system

Abstract

Provide a system. A means for inputting household income and expenditure data; A means for analyzing input income and expenditure data and making income and expenditure forecasts; A means for generating an asset management plan based on the analysis results; a means for presenting the generated asset management plan to a user and requesting approval or modification from the user; means for automatically making investments based on a user-approved investment plan; A means to continually monitor the performance of your investments and adjust your investment plan as needed; A system including:

Description

[Technical Field]

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

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

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

[0004] In today's busy lifestyles, it is extremely important for each household to efficiently and effectively manage their income and expenses and optimally manage their assets. However, traditional methods require a significant amount of time and effort to manage income and expenses and asset management, making it particularly difficult for users with low financial literacy. Furthermore, there is a lack of systems that automatically execute investments and continuously monitor and adjust their performance. Therefore, there is a need for a system that efficiently manages everything from household income and expenses management to asset management all at once. [Means for solving the problem]

[0005] The present invention provides a system including a means for inputting household income and expenditure data, a means for analyzing the input income and expenditure data and generating income and expenditure forecasts, a means for generating an asset management plan based on the analysis results, a means for presenting the generated asset management plan to a user and requesting the user's approval or modification, a means for automatically investing based on the asset management plan approved by the user, and a means for continuously monitoring investment performance and adjusting the investment plan as necessary. Furthermore, by including a machine learning module for analyzing the income and expenditure data and learning the user's spending patterns, and a means for generating multiple asset management plans based on the user's risk tolerance and performing risk assessment for each plan, it is possible to provide a more accurate asset management plan. In this way, the present invention enables individual households to efficiently and effectively manage their income and expenditures and manage their assets.

[0006] A "household" is a family unit living in an individual dwelling and having self-control over its income and expenditure.

[0007] "Income" refers to all economic benefits a household receives, such as salary, business income, and investment income.

[0008] "Expenses" refers to all the costs a household pays to maintain its lifestyle, such as rent, utilities, and food.

[0009] "Assets" refers to all real and financial assets of economic value held by a household, such as cash, savings, real estate, and stocks.

[0010] "Investment" refers to the act of allocating assets to specific financial instruments or real assets in order to obtain future economic benefits.

[0011] "Spending patterns" refer to the tendency of households to spend how much on what items during a particular period.

[0012] "Income and expenditure forecasting" refers to predicting what income and expenditure a household will have in the future based on current and past income and expenditure data.

[0013] An "asset management plan" refers to a specific plan for optimally managing a household's assets and maximizing future economic benefits.

[0014] "Risk tolerance" is a measure of how much risk a household can accept in an investment.

[0015] A "machine learning module" refers to a software component that learns patterns and rules based on data and enables technology to make future predictions and classifications.

[0016] "Risk assessment" refers to the process of quantitatively assessing the risks associated with a particular investment plan.

[0017] "Investment performance" is an indicator that measures the profitability and effectiveness of an investment over a specific period of time.

[0018] "Plan adjustment" refers to the process of reviewing an existing investment plan and making necessary adjustments based on the current investment situation and future outlook. [Brief explanation of the drawings]

[0019] [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 illustrating 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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a system that efficiently manages everything from household income and expenditure management to asset management. Users input their income, expenditure information, and investment preferences, and the system then automatically analyzes the data to propose and implement an effective asset management plan, while continuously monitoring and adjusting it.

[0041] Program processing

[0042] Collecting input data

[0043] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[0044] The user enters information into the application's input form, such as monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount available for investment, and investment preferences (risk tolerance, etc.).

[0045] After entering the data, click the send button to send the data from the terminal to the server.

[0046] Data analysis

[0047] The server analyzes the received data and determines the user's income and expenditure status and current asset status.

[0048] The server stores the received data in a database, and the income and expenditure analysis module analyzes this data.

[0049] Based on the analysis results, the current savings, investment potential, and balance between income and expenditure are calculated.

[0050] The server uses AI models to predict users' spending patterns and future income and expenditures.

[0051] The server uses a machine learning module to learn the user's spending patterns from past income and expenditure data.

[0052] Based on this data, future income and expenditure forecasts are made, and fluctuations in the user's future income and expenditures are predicted.

[0053] Generate a financial plan

[0054] The server generates an optimal asset management plan based on the data obtained.

[0055] The server evaluates various investment options based on the user's income and expenditure forecast data and investment preferences, and generates an optimal asset management plan.

[0056] The generated plans include low-risk, medium-risk, and high-risk investment plans and their return projections.

[0057] The plan includes multiple investment options and risk assessments.

[0058] The server uses a risk assessment module to perform a risk assessment for each investment plan.

[0059] Calculate a risk score for each plan and select the most appropriate plan based on the user's risk tolerance.

[0060] Feedback and Suggestions

[0061] The server sends the generated plan to the terminal.

[0062] The server sends the generated asset management plan in JSON format to the terminal.

[0063] The terminal displays the plan to the user, who then approves or modifies it.

[0064] The device displays the received plan on the application's UI.

[0065] The user reviews the proposed plan and makes any necessary modifications.

[0066] After the user approves, the terminal sends the final plan to the server.

[0067] If the user approves the final plan, the terminal transmits the final plan to the server.

[0068] The server stores this final plan in a database.

[0069] Execution and monitoring

[0070] The server starts asset management based on the approved plan.

[0071] The server automatically executes investment operations through the specified investment API.

[0072] The server continuously monitors the performance of the investments and adjusts the investment plan as needed.

[0073] The server periodically evaluates market data and the user's investment performance and makes any necessary adjustments.

[0074] If there is an important change, the server will send a notification to the terminal and ask the user for confirmation.

[0075] Specific examples

[0076] Initial Setup and Plan Generation

[0077] 1. The user enters the following information into the terminal:

[0078] Monthly income: 500,000 yen

[0079] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0080] Investment limit: 100,000 yen

[0081] Investment preference: Low risk

[0082] 2. The server analyzes the balance of income and expenditures and generates an optimal asset management plan.

[0083] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen in cash as an emergency reserve.

[0084] 3. The user reviews and approves the proposed plan.

[0085] If the user approves, the server automatically executes the investment and makes the mutual fund purchase.

[0086] Continuous monitoring and adjustment

[0087] 1. The server monitors the performance and balance of your investments monthly.

[0088] If your income increases and your investment trust returns are high, your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[0089] 2. The server notifies the terminal of the adjustment results and asks the user for confirmation.

[0090] Once the user approves the changes, the new investment plan will be applied.

[0091] In this way, the system of the present invention can efficiently manage household income and expenditures and asset management all at once, allowing users to optimally manage their assets without any hassle.

[0092] The processing flow will be explained below.

[0093] Step 1:

[0094] The user enters information about their income, expenses, assets, and desired investments from their device. Specifically, the user enters their monthly income, fixed monthly expenses (e.g., rent, utilities, food, etc.), available investment amount, and desired investments (risk tolerance, etc.) into the application form and clicks the submit button.

[0095] Step 2:

[0096] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0097] Step 3:

[0098] The server analyzes the received data to understand the user's current income and expenditure situation and assets. The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes the data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[0099] Step 4:

[0100] The server uses machine learning modules to analyze the user's spending patterns, performs feature extraction based on past data, and extracts spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[0101] Step 5:

[0102] The server generates an optimal investment plan for the user based on the income and expenditure forecast data. The server evaluates investment options and creates multiple investment plans based on risk tolerance. Each plan includes specific investments (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[0103] Step 6:

[0104] The server performs risk assessment of the generated investment plans. The risk assessment module calculates a risk score for each plan and sorts the plans based on the user's risk tolerance. The most suitable plan is selected.

[0105] Step 7:

[0106] The server sends the generated asset management plan to the terminal. The server then sends the selected plan to the terminal in JSON format.

[0107] Step 8:

[0108] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[0109] Step 9:

[0110] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device sends the final plan to the server again. The server saves the approved plan in the database.

[0111] Step 10:

[0112] The server will start asset management based on the approved plan, and then execute specific investment operations such as purchasing stocks or mutual funds through the specified investment API. Once the transaction is completed, the server will update the user's asset status.

[0113] Step 11:

[0114] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or presents new investment opportunities. If there are any major changes, the server sends a notification to your device and asks for your confirmation.

[0115] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management.

[0116] Example 1

[0117] 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."

[0118] Currently, efficient household income and expenditure management and asset management requires multiple different applications and manual work, placing a heavy burden on users. Furthermore, analyzing income and expenditure data and making future predictions requires advanced knowledge, making it difficult for average users to use. Furthermore, there is a lack of systems that can consistently create, execute, and monitor asset management plans.

[0119] 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.

[0120] In this invention, the server includes means for transmitting and storing input income and expenditure data to the server, means for analyzing the stored data and making income and expenditure forecasts, means for generating an asset management plan based on the income and expenditure forecasts, means for transmitting the generated asset management plan to a terminal and presenting it to the user, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This allows users to efficiently and automatically perform everything from income and expenditure management to asset management through a single system.

[0121] "Income" is a general term for the money and assets that a household or individual receives within a certain period of time.

[0122] "Expenditure" is a general term for the money consumed by households and individuals within a certain period of time.

[0123] A "server" is a computer system that stores and processes data on a network.

[0124] A "database" is an information system for efficiently storing, searching, and updating data.

[0125] "Analysis" is the process of organizing and analyzing data to extract useful information.

[0126] "Income and expenditure forecasting" refers to predicting future income and expenditure based on past income and expenditure data.

[0127] An "asset management plan" is a plan that proposes an efficient method of managing assets, taking into account the balance of income and expenditure of individuals or households.

[0128] "Terminal" refers to a computing facility that is directly operated by a user and is a device for input and display.

[0129] A "machine learning module" is a software module that learns rules and patterns from data and uses them to make predictions and classifications.

[0130] "Risk tolerance" refers to the range and degree of risk that a user can accept in investment or asset management.

[0131] An "investment API" is a program interface that works in conjunction with external systems to automate investment operations.

[0132] The present invention is a system that inputs household income and expenditure data, analyzes the data to generate income and expenditure forecasts and asset management plans, and then executes and monitors the plans. This system provides a series of functions that users can use easily and efficiently to manage their assets.

[0133] System Program Overview

[0134] Collecting input data

[0135] The user inputs income, expenses, asset information, and desired investment information from the terminal. The terminal collects this data in the application's input form. For example, monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), investment amount, and risk tolerance are input. The input data is sent from the terminal to the server by clicking the send button. The data is converted to JSON format and securely sent to the server using the HTTPS protocol.

[0136] Data analysis

[0137] The server stores and analyzes the received data. The server stores the data in a database (for example, MySQL (registered trademark) or PostgreSQL). An income / expense analysis module (for example, using the pandas and numpy libraries) analyzes the data and calculates the current savings, investment amount, and balance of income and expenses.

[0138] Revenue and expenditure forecast

[0139] The server uses machine learning modules (such as Tensorflow (registered trademark) and scikit-learn) to learn the user's spending patterns from past income and expenditure data. Based on the learning results, it predicts future income and expenditure and possible investment amounts, and generates income and expenditure forecast data.

[0140] Generate a financial plan

[0141] The server evaluates various investment options based on income / expense forecast data and investment preference information, and generates an optimal asset management plan. This uses the Markowitz model to evaluate risk and return. The generated plan includes multiple investment options, each of which has been risk assessed in the risk assessment module.

[0142] Plan presentation and approval

[0143] The generated asset management plan is sent to the terminal. The terminal displays the received plan on the UI and presents it to the user. The user checks the plan, modifies it if necessary, and approves the final plan. The approved plan is sent back to the server and saved in the database.

[0144] Asset management execution

[0145] The server automatically executes investment operations based on the approved plan through a designated investment API (e.g., Alpha Vantage API).

[0146] Continuous monitoring and adjustment

[0147] The server regularly monitors market data and the user's investment performance, adjusting the investment plan as needed. If there are any significant changes, the server sends a notification to the terminal and asks the user for confirmation.

[0148] Specific examples of operation

[0149] The user types:

[0150] Monthly income: 500,000 yen

[0151] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0152] Investment limit: 100,000 yen

[0153] Investment preference: Low risk

[0154] The server analyzes the income and expenditure balance and generates an optimal asset management plan. For example, it proposes a plan to invest 100,000 yen per month in low-risk mutual funds and keep 50,000 yen in cash as an emergency reserve. If the user approves this plan, the server automatically purchases the mutual funds and regularly monitors the investment performance. If income increases and the mutual fund returns are high, the server automatically adjusts the investment amount from 100,000 yen to 150,000 yen. If the user approves the change, the new investment plan is applied.

[0155] This system allows users to manage their assets optimally without any hassle.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1: Collect input data

[0158] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[0159] Users enter their monthly income, fixed expenses, investment amount, and risk tolerance in the application's input form.

[0160] As a specific example, suppose your monthly income is 500,000 yen, your monthly fixed expenses are 300,000 yen, the amount you can invest is 100,000 yen, and you choose low risk as your investment preference.

[0161] The input data is transmitted from the terminal to the server.

[0162] The input data is converted to JSON format and sent securely to the server using the HTTPS protocol.

[0163] Step 2: Save your data

[0164] The server stores the received data in a database.

[0165] The server parses the received JSON data and stores it in a database (e.g., MySQL or PostgreSQL).

[0166] The input data is saved, and the database stores the user's income, expenses, and investment preferences.

[0167] Step 3: Balance analysis

[0168] The server analyzes the income and expenditure data.

[0169] The saved data is read and analyzed using a balance analysis module (for example, using the pandas or numpy library).

[0170] Specifically, subtract fixed expenses from monthly income to calculate the amount available for investment.

[0171] The output is the current savings amount and monthly income and expenditure balance.

[0172] Step 4: Revenue and Expense Forecast

[0173] The server uses a machine learning module to make income and expenditure predictions.

[0174] It uses machine learning libraries such as TensorFlow and scikit-learn to learn users' spending patterns based on past income and expenditure data.

[0175] Based on the learning results, future income and expenditures are predicted and specific income and expenditure forecast data is generated.

[0176] The output is a forecast of future income and expenditure.

[0177] Step 5: Create a financial plan

[0178] The server generates an asset management plan based on the income and expenditure forecast data and desired investment information.

[0179] Uses the Markowitz model to evaluate risk and return and generate optimal investment plans.

[0180] The risk assessment module performs a risk assessment for each investment option and calculates a risk score.

[0181] The output is low-risk, medium-risk, and high-risk investment plans, each with a return forecast.

[0182] Step 6: Plan presentation and approval

[0183] The asset management plan generated by the server is sent to the terminal.

[0184] The generated asset management plan is sent to the terminal in JSON format.

[0185] The terminal displays the plan to the user.

[0186] The terminal displays the plan in the application's UI (using, for example, React or Vue.js).

[0187] The user checks the plan and makes any necessary modifications. The modified plan is also sent from the device to the server.

[0188] The output is a user-approved financial plan.

[0189] Step 7: Implementing asset management

[0190] The server starts asset management based on the approved plan.

[0191] Based on the asset management plan, investment operations are automatically executed through the specified investment API (e.g., Alpha Vantage API).

[0192] Specifically, operations such as purchasing and selling investment trusts are carried out periodically.

[0193] As an output, we get a log of the investment operations that were performed.

[0194] Step 8: Continuously monitor and adjust

[0195] The server periodically monitors the performance of the investment.

[0196] Evaluate market data and your investment performance and adjust your investment plan as needed.

[0197] If there is an important change, a notification is sent to the terminal and the user is asked to confirm.

[0198] The output is a new adjusted investment plan or change notice.

[0199] By following the above steps, users can easily and efficiently manage their household income and expenditures and asset management all at once.

[0200] (Application example 1)

[0201] 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."

[0202] Improving the efficiency of household income and expenditure management and asset management is an important issue for many households. However, manual income and expenditure management is cumbersome and difficult for users without knowledge or experience in asset management. Furthermore, existing systems lack real-time management of income and expenditure data and automatic asset management plan generation, resulting in poor usability. In addition, integration with electronic payment services is incomplete, and functions for collecting and managing income and expenditure information in real time are lacking, making it difficult for users to obtain accurate data for asset management.

[0203] 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.

[0204] In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for presenting the generated asset management plan to a user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for collecting and managing income and expenditure information in real time in cooperation with an electronic payment service. This enables efficient, integrated management of everything from household income and expenditure management to asset management, allowing users to optimally manage their assets based on accurate and timely data.

[0205] "Income and expenditure data" refers to monetary information on household and individual income sources (salary, bonuses, secondary income, etc.) and expenditures (rent, utilities, food, loans, etc.).

[0206] "Analysis" refers to the detailed analysis of collected data using machine learning and other algorithms to extract trends and patterns.

[0207] "Income and expenditure forecast" is the prediction of future trends in income and expenditure based on past income and expenditure data.

[0208] An "asset management plan" is a plan that shows the optimal asset management strategy, including investment targets and investment amounts, based on information such as the user's income, expenses, and risk tolerance.

[0209] "Means for seeking approval or amendment" refers to an interface or other method for presenting the generated asset management plan to the user and allowing the user to review the plan and approve or amend it as necessary.

[0210] "Means for automatically executing investments" refers to programs or API interfaces that allow the system to automatically perform investment operations based on the asset management plan approved by the user.

[0211] "Continuous performance monitoring measures" refers to a monitoring system that regularly reviews investment results and market trends and adjusts the investment plan if necessary.

[0212] "Electronic payment services" refers to online and offline payment methods such as credit cards, debit cards, and mobile payments.

[0213] "Means of collecting and managing in real time" refers to the technology and systems for instantly collecting income and expenditure data and storing and managing it in a database.

[0214] These definitions clarify how the elements of the present invention function and interact.

[0215] The system for embodying the present invention inputs income and expenditure data, analyzes the data, predicts income and expenditure, and creates and executes an asset management plan. The system of the present invention includes the following specific means.

[0216] Hardware and software used

[0217] Hardware

[0218] Smartphone

[0219] software

[0220] Payment API

[0221] Machine learning module (TensorFlow)

[0222] Database (MySQL)

[0223] Backend server (Django)

[0224] Program processing

[0225] 1. Enter user data

[0226] Users use their smartphones to input data such as income, expenses, and investment preferences, including monthly income, fixed expenses, investment capacity, and risk tolerance.

[0227] This data is collected through an input form and sent to the server when the user presses the submit button.

[0228] 2. Data collection and analysis

[0229] The server receives the entered data and stores it in a database.

[0230] Real-time data on users' income and expenditures is collected through a payment API, which is then analyzed using a machine learning module to determine users' spending patterns and forecast income and expenditures.

[0231] Based on the collected and stored data, the server analyzes the balance of income and expenditure and makes predictions about future income and expenditure.

[0232] 3. Generate an investment plan

[0233] The server generates an optimal asset management plan based on the income and expenditure forecast data and the user's investment preferences. Specifically, it creates low-risk, medium-risk, and high-risk plans and performs a risk assessment for each.

[0234] The generated plan includes investment options and their expected returns based on the user's risk tolerance.

[0235] 4. Feedback and Suggestions

[0236] The server sends the generated operational plan in JSON format to the terminal, and the smartphone application displays it to the user.

[0237] The user operates an interface to review the proposed plan and approve or modify it.

[0238] 5. Execution and Monitoring

[0239] Once the user approves the final plan, the server will automatically execute the investment operation through the specified investment API.

[0240] The server continuously monitors the performance of the investments and makes necessary adjustments based on market data and the user's investment performance.

[0241] Specific examples

[0242] Example of input data

[0243] The user uses a smartphone to enter the following data:

[0244] Monthly income: 500,000 yen

[0245] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0246] Investment limit: 100,000 yen

[0247] Investment preference: Low risk

[0248] Example prompts to be input to the generative AI model

[0249] text

[0250] User-entered data:

[0251] Monthly salary: 450,000 yen

[0252] Monthly fixed costs: 250,000 yen

[0253] Investment limit: 100,000 yen

[0254] Risk tolerance: Medium risk

[0255] Historical Spending Data:

[0256] Food expenses: 50,000 yen / month

[0257] Transportation fee: 10,000 yen / month

[0258] Entertainment expenses: 30,000 yen / month

[0259] Future income projections:

[0260] Projected revenue growth: 5% / year

[0261] Asset Management Plan:

[0262] Generate investment portfolios based on risk tolerance

[0263] Medium-risk domestic stocks: 40%

[0264] Medium-risk index mutual funds: 60%

[0265] Plan generation:

[0266] Execute the generated plan and monitor performance monthly, making any necessary adjustments accordingly.

