System

Abstract

A system is provided.SOLUTION: A system comprising: input means for inputting financial information of a user; data collection means for collecting the financial information of the user; analysis means for analyzing the collected financial information; plan generation means for generating a financial plan based on a result of the analysis; and presentation means for presenting the generated financial plan to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When users have goals such as future asset growth, living expenses, securing retirement funds, or saving for education, they often do not know how to build assets or which investment strategy to choose. In particular, users who are busy with their daily lives and are unable to act with an awareness of risk face the problem of difficulty in making efficient and safe financial plans. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes an input means for inputting a user's financial information, a data collection means for collecting the user's financial information, an analysis means for analyzing the collected financial information, a plan generation means for generating a financial plan based on the analysis results, and a presentation means for presenting the generated financial plan to the user. Specifically, the system collects a user's financial information, including past transaction data and consumption patterns, and analyzes the user's financial information taking into account their lifestyle and risk tolerance to provide an individually tailored financial plan.

[0006] "Input Method" means the device or interface through which a user provides financial information to the system.

[0007] "Data collection means" refers to the functions and methods for collecting necessary information, such as past transaction data and consumption patterns, including users' financial information.

[0008] "Analysis methods" refer to algorithms and models that analyze collected data, taking into account the user's lifestyle, risk tolerance, etc.

[0009] "Plan generation means" refers to a function or method for generating a financial plan suitable for a user based on the analysis results obtained from the analysis means.

[0010] "Presentation vehicle" refers to a device or interface for displaying or informing a user of a generated financial plan.

[0011] So, here are the definitions of the important words included in the claims. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system is mainly composed of the following elements: input means, data collection means, analysis means, plan generation means, and presentation means.

[0034] System Overview

[0035] 1. Input Method

[0036] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[0037] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0038] 2. Data Collection Methods

[0039] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[0040] This includes obtaining data through APIs from banks, credit card companies, etc.

[0041] 3. Analysis method

[0042] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[0043] User income and expenditure patterns

[0044] User risk tolerance

[0045] User lifestyle

[0046] 4. Plan Generation Method

[0047] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[0048] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0049] 5. Presentation means

[0050] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[0051] It also includes an interface that allows users to review the plan and provide approval or feedback.

[0052] Specific examples

[0053] Data Collection Example

[0054] A user accesses the system for the first time and creates an account.

[0055] A user uses a financial information entry form to enter the following information:

[0056] Monthly salary: 500,000 yen

[0057] Fixed expenses (rent, etc.): 100,000 yen

[0058] Variable expenses (food, etc.): 50,000 yen

[0059] Current assets: 2,000,000 yen

[0060] Current debt: 500,000 yen

[0061] The terminal sends the input data to the server, which stores it in a database.

[0062] Analysis example

[0063] The server then obtains the user's past transaction data and passes it to the generation AI.

[0064] The generative AI model produces the following analysis results:

[0065] Monthly surplus: 350,000 yen

[0066] Recommended investment amount: 200,000 yen

[0067] Recommended savings amount: 100,000 yen

[0068] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0069] Risk Level: Medium

[0070] Plan Generation and Presentation Example

[0071] The server creates a financial plan based on the analysis results:

[0072] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0073] 60% domestic stocks

[0074] 20% foreign stocks

[0075] 10% bond

[0076] 10% cash

[0077] The server sends the generated plan to the user's terminal, which displays it to the user.

[0078] The user reviews the plan and provides approval or feedback.

[0079] The above is an embodiment of the present invention, and the system allows users to quickly and easily obtain the financial plan that is best suited to them.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] A user accesses the system for the first time and enters the information required to create an account (such as name, email address, and password).

[0083] The terminal sends the input data to the server, which stores it in a database.

[0084] Step 2:

[0085] Users log in and fill out a financial information form, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0086] The terminal sends the input data to the server, which stores it in a database.

[0087] Step 3:

[0088] The server uses an API to retrieve the user's bank and credit card transaction data.

[0089] The server stores these transaction data in a database.

[0090] Step 4:

[0091] The server formats the user's collected data to be passed to the generative AI model, specifically by converting the data into a format that is easy to analyze.

[0092] For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance can be compiled in JSON format.

[0093] Step 5:

[0094] The server passes the formatted data to the generative AI model and begins analysis.

[0095] The generative AI model analyzes the user's income and expenditure patterns, lifestyle, risk tolerance, etc., and obtains analysis results to generate the optimal financial plan.

[0096] Step 6:

[0097] The server receives the analysis results from the generative AI model and generates a financial plan based on them.

[0098] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0099] Step 7:

[0100] The server sends the generated financial plan to the user's terminal.

[0101] The device displays the received plan to the user, who then confirms the plan details.

[0102] Step 8:

[0103] The user reviews the financial plan and provides approval or feedback as needed.

[0104] The device sends feedback to the server and the plan is readjusted if necessary.

[0105] The above is a description of the specific processing steps of the program.

[0106] Example 1

[0107] 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."

[0108] Conventional financial planning systems struggled to fully consider a user's diverse financial information, lifestyle, risk tolerance, and other factors. They also required users to manually input information, and the accuracy of analyzing the collected data was limited. This made it difficult to provide users with optimal financial plans. Furthermore, there was a lack of a way to quickly incorporate user feedback.

[0109] 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.

[0110] In this invention, the server includes a data input means for inputting the user's financial information, a data communication means for collecting the user's financial information and past transaction data, a data analysis means for analyzing the collected financial information and transaction data, a plan generation means for generating an optimal financial plan, a plan provision means for providing the generated financial plan, and an interaction means for the user to review the plan and provide feedback. This makes it possible to efficiently collect and analyze a wide range of user information and quickly provide more accurate financial plans. Furthermore, the plan can be improved by reflecting user feedback in real time.

[0111] 1. "User" means an entity that uses the System and provides personal financial information.

[0112] 2. "Financial Information" means your economic data, such as your income, expenses, assets, and liabilities.

[0113] 3. "Data Entry Means" means the interface through which a user enters financial information into the system.

[0114] 4. "Data communication means" refers to the means for transmitting user input data and transaction data to a server and for obtaining information from external organizations.

[0115] 5. "Data analysis means" means means for analyzing collected data and evaluating a user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[0116] 6. "Generative AI Model" means an artificial intelligence model used for data analysis to generate the optimal plan for the user.

[0117] 7. "Plan generation means" means a means for generating a financial plan based on the analysis results of the generative AI model.

[0118] 8. "Plan Provision Means" means a means for providing the generated financial plan to the user.

[0119] 9. "Interaction methods" are the methods by which users can review the generated plans and provide feedback.

[0120] 10. "API" means an application programming interface for communicating with external systems to obtain data.

[0121] The system of the present invention proposes an optimal financial plan based on the user's financial information. This system is mainly composed of a data input means, a data communication means, a data analysis means, a plan generation means, a plan provision means, and an interaction means.

[0122] System configuration

[0123] 1. Data entry method

[0124] Users enter their financial information through an interface such as a web form or a mobile application.

[0125] Specific financial information includes monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0126] 2. Data communication means

[0127] The device sends the user's input data to the server.

[0128] To gather further data, the server obtains past transaction data and spending patterns through APIs from banks and credit card companies.

[0129] 3. Data Analysis Methods

[0130] The server passes the data collected from users to a generative AI model for detailed analysis.

[0131] This analysis includes the user's income and spending patterns, risk tolerance, lifestyle, etc.

[0132] 4. Plan Generation Method

[0133] The server creates the optimal financial plan for the user based on the analysis results of the generated AI model.

[0134] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0135] 5. Plan Delivery Method

[0136] The server sends the generated financial plan to the user's terminal, which displays it to the user.

[0137] 6. Interaction methods

[0138] An interface is included for users to review the plan and provide approval or feedback.

[0139] Specific operation of the system

[0140] Examples of data collection

[0141] A user accesses the system and creates a new account.

[0142] The user fills in the form with the following information:

[0143] text

[0144] Monthly salary: 500,000 yen

[0145] Fixed expenses: 100,000 yen

[0146] Variable expenses: 50,000 yen

[0147] Current assets: 2,000,000 yen

[0148] Current debt: 500,000 yen

[0149] The terminal sends the input data to the server, which stores it in a database.

[0150] Specific examples of data analysis

[0151] The server collects past transaction data and passes it to the generative AI model.

[0152] A generative AI model analyzes the data and produces results such as:

[0153] text

[0154] Monthly surplus: 350,000 yen

[0155] Recommended investment amount: 200,000 yen

[0156] Recommended savings amount: 100,000 yen

[0157] Diversified investment portfolio:

[0158] 60% domestic stocks

[0159] 20% foreign stocks

[0160] 10% bond

[0161] 10% cash

[0162] Risk Level: Medium

[0163] Example of plan generation and presentation

[0164] The server creates a specific financial plan based on the analysis results:

[0165] text

[0166] Recommended plan:

[0167] Invest 200,000 yen per month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0168] 60% domestic stocks

[0169] 20% foreign stocks

[0170] 10% bond

[0171] 10% cash

[0172] The server sends the generated plan to the user's terminal, which displays it to the user.

[0173] The user reviews the plan and provides approval or feedback.

[0174] This concludes the "Mode for Carrying Out the Invention." This system allows users to quickly and easily obtain the financial plan that is best for them and incorporates feedback in real time.

[0175] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0176] Step 1:

[0177] A user inputs financial information using a data entry device, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance. This data is collected through the data entry device.

[0178] Input: User's financial information (e.g. monthly income, expenses, assets, liabilities)

[0179] Output: Input data

[0180] Action: A user enters information into a form or application and clicks the submit button.

[0181] Step 2:

[0182] The device sends the entered financial information to a server, which stores it in a database. Furthermore, if the user consents, the server uses the APIs of banks and credit card companies to collect past transaction data and spending patterns.

[0183] Input: Input data, user consent

[0184] Output: Collected transaction data

[0185] How it works: The device sends data to the server, which stores it in a database and optionally collects additional data through external APIs.

[0186] Step 3:

[0187] The server passes the collected financial information and transaction data to the generative AI model for data analysis. The analysis includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model analyzes this data and generates the basic information for an appropriate financial plan.

[0188] Input: Financial information, transaction data

[0189] Output: Analysis results (e.g. monthly surplus, recommended investment amount)

[0190] How it works: A server provides data to a generative AI model, which then analyzes the data.

[0191] Step 4:

[0192] Based on the analysis results, the server generates an optimal financial plan, which includes monthly investment amounts, savings amounts, and investment portfolio ratios (domestic stocks, international stocks, bonds, cash, etc.).

[0193] Input: Analysis results

[0194] Output: Financial Plan

[0195] Operation: The server receives the analysis results and uses a plan generation algorithm to create a financial plan.

[0196] Step 5:

[0197] The server sends the generated financial plan to the user's terminal and displays it to the user through the plan providing means, and the user can review the plan and provide approval or feedback.

[0198] Enter: Financial Plan

[0199] Output: Show plan to user

[0200] How it works: The server sends the plan to the device, which displays it on an interface where the user can review the plan.

[0201] Step 6:

[0202] The user reviews the generated financial plan and provides feedback if necessary, which is then sent back to the server via the data input means.

[0203] Input: User feedback

[0204] Output: Updated financial plan (if needed)

[0205] How it works: The user enters their feedback and requests for changes to the plan and sends it to the server. If feedback is received, the server re-analyzes the plan to reflect that feedback.

[0206] (Application example 1)

[0207] 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."

[0208] Conventional financial planning systems require users to manually create plans based on financial information collected by themselves, making it difficult to optimize the plans and preventing them from automatically generating plans that take into account the user's lifestyle and risk tolerance. Furthermore, there is a lack of means to quickly implement the generated plans, which creates the challenge of requiring a lot of effort after the user approves them.

[0209] 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.

[0210] In this invention, the server includes a data collection means for collecting the user's financial information, an analysis means for analyzing the collected financial information, and a plan generation means for generating a financial plan based on the analysis results, thereby enabling the automatic generation of an optimal financial plan that takes into account the user's lifestyle and risk tolerance.

[0211] The system also includes an interface for obtaining user approval and a settlement mechanism for automatically executing approved plans, allowing the created plans to be executed quickly and easily, thereby reducing the user's workload and enabling efficient financial planning.

[0212] "User" means any person or entity that uses the System to enter their financial information and receive a Financial Plan.

[0213] "Financial information" refers to general information such as a user's monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0214] "Input Method" refers to the interface, such as a web form or mobile application, that a user uses to provide financial information to the system.

[0215] "Data Collection Measures" means mechanisms for collecting input financial information and related historical transaction data and spending patterns.

[0216] "Analytical Tools" refers to the generative AI models and computational algorithms used to analyze collected financial information and generate detailed risk assessments and investment plans.

[0217] The "plan generation means" refers to a process or device for creating an optimal financial plan for the user based on the analysis results.

[0218] "Presentation means" refers to the interface for presenting the generated financial plan to the user and obtaining confirmation or approval.

[0219] "Interface" means the means by which a user provides approval and feedback on a generated financial plan.

[0220] "Payment Mechanism" refers to an electronic payment service or related system for automatically executing a plan approved by a user.

[0221] The system of the present invention is a FinTech application that proposes and automatically executes an optimal financial plan based on the user's financial information. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an interface, and a payment mechanism.

[0222] System Overview

[0223] 1. Input Method

[0224] A web form or mobile application interface allows users to enter financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0225] 2. Data Collection Methods

[0226] The device sends the user's input data to the server, and also uses APIs to collect data from banks and credit card companies, as well as past transaction data and spending patterns.

[0227] 3. Analysis method

[0228] The server passes the collected data to a generative AI model for detailed analysis, which takes into account the user's income and expenditure patterns, risk tolerance, and lifestyle. Specifically, the generative AI model is used to generate an optimal financial plan.

[0229] 4. Plan Generation Method

[0230] The server then uses the analysis results from the AI ​​model to create a financial plan optimized for the user, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (e.g., domestic stocks, international stocks, bonds, cash, etc.).

[0231] 5. Presentation means

[0232] The server sends the generated financial plan to the user's device, which presents it to the user, including an interface that allows the user to review the plan's contents and provide approval or feedback.

[0233] 6. Interface

[0234] A means for users to provide approval or feedback on a presented financial plan. Through this interface, users can review and approve the plan.

[0235] 7. Payment Systems

[0236] After the server receives the user's approval, it automatically executes the investment using an electronic payment service, reducing the burden on the user and enabling quick investment execution.

[0237] Specific examples

[0238] Data Collection Example

[0239] A user accesses the system for the first time and creates an account.

[0240] A user uses a financial information entry form to enter the following information:

[0241] Monthly salary: 500,000 yen

[0242] Fixed expenses (rent, etc.): 100,000 yen

[0243] Variable expenses (food, etc.): 50,000 yen

[0244] Current assets: 2,000,000 yen

[0245] Current debt: 500,000 yen

[0246] The terminal sends the input data to the server, which stores it in a database.

[0247] Analysis example

[0248] The server then obtains the user's past transaction data via an API and passes it to the generation AI.

[0249] The generative AI model produces the following analysis results:

[0250] Monthly surplus: 350,000 yen

[0251] Recommended investment amount: 200,000 yen

[0252] Recommended savings amount: 100,000 yen

[0253] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0254] Risk Level: Medium

[0255] Plan Generation and Presentation Example

[0256] The server creates a financial plan based on the analysis results:

[0257] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0258] 60% domestic stocks

[0259] 20% foreign stocks

[0260] 10% bond

[0261] 10% cash

[0262] The server sends the generated plan to the user's terminal, which displays it to the user.

[0263] The user reviews the plan and provides approval or feedback.

[0264] Hardware and software used

[0265] Device: A smartphone or computer where users enter their financial information and view their plan.

[0266] Server: A server that collects data, analyzes, generates plans, presents plans, and executes investments.

[0267] Generative AI model: An AI model that analyzes a user's financial data and generates an optimal financial plan. We will use a "generative AI model" as an example.

[0268] API: API for obtaining data from banks, credit card companies, etc.

[0269] Electronic Payment Service: A payment mechanism for the execution of automated investments.

[0270] The above is an embodiment of the present invention. This system allows users to quickly and easily obtain and efficiently implement a financial plan that is best suited to them.

[0271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0272] Step 1:

[0273] A user uses a smartphone or PC to access a web form or mobile application interface and enters financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc. The input data is stored on the device and sent to a server.

[0274] input:

[0275] Monthly salary: 500,000 yen

[0276] Fixed expenses: 100,000 yen

[0277] Variable expenses: 50,000 yen

[0278] Current assets: 2,000,000 yen

[0279] Current debt: 500,000 yen

[0280] Lifestyle: Normal

[0281] Risk tolerance: Medium

[0282] output:

[0283] A database on the server stores users' financial information.

[0284] Step 2:

[0285] The server receives the user's input data and uses APIs to retrieve bank and credit card transaction data, which includes retrieving the user's past transaction data and spending patterns through APIs.

[0286] input:

[0287] User financial information and API requests.

[0288] output:

[0289] Bank and credit card transaction data is stored on the server.

[0290] Step 3:

[0291] The server sends the collected data to the generative AI model for analysis, which includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model then performs a detailed analysis based on the generated prompt.

[0292] input:

[0293] Your financial and transactional data.

[0294] User's monthly income: 500,000 yen

[0295] Fixed expenses: 100,000 yen

[0296] Variable expenses: 50,000 yen

[0297] Current assets: 2,000,000 yen

[0298] Current debt: 500,000 yen

[0299] Historical Transaction Data: [{"date":"2023-01-01", "amount":-2000, "category":"food"}, {"date":"2023-01-02", "amount":-5000, "category":"transport"}]

[0300] Risk tolerance: Medium

[0301] Lifestyle: Normal

[0302] output:

[0303] Analysis results obtained from a generative AI model. Example: "Monthly surplus: 350,000 yen, recommended investment amount: 200,000 yen, recommended savings amount: 100,000 yen, diversified investment portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash."

[0304] Step 4:

[0305] Based on the analysis results, the server uses a generative AI model to generate an optimal financial plan, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[0306] input:

[0307] Analysis results.

[0308] Monthly surplus: 350,000 yen

[0309] Recommended investment amount: 200,000 yen

[0310] Recommended savings amount: 100,000 yen

[0311] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0312] output:

[0313] Financial plan.

[0314] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0315] 60% domestic stocks

[0316] 20% foreign stocks

[0317] 10% bond

[0318] 10% cash

[0319] Step 5:

[0320] The server sends the generated financial plan to the user's terminal, which then presents it to the user, who then reviews the content of the presented plan and provides approval or feedback through the interface.

[0321] input:

[0322] Financial plan.

[0323] output:

[0324] User acknowledgement or feedback.

[0325] Step 6:

[0326] The server, having received the user's approval, automatically executes the investment using the electronic settlement service, and the settlement mechanism processes the settlement according to the investment portfolio.

[0327] input:

[0328] User approval.

[0329] output:

[0330] Automatically executed investment transactions.

[0331] 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.

[0332] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system mainly consists of the following elements: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, and an emotion recognition means.

