System

Abstract

A system is provided.SOLUTION: This system is provided with a means for unitarily managing expenditure data generated by various different payment methods, a means for collecting the income data and expenditure data of each home, and for making a future expenditure prediction and saving plan based on the collected data, and a means for comparing the expenditure pattern of each user with a national average or a regional average.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's households, the diversification of payment methods makes it difficult to manage them centrally. Furthermore, because each household has different economic situations and life events, individualized financial plans are required, but achieving this is not easy. Furthermore, there is a need to easily understand how one's standard of living compares nationally or regionally, but there is a lack of tools to meet this need. To address these challenges, it is necessary to provide a system that enables centralized management of expenses, individualized financial plans, and the comparison of living standards. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for centrally managing expenditure data generated by various different payment methods, a means for collecting income and expenditure data for each household and using the data to predict future expenditures and create savings plans, and a means for comparing each user's expenditure pattern with the national or regional average. This system not only enables users to manage expenditure data in real time and create future financial plans, but also allows them to visually compare their own living standards. Furthermore, by adding a means for generating and proposing financial plans tailored to specific life events to users, more specific and personalized financial management becomes possible.

[0006] "Expense data" refers to data that includes detailed information such as the amount incurred by payments made by the user, the payment method, the date and time of use, and the destination of use.

[0007] "Centralized management means" refers to a method or device for collecting expenditure data arising from different payment methods in a unified format and storing and managing it in a database.

[0008] "Income data" refers to data that includes detailed information such as the amount of income earned by a user, the source of income, and the date and time of deposit.

[0009] "Expense forecasting" refers to the process and results of estimating and forecasting future expenses based on past expense and income data.

[0010] "Savings planning" refers to a process or method by which a user plans future savings based on income and expenses over a certain period of time.

[0011] A "financial plan" is a systematic proposal or strategy for achieving financial goals and plans that takes into account a user's income, expenses, and life events.

[0012] "Life events" refer to important events and financial milestones in a user's life, such as marriage, childbirth, home purchases, and education expenses.

[0013] The "national average" is the average value calculated as a nationwide aggregate result for a particular data item.

[0014] "Regional average" refers to the average value of a data item aggregated in a specific region.

[0015] "Means for comparison" refers to a method or device for comparing a user's individual data with other data to reveal relative positions and trends. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0037] The present invention is a system that centrally manages expenditures via various payment methods and provides a financial plan based on the household's economic situation, and is implemented in the following form: Cooperation between a server, a terminal, and a user is required to efficiently collect expenditure data and income data and to perform predictions and comparisons.

[0038] 1. Data collection and centralized management

[0039] server:

[0040] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[0041] Device:

[0042] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0043] User:

[0044] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0045] 2. Financial Planning

[0046] server:

[0047] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0048] Device:

[0049] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[0050] 3. Comparison of national living standards

[0051] server:

[0052] The server anonymizes, aggregates, and analyzes the spending data collected from all users. It calculates national and regional averages and generates a benchmark to compare with each user's spending data. This data provides users with a reference for their relative standard of living.

[0053] Device:

[0054] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[0055] Specific examples

[0056] Example 1:

[0057] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[0058] Example 2:

[0059] If User B plans to get married in the next fiscal year, he or she can enter that information into the app, and the device will send it to the server. The AI ​​will analyze the data, predict wedding-related expenses, and generate an appropriate savings plan. User B's device will then visually display this savings plan and predicted spending.

[0060] Example 3:

[0061] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0062] The above is an embodiment of the present invention, which enables a user to efficiently manage their household finances and make financial plans for the future.

[0063] The processing flow will be explained below.

[0064] 1. Data collection and centralized management

[0065] Step 1:

[0066] User: Makes a payment using a QR code at a store, etc.

[0067] Step 2:

[0068] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[0069] Step 3:

[0070] Terminal: In the background, the acquired payment information is sent to the server.

[0071] Step 4:

[0072] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[0073] Step 5:

[0074] User: If necessary, enter extra income or manual expenditure information through the terminal.

[0075] Step 6:

[0076] Terminal: Sends manually entered data to the server.

[0077] Step 7:

[0078] Server: Manually entered data is also stored in the database.

[0079] 2. Financial Planning

[0080] Step 1:

[0081] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[0082] Step 2:

[0083] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[0084] Step 3:

[0085] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[0086] Step 4:

[0087] Server: Save the generated financial plan in the database and associate it with the user ID.

[0088] Step 5:

[0089] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[0090] 3. Comparison of national living standards

[0091] Step 1:

[0092] Server: Spending data collected from all users is anonymized and aggregated into national data.

[0093] Step 2:

[0094] Server: Calculates national and regional average spending data, including average spending by category.

[0095] Step 3:

[0096] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[0097] Step 4:

[0098] Server: When requested by the user, generates the comparison result and sends it to the terminal.

[0099] Step 5:

[0100] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[0101] Specific examples

[0102] Example 1: Managing QR code payments

[0103] Step 1:

[0104] User: Makes a QR code payment at the store (3,000 yen).

[0105] Step 2:

[0106] Terminal: Get payment information.

[0107] Step 3:

[0108] Terminal: Sends payment information to the server.

[0109] Step 4:

[0110] Server: Stores payment information in a database.

[0111] Example 2: Generating a Financial Plan

[0112] Step 1:

[0113] User: Enters next year's wedding plans into the app.

[0114] Step 2:

[0115] Terminal: Sends home configuration information to the server.

[0116] Step 3:

[0117] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[0118] Step 4:

[0119] Server: The generated plan is saved as user data.

[0120] Step 5:

[0121] Device: Shows users savings plans and spending forecasts.

[0122] Example 3: Comparing national standards of living

[0123] Step 1:

[0124] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[0125] Step 2:

[0126] User: Selects Living Standard Comparison from the app.

[0127] Step 3:

[0128] Terminal: Sends a request to the server.

[0129] Step 4:

[0130] Server: Compare user spending data with national averages.

[0131] Step 5:

[0132] Server: Sends the comparison results to the terminal.

[0133] Step 6:

[0134] Device: Displays the comparison of the user's standard of living with the national average.

[0135] The above are the specific processing steps of the program for carrying out the present invention.

[0136] Example 1

[0137] 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."

[0138] Current household management systems are cumbersome in collecting expenditure and income data, and it is difficult to centrally manage data from different payment methods. Furthermore, it is not easy to predict future expenditures or create savings plans from the collected data, and the benchmarks for comparing living standards with those of the nation or region are unclear. There is a need to address these issues and provide a household management system that is user-friendly.

[0139] 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.

[0140] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with national or regional averages, means for receiving payment information transmitted from the user's terminal, formatting it into an appropriate data format, and storing it in a database, means for analyzing the collected income and expenditure data, visualizing the user's financial situation, and proposing future expenditure forecasts and savings plans, and means for anonymizing the aggregated expenditure data, calculating national or regional averages, and generating standards for comparison with each user's expenditure data, thereby enabling users to efficiently manage their household finances and create future economic plans.