[0267] This allows users to efficiently manage their daily income and expenditures and manage their assets without any hassle. The system offers a high degree of automation and real-time data collection and analysis capabilities, enabling users to optimize their assets.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] The user enters information about their income, expenses, and desired investments on their smartphone. Specifically, they enter data such as monthly income, fixed expenses, available investment amount, and risk tolerance into the application's input form. The entered data is temporarily saved in the device's memory. When the user presses the "Send" button, the data is sent to the server in JSON format. The entered data and the sent data are as follows:

[0271] Input data: monthly income, fixed expenses, investment amount, risk tolerance

[0272] Output data: User data in JSON format

[0273] Step 2:

[0274] The server analyzes the received JSON format data. The server saves the data in a database, and then analyzes this data in the income and expenditure analysis module. Specifically, it calculates monthly income and expenses, the amount available for investment, and calculates the income and expenditure balance. The analysis results are saved in the database, and the AI ​​model uses the analysis results to make income and expenditure predictions. This process has the following inputs and outputs:

[0275] Input data: User data in JSON format

[0276] Output data: Income and expenditure balance, income and expenditure forecast data

[0277] Step 3:

[0278] The server generates an investment plan using the balance and forecast data. The investment plan generation module evaluates various investment options and creates low-risk, medium-risk, and high-risk plans. Each plan also includes a risk assessment score, and the most suitable plan is selected based on the user's risk tolerance. The generated plans are saved in JSON format. This process has the following inputs and outputs:

[0279] Input data: Income and expenditure balance, income and expenditure forecast data, risk tolerance

[0280] Output data: Asset management plan (low risk, medium risk, high risk)

[0281] Step 4:

[0282] The server sends the generated asset management plan to the device and presents it to the user. The device receives the plan and displays it to the user through the application's UI. The user reviews the proposed plan and makes any necessary modifications or approves it. Once the user has completed the operation, the selected plan is sent from the device to the server. This process has the following inputs and outputs:

[0283] Input data: Asset management plan (low risk, medium risk, high risk)

[0284] Output data: User feedback (corrections, approvals)

[0285] Step 5:

[0286] The server automatically executes the investment based on the final plan approved by the user. The server performs investment operations through the specified investment API. For example, purchasing mutual funds or trading stocks is performed automatically. This process has the following inputs and outputs:

[0287] Input data: Final plan approved by the user

[0288] Output data: Investment execution results

[0289] Step 6:

[0290] The server continuously monitors the performance of the investments and adjusts the investment plan as needed. The server periodically evaluates market data and the user's investment performance and notifies the user if it determines that an adjustment to the investment plan is necessary. Once the user approves the changes, the new investment plan is applied. This process has the following inputs and outputs:

[0291] Input data: market data, investment performance data

[0292] Output data: adjusted investment plan, notification to user

[0293] 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.

[0294] This invention is a system that efficiently manages everything from household income and expenditure management to asset management, and by integrating an emotion engine that recognizes the user's emotions, it realizes more personalized and optimized asset management. Users input information about their income, expenses, investment preferences, and emotions, and the system then automatically analyzes the data, proposes and implements an effective asset management plan, and continuously monitors and adjusts it.

[0295] Program processing

[0296] Collecting input data

[0297] The user inputs income, expenditure, asset information, investment preference information, and emotional data from the terminal.

[0298] Users fill out the application form with their monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount they can invest, their investment preferences (risk tolerance, etc.), and their emotional state at the time (e.g., stress, sense of security), and click the submit button.

[0299] Data collection and emotion recognition

[0300] The terminal sends the input data to the server.

[0301] The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0302] The server analyzes the user's emotion data using an emotion engine.

[0303] The server uses an emotion engine to analyze the user's input emotional data and evaluate their psychological state. For example, if their stress level is high, it will prioritize low-risk plans.

[0304] Data analysis

[0305] The server analyzes the received income and expenditure data to grasp the user's income and expenditure situation and current state of assets.

[0306] The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes this data to calculate the balance between income and expenditure, current savings, and available investments.

[0307] The server uses a machine learning module to analyze the user's spending patterns.

[0308] The server performs feature extraction based on past data to extract spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[0309] Generate a financial plan

[0310] The server generates an optimal asset management plan based on the analysis results and emotional data.

[0311] The server generates an optimal investment plan based on the income and expenditure forecast data and the user's sentiment assessment, which includes specific investment options (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[0312] Feedback and Suggestions

[0313] The server performs a risk assessment of the generated investment plan and makes adjustments based on the sentiment data.

[0314] The risk assessment module calculates a risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, a low-risk plan will be prioritized.

[0315] The server transmits the generated plan to the terminal, which displays the plan to the user.

[0316] The server sends the generated asset management plan in JSON format to the terminal, and the terminal displays the received plan in the application UI. The user can check the plan and make any necessary modifications.

[0317] Execution and monitoring

[0318] The user approves the plan and the terminal sends the final plan to the server.

[0319] When the user clicks the approve button, the terminal again sends the final plan to the server, and the server stores the approved plan in the database.

[0320] The server initiates asset management based on the approved plan and continuously monitors and adjusts it.

[0321] The server executes specific investment operations through the specified investment API. It also periodically evaluates market data and user asset data to monitor investment performance, rebalancing plans and presenting new investment opportunities when necessary. When significant changes occur, the server sends notifications to the terminal and asks for user confirmation.

[0322] Specific examples

[0323] Collection and analysis of income, expenditure and sentiment data

[0324] 1. The user enters the following information into the terminal:

[0325] Monthly income: 500,000 yen

[0326] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0327] Investment limit: 100,000 yen

[0328] Investment preference: Low risk

[0329] Emotional data: High stress levels

[0330] 2. The server uses an emotion engine to analyze the emotion data and predicts income and expenditure based on the income and expenditure data.

[0331] 3. The server generates an optimal asset management plan based on income and expenditure forecast data, emotional data, and investment preference information, and performs risk assessment.

[0332] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency reserve.

[0333] 4. The user reviews and approves the proposed plan.

[0334] If the user approves, the server automatically executes the investment and completes the mutual fund purchase.

[0335] Continuous monitoring and adjustment

[0336] 1. The server monitors the performance and balance of your investments monthly, and also periodically evaluates sentiment data.

[0337] If your income increases and your investment trust returns are high, your stress level will be recognized as decreasing, and your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[0338] 2. The server notifies the device of the adjustments and asks the user for confirmation.

[0339] Once the user approves the changes, the new investment plan will be applied.

[0340] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[0341] The processing flow will be explained below.

[0342] Step 1:

[0343] The user inputs their income, expenses, asset information, investment preferences, and emotional data from their device. Specifically, the user inputs their monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), available investment amount, investment preferences (risk tolerance, etc.), and current emotional state (e.g., stress, sense of security) into the application form, and clicks the submit button.

[0344] Step 2:

[0345] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0346] Step 3:

[0347] The server uses an emotion engine to analyze the user's emotional data. The server uses the emotion engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[0348] Step 4:

[0349] The server analyzes the received income and expenditure data to understand the current state of the user's income and expenditure and assets. The server retrieves the income and expenditure data from the database, and the income and expenditure analysis module analyzes this data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[0350] Step 5:

[0351] The server uses machine learning modules to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[0352] Step 6:

[0353] The server generates an optimal asset management plan based on the analysis results and emotion data. The server generates an optimal asset management plan based on income and expenditure forecast data and the user's emotion evaluation. This plan includes specific investment targets (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[0354] Step 7:

[0355] The server evaluates the risk of the generated asset management plan and makes adjustments based on emotional data. The risk assessment module calculates the risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state as assessed. For example, if the user is in a high-stress state, a low-risk plan will be selected first.

[0356] Step 8:

[0357] The server sends the generated plan to the terminal. The server sends the generated asset management plan in JSON format to the terminal.

[0358] Step 9:

[0359] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[0360] Step 10:

[0361] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device again sends the final plan to the server, and the server stores the approved plan in the database.

[0362] Step 11:

[0363] The server starts asset management based on the approved plan. The server then executes specific investment operations through the specified investment API, such as purchasing mutual funds or trading stocks. Once the transaction is complete, the server updates the user's asset status.

[0364] Step 12:

[0365] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or suggests new investment opportunities. If any significant changes occur, the server sends a notification to your device, asking for your confirmation.

[0366] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management. In addition, by taking emotion data into consideration, the psychological burden on users can be reduced.

[0367] Example 2

[0368] 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."

[0369] Current income / expense management and asset management systems do not take into account the user's psychological state or emotions, and do not adequately adjust risk assessments or asset management plans. As a result, they are unable to provide an appropriate asset management plan when the user is under high stress or when their risk tolerance fluctuates, which could lead to inappropriate investment risks. This increases the user's psychological burden and makes it difficult to achieve optimal financial planning.

[0370] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0371] In this invention, the server includes means for inputting income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for evaluating the user's psychological state based on the income, expenditure, and emotional data, means for generating an asset management plan based on the analysis results, means for adding a risk assessment to the generated asset management plan and adjusting the risk according to the user's psychological state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This makes it possible to provide an optimal asset management plan taking into account the user's psychological state and achieve effective financial planning while reducing the user's psychological burden.

[0372] "Income and expenditure data" is numerical information about the household's economic situation, such as monthly household income, monthly fixed expenditures, and available investment amounts.

[0373] The "means for inputting" is an interface for the user to input income, expenditure, and emotion data into the terminal.

[0374] The "analyzing means" refers to algorithms and modules that allow the server to obtain income and expenditure data and make income and expenditure forecasts based on this data.

[0375] The "emotion analysis means" is an engine and corresponding software for analyzing the emotion data input by the user and evaluating the user's psychological state.

[0376] An "asset management plan" is an investment and asset management plan suited to a user, generated based on income and expenditure data and emotion data.

[0377] The "means for adding risk assessment" is a module for calculating a risk score for the generated asset management plan and adjusting the risk according to the user's psychological state.

[0378] The "means for requesting approval or modification" is an interface for presenting the generated asset management plan to the user and requesting the user to approve or modify the plan.

[0379] "Means for automatically executing investments" refers to a system and API for automatically performing investment operations based on an asset management plan approved by a user.

[0380] "Means for continuous monitoring of investment performance" are modules and software for periodically evaluating the results of investments and adjusting investment plans as necessary.

[0381] A "machine learning module" is an algorithm and corresponding software for analyzing income and expense data and learning a user's spending patterns.

[0382] "Risk tolerance" is a standard indicating how much risk a user can tolerate, and is an important factor when generating an asset management plan.

[0383] This invention is a system for efficiently managing household income and expenditures and asset management, and realizes individually optimized asset management by integrating an emotion engine that recognizes the user's emotions. The system inputs income, expenses, asset information, investment preferences, and emotion data, performs income and expenditure forecasts and emotion analysis, then generates an optimal asset management plan, automatically executes investments, and continuously monitors and adjusts them.

[0384] Hardware and Software

[0385] The system consists of the following hardware and software:

[0386] User Device: Used by users to input income, expenses, asset information, investment preferences, and emotional data. This includes smartphones, tablets, and PCs.

[0387] Server: Receives data, analyzes it, analyzes sentiment, and generates investment plans. The server includes a database, sentiment engine, machine learning module, and risk assessment module. For example, a cloud platform such as Amazon Web Services (AWS (registered trademark)) can be used.

[0388] Application software: A program that runs on user terminals and servers and is responsible for inputting income and expenditure data, sending and receiving data, displaying analytical results, and creating and executing asset management plans.

[0389] Data processing and calculation

[0390] 1. Collect input data:

[0391] The user uses a terminal to input income, expenses, asset information, investment preferences, and emotional data into the application form.

[0392] Example: A user inputs a monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, investment amount of 100,000 yen, investment preference as low risk, and emotional data as high stress level.

[0393] 2. Data transmission and storage:

[0394] The terminal converts the input data into JSON format and sends it to the server's API endpoint via HTTPS.

[0395] The server stores the received data in a database.

[0396] 3. Emotion analysis:

[0397] The server uses an emotion engine to analyze the user's emotion data.

[0398] For example, the user's stress level is high, so the emotion engine evaluates it as "high stress."

[0399] 4. Balance analysis:

[0400] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts.

[0401] The income and expenditure analysis module analyzes the balance between income and expenditure, savings, and investment potential.

[0402] 5. Analysis of spending patterns:

[0403] The server runs a machine learning module based on past data to model spending patterns.

[0404] Example: Using past data, predict "average monthly expenditure of 250,000 yen and savings of 100,000 yen."

[0405] 6. Generate a financial plan:

[0406] The server generates an optimal asset management plan based on income and expenditure forecast data and emotional data.

[0407] Example: A plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen for emergencies.

[0408] 7. Risk Assessment and Adjustment:

[0409] The server performs risk assessment on the generated asset management plan and adjusts the risk as necessary.

[0410] Example: determining that low-risk mutual funds are suitable for a user in a high-stress state.

[0411] 8. Viewing and Approving the Plan:

[0412] The terminal displays the generated asset management plan to the user and requests approval or modification.

[0413] The user clicks the approve button and the final plan is sent to the server.

[0414] 9. Asset management execution and monitoring:

[0415] The server starts asset management based on the approved plan and executes investment operations through the specified investment API.

[0416] Continually evaluate market data and your asset data to rebalance your investment plan or suggest new investment opportunities as needed.

[0417] Example prompt

[0418] As a concrete example, a user enters the following information:

[0419] Monthly income: 500,000 yen

[0420] Fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0421] Investment limit: 100,000 yen

[0422] Investment preference: Low risk

[0423] Emotional data: High stress levels

[0424] In this way, the system of the present invention can efficiently and effectively manage household income and expenditures and manage assets, and realize optimal asset management that takes into account the user's psychological state.

[0425] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0426] Step 1: User Enters Information

[0427] The user uses a dedicated application to input their monthly income, monthly fixed expenses, available investment amount, investment preferences, and emotional data (e.g., stress level). After filling out each item in the input form and clicking the submit button, the data is entered into the terminal.

[0428] Input: Income, expenses, investment preferences, emotional data

[0429] Output: Input data

[0430] Step 2: The device sends the data

[0431] The device converts the input data into JSON format, which makes it possible to send the data to the server.The device then sends the data to the server's API endpoint via HTTPS.

[0432] Input: Data entered by the user

[0433] Output: JSON format data

[0434] Step 3: The server receives and stores the data

[0435] The server receives HTTP requests sent from the device. It analyzes the received data and stores it in a database. The database stores all data necessary for income and expenditure management and sentiment analysis.

[0436] Input: JSON format data

[0437] Output: Data stored in the database

[0438] Step 4: The server analyzes the emotion data

[0439] The server launches the emotion engine and analyzes the emotion data from the user's input data. For example, if the server recognizes that the stress level is high, the emotion engine will judge it as "high stress." This result will be used to generate a subsequent asset management plan.

[0440] Input: Emotion data

[0441] Output: Emotion analysis results

[0442] Step 5: The server analyzes the data

[0443] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts. The income and expenditure analysis module analyzes this data and calculates the balance between income and expenditure, current savings, and available investment amounts.

[0444] Input: Income data, expenditure data

[0445] Output: Profit and loss forecast results

[0446] Step 6: The server analyzes spending patterns

[0447] The server runs a machine learning module using historical data to analyze spending patterns, perform feature extraction, and model spending trends and patterns, which can then be used to predict future spending.

[0448] Input: Past income and expenditure data

[0449] Output: A model of spending patterns

[0450] Step 7: The server generates the investment plan.

[0451] The server generates an optimal asset management plan based on the income / expense forecast data and sentiment analysis results. This plan includes specific investment targets and predicted returns. For example, a specific plan such as "invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency fund" may be proposed.

[0452] Input: Revenue and expenditure forecast data, emotion analysis results

[0453] Output: Wealth Management Plan

[0454] Step 8: Server performs risk assessment and adjustments

[0455] The server performs risk assessment on the asset management plan generated and makes adjustments as necessary. The risk assessment module calculates a risk score for each plan and adjusts the risk based on the results of sentiment analysis. For example, if a user is in a high stress state, it will prioritize low-risk plans.

[0456] Input: Asset management plan, sentiment analysis results

[0457] Output: Adjusted financial plan

[0458] Step 9: The device displays the plan to the user

[0459] The server sends the generated financial plan in JSON format to the terminal, which receives this data and displays it in the application's user interface. The user can then review the plan and make any necessary modifications.

[0460] Input: Generated Investment Plan

[0461] Output: The plan displayed to the user

[0462] Step 10: User approves the plan

[0463] If the user is satisfied with the proposed asset management plan, he or she clicks the approval button. This operation causes the terminal to send the final approved plan back to the server.

[0464] Input: User approval button click

[0465] Output: Final approved plan

[0466] Step 11: The server starts and monitors the asset management

[0467] The server starts asset management based on the approved plan. It executes specific investment operations through the specified investment API and periodically evaluates market data and the user's asset data to monitor investment performance. It rebalances the plan and presents new investment opportunities as needed. If any important changes occur, it sends a notification to the terminal and asks the user for confirmation.

[0468] Input: Final Approved Plan

[0469] Output: Executed asset operations, ongoing monitoring results

[0470] (Application example 2)

[0471] 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."

[0472] Conventional income / expense management and asset management systems are limited to analyzing income and expenditure data and are unable to consider the user's psychological state, resulting in the problem of only being able to provide a uniform asset management plan. Furthermore, these systems are inadequate for continuous investment performance monitoring and investment plan adjustment, often resulting in suboptimal user investment activities. Furthermore, a lack of integration with electronic payment services limits automation when implementing investment plans, placing a heavy burden on users.

[0473] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for recognizing the user's emotional data and evaluating the emotional state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for making electronic payments for implementing the investment plan generated by the system. This makes it possible to provide an individually optimized asset management plan that takes the user's emotional state into consideration, and to automatically execute investments and continuously monitor and adjust them.

[0474] The "means for inputting household income and expenditure data" is an interface through which a user provides information about his or her income and expenditure to the system.

[0475] The "means for analyzing input income and expenditure data and forecasting income and expenditure" is an analysis module for forecasting future income and expenditure based on income and expenditure data.

[0476] The "means for generating an asset management plan" is a system for proposing investment strategies and asset allocations based on the analysis results.

[0477] The "means for recognizing the user's emotional data and evaluating the emotional state" is a module that has the function of analyzing the user's psychological state using an emotion engine or the like and evaluating that state.

[0478] The "means for presenting the generated asset management plan to the user and requesting approval or modification from the user" is a user interface for notifying the user of the investment plan generated by the system and receiving instructions for approval or modification.

[0479] The "means for automatically executing investments based on an asset management plan approved by a user" is a function for automatically performing investment operations in accordance with an investment plan approved by a user.

[0480] "Means for continuously monitoring the performance of investments and adjusting the investment plan as needed" means a system for regularly monitoring the results of investments and changing the investment strategy as needed.

[0481] "Means for making electronic payments to implement the investment plan generated by the system" refers to a function for electronically executing the necessary payments and transactions based on the investment plan generated.

[0482] This invention is a system that efficiently manages household income and expenditures and asset management all at once, and by integrating an emotion engine that recognizes the user's emotions, it achieves more individually optimized asset management.

[0483] The system provides a means for users to input information about their income, expenses, investment aspirations, and emotions. Specifically, a smartphone app provides an interface for collecting this data. Users enter their monthly income, fixed expenses, available investment amount, investment aspirations, and emotional state into the application form and click the submit button.

[0484] The terminal sends the entered data to the server, which converts the data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0485] The server uses the Emotion Engine to analyze the user's emotional data. Specifically, the server uses the Emotion Engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[0486] The server analyzes the received income and expenditure data to understand the user's current income and expenditure status and assets. The server also uses a machine learning module to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[0487] Based on the analysis results and the emotional data, the server generates an optimal asset management plan. Based on the income and expenditure forecast data and the user's emotional evaluation, the server creates an investment plan that includes specific investment targets and predicted returns.

[0488] The server evaluates the risk of the generated asset management plan and adjusts it based on the emotional data. The risk assessment module calculates the risk score for each plan, and the emotional engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, it will prioritize low-risk plans.

[0489] The server sends the generated plan to the terminal, which displays it to the user. The user can check the plan and make any necessary modifications. When the user clicks the approve button, the terminal sends the final plan back to the server, which then stores the approved plan in the database.

[0490] The server initiates asset management based on the approved plan and continuously monitors and adjusts it. The server executes specific investment operations through the designated investment API, periodically evaluates market data and the user's asset data to monitor investment performance, and rebalances the plan or presents new investment opportunities when necessary. If any significant changes occur, the server will send a notification to the terminal and ask the user for confirmation.

[0491] As a concrete example, consider a case where a user has a monthly income of 500,000 yen, fixed monthly expenses of 300,000 yen, and an investment capacity of 100,000 yen, and is currently in a high stress state. In this case, the application will propose a plan to invest 90,000 yen per month in a low-risk mutual fund and keep 10,000 yen as a reserve.