[0333] System Overview

[0334] 1. Input Method

[0335] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[0336] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0337] 2. Data Collection Methods

[0338] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[0339] This includes obtaining data through APIs from banks, credit card companies, etc.

[0340] 3. Analysis method

[0341] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[0342] User income and expenditure patterns

[0343] User risk tolerance

[0344] User lifestyle

[0345] 4. Plan Generation Method

[0346] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[0347] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0348] 5. Presentation means

[0349] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[0350] It also includes an interface that allows users to review the plan and provide approval or feedback.

[0351] 6. Emotion recognition means

[0352] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[0353] The server passes the data to an emotion recognition engine that analyzes the user's emotional state, which includes techniques such as voice analysis, facial expression analysis, and text analysis.

[0354] Specific examples

[0355] Data Collection Example

[0356] A user accesses the system for the first time and creates an account.

[0357] A user uses a financial information entry form to enter the following information:

[0358] Monthly salary: 500,000 yen

[0359] Fixed expenses (rent, etc.): 100,000 yen

[0360] Variable expenses (food, etc.): 50,000 yen

[0361] Current assets: 2,000,000 yen

[0362] Current debt: 500,000 yen

[0363] Lifestyle: secure

[0364] Risk tolerance: medium

[0365] The terminal sends the input data to the server, which stores it in a database.

[0366] Example of emotion data collection

[0367] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[0368] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[0369] The device sends this emotion data to a server and stores it in a database.

[0370] Analysis example

[0371] The server then obtains the user's past transaction data and passes it to the generation AI.

[0372] The generative AI model produces the following analysis results:

[0373] Monthly surplus: 350,000 yen

[0374] Recommended investment amount: 200,000 yen

[0375] Recommended savings amount: 100,000 yen

[0376] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0377] Risk Level: Medium

[0378] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[0379] Plan Generation and Presentation Example

[0380] The server creates a financial plan based on the analysis results:

[0381] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[0382] 50% domestic stocks

[0383] 20% foreign stocks

[0384] 20% bond

[0385] 10% cash

[0386] The server sends the generated plan to the user's terminal, which displays it to the user.

[0387] The user reviews the plan and provides approval or feedback.

[0388] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[0389] The processing flow will be explained below.

[0390] Step 1:

[0391] When a user accesses the system for the first time, they enter the information required to create an account (such as name, email address, and password). The terminal sends the input data to the server, which then stores it in a database.

[0392] Step 2:

[0393] The user logs in and enters information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a financial information input form. The terminal sends the input data to the server, which then stores it in a database.

[0394] Step 3:

[0395] The server uses an API to retrieve the user's bank and credit card transaction data, which it then stores in a database.

[0396] Step 4:

[0397] The server formats the user's collected data to be passed to the generative AI model. Specifically, the data is converted into a format that is easy to analyze. For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance is compiled into a JSON format.

[0398] Step 5:

[0399] The server passes the formatted data to the generative AI model, which then analyzes it based on the user's income and expenditure patterns, lifestyle, risk tolerance, etc., to obtain analytical results that will help generate an optimal financial plan.

[0400] Step 6:

[0401] The server receives the analysis results from the generative AI model and generates a financial plan based on them, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0402] Step 7:

[0403] The device collects emotional data from the user's voice and text and sends it to the server. For example, when a user enters feedback on a plan, the device analyzes the user's emotions.

[0404] Step 8:

[0405] The server passes the emotional data to an emotion recognition engine, which analyzes the user's emotional state using voice analysis, facial expression analysis, text analysis, and other methods.

[0406] Step 9:

[0407] The server adjusts the financial plan based on the emotion recognition results. For example, if the user is feeling excessively stressed, it will set a lower risk level and regenerate a plan that emphasizes safety.

[0408] Step 10:

[0409] The server sends the adjusted financial plan to the user's device, which displays it to the user, who then reviews the plan and provides approval or feedback.

[0410] The above is a description of the specific processing steps of the system that combines emotion engines.

[0411] Example 2

[0412] 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."

[0413] Conventional financial planning systems generate plans based on a user's financial information, but are unable to consider the user's psychological state or emotions. As a result, the financial plans provided often do not match the user's actual psychological state, making it difficult to implement and maintain the plan. Furthermore, there are insufficient means to specifically reflect the user's lifestyle and risk tolerance, making it difficult to propose personalized plans. The problem that this invention aims to solve is to provide a more personalized and realistic financial plan that takes into account the user's psychological state and lifestyle.

[0414] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for inputting the user's financial information, a data collection means for collecting the user's financial information, and an analysis means for analyzing the collected financial information. This enables detailed data analysis and the provision of a financial plan based on the user's financial situation. Furthermore, by adding an emotion recognition means for recognizing the user's emotional state and collecting that data, and a means for adjusting the financial plan generated based on the emotional state, it becomes possible to optimize the plan taking the user's psychological state into consideration. This makes it possible to provide a feasible and sustainable financial plan for the user.

[0415] "Input means" refers to the interface through which a user provides financial information to the system.

[0416] "Data Collection Method" refers to the method or device used to capture a user's financial information into the system.

[0417] "Analysis means" refers to the methods and functions for conducting data analysis based on collected financial information.

[0418] The "plan generation means" refers to a function or device that creates a financial plan based on the information obtained by the analysis means.

[0419] "Presentation Means" refers to the method or device for displaying and providing the generated financial plan to the user.

[0420] "Emotion recognition means" refers to a method or device for grasping a user's emotional state and incorporating that data into the system.

[0421] The "adjustment means" refers to a method or function for adjusting the financial plan based on the emotion data obtained by the emotion recognition means.

[0422] The system of the present invention proposes an optimal financial plan based on a user's financial information. The system is composed of the following main components: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and an adjustment means.

[0423] System Overview

[0424] 1. Input Method

[0425] The interface through which users provide financial information to the system, such as a web form or a mobile application.

[0426] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0427] 2. Data Collection Methods

[0428] The device uses methods and APIs to send user input data to the server and, if necessary, collect past transaction data and consumption patterns.

[0429] For example, transaction data is obtained from banks and credit card companies via API.

[0430] 3. Analysis method

[0431] The server passes the user's collected data to the generative AI model, which then analyzes the data.

[0432] This analysis includes the user's income and spending patterns, risk tolerance and lifestyle.

[0433] 4. Plan Generation Method

[0434] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[0435] A specific plan includes monthly investment amounts, savings amounts, and investment portfolios.

[0436] 5. Presentation means

[0437] The server sends the generated financial plan to the user's terminal, which then presents it to the user.

[0438] Users can review the plan and provide approval or feedback.

[0439] 6. Emotion recognition means

[0440] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[0441] The server passes the data to an emotion recognition engine to analyze the user's emotional state, which includes voice analysis, facial expression analysis, and text analysis.

[0442] 7. Adjustment means

[0443] The server adjusts the financial plan based on the sentiment data.

[0444] For example, if a user is feeling stressed, the risk level of the plan can be adjusted accordingly.

[0445] Specific examples

[0446] Data Collection Example

[0447] A user accesses the system for the first time and creates an account.

[0448] A user uses a financial information entry form to enter the following information:

[0449] Monthly salary: 500,000 yen

[0450] Fixed expenses (rent, etc.): 100,000 yen

[0451] Variable expenses (food, etc.): 50,000 yen

[0452] Current assets: 2,000,000 yen

[0453] Current debt: 500,000 yen

[0454] Lifestyle: secure

[0455] Risk tolerance: medium

[0456] The terminal sends the input data to the server, which stores it in a database.

[0457] Example of emotion data collection

[0458] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[0459] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[0460] The device sends this emotion data to a server and stores it in a database.

[0461] Analysis example

[0462] The server obtains the user's input data and past transaction data and passes it to the generative AI model.

[0463] The generative AI model produces the following analysis results:

[0464] Monthly surplus: 350,000 yen

[0465] Recommended investment amount: 200,000 yen

[0466] Recommended savings amount: 100,000 yen

[0467] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0468] Risk Level: Medium

[0469] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted to "low."

[0470] Plan Generation and Presentation Example

[0471] The server creates a financial plan based on the analysis results as follows:

[0472] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[0473] 50% domestic stocks

[0474] 20% foreign stocks

[0475] 20% bond

[0476] 10% cash

[0477] The server sends the generated plan to the user's terminal, which displays it to the user.

[0478] The user reviews the plan and provides approval or feedback.

[0479] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[0480] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0481] Step 1:

[0482] User enters financial information

[0483] The user enters financial information (e.g., monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc.) into the system's web form or mobile app.

[0484] Input data: monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance

[0485] Output data: Initial financial information entered by the user

[0486] Step 2:

[0487] The device sends the input data to the server

[0488] The terminal temporarily stores the financial information entered by the user and sends it to the server.

[0489] Input Data: Initial financial information entered by the user

[0490] Output data: Financial information sent to the server

[0491] Step 3:

[0492] The server collects additional data

[0493] If necessary, the server retrieves the user's transaction history and spending patterns from banks and credit card companies via API and adds them to the financial information.

[0494] Input data: Financial information sent to the server

[0495] Output data: Complete financial information with additional transaction history and spending patterns

[0496] Step 4:

[0497] The server passes the data to the generative AI model

[0498] The server passes the collected complete financial information to the generative AI model for detailed analysis, and issues a prompt saying, "Generate the optimal financial plan based on the user's income, expenses, assets, liabilities, and other data."

[0499] Input data: complete financial information, and prompt statements

[0500] Output data: Analysis request passed to the generative AI model

[0501] Step 5:

[0502] Generative AI models analyze data

[0503] The generative AI model analyzes the data passed from the server and generates the optimal financial plan for the user, including the monthly surplus, recommended investment amount, recommended savings amount, diversified investment portfolio, risk level, etc.

[0504] Input data: Complete financial information

[0505] Output data: Analysis results (e.g. monthly surplus, recommended investment amount, recommended savings amount, portfolio, risk level)

[0506] Step 6:

[0507] The server generates a plan based on the analysis results.

[0508] The server generates an optimal financial plan for the user based on the analysis results of the AI ​​model. For example, invest 200,000 yen per month and save 100,000 yen. The recommended portfolio would be 50% domestic stocks, 20% international stocks, 20% bonds, and 10% cash.

[0509] Input data: Analysis results from the generative AI model

[0510] Output data: Generated financial plan

[0511] Step 7:

[0512] The device collects the user's emotional data and sends it to the server.

[0513] The device collects emotional data from user interactions, input text, and voice, and sends this data to a server.

[0514] Input data: user dialogue text and voice

[0515] Output data: Emotion data sent to the server

[0516] Step 8:

[0517] The server performs emotion analysis using an emotion recognition engine.

[0518] The server passes the emotion data to an emotion recognition engine to analyze the user's emotional state, for example, to determine whether the user is feeling stressed.

[0519] Input data: User emotion data

[0520] Output data: Analysis results on the user's emotional state

[0521] Step 9:

[0522] The server adjusts the plan taking into account the emotional data.

[0523] The server adjusts the risk level of the generated financial plan based on the analysis results of the emotion recognition engine. For example, if the user is feeling stressed, the risk level will be changed from "medium" to "low."

[0524] Input data: Generated financial plan and analysis results on emotional state

[0525] Output data: adjusted financial plan

[0526] Step 10:

[0527] The server sends the final plan to the device.

[0528] The server sends the final financial plan to the user's terminal, which then presents it to the user.

[0529] Input data: Adjusted financial plan

[0530] Output data: The final financial plan sent to the user's device

[0531] Step 11:

[0532] Users review the plan and provide feedback

[0533] The user reviews the proposed financial plan and provides feedback or approval as needed. The provided feedback is then sent back to the server as a reference for generating the next plan.

[0534] Input data: Financial plan confirmed by the user

[0535] Output data: User feedback

[0536] (Application example 2)

[0537] 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."

[0538] Conventional financial plan generation systems provide optimal plans based on a user's financial information, but do not take the user's emotional state into consideration. As a result, there is a risk that an appropriate plan will not be provided if the user is in a high-stress or abnormal emotional state. In terms of security, there is also a lack of mechanisms to recognize the user's emotional state and detect abnormal login attempts. Therefore, by combining emotion recognition, it is necessary to provide detailed plans that take the user's psychological state into consideration and strengthen security.

[0539] 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.

[0540] In this invention, the server includes an input means for inputting the user's financial information, a data collection means, and a data analysis means, which enables the presentation of a financial plan that takes into account the user's emotional state and enhances security.

[0541] "Input means" means an interface through which a user provides financial information to the system.

[0542] "Data collection means" refers to a system for collecting information such as users' financial information, past transaction data, and consumption patterns.

[0543] "Analysis means" refers to a device that analyzes collected financial information and evaluates the user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[0544] The "plan generation means" is a system that generates an optimal financial plan for the user based on the results obtained by the analysis means.

[0545] The "presentation means" is an interface for displaying the generated financial plan to the user and providing confirmation and feedback.

[0546] An "emotion recognition means" is a system for recognizing a user's emotional state and collecting that data.

[0547] The "security assessment means" is a system that detects anomalies in a user's login attempt based on the emotional state recognized by the emotion recognition means and requests additional authentication processes.

[0548] A "generative AI model" is an artificial intelligence model used to analyze a user's financial information and emotional state to provide optimal financial plans and security assessments.

[0549] The system of the present invention proposes an optimal financial plan based on a user's financial information and emotional state, and also performs security assessment. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and a security assessment means. The operation of each element is described in detail below.

[0550] System configuration

[0551] 1. Input Method

[0552] Web forms and mobile applications are used as interfaces for users to provide financial information to the system.

[0553] Users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0554] 2. Data Collection Methods

[0555] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[0556] This includes obtaining data through APIs from banks, credit card companies, etc.

[0557] 3. Analysis method

[0558] The server passes the collected data of the user to a generative AI model that evaluates their income and spending patterns, risk tolerance, and lifestyle.

[0559] Machine learning frameworks such as TensorFlow and PyTorch are used as generative AI models.

[0560] 4. Plan Generation Method

[0561] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[0562] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0563] 5. Presentation means

[0564] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[0565] An interface is also provided where users can review the plan and provide approval or feedback.

[0566] 6. Emotion recognition means

[0567] It is a means for a device to capture a user's facial expressions and voice using a camera and microphone to recognize their emotional state.

[0568] This data is sent to a server, where the emotional state is assessed using techniques such as voice analysis, facial expression analysis, and text analysis.

[0569] 7. Security Evaluation Methods

[0570] The server evaluates the user's emotional state based on the data obtained by the emotion recognition means and detects abnormal login attempts.

[0571] If an abnormal emotional state is detected, additional authentication processes (e.g., two-factor authentication or security questions) will be required.

[0572] Web frameworks such as Flask and Django are used for implementation.

[0573] Specific examples

[0574] Examples of data collection:

[0575] A user accesses the system for the first time and creates an account.

[0576] A user uses a financial information entry form to enter the following information:

[0577] Monthly salary: 500,000 yen

[0578] Fixed expenses (rent, etc.): 100,000 yen

[0579] Variable expenses (food, etc.): 50,000 yen

[0580] Current assets: 2,000,000 yen

[0581] Current debt: 500,000 yen

[0582] Lifestyle: secure

[0583] Risk tolerance: medium

[0584] The terminal sends the input data to the server, which stores it in a database.

[0585] Examples of emotion data collection:

[0586] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[0587] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[0588] The device sends this emotion data to a server and stores it in a database.

[0589] Analysis example:

[0590] The server then obtains the user's past transaction data and passes it to the generation AI.

[0591] The generative AI model produces the following analysis results:

[0592] Monthly surplus: 350,000 yen

[0593] Recommended investment amount: 200,000 yen

[0594] Recommended savings amount: 100,000 yen

[0595] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0596] Risk Level: Medium

[0597] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[0598] Example of a security assessment prompt:

[0599] User A attempted to log in while in an unusual emotional state. During normal logins, a calm voice tone and stable facial expression are recorded, but this time the user's voice was trembling and their facial expression showed signs of tension. Please explain how you would analyze this situation and take security measures.

[0600] The system allows users to quickly and easily obtain the financial plan that best suits them, while also reducing security risks caused by abnormal login attempts.

[0601] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0602] Step 1:

[0603] Input: The user enters financial information.

[0604] How it works: Users enter their monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a web form or mobile application.

[0605] Output: Financial information data entered by the user.

[0606] Step 2:

[0607] Input: Financial information data.

[0608] How it works: The device sends the user's input data to a server, while also collecting past transaction data and spending patterns through the APIs of banks and credit card companies.

[0609] Output: Financial information data sent to the server and collected historical transaction and spending pattern data.

[0610] Step 3:

[0611] Inputs: Financial information data and historical transaction and consumption pattern data.

[0612] How it works: The server passes collected data to a generative AI model that evaluates income and spending patterns, risk tolerance, lifestyle, etc. This processing uses machine learning frameworks such as TensorFlow and PyTorch.

[0613] Output: Analysis result data (income and expenditure patterns, risk tolerance, lifestyle assessment).

[0614] Step 4:

[0615] Input: Analysis result data.

[0616] How it works: The server uses the analysis results from the generative AI model to create a financial plan that is optimal for the user, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[0617] Output: The generated financial plan.

[0618] Step 5:

[0619] Input: The generated financial plan.

[0620] How it works: The server sends the financial plan to the user's device, which presents it to the user and provides an interface for the user to review the plan.

[0621] Output: The financial plan displayed to the user.

[0622] Step 6:

[0623] Input: User voice and facial expression data.

[0624] How it works: The device uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes them using emotion recognition techniques, including voice analysis, facial expression analysis, and text analysis.

[0625] Output: Emotion recognition result data (e.g., user is in stress state).

[0626] Step 7:

[0627] Input: Emotion recognition result data.

[0628] How it works: The server evaluates the user's emotional state based on the emotion recognition result data and detects abnormal login attempts. If an abnormality is detected, an additional authentication process is required.

[0629] Output: Security assessment result (e.g., additional authentication required).

[0630] Step 8:

[0631] Input: Enter the prompt statement.

[0632] How it works: A server or generative AI model uses prompts to generate countermeasures for security anomaly detection and emotional states.

[0633] Output: Suggested security measures (e.g., requiring two-factor authentication, presenting security questions, etc.).

[0634] 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.

[0635] 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.

[0636] 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.

[0637] [Second embodiment]

[0638] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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).

[0644] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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."

[0650] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system is mainly composed of the following elements: input means, data collection means, analysis means, plan generation means, and presentation means.

[0651] System Overview

[0652] 1. Input Method

[0653] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[0654] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0655] 2. Data Collection Methods

[0656] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[0657] This includes obtaining data through APIs from banks, credit card companies, etc.

[0658] 3. Analysis method

[0659] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[0660] User income and expenditure patterns

[0661] User risk tolerance

[0662] User lifestyle

[0663] 4. Plan Generation Method

[0664] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[0665] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0666] 5. Presentation means

[0667] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[0668] It also includes an interface that allows users to review the plan and provide approval or feedback.

[0669] Specific examples

[0670] Data Collection Example

[0671] A user accesses the system for the first time and creates an account.