[0141] "Expense data" refers to financial information relating to purchases and service usage made by a user using various payment methods.

[0142] "Income Data" refers to information about the income or earnings earned by a User.

[0143] "Centralized management means" refers to a means for uniformly collecting and managing expenditure data obtained from various payment methods.

[0144] "Future expenditure forecasting" refers to predicting future expenditure amounts and spending patterns based on collected data.

[0145] A "savings plan" is a detailed plan for a user to effectively save money according to their future goals and needs.

[0146] "Spending patterns" refer to a user's past payment history and spending behavior tendencies.

[0147] The "national average" refers to data on average expenditures and income across Japan.

[0148] "Regional averages" refer to data on average expenditures and incomes in specific regions of Japan.

[0149] "Database" means a data storage system for properly managing and storing received expenditure and income data.

[0150] An "AI engine" is an engine that uses artificial intelligence technology to analyze collected data and predict future economic conditions.

[0151] "Anonymization" means removing information that identifies a user personally and processing the data so that it cannot be linked to a specific individual.

[0152] "Analysis" refers to the process of extracting information and finding meaning from collected data using statistical methods and algorithms.

[0153] An "API endpoint" is an interface for sending and receiving data between different systems.

[0154] "Notification" refers to a means of providing information to inform the user of the analysis results and the generated plan.

[0155] This invention is a system that manages expenditures by various payment methods in an integrated manner and provides a financial plan based on the household's economic situation. Cooperation between the server, terminals, and users is required to efficiently collect expenditure and income data and to make predictions and comparisons.

[0156] 1. Data collection and centralized management

[0157] server:

[0158] The server provides an API endpoint to receive spending data sent from users' devices using various payment methods (QR code, credit card, manual entry, etc.). The hardware used includes a high-performance server computer, and the software includes a database management system (DBMS) and an API server. The received data is then properly formatted and stored in the database.

[0159] Device:

[0160] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to a server. The specific software used can be a mobile application or a web browser. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0161] User:

[0162] Users go about their daily routine using their usual payment methods to make purchases, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0163] 2. Financial Planning

[0164] server:

[0165] The AI ​​engine located on the server analyzes income and expenditure data collected daily. The software used includes machine learning libraries and statistical analysis tools. Based on the analysis results, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0166] Device:

[0167] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. Users can check this information through the app, gain a concrete understanding of their financial future, and take action based on that information.

[0168] 3. Comparison of national living standards

[0169] server:

[0170] The server anonymizes, aggregates, and analyzes the spending data collected from all users. The software used includes data anonymization tools and statistical analysis software. It calculates national and regional averages and generates a benchmark for comparison with each user's spending data. This data provides users with a reference for their relative standard of living.

[0171] Device:

[0172] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[0173] Specific examples

[0174] Example 1:

[0175] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server via a secure HTTP request, and the server stores the information in a database. At the same time, this amount is reflected in User A's daily spending in real time.

[0176] Example 2:

[0177] If User B plans to get married in the next fiscal year, he or she enters that information into the app. The device sends this information to the server, where an AI engine analyzes it to predict wedding-related expenses and generate an appropriate savings plan. The prediction and savings plan are then visually displayed on User B's device.

[0178] Example 3:

[0179] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device then displays the results in a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0180] Prompt Sentence Examples

[0181] "Please forecast our wedding expenses for the next year and suggest a savings plan."

[0182] "I want to know how my spending compares to the national average."

[0183] "I want to record payments using QR codes and perform spending analysis."

[0184] The above is an embodiment of the present invention. This system allows users to efficiently manage their household finances and make financial plans for the future.

[0185] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0186] Step 1: Data collection

[0187] Terminal: When a user makes a purchase, they use a QR code or credit card to pay and capture the payment information. Specifically, for example, a smartphone app automatically records payment details (date, time, location, amount, category, etc.). It receives payment information as input and generates organized payment data as output.

[0188] Step 2: Submit your payment information

[0189] Terminal: Sends the acquired payment information to the server via a secure HTTP request. The process runs in the background, and the information is sent transparently to the user. It receives payment data as input and generates data to be sent to the server as output.

[0190] Step 3: Receive and store payment information

[0191] Server: Receives payment information sent from the terminal at the API endpoint. After receiving the information, it formats it into the appropriate data format and stores it in the database. Specifically, it analyzes the received data and inserts it into the appropriate table. It receives data sent from the terminal as input and stores it in the database as output.

[0192] Step 4: Manage manually entered data

[0193] User: When a user spends cash or uses other payment methods, they manually enter that information through the app. For example, if a user pays for a 500 yen lunch with cash, they enter the details into the app. It takes the expenditure details as input and generates the manually entered data as output.

[0194] Terminal: Sends manually entered expenditure information to the server. Specifically, when the user completes the input, they press the send button or the information is sent automatically. It receives manually entered expenditure data as input and generates data to be sent to the server as output.

[0195] Step 5: Analyze your income and expense data

[0196] Server: Analyzes collected income and expenditure data and visualizes the user's financial situation. The AI ​​engine performs the analysis using statistical analysis and machine learning algorithms. Specific operations include generating graphs and dashboards and calculating the balance between income and expenditure. It receives integrated income and expenditure data as input and generates analysis results as output.

[0197] Step 6: Generate a financial plan

[0198] Server: The AI ​​engine generates predicted spending and savings plans based on the user's goals (e.g., marriage, home purchase, etc.). Specifically, it predicts future spending trends based on past spending data and proposes savings plans based on the results. It receives the user's goal information and past data as input and generates a financial plan as output.

[0199] Step 7: Present and communicate your plan

[0200] Device: Displays the generated financial plan and notifications to the user. The user can check this information at any time through the app. Specifically, the latest financial status report is automatically pushed once a week. The device receives the generated plan data as input and generates notification data for the user as output.

[0201] Step 8: Compare living standards across countries

[0202] Server: Anonymizes the spending data collected from all users and calculates regional and national averages. Specifically, it removes information that identifies individual users and statistically processes the data. It receives the collected spending data as input and produces aggregate results as output.

[0203] Step 9: Expressing the basis of comparison

[0204] Server: Generates a comparison standard based on each user's expenditure data and the national average. Specifically, it calculates how many percentage points higher or lower a user's expenditure is than the national average. It receives statistical data and user data as input and generates a comparison standard as output.

[0205] Step 10: View the comparison results

[0206] Device: Visually presents the comparison results sent from the server to the user. For example, the app uses graphs and tables to help users intuitively understand their living standards. It receives the comparison results from the server as input and generates the data to be presented visually as output.

[0207] The above is the specific processing flow of this system. This configuration allows users to efficiently manage their household finances and make future economic plans.