[0492] An example of a prompt to input to a generative AI model is as follows:

[0493] Generate an optimal financial plan based on the user's monthly income, monthly fixed expenses, investment capacity, investment preferences, and emotional data. If the user's stress level is high, suggest a low-risk plan. Below is a sample of user data:

[0494] Monthly income: 500,000 yen

[0495] Monthly fixed expenses: 300,000 yen

[0496] Investment limit: 100,000 yen

[0497] Investment preference: Low risk

[0498] Emotional data: High stress levels

[0499] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[0500] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0501] Step 1:

[0502] The user inputs income, expenses, investment preferences, and emotional data. The user enters monthly income, fixed expenses (e.g., rent, utilities, food, etc.), available investment amount, investment preferences, and emotional state (e.g., high stress level) into a form on the smartphone app and clicks the submit button. The input data is monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, available investment amount of 100,000 yen, investment preferences are low risk, and the emotional data is high stress.

[0503] Step 2:

[0504] The terminal sends the input data to the server. The terminal converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The input data includes monthly income, fixed expenses, available investment amount, investment preference, and sentiment data. The server receives this data and stores it in a database.

[0505] Step 3:

[0506] The server analyzes the emotion data. The server uses an emotion engine to evaluate the user's psychological state based on the emotion data entered by the user. Specifically, the emotion engine analyzes the user's stress level and determines that the user's stress level is high as a result of the evaluation.

[0507] Step 4:

[0508] The server analyzes the income and expenditure data and grasps the income and expenditure situation. The server retrieves the income and expenditure data from the database and analyzes the data in the income and expenditure analysis module. This outputs the balance of income and expenditure, the current amount of savings, and the amount available for investment.

[0509] Step 5:

[0510] The server analyzes spending patterns using a machine learning module. The server performs feature extraction using past income and expenditure data to extract spending trends and patterns. This outputs future income and expenditure forecast data and generates a spending pattern model for the user.

[0511] Step 6:

[0512] The server generates an optimal asset management plan based on the analysis results and emotional data. Based on income / expense forecast data and emotional evaluation, the server creates an investment plan that includes specific investment targets (e.g., low-risk investment trusts) and predicted returns. Users with high stress levels are suggested low-risk plans, and each plan is also risk-assessed.

[0513] Step 7:

[0514] The server sends the generated asset management plan to the terminal, which then displays the plan to the user. The server also sends the generated management plan in JSON format to the terminal, which then displays it on the application UI based on the received data. This allows the user to check the plan and approve or modify it.

[0515] Step 8:

[0516] The user approves the plan, and the device sends the final plan to the server. The user clicks the approve button, and this action causes the device to send the final plan back to the server. The server saves the approved plan in its database.

[0517] Step 9:

[0518] The server initiates asset management based on the approved plan and continuously monitors and adjusts it. The server executes specific investment operations through the designated investment API, periodically evaluates market data and the user's asset data to monitor investment performance, and rebalances the plan or presents new investment opportunities when necessary, and sends notifications to the terminal when important changes occur.

[0519] In this way, all processing steps are carried out in a single flow, and the system automatically implements optimal asset management while taking into account the user's emotional state.

[0520] 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.

[0521] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0522] 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.

[0523] [Second embodiment]

[0524] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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."

[0536] This invention is a system that efficiently manages everything from household income and expenditure management to asset management. Users input their income, expenditure information, and investment preferences, and the system then automatically analyzes the data to propose and implement an effective asset management plan, while continuously monitoring and adjusting it.

[0537] Program processing

[0538] Collecting input data

[0539] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[0540] The user enters information into the application's input form, such as monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount available for investment, and investment preferences (risk tolerance, etc.).

[0541] After entering the data, click the send button to send the data from the terminal to the server.

[0542] Data analysis

[0543] The server analyzes the received data and determines the user's income and expenditure status and current asset status.

[0544] The server stores the received data in a database, and the income and expenditure analysis module analyzes this data.

[0545] Based on the analysis results, the current savings, investment potential, and balance between income and expenditure are calculated.

[0546] The server uses AI models to predict users' spending patterns and future income and expenditures.

[0547] The server uses a machine learning module to learn the user's spending patterns from past income and expenditure data.

[0548] Based on this data, future income and expenditure forecasts are made, and fluctuations in the user's future income and expenditures are predicted.

[0549] Generate a financial plan

[0550] The server generates an optimal asset management plan based on the data obtained.

[0551] The server evaluates various investment options based on the user's income and expenditure forecast data and investment preferences, and generates an optimal asset management plan.

[0552] The generated plans include low-risk, medium-risk, and high-risk investment plans and their return projections.

[0553] The plan includes multiple investment options and risk assessments.

[0554] The server uses a risk assessment module to perform a risk assessment for each investment plan.

[0555] Calculate a risk score for each plan and select the most appropriate plan based on the user's risk tolerance.

[0556] Feedback and Suggestions

[0557] The server sends the generated plan to the terminal.

[0558] The server sends the generated asset management plan in JSON format to the terminal.

[0559] The terminal displays the plan to the user, who then approves or modifies it.

[0560] The device displays the received plan on the application's UI.

[0561] The user reviews the proposed plan and makes any necessary modifications.

[0562] After the user approves, the terminal sends the final plan to the server.

[0563] If the user approves the final plan, the terminal transmits the final plan to the server.

[0564] The server stores this final plan in a database.

[0565] Execution and monitoring

[0566] The server starts asset management based on the approved plan.

[0567] The server automatically executes investment operations through the specified investment API.

[0568] The server continuously monitors the performance of the investments and adjusts the investment plan as needed.

[0569] The server periodically evaluates market data and the user's investment performance and makes any necessary adjustments.

[0570] If there is an important change, the server will send a notification to the terminal and ask the user for confirmation.

[0571] Specific examples

[0572] Initial Setup and Plan Generation

[0573] 1. The user enters the following information into the terminal:

[0574] Monthly income: 500,000 yen

[0575] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0576] Investment limit: 100,000 yen

[0577] Investment preference: Low risk

[0578] 2. The server analyzes the balance of income and expenditures and generates an optimal asset management plan.

[0579] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen in cash as an emergency reserve.

[0580] 3. The user reviews and approves the proposed plan.

[0581] If the user approves, the server automatically executes the investment and makes the mutual fund purchase.

[0582] Continuous monitoring and adjustment

[0583] 1. The server monitors the performance and balance of your investments monthly.

[0584] If your income increases and your investment trust returns are high, your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[0585] 2. The server notifies the terminal of the adjustment results and asks the user for confirmation.

[0586] Once the user approves the changes, the new investment plan will be applied.

[0587] In this way, the system of the present invention can efficiently manage household income and expenditures and asset management all at once, allowing users to optimally manage their assets without any hassle.

[0588] The processing flow will be explained below.

[0589] Step 1:

[0590] The user enters information about their income, expenses, assets, and desired investments from their device. Specifically, the user enters their monthly income, fixed monthly expenses (e.g., rent, utilities, food, etc.), available investment amount, and desired investments (risk tolerance, etc.) into the application form and clicks the submit button.

[0591] Step 2:

[0592] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0593] Step 3:

[0594] The server analyzes the received data to understand the user's current income and expenditure situation and assets. The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes the data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[0595] Step 4:

[0596] The server uses machine learning modules to analyze the user's spending patterns, performs feature extraction based on past data, and extracts spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[0597] Step 5:

[0598] The server generates an optimal investment plan for the user based on the income and expenditure forecast data. The server evaluates investment options and creates multiple investment plans based on risk tolerance. Each plan includes specific investments (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[0599] Step 6:

[0600] The server performs risk assessment of the generated investment plans. The risk assessment module calculates a risk score for each plan and sorts the plans based on the user's risk tolerance. The most suitable plan is selected.

[0601] Step 7:

[0602] The server sends the generated asset management plan to the terminal. The server then sends the selected plan to the terminal in JSON format.

[0603] Step 8:

[0604] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[0605] Step 9:

[0606] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device sends the final plan to the server again. The server saves the approved plan in the database.

[0607] Step 10:

[0608] The server will start asset management based on the approved plan, and then execute specific investment operations such as purchasing stocks or mutual funds through the specified investment API. Once the transaction is completed, the server will update the user's asset status.

[0609] Step 11:

[0610] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or presents new investment opportunities. If there are any major changes, the server sends a notification to your device and asks for your confirmation.

[0611] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management.

[0612] Example 1

[0613] 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."

[0614] Currently, efficient household income and expenditure management and asset management requires multiple different applications and manual work, placing a heavy burden on users. Furthermore, analyzing income and expenditure data and making future predictions requires advanced knowledge, making it difficult for average users to use. Furthermore, there is a lack of systems that can consistently create, execute, and monitor asset management plans.

[0615] 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.

[0616] In this invention, the server includes means for transmitting and storing input income and expenditure data to the server, means for analyzing the stored data and making income and expenditure forecasts, means for generating an asset management plan based on the income and expenditure forecasts, means for transmitting the generated asset management plan to a terminal and presenting it to the user, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This allows users to efficiently and automatically perform everything from income and expenditure management to asset management through a single system.

[0617] "Income" is a general term for the money and assets that a household or individual receives within a certain period of time.

[0618] "Expenditure" is a general term for the money consumed by households and individuals within a certain period of time.

[0619] A "server" is a computer system that stores and processes data on a network.

[0620] A "database" is an information system for efficiently storing, searching, and updating data.

[0621] "Analysis" is the process of organizing and analyzing data to extract useful information.

[0622] "Income and expenditure forecasting" refers to predicting future income and expenditure based on past income and expenditure data.

[0623] An "asset management plan" is a plan that proposes an efficient method of managing assets, taking into account the balance of income and expenditure of individuals or households.

[0624] "Terminal" refers to a computing facility that is directly operated by a user and is a device for input and display.

[0625] A "machine learning module" is a software module that learns rules and patterns from data and uses them to make predictions and classifications.

[0626] "Risk tolerance" refers to the range and degree of risk that a user can accept in investment or asset management.

[0627] An "investment API" is a program interface that works in conjunction with external systems to automate investment operations.

[0628] The present invention is a system that inputs household income and expenditure data, analyzes the data to generate income and expenditure forecasts and asset management plans, and then executes and monitors the plans. This system provides a series of functions that users can use easily and efficiently to manage their assets.

[0629] System Program Overview

[0630] Collecting input data

[0631] The user inputs income, expenses, asset information, and desired investment information from the terminal. The terminal collects this data in the application's input form. For example, monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), investment amount, and risk tolerance are input. The input data is sent from the terminal to the server by clicking the send button. The data is converted to JSON format and securely sent to the server using the HTTPS protocol.

[0632] Data analysis

[0633] The server stores and analyzes the received data. The server stores the data in a database (e.g., MySQL or PostgreSQL). An income / expense analysis module (e.g., using the pandas and numpy libraries) analyzes the data and calculates the current savings, investment potential, and balance of income and expenses.

[0634] Revenue and expenditure forecast

[0635] The server uses machine learning modules (such as TensorFlow and scikit-learn) to learn the user's spending patterns from past income and expenditure data. Based on the learning results, it predicts future income and expenditure and investment potential, and generates income and expenditure forecast data.

[0636] Generate a financial plan

[0637] The server evaluates various investment options based on income / expense forecast data and investment preference information, and generates an optimal asset management plan. This uses the Markowitz model to evaluate risk and return. The generated plan includes multiple investment options, each of which has been risk assessed in the risk assessment module.

[0638] Plan presentation and approval

[0639] The generated asset management plan is sent to the terminal. The terminal displays the received plan on the UI and presents it to the user. The user checks the plan, modifies it if necessary, and approves the final plan. The approved plan is sent back to the server and saved in the database.

[0640] Asset management execution

[0641] The server automatically executes investment operations based on the approved plan through a designated investment API (e.g., Alpha Vantage API).

[0642] Continuous monitoring and adjustment

[0643] The server regularly monitors market data and the user's investment performance, adjusting the investment plan as needed. If there are any significant changes, the server sends a notification to the terminal and asks the user for confirmation.

[0644] Specific examples of operation

[0645] The user types:

[0646] Monthly income: 500,000 yen

[0647] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0648] Investment limit: 100,000 yen

[0649] Investment preference: Low risk

[0650] The server analyzes the income and expenditure balance and generates an optimal asset management plan. For example, it proposes a plan to invest 100,000 yen per month in low-risk mutual funds and keep 50,000 yen in cash as an emergency reserve. If the user approves this plan, the server automatically purchases the mutual funds and regularly monitors the investment performance. If income increases and the mutual fund returns are high, the server automatically adjusts the investment amount from 100,000 yen to 150,000 yen. If the user approves the change, the new investment plan is applied.

[0651] This system allows users to manage their assets optimally without any hassle.

[0652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0653] Step 1: Collect input data

[0654] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[0655] Users enter their monthly income, fixed expenses, investment amount, and risk tolerance in the application's input form.

[0656] As a specific example, suppose your monthly income is 500,000 yen, your monthly fixed expenses are 300,000 yen, the amount you can invest is 100,000 yen, and you choose low risk as your investment preference.

[0657] The input data is transmitted from the terminal to the server.

[0658] The input data is converted to JSON format and sent securely to the server using the HTTPS protocol.

[0659] Step 2: Save your data

[0660] The server stores the received data in a database.

[0661] The server parses the received JSON data and stores it in a database (e.g., MySQL or PostgreSQL).

[0662] The input data is saved, and the database stores the user's income, expenses, and investment preferences.

[0663] Step 3: Balance analysis

[0664] The server analyzes the income and expenditure data.

[0665] The saved data is read and analyzed using a balance analysis module (for example, using the pandas or numpy library).

[0666] Specifically, subtract fixed expenses from monthly income to calculate the amount available for investment.

[0667] The output is the current savings amount and monthly income and expenditure balance.

[0668] Step 4: Revenue and Expense Forecast

[0669] The server uses a machine learning module to make income and expenditure predictions.

[0670] It uses machine learning libraries such as TensorFlow and scikit-learn to learn users' spending patterns based on past income and expenditure data.

[0671] Based on the learning results, future income and expenditures are predicted and specific income and expenditure forecast data is generated.

[0672] The output is a forecast of future income and expenditure.

[0673] Step 5: Create a financial plan

[0674] The server generates an asset management plan based on the income and expenditure forecast data and desired investment information.

[0675] Uses the Markowitz model to evaluate risk and return and generate optimal investment plans.

[0676] The risk assessment module performs a risk assessment for each investment option and calculates a risk score.

[0677] The output is low-risk, medium-risk, and high-risk investment plans, each with a return forecast.

[0678] Step 6: Plan presentation and approval

[0679] The asset management plan generated by the server is sent to the terminal.

[0680] The generated asset management plan is sent to the terminal in JSON format.

[0681] The terminal displays the plan to the user.

[0682] The terminal displays the plan in the application's UI (using, for example, React or Vue.js).

[0683] The user checks the plan and makes any necessary modifications. The modified plan is also sent from the device to the server.

[0684] The output is a user-approved financial plan.

[0685] Step 7: Implementing asset management

[0686] The server starts asset management based on the approved plan.

[0687] Based on the asset management plan, investment operations are automatically executed through the specified investment API (e.g., Alpha Vantage API).

[0688] Specifically, operations such as purchasing and selling investment trusts are carried out periodically.

[0689] As an output, we get a log of the investment operations that were performed.

[0690] Step 8: Continuously monitor and adjust

[0691] The server periodically monitors the performance of the investment.

[0692] Evaluate market data and your investment performance and adjust your investment plan as needed.

[0693] If there is an important change, a notification is sent to the terminal and the user is asked to confirm.

[0694] The output is a new adjusted investment plan or change notice.

[0695] By following the above steps, users can easily and efficiently manage their household income and expenditures and asset management all at once.

[0696] (Application example 1)

[0697] 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."

[0698] Improving the efficiency of household income and expenditure management and asset management is an important issue for many households. However, manual income and expenditure management is cumbersome and difficult for users without knowledge or experience in asset management. Furthermore, existing systems lack real-time management of income and expenditure data and automatic asset management plan generation, resulting in poor usability. In addition, integration with electronic payment services is incomplete, and functions for collecting and managing income and expenditure information in real time are lacking, making it difficult for users to obtain accurate data for asset management.

[0699] 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.

[0700] In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for presenting the generated asset management plan to a user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for collecting and managing income and expenditure information in real time in cooperation with an electronic payment service. This enables efficient, integrated management of everything from household income and expenditure management to asset management, allowing users to optimally manage their assets based on accurate and timely data.

[0701] "Income and expenditure data" refers to monetary information on household and individual income sources (salary, bonuses, secondary income, etc.) and expenditures (rent, utilities, food, loans, etc.).

[0702] "Analysis" refers to the detailed analysis of collected data using machine learning and other algorithms to extract trends and patterns.

[0703] "Income and expenditure forecast" is the prediction of future trends in income and expenditure based on past income and expenditure data.

[0704] An "asset management plan" is a plan that shows the optimal asset management strategy, including investment targets and investment amounts, based on information such as the user's income, expenses, and risk tolerance.

[0705] "Means for seeking approval or amendment" refers to an interface or other method for presenting the generated asset management plan to the user and allowing the user to review the plan and approve or amend it as necessary.

[0706] "Means for automatically executing investments" refers to programs or API interfaces that allow the system to automatically perform investment operations based on the asset management plan approved by the user.

[0707] "Continuous performance monitoring measures" refers to a monitoring system that regularly reviews investment results and market trends and adjusts the investment plan if necessary.

[0708] "Electronic payment services" refers to online and offline payment methods such as credit cards, debit cards, and mobile payments.

[0709] "Means of collecting and managing in real time" refers to the technology and systems for instantly collecting income and expenditure data and storing and managing it in a database.

[0710] These definitions clarify how the elements of the present invention function and interact.

[0711] The system for embodying the present invention inputs income and expenditure data, analyzes the data, predicts income and expenditure, and creates and executes an asset management plan. The system of the present invention includes the following specific means.

[0712] Hardware and software used

[0713] Hardware

[0714] Smartphone

[0715] software

[0716] Payment API

[0717] Machine learning module (TensorFlow)

[0718] Database (MySQL)

[0719] Backend server (Django)

[0720] Program processing

[0721] 1. Enter user data

[0722] Users use their smartphones to input data such as income, expenses, and investment preferences, including monthly income, fixed expenses, investment capacity, and risk tolerance.

[0723] This data is collected through an input form and sent to the server when the user presses the submit button.

[0724] 2. Data collection and analysis

[0725] The server receives the entered data and stores it in a database.

[0726] Real-time data on users' income and expenditures is collected through a payment API, which is then analyzed using a machine learning module to determine users' spending patterns and forecast income and expenditures.

[0727] Based on the collected and stored data, the server analyzes the balance of income and expenditure and makes predictions about future income and expenditure.

[0728] 3. Generate an investment plan

[0729] The server generates an optimal asset management plan based on the income and expenditure forecast data and the user's investment preferences. Specifically, it creates low-risk, medium-risk, and high-risk plans and performs a risk assessment for each.

[0730] The generated plan includes investment options and their expected returns based on the user's risk tolerance.

[0731] 4. Feedback and Suggestions

[0732] The server sends the generated operational plan in JSON format to the terminal, and the smartphone application displays it to the user.

[0733] The user operates an interface to review the proposed plan and approve or modify it.

[0734] 5. Execution and Monitoring

[0735] Once the user approves the final plan, the server will automatically execute the investment operation through the specified investment API.

[0736] The server continuously monitors the performance of the investments and makes necessary adjustments based on market data and the user's investment performance.

[0737] Specific examples

[0738] Example of input data

[0739] The user uses a smartphone to enter the following data:

[0740] Monthly income: 500,000 yen

[0741] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0742] Investment limit: 100,000 yen

[0743] Investment preference: Low risk

[0744] Example prompts to be input to the generative AI model

[0745] text

[0746] User-entered data:

[0747] Monthly salary: 450,000 yen

[0748] Monthly fixed costs: 250,000 yen

[0749] Investment limit: 100,000 yen

[0750] Risk tolerance: Medium risk

[0751] Historical Spending Data:

[0752] Food expenses: 50,000 yen / month

[0753] Transportation fee: 10,000 yen / month

[0754] Entertainment expenses: 30,000 yen / month

[0755] Future income projections:

[0756] Projected revenue growth: 5% / year

[0757] Asset Management Plan:

[0758] Generate investment portfolios based on risk tolerance

[0759] Medium-risk domestic stocks: 40%

[0760] Medium-risk index mutual funds: 60%

[0761] Plan generation:

[0762] Execute the generated plan and monitor performance monthly, making any necessary adjustments accordingly.

[0763] This allows users to efficiently manage their daily income and expenditures and manage their assets without any hassle. The system offers a high degree of automation and real-time data collection and analysis capabilities, enabling users to optimize their assets.

