[0940] Input: Personal information entered by the user.

[0941] What happens: The device encodes the personal information entered by the user into JSON format.

[0942] Data processing: The encoded data is sent to the server using the HTTPS protocol.

[0943] Output: The server receives the encoded data sent by the device.

[0944] Step 3: Save data

[0945] Input: The encoded data sent.

[0946] Specific operation: The server analyzes the received data and stores it in a database.

[0947] Data calculation: Preprocessing data stored in the database and preparing it as input data for the AI ​​model.

[0948] Output: The formatted data is ready.

[0949] Step 4: Generate your insurance plan

[0950] Input: Formatted user data.

[0951] Specific operation: The server inputs the formatted data into the AI ​​model.

[0952] Data Computation: The AI ​​model uses pre-trained machine learning algorithms to generate optimal insurance plans.

[0953] Output: The generated insurance plan is saved on the server.

[0954] Step 5: View your insurance plan

[0955] Input: The generated insurance plan.

[0956] Specific operation: The server sends the generated insurance plan to the user's device.

[0957] Output: The device displays the insurance plan that was sent. The user can view the insurance plan in the app.

[0958] Step 6: Select and apply for an insurance plan

[0959] Input: The insurance plan selected by the user.

[0960] Specific behavior: The user selects the most suitable insurance plan from the displayed options and clicks the apply button.

[0961] Output: The selection is saved to the device.

[0962] Step 7: Submit your selections

[0963] Input: Information about the insurance plan selected by the user.

[0964] Specific operation: The device sends the selected plan information to the server.

[0965] Output: The server receives the selection information sent by the device.

[0966] Step 8: Apply employee perks discounts

[0967] Input: Received selection information and user ID.

[0968] Specific operation: The server searches the database for employee information based on the user ID.

[0969] Data calculation: Apply discounts, if applicable, to calculate the final premium.

[0970] Output: The final insurance premium is calculated and notified to the user.

[0971] Step 9: Additional proposals for asset formation plans

[0972] Input: User data and insurance plan.

[0973] Specific operation: The server inputs data about the asset formation plan into the AI ​​model.

[0974] Data calculation: Generate optimal asset formation plans.

[0975] Output: The generated asset formation plan is sent to the user's terminal and displayed.

[0976] Step 10: Emotion Recognition with the Emotion Engine

[0977] Input: User's voice tone and facial expression data.

[0978] How it works: While the user is using the app, the emotion engine performs real-time voice tone and facial recognition.

[0979] Data processing: Analyze the emotion data and encode it into JSON format.

[0980] Output: The analyzed emotion data is sent to the server.

[0981] Step 11: Adjust content based on emotional state

[0982] Input: Parsed emotion data.

[0983] Specific operation: The server inputs the received emotion data into the AI ​​model.

[0984] Data Computation: Adjusting insurance and wealth plans based on emotional state.

[0985] Output: The adjusted insurance plan or asset formation plan is sent to the user's device and displayed.

[0986] (Application example 2)

[0987] 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."

[0988] Conventional insurance plan recommendation systems can present optimal insurance plans based on a user's personal information, but do not take into account the user's emotional state. This creates the problem of making it difficult for users to select an appropriate insurance plan when they are feeling anxious or worried. Furthermore, asset formation plans are proposed solely based on personal information, and the contents of these plans are not adjusted to fit the user's emotional state. This results in lower user satisfaction and the possibility of users being unable to select the optimal plan.

[0989] 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.

[0990] In this invention, the server includes a means for recognizing the emotional state of the user and adjusting the contents of the insurance plan based on that emotional state, a means for generating an asset formation plan and adjusting the contents based on the emotional state of the user, and a means for inputting personal information from a terminal and transmitting it to the server, thereby making it possible to propose the optimal insurance plan and asset formation plan according to the emotional state of the user.

[0991] "Personal information" refers to information that identifies a user, such as the user's name, age, gender, occupation, and family composition.

[0992] An "AI model" is a model that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on input data.

[0993] An "insurance plan" is a combination of various insurance products and their terms and conditions offered by an insurance company and proposed to a user.

[0994] "Emotional state" refers to the psychological state of the user recognized through voice tone and facial expression analysis, and refers to emotions such as anxiety, joy, and sadness.

[0995] An "asset formation plan" includes investment and savings plans to increase a user's assets, and is proposed together with an insurance plan.

[0996] A "terminal" is an electronic device used by a user, such as a smartphone or PC, that is used to input personal information and recognize emotional states.

[0997] "Server" means a central computer system that processes data submitted by users and generates insurance and wealth plans.

[0998] The present invention relates to a system that automates the process by which a user selects, adjusts, and enrolls in an insurance or wealth plan.

[0999] Specific methods for carrying out the present invention will now be described.

[1000] System configuration

[1001] User's device

[1002] The user's device is an electronic device such as a smartphone or PC. The device has the following functions:

[1003] It provides a form for entering personal information, allowing users to enter personal information such as name, age, gender, occupation, and family composition.

[1004] It uses the smartphone's camera and microphone to recognize the user's emotional state and collect that data.

[1005] The device encodes the collected data in JSON format and sends it to the server using the secure HTTPS protocol.

[1006] server

[1007] The server processes the data and has the following functions:

[1008] Receive personal information and emotional data sent by users and store it in a database.

[1009] The pre-processed data is input into an AI model to generate optimal insurance and asset formation plans.

[1010] An emotion recognition engine is used to adjust the generated plan content based on the user's emotional state.

[1011] The optimal plan is sent to the device and presented to the user.

[1012] Hardware and Software

[1013] Specific examples of hardware and software used include:

[1014] Hardware: Smartphone (with camera and microphone), PC, server (cloud server is also acceptable)

[1015] Software: Flask (web framework), TensorFlow (machine learning library), OpenCV (image processing library), DeepFace (emotion recognition library)

[1016] Processing flow

[1017] Enter your personal information

[1018] User: Launches the application and fills in the personal information form.

[1019] On the device: The personal information entered is encoded in JSON format and sent to the server using the HTTPS protocol.

[1020] Generate an insurance plan

[1021] Server: Stores the received personal information in a database and inputs it into an AI model to generate an appropriate insurance plan.

[1022] Server: Sends the generated insurance plan to the device and displays it in the app.

[1023] Recognition of emotional states

[1024] User: Emotion recognition is activated while using the application, using the camera or microphone to collect your emotional state.

[1025] Emotion engine (terminal): Sends recognized emotion data to the server and analyzes the emotional state.

[1026] Emotion-Based Adjustment

[1027] Server: Adjusts the content of the plan generated by the AI ​​model based on emotional data and personal information.

[1028] Server: Sends the adjusted plan to the device and displays it to the user.

[1029] Specific examples

[1030] Prompt Sentence Examples

[1031] The prompt that users see when entering their personal information:

[1032] Please enter your name, age, gender, occupation, family composition, and ages of your family members.

[1033] For emotion recognition, the prompt displayed to the user is:

[1034] To recognize your emotional state while using the app, please face the camera and be in a stable position.

[1035] This system allows users to easily select the most suitable insurance and asset formation plan based on their emotional state and personal information. In particular, even if they are feeling anxious or worried, the system will suggest an appropriate plan based on their emotions, which will increase user satisfaction.

[1036] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1037] Step 1:

[1038] User: Launches the application on their smartphone or PC and enters their name, age, gender, occupation, family composition, and ages of family members into the personal information input form. This information is entered into the application as personal information.

[1039] Step 2:

[1040] On the device, the personal information entered is encoded in JSON format and sent to the server using the secure HTTPS protocol. The input data is divided into fields (name, age, gender, occupation, and family information) and properly formatted.

[1041] Step 3:

[1042] Server: Stores the received personal information in a database, mapping each field to the appropriate table and preprocessing the data. For example, age is treated as numeric data, while gender and occupation are converted to predefined categorical data.

[1043] Step 4:

[1044] Server: The preprocessed personal information is input into the AI ​​model to generate the optimal insurance plan for the user profile. The AI ​​model uses machine learning algorithms to compare the input data with past data and select the optimal insurance plan. For example, it generates the optimal life insurance and car insurance plan for a 30-year-old engineer with a family.

[1045] Step 5:

[1046] Server: The generated insurance plan is sent to the device and displayed in the application. The sent format is JSON, and includes information such as the insurance type, plan details, and premium.

[1047] Step 6:

[1048] User: While using the application, the user faces the camera to recognize their emotional state, using facial recognition and voice tone analysis.

[1049] Step 7:

[1050] Device: The smartphone's camera and microphone are used to recognize the user's emotional state and capture the data. The analysis results are processed in real time through the emotion engine and sent to the server in JSON format.

[1051] Step 8:

[1052] Server: Analyzes the received emotional state data and adjusts the insurance plan content. For example, if the user is feeling anxious, the content is changed to prioritize insurance plans with low risks.

[1053] Step 9:

[1054] Server: The adjusted insurance plan is sent back to the device and displayed in the application, receiving feedback based on the user's emotional state to make more relevant recommendations.

[1055] Step 10:

[1056] User: Selects the desired plan from the insurance plans presented and applies. This action sends the selected plan information to the server.

[1057] Step 11:

[1058] Server: Checks the user database and applies any benefits, such as employee discounts, that apply to the selected insurance plan. The final calculated premium is calculated and notified to the user.

[1059] Step 12:

[1060] Server: The server connects the finalized insurance plan information to the insurance company's system, allowing the user to officially enroll in the insurance.

[1061] Thus, this system allows users to easily select the optimal insurance plan and asset formation plan based on their emotional state and personal information, and provides appropriate plans even when they are feeling particularly anxious or worried, thereby improving user satisfaction.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] [Third embodiment]

[1066] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1067] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1068] 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).

[1069] 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.

[1070] 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.

[1071] 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).

[1072] 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. 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.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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.

[1077] 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."

[1078] The system of the present invention automates the process in which a user inputs personal information and then proposes an appropriate insurance plan based on that information. A specific embodiment of the system will be described below.

[1079] The system consists of three main components: the user's device, the server that processes the data, and the AI ​​model. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[1080] User data input processing

[1081] User:

[1082] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[1083] Device:

[1084] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[1085] server:

[1086] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[1087] Insurance plan proposal processing

[1088] server:

[1089] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[1090] Device:

[1091] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[1092] Employee benefit discount processing

[1093] User:

[1094] The user selects an insurance plan and clicks the apply button.

[1095] Device:

[1096] The terminal transmits the selected plan information to the server.

[1097] server:

[1098] The server searches the database for employee information based on the user ID, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user.

[1099] Additional proposals for asset formation plans

[1100] server:

[1101] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[1102] Specific examples

[1103] 1. Enter your personal information

[1104] User: "I'm a 35-year-old male engineer with a wife and two children."

[1105] Device: "Personal and family information will be sent to the server."

[1106] Server: "Data received, thank you."

[1107] 2. Insurance plan proposals

[1108] Server: "Based on the user's data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1109] Device: "The following insurance plans are suitable."

[1110] 3. Employee discount processing

[1111] User: "Apply for car insurance."

[1112] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[1113] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees, and providing a more comprehensive offering by adding asset formation plans.

[1114] The processing flow will be explained below.