[0208] (Application example 1)

[0209] 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."

[0210] In today's society, there are many different payment methods, and the resulting expenditure data is managed in a decentralized manner. This makes it difficult to grasp a household's financial situation in a unified manner and create an appropriate financial plan. It is also difficult for users to compare their own standard of living with the national or regional average. A system that can solve these problems is needed.

[0211] 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.

[0212] In this invention, the server includes: means for centrally managing expenditure data generated by various different payment methods; means for collecting income and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon; means for comparing each user's expenditure pattern with national or regional averages; means for automatically collecting expenditure data from QR codes, credit cards, bank accounts, etc.; means for manually inputting income and expenditures; means for visually displaying the generated financial plan on the user's terminal; and means for comparing the user's expenditure data with national or regional average data to present the user's relative standard of living. This enables centralized management of expenditure data, a detailed understanding of the household's economic situation, and the provision of an appropriate financial plan through predictions and comparisons.

[0213] "Various different payment methods" refers to multiple different payment methods, such as QR codes, credit cards, and bank accounts.

[0214] "Expense data" refers to information about a user's purchases and payments, including information such as the amount, date and time, and category of each transaction.

[0215] "Income Data" refers to information related to a user's income, including information such as salary, bonuses, and investment returns.

[0216] "Centralized management means" refers to a method of integrating data collected from multiple sources and managing it in a unified format.

[0217] "Means for predicting future expenditures and formulating savings plans" refers to methods for predicting future expenditures and formulating savings plans based on collected data.

[0218] "Means for comparison to national or regional averages" refers to a method for comparing a user's data with national or regional average data to calculate relative position.

[0219] "Automatic collection methods" refers to methods of automatically obtaining data from QR codes, credit cards, bank accounts, etc.

[0220] "Means for manual income and expense entry" refers to the methods by which a user manually enters income and expense information into an application or device.

[0221] "Visual display means" refers to a method of presenting information to a user in the form of text, graphs, charts, etc.

[0222] "Means of showing relative standard of living positions" refers to methods of comparing the user's data with national or regional averages and showing the results to the user.

[0223] The present invention is a system that provides financial planning by centrally managing expenses from various payment methods. The system collects income and expenditure data and can predict future expenses and create savings plans based on the data. It also has the function of comparing each user's spending patterns with national or regional averages.

[0224] server

[0225] The server automatically collects expenditure data sent from the user's device using various payment methods (QR code, credit card, bank account, etc.). The received data is then properly formatted and stored in a database. The server receives the collected expenditure data in real time and processes it to store it in the database. It also has an AI engine that generates a financial plan based on income and expenditure data and proposes it to the user. This AI engine has the function of visually displaying the generated financial plan on the user's device. It also has the function of comparing the user's expenditure data with national and regional averages to show the user's relative standard of living.

[0226] Terminal

[0227] When a payment is completed, the user's device (such as a smartphone or PC) sends the payment information to the server. Any income or expenses manually entered by the user are also sent to the server. The device is equipped with an interface for manually entering income and expenses. Furthermore, the user's device has the ability to visually display the financial plan and future savings plan generated by the AI ​​engine. Users can refer to this information to understand their financial future and take appropriate action.

[0228] User

[0229] Users make purchases in their daily lives using the usual payment methods. They make expenditures using QR codes, credit cards, and bank accounts, and the data is automatically sent to the server via their device. If manual input is required, they enter the data through the app. When users plan specific life events (marriage, childbirth, home purchase, etc.), they can enter that information into the app. The AI ​​engine analyzes this information and generates an appropriate financial plan. Users can refer to this plan to prepare for their future financial needs.

[0230] Specific examples

[0231] Example 1: User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily expenditure, and the proportion of expenditure in each category is also updated.

[0232] Example 2: If User B plans to get married next year, he or she enters that information into the app and the device sends it to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending.

[0233] Example 3: User C wants to compare his / her standard of living with the national average, so he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0234] Prompt Sentence Examples

[0235] "Please send data on purchasing home appliances worth 5,000 yen and retrieve financial plans and living standards comparisons for user ID 2 from the server."

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] User makes payment:

[0239] A user completes a payment using a QR code, credit card, bank account, etc. At that time, data about the payment (such as the amount spent, date and time, category, etc.) is generated.

[0240] Input: User payment behavior, expenditure data (expense amount, date and time, category, etc.)

[0241] Output: Expense data

[0242] Step 2:

[0243] The terminal sends the payment information to the server:

[0244] The terminal sends the user-completed payment information to the server, which is then sent to the server through an API endpoint.

[0245] Input: Expense Data

[0246] Output: Sending spending data to the server

[0247] What happens: The device app collects spending data and issues an HTTP POST request to send it to the server's API endpoint.

[0248] Step 3:

[0249] The server receives and stores the spending data:

[0250] The server receives the expenditure data sent from the terminal, converts it into an appropriate format, and stores it in a database.

[0251] Input: Spending data sent to the server

[0252] Output: Spending data stored in a database

[0253] Specific operation: The server receives the expenditure data and inserts it into the expenditure table in the database, while formatting and validating the data.

[0254] Step 4:

[0255] Collecting manually entered data:

[0256] Users manually enter their income and expenses through the terminal app, and the data is sent to the server.

[0257] Input: Manually entered income and expenditure data

[0258] Output: Send income and expenditure data to the server

[0259] Specific behavior: The device app collects income and expenditure data through the user interface and issues an HTTP POST request to send it to the server.

[0260] Step 5:

[0261] Generate a financial plan:

[0262] The AI ​​engine on the server uses collected income and expenditure data to predict future spending and create savings plans.

[0263] Input: Income data, expenditure data in the database

[0264] Output: Financial plan data

[0265] How it works: The AI ​​engine analyzes income and expense data and uses predictive algorithms to generate a financial plan.

[0266] Step 6:

[0267] View the generated financial plan:

[0268] A terminal receives the generated financial plan data from the server and visually displays it on a user interface.

[0269] Input: Financial plan data received from the server

[0270] Output: Financial plan displayed on the terminal

[0271] Specific behavior: The terminal app receives data from the server and displays it in the user interface in text and graph format.

[0272] Step 7:

[0273] Comparison of living standards:

[0274] The user requests a standard of living comparison function, and the server compares the collected expenditure data with the national or regional average. The results are displayed in the user interface.

[0275] Input: Standard of Living Comparison Request, User Expenditure Data

[0276] Output: Results compared to national or regional averages

[0277] What it does: The server retrieves the user's spending data and compares it with national or regional averages. The results are received by the device app and displayed visually.

[0278] To realize this application example, the user, terminal, and server cooperate to collect, manage, and analyze data, and provide the results to the user.

[0279] 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.