[0764] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0765] Step 1:

[0766] The user enters information about their income, expenses, and desired investments on their smartphone. Specifically, they enter data such as monthly income, fixed expenses, available investment amount, and risk tolerance into the application's input form. The entered data is temporarily saved in the device's memory. When the user presses the "Send" button, the data is sent to the server in JSON format. The entered data and the sent data are as follows:

[0767] Input data: monthly income, fixed expenses, investment amount, risk tolerance

[0768] Output data: User data in JSON format

[0769] Step 2:

[0770] The server analyzes the received JSON format data. The server saves the data in a database, and then analyzes this data in the income and expenditure analysis module. Specifically, it calculates monthly income and expenses, the amount available for investment, and calculates the income and expenditure balance. The analysis results are saved in the database, and the AI ​​model uses the analysis results to make income and expenditure predictions. This process has the following inputs and outputs:

[0771] Input data: User data in JSON format

[0772] Output data: Income and expenditure balance, income and expenditure forecast data

[0773] Step 3:

[0774] The server generates an investment plan using the balance and forecast data. The investment plan generation module evaluates various investment options and creates low-risk, medium-risk, and high-risk plans. Each plan also includes a risk assessment score, and the most suitable plan is selected based on the user's risk tolerance. The generated plans are saved in JSON format. This process has the following inputs and outputs:

[0775] Input data: Income and expenditure balance, income and expenditure forecast data, risk tolerance

[0776] Output data: Asset management plan (low risk, medium risk, high risk)

[0777] Step 4:

[0778] The server sends the generated asset management plan to the device and presents it to the user. The device receives the plan and displays it to the user through the application's UI. The user reviews the proposed plan and makes any necessary modifications or approves it. Once the user has completed the operation, the selected plan is sent from the device to the server. This process has the following inputs and outputs:

[0779] Input data: Asset management plan (low risk, medium risk, high risk)

[0780] Output data: User feedback (corrections, approvals)

[0781] Step 5:

[0782] The server automatically executes the investment based on the final plan approved by the user. The server performs investment operations through the specified investment API. For example, purchasing mutual funds or trading stocks is performed automatically. This process has the following inputs and outputs:

[0783] Input data: Final plan approved by the user

[0784] Output data: Investment execution results

[0785] Step 6:

[0786] The server continuously monitors the performance of the investments and adjusts the investment plan as needed. The server periodically evaluates market data and the user's investment performance and notifies the user if it determines that an adjustment to the investment plan is necessary. Once the user approves the changes, the new investment plan is applied. This process has the following inputs and outputs:

[0787] Input data: market data, investment performance data

[0788] Output data: adjusted investment plan, notification to user

[0789] 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.

[0790] This invention is a system that efficiently manages everything from household income and expenditure management to asset management, and by integrating an emotion engine that recognizes the user's emotions, it realizes more personalized and optimized asset management. Users input information about their income, expenses, investment preferences, and emotions, and the system then automatically analyzes the data, proposes and implements an effective asset management plan, and continuously monitors and adjusts it.

[0791] Program processing

[0792] Collecting input data

[0793] The user inputs income, expenditure, asset information, investment preference information, and emotional data from the terminal.

[0794] Users fill out the application form with their monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount they can invest, their investment preferences (risk tolerance, etc.), and their emotional state at the time (e.g., stress, sense of security), and click the submit button.

[0795] Data collection and emotion recognition

[0796] The terminal sends the input data to the server.

[0797] The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0798] The server analyzes the user's emotion data using an emotion engine.

[0799] The server uses an emotion engine to analyze the user's input emotional data and evaluate their psychological state. For example, if their stress level is high, it will prioritize low-risk plans.

[0800] Data analysis

[0801] The server analyzes the received income and expenditure data to grasp the user's income and expenditure situation and current state of assets.

[0802] The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes this data to calculate the balance between income and expenditure, current savings, and available investments.

[0803] The server uses a machine learning module to analyze the user's spending patterns.

[0804] The server performs feature extraction based on past data to extract spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[0805] Generate a financial plan

[0806] The server generates an optimal asset management plan based on the analysis results and emotional data.

[0807] The server generates an optimal investment plan based on the income and expenditure forecast data and the user's sentiment assessment, which includes specific investment options (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[0808] Feedback and Suggestions

[0809] The server performs a risk assessment of the generated investment plan and makes adjustments based on the sentiment data.

[0810] The risk assessment module calculates a risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, a low-risk plan will be prioritized.

[0811] The server transmits the generated plan to the terminal, which displays the plan to the user.

[0812] The server sends the generated asset management plan in JSON format to the terminal, and the terminal displays the received plan in the application UI. The user can check the plan and make any necessary modifications.

[0813] Execution and monitoring

[0814] The user approves the plan and the terminal sends the final plan to the server.

[0815] When the user clicks the approve button, the terminal again sends the final plan to the server, and the server stores the approved plan in the database.

[0816] The server initiates asset management based on the approved plan and continuously monitors and adjusts it.

[0817] The server executes specific investment operations through the specified investment API. It also periodically evaluates market data and user asset data to monitor investment performance, rebalancing plans and presenting new investment opportunities when necessary. When significant changes occur, the server sends notifications to the terminal and asks for user confirmation.

[0818] Specific examples

[0819] Collection and analysis of income, expenditure and sentiment data

[0820] 1. The user enters the following information into the terminal:

[0821] Monthly income: 500,000 yen

[0822] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0823] Investment limit: 100,000 yen

[0824] Investment preference: Low risk

[0825] Emotional data: High stress levels

[0826] 2. The server uses an emotion engine to analyze the emotion data and predicts income and expenditure based on the income and expenditure data.

[0827] 3. The server generates an optimal asset management plan based on income and expenditure forecast data, emotional data, and investment preference information, and performs risk assessment.

[0828] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency reserve.

[0829] 4. The user reviews and approves the proposed plan.

[0830] If the user approves, the server automatically executes the investment and completes the mutual fund purchase.

[0831] Continuous monitoring and adjustment

[0832] 1. The server monitors the performance and balance of your investments monthly, and also periodically evaluates sentiment data.

[0833] If your income increases and your investment trust returns are high, your stress level will be recognized as decreasing, and your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[0834] 2. The server notifies the device of the adjustments and asks the user for confirmation.

[0835] Once the user approves the changes, the new investment plan will be applied.

[0836] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[0837] The processing flow will be explained below.

[0838] Step 1:

[0839] The user inputs their income, expenses, asset information, investment preferences, and emotional data from their device. Specifically, the user inputs their monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), available investment amount, investment preferences (risk tolerance, etc.), and current emotional state (e.g., stress, sense of security) into the application form, and clicks the submit button.

[0840] Step 2:

[0841] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0842] Step 3:

[0843] The server uses an emotion engine to analyze the user's emotional data. The server uses the emotion engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[0844] Step 4:

[0845] The server analyzes the received income and expenditure data to understand the current state of the user's income and expenditure and assets. The server retrieves the income and expenditure data from the database, and the income and expenditure analysis module analyzes this data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[0846] Step 5:

[0847] The server uses machine learning modules to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[0848] Step 6:

[0849] The server generates an optimal asset management plan based on the analysis results and emotion data. The server generates an optimal asset management plan based on income and expenditure forecast data and the user's emotion evaluation. This plan includes specific investment targets (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[0850] Step 7:

[0851] The server evaluates the risk of the generated asset management plan and makes adjustments based on emotional data. The risk assessment module calculates the risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state as assessed. For example, if the user is in a high-stress state, a low-risk plan will be selected first.

[0852] Step 8:

[0853] The server sends the generated plan to the terminal. The server sends the generated asset management plan in JSON format to the terminal.

[0854] Step 9:

[0855] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[0856] Step 10:

[0857] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device again sends the final plan to the server, and the server stores the approved plan in the database.

[0858] Step 11:

[0859] The server starts asset management based on the approved plan. The server then executes specific investment operations through the specified investment API, such as purchasing mutual funds or trading stocks. Once the transaction is complete, the server updates the user's asset status.

[0860] Step 12:

[0861] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or suggests new investment opportunities. If any significant changes occur, the server sends a notification to your device, asking for your confirmation.

[0862] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management. In addition, by taking emotion data into consideration, the psychological burden on users can be reduced.

[0863] Example 2

[0864] 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."

[0865] Current income / expense management and asset management systems do not take into account the user's psychological state or emotions, and do not adequately adjust risk assessments or asset management plans. As a result, they are unable to provide an appropriate asset management plan when the user is under high stress or when their risk tolerance fluctuates, which could lead to inappropriate investment risks. This increases the user's psychological burden and makes it difficult to achieve optimal financial planning.

[0866] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0867] In this invention, the server includes means for inputting income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for evaluating the user's psychological state based on the income, expenditure, and emotional data, means for generating an asset management plan based on the analysis results, means for adding a risk assessment to the generated asset management plan and adjusting the risk according to the user's psychological state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This makes it possible to provide an optimal asset management plan taking into account the user's psychological state and achieve effective financial planning while reducing the user's psychological burden.

[0868] "Income and expenditure data" is numerical information about the household's economic situation, such as monthly household income, monthly fixed expenditures, and available investment amounts.

[0869] The "means for inputting" is an interface for the user to input income, expenditure, and emotion data into the terminal.

[0870] The "analyzing means" refers to algorithms and modules that allow the server to obtain income and expenditure data and make income and expenditure forecasts based on this data.

[0871] The "emotion analysis means" is an engine and corresponding software for analyzing the emotion data input by the user and evaluating the user's psychological state.

[0872] An "asset management plan" is an investment and asset management plan suited to a user, generated based on income and expenditure data and emotion data.

[0873] The "means for adding risk assessment" is a module for calculating a risk score for the generated asset management plan and adjusting the risk according to the user's psychological state.

[0874] The "means for requesting approval or modification" is an interface for presenting the generated asset management plan to the user and requesting the user to approve or modify the plan.

[0875] "Means for automatically executing investments" refers to a system and API for automatically performing investment operations based on an asset management plan approved by a user.

[0876] "Means for continuous monitoring of investment performance" are modules and software for periodically evaluating the results of investments and adjusting investment plans as necessary.

[0877] A "machine learning module" is an algorithm and corresponding software for analyzing income and expense data and learning a user's spending patterns.

[0878] "Risk tolerance" is a standard indicating how much risk a user can tolerate, and is an important factor when generating an asset management plan.

[0879] This invention is a system for efficiently managing household income and expenditures and asset management, and realizes individually optimized asset management by integrating an emotion engine that recognizes the user's emotions. The system inputs income, expenses, asset information, investment preferences, and emotion data, performs income and expenditure forecasts and emotion analysis, then generates an optimal asset management plan, automatically executes investments, and continuously monitors and adjusts them.

[0880] Hardware and Software

[0881] The system consists of the following hardware and software:

[0882] User Device: Used by users to input income, expenses, asset information, investment preferences, and emotional data. This includes smartphones, tablets, and PCs.

[0883] Server: Receives data, analyzes it, analyzes sentiment, and generates investment plans. The server includes a database, sentiment engine, machine learning module, and risk assessment module. For example, a cloud platform such as Amazon Web Services (AWS) can be used.

[0884] Application software: A program that runs on user terminals and servers and is responsible for inputting income and expenditure data, sending and receiving data, displaying analytical results, and creating and executing asset management plans.

[0885] Data processing and calculation

[0886] 1. Collect input data:

[0887] The user uses a terminal to input income, expenses, asset information, investment preferences, and emotional data into the application form.

[0888] Example: A user inputs a monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, investment amount of 100,000 yen, investment preference as low risk, and emotional data as high stress level.

[0889] 2. Data transmission and storage:

[0890] The terminal converts the input data into JSON format and sends it to the server's API endpoint via HTTPS.

[0891] The server stores the received data in a database.

[0892] 3. Emotion analysis:

[0893] The server uses an emotion engine to analyze the user's emotion data.

[0894] For example, the user's stress level is high, so the emotion engine evaluates it as "high stress."

[0895] 4. Balance analysis:

[0896] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts.

[0897] The income and expenditure analysis module analyzes the balance between income and expenditure, savings, and investment potential.

[0898] 5. Analysis of spending patterns:

[0899] The server runs a machine learning module based on past data to model spending patterns.

[0900] Example: Using past data, predict "average monthly expenditure of 250,000 yen and savings of 100,000 yen."

[0901] 6. Generate a financial plan:

[0902] The server generates an optimal asset management plan based on income and expenditure forecast data and emotional data.

[0903] Example: A plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen for emergencies.

[0904] 7. Risk Assessment and Adjustment:

[0905] The server performs risk assessment on the generated asset management plan and adjusts the risk as necessary.

[0906] Example: determining that low-risk mutual funds are suitable for a user in a high-stress state.

[0907] 8. Viewing and Approving the Plan:

[0908] The terminal displays the generated asset management plan to the user and requests approval or modification.

[0909] The user clicks the approve button and the final plan is sent to the server.

[0910] 9. Asset management execution and monitoring:

[0911] The server starts asset management based on the approved plan and executes investment operations through the specified investment API.

[0912] Continually evaluate market data and your asset data to rebalance your investment plan or suggest new investment opportunities as needed.

[0913] Example prompt

[0914] As a concrete example, a user enters the following information:

[0915] Monthly income: 500,000 yen

[0916] Fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[0917] Investment limit: 100,000 yen

[0918] Investment preference: Low risk

[0919] Emotional data: High stress levels

[0920] In this way, the system of the present invention can efficiently and effectively manage household income and expenditures and manage assets, and realize optimal asset management that takes into account the user's psychological state.

[0921] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0922] Step 1: User Enters Information

[0923] The user uses a dedicated application to input their monthly income, monthly fixed expenses, available investment amount, investment preferences, and emotional data (e.g., stress level). After filling out each item in the input form and clicking the submit button, the data is entered into the terminal.

[0924] Input: Income, expenses, investment preferences, emotional data

[0925] Output: Input data

[0926] Step 2: The device sends the data

[0927] The device converts the input data into JSON format, which makes it possible to send the data to the server.The device then sends the data to the server's API endpoint via HTTPS.

[0928] Input: Data entered by the user

[0929] Output: JSON format data

[0930] Step 3: The server receives and stores the data

[0931] The server receives HTTP requests sent from the device. It analyzes the received data and stores it in a database. The database stores all data necessary for income and expenditure management and sentiment analysis.

[0932] Input: JSON format data

[0933] Output: Data stored in the database

[0934] Step 4: The server analyzes the emotion data

[0935] The server launches the emotion engine and analyzes the emotion data from the user's input data. For example, if the server recognizes that the stress level is high, the emotion engine will judge it as "high stress." This result will be used to generate a subsequent asset management plan.

[0936] Input: Emotion data

[0937] Output: Emotion analysis results

[0938] Step 5: The server analyzes the data

[0939] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts. The income and expenditure analysis module analyzes this data and calculates the balance between income and expenditure, current savings, and available investment amounts.

[0940] Input: Income data, expenditure data

[0941] Output: Profit and loss forecast results

[0942] Step 6: The server analyzes spending patterns

[0943] The server runs a machine learning module using historical data to analyze spending patterns, perform feature extraction, and model spending trends and patterns, which can then be used to predict future spending.

[0944] Input: Past income and expenditure data

[0945] Output: A model of spending patterns

[0946] Step 7: The server generates the investment plan.

[0947] The server generates an optimal asset management plan based on the income / expense forecast data and sentiment analysis results. This plan includes specific investment targets and predicted returns. For example, a specific plan such as "invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency fund" may be proposed.

[0948] Input: Revenue and expenditure forecast data, emotion analysis results

[0949] Output: Wealth Management Plan

[0950] Step 8: Server performs risk assessment and adjustments

[0951] The server performs risk assessment on the asset management plan generated and makes adjustments as necessary. The risk assessment module calculates a risk score for each plan and adjusts the risk based on the results of sentiment analysis. For example, if a user is in a high stress state, it will prioritize low-risk plans.

[0952] Input: Asset management plan, sentiment analysis results

[0953] Output: Adjusted financial plan

[0954] Step 9: The device displays the plan to the user

[0955] The server sends the generated financial plan in JSON format to the terminal, which receives this data and displays it in the application's user interface. The user can then review the plan and make any necessary modifications.

[0956] Input: Generated Investment Plan

[0957] Output: The plan displayed to the user

[0958] Step 10: User approves the plan

[0959] If the user is satisfied with the proposed asset management plan, he or she clicks the approval button. This operation causes the terminal to send the final approved plan back to the server.

[0960] Input: User approval button click

[0961] Output: Final approved plan

[0962] Step 11: The server starts and monitors the asset management

[0963] The server starts asset management based on the approved plan. It executes specific investment operations through the specified investment API and periodically evaluates market data and the user's asset data to monitor investment performance. It rebalances the plan and presents new investment opportunities as needed. If any important changes occur, it sends a notification to the terminal and asks the user for confirmation.

[0964] Input: Final Approved Plan

[0965] Output: Executed asset operations, ongoing monitoring results

[0966] (Application example 2)

[0967] 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."

[0968] Conventional income / expense management and asset management systems are limited to analyzing income and expenditure data and are unable to consider the user's psychological state, resulting in the problem of only being able to provide a uniform asset management plan. Furthermore, these systems are inadequate for continuous investment performance monitoring and investment plan adjustment, often resulting in suboptimal user investment activities. Furthermore, a lack of integration with electronic payment services limits automation when implementing investment plans, placing a heavy burden on users.

[0969] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for recognizing the user's emotional data and evaluating the emotional state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for making electronic payments for implementing the investment plan generated by the system. This makes it possible to provide an individually optimized asset management plan that takes the user's emotional state into consideration, and to automatically execute investments and continuously monitor and adjust them.

[0970] The "means for inputting household income and expenditure data" is an interface through which a user provides information about his or her income and expenditure to the system.

[0971] The "means for analyzing input income and expenditure data and forecasting income and expenditure" is an analysis module for forecasting future income and expenditure based on income and expenditure data.

[0972] The "means for generating an asset management plan" is a system for proposing investment strategies and asset allocations based on the analysis results.

[0973] The "means for recognizing the user's emotional data and evaluating the emotional state" is a module that has the function of analyzing the user's psychological state using an emotion engine or the like and evaluating that state.

[0974] The "means for presenting the generated asset management plan to the user and requesting approval or modification from the user" is a user interface for notifying the user of the investment plan generated by the system and receiving instructions for approval or modification.

[0975] The "means for automatically executing investments based on an asset management plan approved by a user" is a function for automatically performing investment operations in accordance with an investment plan approved by a user.

[0976] "Means for continuously monitoring the performance of investments and adjusting the investment plan as needed" means a system for regularly monitoring the results of investments and changing the investment strategy as needed.

[0977] "Means for making electronic payments to implement the investment plan generated by the system" refers to a function for electronically executing the necessary payments and transactions based on the investment plan generated.

[0978] This invention is a system that efficiently manages household income and expenditures and asset management all at once, and by integrating an emotion engine that recognizes the user's emotions, it achieves more individually optimized asset management.

[0979] The system provides a means for users to input information about their income, expenses, investment aspirations, and emotions. Specifically, a smartphone app provides an interface for collecting this data. Users enter their monthly income, fixed expenses, available investment amount, investment aspirations, and emotional state into the application form and click the submit button.

[0980] The terminal sends the entered data to the server, which converts the data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[0981] The server uses the Emotion Engine to analyze the user's emotional data. Specifically, the server uses the Emotion Engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[0982] The server analyzes the received income and expenditure data to understand the user's current income and expenditure status and assets. The server also uses a machine learning module to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[0983] Based on the analysis results and the emotional data, the server generates an optimal asset management plan. Based on the income and expenditure forecast data and the user's emotional evaluation, the server creates an investment plan that includes specific investment targets and predicted returns.

[0984] The server evaluates the risk of the generated asset management plan and adjusts it based on the emotional data. The risk assessment module calculates the risk score for each plan, and the emotional engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, it will prioritize low-risk plans.

[0985] The server sends the generated plan to the terminal, which displays it to the user. The user can check the plan and make any necessary modifications. When the user clicks the approve button, the terminal sends the final plan back to the server, which then stores the approved plan in the database.

[0986] The server initiates asset management based on the approved plan and continuously monitors and adjusts it. The server executes specific investment operations through the designated investment API, periodically evaluates market data and the user's asset data to monitor investment performance, and rebalances the plan or presents new investment opportunities when necessary. If any significant changes occur, the server will send a notification to the terminal and ask the user for confirmation.

[0987] As a concrete example, consider a case where a user has a monthly income of 500,000 yen, fixed monthly expenses of 300,000 yen, and an investment capacity of 100,000 yen, and is currently in a high stress state. In this case, the application will propose a plan to invest 90,000 yen per month in a low-risk mutual fund and keep 10,000 yen as a reserve.