[1115] Step 1: Displaying the user data entry form

[1116] Server: When the insurance app is launched, it sends the HTML of a form for entering personal and family information to the device, allowing the user to enter the required data.

[1117] Step 2: Enter user data

[1118] User: Enters name, age, gender, occupation, family composition, and desired insurance conditions, and presses the submit button, which provides the necessary information to the system.

[1119] Step 3: Send input data

[1120] Terminal: The entered data is sent to the server in JSON format, encrypted using the secure HTTPS protocol.

[1121] Step 4: Save user data

[1122] Server: Stores the received data in a database, which also includes security measures such as SQL injection.

[1123] Step 5: Invoke the AI ​​model

[1124] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the data.

[1125] Step 6: Generate your insurance plan

[1126] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[1127] Step 7: Distributing the insurance plan

[1128] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[1129] Step 8: View your plan

[1130] Terminal: The received insurance plan is displayed on the screen, formatted for easy visual understanding by the user.

[1131] Step 9: Choose a plan and sign up

[1132] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[1133] Step 10: Submit Selected Data

[1134] Device: Sends the selected plan information to the server. It is sent in JSON format again.

[1135] Step 11: Employee Verification and Discount Processing

[1136] Server: Searches for employee information in the database based on the user ID, applies discounts if applicable, and calculates the final discounted price.

[1137] Step 12: Final price notification

[1138] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[1139] Step 13: Propose an asset formation plan

[1140] Server: Asset formation data is input into the AI ​​model in the same way as insurance plans, and appropriate proposals are generated. This data is also sent to the device.

[1141] Step 14: View your wealth plan

[1142] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[1143] Example 1

[1144] 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."

[1145] Conventional insurance plan proposal systems lack the functionality to automatically propose the most suitable plan based on the user's personal information. Also, the application of employee benefit discounts is often done manually, reducing user convenience. Another issue is the lack of a system that comprehensively proposes not only insurance plans but also asset formation plans for users.

[1146] 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.

[1147] In this invention, the server includes a means for inputting personal information, a means for storing the input personal information, and a means for inputting the stored personal information into a generative AI model to generate an insurance plan. This makes it possible to automatically propose optimal insurance plans and asset formation plans based on the user's personal information. Furthermore, it is possible to verify whether the user is an employee based on individual identification information and automatically apply discounts, improving user convenience.

[1148] "Personal information" refers to information such as a user's name, age, gender, occupation, and family composition.

[1149] "Retention" means saving the input data in a storage such as a database.

[1150] A "generative AI model" refers to an artificial intelligence model that is trained using machine learning algorithms and generates appropriate insurance plans or asset formation plans based on input data.

[1151] "Insurance Plans" refers to detailed plans such as life insurance and car insurance that are proposed based on the user's personal information.

[1152] "Asset formation plan" refers to a plan that includes suggestions regarding the user's asset management.

[1153] "Terminal" refers to a computer device used by a user, such as a smartphone or PC.

[1154] "Server" refers to a computer system that receives and processes data sent from a user's terminal.

[1155] "Individual identification information" refers to information that uniquely identifies a user and is primarily used to verify employee information.

[1156] "Discount" refers to a reduction in the price applied to insurance premiums.

[1157] The system of the present invention automatically proposes appropriate insurance plans and asset formation plans based on personal information entered by the user. The hardware and software required to implement this system, as well as the data processing method, are described in detail below.

[1158] User data input processing

[1159] User:

[1160] Users launch an insurance app on their smartphone or PC and enter their personal information (name, age, gender, occupation, family composition, etc.). For example, they might enter, "I am a 35-year-old man, an engineer, and have a wife and two children."

[1161] Data transmission process

[1162] Device:

[1163] The device, which can be a smartphone or PC, encodes the input data into JSON format and sends it to the server using the secure HTTPS protocol.

[1164] Data storage and preprocessing

[1165] server:

[1166] The server stores the received user data in a database. The stored data undergoes preprocessing to be input into the generative AI model. This preprocessing includes data normalization and cleaning. The server used is a common cloud service (e.g., AWS, Google Cloud).

[1167] Insurance plan generation process

[1168] server:

[1169] The server then inputs the preprocessed data into a generative AI model to generate the optimal insurance plan for the user. This generative AI model is based on a pre-trained machine learning algorithm and utilizes machine learning libraries such as TensorFlow and PyTorch.

[1170] Insurance plan display processing

[1171] Device:

[1172] The generated insurance plan is sent from the server to the user's device and displayed within the app. The user can choose from multiple insurance plans. The display format uses an easy-to-understand graphical user interface (GUI).

[1173] Employee benefit discount processing

[1174] User:

[1175] The user selects one of the insurance plans offered and clicks the apply button. For example, select "Apply for car insurance."

[1176] Device:

[1177] The selected insurance plan information is sent to the server.

[1178] server:

[1179] The server searches the database for employee information based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user's terminal. For example, the information notified may be "Automobile insurance (after discount): 79,200 yen."

[1180] Additional proposals for asset formation plans

[1181] server:

[1182] Data on wealth planning is also collected and fed into a generative AI model, similar to insurance plans, to generate appropriate proposals, which are then sent to the device and displayed to the user.

[1183] Examples and prompts

[1184] As a concrete example, the case where a user inputs his / her personal information is shown below.

[1185] User: "I'm a 35-year-old male engineer with a wife and two children."

[1186] Device: "Personal and family information will be sent to the server."

[1187] Server: "Data received, thank you."

[1188] Examples of insurance plans that are offered include:

[1189] Server: "Based on the user data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1190] Device: "The following insurance plans are suitable."

[1191] Examples of employee discounts include:

[1192] User: "I'm applying for car insurance."

[1193] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[1194] The system of the present invention allows users to receive proposals for optimal insurance plans and asset formation plans, and in particular, it greatly improves convenience by automatically applying discounts to company employees.

[1195] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1196] Step 1: User data input process

[1197] User:

[1198] The user launches an insurance app from their smartphone or PC and enters their personal information (name, age, gender, occupation, family composition, etc.). The data is entered manually into the app's form fields. Specifically, the user enters "Name: Taro Tanaka, Age: 35, Gender: Male, Occupation: Engineer, Family Composition: Wife, 2 Children." The input data is converted to JSON format.

[1199] Input: User's personal and family information

[1200] Output: User data encoded in JSON format

[1201] Step 2: Data transmission process

[1202] Device:

[1203] The device encodes the entered user data and sends it to the server using the secure HTTPS protocol. Specifically, when the user clicks the "Send" button, the data is converted to JSON format within the app and sent to the server as an HTTPS request.

[1204] Input: User data encoded in JSON format

[1205] Output: HTTPS request sent to the server

[1206] Step 3: Data storage and preprocessing

[1207] server:

[1208] The server stores the received user data in a database. After storing it, it performs preprocessing to prepare it for input to the AI ​​model. This preprocessing includes normalizing and cleaning the data. Specifically, the server executes an SQL query to insert the data into the database, and then normalizes it using a Python script.

[1209] Input: HTTPS request sent to the server

[1210] Output: User data stored in a database, preprocessed data

[1211] Step 4: Insurance plan generation process

[1212] server:

[1213] The server then inputs the preprocessed data into a generative AI model to generate an insurance plan. This generative AI model uses pre-trained machine learning algorithms, such as TensorFlow and PyTorch. The generated insurance plan includes the optimal plan based on the user's profile.

[1214] Input: Preprocessed data

[1215] Output: Generated insurance plan

[1216] Step 5: Insurance plan display process

[1217] Device:

[1218] The generated insurance plan is sent from the server to the user's device and displayed within the app. Specifically, the device parses the JSON response from the server and uses it to display data in a graphical user interface (GUI). The user can choose from multiple insurance plans.

[1219] Input: Generated insurance plan (JSON format)

[1220] Output: App screen showing insurance plans

[1221] Step 6: Process employee benefit discounts

[1222] User:

[1223] The user selects the desired insurance plan and clicks the "Apply" button. Specifically, the user performs an operation such as "apply for car insurance."

[1224] Input: The insurance plan selected by the user

[1225] Output: Application request

[1226] Device:

[1227] The selected insurance plan information is sent to the server. Specifically, the device encodes the user's selection information again into JSON format and sends it to the server as an HTTPS request.

[1228] Input: User-selected insurance plan (JSON format)

[1229] Output: The application request sent to the server

[1230] server:

[1231] The server searches for employee information from a database based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is again notified to the user's terminal. For example, it sends information such as "Automobile insurance (after discount): 79,200 yen." Specifically, the server executes an SQL query to obtain employee information and calculates the discount.

[1232] Input: User application information, employee information in the database

[1233] Output: Final premium with discount applied

[1234] Step 7: Additional proposals for asset formation plans

[1235] server:

[1236] Data on asset formation plans is also collected and input into a generative AI model, just like insurance plans, to generate appropriate proposals. The generated asset formation plans are sent to the device and displayed to the user. Specifically, the server inputs the asset formation data into a Python AI model and sends the generated results in JSON format to the device.

[1237] Input: Asset formation data

[1238] Output: Generated wealth plan (JSON format)

[1239] (Application example 1)

[1240] 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."

[1241] Conventional food delivery services did not adequately provide personalized suggestions that took into account users' individual preferences, health information, allergies, etc. This meant that they were unable to recommend the optimal food or health plans that users truly needed, potentially resulting in lower satisfaction. Furthermore, applying discounts based on specific conditions had to be done manually, which was inefficient.

[1242] 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.

[1243] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model to generate a personalized food plan, a means for displaying the generated food plan, a means for selecting and applying for a food plan, and a means for checking specific conditions and applying discounts based on the conditions. This makes it possible to automatically propose optimal food plans and health plans according to the individual needs of the user and efficiently apply discounts.

[1244] "Personal Information" is information that can individually identify a user, including information about the user's age, gender, dietary preferences, allergy information, and health information.

[1245] "Means for saving" refers to the function of saving input data in storage such as a database.

[1246] "AI Model" is an artificial intelligence model that uses machine learning algorithms to generate optimal food and health plans based on a user's profile and preferences.

[1247] A "food plan" is a meal plan suggested based on a user's dietary preferences and health information.

[1248] A "means for displaying" is a digital component that displays the generated plan on a terminal in a form that can be viewed by a user.

[1249] "Conditions" are specific criteria or requirements that a User must meet in order to receive special services or discounts.

[1250] A "means for applying discounts" is a function that automatically applies discounts, such as price reductions, when a user meets certain conditions.

[1251] A "health plan" is a plan that suggests optimal exercise and diet based on the user's health goals and situation.

[1252] A "terminal" is a device such as a smartphone, computer, or tablet that allows a user to enter data or view plans.

[1253] A "server" is a computer system with the computational resources to receive and store data submitted by users, input it into an AI model, and generate a plan.

[1254] A "system" is an integrated platform in which multiple components work together.

[1255] The system of the present invention allows a user to input personal information and then proposes optimal food and health plans based on that information. A specific embodiment of the system will be described below.

[1256] User data input processing

[1257] User:

[1258] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. The application provides a form for users to enter their personal information (name, age, gender, allergy information, health information).