[0280] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[0281] 1. Data collection and centralized management

[0282] server:

[0283] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[0284] Device:

[0285] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0286] User:

[0287] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0288] 2. Financial Planning

[0289] server:

[0290] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0291] Device:

[0292] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[0293] 3. Incorporating an Emotional Engine

[0294] server:

[0295] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends.

[0296] Device:

[0297] The user device has the ability to transmit emotional data obtained from the emotion engine to the server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[0298] User:

[0299] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[0300] 4. Adjust your plan based on emotions

[0301] server:

[0302] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[0303] Device:

[0304] The user terminal displays the tailored financial plan and provides emotion-based advice and suggestions.

[0305] 5. Comparison of national living standards

[0306] server:

[0307] The server anonymizes the spending data collected from all users and aggregates it into national data. It calculates national and regional averages and generates a baseline against which each user's spending data can be compared. This data provides users with a reference for their relative standard of living.

[0308] Device:

[0309] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[0310] Specific examples

[0311] Example 1:

[0312] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[0313] Example 2:

[0314] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. Additionally, if User B's current emotional state is "stress," suggestions for reducing that risk are also provided.

[0315] Example 3:

[0316] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0317] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, it is possible to provide a more personalized financial plan that is more suited to the user.

[0318] The processing flow will be explained below.

[0319] 1. Data collection and centralized management

[0320] Step 1:

[0321] User: Makes a payment using a QR code at a store, etc.

[0322] Step 2:

[0323] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[0324] Step 3:

[0325] Terminal: In the background, the acquired payment information is sent to the server.

[0326] Step 4:

[0327] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[0328] Step 5:

[0329] User: If necessary, enter extra income or manual expenditure information through the terminal.

[0330] Step 6:

[0331] Terminal: Sends manually entered data to the server.

[0332] Step 7:

[0333] Server: Manually entered data is also stored in the database.

[0334] 2. Financial Planning

[0335] Step 1:

[0336] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[0337] Step 2:

[0338] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[0339] Step 3:

[0340] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[0341] Step 4:

[0342] Server: Save the generated financial plan in the database and associate it with the user ID.

[0343] Step 5:

[0344] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[0345] 3. Incorporating an Emotional Engine

[0346] Step 1:

[0347] User: Expresses emotions by voice or text input. If the device is equipped with a biometric sensor, emotional data is automatically acquired.

[0348] Step 2:

[0349] Terminal: Collects emotion data in real time and sends it to the server.

[0350] Step 3:

[0351] Server: Stores the emotion data analyzed by the emotion engine in a database.

[0352] 4. Adjust your plan based on emotions

[0353] Step 1:

[0354] Server: Based on the emotional data obtained from the emotion engine, the AI ​​engine adjusts the financial plan.

[0355] Step 2:

[0356] Server: Save the new financial plan, reflecting the user's emotional state, in the database.

[0357] Step 3:

[0358] On the device: When users open the app, they are presented with a tailored financial plan, along with emotional advice and recommendations.

[0359] 5. Comparison of national living standards

[0360] Step 1:

[0361] Server: Spending data collected from all users is anonymized and aggregated into national data.

[0362] Step 2:

[0363] Server: Calculates national and regional average spending data, including average spending by category.

[0364] Step 3:

[0365] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[0366] Step 4:

[0367] Server: When a user makes a request, it generates a comparison result and sends it to the terminal.

[0368] Step 5:

[0369] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[0370] Specific examples

[0371] Example 1: Managing QR code payments

[0372] Step 1:

[0373] User: Makes a QR code payment at the store (3,000 yen).

[0374] Step 2:

[0375] Terminal: Get payment information.

[0376] Step 3:

[0377] Terminal: Sends payment information to the server.

[0378] Step 4:

[0379] Server: Stores payment information in a database.

[0380] Example 2: Generating a Financial Plan

[0381] Step 1:

[0382] User: Enters next year's wedding plans into the app.

[0383] Step 2:

[0384] Terminal: Sends home configuration information to the server.

[0385] Step 3:

[0386] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[0387] Step 4:

[0388] Server: The generated plan is saved as user data.

[0389] Step 5:

[0390] Device: Shows users savings plans and spending forecasts.

[0391] Example 3: Comparing national standards of living

[0392] Step 1:

[0393] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[0394] Step 2:

[0395] User: Selects Living Standard Comparison from the app.

[0396] Step 3:

[0397] Terminal: Sends a request to the server.

[0398] Step 4:

[0399] Server: Compare user spending data with national averages.

[0400] Step 5:

[0401] Server: Sends the comparison results to the terminal.

[0402] Step 6:

[0403] Device: Displays the comparison of the user's standard of living with the national average.

[0404] Example 4: Adjusting your financial plan based on emotions

[0405] Step 1:

[0406] User: Enters "I've been feeling stressed lately" using voice input in the app.

[0407] Step 2:

[0408] Terminal: Sends emotion data to the server.

[0409] Step 3:

[0410] Server: Analyzes the received emotion data and stores it in a database.

[0411] Step 4:

[0412] Server (AI engine): Adjusts the current financial plan based on the emotional data, for example adding advice to avoid risks.

[0413] Step 5:

[0414] Device: Presents emotion-based adjustment plans to users.

[0415] The above are the specific processing steps of the program for implementing the present invention. The present invention allows users to manage their expenses and income in an integrated manner and obtain a highly personalized financial plan. Furthermore, by taking emotional data into consideration, flexible proposals tailored to the user's financial behavior are provided.

[0416] Example 2

[0417] 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."

[0418] Traditional household financial planning tools are unable to centrally manage spending data based on multiple payment methods and income sources, making it difficult to grasp a household's overall financial situation. They also fail to take into account the user's emotional state when predicting future spending and creating savings plans, making it difficult to provide personalized plans that are appropriate for each user. Furthermore, it is impossible to compare each user's spending patterns with national or regional averages, making it difficult to evaluate their relative standard of living.

[0419] 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.

[0420] In this invention, the server includes a means for centrally managing expenditure data generated by various different payment methods, a means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, a means for comparing each user's expenditure pattern with the national average or regional average, and a means for collecting user emotional data and adjusting a financial plan based on the user's emotional state. This allows for comprehensive management of user expenditure data, enabling future economic planning based on income and expenditure data, and providing personalized plans that take emotional state into consideration. Furthermore, by comparing each user's expenditure pattern with the national average or regional average, a relative evaluation of living standards is possible.

[0421] "Spending Data" refers to information about spending made by a user using various payment methods.

[0422] "Centralized management" refers to aggregating expenditure data obtained from multiple different payment methods and managing it in a single database or system.

[0423] "Income data" refers to information regarding various types of income earned by a user.

[0424] "Future expenditure forecasting" refers to forecasting future expenditures based on current and past income and expenditure data.

[0425] A "savings plan" is a specific plan for how to increase savings based on the user's future financial goals.

[0426] "Spending patterns" refers to data that indicates the characteristics and tendencies of a user's spending.