[0988] An example of a prompt to input to a generative AI model is as follows:

[0989] Generate an optimal financial plan based on the user's monthly income, monthly fixed expenses, investment capacity, investment preferences, and emotional data. If the user's stress level is high, suggest a low-risk plan. Below is a sample of user data:

[0990] Monthly income: 500,000 yen

[0991] Monthly fixed expenses: 300,000 yen

[0992] Investment limit: 100,000 yen

[0993] Investment preference: Low risk

[0994] Emotional data: High stress levels

[0995] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[0996] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0997] Step 1:

[0998] The user inputs income, expenses, investment preferences, and emotional data. The user enters monthly income, fixed expenses (e.g., rent, utilities, food, etc.), available investment amount, investment preferences, and emotional state (e.g., high stress level) into a form on the smartphone app and clicks the submit button. The input data is monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, available investment amount of 100,000 yen, investment preferences are low risk, and the emotional data is high stress.

[0999] Step 2:

[1000] The terminal sends the input data to the server. The terminal converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The input data includes monthly income, fixed expenses, available investment amount, investment preference, and sentiment data. The server receives this data and stores it in a database.

[1001] Step 3:

[1002] The server analyzes the emotion data. The server uses an emotion engine to evaluate the user's psychological state based on the emotion data entered by the user. Specifically, the emotion engine analyzes the user's stress level and determines that the user's stress level is high as a result of the evaluation.

[1003] Step 4:

[1004] The server analyzes the income and expenditure data and grasps the income and expenditure situation. The server retrieves the income and expenditure data from the database and analyzes the data in the income and expenditure analysis module. This outputs the balance of income and expenditure, the current amount of savings, and the amount available for investment.

[1005] Step 5:

[1006] The server analyzes spending patterns using a machine learning module. The server performs feature extraction using past income and expenditure data to extract spending trends and patterns. This outputs future income and expenditure forecast data and generates a spending pattern model for the user.

[1007] Step 6:

[1008] The server generates an optimal asset management plan based on the analysis results and emotional data. Based on income / expense forecast data and emotional evaluation, the server creates an investment plan that includes specific investment targets (e.g., low-risk investment trusts) and predicted returns. Users with high stress levels are suggested low-risk plans, and each plan is also risk-assessed.

[1009] Step 7:

[1010] The server sends the generated asset management plan to the terminal, which then displays the plan to the user. The server also sends the generated management plan in JSON format to the terminal, which then displays it on the application UI based on the received data. This allows the user to check the plan and approve or modify it.

[1011] Step 8:

[1012] The user approves the plan, and the device sends the final plan to the server. The user clicks the approve button, and this action causes the device to send the final plan back to the server. The server saves the approved plan in its database.

[1013] Step 9:

[1014] The server initiates asset management based on the approved plan and continuously monitors and adjusts it. The server executes specific investment operations through the designated investment API, periodically evaluates market data and the user's asset data to monitor investment performance, and rebalances the plan or presents new investment opportunities when necessary, and sends notifications to the terminal when important changes occur.

[1015] In this way, all processing steps are carried out in a single flow, and the system automatically implements optimal asset management while taking into account the user's emotional state.

[1016] 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.

[1017] 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.

[1018] 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.

[1019] [Third embodiment]

[1020] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1021] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1022] 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).

[1023] 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.

[1024] 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.

[1025] 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).

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] 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."

[1032] This invention is a system that efficiently manages everything from household income and expenditure management to asset management. Users input their income, expenditure information, and investment preferences, and the system then automatically analyzes the data to propose and implement an effective asset management plan, while continuously monitoring and adjusting it.

[1033] Program processing

[1034] Collecting input data

[1035] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[1036] The user enters information into the application's input form, such as monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount available for investment, and investment preferences (risk tolerance, etc.).

[1037] After entering the data, click the send button to send the data from the terminal to the server.

[1038] Data analysis

[1039] The server analyzes the received data and determines the user's income and expenditure status and current asset status.

[1040] The server stores the received data in a database, and the income and expenditure analysis module analyzes this data.

[1041] Based on the analysis results, the current savings, investment potential, and balance between income and expenditure are calculated.

[1042] The server uses AI models to predict users' spending patterns and future income and expenditures.

[1043] The server uses a machine learning module to learn the user's spending patterns from past income and expenditure data.

[1044] Based on this data, future income and expenditure forecasts are made, and fluctuations in the user's future income and expenditures are predicted.

[1045] Generate a financial plan

[1046] The server generates an optimal asset management plan based on the data obtained.

[1047] The server evaluates various investment options based on the user's income and expenditure forecast data and investment preferences, and generates an optimal asset management plan.

[1048] The generated plans include low-risk, medium-risk, and high-risk investment plans and their return projections.

[1049] The plan includes multiple investment options and risk assessments.

[1050] The server uses a risk assessment module to perform a risk assessment for each investment plan.

[1051] Calculate a risk score for each plan and select the most appropriate plan based on the user's risk tolerance.

[1052] Feedback and Suggestions

[1053] The server sends the generated plan to the terminal.

[1054] The server sends the generated asset management plan in JSON format to the terminal.

[1055] The terminal displays the plan to the user, who then approves or modifies it.

[1056] The device displays the received plan on the application's UI.

[1057] The user reviews the proposed plan and makes any necessary modifications.

[1058] After the user approves, the terminal sends the final plan to the server.

[1059] If the user approves the final plan, the terminal transmits the final plan to the server.

[1060] The server stores this final plan in a database.

[1061] Execution and monitoring

[1062] The server starts asset management based on the approved plan.

[1063] The server automatically executes investment operations through the specified investment API.

[1064] The server continuously monitors the performance of the investments and adjusts the investment plan as needed.

[1065] The server periodically evaluates market data and the user's investment performance and makes any necessary adjustments.

[1066] If there is an important change, the server will send a notification to the terminal and ask the user for confirmation.

[1067] Specific examples

[1068] Initial Setup and Plan Generation

[1069] 1. The user enters the following information into the terminal:

[1070] Monthly income: 500,000 yen

[1071] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1072] Investment limit: 100,000 yen

[1073] Investment preference: Low risk

[1074] 2. The server analyzes the balance of income and expenditures and generates an optimal asset management plan.

[1075] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen in cash as an emergency reserve.

[1076] 3. The user reviews and approves the proposed plan.

[1077] If the user approves, the server automatically executes the investment and makes the mutual fund purchase.

[1078] Continuous monitoring and adjustment

[1079] 1. The server monitors the performance and balance of your investments monthly.

[1080] If your income increases and your investment trust returns are high, your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[1081] 2. The server notifies the terminal of the adjustment results and asks the user for confirmation.

[1082] Once the user approves the changes, the new investment plan will be applied.

[1083] In this way, the system of the present invention can efficiently manage household income and expenditures and asset management all at once, allowing users to optimally manage their assets without any hassle.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The user enters information about their income, expenses, assets, and desired investments from their device. Specifically, the user enters their monthly income, fixed monthly expenses (e.g., rent, utilities, food, etc.), available investment amount, and desired investments (risk tolerance, etc.) into the application form and clicks the submit button.

[1087] Step 2:

[1088] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1089] Step 3:

[1090] The server analyzes the received data to understand the user's current income and expenditure situation and assets. The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes the data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[1091] Step 4:

[1092] The server uses machine learning modules to analyze the user's spending patterns, performs feature extraction based on past data, and extracts spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[1093] Step 5:

[1094] The server generates an optimal investment plan for the user based on the income and expenditure forecast data. The server evaluates investment options and creates multiple investment plans based on risk tolerance. Each plan includes specific investments (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[1095] Step 6:

[1096] The server performs risk assessment of the generated investment plans. The risk assessment module calculates a risk score for each plan and sorts the plans based on the user's risk tolerance. The most suitable plan is selected.

[1097] Step 7:

[1098] The server sends the generated asset management plan to the terminal. The server then sends the selected plan to the terminal in JSON format.

[1099] Step 8:

[1100] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[1101] Step 9:

[1102] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device sends the final plan to the server again. The server saves the approved plan in the database.

[1103] Step 10:

[1104] The server will start asset management based on the approved plan, and then execute specific investment operations such as purchasing stocks or mutual funds through the specified investment API. Once the transaction is completed, the server will update the user's asset status.

[1105] Step 11:

[1106] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or presents new investment opportunities. If there are any major changes, the server sends a notification to your device and asks for your confirmation.

[1107] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management.

[1108] Example 1

[1109] 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."

[1110] Currently, efficient household income and expenditure management and asset management requires multiple different applications and manual work, placing a heavy burden on users. Furthermore, analyzing income and expenditure data and making future predictions requires advanced knowledge, making it difficult for average users to use. Furthermore, there is a lack of systems that can consistently create, execute, and monitor asset management plans.

[1111] 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.

[1112] In this invention, the server includes means for transmitting and storing input income and expenditure data to the server, means for analyzing the stored data and making income and expenditure forecasts, means for generating an asset management plan based on the income and expenditure forecasts, means for transmitting the generated asset management plan to a terminal and presenting it to the user, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This allows users to efficiently and automatically perform everything from income and expenditure management to asset management through a single system.

[1113] "Income" is a general term for the money and assets that a household or individual receives within a certain period of time.

[1114] "Expenditure" is a general term for the money consumed by households and individuals within a certain period of time.

[1115] A "server" is a computer system that stores and processes data on a network.

[1116] A "database" is an information system for efficiently storing, searching, and updating data.

[1117] "Analysis" is the process of organizing and analyzing data to extract useful information.

[1118] "Income and expenditure forecasting" refers to predicting future income and expenditure based on past income and expenditure data.

[1119] An "asset management plan" is a plan that proposes an efficient method of managing assets, taking into account the balance of income and expenditure of individuals or households.

[1120] "Terminal" refers to a computing facility that is directly operated by a user and is a device for input and display.

[1121] A "machine learning module" is a software module that learns rules and patterns from data and uses them to make predictions and classifications.

[1122] "Risk tolerance" refers to the range and degree of risk that a user can accept in investment or asset management.

[1123] An "investment API" is a program interface that works in conjunction with external systems to automate investment operations.

[1124] The present invention is a system that inputs household income and expenditure data, analyzes the data to generate income and expenditure forecasts and asset management plans, and then executes and monitors the plans. This system provides a series of functions that users can use easily and efficiently to manage their assets.

[1125] System Program Overview

[1126] Collecting input data

[1127] The user inputs income, expenses, asset information, and desired investment information from the terminal. The terminal collects this data in the application's input form. For example, monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), investment amount, and risk tolerance are input. The input data is sent from the terminal to the server by clicking the send button. The data is converted to JSON format and securely sent to the server using the HTTPS protocol.

[1128] Data analysis

[1129] The server stores and analyzes the received data. The server stores the data in a database (e.g., MySQL or PostgreSQL). An income / expense analysis module (e.g., using the pandas and numpy libraries) analyzes the data and calculates the current savings, investment potential, and balance of income and expenses.

[1130] Revenue and expenditure forecast

[1131] The server uses machine learning modules (such as TensorFlow and scikit-learn) to learn the user's spending patterns from past income and expenditure data. Based on the learning results, it predicts future income and expenditure and investment potential, and generates income and expenditure forecast data.

[1132] Generate a financial plan

[1133] The server evaluates various investment options based on income / expense forecast data and investment preference information, and generates an optimal asset management plan. This uses the Markowitz model to evaluate risk and return. The generated plan includes multiple investment options, each of which has been risk assessed in the risk assessment module.

[1134] Plan presentation and approval

[1135] The generated asset management plan is sent to the terminal. The terminal displays the received plan on the UI and presents it to the user. The user checks the plan, modifies it if necessary, and approves the final plan. The approved plan is sent back to the server and saved in the database.

[1136] Asset management execution

[1137] The server automatically executes investment operations based on the approved plan through a designated investment API (e.g., Alpha Vantage API).

[1138] Continuous monitoring and adjustment

[1139] The server regularly monitors market data and the user's investment performance, adjusting the investment plan as needed. If there are any significant changes, the server sends a notification to the terminal and asks the user for confirmation.

[1140] Specific examples of operation

[1141] The user types:

[1142] Monthly income: 500,000 yen

[1143] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1144] Investment limit: 100,000 yen

[1145] Investment preference: Low risk

[1146] The server analyzes the income and expenditure balance and generates an optimal asset management plan. For example, it proposes a plan to invest 100,000 yen per month in low-risk mutual funds and keep 50,000 yen in cash as an emergency reserve. If the user approves this plan, the server automatically purchases the mutual funds and regularly monitors the investment performance. If income increases and the mutual fund returns are high, the server automatically adjusts the investment amount from 100,000 yen to 150,000 yen. If the user approves the change, the new investment plan is applied.

[1147] This system allows users to manage their assets optimally without any hassle.

[1148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1149] Step 1: Collect input data

[1150] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[1151] Users enter their monthly income, fixed expenses, investment amount, and risk tolerance in the application's input form.

[1152] As a specific example, suppose your monthly income is 500,000 yen, your monthly fixed expenses are 300,000 yen, the amount you can invest is 100,000 yen, and you choose low risk as your investment preference.

[1153] The input data is transmitted from the terminal to the server.

[1154] The input data is converted to JSON format and sent securely to the server using the HTTPS protocol.

[1155] Step 2: Save your data

[1156] The server stores the received data in a database.

[1157] The server parses the received JSON data and stores it in a database (e.g., MySQL or PostgreSQL).

[1158] The input data is saved, and the database stores the user's income, expenses, and investment preferences.

[1159] Step 3: Balance analysis

[1160] The server analyzes the income and expenditure data.

[1161] The saved data is read and analyzed using a balance analysis module (for example, using the pandas or numpy library).

[1162] Specifically, subtract fixed expenses from monthly income to calculate the amount available for investment.

[1163] The output is the current savings amount and monthly income and expenditure balance.

[1164] Step 4: Revenue and Expense Forecast

[1165] The server uses a machine learning module to make income and expenditure predictions.

[1166] It uses machine learning libraries such as TensorFlow and scikit-learn to learn users' spending patterns based on past income and expenditure data.

[1167] Based on the learning results, future income and expenditures are predicted and specific income and expenditure forecast data is generated.

[1168] The output is a forecast of future income and expenditure.

[1169] Step 5: Create a financial plan

[1170] The server generates an asset management plan based on the income and expenditure forecast data and desired investment information.

[1171] Uses the Markowitz model to evaluate risk and return and generate optimal investment plans.

[1172] The risk assessment module performs a risk assessment for each investment option and calculates a risk score.

[1173] The output is low-risk, medium-risk, and high-risk investment plans, each with a return forecast.

[1174] Step 6: Plan presentation and approval

[1175] The asset management plan generated by the server is sent to the terminal.

[1176] The generated asset management plan is sent to the terminal in JSON format.

[1177] The terminal displays the plan to the user.

[1178] The terminal displays the plan in the application's UI (using, for example, React or Vue.js).

[1179] The user checks the plan and makes any necessary modifications. The modified plan is also sent from the device to the server.

[1180] The output is a user-approved financial plan.

[1181] Step 7: Implementing asset management

[1182] The server starts asset management based on the approved plan.

[1183] Based on the asset management plan, investment operations are automatically executed through the specified investment API (e.g., Alpha Vantage API).

[1184] Specifically, operations such as purchasing and selling investment trusts are carried out periodically.

[1185] As an output, we get a log of the investment operations that were performed.

[1186] Step 8: Continuously monitor and adjust

[1187] The server periodically monitors the performance of the investment.

[1188] Evaluate market data and your investment performance and adjust your investment plan as needed.

[1189] If there is an important change, a notification is sent to the terminal and the user is asked to confirm.

[1190] The output is a new adjusted investment plan or change notice.

[1191] By following the above steps, users can easily and efficiently manage their household income and expenditures and asset management all at once.

[1192] (Application example 1)

[1193] 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."

[1194] Improving the efficiency of household income and expenditure management and asset management is an important issue for many households. However, manual income and expenditure management is cumbersome and difficult for users without knowledge or experience in asset management. Furthermore, existing systems lack real-time management of income and expenditure data and automatic asset management plan generation, resulting in poor usability. In addition, integration with electronic payment services is incomplete, and functions for collecting and managing income and expenditure information in real time are lacking, making it difficult for users to obtain accurate data for asset management.

[1195] 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.

[1196] In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for presenting the generated asset management plan to a user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for collecting and managing income and expenditure information in real time in cooperation with an electronic payment service. This enables efficient, integrated management of everything from household income and expenditure management to asset management, allowing users to optimally manage their assets based on accurate and timely data.

[1197] "Income and expenditure data" refers to monetary information on household and individual income sources (salary, bonuses, secondary income, etc.) and expenditures (rent, utilities, food, loans, etc.).

[1198] "Analysis" refers to the detailed analysis of collected data using machine learning and other algorithms to extract trends and patterns.

[1199] "Income and expenditure forecast" is the prediction of future trends in income and expenditure based on past income and expenditure data.

[1200] An "asset management plan" is a plan that shows the optimal asset management strategy, including investment targets and investment amounts, based on information such as the user's income, expenses, and risk tolerance.

[1201] "Means for seeking approval or amendment" refers to an interface or other method for presenting the generated asset management plan to the user and allowing the user to review the plan and approve or amend it as necessary.

[1202] "Means for automatically executing investments" refers to programs or API interfaces that allow the system to automatically perform investment operations based on the asset management plan approved by the user.

[1203] "Continuous performance monitoring measures" refers to a monitoring system that regularly reviews investment results and market trends and adjusts the investment plan if necessary.

[1204] "Electronic payment services" refers to online and offline payment methods such as credit cards, debit cards, and mobile payments.

[1205] "Means of collecting and managing in real time" refers to the technology and systems for instantly collecting income and expenditure data and storing and managing it in a database.

[1206] These definitions clarify how the elements of the present invention function and interact.

[1207] The system for embodying the present invention inputs income and expenditure data, analyzes the data, predicts income and expenditure, and creates and executes an asset management plan. The system of the present invention includes the following specific means.

[1208] Hardware and software used

[1209] Hardware

[1210] Smartphone

[1211] software

[1212] Payment API

[1213] Machine learning module (TensorFlow)

[1214] Database (MySQL)

[1215] Backend server (Django)

[1216] Program processing

[1217] 1. Enter user data

[1218] Users use their smartphones to input data such as income, expenses, and investment preferences, including monthly income, fixed expenses, investment capacity, and risk tolerance.

[1219] This data is collected through an input form and sent to the server when the user presses the submit button.

[1220] 2. Data collection and analysis

[1221] The server receives the entered data and stores it in a database.

[1222] Real-time data on users' income and expenditures is collected through a payment API, which is then analyzed using a machine learning module to determine users' spending patterns and forecast income and expenditures.

[1223] Based on the collected and stored data, the server analyzes the balance of income and expenditure and makes predictions about future income and expenditure.

[1224] 3. Generate an investment plan

[1225] The server generates an optimal asset management plan based on the income and expenditure forecast data and the user's investment preferences. Specifically, it creates low-risk, medium-risk, and high-risk plans and performs a risk assessment for each.

[1226] The generated plan includes investment options and their expected returns based on the user's risk tolerance.

[1227] 4. Feedback and Suggestions

[1228] The server sends the generated operational plan in JSON format to the terminal, and the smartphone application displays it to the user.

[1229] The user operates an interface to review the proposed plan and approve or modify it.

[1230] 5. Execution and Monitoring

[1231] Once the user approves the final plan, the server will automatically execute the investment operation through the specified investment API.

[1232] The server continuously monitors the performance of the investments and makes necessary adjustments based on market data and the user's investment performance.

[1233] Specific examples

[1234] Example of input data

[1235] The user uses a smartphone to enter the following data:

[1236] Monthly income: 500,000 yen

[1237] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1238] Investment limit: 100,000 yen

[1239] Investment preference: Low risk

[1240] Example prompts to be input to the generative AI model

[1241] text

[1242] User-entered data:

[1243] Monthly salary: 450,000 yen

[1244] Monthly fixed costs: 250,000 yen

[1245] Investment limit: 100,000 yen

[1246] Risk tolerance: Medium risk

[1247] Historical Spending Data:

[1248] Food expenses: 50,000 yen / month

[1249] Transportation fee: 10,000 yen / month

[1250] Entertainment expenses: 30,000 yen / month

[1251] Future income projections:

[1252] Projected revenue growth: 5% / year

[1253] Asset Management Plan:

[1254] Generate investment portfolios based on risk tolerance

[1255] Medium-risk domestic stocks: 40%

[1256] Medium-risk index mutual funds: 60%

[1257] Plan generation:

[1258] Execute the generated plan and monitor performance monthly, making any necessary adjustments accordingly.

[1259] This allows users to efficiently manage their daily income and expenditures and manage their assets without any hassle. The system offers a high degree of automation and real-time data collection and analysis capabilities, enabling users to optimize their assets.