[1259] Device:

[1260] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[1261] server:

[1262] The server stores the received user data in a database, while also preprocessing the data and formatting it as input data for the AI ​​model.

[1263] Food plan proposal processing

[1264] server:

[1265] The server then inputs the formatted user data into an AI model to generate a meal plan, which uses pre-trained machine learning algorithms to suggest optimal meal plans based on individual user preferences and health information.

[1266] Device:

[1267] The generated food plan is sent to the user's device and displayed within the app, where the user can choose the most appropriate one from multiple suggested food plans.

[1268] Additional health plan proposals

[1269] server:

[1270] Similarly, the health plan is generated based on user data and fed into an AI model to generate optimal recommendations, which are then sent to the user's device along with the food plan.

[1271] Discount Processing

[1272] User:

[1273] The user selects a food plan and clicks the sign up button.

[1274] server:

[1275] The server checks the database for specific conditions based on the user ID. If the conditions are met, it applies a certain discount (for example, 10%). The final price after the discount is applied is notified to the user.

[1276] Specific examples

[1277] Enter your personal information

[1278] User: "I'm a 30-year-old man who loves sushi but is allergic to peanuts. I'm on a diet and my daily calorie goal is 2000 kcal."

[1279] Device: "Personal and health information will be sent to the server."

[1280] Server: "Data received, thank you."

[1281] Food plan suggestions

[1282] Server: "Based on your data, we'll suggest the following food plans: low-carb set lunch, low-calorie set dinner."

[1283] Device: "The following food plan is suitable for you."

[1284] Health plan proposals

[1285] Server: "At the same time, we'll suggest the following health plan: exercise twice a week, and record your daily walking."

[1286] Discount Processing

[1287] User: "Order a low-calorie dinner set."

[1288] Server: "Checking... You meet the requirements, so we'll give you a 10% discount. Your final price is 1800 yen."

[1289] Example prompt for a generative AI model:

[1290] "A 30-year-old man loves sushi and has a peanut allergy. He is on a calorie restriction diet, with a daily goal of 2000 kcal. Can you suggest a weekly meal plan that would be optimal for him?"

[1291] In this way, the system of the present invention streamlines the process of selecting a personalized food plan for users, improves convenience by automatically applying discounts when certain conditions are met, and enables more comprehensive offerings by adding health plans.

[1292] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1293] Step 1:

[1294] The user launches a dedicated application using a device such as a smartphone, tablet, or PC. The application provides the user with a form to enter personal information (name, age, gender, allergy information, health information). This input information becomes the data to be sent to the server in the next step.

[1295] Input: Personal and health information you enter.

[1296] Output: User data encoded in JSON format.

[1297] Step 2:

[1298] The terminal encodes the data entered by the user in JSON format and sends it to the server using the secure HTTPS protocol. Once the data has been sent, the server receives it in the next step.

[1299] Input: User data in JSON format.

[1300] Output: Notification of completion of transmission to the server.

[1301] Step 3:

[1302] The server stores the user data received from the device in a database. At the same time, it preprocesses the data and formats it as input data for the AI ​​model. This process converts the user data into a format that can be used by the AI ​​model.

[1303] Input: User data in JSON format sent to the server.

[1304] Output: User data stored in a database, formatted data for the AI ​​model.

[1305] Step 4:

[1306] The server inputs the formatted user data into an AI model to generate a food plan. The AI ​​model uses pre-trained machine learning algorithms to generate an optimal food plan based on the individual user's preferences and health information. This step results in several candidate food plans.

[1307] Input: User data formatted for the AI ​​model.

[1308] Output: The generated food plan.

[1309] Step 5:

[1310] The generated food plan is sent from the server to the user's device and displayed in the app. The user selects one of the proposed food plans. This selection data is used in the next step.

[1311] Input: The generated food plan.

[1312] Output: The food plan displayed on the user's device.

[1313] Step 6:

[1314] The user selects the most suitable food plan from the displayed options and clicks the "Apply" button. This selection information is sent from the device to the server.

[1315] Input: The food plan selected by the user.

[1316] Output: Data sent to the server (selected food plan).

[1317] Step 7:

[1318] The server checks the database for specific conditions (e.g., special offers or discounts) based on the user ID. If these conditions are met, it applies a certain discount (e.g., 10%). The final price with the discount applied is notified to the user.

[1319] Input: User selection data and discount conditions.

[1320] Output: Final price after applying discounts.

[1321] Step 8:

[1322] The server also generates a health plan and proposes it alongside the food plan, analyzing the user's health information based on an AI model and providing optimal exercise plans and dietary advice.

[1323] Input: User data required to generate a health plan.

[1324] Output: The generated health plan.

[1325] These are the processing steps of the system that realizes this application example. At each step, the specific operations performed by the server, terminal, and user, as well as their inputs and outputs, are clearly shown. This allows us to realize a system that can provide optimal food and health plans to users and efficiently apply discounts based on specific conditions.

[1326] 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.

[1327] The system of the present invention automates the process of suggesting appropriate insurance plans based on the user's personal information. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, allowing it to tailor the proposals to suit the user's emotional state.

[1328] The system consists of four main components: the user's device, the server that processes the data, the AI ​​model, and the emotion engine. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[1329] User data input processing

[1330] User:

[1331] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[1332] Device:

[1333] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[1334] server:

[1335] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[1336] Insurance plan proposal processing

[1337] server:

[1338] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[1339] Device:

[1340] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[1341] Employee benefit discount processing

[1342] User:

[1343] The user selects an insurance plan and clicks the apply button.

[1344] Device:

[1345] The terminal transmits the selected plan information to the server.

[1346] server:

[1347] The server searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[1348] Additional proposals for asset formation plans

[1349] server:

[1350] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[1351] Emotion recognition by emotion engine

[1352] User:

[1353] While a user is using the insurance app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition.

[1354] Device:

[1355] The emotion engine analyzes the emotion data and sends it to the server. The emotion data is encoded in JSON format and sent.

[1356] server:

[1357] The server inputs the received emotional data into an AI model and adjusts the contents of insurance and asset formation plans based on the user's emotional state. For example, if the user is feeling anxious, it will prioritize insurance plans with low risks.

[1358] Specific examples

[1359] 1. Enter your personal information

[1360] User: "I'm a 35-year-old male engineer with a wife and two children."

[1361] Device: "Personal and family information will be sent to the server."

[1362] Server: "Data received, thank you."

[1363] 2. Insurance plan proposals

[1364] Server: "Based on the user's data, we will recommend life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1365] Device: "The following insurance plans are suitable."

[1366] 3. Employee discount processing

[1367] User: "Apply for car insurance."

[1368] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[1369] 4. Emotional Engine Adjustment

[1370] User: Shows "anxious facial expression" while using the app.

[1371] Emotion engine: Recognizing "anxiety."

[1372] Server: "Show insurance plans with low risk (e.g., life insurance exclusion plans)."

[1373] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees. The addition of asset-building plans also enables more comprehensive proposals. Furthermore, by incorporating an emotion engine, the system can tailor insurance plans and proposals based on the user's emotional state, providing a more personalized service to users.

[1374] The processing flow will be explained below.

[1375] Step 1: Displaying the user data entry form

[1376] Server: When the insurance app is launched, it sends an HTML form to the device for entering personal and family information. This form includes fields such as name, age, gender, occupation, and family composition.

[1377] Step 2: Enter user data

[1378] User: Enters name, age, gender, occupation, family composition, etc. into the specified form and presses the submit button. This provides the necessary information to the system.

[1379] Step 3: Send input data

[1380] Terminal: The entered data is sent to the server in JSON format. The data is encrypted and sent securely using the HTTPS protocol.

[1381] Step 4: Save user data

[1382] Server: The received data is saved in a database. Security measures such as SQL injection are implemented when saving the data in the database.

[1383] Step 5: Invoke the AI ​​model

[1384] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the user's situation and desired conditions.

[1385] Step 6: Generate your insurance plan

[1386] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[1387] Step 7: Distributing the insurance plan

[1388] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[1389] Step 8: View your plan

[1390] Device: The insurance plan you received will be displayed on the screen, visually showing the plan details and features.

[1391] Step 9: Choose a plan and sign up

[1392] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[1393] Step 10: Submit Selected Data

[1394] Device: Sends the selected plan information to the server. The data is sent in JSON format again.

[1395] Step 11: Employee Verification and Discount Processing

[1396] Server: Searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[1397] Step 12: Final price notification

[1398] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[1399] Step 13: Propose an asset formation plan

[1400] Server: Asset formation data, along with the insurance plan, is input into the AI ​​model to generate appropriate proposals. The generated asset formation plan is sent to the user's device along with the insurance plan.

[1401] Step 14: View your wealth plan

[1402] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[1403] Step 15: Working with the Emotional Engine

[1404] User: Emotions are detected through tone of voice and facial expressions while using the app.

[1405] On the device: The detected emotions are analyzed by the emotion engine and the results are sent to the server in JSON format.

[1406] Step 16: Processing Emotion Data

[1407] Server: Analyzes the received emotional data and adjusts the proposed insurance and asset building plans based on the user's emotional state. If the user feels anxious, the plan will be customized to suggest a low-risk plan.

[1408] Step 17: Deliver the adjusted plan

[1409] Server: The adjusted insurance plan or asset formation plan is sent to the device in JSON format. The adjustment results are reflected in real time.

[1410] Step 18: View the adjusted plan

[1411] Device: The adjusted insurance plan or asset formation plan is redisplayed on the screen, allowing the user to see the optimal plan based on their emotional state.

[1412] Example 2

[1413] 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."

[1414] Conventional insurance plan recommendation systems have the following problems. First, the process of properly inputting the user's personal information and family composition information and then proposing the most suitable insurance plan based on that information is done manually, which is inefficient. Second, the lack of a function to automatically apply discount benefits to company employees leads to an inconsistent user experience. Third, recommendations are made without taking the user's emotional state into consideration, which may result in the recommendation not being the most suitable for the user. Furthermore, there is a need to provide a more comprehensive service by proposing asset formation plans in addition to insurance plans.

[1415] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1416] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model and generating an insurance plan, a means for displaying the generated insurance plan, a means for selecting and applying for an insurance plan, a means for verifying whether the user is an employee and applying a discount if so, a means for recognizing the user's emotions and adjusting the proposal content based on the emotions, and a means for generating an asset formation plan. This allows the user to simply input their personal information and receive a proposal for the optimal insurance plan or asset formation plan, and further enables personalized proposals based on the user's emotional state. Furthermore, discounts are automatically applied to company employees, improving convenience.

[1417] "Personal information" refers to information such as the user's name, age, gender, occupation, and family composition and age.

[1418] "Saving" means storing input data in a storage device such as a database.

[1419] An "AI model" is a program that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on user data.

[1420] An "insurance plan" is a proposal for an insurance product, such as life insurance or auto insurance, offered to a user.

[1421] "Display" refers to visually showing the generated insurance plan on the user's terminal.

[1422] "Selection" refers to the act of a user choosing and deciding on the most suitable insurance plan from multiple options.

[1423] "Applying" means going through the process of officially enrolling in the insurance plan selected by the user.

[1424] An "employee" is an individual who belongs to a particular company and is entitled to the company's benefits and discounts.