[0427] "National average" refers to the average value of various expenditures across the country.

[0428] "Regional average" refers to the average value of various expenditures in a particular region.

[0429] "Emotional data" refers to information about a user's emotional state, such as data obtained from voice input, text input, or biometric sensors.

[0430] An "emotion engine" is a system or service for analyzing emotional data obtained from voice input, text input, or biometric sensors.

[0431] A "financial plan" is a plan that presents a future economic plan based on a user's income and expenditure data.

[0432] A "life event" refers to an important event in a user's life (e.g., marriage, childbirth, home purchase, etc.) for which the associated costs need to be predicted and planned.

[0433] "Personalization" refers to providing plans and services that are optimized for individual users.

[0434] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[0435] 1. Data collection and centralized management

[0436] server:

[0437] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then appropriately formatted and stored in a database such as MySQL or PostgreSQL.

[0438] Device:

[0439] Once a payment is completed, the device (such as a smartphone or PC) sends the payment information to the server. The data is also sent to the server regarding expenses and income manually entered by the user. The data is transmitted securely using the HTTPS protocol.

[0440] User:

[0441] Users go about their daily lives making purchases using their usual payment methods, and when manual input is required, they enter that data through the app, for example, cash expenditures or salary income.

[0442] 2. Financial Planning

[0443] server:

[0444] The AI ​​engine (such as TensorFlow or PyTorch) on the server analyzes the income and expenditure data collected daily. Based on the results of this analysis, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. Statistical algorithms are used for the analysis to extract past data patterns and trends. For example, if a user plans to purchase a home in the future, the system can estimate the cost and suggest an appropriate savings plan.

[0445] Device:

[0446] The device displays a financial plan and future savings plan generated by an AI engine, and users can open the app to get a concrete understanding of their financial future through various graphs and charts.

[0447] 3. Incorporating an Emotional Engine

[0448] server:

[0449] The server incorporates an emotion engine (e.g., emotion recognition API) to recognize the user's emotions and obtains emotion data from the user's voice input (voice recognition technology), text input, or biometric sensors (health monitoring devices). This data is analyzed and stored in a database. The analysis results are used for next planning based on the user's emotional patterns.

[0450] Device:

[0451] The device has the ability to transmit emotional data in real time to a server, which can then be sent via a RESTful API using scripts written in Python or Ruby, allowing financial plans to be adjusted to reflect the user's current emotional state.

[0452] User:

[0453] Users can express their emotions through voice or text input. Furthermore, if the device is equipped with a built-in biometric sensor, emotional data can be automatically acquired. For example, the emotion engine can analyze heart rate data to determine whether stress levels are high.

[0454] 4. Adjust your plan based on emotions

[0455] server:

[0456] The server uses an AI engine to adjust the financial plan based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest low-risk savings and investment plans. This adjustment is made using machine learning libraries such as Scikit-learn.

[0457] Device:

[0458] The device displays an adjusted financial plan and offers emotion-based advice and suggestions: for example, when stress levels are high, a notification will appear suggesting relaxation techniques to the user.

[0459] 5. Comparison of national living standards

[0460] server:

[0461] The server anonymizes all users' spending data and aggregates it into large datasets for analysis. The aggregation process is performed using Hadoop, Apache Spark, or similar tools. This allows for calculation of national and regional averages, generating a baseline for comparison with users' spending data.

[0462] Device:

[0463] The device has the function of displaying the comparison results sent from the server. Users can request a comparison of their living standards through the app and see the results of how their living standards compare with the national and regional averages. The results are displayed as graphs and heat maps.

[0464] Specific examples

[0465] Example 1:

[0466] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server using Python's requests library, and the server saves the information in the database. This amount is reflected in the daily expenditure, and the expenditure percentage for each category is also updated.

[0467] Example 2:

[0468] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. At the same time, if User B's current emotional state is "stressed," it also provides suggestions to reduce the risk.

[0469] Example 3:

[0470] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0471] Example prompts to be input to the generative AI model:

[0472] Generate a personalized financial plan based on the user's spending and emotional data. If the emotional state is stressed, provide advice that includes a savings plan to mitigate risk.

[0473] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, more personalized financial plans can be provided.

[0474] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0475] Step 1: Data collection

[0476] Input: User's payment information (QR code, credit card, manual entry, etc.)

[0477] How it works: When a user makes a payment, the terminal captures the payment information (payment amount, date and time, payment method, etc.).

[0478] Specific operation: When a user makes a purchase of 3,000 yen using a QR code, the information is recorded in the device's app.

[0479] Step 2: Send data

[0480] Input: Captured payment information

[0481] How it works: The terminal sends the acquired payment information to the server using the HTTPS protocol. The data is structured in JSON format.

[0482] What happens: The device sends an HTTP POST request to the server, sending JSON data containing payment information.

[0483] Step 3: Receiving and storing data

[0484] Input: Payment information sent from the terminal

[0485] How it works: The server receives the incoming payment information, converts it into the appropriate format, and stores it in a database.

[0486] What it does: The server stores the received payment information in the "Expenses" table in the MySQL database.

[0487] Step 4: Generate a financial plan

[0488] Input: Income and expenditure data retrieved from the database

[0489] How it works: An AI engine on the server analyzes income and expenditure data and creates future spending forecasts and savings plans.

[0490] How it works: The AI ​​engine analyzes trends based on spending data from the past six months and suggests savings goals for the next year.

[0491] Step 5: Submit your plan

[0492] Input: Generated financial plan

[0493] Operation: The server sends the generated financial plan to the terminal. The data is structured in JSON format.

[0494] Specific operation: The server sends the generated financial plan in JSON format to the terminal as an HTTP response.

[0495] Step 6: View your plan

[0496] Input: Financial plan sent from server

[0497] How it works: The device visually displays the financial plan it receives.

[0498] What it does: The app displays a financial plan as graphs and charts for the user to review.

[0499] Step 7: Obtaining emotion data

[0500] Input: User emotion input (voice, text, biometric sensors)

[0501] Operation: The device acquires the user's emotion data.

[0502] Specific operation: When the user speaks "I'm stressed," the speech is converted into text and recorded.

[0503] Step 8: Sending Emotion Data

[0504] Input: Captured emotion data

[0505] Operation: The device transmits the acquired emotion data to the server in real time.

[0506] How it works: The device sends its emotional state and its intensity to the server using an HTTP POST request.

[0507] Step 9: Analyze the sentiment data

[0508] Input: Emotion data sent from the device

[0509] How it works: The server analyzes the emotion data using the emotion engine, and the analysis results are stored in a database.

[0510] Specific operation: The server analyzes heart rate data from the biometric sensor and determines the user's emotional state as "stressed."

[0511] Step 10: Adjust your plan

[0512] Input: Parsed emotion data

[0513] How it works: The server adjusts financial plans based on emotional data. For example, if the user is stressed, it suggests a plan with less risk.