[0672] A user uses a financial information entry form to enter the following information:

[0673] Monthly salary: 500,000 yen

[0674] Fixed expenses (rent, etc.): 100,000 yen

[0675] Variable expenses (food, etc.): 50,000 yen

[0676] Current assets: 2,000,000 yen

[0677] Current debt: 500,000 yen

[0678] The terminal sends the input data to the server, which stores it in a database.

[0679] Analysis example

[0680] The server then obtains the user's past transaction data and passes it to the generation AI.

[0681] The generative AI model produces the following analysis results:

[0682] Monthly surplus: 350,000 yen

[0683] Recommended investment amount: 200,000 yen

[0684] Recommended savings amount: 100,000 yen

[0685] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0686] Risk Level: Medium

[0687] Plan Generation and Presentation Example

[0688] The server creates a financial plan based on the analysis results:

[0689] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0690] 60% domestic stocks

[0691] 20% foreign stocks

[0692] 10% bond

[0693] 10% cash

[0694] The server sends the generated plan to the user's terminal, which displays it to the user.

[0695] The user reviews the plan and provides approval or feedback.

[0696] The above is an embodiment of the present invention, and the system allows users to quickly and easily obtain the financial plan that is best suited to them.

[0697] The processing flow will be explained below.

[0698] Step 1:

[0699] A user accesses the system for the first time and enters the information required to create an account (such as name, email address, and password).

[0700] The terminal sends the input data to the server, which stores it in a database.

[0701] Step 2:

[0702] Users log in and fill out a financial information form, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0703] The terminal sends the input data to the server, which stores it in a database.

[0704] Step 3:

[0705] The server uses an API to retrieve the user's bank and credit card transaction data.

[0706] The server stores these transaction data in a database.

[0707] Step 4:

[0708] The server formats the user's collected data to be passed to the generative AI model, specifically by converting the data into a format that is easy to analyze.

[0709] For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance can be compiled in JSON format.

[0710] Step 5:

[0711] The server passes the formatted data to the generative AI model and begins analysis.

[0712] The generative AI model analyzes the user's income and expenditure patterns, lifestyle, risk tolerance, etc., and obtains analysis results to generate the optimal financial plan.

[0713] Step 6:

[0714] The server receives the analysis results from the generative AI model and generates a financial plan based on them.

[0715] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0716] Step 7:

[0717] The server sends the generated financial plan to the user's terminal.

[0718] The device displays the received plan to the user, who then confirms the plan details.

[0719] Step 8:

[0720] The user reviews the financial plan and provides approval or feedback as needed.

[0721] The device sends feedback to the server and the plan is readjusted if necessary.

[0722] The above is a description of the specific processing steps of the program.

[0723] Example 1

[0724] 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."

[0725] Conventional financial planning systems struggled to fully consider a user's diverse financial information, lifestyle, risk tolerance, and other factors. They also required users to manually input information, and the accuracy of analyzing the collected data was limited. This made it difficult to provide users with optimal financial plans. Furthermore, there was a lack of a way to quickly incorporate user feedback.

[0726] 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.

[0727] In this invention, the server includes a data input means for inputting the user's financial information, a data communication means for collecting the user's financial information and past transaction data, a data analysis means for analyzing the collected financial information and transaction data, a plan generation means for generating an optimal financial plan, a plan provision means for providing the generated financial plan, and an interaction means for the user to review the plan and provide feedback. This makes it possible to efficiently collect and analyze a wide range of user information and quickly provide more accurate financial plans. Furthermore, the plan can be improved by reflecting user feedback in real time.

[0728] 1. "User" means an entity that uses the System and provides personal financial information.

[0729] 2. "Financial Information" means your economic data, such as your income, expenses, assets, and liabilities.

[0730] 3. "Data Entry Means" means the interface through which a user enters financial information into the system.

[0731] 4. "Data communication means" refers to the means for transmitting user input data and transaction data to a server and for obtaining information from external organizations.

[0732] 5. "Data analysis means" means means for analyzing collected data and evaluating a user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[0733] 6. "Generative AI Model" means an artificial intelligence model used for data analysis to generate the optimal plan for the user.

[0734] 7. "Plan generation means" means a means for generating a financial plan based on the analysis results of the generative AI model.

[0735] 8. "Plan Provision Means" means a means for providing the generated financial plan to the user.

[0736] 9. "Interaction methods" are the methods by which users can review the generated plans and provide feedback.

[0737] 10. "API" means an application programming interface for communicating with external systems to obtain data.

[0738] The system of the present invention proposes an optimal financial plan based on the user's financial information. This system is mainly composed of a data input means, a data communication means, a data analysis means, a plan generation means, a plan provision means, and an interaction means.

[0739] System configuration

[0740] 1. Data entry method

[0741] Users enter their financial information through an interface such as a web form or a mobile application.

[0742] Specific financial information includes monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0743] 2. Data communication means

[0744] The device sends the user's input data to the server.

[0745] To gather further data, the server obtains past transaction data and spending patterns through APIs from banks and credit card companies.

[0746] 3. Data Analysis Methods

[0747] The server passes the data collected from users to a generative AI model for detailed analysis.

[0748] This analysis includes the user's income and spending patterns, risk tolerance, lifestyle, etc.

[0749] 4. Plan Generation Method

[0750] The server creates the optimal financial plan for the user based on the analysis results of the generated AI model.

[0751] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0752] 5. Plan Delivery Method

[0753] The server sends the generated financial plan to the user's terminal, which displays it to the user.

[0754] 6. Interaction methods

[0755] An interface is included for users to review the plan and provide approval or feedback.

[0756] Specific operation of the system

[0757] Examples of data collection

[0758] A user accesses the system and creates a new account.

[0759] The user fills in the form with the following information:

[0760] text

[0761] Monthly salary: 500,000 yen

[0762] Fixed expenses: 100,000 yen

[0763] Variable expenses: 50,000 yen

[0764] Current assets: 2,000,000 yen

[0765] Current debt: 500,000 yen

[0766] The terminal sends the input data to the server, which stores it in a database.

[0767] Specific examples of data analysis

[0768] The server collects past transaction data and passes it to the generative AI model.

[0769] A generative AI model analyzes the data and produces results such as:

[0770] text

[0771] Monthly surplus: 350,000 yen

[0772] Recommended investment amount: 200,000 yen

[0773] Recommended savings amount: 100,000 yen

[0774] Diversified investment portfolio:

[0775] 60% domestic stocks

[0776] 20% foreign stocks

[0777] 10% bond

[0778] 10% cash

[0779] Risk Level: Medium

[0780] Example of plan generation and presentation

[0781] The server creates a specific financial plan based on the analysis results:

[0782] text

[0783] Recommended plan:

[0784] Invest 200,000 yen per month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0785] 60% domestic stocks

[0786] 20% foreign stocks

[0787] 10% bond

[0788] 10% cash

[0789] The server sends the generated plan to the user's terminal, which displays it to the user.

[0790] The user reviews the plan and provides approval or feedback.

[0791] This concludes the "Mode for Carrying Out the Invention." This system allows users to quickly and easily obtain the financial plan that is best for them and incorporates feedback in real time.

[0792] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0793] Step 1:

[0794] A user inputs financial information using a data entry device, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance. This data is collected through the data entry device.

[0795] Input: User's financial information (e.g. monthly income, expenses, assets, liabilities)

[0796] Output: Input data

[0797] Action: A user enters information into a form or application and clicks the submit button.

[0798] Step 2:

[0799] The device sends the entered financial information to a server, which stores it in a database. Furthermore, if the user consents, the server uses the APIs of banks and credit card companies to collect past transaction data and spending patterns.

[0800] Input: Input data, user consent

[0801] Output: Collected transaction data

[0802] How it works: The device sends data to the server, which stores it in a database and optionally collects additional data through external APIs.

[0803] Step 3:

[0804] The server passes the collected financial information and transaction data to the generative AI model for data analysis. The analysis includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model analyzes this data and generates the basic information for an appropriate financial plan.

[0805] Input: Financial information, transaction data

[0806] Output: Analysis results (e.g. monthly surplus, recommended investment amount)

[0807] How it works: A server provides data to a generative AI model, which then analyzes the data.

[0808] Step 4:

[0809] Based on the analysis results, the server generates an optimal financial plan, which includes monthly investment amounts, savings amounts, and investment portfolio ratios (domestic stocks, international stocks, bonds, cash, etc.).

[0810] Input: Analysis results

[0811] Output: Financial Plan

[0812] Operation: The server receives the analysis results and uses a plan generation algorithm to create a financial plan.

[0813] Step 5:

[0814] The server sends the generated financial plan to the user's terminal and displays it to the user through the plan providing means, and the user can review the plan and provide approval or feedback.

[0815] Enter: Financial Plan

[0816] Output: Show plan to user

[0817] How it works: The server sends the plan to the device, which displays it on an interface where the user can review the plan.

[0818] Step 6:

[0819] The user reviews the generated financial plan and provides feedback if necessary, which is then sent back to the server via the data input means.

[0820] Input: User feedback

[0821] Output: Updated financial plan (if needed)

[0822] How it works: The user enters their feedback and requests for changes to the plan and sends it to the server. If feedback is received, the server re-analyzes the plan to reflect that feedback.

[0823] (Application example 1)

[0824] 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."

[0825] Conventional financial planning systems require users to manually create plans based on financial information collected by themselves, making it difficult to optimize the plans and preventing them from automatically generating plans that take into account the user's lifestyle and risk tolerance. Furthermore, there is a lack of means to quickly implement the generated plans, which creates the challenge of requiring a lot of effort after the user approves them.

[0826] 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.

[0827] In this invention, the server includes a data collection means for collecting the user's financial information, an analysis means for analyzing the collected financial information, and a plan generation means for generating a financial plan based on the analysis results, thereby enabling the automatic generation of an optimal financial plan that takes into account the user's lifestyle and risk tolerance.

[0828] The system also includes an interface for obtaining user approval and a settlement mechanism for automatically executing approved plans, allowing the created plans to be executed quickly and easily, thereby reducing the user's workload and enabling efficient financial planning.

[0829] "User" means any person or entity that uses the System to enter their financial information and receive a Financial Plan.

[0830] "Financial information" refers to general information such as a user's monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0831] "Input Method" refers to the interface, such as a web form or mobile application, that a user uses to provide financial information to the system.

[0832] "Data Collection Measures" means mechanisms for collecting input financial information and related historical transaction data and spending patterns.

[0833] "Analytical Tools" refers to the generative AI models and computational algorithms used to analyze collected financial information and generate detailed risk assessments and investment plans.

[0834] The "plan generation means" refers to a process or device for creating an optimal financial plan for the user based on the analysis results.

[0835] "Presentation means" refers to the interface for presenting the generated financial plan to the user and obtaining confirmation or approval.

[0836] "Interface" means the means by which a user provides approval and feedback on a generated financial plan.

[0837] "Payment Mechanism" refers to an electronic payment service or related system for automatically executing a plan approved by a user.

[0838] The system of the present invention is a FinTech application that proposes and automatically executes an optimal financial plan based on the user's financial information. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an interface, and a payment mechanism.

[0839] System Overview

[0840] 1. Input Method

[0841] A web form or mobile application interface allows users to enter financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0842] 2. Data Collection Methods

[0843] The device sends the user's input data to the server, and also uses APIs to collect data from banks and credit card companies, as well as past transaction data and spending patterns.

[0844] 3. Analysis method

[0845] The server passes the collected data to a generative AI model for detailed analysis, which takes into account the user's income and expenditure patterns, risk tolerance, and lifestyle. Specifically, the generative AI model is used to generate an optimal financial plan.

[0846] 4. Plan Generation Method

[0847] The server then uses the analysis results from the AI ​​model to create a financial plan optimized for the user, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (e.g., domestic stocks, international stocks, bonds, cash, etc.).

[0848] 5. Presentation means

[0849] The server sends the generated financial plan to the user's device, which presents it to the user, including an interface that allows the user to review the plan's contents and provide approval or feedback.

[0850] 6. Interface

[0851] A means for users to provide approval or feedback on a presented financial plan. Through this interface, users can review and approve the plan.

[0852] 7. Payment Systems

[0853] After the server receives the user's approval, it automatically executes the investment using an electronic payment service, reducing the burden on the user and enabling quick investment execution.

[0854] Specific examples

[0855] Data Collection Example

[0856] A user accesses the system for the first time and creates an account.

[0857] A user uses a financial information entry form to enter the following information:

[0858] Monthly salary: 500,000 yen

[0859] Fixed expenses (rent, etc.): 100,000 yen

[0860] Variable expenses (food, etc.): 50,000 yen

[0861] Current assets: 2,000,000 yen

[0862] Current debt: 500,000 yen

[0863] The terminal sends the input data to the server, which stores it in a database.

[0864] Analysis example

[0865] The server then obtains the user's past transaction data via an API and passes it to the generation AI.

[0866] The generative AI model produces the following analysis results:

[0867] Monthly surplus: 350,000 yen

[0868] Recommended investment amount: 200,000 yen

[0869] Recommended savings amount: 100,000 yen

[0870] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0871] Risk Level: Medium

[0872] Plan Generation and Presentation Example

[0873] The server creates a financial plan based on the analysis results:

[0874] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0875] 60% domestic stocks

[0876] 20% foreign stocks

[0877] 10% bond

[0878] 10% cash

[0879] The server sends the generated plan to the user's terminal, which displays it to the user.

[0880] The user reviews the plan and provides approval or feedback.

[0881] Hardware and software used

[0882] Device: A smartphone or computer where users enter their financial information and view their plan.

[0883] Server: A server that collects data, analyzes, generates plans, presents plans, and executes investments.

[0884] Generative AI model: An AI model that analyzes a user's financial data and generates an optimal financial plan. We will use a "generative AI model" as an example.

[0885] API: API for obtaining data from banks, credit card companies, etc.

[0886] Electronic Payment Service: A payment mechanism for the execution of automated investments.

[0887] The above is an embodiment of the present invention. This system allows users to quickly and easily obtain and efficiently implement a financial plan that is best suited to them.

[0888] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0889] Step 1:

[0890] A user uses a smartphone or PC to access a web form or mobile application interface and enters financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc. The input data is stored on the device and sent to a server.

[0891] input:

[0892] Monthly salary: 500,000 yen

[0893] Fixed expenses: 100,000 yen

[0894] Variable expenses: 50,000 yen

[0895] Current assets: 2,000,000 yen

[0896] Current debt: 500,000 yen

[0897] Lifestyle: Normal

[0898] Risk tolerance: Medium

[0899] output:

[0900] A database on the server stores users' financial information.

[0901] Step 2:

[0902] The server receives the user's input data and uses APIs to retrieve bank and credit card transaction data, which includes retrieving the user's past transaction data and spending patterns through APIs.

[0903] input:

[0904] User financial information and API requests.

[0905] output:

[0906] Bank and credit card transaction data is stored on the server.

[0907] Step 3:

[0908] The server sends the collected data to the generative AI model for analysis, which includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model then performs a detailed analysis based on the generated prompt.

[0909] input:

[0910] Your financial and transactional data.

[0911] User's monthly income: 500,000 yen

[0912] Fixed expenses: 100,000 yen

[0913] Variable expenses: 50,000 yen

[0914] Current assets: 2,000,000 yen

[0915] Current debt: 500,000 yen

[0916] Historical Transaction Data: [{"date":"2023-01-01", "amount":-2000, "category":"food"}, {"date":"2023-01-02", "amount":-5000, "category":"transport"}]

[0917] Risk tolerance: Medium

[0918] Lifestyle: Normal

[0919] output:

[0920] Analysis results obtained from a generative AI model. Example: "Monthly surplus: 350,000 yen, recommended investment amount: 200,000 yen, recommended savings amount: 100,000 yen, diversified investment portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash."

[0921] Step 4:

[0922] Based on the analysis results, the server uses a generative AI model to generate an optimal financial plan, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[0923] input:

[0924] Analysis results.

[0925] Monthly surplus: 350,000 yen

[0926] Recommended investment amount: 200,000 yen

[0927] Recommended savings amount: 100,000 yen

[0928] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0929] output:

[0930] Financial plan.

[0931] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[0932] 60% domestic stocks

[0933] 20% foreign stocks

[0934] 10% bond

[0935] 10% cash

[0936] Step 5:

[0937] The server sends the generated financial plan to the user's terminal, which then presents it to the user, who then reviews the content of the presented plan and provides approval or feedback through the interface.

[0938] input:

[0939] Financial plan.

[0940] output:

[0941] User acknowledgement or feedback.

[0942] Step 6:

[0943] The server, having received the user's approval, automatically executes the investment using the electronic settlement service, and the settlement mechanism processes the settlement according to the investment portfolio.

[0944] input:

[0945] User approval.

[0946] output:

[0947] Automatically executed investment transactions.

[0948] 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.

[0949] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system mainly consists of the following elements: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, and an emotion recognition means.

[0950] System Overview

[0951] 1. Input Method

[0952] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[0953] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[0954] 2. Data Collection Methods

[0955] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[0956] This includes obtaining data through APIs from banks, credit card companies, etc.

[0957] 3. Analysis method

[0958] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[0959] User income and expenditure patterns

[0960] User risk tolerance

[0961] User lifestyle

[0962] 4. Plan Generation Method

[0963] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[0964] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[0965] 5. Presentation means

[0966] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[0967] It also includes an interface that allows users to review the plan and provide approval or feedback.

[0968] 6. Emotion recognition means

[0969] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[0970] The server passes the data to an emotion recognition engine that analyzes the user's emotional state, which includes techniques such as voice analysis, facial expression analysis, and text analysis.

[0971] Specific examples

[0972] Data Collection Example

[0973] A user accesses the system for the first time and creates an account.

[0974] A user uses a financial information entry form to enter the following information:

[0975] Monthly salary: 500,000 yen

[0976] Fixed expenses (rent, etc.): 100,000 yen

[0977] Variable expenses (food, etc.): 50,000 yen

[0978] Current assets: 2,000,000 yen

[0979] Current debt: 500,000 yen

[0980] Lifestyle: secure

[0981] Risk tolerance: medium

[0982] The terminal sends the input data to the server, which stores it in a database.

[0983] Example of emotion data collection

[0984] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[0985] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[0986] The device sends this emotion data to a server and stores it in a database.

[0987] Analysis example

[0988] The server then obtains the user's past transaction data and passes it to the generation AI.

[0989] The generative AI model produces the following analysis results:

[0990] Monthly surplus: 350,000 yen

[0991] Recommended investment amount: 200,000 yen

[0992] Recommended savings amount: 100,000 yen

[0993] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[0994] Risk Level: Medium

[0995] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[0996] Plan Generation and Presentation Example

[0997] The server creates a financial plan based on the analysis results:

[0998] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[0999] 50% domestic stocks

[1000] 20% foreign stocks

[1001] 20% bond

[1002] 10% cash

[1003] The server sends the generated plan to the user's terminal, which displays it to the user.

[1004] The user reviews the plan and provides approval or feedback.

[1005] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[1006] The processing flow will be explained below.