[1425] A "discount" is a reduction of a certain amount or percentage from the original insurance premium based on certain criteria.

[1426] "Emotion recognition" refers to determining a user's current emotional state using technologies such as voice tone and facial recognition.

[1427] "Adjustment" refers to changing or modifying the contents of an insurance or wealth plan in response to a perceived emotional state.

[1428] An "asset formation plan" is a proposal for asset management and investment tailored to the user's financial goals and situation.

[1429] The system of the present invention allows users to input their personal information and, based on that information, provides the function of proposing optimal insurance plans. It also applies special discounts to corporate employees and can adjust the proposals based on the user's emotional state. The system is broadly composed of four components: the user's device, a server, an AI model, and an emotion engine. It also provides comprehensive services to users by proposing additional asset formation plans.

[1430] User data input processing

[1431] Users launch the insurance app on their smartphone, PC, or other device and enter their name, age, gender, occupation, and family information. This information is encoded into JSON format by the device and sent to the server via the secure HTTPS protocol.

[1432] Specific examples

[1433] "I'm a 35-year-old man, an engineer by profession, with a wife and two children."

[1434] Data transmission and storage

[1435] The device sends the input data to the server, which stores the received user data in a database, preprocesses the data, and formats it as input data for the AI ​​model.

[1436] Insurance plan proposal processing

[1437] The server inputs the formatted user data into an AI model to generate the optimal insurance plan. The AI ​​model uses pre-trained machine learning algorithms to suggest the insurance plan that best suits the user's profile. The generated insurance plan is sent to the user's device and displayed within the app.

[1438] Specific examples

[1439] "We offer life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1440] Employee benefit discount processing

[1441] When a user selects an insurance plan and applies, the device sends the selection information to the server, which searches the database for employee information based on the user ID and applies discounts if applicable. The final premium amount is then notified to the user.

[1442] Specific examples

[1443] "Checking... Employee discount will be applied. Final price is 79,200 yen."

[1444] Additional proposals for asset formation plans

[1445] The server also inputs data about the asset formation plan into the AI ​​model to generate optimal proposals, which are then sent to the user's device along with the insurance plan.

[1446] Emotion recognition by emotion engine

[1447] While a user is using the app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition. The analysis results are encoded in JSON format and sent to the server. The server then uses an AI model to adjust the suggestions based on the received emotional data and provide a plan that matches the user's emotional state.

[1448] Specific examples

[1449] If the user shows an "anxious expression," the emotion engine recognizes it and the server displays a low-risk insurance plan (e.g., a life insurance exception coverage plan).

[1450] In this way, the system of the present invention helps users efficiently select insurance plans, automatically applying discounts to corporate employees in particular. It also offers additional asset formation plans, providing more comprehensive insurance and asset management solutions. Furthermore, the emotion engine provides personalized services based on the user's emotional state.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1: User Data Input

[1453] Input: The user enters personal information.

[1454] Specific operations: Users launch the app on their smartphone or PC and enter their name, age, gender, occupation, and family information into a dedicated form.

[1455] Output: The personal information entered is saved on the device.

[1456] Step 2: Send data

[1457] Input: Personal information entered by the user.

[1458] What happens: The device encodes the personal information entered by the user into JSON format.

[1459] Data processing: The encoded data is sent to the server using the HTTPS protocol.

[1460] Output: The server receives the encoded data sent by the device.

[1461] Step 3: Save data

[1462] Input: The encoded data sent.

[1463] Specific operation: The server analyzes the received data and stores it in a database.

[1464] Data calculation: Preprocessing data stored in the database and preparing it as input data for the AI ​​model.

[1465] Output: The formatted data is ready.

[1466] Step 4: Generate your insurance plan

[1467] Input: Formatted user data.

[1468] Specific operation: The server inputs the formatted data into the AI ​​model.

[1469] Data Computation: The AI ​​model uses pre-trained machine learning algorithms to generate optimal insurance plans.

[1470] Output: The generated insurance plan is saved on the server.

[1471] Step 5: View your insurance plan

[1472] Input: The generated insurance plan.

[1473] Specific operation: The server sends the generated insurance plan to the user's device.

[1474] Output: The device displays the insurance plan that was sent. The user can view the insurance plan in the app.

[1475] Step 6: Select and apply for an insurance plan

[1476] Input: The insurance plan selected by the user.

[1477] Specific behavior: The user selects the most suitable insurance plan from the displayed options and clicks the apply button.

[1478] Output: The selection is saved to the device.

[1479] Step 7: Submit your selections

[1480] Input: Information about the insurance plan selected by the user.

[1481] Specific operation: The device sends the selected plan information to the server.

[1482] Output: The server receives the selection information sent by the device.

[1483] Step 8: Apply employee perks discounts

[1484] Input: Received selection information and user ID.

[1485] Specific operation: The server searches the database for employee information based on the user ID.

[1486] Data calculation: Apply discounts, if applicable, to calculate the final premium.

[1487] Output: The final insurance premium is calculated and notified to the user.

[1488] Step 9: Additional proposals for asset formation plans

[1489] Input: User data and insurance plan.

[1490] Specific operation: The server inputs data about the asset formation plan into the AI ​​model.

[1491] Data calculation: Generate optimal asset formation plans.

[1492] Output: The generated asset formation plan is sent to the user's terminal and displayed.

[1493] Step 10: Emotion Recognition with the Emotion Engine

[1494] Input: User's voice tone and facial expression data.

[1495] How it works: While the user is using the app, the emotion engine performs real-time voice tone and facial recognition.

[1496] Data processing: Analyze the emotion data and encode it into JSON format.

[1497] Output: The analyzed emotion data is sent to the server.

[1498] Step 11: Adjust content based on emotional state

[1499] Input: Parsed emotion data.

[1500] Specific operation: The server inputs the received emotion data into the AI ​​model.

[1501] Data Computation: Adjusting insurance and wealth plans based on emotional state.

[1502] Output: The adjusted insurance plan or asset formation plan is sent to the user's device and displayed.

[1503] (Application example 2)

[1504] 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."

[1505] Conventional insurance plan recommendation systems can present optimal insurance plans based on a user's personal information, but do not take into account the user's emotional state. This creates the problem of making it difficult for users to select an appropriate insurance plan when they are feeling anxious or worried. Furthermore, asset formation plans are proposed solely based on personal information, and the contents of these plans are not adjusted to fit the user's emotional state. This results in lower user satisfaction and the possibility of users being unable to select the optimal plan.

[1506] 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.

[1507] In this invention, the server includes a means for recognizing the emotional state of the user and adjusting the contents of the insurance plan based on that emotional state, a means for generating an asset formation plan and adjusting the contents based on the emotional state of the user, and a means for inputting personal information from a terminal and transmitting it to the server, thereby making it possible to propose the optimal insurance plan and asset formation plan according to the emotional state of the user.

[1508] "Personal information" refers to information that identifies a user, such as the user's name, age, gender, occupation, and family composition.

[1509] An "AI model" is a model that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on input data.

[1510] An "insurance plan" is a combination of various insurance products and their terms and conditions offered by an insurance company and proposed to a user.

[1511] "Emotional state" refers to the psychological state of the user recognized through voice tone and facial expression analysis, and refers to emotions such as anxiety, joy, and sadness.

[1512] An "asset formation plan" includes investment and savings plans to increase a user's assets, and is proposed together with an insurance plan.

[1513] A "terminal" is an electronic device used by a user, such as a smartphone or PC, that is used to input personal information and recognize emotional states.

[1514] "Server" means a central computer system that processes data submitted by users and generates insurance and wealth plans.

[1515] The present invention relates to a system that automates the process by which a user selects, adjusts, and enrolls in an insurance or wealth plan.

[1516] Specific methods for carrying out the present invention will now be described.

[1517] System configuration

[1518] User's device

[1519] The user's device is an electronic device such as a smartphone or PC. The device has the following functions:

[1520] It provides a form for entering personal information, allowing users to enter personal information such as name, age, gender, occupation, and family composition.

[1521] It uses the smartphone's camera and microphone to recognize the user's emotional state and collect that data.

[1522] The device encodes the collected data in JSON format and sends it to the server using the secure HTTPS protocol.

[1523] server

[1524] The server processes the data and has the following functions:

[1525] Receive personal information and emotional data sent by users and store it in a database.

[1526] The pre-processed data is input into an AI model to generate optimal insurance and asset formation plans.

[1527] An emotion recognition engine is used to adjust the generated plan content based on the user's emotional state.

[1528] The optimal plan is sent to the device and presented to the user.

[1529] Hardware and Software

[1530] Specific examples of hardware and software used include:

[1531] Hardware: Smartphone (with camera and microphone), PC, server (cloud server is also acceptable)

[1532] Software: Flask (web framework), TensorFlow (machine learning library), OpenCV (image processing library), DeepFace (emotion recognition library)

[1533] Processing flow

[1534] Enter your personal information

[1535] User: Launches the application and fills in the personal information form.

[1536] On the device: The personal information entered is encoded in JSON format and sent to the server using the HTTPS protocol.

[1537] Generate an insurance plan

[1538] Server: Stores the received personal information in a database and inputs it into an AI model to generate an appropriate insurance plan.

[1539] Server: Sends the generated insurance plan to the device and displays it in the app.

[1540] Recognition of emotional states

[1541] User: Emotion recognition is activated while using the application, using the camera or microphone to collect your emotional state.

[1542] Emotion engine (terminal): Sends recognized emotion data to the server and analyzes the emotional state.

[1543] Emotion-Based Adjustment

[1544] Server: Adjusts the content of the plan generated by the AI ​​model based on emotional data and personal information.

[1545] Server: Sends the adjusted plan to the device and displays it to the user.

[1546] Specific examples

[1547] Prompt Sentence Examples

[1548] The prompt that users see when entering their personal information:

[1549] Please enter your name, age, gender, occupation, family composition, and ages of your family members.

[1550] For emotion recognition, the prompt displayed to the user is:

[1551] To recognize your emotional state while using the app, please face the camera and be in a stable position.

[1552] This system allows users to easily select the most suitable insurance and asset formation plan based on their emotional state and personal information. In particular, even if they are feeling anxious or worried, the system will suggest an appropriate plan based on their emotions, which will increase user satisfaction.

[1553] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1554] Step 1:

[1555] User: Launches the application on their smartphone or PC and enters their name, age, gender, occupation, family composition, and ages of family members into the personal information input form. This information is entered into the application as personal information.

[1556] Step 2:

[1557] On the device, the personal information entered is encoded in JSON format and sent to the server using the secure HTTPS protocol. The input data is divided into fields (name, age, gender, occupation, and family information) and properly formatted.

[1558] Step 3:

[1559] Server: Stores the received personal information in a database, mapping each field to the appropriate table and preprocessing the data. For example, age is treated as numeric data, while gender and occupation are converted to predefined categorical data.

[1560] Step 4:

[1561] Server: The preprocessed personal information is input into the AI ​​model to generate the optimal insurance plan for the user profile. The AI ​​model uses machine learning algorithms to compare the input data with past data and select the optimal insurance plan. For example, it generates the optimal life insurance and car insurance plan for a 30-year-old engineer with a family.