[1260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1261] Step 1:

[1262] The user enters information about their income, expenses, and desired investments on their smartphone. Specifically, they enter data such as monthly income, fixed expenses, available investment amount, and risk tolerance into the application's input form. The entered data is temporarily saved in the device's memory. When the user presses the "Send" button, the data is sent to the server in JSON format. The entered data and the sent data are as follows:

[1263] Input data: monthly income, fixed expenses, investment amount, risk tolerance

[1264] Output data: User data in JSON format

[1265] Step 2:

[1266] The server analyzes the received JSON format data. The server saves the data in a database, and then analyzes this data in the income and expenditure analysis module. Specifically, it calculates monthly income and expenses, the amount available for investment, and calculates the income and expenditure balance. The analysis results are saved in the database, and the AI ​​model uses the analysis results to make income and expenditure predictions. This process has the following inputs and outputs:

[1267] Input data: User data in JSON format

[1268] Output data: Income and expenditure balance, income and expenditure forecast data

[1269] Step 3:

[1270] The server generates an investment plan using the balance and forecast data. The investment plan generation module evaluates various investment options and creates low-risk, medium-risk, and high-risk plans. Each plan also includes a risk assessment score, and the most suitable plan is selected based on the user's risk tolerance. The generated plans are saved in JSON format. This process has the following inputs and outputs:

[1271] Input data: Income and expenditure balance, income and expenditure forecast data, risk tolerance

[1272] Output data: Asset management plan (low risk, medium risk, high risk)

[1273] Step 4:

[1274] The server sends the generated asset management plan to the device and presents it to the user. The device receives the plan and displays it to the user through the application's UI. The user reviews the proposed plan and makes any necessary modifications or approves it. Once the user has completed the operation, the selected plan is sent from the device to the server. This process has the following inputs and outputs:

[1275] Input data: Asset management plan (low risk, medium risk, high risk)

[1276] Output data: User feedback (corrections, approvals)

[1277] Step 5:

[1278] The server automatically executes the investment based on the final plan approved by the user. The server performs investment operations through the specified investment API. For example, purchasing mutual funds or trading stocks is performed automatically. This process has the following inputs and outputs:

[1279] Input data: Final plan approved by the user

[1280] Output data: Investment execution results

[1281] Step 6:

[1282] The server continuously monitors the performance of the investments and adjusts the investment plan as needed. The server periodically evaluates market data and the user's investment performance and notifies the user if it determines that an adjustment to the investment plan is necessary. Once the user approves the changes, the new investment plan is applied. This process has the following inputs and outputs:

[1283] Input data: market data, investment performance data

[1284] Output data: adjusted investment plan, notification to user

[1285] 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.

[1286] This invention is a system that efficiently manages everything from household income and expenditure management to asset management, and by integrating an emotion engine that recognizes the user's emotions, it realizes more personalized and optimized asset management. Users input information about their income, expenses, investment preferences, and emotions, and the system then automatically analyzes the data, proposes and implements an effective asset management plan, and continuously monitors and adjusts it.

[1287] Program processing

[1288] Collecting input data

[1289] The user inputs income, expenditure, asset information, investment preference information, and emotional data from the terminal.

[1290] Users fill out the application form with their monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount they can invest, their investment preferences (risk tolerance, etc.), and their emotional state at the time (e.g., stress, sense of security), and click the submit button.

[1291] Data collection and emotion recognition

[1292] The terminal sends the input data to the server.

[1293] The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1294] The server analyzes the user's emotion data using an emotion engine.

[1295] The server uses an emotion engine to analyze the user's input emotional data and evaluate their psychological state. For example, if their stress level is high, it will prioritize low-risk plans.

[1296] Data analysis

[1297] The server analyzes the received income and expenditure data to grasp the user's income and expenditure situation and current state of assets.

[1298] The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes this data to calculate the balance between income and expenditure, current savings, and available investments.

[1299] The server uses a machine learning module to analyze the user's spending patterns.

[1300] The server performs feature extraction based on past data to extract spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[1301] Generate a financial plan

[1302] The server generates an optimal asset management plan based on the analysis results and emotional data.

[1303] The server generates an optimal investment plan based on the income and expenditure forecast data and the user's sentiment assessment, which includes specific investment options (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[1304] Feedback and Suggestions

[1305] The server performs a risk assessment of the generated investment plan and makes adjustments based on the sentiment data.

[1306] The risk assessment module calculates a risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, a low-risk plan will be prioritized.

[1307] The server transmits the generated plan to the terminal, which displays the plan to the user.

[1308] The server sends the generated asset management plan in JSON format to the terminal, and the terminal displays the received plan in the application UI. The user can check the plan and make any necessary modifications.

[1309] Execution and monitoring

[1310] The user approves the plan and the terminal sends the final plan to the server.

[1311] When the user clicks the approve button, the terminal again sends the final plan to the server, and the server stores the approved plan in the database.

[1312] The server initiates asset management based on the approved plan and continuously monitors and adjusts it.

[1313] The server executes specific investment operations through the specified investment API. It also periodically evaluates market data and user asset data to monitor investment performance, rebalancing plans and presenting new investment opportunities when necessary. When significant changes occur, the server sends notifications to the terminal and asks for user confirmation.

[1314] Specific examples

[1315] Collection and analysis of income, expenditure and sentiment data

[1316] 1. The user enters the following information into the terminal:

[1317] Monthly income: 500,000 yen

[1318] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1319] Investment limit: 100,000 yen

[1320] Investment preference: Low risk

[1321] Emotional data: High stress levels

[1322] 2. The server uses an emotion engine to analyze the emotion data and predicts income and expenditure based on the income and expenditure data.

[1323] 3. The server generates an optimal asset management plan based on income and expenditure forecast data, emotional data, and investment preference information, and performs risk assessment.

[1324] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency reserve.

[1325] 4. The user reviews and approves the proposed plan.

[1326] If the user approves, the server automatically executes the investment and completes the mutual fund purchase.

[1327] Continuous monitoring and adjustment

[1328] 1. The server monitors the performance and balance of your investments monthly, and also periodically evaluates sentiment data.

[1329] If your income increases and your investment trust returns are high, your stress level will be recognized as decreasing, and your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[1330] 2. The server notifies the device of the adjustments and asks the user for confirmation.

[1331] Once the user approves the changes, the new investment plan will be applied.

[1332] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[1333] The processing flow will be explained below.

[1334] Step 1:

[1335] The user inputs their income, expenses, asset information, investment preferences, and emotional data from their device. Specifically, the user inputs their monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), available investment amount, investment preferences (risk tolerance, etc.), and current emotional state (e.g., stress, sense of security) into the application form, and clicks the submit button.

[1336] Step 2:

[1337] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1338] Step 3:

[1339] The server uses an emotion engine to analyze the user's emotional data. The server uses the emotion engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[1340] Step 4:

[1341] The server analyzes the received income and expenditure data to understand the current state of the user's income and expenditure and assets. The server retrieves the income and expenditure data from the database, and the income and expenditure analysis module analyzes this data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[1342] Step 5:

[1343] The server uses machine learning modules to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[1344] Step 6:

[1345] The server generates an optimal asset management plan based on the analysis results and emotion data. The server generates an optimal asset management plan based on income and expenditure forecast data and the user's emotion evaluation. This plan includes specific investment targets (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[1346] Step 7:

[1347] The server evaluates the risk of the generated asset management plan and makes adjustments based on emotional data. The risk assessment module calculates the risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state as assessed. For example, if the user is in a high-stress state, a low-risk plan will be selected first.

[1348] Step 8:

[1349] The server sends the generated plan to the terminal. The server sends the generated asset management plan in JSON format to the terminal.

[1350] Step 9:

[1351] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[1352] Step 10:

[1353] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device again sends the final plan to the server, and the server stores the approved plan in the database.

[1354] Step 11:

[1355] The server starts asset management based on the approved plan. The server then executes specific investment operations through the specified investment API, such as purchasing mutual funds or trading stocks. Once the transaction is complete, the server updates the user's asset status.

[1356] Step 12:

[1357] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or suggests new investment opportunities. If any significant changes occur, the server sends a notification to your device, asking for your confirmation.

[1358] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management. In addition, by taking emotion data into consideration, the psychological burden on users can be reduced.

[1359] Example 2

[1360] 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."

[1361] Current income / expense management and asset management systems do not take into account the user's psychological state or emotions, and do not adequately adjust risk assessments or asset management plans. As a result, they are unable to provide an appropriate asset management plan when the user is under high stress or when their risk tolerance fluctuates, which could lead to inappropriate investment risks. This increases the user's psychological burden and makes it difficult to achieve optimal financial planning.

[1362] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1363] In this invention, the server includes means for inputting income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for evaluating the user's psychological state based on the income, expenditure, and emotional data, means for generating an asset management plan based on the analysis results, means for adding a risk assessment to the generated asset management plan and adjusting the risk according to the user's psychological state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This makes it possible to provide an optimal asset management plan taking into account the user's psychological state and achieve effective financial planning while reducing the user's psychological burden.

[1364] "Income and expenditure data" is numerical information about the household's economic situation, such as monthly household income, monthly fixed expenditures, and available investment amounts.

[1365] The "means for inputting" is an interface for the user to input income, expenditure, and emotion data into the terminal.

[1366] The "analyzing means" refers to algorithms and modules that allow the server to obtain income and expenditure data and make income and expenditure forecasts based on this data.

[1367] The "emotion analysis means" is an engine and corresponding software for analyzing the emotion data input by the user and evaluating the user's psychological state.

[1368] An "asset management plan" is an investment and asset management plan suited to a user, generated based on income and expenditure data and emotion data.

[1369] The "means for adding risk assessment" is a module for calculating a risk score for the generated asset management plan and adjusting the risk according to the user's psychological state.

[1370] The "means for requesting approval or modification" is an interface for presenting the generated asset management plan to the user and requesting the user to approve or modify the plan.

[1371] "Means for automatically executing investments" refers to a system and API for automatically performing investment operations based on an asset management plan approved by a user.

[1372] "Means for continuous monitoring of investment performance" are modules and software for periodically evaluating the results of investments and adjusting investment plans as necessary.

[1373] A "machine learning module" is an algorithm and corresponding software for analyzing income and expense data and learning a user's spending patterns.

[1374] "Risk tolerance" is a standard indicating how much risk a user can tolerate, and is an important factor when generating an asset management plan.

[1375] This invention is a system for efficiently managing household income and expenditures and asset management, and realizes individually optimized asset management by integrating an emotion engine that recognizes the user's emotions. The system inputs income, expenses, asset information, investment preferences, and emotion data, performs income and expenditure forecasts and emotion analysis, then generates an optimal asset management plan, automatically executes investments, and continuously monitors and adjusts them.

[1376] Hardware and Software

[1377] The system consists of the following hardware and software:

[1378] User Device: Used by users to input income, expenses, asset information, investment preferences, and emotional data. This includes smartphones, tablets, and PCs.

[1379] Server: Receives data, analyzes it, analyzes sentiment, and generates investment plans. The server includes a database, sentiment engine, machine learning module, and risk assessment module. For example, a cloud platform such as Amazon Web Services (AWS) can be used.

[1380] Application software: A program that runs on user terminals and servers and is responsible for inputting income and expenditure data, sending and receiving data, displaying analytical results, and creating and executing asset management plans.

[1381] Data processing and calculation

[1382] 1. Collect input data:

[1383] The user uses a terminal to input income, expenses, asset information, investment preferences, and emotional data into the application form.

[1384] Example: A user inputs a monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, investment amount of 100,000 yen, investment preference as low risk, and emotional data as high stress level.

[1385] 2. Data transmission and storage:

[1386] The terminal converts the input data into JSON format and sends it to the server's API endpoint via HTTPS.

[1387] The server stores the received data in a database.

[1388] 3. Emotion analysis:

[1389] The server uses an emotion engine to analyze the user's emotion data.

[1390] For example, the user's stress level is high, so the emotion engine evaluates it as "high stress."

[1391] 4. Balance analysis:

[1392] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts.

[1393] The income and expenditure analysis module analyzes the balance between income and expenditure, savings, and investment potential.

[1394] 5. Analysis of spending patterns:

[1395] The server runs a machine learning module based on past data to model spending patterns.

[1396] Example: Using past data, predict "average monthly expenditure of 250,000 yen and savings of 100,000 yen."

[1397] 6. Generate a financial plan:

[1398] The server generates an optimal asset management plan based on income and expenditure forecast data and emotional data.

[1399] Example: A plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen for emergencies.

[1400] 7. Risk Assessment and Adjustment:

[1401] The server performs risk assessment on the generated asset management plan and adjusts the risk as necessary.

[1402] Example: determining that low-risk mutual funds are suitable for a user in a high-stress state.

[1403] 8. Viewing and Approving the Plan:

[1404] The terminal displays the generated asset management plan to the user and requests approval or modification.

[1405] The user clicks the approve button and the final plan is sent to the server.

[1406] 9. Asset management execution and monitoring:

[1407] The server starts asset management based on the approved plan and executes investment operations through the specified investment API.

[1408] Continually evaluate market data and your asset data to rebalance your investment plan or suggest new investment opportunities as needed.

[1409] Example prompt

[1410] As a concrete example, a user enters the following information:

[1411] Monthly income: 500,000 yen

[1412] Fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1413] Investment limit: 100,000 yen

[1414] Investment preference: Low risk

[1415] Emotional data: High stress levels

[1416] In this way, the system of the present invention can efficiently and effectively manage household income and expenditures and manage assets, and realize optimal asset management that takes into account the user's psychological state.

[1417] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1418] Step 1: User Enters Information

[1419] The user uses a dedicated application to input their monthly income, monthly fixed expenses, available investment amount, investment preferences, and emotional data (e.g., stress level). After filling out each item in the input form and clicking the submit button, the data is entered into the terminal.

[1420] Input: Income, expenses, investment preferences, emotional data

[1421] Output: Input data

[1422] Step 2: The device sends the data

[1423] The device converts the input data into JSON format, which makes it possible to send the data to the server.The device then sends the data to the server's API endpoint via HTTPS.

[1424] Input: Data entered by the user

[1425] Output: JSON format data

[1426] Step 3: The server receives and stores the data

[1427] The server receives HTTP requests sent from the device. It analyzes the received data and stores it in a database. The database stores all data necessary for income and expenditure management and sentiment analysis.

[1428] Input: JSON format data

[1429] Output: Data stored in the database

[1430] Step 4: The server analyzes the emotion data

[1431] The server launches the emotion engine and analyzes the emotion data from the user's input data. For example, if the server recognizes that the stress level is high, the emotion engine will judge it as "high stress." This result will be used to generate a subsequent asset management plan.

[1432] Input: Emotion data

[1433] Output: Emotion analysis results

[1434] Step 5: The server analyzes the data

[1435] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts. The income and expenditure analysis module analyzes this data and calculates the balance between income and expenditure, current savings, and available investment amounts.

[1436] Input: Income data, expenditure data

[1437] Output: Profit and loss forecast results

[1438] Step 6: The server analyzes spending patterns

[1439] The server runs a machine learning module using historical data to analyze spending patterns, perform feature extraction, and model spending trends and patterns, which can then be used to predict future spending.

[1440] Input: Past income and expenditure data

[1441] Output: A model of spending patterns

[1442] Step 7: The server generates the investment plan.

[1443] The server generates an optimal asset management plan based on the income / expense forecast data and sentiment analysis results. This plan includes specific investment targets and predicted returns. For example, a specific plan such as "invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency fund" may be proposed.

[1444] Input: Revenue and expenditure forecast data, emotion analysis results

[1445] Output: Wealth Management Plan

[1446] Step 8: Server performs risk assessment and adjustments

[1447] The server performs risk assessment on the asset management plan generated and makes adjustments as necessary. The risk assessment module calculates a risk score for each plan and adjusts the risk based on the results of sentiment analysis. For example, if a user is in a high stress state, it will prioritize low-risk plans.

[1448] Input: Asset management plan, sentiment analysis results

[1449] Output: Adjusted financial plan

[1450] Step 9: The device displays the plan to the user

[1451] The server sends the generated financial plan in JSON format to the terminal, which receives this data and displays it in the application's user interface. The user can then review the plan and make any necessary modifications.

[1452] Input: Generated Investment Plan

[1453] Output: The plan displayed to the user

[1454] Step 10: User approves the plan

[1455] If the user is satisfied with the proposed asset management plan, he or she clicks the approval button. This operation causes the terminal to send the final approved plan back to the server.

[1456] Input: User approval button click

[1457] Output: Final approved plan

[1458] Step 11: The server starts and monitors the asset management

[1459] The server starts asset management based on the approved plan. It executes specific investment operations through the specified investment API and periodically evaluates market data and the user's asset data to monitor investment performance. It rebalances the plan and presents new investment opportunities as needed. If any important changes occur, it sends a notification to the terminal and asks the user for confirmation.

[1460] Input: Final Approved Plan

[1461] Output: Executed asset operations, ongoing monitoring results

[1462] (Application example 2)

[1463] 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."

[1464] Conventional income / expense management and asset management systems are limited to analyzing income and expenditure data and are unable to consider the user's psychological state, resulting in the problem of only being able to provide a uniform asset management plan. Furthermore, these systems are inadequate for continuous investment performance monitoring and investment plan adjustment, often resulting in suboptimal user investment activities. Furthermore, a lack of integration with electronic payment services limits automation when implementing investment plans, placing a heavy burden on users.

[1465] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for recognizing the user's emotional data and evaluating the emotional state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for making electronic payments for implementing the investment plan generated by the system. This makes it possible to provide an individually optimized asset management plan that takes the user's emotional state into consideration, and to automatically execute investments and continuously monitor and adjust them.

[1466] The "means for inputting household income and expenditure data" is an interface through which a user provides information about his or her income and expenditure to the system.

[1467] The "means for analyzing input income and expenditure data and forecasting income and expenditure" is an analysis module for forecasting future income and expenditure based on income and expenditure data.

[1468] The "means for generating an asset management plan" is a system for proposing investment strategies and asset allocations based on the analysis results.

[1469] The "means for recognizing the user's emotional data and evaluating the emotional state" is a module that has the function of analyzing the user's psychological state using an emotion engine or the like and evaluating that state.

[1470] The "means for presenting the generated asset management plan to the user and requesting approval or modification from the user" is a user interface for notifying the user of the investment plan generated by the system and receiving instructions for approval or modification.

[1471] The "means for automatically executing investments based on an asset management plan approved by a user" is a function for automatically performing investment operations in accordance with an investment plan approved by a user.

[1472] "Means for continuously monitoring the performance of investments and adjusting the investment plan as needed" means a system for regularly monitoring the results of investments and changing the investment strategy as needed.

[1473] "Means for making electronic payments to implement the investment plan generated by the system" refers to a function for electronically executing the necessary payments and transactions based on the investment plan generated.

[1474] This invention is a system that efficiently manages household income and expenditures and asset management all at once, and by integrating an emotion engine that recognizes the user's emotions, it achieves more individually optimized asset management.

[1475] The system provides a means for users to input information about their income, expenses, investment aspirations, and emotions. Specifically, a smartphone app provides an interface for collecting this data. Users enter their monthly income, fixed expenses, available investment amount, investment aspirations, and emotional state into the application form and click the submit button.

[1476] The terminal sends the entered data to the server, which converts the data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1477] The server uses the Emotion Engine to analyze the user's emotional data. Specifically, the server uses the Emotion Engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[1478] The server analyzes the received income and expenditure data to understand the user's current income and expenditure status and assets. The server also uses a machine learning module to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[1479] Based on the analysis results and the emotional data, the server generates an optimal asset management plan. Based on the income and expenditure forecast data and the user's emotional evaluation, the server creates an investment plan that includes specific investment targets and predicted returns.

[1480] The server evaluates the risk of the generated asset management plan and adjusts it based on the emotional data. The risk assessment module calculates the risk score for each plan, and the emotional engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, it will prioritize low-risk plans.

[1481] The server sends the generated plan to the terminal, which displays it to the user. The user can check the plan and make any necessary modifications. When the user clicks the approve button, the terminal sends the final plan back to the server, which then stores the approved plan in the database.

[1482] The server initiates asset management based on the approved plan and continuously monitors and adjusts it. The server executes specific investment operations through the designated investment API, periodically evaluates market data and the user's asset data to monitor investment performance, and rebalances the plan or presents new investment opportunities when necessary. If any significant changes occur, the server will send a notification to the terminal and ask the user for confirmation.

[1483] As a concrete example, consider a case where a user has a monthly income of 500,000 yen, fixed monthly expenses of 300,000 yen, and an investment capacity of 100,000 yen, and is currently in a high stress state. In this case, the application will propose a plan to invest 90,000 yen per month in a low-risk mutual fund and keep 10,000 yen as a reserve.