[1007] Step 1:

[1008] When a user accesses the system for the first time, they enter the information required to create an account (such as name, email address, and password). The terminal sends the input data to the server, which then stores it in a database.

[1009] Step 2:

[1010] The user logs in and enters information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a financial information input form. The terminal sends the input data to the server, which then stores it in a database.

[1011] Step 3:

[1012] The server uses an API to retrieve the user's bank and credit card transaction data, which it then stores in a database.

[1013] Step 4:

[1014] The server formats the user's collected data to be passed to the generative AI model. Specifically, the data is converted into a format that is easy to analyze. For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance is compiled into a JSON format.

[1015] Step 5:

[1016] The server passes the formatted data to the generative AI model, which then analyzes it based on the user's income and expenditure patterns, lifestyle, risk tolerance, etc., to obtain analytical results that will help generate an optimal financial plan.

[1017] Step 6:

[1018] The server receives the analysis results from the generative AI model and generates a financial plan based on them, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1019] Step 7:

[1020] The device collects emotional data from the user's voice and text and sends it to the server. For example, when a user enters feedback on a plan, the device analyzes the user's emotions.

[1021] Step 8:

[1022] The server passes the emotional data to an emotion recognition engine, which analyzes the user's emotional state using voice analysis, facial expression analysis, text analysis, and other methods.

[1023] Step 9:

[1024] The server adjusts the financial plan based on the emotion recognition results. For example, if the user is feeling excessively stressed, it will set a lower risk level and regenerate a plan that emphasizes safety.

[1025] Step 10:

[1026] The server sends the adjusted financial plan to the user's device, which displays it to the user, who then reviews the plan and provides approval or feedback.

[1027] The above is a description of the specific processing steps of the system that combines emotion engines.

[1028] Example 2

[1029] 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."

[1030] Conventional financial planning systems generate plans based on a user's financial information, but are unable to consider the user's psychological state or emotions. As a result, the financial plans provided often do not match the user's actual psychological state, making it difficult to implement and maintain the plan. Furthermore, there are insufficient means to specifically reflect the user's lifestyle and risk tolerance, making it difficult to propose personalized plans. The problem that this invention aims to solve is to provide a more personalized and realistic financial plan that takes into account the user's psychological state and lifestyle.

[1031] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for inputting the user's financial information, a data collection means for collecting the user's financial information, and an analysis means for analyzing the collected financial information. This enables detailed data analysis and the provision of a financial plan based on the user's financial situation. Furthermore, by adding an emotion recognition means for recognizing the user's emotional state and collecting that data, and a means for adjusting the financial plan generated based on the emotional state, it becomes possible to optimize the plan taking the user's psychological state into consideration. This makes it possible to provide a feasible and sustainable financial plan for the user.

[1032] "Input means" refers to the interface through which a user provides financial information to the system.

[1033] "Data Collection Method" refers to the method or device used to capture a user's financial information into the system.

[1034] "Analysis means" refers to the methods and functions for conducting data analysis based on collected financial information.

[1035] The "plan generation means" refers to a function or device that creates a financial plan based on the information obtained by the analysis means.

[1036] "Presentation Means" refers to the method or device for displaying and providing the generated financial plan to the user.

[1037] "Emotion recognition means" refers to a method or device for grasping a user's emotional state and incorporating that data into the system.

[1038] The "adjustment means" refers to a method or function for adjusting the financial plan based on the emotion data obtained by the emotion recognition means.

[1039] The system of the present invention proposes an optimal financial plan based on a user's financial information. The system is composed of the following main components: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and an adjustment means.

[1040] System Overview

[1041] 1. Input Method

[1042] The interface through which users provide financial information to the system, such as a web form or a mobile application.

[1043] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1044] 2. Data Collection Methods

[1045] The device uses methods and APIs to send user input data to the server and, if necessary, collect past transaction data and consumption patterns.

[1046] For example, transaction data is obtained from banks and credit card companies via API.

[1047] 3. Analysis method

[1048] The server passes the user's collected data to the generative AI model, which then analyzes the data.

[1049] This analysis includes the user's income and spending patterns, risk tolerance and lifestyle.

[1050] 4. Plan Generation Method

[1051] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1052] A specific plan includes monthly investment amounts, savings amounts, and investment portfolios.

[1053] 5. Presentation means

[1054] The server sends the generated financial plan to the user's terminal, which then presents it to the user.

[1055] Users can review the plan and provide approval or feedback.

[1056] 6. Emotion recognition means

[1057] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[1058] The server passes the data to an emotion recognition engine to analyze the user's emotional state, which includes voice analysis, facial expression analysis, and text analysis.

[1059] 7. Adjustment means

[1060] The server adjusts the financial plan based on the sentiment data.

[1061] For example, if a user is feeling stressed, the risk level of the plan can be adjusted accordingly.

[1062] Specific examples

[1063] Data Collection Example

[1064] A user accesses the system for the first time and creates an account.

[1065] A user uses a financial information entry form to enter the following information:

[1066] Monthly salary: 500,000 yen

[1067] Fixed expenses (rent, etc.): 100,000 yen

[1068] Variable expenses (food, etc.): 50,000 yen

[1069] Current assets: 2,000,000 yen

[1070] Current debt: 500,000 yen

[1071] Lifestyle: secure

[1072] Risk tolerance: medium

[1073] The terminal sends the input data to the server, which stores it in a database.

[1074] Example of emotion data collection

[1075] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[1076] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[1077] The device sends this emotion data to a server and stores it in a database.

[1078] Analysis example

[1079] The server obtains the user's input data and past transaction data and passes it to the generative AI model.

[1080] The generative AI model produces the following analysis results:

[1081] Monthly surplus: 350,000 yen

[1082] Recommended investment amount: 200,000 yen

[1083] Recommended savings amount: 100,000 yen

[1084] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1085] Risk Level: Medium

[1086] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted to "low."

[1087] Plan Generation and Presentation Example

[1088] The server creates a financial plan based on the analysis results as follows:

[1089] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[1090] 50% domestic stocks

[1091] 20% foreign stocks

[1092] 20% bond

[1093] 10% cash

[1094] The server sends the generated plan to the user's terminal, which displays it to the user.

[1095] The user reviews the plan and provides approval or feedback.

[1096] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[1097] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1098] Step 1:

[1099] User enters financial information

[1100] The user enters financial information (e.g., monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc.) into the system's web form or mobile app.

[1101] Input data: monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance

[1102] Output data: Initial financial information entered by the user

[1103] Step 2:

[1104] The device sends the input data to the server

[1105] The terminal temporarily stores the financial information entered by the user and sends it to the server.

[1106] Input Data: Initial financial information entered by the user

[1107] Output data: Financial information sent to the server

[1108] Step 3:

[1109] The server collects additional data

[1110] If necessary, the server retrieves the user's transaction history and spending patterns from banks and credit card companies via API and adds them to the financial information.

[1111] Input data: Financial information sent to the server

[1112] Output data: Complete financial information with additional transaction history and spending patterns

[1113] Step 4:

[1114] The server passes the data to the generative AI model

[1115] The server passes the collected complete financial information to the generative AI model for detailed analysis, and issues a prompt saying, "Generate the optimal financial plan based on the user's income, expenses, assets, liabilities, and other data."

[1116] Input data: complete financial information, and prompt statements

[1117] Output data: Analysis request passed to the generative AI model

[1118] Step 5:

[1119] Generative AI models analyze data

[1120] The generative AI model analyzes the data passed from the server and generates the optimal financial plan for the user, including the monthly surplus, recommended investment amount, recommended savings amount, diversified investment portfolio, risk level, etc.

[1121] Input data: Complete financial information

[1122] Output data: Analysis results (e.g. monthly surplus, recommended investment amount, recommended savings amount, portfolio, risk level)

[1123] Step 6:

[1124] The server generates a plan based on the analysis results.

[1125] The server generates an optimal financial plan for the user based on the analysis results of the AI ​​model. For example, invest 200,000 yen per month and save 100,000 yen. The recommended portfolio would be 50% domestic stocks, 20% international stocks, 20% bonds, and 10% cash.

[1126] Input data: Analysis results from the generative AI model

[1127] Output data: Generated financial plan

[1128] Step 7:

[1129] The device collects the user's emotional data and sends it to the server.

[1130] The device collects emotional data from user interactions, input text, and voice, and sends this data to a server.

[1131] Input data: user dialogue text and voice

[1132] Output data: Emotion data sent to the server

[1133] Step 8:

[1134] The server performs emotion analysis using an emotion recognition engine.

[1135] The server passes the emotion data to an emotion recognition engine to analyze the user's emotional state, for example, to determine whether the user is feeling stressed.

[1136] Input data: User emotion data

[1137] Output data: Analysis results on the user's emotional state

[1138] Step 9:

[1139] The server adjusts the plan taking into account the emotional data.

[1140] The server adjusts the risk level of the generated financial plan based on the analysis results of the emotion recognition engine. For example, if the user is feeling stressed, the risk level will be changed from "medium" to "low."

[1141] Input data: Generated financial plan and analysis results on emotional state

[1142] Output data: adjusted financial plan

[1143] Step 10:

[1144] The server sends the final plan to the device.

[1145] The server sends the final financial plan to the user's terminal, which then presents it to the user.

[1146] Input data: Adjusted financial plan

[1147] Output data: The final financial plan sent to the user's device

[1148] Step 11:

[1149] Users review the plan and provide feedback

[1150] The user reviews the proposed financial plan and provides feedback or approval as needed. The provided feedback is then sent back to the server as a reference for generating the next plan.

[1151] Input data: Financial plan confirmed by the user

[1152] Output data: User feedback

[1153] (Application example 2)

[1154] 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."

[1155] Conventional financial plan generation systems provide optimal plans based on a user's financial information, but do not take the user's emotional state into consideration. As a result, there is a risk that an appropriate plan will not be provided if the user is in a high-stress or abnormal emotional state. In terms of security, there is also a lack of mechanisms to recognize the user's emotional state and detect abnormal login attempts. Therefore, by combining emotion recognition, it is necessary to provide detailed plans that take the user's psychological state into consideration and strengthen security.

[1156] 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.

[1157] In this invention, the server includes an input means for inputting the user's financial information, a data collection means, and a data analysis means, which enables the presentation of a financial plan that takes into account the user's emotional state and enhances security.

[1158] "Input means" means an interface through which a user provides financial information to the system.

[1159] "Data collection means" refers to a system for collecting information such as users' financial information, past transaction data, and consumption patterns.

[1160] "Analysis means" refers to a device that analyzes collected financial information and evaluates the user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[1161] The "plan generation means" is a system that generates an optimal financial plan for the user based on the results obtained by the analysis means.

[1162] The "presentation means" is an interface for displaying the generated financial plan to the user and providing confirmation and feedback.

[1163] An "emotion recognition means" is a system for recognizing a user's emotional state and collecting that data.

[1164] The "security assessment means" is a system that detects anomalies in a user's login attempt based on the emotional state recognized by the emotion recognition means and requests additional authentication processes.

[1165] A "generative AI model" is an artificial intelligence model used to analyze a user's financial information and emotional state to provide optimal financial plans and security assessments.

[1166] The system of the present invention proposes an optimal financial plan based on a user's financial information and emotional state, and also performs security assessment. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and a security assessment means. The operation of each element is described in detail below.

[1167] System configuration

[1168] 1. Input Method

[1169] Web forms and mobile applications are used as interfaces for users to provide financial information to the system.

[1170] Users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1171] 2. Data Collection Methods

[1172] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[1173] This includes obtaining data through APIs from banks, credit card companies, etc.

[1174] 3. Analysis method

[1175] The server passes the collected data of the user to a generative AI model that evaluates their income and spending patterns, risk tolerance, and lifestyle.

[1176] Machine learning frameworks such as TensorFlow and PyTorch are used as generative AI models.

[1177] 4. Plan Generation Method

[1178] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1179] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1180] 5. Presentation means

[1181] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[1182] An interface is also provided where users can review the plan and provide approval or feedback.

[1183] 6. Emotion recognition means

[1184] It is a means for a device to capture a user's facial expressions and voice using a camera and microphone to recognize their emotional state.

[1185] This data is sent to a server, where the emotional state is assessed using techniques such as voice analysis, facial expression analysis, and text analysis.

[1186] 7. Security Evaluation Methods

[1187] The server evaluates the user's emotional state based on the data obtained by the emotion recognition means and detects abnormal login attempts.

[1188] If an abnormal emotional state is detected, additional authentication processes (e.g., two-factor authentication or security questions) will be required.

[1189] Web frameworks such as Flask and Django are used for implementation.

[1190] Specific examples

[1191] Examples of data collection:

[1192] A user accesses the system for the first time and creates an account.

[1193] A user uses a financial information entry form to enter the following information:

[1194] Monthly salary: 500,000 yen

[1195] Fixed expenses (rent, etc.): 100,000 yen

[1196] Variable expenses (food, etc.): 50,000 yen

[1197] Current assets: 2,000,000 yen

[1198] Current debt: 500,000 yen

[1199] Lifestyle: secure

[1200] Risk tolerance: medium

[1201] The terminal sends the input data to the server, which stores it in a database.

[1202] Examples of emotion data collection:

[1203] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[1204] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[1205] The device sends this emotion data to a server and stores it in a database.

[1206] Analysis example:

[1207] The server then obtains the user's past transaction data and passes it to the generation AI.

[1208] The generative AI model produces the following analysis results:

[1209] Monthly surplus: 350,000 yen

[1210] Recommended investment amount: 200,000 yen

[1211] Recommended savings amount: 100,000 yen

[1212] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1213] Risk Level: Medium

[1214] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[1215] Example of a security assessment prompt:

[1216] User A attempted to log in while in an unusual emotional state. During normal logins, a calm voice tone and stable facial expression are recorded, but this time the user's voice was trembling and their facial expression showed signs of tension. Please explain how you would analyze this situation and take security measures.

[1217] The system allows users to quickly and easily obtain the financial plan that best suits them, while also reducing security risks caused by abnormal login attempts.

[1218] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1219] Step 1:

[1220] Input: The user enters financial information.

[1221] How it works: Users enter their monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a web form or mobile application.

[1222] Output: Financial information data entered by the user.

[1223] Step 2:

[1224] Input: Financial information data.

[1225] How it works: The device sends the user's input data to a server, while also collecting past transaction data and spending patterns through the APIs of banks and credit card companies.

[1226] Output: Financial information data sent to the server and collected historical transaction and spending pattern data.

[1227] Step 3:

[1228] Inputs: Financial information data and historical transaction and consumption pattern data.

[1229] How it works: The server passes collected data to a generative AI model that evaluates income and spending patterns, risk tolerance, lifestyle, etc. This processing uses machine learning frameworks such as TensorFlow and PyTorch.

[1230] Output: Analysis result data (income and expenditure patterns, risk tolerance, lifestyle assessment).

[1231] Step 4:

[1232] Input: Analysis result data.

[1233] How it works: The server uses the analysis results from the generative AI model to create a financial plan that is optimal for the user, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[1234] Output: The generated financial plan.

[1235] Step 5:

[1236] Input: The generated financial plan.

[1237] How it works: The server sends the financial plan to the user's device, which presents it to the user and provides an interface for the user to review the plan.

[1238] Output: The financial plan displayed to the user.

[1239] Step 6:

[1240] Input: User voice and facial expression data.

[1241] How it works: The device uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes them using emotion recognition techniques, including voice analysis, facial expression analysis, and text analysis.

[1242] Output: Emotion recognition result data (e.g., user is in stress state).

[1243] Step 7:

[1244] Input: Emotion recognition result data.

[1245] How it works: The server evaluates the user's emotional state based on the emotion recognition result data and detects abnormal login attempts. If an abnormality is detected, an additional authentication process is required.

[1246] Output: Security assessment result (e.g., additional authentication required).

[1247] Step 8:

[1248] Input: Enter the prompt statement.

[1249] How it works: A server or generative AI model uses prompts to generate countermeasures for security anomaly detection and emotional states.

[1250] Output: Suggested security measures (e.g., requiring two-factor authentication, presenting security questions, etc.).

[1251] 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.

[1252] 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.

[1253] 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.

[1254] [Third embodiment]

[1255] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1256] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1257] 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).

[1258] 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.

[1259] 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.

[1260] 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).

[1261] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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."

[1267] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system is mainly composed of the following elements: input means, data collection means, analysis means, plan generation means, and presentation means.

[1268] System Overview

[1269] 1. Input Method

[1270] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[1271] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1272] 2. Data Collection Methods

[1273] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[1274] This includes obtaining data through APIs from banks, credit card companies, etc.

[1275] 3. Analysis method

[1276] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[1277] User income and expenditure patterns

[1278] User risk tolerance

[1279] User lifestyle

[1280] 4. Plan Generation Method

[1281] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1282] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1283] 5. Presentation means

[1284] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[1285] It also includes an interface that allows users to review the plan and provide approval or feedback.

[1286] Specific examples

[1287] Data Collection Example

[1288] A user accesses the system for the first time and creates an account.

[1289] A user uses a financial information entry form to enter the following information:

[1290] Monthly salary: 500,000 yen

[1291] Fixed expenses (rent, etc.): 100,000 yen

[1292] Variable expenses (food, etc.): 50,000 yen

[1293] Current assets: 2,000,000 yen

[1294] Current debt: 500,000 yen

[1295] The terminal sends the input data to the server, which stores it in a database.

[1296] Analysis example

[1297] The server then obtains the user's past transaction data and passes it to the generation AI.

[1298] The generative AI model produces the following analysis results:

[1299] Monthly surplus: 350,000 yen

[1300] Recommended investment amount: 200,000 yen

[1301] Recommended savings amount: 100,000 yen

[1302] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1303] Risk Level: Medium

[1304] Plan Generation and Presentation Example

[1305] The server creates a financial plan based on the analysis results:

[1306] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[1307] 60% domestic stocks

[1308] 20% foreign stocks

[1309] 10% bond

[1310] 10% cash

[1311] The server sends the generated plan to the user's terminal, which displays it to the user.

[1312] The user reviews the plan and provides approval or feedback.

[1313] The above is an embodiment of the present invention, and the system allows users to quickly and easily obtain the financial plan that is best suited to them.

[1314] The processing flow will be explained below.

[1315] Step 1:

[1316] A user accesses the system for the first time and enters the information required to create an account (such as name, email address, and password).

[1317] The terminal sends the input data to the server, which stores it in a database.

[1318] Step 2:

[1319] Users log in and fill out a financial information form, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1320] The terminal sends the input data to the server, which stores it in a database.

[1321] Step 3:

[1322] The server uses an API to retrieve the user's bank and credit card transaction data.

[1323] The server stores these transaction data in a database.

[1324] Step 4:

[1325] The server formats the user's collected data to be passed to the generative AI model, specifically by converting the data into a format that is easy to analyze.

[1326] For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance can be compiled in JSON format.

[1327] Step 5:

[1328] The server passes the formatted data to the generative AI model and begins analysis.

[1329] The generative AI model analyzes the user's income and expenditure patterns, lifestyle, risk tolerance, etc., and obtains analysis results to generate the optimal financial plan.