[1562] Step 5:

[1563] Server: The generated insurance plan is sent to the device and displayed in the application. The sent format is JSON, and includes information such as the insurance type, plan details, and premium.

[1564] Step 6:

[1565] User: While using the application, the user faces the camera to recognize their emotional state, using facial recognition and voice tone analysis.

[1566] Step 7:

[1567] Device: The smartphone's camera and microphone are used to recognize the user's emotional state and capture the data. The analysis results are processed in real time through the emotion engine and sent to the server in JSON format.

[1568] Step 8:

[1569] Server: Analyzes the received emotional state data and adjusts the insurance plan content. For example, if the user is feeling anxious, the content is changed to prioritize insurance plans with low risks.

[1570] Step 9:

[1571] Server: The adjusted insurance plan is sent back to the device and displayed in the application, receiving feedback based on the user's emotional state to make more relevant recommendations.

[1572] Step 10:

[1573] User: Selects the desired plan from the insurance plans presented and applies. This action sends the selected plan information to the server.

[1574] Step 11:

[1575] Server: Checks the user database and applies any benefits, such as employee discounts, that apply to the selected insurance plan. The final calculated premium is calculated and notified to the user.

[1576] Step 12:

[1577] Server: The server connects the finalized insurance plan information to the insurance company's system, allowing the user to officially enroll in the insurance.

[1578] Thus, this system allows users to easily select the optimal insurance plan and asset formation plan based on their emotional state and personal information, and provides appropriate plans even when they are feeling particularly anxious or worried, thereby improving user satisfaction.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] [Fourth embodiment]

[1583] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1584] 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.

[1585] 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).

[1586] 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.

[1587] 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.

[1588] 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).

[1589] 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. 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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.

[1595] 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."

[1596] The system of the present invention automates the process in which a user inputs personal information and then proposes an appropriate insurance plan based on that information. A specific embodiment of the system will be described below.

[1597] The system consists of three main components: the user's device, the server that processes the data, and the AI ​​model. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[1598] User data input processing

[1599] User:

[1600] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[1601] Device:

[1602] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[1603] server:

[1604] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[1605] Insurance plan proposal processing

[1606] server:

[1607] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[1608] Device:

[1609] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[1610] Employee benefit discount processing

[1611] User:

[1612] The user selects an insurance plan and clicks the apply button.

[1613] Device:

[1614] The terminal transmits the selected plan information to the server.

[1615] server:

[1616] The server searches the database for employee information based on the user ID, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user.

[1617] Additional proposals for asset formation plans

[1618] server:

[1619] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[1620] Specific examples

[1621] 1. Enter your personal information

[1622] User: "I'm a 35-year-old male engineer with a wife and two children."

[1623] Device: "Personal and family information will be sent to the server."

[1624] Server: "Data received, thank you."

[1625] 2. Insurance plan proposals

[1626] Server: "Based on the user's data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1627] Device: "The following insurance plans are suitable."

[1628] 3. Employee discount processing

[1629] User: "Apply for car insurance."

[1630] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[1631] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees, and providing a more comprehensive offering by adding asset formation plans.

[1632] The processing flow will be explained below.

[1633] Step 1: Displaying the user data entry form

[1634] Server: When the insurance app is launched, it sends the HTML of a form for entering personal and family information to the device, allowing the user to enter the required data.

[1635] Step 2: Enter user data

[1636] User: Enters name, age, gender, occupation, family composition, and desired insurance conditions, and presses the submit button, which provides the necessary information to the system.

[1637] Step 3: Send input data

[1638] Terminal: The entered data is sent to the server in JSON format, encrypted using the secure HTTPS protocol.

[1639] Step 4: Save user data

[1640] Server: Stores the received data in a database, which also includes security measures such as SQL injection.

[1641] Step 5: Invoke the AI ​​model

[1642] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the data.

[1643] Step 6: Generate your insurance plan

[1644] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[1645] Step 7: Distributing the insurance plan

[1646] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[1647] Step 8: View your plan

[1648] Terminal: The received insurance plan is displayed on the screen, formatted for easy visual understanding by the user.

[1649] Step 9: Choose a plan and sign up

[1650] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[1651] Step 10: Submit Selected Data

[1652] Device: Sends the selected plan information to the server. It is sent in JSON format again.

[1653] Step 11: Employee Verification and Discount Processing

[1654] Server: Searches for employee information in the database based on the user ID, applies discounts if applicable, and calculates the final discounted price.

[1655] Step 12: Final price notification

[1656] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[1657] Step 13: Propose an asset formation plan

[1658] Server: Asset formation data is input into the AI ​​model in the same way as insurance plans, and appropriate proposals are generated. This data is also sent to the device.

[1659] Step 14: View your wealth plan

[1660] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[1661] Example 1

[1662] 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."

[1663] Conventional insurance plan proposal systems lack the functionality to automatically propose the most suitable plan based on the user's personal information. Also, the application of employee benefit discounts is often done manually, reducing user convenience. Another issue is the lack of a system that comprehensively proposes not only insurance plans but also asset formation plans for users.

[1664] 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.

[1665] In this invention, the server includes a means for inputting personal information, a means for storing the input personal information, and a means for inputting the stored personal information into a generative AI model to generate an insurance plan. This makes it possible to automatically propose optimal insurance plans and asset formation plans based on the user's personal information. Furthermore, it is possible to verify whether the user is an employee based on individual identification information and automatically apply discounts, improving user convenience.

[1666] "Personal information" refers to information such as a user's name, age, gender, occupation, and family composition.

[1667] "Retention" means saving the input data in a storage such as a database.

[1668] A "generative AI model" refers to an artificial intelligence model that is trained using machine learning algorithms and generates appropriate insurance plans or asset formation plans based on input data.

[1669] "Insurance Plans" refers to detailed plans such as life insurance and car insurance that are proposed based on the user's personal information.

[1670] "Asset formation plan" refers to a plan that includes suggestions regarding the user's asset management.

[1671] "Terminal" refers to a computer device used by a user, such as a smartphone or PC.

[1672] "Server" refers to a computer system that receives and processes data sent from a user's terminal.

[1673] "Individual identification information" refers to information that uniquely identifies a user and is primarily used to verify employee information.

[1674] "Discount" refers to a reduction in the price applied to insurance premiums.

[1675] The system of the present invention automatically proposes appropriate insurance plans and asset formation plans based on personal information entered by the user. The hardware and software required to implement this system, as well as the data processing method, are described in detail below.

[1676] User data input processing

[1677] User:

[1678] Users launch an insurance app on their smartphone or PC and enter their personal information (name, age, gender, occupation, family composition, etc.). For example, they might enter, "I am a 35-year-old man, an engineer, and have a wife and two children."

[1679] Data transmission process

[1680] Device:

[1681] The device, which can be a smartphone or PC, encodes the input data into JSON format and sends it to the server using the secure HTTPS protocol.

[1682] Data storage and preprocessing

[1683] server:

[1684] The server stores the received user data in a database. The stored data undergoes preprocessing to be input into the generative AI model. This preprocessing includes data normalization and cleaning. The server used is a common cloud service (e.g., AWS, Google Cloud).

[1685] Insurance plan generation process

[1686] server:

[1687] The server then inputs the preprocessed data into a generative AI model to generate the optimal insurance plan for the user. This generative AI model is based on a pre-trained machine learning algorithm and utilizes machine learning libraries such as TensorFlow and PyTorch.

[1688] Insurance plan display processing

[1689] Device:

[1690] The generated insurance plan is sent from the server to the user's device and displayed within the app. The user can choose from multiple insurance plans. The display format uses an easy-to-understand graphical user interface (GUI).

[1691] Employee benefit discount processing

[1692] User:

[1693] The user selects one of the insurance plans offered and clicks the apply button. For example, select "Apply for car insurance."

[1694] Device:

[1695] The selected insurance plan information is sent to the server.

[1696] server:

[1697] The server searches the database for employee information based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is notified to the user's terminal. For example, the information notified may be "Automobile insurance (after discount): 79,200 yen."

[1698] Additional proposals for asset formation plans

[1699] server:

[1700] Data on wealth planning is also collected and fed into a generative AI model, similar to insurance plans, to generate appropriate proposals, which are then sent to the device and displayed to the user.

[1701] Examples and prompts

[1702] As a concrete example, the case where a user inputs his / her personal information is shown below.

[1703] User: "I'm a 35-year-old male engineer with a wife and two children."

[1704] Device: "Personal and family information will be sent to the server."

[1705] Server: "Data received, thank you."

[1706] Examples of insurance plans that are offered include:

[1707] Server: "Based on the user data, we will propose life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1708] Device: "The following insurance plans are suitable."

[1709] Examples of employee discounts include:

[1710] User: "I'm applying for car insurance."

[1711] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[1712] The system of the present invention allows users to receive proposals for optimal insurance plans and asset formation plans, and in particular, it greatly improves convenience by automatically applying discounts to company employees.

[1713] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1714] Step 1: User data input process

[1715] User:

[1716] The user launches an insurance app from their smartphone or PC and enters their personal information (name, age, gender, occupation, family composition, etc.). The data is entered manually into the app's form fields. Specifically, the user enters "Name: Taro Tanaka, Age: 35, Gender: Male, Occupation: Engineer, Family Composition: Wife, 2 Children." The input data is converted to JSON format.

[1717] Input: User's personal and family information

[1718] Output: User data encoded in JSON format

[1719] Step 2: Data transmission process

[1720] Device:

[1721] The device encodes the entered user data and sends it to the server using the secure HTTPS protocol. Specifically, when the user clicks the "Send" button, the data is converted to JSON format within the app and sent to the server as an HTTPS request.

[1722] Input: User data encoded in JSON format

[1723] Output: HTTPS request sent to the server

[1724] Step 3: Data storage and preprocessing

[1725] server:

[1726] The server stores the received user data in a database. After storing it, it performs preprocessing to prepare it for input to the AI ​​model. This preprocessing includes normalizing and cleaning the data. Specifically, the server executes an SQL query to insert the data into the database, and then normalizes it using a Python script.

[1727] Input: HTTPS request sent to the server

[1728] Output: User data stored in a database, preprocessed data

[1729] Step 4: Insurance plan generation process

[1730] server:

[1731] The server then inputs the preprocessed data into a generative AI model to generate an insurance plan. This generative AI model uses pre-trained machine learning algorithms, such as TensorFlow and PyTorch. The generated insurance plan includes the optimal plan based on the user's profile.

[1732] Input: Preprocessed data

[1733] Output: Generated insurance plan

[1734] Step 5: Insurance plan display process

[1735] Device:

[1736] The generated insurance plan is sent from the server to the user's device and displayed within the app. Specifically, the device parses the JSON response from the server and uses it to display data in a graphical user interface (GUI). The user can choose from multiple insurance plans.

[1737] Input: Generated insurance plan (JSON format)

[1738] Output: App screen showing insurance plans

[1739] Step 6: Process employee benefit discounts

[1740] User:

[1741] The user selects the desired insurance plan and clicks the "Apply" button. Specifically, the user performs an operation such as "apply for car insurance."