[1484] An example of a prompt to input to a generative AI model is as follows:

[1485] Generate an optimal financial plan based on the user's monthly income, monthly fixed expenses, investment capacity, investment preferences, and emotional data. If the user's stress level is high, suggest a low-risk plan. Below is a sample of user data:

[1486] Monthly income: 500,000 yen

[1487] Monthly fixed expenses: 300,000 yen

[1488] Investment limit: 100,000 yen

[1489] Investment preference: Low risk

[1490] Emotional data: High stress levels

[1491] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[1492] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1493] Step 1:

[1494] The user inputs income, expenses, investment preferences, and emotional data. The user enters monthly income, fixed expenses (e.g., rent, utilities, food, etc.), available investment amount, investment preferences, and emotional state (e.g., high stress level) into a form on the smartphone app and clicks the submit button. The input data is monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, available investment amount of 100,000 yen, investment preferences are low risk, and the emotional data is high stress.

[1495] Step 2:

[1496] The terminal sends the input data to the server. The terminal converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The input data includes monthly income, fixed expenses, available investment amount, investment preference, and sentiment data. The server receives this data and stores it in a database.

[1497] Step 3:

[1498] The server analyzes the emotion data. The server uses an emotion engine to evaluate the user's psychological state based on the emotion data entered by the user. Specifically, the emotion engine analyzes the user's stress level and determines that the user's stress level is high as a result of the evaluation.

[1499] Step 4:

[1500] The server analyzes the income and expenditure data and grasps the income and expenditure situation. The server retrieves the income and expenditure data from the database and analyzes the data in the income and expenditure analysis module. This outputs the balance of income and expenditure, the current amount of savings, and the amount available for investment.

[1501] Step 5:

[1502] The server analyzes spending patterns using a machine learning module. The server performs feature extraction using past income and expenditure data to extract spending trends and patterns. This outputs future income and expenditure forecast data and generates a spending pattern model for the user.

[1503] Step 6:

[1504] The server generates an optimal asset management plan based on the analysis results and emotional data. Based on income / expense forecast data and emotional evaluation, the server creates an investment plan that includes specific investment targets (e.g., low-risk investment trusts) and predicted returns. Users with high stress levels are suggested low-risk plans, and each plan is also risk-assessed.

[1505] Step 7:

[1506] The server sends the generated asset management plan to the terminal, which then displays the plan to the user. The server also sends the generated management plan in JSON format to the terminal, which then displays it on the application UI based on the received data. This allows the user to check the plan and approve or modify it.

[1507] Step 8:

[1508] The user approves the plan, and the device sends the final plan to the server. The user clicks the approve button, and this action causes the device to send the final plan back to the server. The server saves the approved plan in its database.

[1509] Step 9:

[1510] The server initiates asset management based on the approved plan and continuously monitors and adjusts it. The server executes specific investment operations through the designated investment API, periodically evaluates market data and the user's asset data to monitor investment performance, and rebalances the plan or presents new investment opportunities when necessary, and sends notifications to the terminal when important changes occur.

[1511] In this way, all processing steps are carried out in a single flow, and the system automatically implements optimal asset management while taking into account the user's emotional state.

[1512] 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.

[1513] 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.

[1514] 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.

[1515] [Fourth embodiment]

[1516] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1517] 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.

[1518] 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).

[1519] 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.

[1520] 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.

[1521] 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).

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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."

[1529] This invention is a system that efficiently manages everything from household income and expenditure management to asset management. Users input their income, expenditure information, and investment preferences, and the system then automatically analyzes the data to propose and implement an effective asset management plan, while continuously monitoring and adjusting it.

[1530] Program processing

[1531] Collecting input data

[1532] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[1533] The user enters information into the application's input form, such as monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount available for investment, and investment preferences (risk tolerance, etc.).

[1534] After entering the data, click the send button to send the data from the terminal to the server.

[1535] Data analysis

[1536] The server analyzes the received data and determines the user's income and expenditure status and current asset status.

[1537] The server stores the received data in a database, and the income and expenditure analysis module analyzes this data.

[1538] Based on the analysis results, the current savings, investment potential, and balance between income and expenditure are calculated.

[1539] The server uses AI models to predict users' spending patterns and future income and expenditures.

[1540] The server uses a machine learning module to learn the user's spending patterns from past income and expenditure data.

[1541] Based on this data, future income and expenditure forecasts are made, and fluctuations in the user's future income and expenditures are predicted.

[1542] Generate a financial plan

[1543] The server generates an optimal asset management plan based on the data obtained.

[1544] The server evaluates various investment options based on the user's income and expenditure forecast data and investment preferences, and generates an optimal asset management plan.

[1545] The generated plans include low-risk, medium-risk, and high-risk investment plans and their return projections.

[1546] The plan includes multiple investment options and risk assessments.

[1547] The server uses a risk assessment module to perform a risk assessment for each investment plan.

[1548] Calculate a risk score for each plan and select the most appropriate plan based on the user's risk tolerance.

[1549] Feedback and Suggestions

[1550] The server sends the generated plan to the terminal.

[1551] The server sends the generated asset management plan in JSON format to the terminal.

[1552] The terminal displays the plan to the user, who then approves or modifies it.

[1553] The device displays the received plan on the application's UI.

[1554] The user reviews the proposed plan and makes any necessary modifications.

[1555] After the user approves, the terminal sends the final plan to the server.

[1556] If the user approves the final plan, the terminal transmits the final plan to the server.

[1557] The server stores this final plan in a database.

[1558] Execution and monitoring

[1559] The server starts asset management based on the approved plan.

[1560] The server automatically executes investment operations through the specified investment API.

[1561] The server continuously monitors the performance of the investments and adjusts the investment plan as needed.

[1562] The server periodically evaluates market data and the user's investment performance and makes any necessary adjustments.

[1563] If there is an important change, the server will send a notification to the terminal and ask the user for confirmation.

[1564] Specific examples

[1565] Initial Setup and Plan Generation

[1566] 1. The user enters the following information into the terminal:

[1567] Monthly income: 500,000 yen

[1568] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1569] Investment limit: 100,000 yen

[1570] Investment preference: Low risk

[1571] 2. The server analyzes the balance of income and expenditures and generates an optimal asset management plan.

[1572] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen in cash as an emergency reserve.

[1573] 3. The user reviews and approves the proposed plan.

[1574] If the user approves, the server automatically executes the investment and makes the mutual fund purchase.

[1575] Continuous monitoring and adjustment

[1576] 1. The server monitors the performance and balance of your investments monthly.

[1577] If your income increases and your investment trust returns are high, your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[1578] 2. The server notifies the terminal of the adjustment results and asks the user for confirmation.

[1579] Once the user approves the changes, the new investment plan will be applied.

[1580] In this way, the system of the present invention can efficiently manage household income and expenditures and asset management all at once, allowing users to optimally manage their assets without any hassle.

[1581] The processing flow will be explained below.

[1582] Step 1:

[1583] The user enters information about their income, expenses, assets, and desired investments from their device. Specifically, the user enters their monthly income, fixed monthly expenses (e.g., rent, utilities, food, etc.), available investment amount, and desired investments (risk tolerance, etc.) into the application form and clicks the submit button.

[1584] Step 2:

[1585] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1586] Step 3:

[1587] The server analyzes the received data to understand the user's current income and expenditure situation and assets. The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes the data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[1588] Step 4:

[1589] The server uses machine learning modules to analyze the user's spending patterns, performs feature extraction based on past data, and extracts spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[1590] Step 5:

[1591] The server generates an optimal investment plan for the user based on the income and expenditure forecast data. The server evaluates investment options and creates multiple investment plans based on risk tolerance. Each plan includes specific investments (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[1592] Step 6:

[1593] The server performs risk assessment of the generated investment plans. The risk assessment module calculates a risk score for each plan and sorts the plans based on the user's risk tolerance. The most suitable plan is selected.

[1594] Step 7:

[1595] The server sends the generated asset management plan to the terminal. The server then sends the selected plan to the terminal in JSON format.

[1596] Step 8:

[1597] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[1598] Step 9:

[1599] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device sends the final plan to the server again. The server saves the approved plan in the database.

[1600] Step 10:

[1601] The server will start asset management based on the approved plan, and then execute specific investment operations such as purchasing stocks or mutual funds through the specified investment API. Once the transaction is completed, the server will update the user's asset status.

[1602] Step 11:

[1603] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or presents new investment opportunities. If there are any major changes, the server sends a notification to your device and asks for your confirmation.

[1604] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management.

[1605] Example 1

[1606] 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."

[1607] Currently, efficient household income and expenditure management and asset management requires multiple different applications and manual work, placing a heavy burden on users. Furthermore, analyzing income and expenditure data and making future predictions requires advanced knowledge, making it difficult for average users to use. Furthermore, there is a lack of systems that can consistently create, execute, and monitor asset management plans.

[1608] 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.

[1609] In this invention, the server includes means for transmitting and storing input income and expenditure data to the server, means for analyzing the stored data and making income and expenditure forecasts, means for generating an asset management plan based on the income and expenditure forecasts, means for transmitting the generated asset management plan to a terminal and presenting it to the user, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This allows users to efficiently and automatically perform everything from income and expenditure management to asset management through a single system.

[1610] "Income" is a general term for the money and assets that a household or individual receives within a certain period of time.

[1611] "Expenditure" is a general term for the money consumed by households and individuals within a certain period of time.

[1612] A "server" is a computer system that stores and processes data on a network.

[1613] A "database" is an information system for efficiently storing, searching, and updating data.

[1614] "Analysis" is the process of organizing and analyzing data to extract useful information.

[1615] "Income and expenditure forecasting" refers to predicting future income and expenditure based on past income and expenditure data.

[1616] An "asset management plan" is a plan that proposes an efficient method of managing assets, taking into account the balance of income and expenditure of individuals or households.

[1617] "Terminal" refers to a computing facility that is directly operated by a user and is a device for input and display.

[1618] A "machine learning module" is a software module that learns rules and patterns from data and uses them to make predictions and classifications.

[1619] "Risk tolerance" refers to the range and degree of risk that a user can accept in investment or asset management.

[1620] An "investment API" is a program interface that works in conjunction with external systems to automate investment operations.

[1621] The present invention is a system that inputs household income and expenditure data, analyzes the data to generate income and expenditure forecasts and asset management plans, and then executes and monitors the plans. This system provides a series of functions that users can use easily and efficiently to manage their assets.

[1622] System Program Overview

[1623] Collecting input data

[1624] The user inputs income, expenses, asset information, and desired investment information from the terminal. The terminal collects this data in the application's input form. For example, monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), investment amount, and risk tolerance are input. The input data is sent from the terminal to the server by clicking the send button. The data is converted to JSON format and securely sent to the server using the HTTPS protocol.

[1625] Data analysis

[1626] The server stores and analyzes the received data. The server stores the data in a database (e.g., MySQL or PostgreSQL). An income / expense analysis module (e.g., using the pandas and numpy libraries) analyzes the data and calculates the current savings, investment potential, and balance of income and expenses.

[1627] Revenue and expenditure forecast

[1628] The server uses machine learning modules (such as TensorFlow and scikit-learn) to learn the user's spending patterns from past income and expenditure data. Based on the learning results, it predicts future income and expenditure and investment potential, and generates income and expenditure forecast data.

[1629] Generate a financial plan

[1630] The server evaluates various investment options based on income / expense forecast data and investment preference information, and generates an optimal asset management plan. This uses the Markowitz model to evaluate risk and return. The generated plan includes multiple investment options, each of which has been risk assessed in the risk assessment module.

[1631] Plan presentation and approval

[1632] The generated asset management plan is sent to the terminal. The terminal displays the received plan on the UI and presents it to the user. The user checks the plan, modifies it if necessary, and approves the final plan. The approved plan is sent back to the server and saved in the database.

[1633] Asset management execution

[1634] The server automatically executes investment operations based on the approved plan through a designated investment API (e.g., Alpha Vantage API).

[1635] Continuous monitoring and adjustment

[1636] The server regularly monitors market data and the user's investment performance, adjusting the investment plan as needed. If there are any significant changes, the server sends a notification to the terminal and asks the user for confirmation.

[1637] Specific examples of operation

[1638] The user types:

[1639] Monthly income: 500,000 yen

[1640] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1641] Investment limit: 100,000 yen

[1642] Investment preference: Low risk

[1643] The server analyzes the income and expenditure balance and generates an optimal asset management plan. For example, it proposes a plan to invest 100,000 yen per month in low-risk mutual funds and keep 50,000 yen in cash as an emergency reserve. If the user approves this plan, the server automatically purchases the mutual funds and regularly monitors the investment performance. If income increases and the mutual fund returns are high, the server automatically adjusts the investment amount from 100,000 yen to 150,000 yen. If the user approves the change, the new investment plan is applied.

[1644] This system allows users to manage their assets optimally without any hassle.

[1645] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1646] Step 1: Collect input data

[1647] The user inputs income, expenses, asset information, and desired investment information from the terminal.

[1648] Users enter their monthly income, fixed expenses, investment amount, and risk tolerance in the application's input form.

[1649] As a specific example, suppose your monthly income is 500,000 yen, your monthly fixed expenses are 300,000 yen, the amount you can invest is 100,000 yen, and you choose low risk as your investment preference.

[1650] The input data is transmitted from the terminal to the server.

[1651] The input data is converted to JSON format and sent securely to the server using the HTTPS protocol.

[1652] Step 2: Save your data

[1653] The server stores the received data in a database.

[1654] The server parses the received JSON data and stores it in a database (e.g., MySQL or PostgreSQL).

[1655] The input data is saved, and the database stores the user's income, expenses, and investment preferences.

[1656] Step 3: Balance analysis

[1657] The server analyzes the income and expenditure data.

[1658] The saved data is read and analyzed using a balance analysis module (for example, using the pandas or numpy library).

[1659] Specifically, subtract fixed expenses from monthly income to calculate the amount available for investment.

[1660] The output is the current savings amount and monthly income and expenditure balance.

[1661] Step 4: Revenue and Expense Forecast

[1662] The server uses a machine learning module to make income and expenditure predictions.

[1663] It uses machine learning libraries such as TensorFlow and scikit-learn to learn users' spending patterns based on past income and expenditure data.

[1664] Based on the learning results, future income and expenditures are predicted and specific income and expenditure forecast data is generated.

[1665] The output is a forecast of future income and expenditure.

[1666] Step 5: Create a financial plan

[1667] The server generates an asset management plan based on the income and expenditure forecast data and desired investment information.

[1668] Uses the Markowitz model to evaluate risk and return and generate optimal investment plans.

[1669] The risk assessment module performs a risk assessment for each investment option and calculates a risk score.

[1670] The output is low-risk, medium-risk, and high-risk investment plans, each with a return forecast.

[1671] Step 6: Plan presentation and approval

[1672] The asset management plan generated by the server is sent to the terminal.

[1673] The generated asset management plan is sent to the terminal in JSON format.

[1674] The terminal displays the plan to the user.

[1675] The terminal displays the plan in the application's UI (using, for example, React or Vue.js).

[1676] The user checks the plan and makes any necessary modifications. The modified plan is also sent from the device to the server.

[1677] The output is a user-approved financial plan.

[1678] Step 7: Implementing asset management

[1679] The server starts asset management based on the approved plan.

[1680] Based on the asset management plan, investment operations are automatically executed through the specified investment API (e.g., Alpha Vantage API).

[1681] Specifically, operations such as purchasing and selling investment trusts are carried out periodically.

[1682] As an output, we get a log of the investment operations that were performed.

[1683] Step 8: Continuously monitor and adjust

[1684] The server periodically monitors the performance of the investment.

[1685] Evaluate market data and your investment performance and adjust your investment plan as needed.

[1686] If there is an important change, a notification is sent to the terminal and the user is asked to confirm.

[1687] The output is a new adjusted investment plan or change notice.

[1688] By following the above steps, users can easily and efficiently manage their household income and expenditures and asset management all at once.

[1689] (Application example 1)

[1690] 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."

[1691] Improving the efficiency of household income and expenditure management and asset management is an important issue for many households. However, manual income and expenditure management is cumbersome and difficult for users without knowledge or experience in asset management. Furthermore, existing systems lack real-time management of income and expenditure data and automatic asset management plan generation, resulting in poor usability. In addition, integration with electronic payment services is incomplete, and functions for collecting and managing income and expenditure information in real time are lacking, making it difficult for users to obtain accurate data for asset management.

[1692] 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.

[1693] In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for presenting the generated asset management plan to a user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for collecting and managing income and expenditure information in real time in cooperation with an electronic payment service. This enables efficient, integrated management of everything from household income and expenditure management to asset management, allowing users to optimally manage their assets based on accurate and timely data.

[1694] "Income and expenditure data" refers to monetary information on household and individual income sources (salary, bonuses, secondary income, etc.) and expenditures (rent, utilities, food, loans, etc.).

[1695] "Analysis" refers to the detailed analysis of collected data using machine learning and other algorithms to extract trends and patterns.

[1696] "Income and expenditure forecast" is the prediction of future trends in income and expenditure based on past income and expenditure data.

[1697] An "asset management plan" is a plan that shows the optimal asset management strategy, including investment targets and investment amounts, based on information such as the user's income, expenses, and risk tolerance.

[1698] "Means for seeking approval or amendment" refers to an interface or other method for presenting the generated asset management plan to the user and allowing the user to review the plan and approve or amend it as necessary.

[1699] "Means for automatically executing investments" refers to programs or API interfaces that allow the system to automatically perform investment operations based on the asset management plan approved by the user.

[1700] "Continuous performance monitoring measures" refers to a monitoring system that regularly reviews investment results and market trends and adjusts the investment plan if necessary.

[1701] "Electronic payment services" refers to online and offline payment methods such as credit cards, debit cards, and mobile payments.

[1702] "Means of collecting and managing in real time" refers to the technology and systems for instantly collecting income and expenditure data and storing and managing it in a database.

[1703] These definitions clarify how the elements of the present invention function and interact.

[1704] The system for embodying the present invention inputs income and expenditure data, analyzes the data, predicts income and expenditure, and creates and executes an asset management plan. The system of the present invention includes the following specific means.

[1705] Hardware and software used

[1706] Hardware

[1707] Smartphone

[1708] software

[1709] Payment API

[1710] Machine learning module (TensorFlow)

[1711] Database (MySQL)

[1712] Backend server (Django)

[1713] Program processing

[1714] 1. Enter user data

[1715] Users use their smartphones to input data such as income, expenses, and investment preferences, including monthly income, fixed expenses, investment capacity, and risk tolerance.

[1716] This data is collected through an input form and sent to the server when the user presses the submit button.

[1717] 2. Data collection and analysis

[1718] The server receives the entered data and stores it in a database.

[1719] Real-time data on users' income and expenditures is collected through a payment API, which is then analyzed using a machine learning module to determine users' spending patterns and forecast income and expenditures.

[1720] Based on the collected and stored data, the server analyzes the balance of income and expenditure and makes predictions about future income and expenditure.

[1721] 3. Generate an investment plan

[1722] The server generates an optimal asset management plan based on the income and expenditure forecast data and the user's investment preferences. Specifically, it creates low-risk, medium-risk, and high-risk plans and performs a risk assessment for each.

[1723] The generated plan includes investment options and their expected returns based on the user's risk tolerance.

[1724] 4. Feedback and Suggestions

[1725] The server sends the generated operational plan in JSON format to the terminal, and the smartphone application displays it to the user.

[1726] The user operates an interface to review the proposed plan and approve or modify it.

[1727] 5. Execution and Monitoring

[1728] Once the user approves the final plan, the server will automatically execute the investment operation through the specified investment API.

[1729] The server continuously monitors the performance of the investments and makes necessary adjustments based on market data and the user's investment performance.

[1730] Specific examples

[1731] Example of input data

[1732] The user uses a smartphone to enter the following data:

[1733] Monthly income: 500,000 yen

[1734] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1735] Investment limit: 100,000 yen

[1736] Investment preference: Low risk

[1737] Example prompts to be input to the generative AI model

[1738] text

[1739] User-entered data:

[1740] Monthly salary: 450,000 yen

[1741] Monthly fixed costs: 250,000 yen

[1742] Investment limit: 100,000 yen

[1743] Risk tolerance: Medium risk

[1744] Historical Spending Data:

[1745] Food expenses: 50,000 yen / month

[1746] Transportation fee: 10,000 yen / month

[1747] Entertainment expenses: 30,000 yen / month

[1748] Future income projections:

[1749] Projected revenue growth: 5% / year

[1750] Asset Management Plan:

[1751] Generate investment portfolios based on risk tolerance

[1752] Medium-risk domestic stocks: 40%

[1753] Medium-risk index mutual funds: 60%

[1754] Plan generation:

[1755] Execute the generated plan and monitor performance monthly, making any necessary adjustments accordingly.

[1756] This allows users to efficiently manage their daily income and expenditures and manage their assets without any hassle. The system offers a high degree of automation and real-time data collection and analysis capabilities, enabling users to optimize their assets.