[1330] Step 6:

[1331] The server receives the analysis results from the generative AI model and generates a financial plan based on them.

[1332] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1333] Step 7:

[1334] The server sends the generated financial plan to the user's terminal.

[1335] The device displays the received plan to the user, who then confirms the plan details.

[1336] Step 8:

[1337] The user reviews the financial plan and provides approval or feedback as needed.

[1338] The device sends feedback to the server and the plan is readjusted if necessary.

[1339] The above is a description of the specific processing steps of the program.

[1340] Example 1

[1341] 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."

[1342] Conventional financial planning systems struggled to fully consider a user's diverse financial information, lifestyle, risk tolerance, and other factors. They also required users to manually input information, and the accuracy of analyzing the collected data was limited. This made it difficult to provide users with optimal financial plans. Furthermore, there was a lack of a way to quickly incorporate user feedback.

[1343] 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.

[1344] In this invention, the server includes a data input means for inputting the user's financial information, a data communication means for collecting the user's financial information and past transaction data, a data analysis means for analyzing the collected financial information and transaction data, a plan generation means for generating an optimal financial plan, a plan provision means for providing the generated financial plan, and an interaction means for the user to review the plan and provide feedback. This makes it possible to efficiently collect and analyze a wide range of user information and quickly provide more accurate financial plans. Furthermore, the plan can be improved by reflecting user feedback in real time.

[1345] 1. "User" means an entity that uses the System and provides personal financial information.

[1346] 2. "Financial Information" means your economic data, such as your income, expenses, assets, and liabilities.

[1347] 3. "Data Entry Means" means the interface through which a user enters financial information into the system.

[1348] 4. "Data communication means" refers to the means for transmitting user input data and transaction data to a server and for obtaining information from external organizations.

[1349] 5. "Data analysis means" means means for analyzing collected data and evaluating a user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[1350] 6. "Generative AI Model" means an artificial intelligence model used for data analysis to generate the optimal plan for the user.

[1351] 7. "Plan generation means" means a means for generating a financial plan based on the analysis results of the generative AI model.

[1352] 8. "Plan Provision Means" means a means for providing the generated financial plan to the user.

[1353] 9. "Interaction methods" are the methods by which users can review the generated plans and provide feedback.

[1354] 10. "API" means an application programming interface for communicating with external systems to obtain data.

[1355] The system of the present invention proposes an optimal financial plan based on the user's financial information. This system is mainly composed of a data input means, a data communication means, a data analysis means, a plan generation means, a plan provision means, and an interaction means.

[1356] System configuration

[1357] 1. Data entry method

[1358] Users enter their financial information through an interface such as a web form or a mobile application.

[1359] Specific financial information includes monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1360] 2. Data communication means

[1361] The device sends the user's input data to the server.

[1362] To gather further data, the server obtains past transaction data and spending patterns through APIs from banks and credit card companies.

[1363] 3. Data Analysis Methods

[1364] The server passes the data collected from users to a generative AI model for detailed analysis.

[1365] This analysis includes the user's income and spending patterns, risk tolerance, lifestyle, etc.

[1366] 4. Plan Generation Method

[1367] The server creates the optimal financial plan for the user based on the analysis results of the generated AI model.

[1368] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1369] 5. Plan Delivery Method

[1370] The server sends the generated financial plan to the user's terminal, which displays it to the user.

[1371] 6. Interaction methods

[1372] An interface is included for users to review the plan and provide approval or feedback.

[1373] Specific operation of the system

[1374] Examples of data collection

[1375] A user accesses the system and creates a new account.

[1376] The user fills in the form with the following information:

[1377] text

[1378] Monthly salary: 500,000 yen

[1379] Fixed expenses: 100,000 yen

[1380] Variable expenses: 50,000 yen

[1381] Current assets: 2,000,000 yen

[1382] Current debt: 500,000 yen

[1383] The terminal sends the input data to the server, which stores it in a database.

[1384] Specific examples of data analysis

[1385] The server collects past transaction data and passes it to the generative AI model.

[1386] A generative AI model analyzes the data and produces results such as:

[1387] text

[1388] Monthly surplus: 350,000 yen

[1389] Recommended investment amount: 200,000 yen

[1390] Recommended savings amount: 100,000 yen

[1391] Diversified investment portfolio:

[1392] 60% domestic stocks

[1393] 20% foreign stocks

[1394] 10% bond

[1395] 10% cash

[1396] Risk Level: Medium

[1397] Example of plan generation and presentation

[1398] The server creates a specific financial plan based on the analysis results:

[1399] text

[1400] Recommended plan:

[1401] Invest 200,000 yen per month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[1402] 60% domestic stocks

[1403] 20% foreign stocks

[1404] 10% bond

[1405] 10% cash

[1406] The server sends the generated plan to the user's terminal, which displays it to the user.

[1407] The user reviews the plan and provides approval or feedback.

[1408] This concludes the "Mode for Carrying Out the Invention." This system allows users to quickly and easily obtain the financial plan that is best for them and incorporates feedback in real time.

[1409] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1410] Step 1:

[1411] A user inputs financial information using a data entry device, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance. This data is collected through the data entry device.

[1412] Input: User's financial information (e.g. monthly income, expenses, assets, liabilities)

[1413] Output: Input data

[1414] Action: A user enters information into a form or application and clicks the submit button.

[1415] Step 2:

[1416] The device sends the entered financial information to a server, which stores it in a database. Furthermore, if the user consents, the server uses the APIs of banks and credit card companies to collect past transaction data and spending patterns.

[1417] Input: Input data, user consent

[1418] Output: Collected transaction data

[1419] How it works: The device sends data to the server, which stores it in a database and optionally collects additional data through external APIs.

[1420] Step 3:

[1421] The server passes the collected financial information and transaction data to the generative AI model for data analysis. The analysis includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model analyzes this data and generates the basic information for an appropriate financial plan.

[1422] Input: Financial information, transaction data

[1423] Output: Analysis results (e.g. monthly surplus, recommended investment amount)

[1424] How it works: A server provides data to a generative AI model, which then analyzes the data.

[1425] Step 4:

[1426] Based on the analysis results, the server generates an optimal financial plan, which includes monthly investment amounts, savings amounts, and investment portfolio ratios (domestic stocks, international stocks, bonds, cash, etc.).

[1427] Input: Analysis results

[1428] Output: Financial Plan

[1429] Operation: The server receives the analysis results and uses a plan generation algorithm to create a financial plan.

[1430] Step 5:

[1431] The server sends the generated financial plan to the user's terminal and displays it to the user through the plan providing means, and the user can review the plan and provide approval or feedback.

[1432] Enter: Financial Plan

[1433] Output: Show plan to user

[1434] How it works: The server sends the plan to the device, which displays it on an interface where the user can review the plan.

[1435] Step 6:

[1436] The user reviews the generated financial plan and provides feedback if necessary, which is then sent back to the server via the data input means.

[1437] Input: User feedback

[1438] Output: Updated financial plan (if needed)

[1439] How it works: The user enters their feedback and requests for changes to the plan and sends it to the server. If feedback is received, the server re-analyzes the plan to reflect that feedback.

[1440] (Application example 1)

[1441] 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."

[1442] Conventional financial planning systems require users to manually create plans based on financial information collected by themselves, making it difficult to optimize the plans and preventing them from automatically generating plans that take into account the user's lifestyle and risk tolerance. Furthermore, there is a lack of means to quickly implement the generated plans, which creates the challenge of requiring a lot of effort after the user approves them.

[1443] 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.

[1444] In this invention, the server includes a data collection means for collecting the user's financial information, an analysis means for analyzing the collected financial information, and a plan generation means for generating a financial plan based on the analysis results, thereby enabling the automatic generation of an optimal financial plan that takes into account the user's lifestyle and risk tolerance.

[1445] The system also includes an interface for obtaining user approval and a settlement mechanism for automatically executing approved plans, allowing the created plans to be executed quickly and easily, thereby reducing the user's workload and enabling efficient financial planning.

[1446] "User" means any person or entity that uses the System to enter their financial information and receive a Financial Plan.

[1447] "Financial information" refers to general information such as a user's monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1448] "Input Method" refers to the interface, such as a web form or mobile application, that a user uses to provide financial information to the system.

[1449] "Data Collection Measures" means mechanisms for collecting input financial information and related historical transaction data and spending patterns.

[1450] "Analytical Tools" refers to the generative AI models and computational algorithms used to analyze collected financial information and generate detailed risk assessments and investment plans.

[1451] The "plan generation means" refers to a process or device for creating an optimal financial plan for the user based on the analysis results.

[1452] "Presentation means" refers to the interface for presenting the generated financial plan to the user and obtaining confirmation or approval.

[1453] "Interface" means the means by which a user provides approval and feedback on a generated financial plan.

[1454] "Payment Mechanism" refers to an electronic payment service or related system for automatically executing a plan approved by a user.

[1455] The system of the present invention is a FinTech application that proposes and automatically executes an optimal financial plan based on the user's financial information. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an interface, and a payment mechanism.

[1456] System Overview

[1457] 1. Input Method

[1458] A web form or mobile application interface allows users to enter financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1459] 2. Data Collection Methods

[1460] The device sends the user's input data to the server, and also uses APIs to collect data from banks and credit card companies, as well as past transaction data and spending patterns.

[1461] 3. Analysis method

[1462] The server passes the collected data to a generative AI model for detailed analysis, which takes into account the user's income and expenditure patterns, risk tolerance, and lifestyle. Specifically, the generative AI model is used to generate an optimal financial plan.

[1463] 4. Plan Generation Method

[1464] The server then uses the analysis results from the AI ​​model to create a financial plan optimized for the user, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (e.g., domestic stocks, international stocks, bonds, cash, etc.).

[1465] 5. Presentation means

[1466] The server sends the generated financial plan to the user's device, which presents it to the user, including an interface that allows the user to review the plan's contents and provide approval or feedback.

[1467] 6. Interface

[1468] A means for users to provide approval or feedback on a presented financial plan. Through this interface, users can review and approve the plan.

[1469] 7. Payment Systems

[1470] After the server receives the user's approval, it automatically executes the investment using an electronic payment service, reducing the burden on the user and enabling quick investment execution.

[1471] Specific examples

[1472] Data Collection Example

[1473] A user accesses the system for the first time and creates an account.

[1474] A user uses a financial information entry form to enter the following information:

[1475] Monthly salary: 500,000 yen

[1476] Fixed expenses (rent, etc.): 100,000 yen

[1477] Variable expenses (food, etc.): 50,000 yen

[1478] Current assets: 2,000,000 yen

[1479] Current debt: 500,000 yen

[1480] The terminal sends the input data to the server, which stores it in a database.

[1481] Analysis example

[1482] The server then obtains the user's past transaction data via an API and passes it to the generation AI.

[1483] The generative AI model produces the following analysis results:

[1484] Monthly surplus: 350,000 yen

[1485] Recommended investment amount: 200,000 yen

[1486] Recommended savings amount: 100,000 yen

[1487] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1488] Risk Level: Medium

[1489] Plan Generation and Presentation Example

[1490] The server creates a financial plan based on the analysis results:

[1491] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[1492] 60% domestic stocks

[1493] 20% foreign stocks

[1494] 10% bond

[1495] 10% cash

[1496] The server sends the generated plan to the user's terminal, which displays it to the user.

[1497] The user reviews the plan and provides approval or feedback.

[1498] Hardware and software used

[1499] Device: A smartphone or computer where users enter their financial information and view their plan.

[1500] Server: A server that collects data, analyzes, generates plans, presents plans, and executes investments.

[1501] Generative AI model: An AI model that analyzes a user's financial data and generates an optimal financial plan. We will use a "generative AI model" as an example.

[1502] API: API for obtaining data from banks, credit card companies, etc.

[1503] Electronic Payment Service: A payment mechanism for the execution of automated investments.

[1504] The above is an embodiment of the present invention. This system allows users to quickly and easily obtain and efficiently implement a financial plan that is best suited to them.

[1505] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1506] Step 1:

[1507] A user uses a smartphone or PC to access a web form or mobile application interface and enters financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc. The input data is stored on the device and sent to a server.

[1508] input:

[1509] Monthly salary: 500,000 yen

[1510] Fixed expenses: 100,000 yen

[1511] Variable expenses: 50,000 yen

[1512] Current assets: 2,000,000 yen

[1513] Current debt: 500,000 yen

[1514] Lifestyle: Normal

[1515] Risk tolerance: Medium

[1516] output:

[1517] A database on the server stores users' financial information.

[1518] Step 2:

[1519] The server receives the user's input data and uses APIs to retrieve bank and credit card transaction data, which includes retrieving the user's past transaction data and spending patterns through APIs.

[1520] input:

[1521] User financial information and API requests.

[1522] output:

[1523] Bank and credit card transaction data is stored on the server.

[1524] Step 3:

[1525] The server sends the collected data to the generative AI model for analysis, which includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model then performs a detailed analysis based on the generated prompt.

[1526] input:

[1527] Your financial and transactional data.

[1528] User's monthly income: 500,000 yen

[1529] Fixed expenses: 100,000 yen

[1530] Variable expenses: 50,000 yen

[1531] Current assets: 2,000,000 yen

[1532] Current debt: 500,000 yen

[1533] Historical Transaction Data: [{"date":"2023-01-01", "amount":-2000, "category":"food"}, {"date":"2023-01-02", "amount":-5000, "category":"transport"}]

[1534] Risk tolerance: Medium

[1535] Lifestyle: Normal

[1536] output:

[1537] Analysis results obtained from a generative AI model. Example: "Monthly surplus: 350,000 yen, recommended investment amount: 200,000 yen, recommended savings amount: 100,000 yen, diversified investment portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash."

[1538] Step 4:

[1539] Based on the analysis results, the server uses a generative AI model to generate an optimal financial plan, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[1540] input:

[1541] Analysis results.

[1542] Monthly surplus: 350,000 yen

[1543] Recommended investment amount: 200,000 yen

[1544] Recommended savings amount: 100,000 yen

[1545] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1546] output:

[1547] Financial plan.

[1548] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[1549] 60% domestic stocks

[1550] 20% foreign stocks

[1551] 10% bond

[1552] 10% cash

[1553] Step 5:

[1554] The server sends the generated financial plan to the user's terminal, which then presents it to the user, who then reviews the content of the presented plan and provides approval or feedback through the interface.

[1555] input:

[1556] Financial plan.

[1557] output:

[1558] User acknowledgement or feedback.

[1559] Step 6:

[1560] The server, having received the user's approval, automatically executes the investment using the electronic settlement service, and the settlement mechanism processes the settlement according to the investment portfolio.

[1561] input:

[1562] User approval.

[1563] output:

[1564] Automatically executed investment transactions.

[1565] 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.

[1566] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system mainly consists of the following elements: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, and an emotion recognition means.

[1567] System Overview

[1568] 1. Input Method

[1569] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[1570] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1571] 2. Data Collection Methods

[1572] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[1573] This includes obtaining data through APIs from banks, credit card companies, etc.

[1574] 3. Analysis method

[1575] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[1576] User income and expenditure patterns

[1577] User risk tolerance

[1578] User lifestyle

[1579] 4. Plan Generation Method

[1580] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1581] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1582] 5. Presentation means

[1583] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[1584] It also includes an interface that allows users to review the plan and provide approval or feedback.

[1585] 6. Emotion recognition means

[1586] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[1587] The server passes the data to an emotion recognition engine that analyzes the user's emotional state, which includes techniques such as voice analysis, facial expression analysis, and text analysis.

[1588] Specific examples

[1589] Data Collection Example

[1590] A user accesses the system for the first time and creates an account.

[1591] A user uses a financial information entry form to enter the following information:

[1592] Monthly salary: 500,000 yen

[1593] Fixed expenses (rent, etc.): 100,000 yen

[1594] Variable expenses (food, etc.): 50,000 yen

[1595] Current assets: 2,000,000 yen

[1596] Current debt: 500,000 yen

[1597] Lifestyle: secure

[1598] Risk tolerance: medium

[1599] The terminal sends the input data to the server, which stores it in a database.

[1600] Example of emotion data collection

[1601] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[1602] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[1603] The device sends this emotion data to a server and stores it in a database.

[1604] Analysis example

[1605] The server then obtains the user's past transaction data and passes it to the generation AI.

[1606] The generative AI model produces the following analysis results:

[1607] Monthly surplus: 350,000 yen

[1608] Recommended investment amount: 200,000 yen

[1609] Recommended savings amount: 100,000 yen

[1610] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1611] Risk Level: Medium

[1612] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[1613] Plan Generation and Presentation Example

[1614] The server creates a financial plan based on the analysis results:

[1615] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[1616] 50% domestic stocks

[1617] 20% foreign stocks

[1618] 20% bond

[1619] 10% cash

[1620] The server sends the generated plan to the user's terminal, which displays it to the user.

[1621] The user reviews the plan and provides approval or feedback.

[1622] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[1623] The processing flow will be explained below.

[1624] Step 1:

[1625] When a user accesses the system for the first time, they enter the information required to create an account (such as name, email address, and password). The terminal sends the input data to the server, which then stores it in a database.

[1626] Step 2:

[1627] The user logs in and enters information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a financial information input form. The terminal sends the input data to the server, which then stores it in a database.

[1628] Step 3:

[1629] The server uses an API to retrieve the user's bank and credit card transaction data, which it then stores in a database.

[1630] Step 4:

[1631] The server formats the user's collected data to be passed to the generative AI model. Specifically, the data is converted into a format that is easy to analyze. For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance is compiled into a JSON format.

[1632] Step 5:

[1633] The server passes the formatted data to the generative AI model, which then analyzes it based on the user's income and expenditure patterns, lifestyle, risk tolerance, etc., to obtain analytical results that will help generate an optimal financial plan.

[1634] Step 6:

[1635] The server receives the analysis results from the generative AI model and generates a financial plan based on them, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1636] Step 7:

[1637] The device collects emotional data from the user's voice and text and sends it to the server. For example, when a user enters feedback on a plan, the device analyzes the user's emotions.

[1638] Step 8:

[1639] The server passes the emotional data to an emotion recognition engine, which analyzes the user's emotional state using voice analysis, facial expression analysis, text analysis, and other methods.

[1640] Step 9:

[1641] The server adjusts the financial plan based on the emotion recognition results. For example, if the user is feeling excessively stressed, it will set a lower risk level and regenerate a plan that emphasizes safety.

[1642] Step 10:

[1643] The server sends the adjusted financial plan to the user's device, which displays it to the user, who then reviews the plan and provides approval or feedback.

[1644] The above is a description of the specific processing steps of the system that combines emotion engines.

[1645] Example 2

[1646] 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."

[1647] Conventional financial planning systems generate plans based on a user's financial information, but are unable to consider the user's psychological state or emotions. As a result, the financial plans provided often do not match the user's actual psychological state, making it difficult to implement and maintain the plan. Furthermore, there are insufficient means to specifically reflect the user's lifestyle and risk tolerance, making it difficult to propose personalized plans. The problem that this invention aims to solve is to provide a more personalized and realistic financial plan that takes into account the user's psychological state and lifestyle.