[1742] Input: The insurance plan selected by the user

[1743] Output: Application request

[1744] Device:

[1745] The selected insurance plan information is sent to the server. Specifically, the device encodes the user's selection information again into JSON format and sends it to the server as an HTTPS request.

[1746] Input: User-selected insurance plan (JSON format)

[1747] Output: The application request sent to the server

[1748] server:

[1749] The server searches for employee information from a database based on the user's individual identification information, and if the user is a company employee, applies a specified discount (for example, 1%). The final calculated insurance premium is again notified to the user's terminal. For example, it sends information such as "Automobile insurance (after discount): 79,200 yen." Specifically, the server executes an SQL query to obtain employee information and calculates the discount.

[1750] Input: User application information, employee information in the database

[1751] Output: Final premium with discount applied

[1752] Step 7: Additional proposals for asset formation plans

[1753] server:

[1754] Data on asset formation plans is also collected and input into a generative AI model, just like insurance plans, to generate appropriate proposals. The generated asset formation plans are sent to the device and displayed to the user. Specifically, the server inputs the asset formation data into a Python AI model and sends the generated results in JSON format to the device.

[1755] Input: Asset formation data

[1756] Output: Generated wealth plan (JSON format)

[1757] (Application example 1)

[1758] 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."

[1759] Conventional food delivery services did not adequately provide personalized suggestions that took into account users' individual preferences, health information, allergies, etc. This meant that they were unable to recommend the optimal food or health plans that users truly needed, potentially resulting in lower satisfaction. Furthermore, applying discounts based on specific conditions had to be done manually, which was inefficient.

[1760] 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.

[1761] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model to generate a personalized food plan, a means for displaying the generated food plan, a means for selecting and applying for a food plan, and a means for checking specific conditions and applying discounts based on the conditions. This makes it possible to automatically propose optimal food plans and health plans according to the individual needs of the user and efficiently apply discounts.

[1762] "Personal Information" is information that can individually identify a user, including information about the user's age, gender, dietary preferences, allergy information, and health information.

[1763] "Means for saving" refers to the function of saving input data in storage such as a database.

[1764] "AI Model" is an artificial intelligence model that uses machine learning algorithms to generate optimal food and health plans based on a user's profile and preferences.

[1765] A "food plan" is a meal plan suggested based on a user's dietary preferences and health information.

[1766] A "means for displaying" is a digital component that displays the generated plan on a terminal in a form that can be viewed by a user.

[1767] "Conditions" are specific criteria or requirements that a User must meet in order to receive special services or discounts.

[1768] A "means for applying discounts" is a function that automatically applies discounts, such as price reductions, when a user meets certain conditions.

[1769] A "health plan" is a plan that suggests optimal exercise and diet based on the user's health goals and situation.

[1770] A "terminal" is a device such as a smartphone, computer, or tablet that allows a user to enter data or view plans.

[1771] A "server" is a computer system with the computational resources to receive and store data submitted by users, input it into an AI model, and generate a plan.

[1772] A "system" is an integrated platform in which multiple components work together.

[1773] The system of the present invention allows a user to input personal information and then proposes optimal food and health plans based on that information. A specific embodiment of the system will be described below.

[1774] User data input processing

[1775] User:

[1776] Users launch a dedicated application using a device such as a smartphone, tablet, or PC. The application provides a form for users to enter their personal information (name, age, gender, allergy information, health information).

[1777] Device:

[1778] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[1779] server:

[1780] The server stores the received user data in a database, while also preprocessing the data and formatting it as input data for the AI ​​model.

[1781] Food plan proposal processing

[1782] server:

[1783] The server then inputs the formatted user data into an AI model to generate a meal plan, which uses pre-trained machine learning algorithms to suggest optimal meal plans based on individual user preferences and health information.

[1784] Device:

[1785] The generated food plan is sent to the user's device and displayed within the app, where the user can choose the most appropriate one from multiple suggested food plans.

[1786] Additional health plan proposals

[1787] server:

[1788] Similarly, the health plan is generated based on user data and fed into an AI model to generate optimal recommendations, which are then sent to the user's device along with the food plan.

[1789] Discount Processing

[1790] User:

[1791] The user selects a food plan and clicks the sign up button.

[1792] server:

[1793] The server checks the database for specific conditions based on the user ID. If the conditions are met, it applies a certain discount (for example, 10%). The final price after the discount is applied is notified to the user.

[1794] Specific examples

[1795] Enter your personal information

[1796] User: "I'm a 30-year-old man who loves sushi but is allergic to peanuts. I'm on a diet and my daily calorie goal is 2000 kcal."

[1797] Device: "Personal and health information will be sent to the server."

[1798] Server: "Data received, thank you."

[1799] Food plan suggestions

[1800] Server: "Based on your data, we'll suggest the following food plans: low-carb set lunch, low-calorie set dinner."

[1801] Device: "The following food plan is suitable for you."

[1802] Health plan proposals

[1803] Server: "At the same time, we'll suggest the following health plan: exercise twice a week, and record your daily walking."

[1804] Discount Processing

[1805] User: "Order a low-calorie dinner set."

[1806] Server: "Checking... You meet the requirements, so we'll give you a 10% discount. Your final price is 1800 yen."

[1807] Example prompt for a generative AI model:

[1808] "A 30-year-old man loves sushi and has a peanut allergy. He is on a calorie restriction diet, with a daily goal of 2000 kcal. Can you suggest a weekly meal plan that would be optimal for him?"

[1809] In this way, the system of the present invention streamlines the process of selecting a personalized food plan for users, improves convenience by automatically applying discounts when certain conditions are met, and enables more comprehensive offerings by adding health plans.

[1810] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1811] Step 1:

[1812] The user launches a dedicated application using a device such as a smartphone, tablet, or PC. The application provides the user with a form to enter personal information (name, age, gender, allergy information, health information). This input information becomes the data to be sent to the server in the next step.

[1813] Input: Personal and health information you enter.

[1814] Output: User data encoded in JSON format.

[1815] Step 2:

[1816] The terminal encodes the data entered by the user in JSON format and sends it to the server using the secure HTTPS protocol. Once the data has been sent, the server receives it in the next step.

[1817] Input: User data in JSON format.

[1818] Output: Notification of completion of transmission to the server.

[1819] Step 3:

[1820] The server stores the user data received from the device in a database. At the same time, it preprocesses the data and formats it as input data for the AI ​​model. This process converts the user data into a format that can be used by the AI ​​model.

[1821] Input: User data in JSON format sent to the server.

[1822] Output: User data stored in a database, formatted data for the AI ​​model.

[1823] Step 4:

[1824] The server inputs the formatted user data into an AI model to generate a food plan. The AI ​​model uses pre-trained machine learning algorithms to generate an optimal food plan based on the individual user's preferences and health information. This step results in several candidate food plans.

[1825] Input: User data formatted for the AI ​​model.

[1826] Output: The generated food plan.

[1827] Step 5:

[1828] The generated food plan is sent from the server to the user's device and displayed in the app. The user selects one of the proposed food plans. This selection data is used in the next step.

[1829] Input: The generated food plan.

[1830] Output: The food plan displayed on the user's device.

[1831] Step 6:

[1832] The user selects the most suitable food plan from the displayed options and clicks the "Apply" button. This selection information is sent from the device to the server.

[1833] Input: The food plan selected by the user.

[1834] Output: Data sent to the server (selected food plan).

[1835] Step 7:

[1836] The server checks the database for specific conditions (e.g., special offers or discounts) based on the user ID. If these conditions are met, it applies a certain discount (e.g., 10%). The final price with the discount applied is notified to the user.

[1837] Input: User selection data and discount conditions.

[1838] Output: Final price after applying discounts.

[1839] Step 8:

[1840] The server also generates a health plan and proposes it alongside the food plan, analyzing the user's health information based on an AI model and providing optimal exercise plans and dietary advice.

[1841] Input: User data required to generate a health plan.

[1842] Output: The generated health plan.

[1843] These are the processing steps of the system that realizes this application example. At each step, the specific operations performed by the server, terminal, and user, as well as their inputs and outputs, are clearly shown. This allows us to realize a system that can provide optimal food and health plans to users and efficiently apply discounts based on specific conditions.

[1844] 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.

[1845] The system of the present invention automates the process of suggesting appropriate insurance plans based on the user's personal information. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions, allowing it to tailor the proposals to suit the user's emotional state.

[1846] The system consists of four main components: the user's device, the server that processes the data, the AI ​​model, and the emotion engine. It also offers an asset formation plan, providing users with comprehensive insurance and asset management solutions.

[1847] User data input processing

[1848] User:

[1849] Users launch the insurance app on their smartphone, PC, or other device. The app provides a form for users to enter their personal information (name, age, gender, occupation) and family information (family composition, age, etc.).

[1850] Device:

[1851] The terminal sends the data entered by the user to the server, where it is encoded in JSON format and transmitted using the secure HTTPS protocol.

[1852] server:

[1853] The server stores the received user data in a database, while also preprocessing the data and preparing it for input into the AI ​​model.

[1854] Insurance plan proposal processing

[1855] server:

[1856] On the server, the formatted user data is fed into an AI model to generate insurance plans, which uses pre-trained machine learning algorithms to suggest the best insurance plan for the user's profile.

[1857] Device:

[1858] The generated insurance plan is sent to the user's device and displayed in the app, where the user can choose the most appropriate plan from multiple options.

[1859] Employee benefit discount processing

[1860] User:

[1861] The user selects an insurance plan and clicks the apply button.

[1862] Device:

[1863] The terminal transmits the selected plan information to the server.

[1864] server:

[1865] The server searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[1866] Additional proposals for asset formation plans

[1867] server:

[1868] Data on the wealth plan, like the insurance plan, is input into the AI ​​model to generate appropriate proposals, which are then sent to the user's device along with the insurance plan.

[1869] Emotion recognition by emotion engine

[1870] User:

[1871] While a user is using the insurance app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition.

[1872] Device:

[1873] The emotion engine analyzes the emotion data and sends it to the server. The emotion data is encoded in JSON format and sent.

[1874] server:

[1875] The server inputs the received emotional data into an AI model and adjusts the contents of insurance and asset formation plans based on the user's emotional state. For example, if the user is feeling anxious, it will prioritize insurance plans with low risks.

[1876] Specific examples

[1877] 1. Enter your personal information

[1878] User: "I'm a 35-year-old male engineer with a wife and two children."

[1879] Device: "Personal and family information will be sent to the server."

[1880] Server: "Data received, thank you."

[1881] 2. Insurance plan proposals

[1882] Server: "Based on the user's data, we will recommend life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1883] Device: "The following insurance plans are suitable."

[1884] 3. Employee discount processing

[1885] User: "Apply for car insurance."

[1886] Server: "Checking... Employee discount applied. Final price is 79,200 yen."

[1887] 4. Emotional Engine Adjustment

[1888] User: Shows "anxious facial expression" while using the app.

[1889] Emotion engine: Recognizing "anxiety."

[1890] Server: "Show insurance plans with low risk (e.g., life insurance exclusion plans)."

[1891] In this way, the system of the present invention streamlines the process of selecting an insurance plan for users, improving convenience by automatically applying discounts to corporate employees. The addition of asset-building plans also enables more comprehensive proposals. Furthermore, by incorporating an emotion engine, the system can tailor insurance plans and proposals based on the user's emotional state, providing a more personalized service to users.