[1757] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1758] Step 1:

[1759] The user enters information about their income, expenses, and desired investments on their smartphone. Specifically, they enter data such as monthly income, fixed expenses, available investment amount, and risk tolerance into the application's input form. The entered data is temporarily saved in the device's memory. When the user presses the "Send" button, the data is sent to the server in JSON format. The entered data and the sent data are as follows:

[1760] Input data: monthly income, fixed expenses, investment amount, risk tolerance

[1761] Output data: User data in JSON format

[1762] Step 2:

[1763] The server analyzes the received JSON format data. The server saves the data in a database, and then analyzes this data in the income and expenditure analysis module. Specifically, it calculates monthly income and expenses, the amount available for investment, and calculates the income and expenditure balance. The analysis results are saved in the database, and the AI ​​model uses the analysis results to make income and expenditure predictions. This process has the following inputs and outputs:

[1764] Input data: User data in JSON format

[1765] Output data: Income and expenditure balance, income and expenditure forecast data

[1766] Step 3:

[1767] The server generates an investment plan using the balance and forecast data. The investment plan generation module evaluates various investment options and creates low-risk, medium-risk, and high-risk plans. Each plan also includes a risk assessment score, and the most suitable plan is selected based on the user's risk tolerance. The generated plans are saved in JSON format. This process has the following inputs and outputs:

[1768] Input data: Income and expenditure balance, income and expenditure forecast data, risk tolerance

[1769] Output data: Asset management plan (low risk, medium risk, high risk)

[1770] Step 4:

[1771] The server sends the generated asset management plan to the device and presents it to the user. The device receives the plan and displays it to the user through the application's UI. The user reviews the proposed plan and makes any necessary modifications or approves it. Once the user has completed the operation, the selected plan is sent from the device to the server. This process has the following inputs and outputs:

[1772] Input data: Asset management plan (low risk, medium risk, high risk)

[1773] Output data: User feedback (corrections, approvals)

[1774] Step 5:

[1775] The server automatically executes the investment based on the final plan approved by the user. The server performs investment operations through the specified investment API. For example, purchasing mutual funds or trading stocks is performed automatically. This process has the following inputs and outputs:

[1776] Input data: Final plan approved by the user

[1777] Output data: Investment execution results

[1778] Step 6:

[1779] The server continuously monitors the performance of the investments and adjusts the investment plan as needed. The server periodically evaluates market data and the user's investment performance and notifies the user if it determines that an adjustment to the investment plan is necessary. Once the user approves the changes, the new investment plan is applied. This process has the following inputs and outputs:

[1780] Input data: market data, investment performance data

[1781] Output data: adjusted investment plan, notification to user

[1782] 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.

[1783] This invention is a system that efficiently manages everything from household income and expenditure management to asset management, and by integrating an emotion engine that recognizes the user's emotions, it realizes more personalized and optimized asset management. Users input information about their income, expenses, investment preferences, and emotions, and the system then automatically analyzes the data, proposes and implements an effective asset management plan, and continuously monitors and adjusts it.

[1784] Program processing

[1785] Collecting input data

[1786] The user inputs income, expenditure, asset information, investment preference information, and emotional data from the terminal.

[1787] Users fill out the application form with their monthly income, monthly fixed expenses (e.g., rent, utilities, food, etc.), the amount they can invest, their investment preferences (risk tolerance, etc.), and their emotional state at the time (e.g., stress, sense of security), and click the submit button.

[1788] Data collection and emotion recognition

[1789] The terminal sends the input data to the server.

[1790] The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1791] The server analyzes the user's emotion data using an emotion engine.

[1792] The server uses an emotion engine to analyze the user's input emotional data and evaluate their psychological state. For example, if their stress level is high, it will prioritize low-risk plans.

[1793] Data analysis

[1794] The server analyzes the received income and expenditure data to grasp the user's income and expenditure situation and current state of assets.

[1795] The server retrieves income and expenditure data from the database, and the income and expenditure analysis module analyzes this data to calculate the balance between income and expenditure, current savings, and available investments.

[1796] The server uses a machine learning module to analyze the user's spending patterns.

[1797] The server performs feature extraction based on past data to extract spending trends and patterns, which are then used to predict future income and expenditures and build a model of the user's spending patterns.

[1798] Generate a financial plan

[1799] The server generates an optimal asset management plan based on the analysis results and emotional data.

[1800] The server generates an optimal investment plan based on the income and expenditure forecast data and the user's sentiment assessment, which includes specific investment options (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[1801] Feedback and Suggestions

[1802] The server performs a risk assessment of the generated investment plan and makes adjustments based on the sentiment data.

[1803] The risk assessment module calculates a risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state. For example, if the user is in a high-stress state, a low-risk plan will be prioritized.

[1804] The server transmits the generated plan to the terminal, which displays the plan to the user.

[1805] The server sends the generated asset management plan in JSON format to the terminal, and the terminal displays the received plan in the application UI. The user can check the plan and make any necessary modifications.

[1806] Execution and monitoring

[1807] The user approves the plan and the terminal sends the final plan to the server.

[1808] When the user clicks the approve button, the terminal again sends the final plan to the server, and the server stores the approved plan in the database.

[1809] The server initiates asset management based on the approved plan and continuously monitors and adjusts it.

[1810] The server executes specific investment operations through the specified investment API. It also periodically evaluates market data and user asset data to monitor investment performance, rebalancing plans and presenting new investment opportunities when necessary. When significant changes occur, the server sends notifications to the terminal and asks for user confirmation.

[1811] Specific examples

[1812] Collection and analysis of income, expenditure and sentiment data

[1813] 1. The user enters the following information into the terminal:

[1814] Monthly income: 500,000 yen

[1815] Monthly fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1816] Investment limit: 100,000 yen

[1817] Investment preference: Low risk

[1818] Emotional data: High stress levels

[1819] 2. The server uses an emotion engine to analyze the emotion data and predicts income and expenditure based on the income and expenditure data.

[1820] 3. The server generates an optimal asset management plan based on income and expenditure forecast data, emotional data, and investment preference information, and performs risk assessment.

[1821] For example, we suggest a plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency reserve.

[1822] 4. The user reviews and approves the proposed plan.

[1823] If the user approves, the server automatically executes the investment and completes the mutual fund purchase.

[1824] Continuous monitoring and adjustment

[1825] 1. The server monitors the performance and balance of your investments monthly, and also periodically evaluates sentiment data.

[1826] If your income increases and your investment trust returns are high, your stress level will be recognized as decreasing, and your investment amount will be automatically adjusted from 100,000 yen to 150,000 yen.

[1827] 2. The server notifies the device of the adjustments and asks the user for confirmation.

[1828] Once the user approves the changes, the new investment plan will be applied.

[1829] In this way, the system of the present invention not only enables efficient management of household income and expenditure and asset management all at once, but also realizes optimal asset management that takes into account the user's feelings.

[1830] The processing flow will be explained below.

[1831] Step 1:

[1832] The user inputs their income, expenses, asset information, investment preferences, and emotional data from their device. Specifically, the user inputs their monthly income, monthly fixed expenses (e.g., rent, utility bills, food, etc.), available investment amount, investment preferences (risk tolerance, etc.), and current emotional state (e.g., stress, sense of security) into the application form, and clicks the submit button.

[1833] Step 2:

[1834] The device sends the input data to the server. The device converts the input data into JSON format and sends an HTTP POST request to the specified API endpoint. The server receives the request and saves the data in a database.

[1835] Step 3:

[1836] The server uses an emotion engine to analyze the user's emotional data. The server uses the emotion engine to analyze the emotional data entered by the user and evaluate their psychological state. For example, if the stress level is high, it will prioritize and suggest low-risk plans.

[1837] Step 4:

[1838] The server analyzes the received income and expenditure data to understand the current state of the user's income and expenditure and assets. The server retrieves the income and expenditure data from the database, and the income and expenditure analysis module analyzes this data. This analysis calculates the balance between income and expenditure, current savings, and available investment amount.

[1839] Step 5:

[1840] The server uses machine learning modules to analyze the user's spending patterns. The server performs feature extraction based on past data to extract spending trends and patterns. Based on this, it predicts future income and expenditures and builds a model of the user's spending patterns.

[1841] Step 6:

[1842] The server generates an optimal asset management plan based on the analysis results and emotion data. The server generates an optimal asset management plan based on income and expenditure forecast data and the user's emotion evaluation. This plan includes specific investment targets (e.g., mutual funds, stocks, bonds, etc.) and predicted returns.

[1843] Step 7:

[1844] The server evaluates the risk of the generated asset management plan and makes adjustments based on emotional data. The risk assessment module calculates the risk score for each plan, and the emotion engine adjusts the risk according to the user's psychological state as assessed. For example, if the user is in a high-stress state, a low-risk plan will be selected first.

[1845] Step 8:

[1846] The server sends the generated plan to the terminal. The server sends the generated asset management plan in JSON format to the terminal.

[1847] Step 9:

[1848] The device displays the plan to the user. The device displays the received plan in the application's UI and asks the user for confirmation. The user can review the plan and make any necessary modifications.

[1849] Step 10:

[1850] The user approves the plan, and the device sends the final plan to the server. When the user clicks the approve button, the device again sends the final plan to the server, and the server stores the approved plan in the database.

[1851] Step 11:

[1852] The server starts asset management based on the approved plan. The server then executes specific investment operations through the specified investment API, such as purchasing mutual funds or trading stocks. Once the transaction is complete, the server updates the user's asset status.

[1853] Step 12:

[1854] The server continuously monitors the performance of your investments and automatically adjusts as needed. The server periodically collects market data and your asset data to evaluate performance. If necessary, it rebalances your assets or suggests new investment opportunities. If any significant changes occur, the server sends a notification to your device, asking for your confirmation.

[1855] These specific processing steps allow users to easily and effectively manage their assets and achieve optimal asset management. In addition, by taking emotion data into consideration, the psychological burden on users can be reduced.

[1856] Example 2

[1857] 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."

[1858] Current income / expense management and asset management systems do not take into account the user's psychological state or emotions, and do not adequately adjust risk assessments or asset management plans. As a result, they are unable to provide an appropriate asset management plan when the user is under high stress or when their risk tolerance fluctuates, which could lead to inappropriate investment risks. This increases the user's psychological burden and makes it difficult to achieve optimal financial planning.

[1859] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1860] In this invention, the server includes means for inputting income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for evaluating the user's psychological state based on the income, expenditure, and emotional data, means for generating an asset management plan based on the analysis results, means for adding a risk assessment to the generated asset management plan and adjusting the risk according to the user's psychological state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically making investments based on the asset management plan approved by the user, and means for continuously monitoring investment performance and adjusting the investment plan as necessary. This makes it possible to provide an optimal asset management plan taking into account the user's psychological state and achieve effective financial planning while reducing the user's psychological burden.

[1861] "Income and expenditure data" is numerical information about the household's economic situation, such as monthly household income, monthly fixed expenditures, and available investment amounts.

[1862] The "means for inputting" is an interface for the user to input income, expenditure, and emotion data into the terminal.

[1863] The "analyzing means" refers to algorithms and modules that allow the server to obtain income and expenditure data and make income and expenditure forecasts based on this data.

[1864] The "emotion analysis means" is an engine and corresponding software for analyzing the emotion data input by the user and evaluating the user's psychological state.

[1865] An "asset management plan" is an investment and asset management plan suited to a user, generated based on income and expenditure data and emotion data.

[1866] The "means for adding risk assessment" is a module for calculating a risk score for the generated asset management plan and adjusting the risk according to the user's psychological state.

[1867] The "means for requesting approval or modification" is an interface for presenting the generated asset management plan to the user and requesting the user to approve or modify the plan.

[1868] "Means for automatically executing investments" refers to a system and API for automatically performing investment operations based on an asset management plan approved by a user.

[1869] "Means for continuous monitoring of investment performance" are modules and software for periodically evaluating the results of investments and adjusting investment plans as necessary.

[1870] A "machine learning module" is an algorithm and corresponding software for analyzing income and expense data and learning a user's spending patterns.

[1871] "Risk tolerance" is a standard indicating how much risk a user can tolerate, and is an important factor when generating an asset management plan.

[1872] This invention is a system for efficiently managing household income and expenditures and asset management, and realizes individually optimized asset management by integrating an emotion engine that recognizes the user's emotions. The system inputs income, expenses, asset information, investment preferences, and emotion data, performs income and expenditure forecasts and emotion analysis, then generates an optimal asset management plan, automatically executes investments, and continuously monitors and adjusts them.

[1873] Hardware and Software

[1874] The system consists of the following hardware and software:

[1875] User Device: Used by users to input income, expenses, asset information, investment preferences, and emotional data. This includes smartphones, tablets, and PCs.

[1876] Server: Receives data, analyzes it, analyzes sentiment, and generates investment plans. The server includes a database, sentiment engine, machine learning module, and risk assessment module. For example, a cloud platform such as Amazon Web Services (AWS) can be used.

[1877] Application software: A program that runs on user terminals and servers and is responsible for inputting income and expenditure data, sending and receiving data, displaying analytical results, and creating and executing asset management plans.

[1878] Data processing and calculation

[1879] 1. Collect input data:

[1880] The user uses a terminal to input income, expenses, asset information, investment preferences, and emotional data into the application form.

[1881] Example: A user inputs a monthly income of 500,000 yen, monthly fixed expenses of 300,000 yen, investment amount of 100,000 yen, investment preference as low risk, and emotional data as high stress level.

[1882] 2. Data transmission and storage:

[1883] The terminal converts the input data into JSON format and sends it to the server's API endpoint via HTTPS.

[1884] The server stores the received data in a database.

[1885] 3. Emotion analysis:

[1886] The server uses an emotion engine to analyze the user's emotion data.

[1887] For example, the user's stress level is high, so the emotion engine evaluates it as "high stress."

[1888] 4. Balance analysis:

[1889] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts.

[1890] The income and expenditure analysis module analyzes the balance between income and expenditure, savings, and investment potential.

[1891] 5. Analysis of spending patterns:

[1892] The server runs a machine learning module based on past data to model spending patterns.

[1893] Example: Using past data, predict "average monthly expenditure of 250,000 yen and savings of 100,000 yen."

[1894] 6. Generate a financial plan:

[1895] The server generates an optimal asset management plan based on income and expenditure forecast data and emotional data.

[1896] Example: A plan to invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen for emergencies.

[1897] 7. Risk Assessment and Adjustment:

[1898] The server performs risk assessment on the generated asset management plan and adjusts the risk as necessary.

[1899] Example: determining that low-risk mutual funds are suitable for a user in a high-stress state.

[1900] 8. Viewing and Approving the Plan:

[1901] The terminal displays the generated asset management plan to the user and requests approval or modification.

[1902] The user clicks the approve button and the final plan is sent to the server.

[1903] 9. Asset management execution and monitoring:

[1904] The server starts asset management based on the approved plan and executes investment operations through the specified investment API.

[1905] Continually evaluate market data and your asset data to rebalance your investment plan or suggest new investment opportunities as needed.

[1906] Example prompt

[1907] As a concrete example, a user enters the following information:

[1908] Monthly income: 500,000 yen

[1909] Fixed expenses: 300,000 yen (rent, utilities, food, etc.)

[1910] Investment limit: 100,000 yen

[1911] Investment preference: Low risk

[1912] Emotional data: High stress levels

[1913] In this way, the system of the present invention can efficiently and effectively manage household income and expenditures and manage assets, and realize optimal asset management that takes into account the user's psychological state.

[1914] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1915] Step 1: User Enters Information

[1916] The user uses a dedicated application to input their monthly income, monthly fixed expenses, available investment amount, investment preferences, and emotional data (e.g., stress level). After filling out each item in the input form and clicking the submit button, the data is entered into the terminal.

[1917] Input: Income, expenses, investment preferences, emotional data

[1918] Output: Input data

[1919] Step 2: The device sends the data

[1920] The device converts the input data into JSON format, which makes it possible to send the data to the server.The device then sends the data to the server's API endpoint via HTTPS.

[1921] Input: Data entered by the user

[1922] Output: JSON format data

[1923] Step 3: The server receives and stores the data

[1924] The server receives HTTP requests sent from the device. It analyzes the received data and stores it in a database. The database stores all data necessary for income and expenditure management and sentiment analysis.

[1925] Input: JSON format data

[1926] Output: Data stored in the database

[1927] Step 4: The server analyzes the emotion data

[1928] The server launches the emotion engine and analyzes the emotion data from the user's input data. For example, if the server recognizes that the stress level is high, the emotion engine will judge it as "high stress." This result will be used to generate a subsequent asset management plan.

[1929] Input: Emotion data

[1930] Output: Emotion analysis results

[1931] Step 5: The server analyzes the data

[1932] The server retrieves income and expenditure data from the database and generates income and expenditure forecasts. The income and expenditure analysis module analyzes this data and calculates the balance between income and expenditure, current savings, and available investment amounts.

[1933] Input: Income data, expenditure data

[1934] Output: Profit and loss forecast results

[1935] Step 6: The server analyzes spending patterns

[1936] The server runs a machine learning module using historical data to analyze spending patterns, perform feature extraction, and model spending trends and patterns, which can then be used to predict future spending.

[1937] Input: Past income and expenditure data

[1938] Output: A model of spending patterns

[1939] Step 7: The server generates the investment plan.

[1940] The server generates an optimal asset management plan based on the income / expense forecast data and sentiment analysis results. This plan includes specific investment targets and predicted returns. For example, a specific plan such as "invest 100,000 yen per month in low-risk investment trusts and keep 50,000 yen as an emergency fund" may be proposed.

[1941] Input: Revenue and expenditure forecast data, emotion analysis results

[1942] Output: Wealth Management Plan

[1943] Step 8: Server performs risk assessment and adjustments

[1944] The server performs risk assessment on the asset management plan generated and makes adjustments as necessary. The risk assessment module calculates a risk score for each plan and adjusts the risk based on the results of sentiment analysis. For example, if a user is in a high stress state, it will prioritize low-risk plans.

[1945] Input: Asset management plan, sentiment analysis results

[1946] Output: Adjusted financial plan

[1947] Step 9: The device displays the plan to the user

[1948] The server sends the generated financial plan in JSON format to the terminal, which receives this data and displays it in the application's user interface. The user can then review the plan and make any necessary modifications.

[1949] Input: Generated Investment Plan

[1950] Output: The plan displayed to the user

[1951] Step 10: User approves the plan

[1952] If the user is satisfied with the proposed asset management plan, he or she clicks the approval button. This operation causes the terminal to send the final approved plan back to the server.

[1953] Input: User approval button click

[1954] Output: Final approved plan

[1955] Step 11: The server starts and monitors the asset management

[1956] The server starts asset management based on the approved plan. It executes specific investment operations through the specified investment API and periodically evaluates market data and the user's asset data to monitor investment performance. It rebalances the plan and presents new investment opportunities as needed. If any important changes occur, it sends a notification to the terminal and asks the user for confirmation.

[1957] Input: Final Approved Plan

[1958] Output: Executed asset operations, ongoing monitoring results

[1959] (Application example 2)

[1960] 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."

[1961] Conventional income / expense management and asset management systems are limited to analyzing income and expenditure data and are unable to consider the user's psychological state, resulting in the problem of only being able to provide a uniform asset management plan. Furthermore, these systems are inadequate for continuous investment performance monitoring and investment plan adjustment, often resulting in suboptimal user investment activities. Furthermore, a lack of integration with electronic payment services limits automation when implementing investment plans, placing a heavy burden on users.

[1962] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting household income and expenditure data, means for analyzing the input income and expenditure data and making income and expenditure forecasts, means for generating an asset management plan based on the analysis results, means for recognizing the user's emotional data and evaluating the emotional state, means for presenting the generated asset management plan to the user and requesting the user's approval or modification, means for automatically executing investments based on the asset management plan approved by the user, means for continuously monitoring investment performance and adjusting the investment plan as necessary, and means for making electronic payments for implementing the investment plan generated by the system. This makes it possible to provide an individually optimized asset management plan that takes the user's emotional state into consideration, and to automatically execute investments and continuously monitor and adjust them.

[1963] The "means for inputting household income and expenditure data" is an interface through which a user provides information about his or her income and expenditure to the system.

[1964] The "means ...

Claims

1. a means for inputting household income and expenditure data; A means for analyzing input income and expenditure data and making income and expenditure forecasts; A means for generating an asset management plan based on the analysis results; a means for presenting the generated asset management plan to a user and requesting approval or modification from the user; means for automatically making investments based on a user-approved investment plan; A means to continually monitor the performance of your investments and adjust your investment plan as needed; A system including:

2. The system of claim 1 , further comprising a machine learning module for analyzing income and expense data and learning user spending patterns.

3. 2. The system of claim 1, further comprising means for generating a plurality of asset management plans based on the user's risk tolerance and performing a risk assessment for each plan.

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