[1648] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for inputting the user's financial information, a data collection means for collecting the user's financial information, and an analysis means for analyzing the collected financial information. This enables detailed data analysis and the provision of a financial plan based on the user's financial situation. Furthermore, by adding an emotion recognition means for recognizing the user's emotional state and collecting that data, and a means for adjusting the financial plan generated based on the emotional state, it becomes possible to optimize the plan taking the user's psychological state into consideration. This makes it possible to provide a feasible and sustainable financial plan for the user.

[1649] "Input means" refers to the interface through which a user provides financial information to the system.

[1650] "Data Collection Method" refers to the method or device used to capture a user's financial information into the system.

[1651] "Analysis means" refers to the methods and functions for conducting data analysis based on collected financial information.

[1652] The "plan generation means" refers to a function or device that creates a financial plan based on the information obtained by the analysis means.

[1653] "Presentation Means" refers to the method or device for displaying and providing the generated financial plan to the user.

[1654] "Emotion recognition means" refers to a method or device for grasping a user's emotional state and incorporating that data into the system.

[1655] The "adjustment means" refers to a method or function for adjusting the financial plan based on the emotion data obtained by the emotion recognition means.

[1656] The system of the present invention proposes an optimal financial plan based on a user's financial information. The system is composed of the following main components: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and an adjustment means.

[1657] System Overview

[1658] 1. Input Method

[1659] The interface through which users provide financial information to the system, such as a web form or a mobile application.

[1660] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1661] 2. Data Collection Methods

[1662] The device uses methods and APIs to send user input data to the server and, if necessary, collect past transaction data and consumption patterns.

[1663] For example, transaction data is obtained from banks and credit card companies via API.

[1664] 3. Analysis method

[1665] The server passes the user's collected data to the generative AI model, which then analyzes the data.

[1666] This analysis includes the user's income and spending patterns, risk tolerance and lifestyle.

[1667] 4. Plan Generation Method

[1668] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1669] A specific plan includes monthly investment amounts, savings amounts, and investment portfolios.

[1670] 5. Presentation means

[1671] The server sends the generated financial plan to the user's terminal, which then presents it to the user.

[1672] Users can review the plan and provide approval or feedback.

[1673] 6. Emotion recognition means

[1674] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[1675] The server passes the data to an emotion recognition engine to analyze the user's emotional state, which includes voice analysis, facial expression analysis, and text analysis.

[1676] 7. Adjustment means

[1677] The server adjusts the financial plan based on the sentiment data.

[1678] For example, if a user is feeling stressed, the risk level of the plan can be adjusted accordingly.

[1679] Specific examples

[1680] Data Collection Example

[1681] A user accesses the system for the first time and creates an account.

[1682] A user uses a financial information entry form to enter the following information:

[1683] Monthly salary: 500,000 yen

[1684] Fixed expenses (rent, etc.): 100,000 yen

[1685] Variable expenses (food, etc.): 50,000 yen

[1686] Current assets: 2,000,000 yen

[1687] Current debt: 500,000 yen

[1688] Lifestyle: secure

[1689] Risk tolerance: medium

[1690] The terminal sends the input data to the server, which stores it in a database.

[1691] Example of emotion data collection

[1692] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[1693] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[1694] The device sends this emotion data to a server and stores it in a database.

[1695] Analysis example

[1696] The server obtains the user's input data and past transaction data and passes it to the generative AI model.

[1697] The generative AI model produces the following analysis results:

[1698] Monthly surplus: 350,000 yen

[1699] Recommended investment amount: 200,000 yen

[1700] Recommended savings amount: 100,000 yen

[1701] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1702] Risk Level: Medium

[1703] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted to "low."

[1704] Plan Generation and Presentation Example

[1705] The server creates a financial plan based on the analysis results as follows:

[1706] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[1707] 50% domestic stocks

[1708] 20% foreign stocks

[1709] 20% bond

[1710] 10% cash

[1711] The server sends the generated plan to the user's terminal, which displays it to the user.

[1712] The user reviews the plan and provides approval or feedback.

[1713] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[1714] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1715] Step 1:

[1716] User enters financial information

[1717] The user enters financial information (e.g., monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc.) into the system's web form or mobile app.

[1718] Input data: monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance

[1719] Output data: Initial financial information entered by the user

[1720] Step 2:

[1721] The device sends the input data to the server

[1722] The terminal temporarily stores the financial information entered by the user and sends it to the server.

[1723] Input Data: Initial financial information entered by the user

[1724] Output data: Financial information sent to the server

[1725] Step 3:

[1726] The server collects additional data

[1727] If necessary, the server retrieves the user's transaction history and spending patterns from banks and credit card companies via API and adds them to the financial information.

[1728] Input data: Financial information sent to the server

[1729] Output data: Complete financial information with additional transaction history and spending patterns

[1730] Step 4:

[1731] The server passes the data to the generative AI model

[1732] The server passes the collected complete financial information to the generative AI model for detailed analysis, and issues a prompt saying, "Generate the optimal financial plan based on the user's income, expenses, assets, liabilities, and other data."

[1733] Input data: complete financial information, and prompt statements

[1734] Output data: Analysis request passed to the generative AI model

[1735] Step 5:

[1736] Generative AI models analyze data

[1737] The generative AI model analyzes the data passed from the server and generates the optimal financial plan for the user, including the monthly surplus, recommended investment amount, recommended savings amount, diversified investment portfolio, risk level, etc.

[1738] Input data: Complete financial information

[1739] Output data: Analysis results (e.g. monthly surplus, recommended investment amount, recommended savings amount, portfolio, risk level)

[1740] Step 6:

[1741] The server generates a plan based on the analysis results.

[1742] The server generates an optimal financial plan for the user based on the analysis results of the AI ​​model. For example, invest 200,000 yen per month and save 100,000 yen. The recommended portfolio would be 50% domestic stocks, 20% international stocks, 20% bonds, and 10% cash.

[1743] Input data: Analysis results from the generative AI model

[1744] Output data: Generated financial plan

[1745] Step 7:

[1746] The device collects the user's emotional data and sends it to the server.

[1747] The device collects emotional data from user interactions, input text, and voice, and sends this data to a server.

[1748] Input data: user dialogue text and voice

[1749] Output data: Emotion data sent to the server

[1750] Step 8:

[1751] The server performs emotion analysis using an emotion recognition engine.

[1752] The server passes the emotion data to an emotion recognition engine to analyze the user's emotional state, for example, to determine whether the user is feeling stressed.

[1753] Input data: User emotion data

[1754] Output data: Analysis results on the user's emotional state

[1755] Step 9:

[1756] The server adjusts the plan taking into account the emotional data.

[1757] The server adjusts the risk level of the generated financial plan based on the analysis results of the emotion recognition engine. For example, if the user is feeling stressed, the risk level will be changed from "medium" to "low."

[1758] Input data: Generated financial plan and analysis results on emotional state

[1759] Output data: adjusted financial plan

[1760] Step 10:

[1761] The server sends the final plan to the device.

[1762] The server sends the final financial plan to the user's terminal, which then presents it to the user.

[1763] Input data: Adjusted financial plan

[1764] Output data: The final financial plan sent to the user's device

[1765] Step 11:

[1766] Users review the plan and provide feedback

[1767] The user reviews the proposed financial plan and provides feedback or approval as needed. The provided feedback is then sent back to the server as a reference for generating the next plan.

[1768] Input data: Financial plan confirmed by the user

[1769] Output data: User feedback

[1770] (Application example 2)

[1771] 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."

[1772] Conventional financial plan generation systems provide optimal plans based on a user's financial information, but do not take the user's emotional state into consideration. As a result, there is a risk that an appropriate plan will not be provided if the user is in a high-stress or abnormal emotional state. In terms of security, there is also a lack of mechanisms to recognize the user's emotional state and detect abnormal login attempts. Therefore, by combining emotion recognition, it is necessary to provide detailed plans that take the user's psychological state into consideration and strengthen security.

[1773] 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.

[1774] In this invention, the server includes an input means for inputting the user's financial information, a data collection means, and a data analysis means, which enables the presentation of a financial plan that takes into account the user's emotional state and enhances security.

[1775] "Input means" means an interface through which a user provides financial information to the system.

[1776] "Data collection means" refers to a system for collecting information such as users' financial information, past transaction data, and consumption patterns.

[1777] "Analysis means" refers to a device that analyzes collected financial information and evaluates the user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[1778] The "plan generation means" is a system that generates an optimal financial plan for the user based on the results obtained by the analysis means.

[1779] The "presentation means" is an interface for displaying the generated financial plan to the user and providing confirmation and feedback.

[1780] An "emotion recognition means" is a system for recognizing a user's emotional state and collecting that data.

[1781] The "security assessment means" is a system that detects anomalies in a user's login attempt based on the emotional state recognized by the emotion recognition means and requests additional authentication processes.

[1782] A "generative AI model" is an artificial intelligence model used to analyze a user's financial information and emotional state to provide optimal financial plans and security assessments.

[1783] The system of the present invention proposes an optimal financial plan based on a user's financial information and emotional state, and also performs security assessment. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and a security assessment means. The operation of each element is described in detail below.

[1784] System configuration

[1785] 1. Input Method

[1786] Web forms and mobile applications are used as interfaces for users to provide financial information to the system.

[1787] Users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1788] 2. Data Collection Methods

[1789] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[1790] This includes obtaining data through APIs from banks, credit card companies, etc.

[1791] 3. Analysis method

[1792] The server passes the collected data of the user to a generative AI model that evaluates their income and spending patterns, risk tolerance, and lifestyle.

[1793] Machine learning frameworks such as TensorFlow and PyTorch are used as generative AI models.

[1794] 4. Plan Generation Method

[1795] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1796] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1797] 5. Presentation means

[1798] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[1799] An interface is also provided where users can review the plan and provide approval or feedback.

[1800] 6. Emotion recognition means

[1801] It is a means for a device to capture a user's facial expressions and voice using a camera and microphone to recognize their emotional state.

[1802] This data is sent to a server, where the emotional state is assessed using techniques such as voice analysis, facial expression analysis, and text analysis.

[1803] 7. Security Evaluation Methods

[1804] The server evaluates the user's emotional state based on the data obtained by the emotion recognition means and detects abnormal login attempts.

[1805] If an abnormal emotional state is detected, additional authentication processes (e.g., two-factor authentication or security questions) will be required.

[1806] Web frameworks such as Flask and Django are used for implementation.

[1807] Specific examples

[1808] Examples of data collection:

[1809] A user accesses the system for the first time and creates an account.

[1810] A user uses a financial information entry form to enter the following information:

[1811] Monthly salary: 500,000 yen

[1812] Fixed expenses (rent, etc.): 100,000 yen

[1813] Variable expenses (food, etc.): 50,000 yen

[1814] Current assets: 2,000,000 yen

[1815] Current debt: 500,000 yen

[1816] Lifestyle: secure

[1817] Risk tolerance: medium

[1818] The terminal sends the input data to the server, which stores it in a database.

[1819] Examples of emotion data collection:

[1820] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[1821] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[1822] The device sends this emotion data to a server and stores it in a database.

[1823] Analysis example:

[1824] The server then obtains the user's past transaction data and passes it to the generation AI.

[1825] The generative AI model produces the following analysis results:

[1826] Monthly surplus: 350,000 yen

[1827] Recommended investment amount: 200,000 yen

[1828] Recommended savings amount: 100,000 yen

[1829] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1830] Risk Level: Medium

[1831] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[1832] Example of a security assessment prompt:

[1833] User A attempted to log in while in an unusual emotional state. During normal logins, a calm voice tone and stable facial expression are recorded, but this time the user's voice was trembling and their facial expression showed signs of tension. Please explain how you would analyze this situation and take security measures.

[1834] The system allows users to quickly and easily obtain the financial plan that best suits them, while also reducing security risks caused by abnormal login attempts.

[1835] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1836] Step 1:

[1837] Input: The user enters financial information.

[1838] How it works: Users enter their monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a web form or mobile application.

[1839] Output: Financial information data entered by the user.

[1840] Step 2:

[1841] Input: Financial information data.

[1842] How it works: The device sends the user's input data to a server, while also collecting past transaction data and spending patterns through the APIs of banks and credit card companies.

[1843] Output: Financial information data sent to the server and collected historical transaction and spending pattern data.

[1844] Step 3:

[1845] Inputs: Financial information data and historical transaction and consumption pattern data.

[1846] How it works: The server passes collected data to a generative AI model that evaluates income and spending patterns, risk tolerance, lifestyle, etc. This processing uses machine learning frameworks such as TensorFlow and PyTorch.

[1847] Output: Analysis result data (income and expenditure patterns, risk tolerance, lifestyle assessment).

[1848] Step 4:

[1849] Input: Analysis result data.

[1850] How it works: The server uses the analysis results from the generative AI model to create a financial plan that is optimal for the user, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[1851] Output: The generated financial plan.

[1852] Step 5:

[1853] Input: The generated financial plan.

[1854] How it works: The server sends the financial plan to the user's device, which presents it to the user and provides an interface for the user to review the plan.

[1855] Output: The financial plan displayed to the user.

[1856] Step 6:

[1857] Input: User voice and facial expression data.

[1858] How it works: The device uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes them using emotion recognition techniques, including voice analysis, facial expression analysis, and text analysis.

[1859] Output: Emotion recognition result data (e.g., user is in stress state).

[1860] Step 7:

[1861] Input: Emotion recognition result data.

[1862] How it works: The server evaluates the user's emotional state based on the emotion recognition result data and detects abnormal login attempts. If an abnormality is detected, an additional authentication process is required.

[1863] Output: Security assessment result (e.g., additional authentication required).

[1864] Step 8:

[1865] Input: Enter the prompt statement.

[1866] How it works: A server or generative AI model uses prompts to generate countermeasures for security anomaly detection and emotional states.

[1867] Output: Suggested security measures (e.g., requiring two-factor authentication, presenting security questions, etc.).

[1868] 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.

[1869] 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.

[1870] 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.

[1871] [Fourth embodiment]

[1872] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1873] 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.

[1874] 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).

[1875] 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.

[1876] 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.

[1877] 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).

[1878] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1879] 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.

[1880] 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.

[1881] 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.

[1882] 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.

[1883] 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.

[1884] 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."

[1885] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system is mainly composed of the following elements: input means, data collection means, analysis means, plan generation means, and presentation means.

[1886] System Overview

[1887] 1. Input Method

[1888] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[1889] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1890] 2. Data Collection Methods

[1891] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[1892] This includes obtaining data through APIs from banks, credit card companies, etc.

[1893] 3. Analysis method

[1894] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[1895] User income and expenditure patterns

[1896] User risk tolerance

[1897] User lifestyle

[1898] 4. Plan Generation Method

[1899] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[1900] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1901] 5. Presentation means

[1902] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[1903] It also includes an interface that allows users to review the plan and provide approval or feedback.

[1904] Specific examples

[1905] Data Collection Example

[1906] A user accesses the system for the first time and creates an account.

[1907] A user uses a financial information entry form to enter the following information:

[1908] Monthly salary: 500,000 yen

[1909] Fixed expenses (rent, etc.): 100,000 yen

[1910] Variable expenses (food, etc.): 50,000 yen

[1911] Current assets: 2,000,000 yen

[1912] Current debt: 500,000 yen

[1913] The terminal sends the input data to the server, which stores it in a database.

[1914] Analysis example

[1915] The server then obtains the user's past transaction data and passes it to the generation AI.

[1916] The generative AI model produces the following analysis results:

[1917] Monthly surplus: 350,000 yen

[1918] Recommended investment amount: 200,000 yen

[1919] Recommended savings amount: 100,000 yen

[1920] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[1921] Risk Level: Medium

[1922] Plan Generation and Presentation Example

[1923] The server creates a financial plan based on the analysis results:

[1924] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[1925] 60% domestic stocks

[1926] 20% foreign stocks

[1927] 10% bond

[1928] 10% cash

[1929] The server sends the generated plan to the user's terminal, which displays it to the user.

[1930] The user reviews the plan and provides approval or feedback.

[1931] The above is an embodiment of the present invention, and the system allows users to quickly and easily obtain the financial plan that is best suited to them.

[1932] The processing flow will be explained below.

[1933] Step 1:

[1934] A user accesses the system for the first time and enters the information required to create an account (such as name, email address, and password).

[1935] The terminal sends the input data to the server, which stores it in a database.

[1936] Step 2:

[1937] Users log in and fill out a financial information form, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1938] The terminal sends the input data to the server, which stores it in a database.

[1939] Step 3:

[1940] The server uses an API to retrieve the user's bank and credit card transaction data.

[1941] The server stores these transaction data in a database.

[1942] Step 4:

[1943] The server formats the user's collected data to be passed to the generative AI model, specifically by converting the data into a format that is easy to analyze.

[1944] For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance can be compiled in JSON format.

[1945] Step 5:

[1946] The server passes the formatted data to the generative AI model and begins analysis.

[1947] The generative AI model analyzes the user's income and expenditure patterns, lifestyle, risk tolerance, etc., and obtains analysis results to generate the optimal financial plan.

[1948] Step 6:

[1949] The server receives the analysis results from the generative AI model and generates a financial plan based on them.

[1950] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1951] Step 7:

[1952] The server sends the generated financial plan to the user's terminal.

[1953] The device displays the received plan to the user, who then confirms the plan details.

[1954] Step 8:

[1955] The user reviews the financial plan and provides approval or feedback as needed.

[1956] The device sends feedback to the server and the plan is readjusted if necessary.

[1957] The above is a description of the specific processing steps of the program.

[1958] Example 1

[1959] 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."

[1960] Conventional financial planning systems struggled to fully consider a user's diverse financial information, lifestyle, risk tolerance, and other factors. They also required users to manually input information, and the accuracy of analyzing the collected data was limited. This made it difficult to provide users with optimal financial plans. Furthermore, there was a lack of a way to quickly incorporate user feedback.

[1961] 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.

[1962] In this invention, the server includes a data input means for inputting the user's financial information, a data communication means for collecting the user's financial information and past transaction data, a data analysis means for analyzing the collected financial information and transaction data, a plan generation means for generating an optimal financial plan, a plan provision means for providing the generated financial plan, and an interaction means for the user to review the plan and provide feedback. This makes it possible to efficiently collect and analyze a wide range of user information and quickly provide more accurate financial plans. Furthermore, the plan can be improved by reflecting user feedback in real time.

[1963] 1. "User" means an entity that uses the System and provides personal financial information.

[1964] 2. "Financial Information" means your economic data, such as your income, expenses, assets, and liabilities.

[1965] 3. "Data Entry Means" means the interface through which a user enters financial information into the system.

[1966] 4. "Data communication means" refers to the means for transmitting user input data and transaction data to a server and for obtaining information from external organizations.

[1967] 5. "Data analysis means" means means for analyzing collected data and evaluating a user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[1968] 6. "Generative AI Model" means an artificial intelligence model used for data analysis to generate the optimal plan for the user.