[1892] The processing flow will be explained below.

[1893] Step 1: Displaying the user data entry form

[1894] Server: When the insurance app is launched, it sends an HTML form to the device for entering personal and family information. This form includes fields such as name, age, gender, occupation, and family composition.

[1895] Step 2: Enter user data

[1896] User: Enters name, age, gender, occupation, family composition, etc. into the specified form and presses the submit button. This provides the necessary information to the system.

[1897] Step 3: Send input data

[1898] Terminal: The entered data is sent to the server in JSON format. The data is encrypted and sent securely using the HTTPS protocol.

[1899] Step 4: Save user data

[1900] Server: The received data is saved in a database. Security measures such as SQL injection are implemented when saving the data in the database.

[1901] Step 5: Invoke the AI ​​model

[1902] Server: Retrieves the stored user data, preprocesses it, and then inputs it into the AI ​​model, which then infers the appropriate insurance plan based on the user's situation and desired conditions.

[1903] Step 6: Generate your insurance plan

[1904] Server: Receives the insurance plan data returned by the AI ​​model and converts it into a user-friendly format, including plan details and pricing.

[1905] Step 7: Distributing the insurance plan

[1906] Server: Sends the compiled insurance plans in JSON format to the device, allowing the user to view the plans presented.

[1907] Step 8: View your plan

[1908] Device: The insurance plan you received will be displayed on the screen, visually showing the plan details and features.

[1909] Step 9: Choose a plan and sign up

[1910] User: Select one of the proposed plans and click the Apply button to confirm the selection.

[1911] Step 10: Submit Selected Data

[1912] Device: Sends the selected plan information to the server. The data is sent in JSON format again.

[1913] Step 11: Employee Verification and Discount Processing

[1914] Server: Searches the database for employee information based on the user ID, applies discounts if applicable, and notifies the user of the final calculated premium.

[1915] Step 12: Final price notification

[1916] Server: Sends the final discounted price in JSON format to the terminal. The user confirms the price.

[1917] Step 13: Propose an asset formation plan

[1918] Server: Asset formation data, along with the insurance plan, is input into the AI ​​model to generate appropriate proposals. The generated asset formation plan is sent to the user's device along with the insurance plan.

[1919] Step 14: View your wealth plan

[1920] Device: Asset formation plan proposals are displayed on the screen along with insurance plans, allowing users to see multiple options at once.

[1921] Step 15: Working with the Emotional Engine

[1922] User: Emotions are detected through tone of voice and facial expressions while using the app.

[1923] On the device: The detected emotions are analyzed by the emotion engine and the results are sent to the server in JSON format.

[1924] Step 16: Processing Emotion Data

[1925] Server: Analyzes the received emotional data and adjusts the proposed insurance and asset building plans based on the user's emotional state. If the user feels anxious, the plan will be customized to suggest a low-risk plan.

[1926] Step 17: Deliver the adjusted plan

[1927] Server: The adjusted insurance plan or asset formation plan is sent to the device in JSON format. The adjustment results are reflected in real time.

[1928] Step 18: View the adjusted plan

[1929] Device: The adjusted insurance plan or asset formation plan is redisplayed on the screen, allowing the user to see the optimal plan based on their emotional state.

[1930] Example 2

[1931] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1932] Conventional insurance plan recommendation systems have the following problems. First, the process of properly inputting the user's personal information and family composition information and then proposing the most suitable insurance plan based on that information is done manually, which is inefficient. Second, the lack of a function to automatically apply discount benefits to company employees leads to an inconsistent user experience. Third, recommendations are made without taking the user's emotional state into consideration, which may result in the recommendation not being the most suitable for the user. Furthermore, there is a need to provide a more comprehensive service by proposing asset formation plans in addition to insurance plans.

[1933] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1934] In this invention, the server includes a means for inputting personal information, a means for saving the input personal information, a means for inputting the saved personal information into an AI model and generating an insurance plan, a means for displaying the generated insurance plan, a means for selecting and applying for an insurance plan, a means for verifying whether the user is an employee and applying a discount if so, a means for recognizing the user's emotions and adjusting the proposal content based on the emotions, and a means for generating an asset formation plan. This allows the user to simply input their personal information and receive a proposal for the optimal insurance plan or asset formation plan, and further enables personalized proposals based on the user's emotional state. Furthermore, discounts are automatically applied to company employees, improving convenience.

[1935] "Personal information" refers to information such as the user's name, age, gender, occupation, and family composition and age.

[1936] "Saving" means storing input data in a storage device such as a database.

[1937] An "AI model" is a program that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on user data.

[1938] An "insurance plan" is a proposal for an insurance product, such as life insurance or auto insurance, offered to a user.

[1939] "Display" refers to visually showing the generated insurance plan on the user's terminal.

[1940] "Selection" refers to the act of a user choosing and deciding on the most suitable insurance plan from multiple options.

[1941] "Applying" means going through the process of officially enrolling in the insurance plan selected by the user.

[1942] An "employee" is an individual who belongs to a particular company and is entitled to the company's benefits and discounts.

[1943] A "discount" is a reduction of a certain amount or percentage from the original insurance premium based on certain criteria.

[1944] "Emotion recognition" refers to determining a user's current emotional state using technologies such as voice tone and facial recognition.

[1945] "Adjustment" refers to changing or modifying the contents of an insurance or wealth plan in response to a perceived emotional state.

[1946] An "asset formation plan" is a proposal for asset management and investment tailored to the user's financial goals and situation.

[1947] The system of the present invention allows users to input their personal information and, based on that information, provides the function of proposing optimal insurance plans. It also applies special discounts to corporate employees and can adjust the proposals based on the user's emotional state. The system is broadly composed of four components: the user's device, a server, an AI model, and an emotion engine. It also provides comprehensive services to users by proposing additional asset formation plans.

[1948] User data input processing

[1949] Users launch the insurance app on their smartphone, PC, or other device and enter their name, age, gender, occupation, and family information. This information is encoded into JSON format by the device and sent to the server via the secure HTTPS protocol.

[1950] Specific examples

[1951] "I'm a 35-year-old man, an engineer by profession, with a wife and two children."

[1952] Data transmission and storage

[1953] The device sends the input data to the server, which stores the received user data in a database, preprocesses the data, and formats it as input data for the AI ​​model.

[1954] Insurance plan proposal processing

[1955] The server inputs the formatted user data into an AI model to generate the optimal insurance plan. The AI ​​model uses pre-trained machine learning algorithms to suggest the insurance plan that best suits the user's profile. The generated insurance plan is sent to the user's device and displayed within the app.

[1956] Specific examples

[1957] "We offer life insurance (annual premium: 200,000 yen) and car insurance (annual premium: 80,000 yen)."

[1958] Employee benefit discount processing

[1959] When a user selects an insurance plan and applies, the device sends the selection information to the server, which searches the database for employee information based on the user ID and applies discounts if applicable. The final premium amount is then notified to the user.

[1960] Specific examples

[1961] "Checking... Employee discount will be applied. Final price is 79,200 yen."

[1962] Additional proposals for asset formation plans

[1963] The server also inputs data about the asset formation plan into the AI ​​model to generate optimal proposals, which are then sent to the user's device along with the insurance plan.

[1964] Emotion recognition by emotion engine

[1965] While a user is using the app, the emotion engine analyzes the user's emotional state in real time through voice tone and facial recognition. The analysis results are encoded in JSON format and sent to the server. The server then uses an AI model to adjust the suggestions based on the received emotional data and provide a plan that matches the user's emotional state.

[1966] Specific examples

[1967] If the user shows an "anxious expression," the emotion engine recognizes it and the server displays a low-risk insurance plan (e.g., a life insurance exception coverage plan).

[1968] In this way, the system of the present invention helps users efficiently select insurance plans, automatically applying discounts to corporate employees in particular. It also offers additional asset formation plans, providing more comprehensive insurance and asset management solutions. Furthermore, the emotion engine provides personalized services based on the user's emotional state.

[1969] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1970] Step 1: User Data Input

[1971] Input: The user enters personal information.

[1972] Specific operations: Users launch the app on their smartphone or PC and enter their name, age, gender, occupation, and family information into a dedicated form.

[1973] Output: The personal information entered is saved on the device.

[1974] Step 2: Send data

[1975] Input: Personal information entered by the user.

[1976] What happens: The device encodes the personal information entered by the user into JSON format.

[1977] Data processing: The encoded data is sent to the server using the HTTPS protocol.

[1978] Output: The server receives the encoded data sent by the device.

[1979] Step 3: Save data

[1980] Input: The encoded data sent.

[1981] Specific operation: The server analyzes the received data and stores it in a database.

[1982] Data calculation: Preprocessing data stored in the database and preparing it as input data for the AI ​​model.

[1983] Output: The formatted data is ready.

[1984] Step 4: Generate your insurance plan

[1985] Input: Formatted user data.

[1986] Specific operation: The server inputs the formatted data into the AI ​​model.

[1987] Data Computation: The AI ​​model uses pre-trained machine learning algorithms to generate optimal insurance plans.

[1988] Output: The generated insurance plan is saved on the server.

[1989] Step 5: View your insurance plan

[1990] Input: The generated insurance plan.

[1991] Specific operation: The server sends the generated insurance plan to the user's device.

[1992] Output: The device displays the insurance plan that was sent. The user can view the insurance plan in the app.

[1993] Step 6: Select and apply for an insurance plan

[1994] Input: The insurance plan selected by the user.

[1995] Specific behavior: The user selects the most suitable insurance plan from the displayed options and clicks the apply button.

[1996] Output: The selection is saved to the device.

[1997] Step 7: Submit your selections

[1998] Input: Information about the insurance plan selected by the user.

[1999] Specific operation: The device sends the selected plan information to the server.

[2000] Output: The server receives the selection information sent by the device.

[2001] Step 8: Apply employee perks discounts

[2002] Input: Received selection information and user ID.

[2003] Specific operation: The server searches the database for employee information based on the user ID.

[2004] Data calculation: Apply discounts, if applicable, to calculate the final premium.

[2005] Output: The final insurance premium is calculated and notified to the user.

[2006] Step 9: Additional proposals for asset formation plans

[2007] Input: User data and insurance plan.

[2008] Specific operation: The server inputs data about the asset formation plan into the AI ​​model.

[2009] Data calculation: Generate optimal asset formation plans.

[2010] Output: The generated asset formation plan is sent to the user's terminal and displayed.

[2011] Step 10: Emotion Recognition with the Emotion Engine

[2012] Input: User's voice tone and facial expression data.

[2013] How it works: While the user is using the app, the emotion engine performs real-time voice tone and facial recognition.

[2014] Data processing: Analyze the emotion data and encode it into JSON format.

[2015] Output: The analyzed emotion data is sent to the server.

[2016] Step 11: Adjust content based on emotional state

[2017] Input: Parsed emotion data.

[2018] Specific operation: The server inputs the received emotion data into the AI ​​model.

[2019] Data Computation: Adjusting insurance and wealth plans based on emotional state.