[0514] Specific operation: The AI ​​engine selects and re-proposes safe investment plans based on stress levels.

[0515] Step 11: Submit your adjustment plan

[0516] Input: Coordinated Financial Plan

[0517] Operation: The server sends the re-adjusted financial plan to the terminal.

[0518] Specific operation: The server sends the adjusted plan in JSON format to the terminal and returns it as an HTTP response.

[0519] Step 12: View the adjustment plan

[0520] Input: Adjustment plan sent from the server

[0521] How it works: The terminal visually displays your adjusted financial plan.

[0522] What happens: The device displays the adjusted plan as a graph or chart in the app UI for the user to review.

[0523] Step 13: Compare to the national average

[0524] Input: Spending data collected from all users

[0525] How it works: The server anonymizes and aggregates spending data from all users to calculate national and regional averages.

[0526] What it does: The server aggregates the data using Hadoop and calculates the average standard of living for each region.

[0527] Step 14: Send and display comparison results

[0528] Input: Comparison results with the national average

[0529] How it works: The server sends the results of the comparison with the national average to the device, which then displays the results.

[0530] What it does: The device visualizes the comparison results received from the server as a heat map, allowing the user to check their living standards.

[0531] The above is the content of the specific processing steps of this system.

[0532] (Application example 2)

[0533] 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."

[0534] Conventional financial planning systems collect income and expenditure data and propose future expenditure forecasts and savings plans, but do not consider the user's emotions when planning. While they may provide comparisons of spending patterns with national or regional averages, they do not provide personalized financial plans based on the user's emotional data. This makes it difficult to provide appropriate planning that reflects the user's actual emotional state, making it difficult to propose stress-reducing and reasonable savings plans.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0536] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with the national average or regional average, means for collecting user emotion data and providing a personalized financial plan based thereon, means for centrally managing payment data and comparing living standards, and means for presenting the user with a financial plan created based on their emotions. This makes it possible to provide a reasonable savings plan or a personalized financial plan that takes into account the user's emotional state.

[0537] "Expense Data" is a record of expenses incurred through various different payment methods.

[0538] A "single-source management mechanism" is a mechanism for integrating and managing data generated from different payment methods.

[0539] "Income data" is a record of all income earned by a household or individual.

[0540] "Expense forecasting" is the estimation of future costs based on collected data.

[0541] A "savings plan" is a specific plan for the amount of savings needed to achieve future goals and how to save.

[0542] "Expenditure patterns" are data that indicate how much an individual or household spends on what items.

[0543] The "national average" is the value calculated by averaging all data within Japan.

[0544] The "regional average" is a value calculated by averaging data for a specific region.

[0545] "Emotion data" is data that indicates the user's emotional state and is obtained from voice, text input, biometric sensors, and the like.

[0546] A "personalized financial plan" is a financial plan created based on a user's individual income, expenses, and emotional state.

[0547] "Life events" refer to important personal or family events such as marriage, childbirth, or home purchase.

[0548] An "emotion engine" is a system or software for recognizing and analyzing a user's emotions.

[0549] A "financial plan" is a future financial strategy or plan based on income and expenses.

[0550] A "server" is a computer system that collects, stores, analyzes data, and provides necessary information.

[0551] A "user terminal" is a device used by a user, such as a mobile phone or a personal computer.

[0552] This invention combines a system that centrally manages spending via various payment methods and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[0553] 1. Data collection and centralized management

[0554] server:

[0555] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (e.g., QR code, credit card, manual entry, etc.). The received data is properly formatted and stored in a database. A high-performance server is desirable as the hardware to be used.

[0556] Device:

[0557] The terminal is a smartphone, PC, etc., and once the payment is completed, the payment information is sent to the server. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0558] User:

[0559] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0560] 2. Financial Planning

[0561] server:

[0562] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0563] Device:

[0564] The device has the ability to display financial plans and future savings plans generated by an AI engine, allowing users to gain a concrete understanding of their financial future and take action accordingly.

[0565] 3. Incorporating an Emotional Engine

[0566] server:

[0567] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends. The software used utilizes an emotion recognition API.

[0568] Device:

[0569] The device has the ability to transmit emotional data obtained from the emotion engine to a server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[0570] User:

[0571] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[0572] 4. Adjust your plan based on emotions

[0573] server:

[0574] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[0575] Device:

[0576] The device displays a tailored financial plan and offers emotionally-driven advice and recommendations.

[0577] Specific examples

[0578] Example 1:

[0579] If a user is feeling stressed about their recent high spending, the emotion recognition engine will recognize that emotion and send it along with their payment data to the AI ​​engine, which will then generate a reasonable savings plan to reduce stress and present it to the user.

[0580] Example prompt sentence:

[0581] "I'm worried about my recent expenses."

[0582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0583] Step 1:

[0584] The terminal receives payment information from the user as input and sends it to the server through an API endpoint. Once the payment information is entered, the terminal converts it into a proprietary format so that the spending data is properly stored on the server.

[0585] Step 2:

[0586] The server receives the expenditure data sent from the device and stores it in a database. The server checks the integrity of the data upon receiving it and standardizes the format. This process allows for real-time collection of expenditure data and centralized management.

[0587] Step 3:

[0588] The device receives the user's emotional state as input. When the user expresses their emotion through voice or text input, the device sends the data to the emotion recognition engine. It also receives data from biometric sensors, if available. The emotion recognition engine analyzes the emotional data and identifies the emotional state.

[0589] Step 4:

[0590] The server receives the emotion data sent from the emotion recognition engine and stores it in a database. This data, along with existing income and expenditure data, is input into the AI ​​engine for analysis. The AI ​​engine takes the emotion data into account and generates a customized financial plan.

[0591] Step 5:

[0592] The server then sends the generated financial plan to the device. The AI ​​engine combines emotional and economic data to output a plan that includes reasonable savings plans and low-risk investment suggestions. This plan is personalized to reflect the user's emotional state.

[0593] Step 6:

[0594] The terminal receives the financial plan sent from the server and displays it on the screen. The user can check the details of the plan on the screen and receive advice based on their financial situation and emotional state.

[0595] Step 7:

[0596] The server calculates national or regional averages based on the expenditure data collected from all users and generates comparison results with each user's expenditure patterns. This comparison data is analyzed and stored together with the users' expenditure data to show the relative positions of the users' standard of living.

[0597] Step 8:

[0598] When a user requests a comparison of living standards, the terminal displays the results of the comparison sent from the server on the screen. Users can compare their expenditure data with the national and regional averages and visually check their own living standards.