[1969] 7. "Plan generation means" means a means for generating a financial plan based on the analysis results of the generative AI model.

[1970] 8. "Plan Provision Means" means a means for providing the generated financial plan to the user.

[1971] 9. "Interaction methods" are the methods by which users can review the generated plans and provide feedback.

[1972] 10. "API" means an application programming interface for communicating with external systems to obtain data.

[1973] The system of the present invention proposes an optimal financial plan based on the user's financial information. This system is mainly composed of a data input means, a data communication means, a data analysis means, a plan generation means, a plan provision means, and an interaction means.

[1974] System configuration

[1975] 1. Data entry method

[1976] Users enter their financial information through an interface such as a web form or a mobile application.

[1977] Specific financial information includes monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[1978] 2. Data communication means

[1979] The device sends the user's input data to the server.

[1980] To gather further data, the server obtains past transaction data and spending patterns through APIs from banks and credit card companies.

[1981] 3. Data Analysis Methods

[1982] The server passes the data collected from users to a generative AI model for detailed analysis.

[1983] This analysis includes the user's income and spending patterns, risk tolerance, lifestyle, etc.

[1984] 4. Plan Generation Method

[1985] The server creates the optimal financial plan for the user based on the analysis results of the generated AI model.

[1986] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[1987] 5. Plan Delivery Method

[1988] The server sends the generated financial plan to the user's terminal, which displays it to the user.

[1989] 6. Interaction methods

[1990] An interface is included for users to review the plan and provide approval or feedback.

[1991] Specific operation of the system

[1992] Examples of data collection

[1993] A user accesses the system and creates a new account.

[1994] The user fills in the form with the following information:

[1995] text

[1996] Monthly salary: 500,000 yen

[1997] Fixed expenses: 100,000 yen

[1998] Variable expenses: 50,000 yen

[1999] Current assets: 2,000,000 yen

[2000] Current debt: 500,000 yen

[2001] The terminal sends the input data to the server, which stores it in a database.

[2002] Specific examples of data analysis

[2003] The server collects past transaction data and passes it to the generative AI model.

[2004] A generative AI model analyzes the data and produces results such as:

[2005] text

[2006] Monthly surplus: 350,000 yen

[2007] Recommended investment amount: 200,000 yen

[2008] Recommended savings amount: 100,000 yen

[2009] Diversified investment portfolio:

[2010] 60% domestic stocks

[2011] 20% foreign stocks

[2012] 10% bond

[2013] 10% cash

[2014] Risk Level: Medium

[2015] Example of plan generation and presentation

[2016] The server creates a specific financial plan based on the analysis results:

[2017] text

[2018] Recommended plan:

[2019] Invest 200,000 yen per month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[2020] 60% domestic stocks

[2021] 20% foreign stocks

[2022] 10% bond

[2023] 10% cash

[2024] The server sends the generated plan to the user's terminal, which displays it to the user.

[2025] The user reviews the plan and provides approval or feedback.

[2026] This concludes the "Mode for Carrying Out the Invention." This system allows users to quickly and easily obtain the financial plan that is best for them and incorporates feedback in real time.

[2027] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2028] Step 1:

[2029] A user inputs financial information using a data entry device, including monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance. This data is collected through the data entry device.

[2030] Input: User's financial information (e.g. monthly income, expenses, assets, liabilities)

[2031] Output: Input data

[2032] Action: A user enters information into a form or application and clicks the submit button.

[2033] Step 2:

[2034] The device sends the entered financial information to a server, which stores it in a database. Furthermore, if the user consents, the server uses the APIs of banks and credit card companies to collect past transaction data and spending patterns.

[2035] Input: Input data, user consent

[2036] Output: Collected transaction data

[2037] How it works: The device sends data to the server, which stores it in a database and optionally collects additional data through external APIs.

[2038] Step 3:

[2039] The server passes the collected financial information and transaction data to the generative AI model for data analysis. The analysis includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model analyzes this data and generates the basic information for an appropriate financial plan.

[2040] Input: Financial information, transaction data

[2041] Output: Analysis results (e.g. monthly surplus, recommended investment amount)

[2042] How it works: A server provides data to a generative AI model, which then analyzes the data.

[2043] Step 4:

[2044] Based on the analysis results, the server generates an optimal financial plan, which includes monthly investment amounts, savings amounts, and investment portfolio ratios (domestic stocks, international stocks, bonds, cash, etc.).

[2045] Input: Analysis results

[2046] Output: Financial Plan

[2047] Operation: The server receives the analysis results and uses a plan generation algorithm to create a financial plan.

[2048] Step 5:

[2049] The server sends the generated financial plan to the user's terminal and displays it to the user through the plan providing means, and the user can review the plan and provide approval or feedback.

[2050] Enter: Financial Plan

[2051] Output: Show plan to user

[2052] How it works: The server sends the plan to the device, which displays it on an interface where the user can review the plan.

[2053] Step 6:

[2054] The user reviews the generated financial plan and provides feedback if necessary, which is then sent back to the server via the data input means.

[2055] Input: User feedback

[2056] Output: Updated financial plan (if needed)

[2057] How it works: The user enters their feedback and requests for changes to the plan and sends it to the server. If feedback is received, the server re-analyzes the plan to reflect that feedback.

[2058] (Application example 1)

[2059] 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."

[2060] Conventional financial planning systems require users to manually create plans based on financial information collected by themselves, making it difficult to optimize the plans and preventing them from automatically generating plans that take into account the user's lifestyle and risk tolerance. Furthermore, there is a lack of means to quickly implement the generated plans, which creates the challenge of requiring a lot of effort after the user approves them.

[2061] 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.

[2062] In this invention, the server includes a data collection means for collecting the user's financial information, an analysis means for analyzing the collected financial information, and a plan generation means for generating a financial plan based on the analysis results, thereby enabling the automatic generation of an optimal financial plan that takes into account the user's lifestyle and risk tolerance.

[2063] The system also includes an interface for obtaining user approval and a settlement mechanism for automatically executing approved plans, allowing the created plans to be executed quickly and easily, thereby reducing the user's workload and enabling efficient financial planning.

[2064] "User" means any person or entity that uses the System to enter their financial information and receive a Financial Plan.

[2065] "Financial information" refers to general information such as a user's monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[2066] "Input Method" refers to the interface, such as a web form or mobile application, that a user uses to provide financial information to the system.

[2067] "Data Collection Measures" means mechanisms for collecting input financial information and related historical transaction data and spending patterns.

[2068] "Analytical Tools" refers to the generative AI models and computational algorithms used to analyze collected financial information and generate detailed risk assessments and investment plans.

[2069] The "plan generation means" refers to a process or device for creating an optimal financial plan for the user based on the analysis results.

[2070] "Presentation means" refers to the interface for presenting the generated financial plan to the user and obtaining confirmation or approval.

[2071] "Interface" means the means by which a user provides approval and feedback on a generated financial plan.

[2072] "Payment Mechanism" refers to an electronic payment service or related system for automatically executing a plan approved by a user.

[2073] The system of the present invention is a FinTech application that proposes and automatically executes an optimal financial plan based on the user's financial information. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an interface, and a payment mechanism.

[2074] System Overview

[2075] 1. Input Method

[2076] A web form or mobile application interface allows users to enter financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[2077] 2. Data Collection Methods

[2078] The device sends the user's input data to the server, and also uses APIs to collect data from banks and credit card companies, as well as past transaction data and spending patterns.

[2079] 3. Analysis method

[2080] The server passes the collected data to a generative AI model for detailed analysis, which takes into account the user's income and expenditure patterns, risk tolerance, and lifestyle. Specifically, the generative AI model is used to generate an optimal financial plan.

[2081] 4. Plan Generation Method

[2082] The server then uses the analysis results from the AI ​​model to create a financial plan optimized for the user, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (e.g., domestic stocks, international stocks, bonds, cash, etc.).

[2083] 5. Presentation means

[2084] The server sends the generated financial plan to the user's device, which presents it to the user, including an interface that allows the user to review the plan's contents and provide approval or feedback.

[2085] 6. Interface

[2086] A means for users to provide approval or feedback on a presented financial plan. Through this interface, users can review and approve the plan.

[2087] 7. Payment Systems

[2088] After the server receives the user's approval, it automatically executes the investment using an electronic payment service, reducing the burden on the user and enabling quick investment execution.

[2089] Specific examples

[2090] Data Collection Example

[2091] A user accesses the system for the first time and creates an account.

[2092] A user uses a financial information entry form to enter the following information:

[2093] Monthly salary: 500,000 yen

[2094] Fixed expenses (rent, etc.): 100,000 yen

[2095] Variable expenses (food, etc.): 50,000 yen

[2096] Current assets: 2,000,000 yen

[2097] Current debt: 500,000 yen

[2098] The terminal sends the input data to the server, which stores it in a database.

[2099] Analysis example

[2100] The server then obtains the user's past transaction data via an API and passes it to the generation AI.

[2101] The generative AI model produces the following analysis results:

[2102] Monthly surplus: 350,000 yen

[2103] Recommended investment amount: 200,000 yen

[2104] Recommended savings amount: 100,000 yen

[2105] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[2106] Risk Level: Medium

[2107] Plan Generation and Presentation Example

[2108] The server creates a financial plan based on the analysis results:

[2109] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[2110] 60% domestic stocks

[2111] 20% foreign stocks

[2112] 10% bond

[2113] 10% cash

[2114] The server sends the generated plan to the user's terminal, which displays it to the user.

[2115] The user reviews the plan and provides approval or feedback.

[2116] Hardware and software used

[2117] Device: A smartphone or computer where users enter their financial information and view their plan.

[2118] Server: A server that collects data, analyzes, generates plans, presents plans, and executes investments.

[2119] Generative AI model: An AI model that analyzes a user's financial data and generates an optimal financial plan. We will use a "generative AI model" as an example.

[2120] API: API for obtaining data from banks, credit card companies, etc.

[2121] Electronic Payment Service: A payment mechanism for the execution of automated investments.

[2122] The above is an embodiment of the present invention. This system allows users to quickly and easily obtain and efficiently implement a financial plan that is best suited to them.

[2123] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2124] Step 1:

[2125] A user uses a smartphone or PC to access a web form or mobile application interface and enters financial information, such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc. The input data is stored on the device and sent to a server.

[2126] input:

[2127] Monthly salary: 500,000 yen

[2128] Fixed expenses: 100,000 yen

[2129] Variable expenses: 50,000 yen

[2130] Current assets: 2,000,000 yen

[2131] Current debt: 500,000 yen

[2132] Lifestyle: Normal

[2133] Risk tolerance: Medium

[2134] output:

[2135] A database on the server stores users' financial information.

[2136] Step 2:

[2137] The server receives the user's input data and uses APIs to retrieve bank and credit card transaction data, which includes retrieving the user's past transaction data and spending patterns through APIs.

[2138] input:

[2139] User financial information and API requests.

[2140] output:

[2141] Bank and credit card transaction data is stored on the server.

[2142] Step 3:

[2143] The server sends the collected data to the generative AI model for analysis, which includes the user's income and expenditure patterns, risk tolerance, and lifestyle. The generative AI model then performs a detailed analysis based on the generated prompt.

[2144] input:

[2145] Your financial and transactional data.

[2146] User's monthly income: 500,000 yen

[2147] Fixed expenses: 100,000 yen

[2148] Variable expenses: 50,000 yen

[2149] Current assets: 2,000,000 yen

[2150] Current debt: 500,000 yen

[2151] Historical Transaction Data: [{"date":"2023-01-01", "amount":-2000, "category":"food"}, {"date":"2023-01-02", "amount":-5000, "category":"transport"}]

[2152] Risk tolerance: Medium

[2153] Lifestyle: Normal

[2154] output:

[2155] Analysis results obtained from a generative AI model. Example: "Monthly surplus: 350,000 yen, recommended investment amount: 200,000 yen, recommended savings amount: 100,000 yen, diversified investment portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash."

[2156] Step 4:

[2157] Based on the analysis results, the server uses a generative AI model to generate an optimal financial plan, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[2158] input:

[2159] Analysis results.

[2160] Monthly surplus: 350,000 yen

[2161] Recommended investment amount: 200,000 yen

[2162] Recommended savings amount: 100,000 yen

[2163] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[2164] output:

[2165] Financial plan.

[2166] Invest 200,000 yen each month and save 100,000 yen. The risk level is "medium" and diversification is important. The recommended portfolio is as follows:

[2167] 60% domestic stocks

[2168] 20% foreign stocks

[2169] 10% bond

[2170] 10% cash

[2171] Step 5:

[2172] The server sends the generated financial plan to the user's terminal, which then presents it to the user, who then reviews the content of the presented plan and provides approval or feedback through the interface.

[2173] input:

[2174] Financial plan.

[2175] output:

[2176] User acknowledgement or feedback.

[2177] Step 6:

[2178] The server, having received the user's approval, automatically executes the investment using the electronic settlement service, and the settlement mechanism processes the settlement according to the investment portfolio.

[2179] input:

[2180] User approval.

[2181] output:

[2182] Automatically executed investment transactions.

[2183] 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.

[2184] The system of the present invention proposes an optimal financial plan based on a user's financial information. This system mainly consists of the following elements: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, and an emotion recognition means.

[2185] System Overview

[2186] 1. Input Method

[2187] Users provide financial information to the system through interfaces such as web forms and mobile applications.

[2188] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[2189] 2. Data Collection Methods

[2190] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[2191] This includes obtaining data through APIs from banks, credit card companies, etc.

[2192] 3. Analysis method

[2193] The server passes the collected user data to the generative AI model for detailed analysis, which includes the following elements:

[2194] User income and expenditure patterns

[2195] User risk tolerance

[2196] User lifestyle

[2197] 4. Plan Generation Method

[2198] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[2199] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[2200] 5. Presentation means

[2201] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[2202] It also includes an interface that allows users to review the plan and provide approval or feedback.

[2203] 6. Emotion recognition means

[2204] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[2205] The server passes the data to an emotion recognition engine that analyzes the user's emotional state, which includes techniques such as voice analysis, facial expression analysis, and text analysis.

[2206] Specific examples

[2207] Data Collection Example

[2208] A user accesses the system for the first time and creates an account.

[2209] A user uses a financial information entry form to enter the following information:

[2210] Monthly salary: 500,000 yen

[2211] Fixed expenses (rent, etc.): 100,000 yen

[2212] Variable expenses (food, etc.): 50,000 yen

[2213] Current assets: 2,000,000 yen

[2214] Current debt: 500,000 yen

[2215] Lifestyle: secure

[2216] Risk tolerance: medium

[2217] The terminal sends the input data to the server, which stores it in a database.

[2218] Example of emotion data collection

[2219] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[2220] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[2221] The device sends this emotion data to a server and stores it in a database.

[2222] Analysis example

[2223] The server then obtains the user's past transaction data and passes it to the generation AI.

[2224] The generative AI model produces the following analysis results:

[2225] Monthly surplus: 350,000 yen

[2226] Recommended investment amount: 200,000 yen

[2227] Recommended savings amount: 100,000 yen

[2228] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[2229] Risk Level: Medium

[2230] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[2231] Plan Generation and Presentation Example

[2232] The server creates a financial plan based on the analysis results:

[2233] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[2234] 50% domestic stocks

[2235] 20% foreign stocks

[2236] 20% bond

[2237] 10% cash

[2238] The server sends the generated plan to the user's terminal, which displays it to the user.

[2239] The user reviews the plan and provides approval or feedback.

[2240] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[2241] The processing flow will be explained below.

[2242] Step 1:

[2243] When a user accesses the system for the first time, they enter the information required to create an account (such as name, email address, and password). The terminal sends the input data to the server, which then stores it in a database.

[2244] Step 2:

[2245] The user logs in and enters information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a financial information input form. The terminal sends the input data to the server, which then stores it in a database.

[2246] Step 3:

[2247] The server uses an API to retrieve the user's bank and credit card transaction data, which it then stores in a database.

[2248] Step 4:

[2249] The server formats the user's collected data to be passed to the generative AI model. Specifically, the data is converted into a format that is easy to analyze. For example, each item such as income, expenses, assets, liabilities, lifestyle, and risk tolerance is compiled into a JSON format.

[2250] Step 5:

[2251] The server passes the formatted data to the generative AI model, which then analyzes it based on the user's income and expenditure patterns, lifestyle, risk tolerance, etc., to obtain analytical results that will help generate an optimal financial plan.

[2252] Step 6:

[2253] The server receives the analysis results from the generative AI model and generates a financial plan based on them, including monthly investment amounts, savings amounts, and the percentage of the investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[2254] Step 7:

[2255] The device collects emotional data from the user's voice and text and sends it to the server. For example, when a user enters feedback on a plan, the device analyzes the user's emotions.

[2256] Step 8:

[2257] The server passes the emotional data to an emotion recognition engine, which analyzes the user's emotional state using voice analysis, facial expression analysis, text analysis, and other methods.

[2258] Step 9:

[2259] The server adjusts the financial plan based on the emotion recognition results. For example, if the user is feeling excessively stressed, it will set a lower risk level and regenerate a plan that emphasizes safety.

[2260] Step 10:

[2261] The server sends the adjusted financial plan to the user's device, which displays it to the user, who then reviews the plan and provides approval or feedback.

[2262] The above is a description of the specific processing steps of the system that combines emotion engines.

[2263] Example 2

[2264] 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."

[2265] Conventional financial planning systems generate plans based on a user's financial information, but are unable to consider the user's psychological state or emotions. As a result, the financial plans provided often do not match the user's actual psychological state, making it difficult to implement and maintain the plan. Furthermore, there are insufficient means to specifically reflect the user's lifestyle and risk tolerance, making it difficult to propose personalized plans. The problem that this invention aims to solve is to provide a more personalized and realistic financial plan that takes into account the user's psychological state and lifestyle.

[2266] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an input means for inputting the user's financial information, a data collection means for collecting the user's financial information, and an analysis means for analyzing the collected financial information. This enables detailed data analysis and the provision of a financial plan based on the user's financial situation. Furthermore, by adding an emotion recognition means for recognizing the user's emotional state and collecting that data, and a means for adjusting the financial plan generated based on the emotional state, it becomes possible to optimize the plan taking the user's psychological state into consideration. This makes it possible to provide a feasible and sustainable financial plan for the user.

[2267] "Input means" refers to the interface through which a user provides financial information to the system.

[2268] "Data Collection Method" refers to the method or device used to capture a user's financial information into the system.

[2269] "Analysis means" refers to the methods and functions for conducting data analysis based on collected financial information.

[2270] The "plan generation means" refers to a function or device that creates a financial plan based on the information obtained by the analysis means.

[2271] "Presentation Means" refers to the method or device for displaying and providing the generated financial plan to the user.

[2272] "Emotion recognition means" refers to a method or device for grasping a user's emotional state and incorporating that data into the system.

[2273] The "adjustment means" refers to a method or function for adjusting the financial plan based on the emotion data obtained by the emotion recognition means.

[2274] The system of the present invention proposes an optimal financial plan based on a user's financial information. The system is composed of the following main components: an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and an adjustment means.