[2020] Output: The adjusted insurance plan or asset formation plan is sent to the user's device and displayed.

[2021] (Application example 2)

[2022] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2023] Conventional insurance plan recommendation systems can present optimal insurance plans based on a user's personal information, but do not take into account the user's emotional state. This creates the problem of making it difficult for users to select an appropriate insurance plan when they are feeling anxious or worried. Furthermore, asset formation plans are proposed solely based on personal information, and the contents of these plans are not adjusted to fit the user's emotional state. This results in lower user satisfaction and the possibility of users being unable to select the optimal plan.

[2024] 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.

[2025] In this invention, the server includes a means for recognizing the emotional state of the user and adjusting the contents of the insurance plan based on that emotional state, a means for generating an asset formation plan and adjusting the contents based on the emotional state of the user, and a means for inputting personal information from a terminal and transmitting it to the server, thereby making it possible to propose the optimal insurance plan and asset formation plan according to the emotional state of the user.

[2026] "Personal information" refers to information that identifies a user, such as the user's name, age, gender, occupation, and family composition.

[2027] An "AI model" is a model that uses machine learning algorithms to generate optimal insurance plans and asset formation plans based on input data.

[2028] An "insurance plan" is a combination of various insurance products and their terms and conditions offered by an insurance company and proposed to a user.

[2029] "Emotional state" refers to the psychological state of the user recognized through voice tone and facial expression analysis, and refers to emotions such as anxiety, joy, and sadness.

[2030] An "asset formation plan" includes investment and savings plans to increase a user's assets, and is proposed together with an insurance plan.

[2031] A "terminal" is an electronic device used by a user, such as a smartphone or PC, that is used to input personal information and recognize emotional states.

[2032] "Server" means a central computer system that processes data submitted by users and generates insurance and wealth plans.

[2033] The present invention relates to a system that automates the process by which a user selects, adjusts, and enrolls in an insurance or wealth plan.

[2034] Specific methods for carrying out the present invention will now be described.

[2035] System configuration

[2036] User's device

[2037] The user's device is an electronic device such as a smartphone or PC. The device has the following functions:

[2038] It provides a form for entering personal information, allowing users to enter personal information such as name, age, gender, occupation, and family composition.

[2039] It uses the smartphone's camera and microphone to recognize the user's emotional state and collect that data.

[2040] The device encodes the collected data in JSON format and sends it to the server using the secure HTTPS protocol.

[2041] server

[2042] The server processes the data and has the following functions:

[2043] Receive personal information and emotional data sent by users and store it in a database.

[2044] The pre-processed data is input into an AI model to generate optimal insurance and asset formation plans.

[2045] An emotion recognition engine is used to adjust the generated plan content based on the user's emotional state.

[2046] The optimal plan is sent to the device and presented to the user.

[2047] Hardware and Software

[2048] Specific examples of hardware and software used include:

[2049] Hardware: Smartphone (with camera and microphone), PC, server (cloud server is also acceptable)

[2050] Software: Flask (web framework), TensorFlow (machine learning library), OpenCV (image processing library), DeepFace (emotion recognition library)

[2051] Processing flow

[2052] Enter your personal information

[2053] User: Launches the application and fills in the personal information form.

[2054] On the device: The personal information entered is encoded in JSON format and sent to the server using the HTTPS protocol.

[2055] Generate an insurance plan

[2056] Server: Stores the received personal information in a database and inputs it into an AI model to generate an appropriate insurance plan.

[2057] Server: Sends the generated insurance plan to the device and displays it in the app.

[2058] Recognition of emotional states

[2059] User: Emotion recognition is activated while using the application, using the camera or microphone to collect your emotional state.

[2060] Emotion engine (terminal): Sends recognized emotion data to the server and analyzes the emotional state.

[2061] Emotion-Based Adjustment

[2062] Server: Adjusts the content of the plan generated by the AI ​​model based on emotional data and personal information.

[2063] Server: Sends the adjusted plan to the device and displays it to the user.

[2064] Specific examples

[2065] Prompt Sentence Examples

[2066] The prompt that users see when entering their personal information:

[2067] Please enter your name, age, gender, occupation, family composition, and ages of your family members.

[2068] For emotion recognition, the prompt displayed to the user is:

[2069] To recognize your emotional state while using the app, please face the camera and be in a stable position.

[2070] This system allows users to easily select the most suitable insurance and asset formation plan based on their emotional state and personal information. In particular, even if they are feeling anxious or worried, the system will suggest an appropriate plan based on their emotions, which will increase user satisfaction.

[2071] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2072] Step 1:

[2073] User: Launches the application on their smartphone or PC and enters their name, age, gender, occupation, family composition, and ages of family members into the personal information input form. This information is entered into the application as personal information.

[2074] Step 2:

[2075] On the device, the personal information entered is encoded in JSON format and sent to the server using the secure HTTPS protocol. The input data is divided into fields (name, age, gender, occupation, and family information) and properly formatted.

[2076] Step 3:

[2077] Server: Stores the received personal information in a database, mapping each field to the appropriate table and preprocessing the data. For example, age is treated as numeric data, while gender and occupation are converted to predefined categorical data.

[2078] Step 4:

[2079] Server: The preprocessed personal information is input into the AI ​​model to generate the optimal insurance plan for the user profile. The AI ​​model uses machine learning algorithms to compare the input data with past data and select the optimal insurance plan. For example, it generates the optimal life insurance and car insurance plan for a 30-year-old engineer with a family.

[2080] Step 5:

[2081] Server: The generated insurance plan is sent to the device and displayed in the application. The sent format is JSON, and includes information such as the insurance type, plan details, and premium.

[2082] Step 6:

[2083] User: While using the application, the user faces the camera to recognize their emotional state, using facial recognition and voice tone analysis.

[2084] Step 7:

[2085] Device: The smartphone's camera and microphone are used to recognize the user's emotional state and capture the data. The analysis results are processed in real time through the emotion engine and sent to the server in JSON format.

[2086] Step 8:

[2087] Server: Analyzes the received emotional state data and adjusts the insurance plan content. For example, if the user is feeling anxious, the content is changed to prioritize insurance plans with low risks.

[2088] Step 9:

[2089] Server: The adjusted insurance plan is sent back to the device and displayed in the application, receiving feedback based on the user's emotional state to make more relevant recommendations.

[2090] Step 10:

[2091] User: Selects the desired plan from the insurance plans presented and applies. This action sends the selected plan information to the server.

[2092] Step 11:

[2093] Server: Checks the user database and applies any benefits, such as employee discounts, that apply to the selected insurance plan. The final calculated premium is calculated and notified to the user.

[2094] Step 12:

[2095] Server: The server connects the finalized insurance plan information to the insurance company's system, allowing the user to officially enroll in the insurance.

[2096] Thus, this system allows users to easily select the optimal insurance plan and asset formation plan based on their emotional state and personal information, and provides appropriate plans even when they are feeling particularly anxious or worried, thereby improving user satisfaction.

[2097] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2098] 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.

[2099] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2100] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2101] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2102] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2103] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2104] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2105] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2106] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2107] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2108] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2109] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2110] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2111] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2112] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2113] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2114] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2115] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2116] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2117] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2118] The following is further disclosed regarding the above embodiment.

[2119] (Claim 1)

[2120] A means for entering personal information;

[2121] A means for storing the personal information entered;

[2122] A means for inputting the stored personal information into an AI model to generate an insurance plan;

[2123] a means for displaying the generated insurance plan;

[2124] How to select and apply for an insurance plan;

[2125] A way to verify employee status and apply discounts if they are employees,

[2126] A system including:

[2127] (Claim 2)

[2128] 10. The system of claim 1, further comprising means for generating a wealth creation plan.

[2129] (Claim 3)

[2130] 2. The system according to claim 1, further comprising means for inputting personal information from a terminal and transmitting the personal information to a server.

[2131] "Example 1"

[2132] (Claim 1)

[2133] A means for entering personal information;

[2134] A means for retaining the personal information entered;

[2135] A means for inputting the retained personal information into a generative AI model to generate an insurance plan;

[2136] a means for displaying the generated insurance plan;

[2137] How to select and apply for an insurance plan;

[2138] A method to verify whether a customer is an employee based on individual identification information and apply discounts if the customer is an employee.

[2139] A system including:

[2140] (Claim 2)

[2141] 10. The system of claim 1, further comprising means for generating a wealth creation plan.

[2142] (Claim 3)

[2143] 2. The system according to claim 1, further comprising means for inputting personal information from a terminal and transmitting the information to a server.

[2144] "Application Example 1"

[2145] Newly constructed claims

[2146] (Claim 1)

[2147] A means for entering personal information;

[2148] A means for storing the personal information entered;

[2149] A means of inputting stored personal information into an AI model to generate a personalized food plan;

[2150] a means for displaying the generated food plan;

[2151] How to select and apply for a food plan,

[2152] a means for checking for certain conditions and applying discounts based on those conditions;

[2153] A system including:

[2154] (Claim 2)

[2155] 10. The system of claim 1, further comprising means for generating a health plan.

[2156] (Claim 3)

[2157] 2. The system according to claim 1, further comprising means for inputting personal information from a terminal and transmitting the personal information to a server.

[2158] "Example 2: Combining Emotion Engines"

[2159] (Claim 1)

[2160] A means for entering personal information;

[2161] A means for storing the personal information entered;

[2162] A means for inputting the stored personal information into an AI model to generate an insurance plan;

[2163] a means for displaying the generated insurance plan;

[2164] How to select and apply for an insurance plan;

[2165] A way to verify employee status and apply discounts if they are employees,

[2166] a means of recognizing a user's emotions and tailoring recommendations based on those emotions;

[2167] A system including:

[2168] (Claim 2)

[2169] 10. The system of claim 1, further comprising means for generating a wealth creation plan.

[2170] (Claim 3)

[2171] 2. The system according to claim 1, further comprising means for inputting personal information from a terminal and transmitting the personal information to a server.

[2172] "Application example 2 when combining emotion engines"

[2173] (Claim 1)

[2174] A means for entering personal information;

[2175] A means for storing the personal information entered;

[2176] A means for inputting the stored personal information into an AI model to generate an insurance plan;

[2177] a means for displaying the generated insurance plan;

[2178] How to select and apply for an insurance plan;

[2179] A way to verify employee status and apply discounts if they are employees,

[2180] a means for recognizing a user's emotional state and adjusting the contents of the insurance plan based on the emotional state;

[2181] A system including:

[2182] (Claim 2)

[2183] 10. The system of claim 1, further comprising means for generating a wealth creation plan and adjusting its contents based on the user's emotional state.

[2184] (Claim 3)

[2185] 2. The system according to claim 1, further comprising means for inputting personal information from a terminal and transmitting the personal information to a server. [Explanation of symbols]

[2186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for entering personal information; A means for storing the personal information entered; A means for inputting the stored personal information into an AI model to generate an insurance plan; a means for displaying the generated insurance plan; How to select and apply for an insurance plan; A way to verify employee status and apply discounts if they are employees, A system including:

2. The system of claim 1 further comprising means for generating a wealth creation plan.

3. 2. The system according to claim 1, further comprising means for inputting personal information from a terminal and transmitting the information to a server.

Images