[0599] Examples of specific operations and inputs and outputs:

[0600] The device recognizes the user's voice input, "I'm worried about my recent high expenses," and sends it to the server. The server uses an emotion recognition engine to output the emotion data "stress" from this input data. The server then inputs this emotion data along with the expenditure data into an AI engine, which generates a personalized financial plan including a reasonable savings plan and sends it to the device. The user can review this plan and use it to help with future financial activities.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] [Second embodiment]

[0605] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] In the smart glasses 214, 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.

[0616] 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."

[0617] The present invention is a system that centrally manages expenditures via various payment methods and provides a financial plan based on the household's economic situation, and is implemented in the following form: Cooperation between a server, a terminal, and a user is required to efficiently collect expenditure data and income data and to perform predictions and comparisons.

[0618] 1. Data collection and centralized management

[0619] server:

[0620] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[0621] Device:

[0622] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0623] User:

[0624] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0625] 2. Financial Planning

[0626] server:

[0627] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0628] Device:

[0629] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[0630] 3. Comparison of national living standards

[0631] server:

[0632] The server anonymizes, aggregates, and analyzes the spending data collected from all users. It calculates national and regional averages and generates a benchmark to compare with each user's spending data. This data provides users with a reference for their relative standard of living.

[0633] Device:

[0634] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[0635] Specific examples

[0636] Example 1:

[0637] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[0638] Example 2:

[0639] If User B plans to get married in the next fiscal year, he or she can enter that information into the app, and the device will send it to the server. The AI ​​will analyze the data, predict wedding-related expenses, and generate an appropriate savings plan. User B's device will then visually display this savings plan and predicted spending.

[0640] Example 3:

[0641] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0642] The above is an embodiment of the present invention, which enables a user to efficiently manage their household finances and make financial plans for the future.

[0643] The processing flow will be explained below.

[0644] 1. Data collection and centralized management

[0645] Step 1:

[0646] User: Makes a payment using a QR code at a store, etc.

[0647] Step 2:

[0648] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[0649] Step 3:

[0650] Terminal: In the background, the acquired payment information is sent to the server.

[0651] Step 4:

[0652] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[0653] Step 5:

[0654] User: If necessary, enter extra income or manual expenditure information through the terminal.

[0655] Step 6:

[0656] Terminal: Sends manually entered data to the server.

[0657] Step 7:

[0658] Server: Manually entered data is also stored in the database.

[0659] 2. Financial Planning

[0660] Step 1:

[0661] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[0662] Step 2:

[0663] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[0664] Step 3:

[0665] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[0666] Step 4:

[0667] Server: Save the generated financial plan in the database and associate it with the user ID.

[0668] Step 5:

[0669] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[0670] 3. Comparison of national living standards

[0671] Step 1:

[0672] Server: Spending data collected from all users is anonymized and aggregated into national data.

[0673] Step 2:

[0674] Server: Calculates national and regional average spending data, including average spending by category.

[0675] Step 3:

[0676] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[0677] Step 4:

[0678] Server: When requested by the user, generates the comparison result and sends it to the terminal.

[0679] Step 5:

[0680] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[0681] Specific examples

[0682] Example 1: Managing QR code payments

[0683] Step 1:

[0684] User: Makes a QR code payment at the store (3,000 yen).

[0685] Step 2:

[0686] Terminal: Get payment information.

[0687] Step 3:

[0688] Terminal: Sends payment information to the server.

[0689] Step 4:

[0690] Server: Stores payment information in a database.

[0691] Example 2: Generating a Financial Plan

[0692] Step 1:

[0693] User: Enters next year's wedding plans into the app.

[0694] Step 2:

[0695] Terminal: Sends home configuration information to the server.

[0696] Step 3:

[0697] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[0698] Step 4:

[0699] Server: The generated plan is saved as user data.

[0700] Step 5:

[0701] Device: Shows users savings plans and spending forecasts.

[0702] Example 3: Comparing national standards of living

[0703] Step 1:

[0704] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[0705] Step 2:

[0706] User: Selects Living Standard Comparison from the app.

[0707] Step 3:

[0708] Terminal: Sends a request to the server.

[0709] Step 4:

[0710] Server: Compare user spending data with national averages.

[0711] Step 5:

[0712] Server: Sends the comparison results to the terminal.

[0713] Step 6:

[0714] Device: Displays the comparison of the user's standard of living with the national average.

[0715] The above are the specific processing steps of the program for carrying out the present invention.

[0716] Example 1

[0717] 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."

[0718] Current household management systems are cumbersome in collecting expenditure and income data, and it is difficult to centrally manage data from different payment methods. Furthermore, it is not easy to predict future expenditures or create savings plans from the collected data, and the benchmarks for comparing living standards with those of the nation or region are unclear. There is a need to address these issues and provide a household management system that is user-friendly.

[0719] 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.

[0720] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with national or regional averages, means for receiving payment information transmitted from the user's terminal, formatting it into an appropriate data format, and storing it in a database, means for analyzing the collected income and expenditure data, visualizing the user's financial situation, and proposing future expenditure forecasts and savings plans, and means for anonymizing the aggregated expenditure data, calculating national or regional averages, and generating standards for comparison with each user's expenditure data, thereby enabling users to efficiently manage their household finances and create future economic plans.

[0721] "Expense data" refers to financial information relating to purchases and service usage made by a user using various payment methods.

[0722] "Income Data" refers to information about the income or earnings earned by a User.

[0723] "Centralized management means" refers to a means for uniformly collecting and managing expenditure data obtained from various payment methods.

[0724] "Future expenditure forecasting" refers to predicting future expenditure amounts and spending patterns based on collected data.

[0725] A "savings plan" is a detailed plan for a user to effectively save money according to their future goals and needs.

[0726] "Spending patterns" refer to a user's past payment history and spending behavior tendencies.

[0727] The "national average" refers to data on average expenditures and income across Japan.

[0728] "Regional averages" refer to data on average expenditures and incomes in specific regions of Japan.

[0729] "Database" means a data storage system for properly managing and storing received expenditure and income data.

[0730] An "AI engine" is an engine that uses artificial intelligence technology to analyze collected data and predict future economic conditions.

[0731] "Anonymization" means removing information that identifies a user personally and processing the data so that it cannot be linked to a specific individual.

[0732] "Analysis" refers to the process of extracting information and finding meaning from collected data using statistical methods and algorithms.

[0733] An "API endpoint" is an interface for sending and receiving data between different systems.

[0734] "Notification" refers to a means of providing information to inform the user of the analysis results and the generated plan.

[0735] This invention is a system that manages expenditures by various payment methods in an integrated manner and provides a financial plan based on the household's economic situation. Cooperation between the server, terminals, and users is required to efficiently collect expenditure and income data and to make predictions and comparisons.

[0736] 1. Data collection and centralized management

[0737] server:

[0738] The server provides an API endpoint to receive spending data sent from users' devices using various payment methods (QR code, credit card, manual entry, etc.). The hardware used includes a high-performance server computer, and the software includes a database management system (DBMS) and an API server. The received data is then properly formatted and stored in the database.