[2275] System Overview

[2276] 1. Input Method

[2277] The interface through which users provide financial information to the system, such as a web form or a mobile application.

[2278] For example, users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[2279] 2. Data Collection Methods

[2280] The device uses methods and APIs to send user input data to the server and, if necessary, collect past transaction data and consumption patterns.

[2281] For example, transaction data is obtained from banks and credit card companies via API.

[2282] 3. Analysis method

[2283] The server passes the user's collected data to the generative AI model, which then analyzes the data.

[2284] This analysis includes the user's income and spending patterns, risk tolerance and lifestyle.

[2285] 4. Plan Generation Method

[2286] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[2287] A specific plan includes monthly investment amounts, savings amounts, and investment portfolios.

[2288] 5. Presentation means

[2289] The server sends the generated financial plan to the user's terminal, which then presents it to the user.

[2290] Users can review the plan and provide approval or feedback.

[2291] 6. Emotion recognition means

[2292] The device collects emotional data based on the user's input and dialogue and sends it to the server.

[2293] The server passes the data to an emotion recognition engine to analyze the user's emotional state, which includes voice analysis, facial expression analysis, and text analysis.

[2294] 7. Adjustment means

[2295] The server adjusts the financial plan based on the sentiment data.

[2296] For example, if a user is feeling stressed, the risk level of the plan can be adjusted accordingly.

[2297] Specific examples

[2298] Data Collection Example

[2299] A user accesses the system for the first time and creates an account.

[2300] A user uses a financial information entry form to enter the following information:

[2301] Monthly salary: 500,000 yen

[2302] Fixed expenses (rent, etc.): 100,000 yen

[2303] Variable expenses (food, etc.): 50,000 yen

[2304] Current assets: 2,000,000 yen

[2305] Current debt: 500,000 yen

[2306] Lifestyle: secure

[2307] Risk tolerance: medium

[2308] The terminal sends the input data to the server, which stores it in a database.

[2309] Example of emotion data collection

[2310] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[2311] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[2312] The device sends this emotion data to a server and stores it in a database.

[2313] Analysis example

[2314] The server obtains the user's input data and past transaction data and passes it to the generative AI model.

[2315] The generative AI model produces the following analysis results:

[2316] Monthly surplus: 350,000 yen

[2317] Recommended investment amount: 200,000 yen

[2318] Recommended savings amount: 100,000 yen

[2319] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[2320] Risk Level: Medium

[2321] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted to "low."

[2322] Plan Generation and Presentation Example

[2323] The server creates a financial plan based on the analysis results as follows:

[2324] Invest 200,000 yen each month and save 100,000 yen. Set the risk level to "low" and emphasize diversification. The recommended portfolio is as follows:

[2325] 50% domestic stocks

[2326] 20% foreign stocks

[2327] 20% bond

[2328] 10% cash

[2329] The server sends the generated plan to the user's terminal, which displays it to the user.

[2330] The user reviews the plan and provides approval or feedback.

[2331] The above is an embodiment of the present invention. The addition of an emotion recognition engine allows for more personalized assistance by providing financial plans that take into account the user's psychological state. This system allows users to quickly and easily obtain the financial plan that is best suited to them.

[2332] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2333] Step 1:

[2334] User enters financial information

[2335] The user enters financial information (e.g., monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance, etc.) into the system's web form or mobile app.

[2336] Input data: monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, risk tolerance

[2337] Output data: Initial financial information entered by the user

[2338] Step 2:

[2339] The device sends the input data to the server

[2340] The terminal temporarily stores the financial information entered by the user and sends it to the server.

[2341] Input Data: Initial financial information entered by the user

[2342] Output data: Financial information sent to the server

[2343] Step 3:

[2344] The server collects additional data

[2345] If necessary, the server retrieves the user's transaction history and spending patterns from banks and credit card companies via API and adds them to the financial information.

[2346] Input data: Financial information sent to the server

[2347] Output data: Complete financial information with additional transaction history and spending patterns

[2348] Step 4:

[2349] The server passes the data to the generative AI model

[2350] The server passes the collected complete financial information to the generative AI model for detailed analysis, and issues a prompt saying, "Generate the optimal financial plan based on the user's income, expenses, assets, liabilities, and other data."

[2351] Input data: complete financial information, and prompt statements

[2352] Output data: Analysis request passed to the generative AI model

[2353] Step 5:

[2354] Generative AI models analyze data

[2355] The generative AI model analyzes the data passed from the server and generates the optimal financial plan for the user, including the monthly surplus, recommended investment amount, recommended savings amount, diversified investment portfolio, risk level, etc.

[2356] Input data: Complete financial information

[2357] Output data: Analysis results (e.g. monthly surplus, recommended investment amount, recommended savings amount, portfolio, risk level)

[2358] Step 6:

[2359] The server generates a plan based on the analysis results.

[2360] The server generates an optimal financial plan for the user based on the analysis results of the AI ​​model. For example, invest 200,000 yen per month and save 100,000 yen. The recommended portfolio would be 50% domestic stocks, 20% international stocks, 20% bonds, and 10% cash.

[2361] Input data: Analysis results from the generative AI model

[2362] Output data: Generated financial plan

[2363] Step 7:

[2364] The device collects the user's emotional data and sends it to the server.

[2365] The device collects emotional data from user interactions, input text, and voice, and sends this data to a server.

[2366] Input data: user dialogue text and voice

[2367] Output data: Emotion data sent to the server

[2368] Step 8:

[2369] The server performs emotion analysis using an emotion recognition engine.

[2370] The server passes the emotion data to an emotion recognition engine to analyze the user's emotional state, for example, to determine whether the user is feeling stressed.

[2371] Input data: User emotion data

[2372] Output data: Analysis results on the user's emotional state

[2373] Step 9:

[2374] The server adjusts the plan taking into account the emotional data.

[2375] The server adjusts the risk level of the generated financial plan based on the analysis results of the emotion recognition engine. For example, if the user is feeling stressed, the risk level will be changed from "medium" to "low."

[2376] Input data: Generated financial plan and analysis results on emotional state

[2377] Output data: adjusted financial plan

[2378] Step 10:

[2379] The server sends the final plan to the device.

[2380] The server sends the final financial plan to the user's terminal, which then presents it to the user.

[2381] Input data: Adjusted financial plan

[2382] Output data: The final financial plan sent to the user's device

[2383] Step 11:

[2384] Users review the plan and provide feedback

[2385] The user reviews the proposed financial plan and provides feedback or approval as needed. The provided feedback is then sent back to the server as a reference for generating the next plan.

[2386] Input data: Financial plan confirmed by the user

[2387] Output data: User feedback

[2388] (Application example 2)

[2389] 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."

[2390] Conventional financial plan generation systems provide optimal plans based on a user's financial information, but do not take the user's emotional state into consideration. As a result, there is a risk that an appropriate plan will not be provided if the user is in a high-stress or abnormal emotional state. In terms of security, there is also a lack of mechanisms to recognize the user's emotional state and detect abnormal login attempts. Therefore, by combining emotion recognition, it is necessary to provide detailed plans that take the user's psychological state into consideration and strengthen security.

[2391] 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.

[2392] In this invention, the server includes an input means for inputting the user's financial information, a data collection means, and a data analysis means, which enables the presentation of a financial plan that takes into account the user's emotional state and enhances security.

[2393] "Input means" means an interface through which a user provides financial information to the system.

[2394] "Data collection means" refers to a system for collecting information such as users' financial information, past transaction data, and consumption patterns.

[2395] "Analysis means" refers to a device that analyzes collected financial information and evaluates the user's income and expenditure patterns, risk tolerance, lifestyle, etc.

[2396] The "plan generation means" is a system that generates an optimal financial plan for the user based on the results obtained by the analysis means.

[2397] The "presentation means" is an interface for displaying the generated financial plan to the user and providing confirmation and feedback.

[2398] An "emotion recognition means" is a system for recognizing a user's emotional state and collecting that data.

[2399] The "security assessment means" is a system that detects anomalies in a user's login attempt based on the emotional state recognized by the emotion recognition means and requests additional authentication processes.

[2400] A "generative AI model" is an artificial intelligence model used to analyze a user's financial information and emotional state to provide optimal financial plans and security assessments.

[2401] The system of the present invention proposes an optimal financial plan based on a user's financial information and emotional state, and also performs security assessment. This system is composed of an input means, a data collection means, an analysis means, a plan generation means, a presentation means, an emotion recognition means, and a security assessment means. The operation of each element is described in detail below.

[2402] System configuration

[2403] 1. Input Method

[2404] Web forms and mobile applications are used as interfaces for users to provide financial information to the system.

[2405] Users enter information such as monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance.

[2406] 2. Data Collection Methods

[2407] The device sends the user's input data to the server, and also collects past transaction data and consumption patterns as needed.

[2408] This includes obtaining data through APIs from banks, credit card companies, etc.

[2409] 3. Analysis method

[2410] The server passes the collected data of the user to a generative AI model that evaluates their income and spending patterns, risk tolerance, and lifestyle.

[2411] Machine learning frameworks such as TensorFlow and PyTorch are used as generative AI models.

[2412] 4. Plan Generation Method

[2413] The server creates the optimal financial plan for the user based on the analysis results obtained from the generated AI model.

[2414] A specific plan includes monthly investment amounts, savings amounts, and the percentage of your investment portfolio (domestic stocks, international stocks, bonds, cash, etc.).

[2415] 5. Presentation means

[2416] The server transmits the generated financial plan to the user's terminal, which presents it to the user.

[2417] An interface is also provided where users can review the plan and provide approval or feedback.

[2418] 6. Emotion recognition means

[2419] It is a means for a device to capture a user's facial expressions and voice using a camera and microphone to recognize their emotional state.

[2420] This data is sent to a server, where the emotional state is assessed using techniques such as voice analysis, facial expression analysis, and text analysis.

[2421] 7. Security Evaluation Methods

[2422] The server evaluates the user's emotional state based on the data obtained by the emotion recognition means and detects abnormal login attempts.

[2423] If an abnormal emotional state is detected, additional authentication processes (e.g., two-factor authentication or security questions) will be required.

[2424] Web frameworks such as Flask and Django are used for implementation.

[2425] Specific examples

[2426] Examples of data collection:

[2427] A user accesses the system for the first time and creates an account.

[2428] A user uses a financial information entry form to enter the following information:

[2429] Monthly salary: 500,000 yen

[2430] Fixed expenses (rent, etc.): 100,000 yen

[2431] Variable expenses (food, etc.): 50,000 yen

[2432] Current assets: 2,000,000 yen

[2433] Current debt: 500,000 yen

[2434] Lifestyle: secure

[2435] Risk tolerance: medium

[2436] The terminal sends the input data to the server, which stores it in a database.

[2437] Examples of emotion data collection:

[2438] While the user is interacting with the system, the emotion recognition means analyzes emotions from the voice and input text.

[2439] The device analyzes the user's tone of voice and text content to detect when the user is feeling stressed.

[2440] The device sends this emotion data to a server and stores it in a database.

[2441] Analysis example:

[2442] The server then obtains the user's past transaction data and passes it to the generation AI.

[2443] The generative AI model produces the following analysis results:

[2444] Monthly surplus: 350,000 yen

[2445] Recommended investment amount: 200,000 yen

[2446] Recommended savings amount: 100,000 yen

[2447] Diversified portfolio: 60% domestic stocks, 20% international stocks, 10% bonds, 10% cash

[2448] Risk Level: Medium

[2449] Taking into account the emotion recognition results, if the user is feeling stressed, the risk level is adjusted accordingly.

[2450] Example of a security assessment prompt:

[2451] User A attempted to log in while in an unusual emotional state. During normal logins, a calm voice tone and stable facial expression are recorded, but this time the user's voice was trembling and their facial expression showed signs of tension. Please explain how you would analyze this situation and take security measures.

[2452] The system allows users to quickly and easily obtain the financial plan that best suits them, while also reducing security risks caused by abnormal login attempts.

[2453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2454] Step 1:

[2455] Input: The user enters financial information.

[2456] How it works: Users enter their monthly income, fixed expenses, variable expenses, current assets, liabilities, lifestyle, and risk tolerance into a web form or mobile application.

[2457] Output: Financial information data entered by the user.

[2458] Step 2:

[2459] Input: Financial information data.

[2460] How it works: The device sends the user's input data to a server, while also collecting past transaction data and spending patterns through the APIs of banks and credit card companies.

[2461] Output: Financial information data sent to the server and collected historical transaction and spending pattern data.

[2462] Step 3:

[2463] Inputs: Financial information data and historical transaction and consumption pattern data.

[2464] How it works: The server passes collected data to a generative AI model that evaluates income and spending patterns, risk tolerance, lifestyle, etc. This processing uses machine learning frameworks such as TensorFlow and PyTorch.

[2465] Output: Analysis result data (income and expenditure patterns, risk tolerance, lifestyle assessment).

[2466] Step 4:

[2467] Input: Analysis result data.

[2468] How it works: The server uses the analysis results from the generative AI model to create a financial plan that is optimal for the user, including monthly investment amounts, savings amounts, and investment portfolio percentages.

[2469] Output: The generated financial plan.

[2470] Step 5:

[2471] Input: The generated financial plan.

[2472] How it works: The server sends the financial plan to the user's device, which presents it to the user and provides an interface for the user to review the plan.

[2473] Output: The financial plan displayed to the user.

[2474] Step 6:

[2475] Input: User voice and facial expression data.

[2476] How it works: The device uses a camera and microphone to capture the user's facial expressions and voice, and then analyzes them using emotion recognition techniques, including voice analysis, facial expression analysis, and text analysis.

[2477] Output: Emotion recognition result data (e.g., user is in stress state).

[2478] Step 7:

[2479] Input: Emotion recognition result data.

[2480] How it works: The server evaluates the user's emotional state based on the emotion recognition result data and detects abnormal login attempts. If an abnormality is detected, an additional authentication process is required.

[2481] Output: Security assessment result (e.g., additional authentication required).

[2482] Step 8:

[2483] Input: Enter the prompt statement.

[2484] How it works: A server or generative AI model uses prompts to generate countermeasures for security anomaly detection and emotional states.

[2485] Output: Suggested security measures (e.g., requiring two-factor authentication, presenting security questions, etc.).

[2486] 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.

[2487] 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.

[2488] 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 robot 414.

[2489] 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.

[2490] FIG. 9 illustrates 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 behaviors 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.

[2491] 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.

[2492] 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).

[2493] 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.

[2494] 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."

[2495] 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.

[2496] 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).

[2497] 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.

[2498] 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.

[2499] 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.

[2500] 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.

[2501] 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.

[2502] 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.

[2503] 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.

[2504] 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.

[2505] 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.

[2506] 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.

[2507] The following is further disclosed regarding the above embodiment.

[2508] (Claim 1)

[2509] input means for inputting the user's financial information;

[2510] data collection means for collecting financial information of said users;

[2511] analysis means for analyzing the collected financial information;

[2512] a plan generation means for generating a financial plan based on the analysis results;

[2513] a presentation means for presenting the generated financial plan to a user;

[2514] A system including:

[2515] (Claim 2)

[2516] 10. The system of claim 1, wherein said input means includes means for collecting a user's past transaction data and spending patterns.

[2517] (Claim 3)

[2518] 2. The system of claim 1, wherein the analysis means includes means for performing the analysis taking into account a user's lifestyle and risk tolerance.

[2519] The above is the draft of the patent claims.

[2520] "Example 1"

[2521] (Claim 1)

[2522] data entry means for entering user financial information;

[2523] a data communication means for collecting financial information and past transaction data of said user;

[2524] data analysis means for analyzing the collected financial information and transaction data;

[2525] a plan generation means for generating an optimal financial plan based on the analysis results;

[2526] a plan providing means for providing the generated financial plan to a user;

[2527] interaction means for a user to review the generated plan and provide feedback;

[2528] A system including:

[2529] (Claim 2)

[2530] 2. The system of claim 1, wherein the data analysis means includes means for analyzing a user's income and expenditure patterns, risk tolerance, and lifestyle using a generative AI model.

[2531] (Claim 3)

[2532] 10. The system of claim 1, wherein the data communication means includes means for obtaining information through APIs from banks and credit card companies in the process of collecting data.

[2533] "Application Example 1"

[2534] (Claim 1)

[2535] input means for inputting the user's financial information;

[2536] data collection means for collecting financial information of said users;

[2537] analysis means for analyzing the collected financial information;

[2538] a plan generation means for generating a financial plan based on the analysis results;

[2539] a presentation means for presenting the generated financial plan to a user;

[2540] an interface for obtaining user authorization;

[2541] a settlement mechanism that automatically executes the approved plan;

[2542] A system including:

[2543] (Claim 2)

[2544] 10. The system of claim 1, wherein said input means includes means for collecting a user's past transaction data and spending patterns.

[2545] (Claim 3)

[2546] 2. The system of claim 1, wherein the analysis means includes means for performing the analysis taking into account a user's lifestyle and risk tolerance.

[2547] "Example 2: Combining Emotion Engines"

[2548] (Claim 1)

[2549] input means for inputting the user's financial information;

[2550] data collection means for collecting financial information of said users;

[2551] analysis means for analyzing the collected financial information;

[2552] a plan generation means for generating a financial plan based on the analysis results;

[2553] a presentation means for presenting the generated financial plan to a user;

[2554] an emotion recognition means for recognizing and collecting data on the user's emotional state;

[2555] means for adjusting the generated financial plan based on the emotional state;

[2556] A system including:

[2557] (Claim 2)

[2558] 10. The system of claim 1, wherein said input means includes means for collecting a user's past transaction data and spending patterns.

[2559] (Claim 3)

[2560] 2. The system of claim 1, wherein the analysis means includes means for performing the analysis taking into account a user's lifestyle and risk tolerance.

[2561] "Application example 2 when combining emotion engines"

[2562] (Claim 1)

[2563] input means for inputting the user's financial information;

[2564] data collection means for collecting financial information of said users;

[2565] analysis means for analyzing the collected financial information;

[2566] a plan generation means for generating a financial plan based on the analysis results;

[2567] a presentation means for presenting the generated financial plan to a user;

[2568] emotion recognition means for recognizing an emotional state of a user;

[2569] a security assessment means for requesting a security assessment and an additional authentication process based on the emotional state recognized by the emotion recognition means;

[2570] A system including:

[2571] (Claim 2)

[2572] 10. The system of claim 1, wherein said input means includes means for collecting a user's past transaction data and spending patterns.

[2573] (Claim 3)

[2574] 10. The system of claim 1, wherein the security assessment means includes means for detecting anomalies in user emotional states and login attempts using a generative AI model. [Explanation of symbols]

[2575] 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. input means for inputting the user's financial information; data collection means for collecting financial information of said users; analysis means for analyzing the collected financial information; a plan generation means for generating a financial plan based on the analysis results; a presentation means for presenting the generated financial plan to a user; A system including:

2. 2. The system of claim 1, wherein said input means includes means for collecting user past transaction data and spending patterns.

3. 2. The system of claim 1, wherein the analysis means includes means for performing the analysis taking into account the user's lifestyle and risk tolerance.

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