[0739] Device:

[0740] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to a server. The specific software used can be a mobile application or a web browser. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0741] User:

[0742] Users go about their daily routine using their usual payment methods to make purchases, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0743] 2. Financial Planning

[0744] server:

[0745] The AI ​​engine located on the server analyzes income and expenditure data collected daily. The software used includes machine learning libraries and statistical analysis tools. Based on the analysis results, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0746] Device:

[0747] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. Users can check this information through the app, gain a concrete understanding of their financial future, and take action based on that information.

[0748] 3. Comparison of national living standards

[0749] server:

[0750] The server anonymizes, aggregates, and analyzes the spending data collected from all users. The software used includes data anonymization tools and statistical analysis software. It calculates national and regional averages and generates a benchmark for comparison with each user's spending data. This data provides users with a reference for their relative standard of living.

[0751] Device:

[0752] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[0753] Specific examples

[0754] Example 1:

[0755] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server via a secure HTTP request, and the server stores the information in a database. At the same time, this amount is reflected in User A's daily spending in real time.

[0756] Example 2:

[0757] If User B plans to get married in the next fiscal year, he or she enters that information into the app. The device sends this information to the server, where an AI engine analyzes it to predict wedding-related expenses and generate an appropriate savings plan. The prediction and savings plan are then visually displayed on User B's device.

[0758] Example 3:

[0759] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device then displays the results in a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0760] Prompt Sentence Examples

[0761] "Please forecast our wedding expenses for the next year and suggest a savings plan."

[0762] "I want to know how my spending compares to the national average."

[0763] "I want to record payments using QR codes and perform spending analysis."

[0764] The above is an embodiment of the present invention. This system allows users to efficiently manage their household finances and make financial plans for the future.

[0765] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0766] Step 1: Data collection

[0767] Terminal: When a user makes a purchase, they use a QR code or credit card to pay and capture the payment information. Specifically, for example, a smartphone app automatically records payment details (date, time, location, amount, category, etc.). It receives payment information as input and generates organized payment data as output.

[0768] Step 2: Submit your payment information

[0769] Terminal: Sends the acquired payment information to the server via a secure HTTP request. The process runs in the background, and the information is sent transparently to the user. It receives payment data as input and generates data to be sent to the server as output.

[0770] Step 3: Receive and store payment information

[0771] Server: Receives payment information sent from the terminal at the API endpoint. After receiving the information, it formats it into the appropriate data format and stores it in the database. Specifically, it analyzes the received data and inserts it into the appropriate table. It receives data sent from the terminal as input and stores it in the database as output.

[0772] Step 4: Manage manually entered data

[0773] User: When a user spends cash or uses other payment methods, they manually enter that information through the app. For example, if a user pays for a 500 yen lunch with cash, they enter the details into the app. It takes the expenditure details as input and generates the manually entered data as output.

[0774] Terminal: Sends manually entered expenditure information to the server. Specifically, when the user completes the input, they press the send button or the information is sent automatically. It receives manually entered expenditure data as input and generates data to be sent to the server as output.

[0775] Step 5: Analyze your income and expense data

[0776] Server: Analyzes collected income and expenditure data and visualizes the user's financial situation. The AI ​​engine performs the analysis using statistical analysis and machine learning algorithms. Specific operations include generating graphs and dashboards and calculating the balance between income and expenditure. It receives integrated income and expenditure data as input and generates analysis results as output.

[0777] Step 6: Generate a financial plan

[0778] Server: The AI ​​engine generates predicted spending and savings plans based on the user's goals (e.g., marriage, home purchase, etc.). Specifically, it predicts future spending trends based on past spending data and proposes savings plans based on the results. It receives the user's goal information and past data as input and generates a financial plan as output.

[0779] Step 7: Present and communicate your plan

[0780] Device: Displays the generated financial plan and notifications to the user. The user can check this information at any time through the app. Specifically, the latest financial status report is automatically pushed once a week. The device receives the generated plan data as input and generates notification data for the user as output.

[0781] Step 8: Compare living standards across countries

[0782] Server: Anonymizes the spending data collected from all users and calculates regional and national averages. Specifically, it removes information that identifies individual users and statistically processes the data. It receives the collected spending data as input and produces aggregate results as output.

[0783] Step 9: Expressing the basis of comparison

[0784] Server: Generates a comparison standard based on each user's expenditure data and the national average. Specifically, it calculates how many percentage points higher or lower a user's expenditure is than the national average. It receives statistical data and user data as input and generates a comparison standard as output.

[0785] Step 10: View the comparison results

[0786] Device: Visually presents the comparison results sent from the server to the user. For example, the app uses graphs and tables to help users intuitively understand their living standards. It receives the comparison results from the server as input and generates the data to be presented visually as output.

[0787] The above is the specific processing flow of this system. This configuration allows users to efficiently manage their household finances and make future economic plans.

[0788] (Application example 1)

[0789] 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."

[0790] In today's society, there are many different payment methods, and the resulting expenditure data is managed in a decentralized manner. This makes it difficult to grasp a household's financial situation in a unified manner and create an appropriate financial plan. It is also difficult for users to compare their own standard of living with the national or regional average. A system that can solve these problems is needed.

[0791] 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.

[0792] In this invention, the server includes: means for centrally managing expenditure data generated by various different payment methods; means for collecting income and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon; means for comparing each user's expenditure pattern with national or regional averages; means for automatically collecting expenditure data from QR codes, credit cards, bank accounts, etc.; means for manually inputting income and expenditures; means for visually displaying the generated financial plan on the user's terminal; and means for comparing the user's expenditure data with national or regional average data to present the user's relative standard of living. This enables centralized management of expenditure data, a detailed understanding of the household's economic situation, and the provision of an appropriate financial plan through predictions and comparisons.

[0793] "Various different payment methods" refers to multiple different payment methods, such as QR codes, credit cards, and bank accounts.

[0794] "Expense data" refers to information about a user's purchases and payments, including information such as the amount, date and time, and category of each transaction.

[0795] "Income Data" refers to information related to a user's income, including information such as salary, bonuses, and investment returns.

[0796] "Centralized management means" refers to a method of integrating data collected from multiple sources and managing it in a unified format.

[0797] "Means for predicting future expenditures and formulating savings plans" refers to methods for predicting future expenditures and formulating savings plans based on collected data.

[0798] "Means for comparison to national or regional averages" refers to a method for comparing a user's data with national or regional average data to calculate relative position.

[0799] "Automatic collection methods" refers to methods of automatically obtaining data from QR codes, credit cards, bank accounts, etc.

[0800] "Means for manual income and expense entry" refers to the methods by which a user manually enters income and expense information into an application or device.

[0801] "Visual display means" refers to a method of presenting information to a user in the form of text, graphs, charts, etc.

[0802] "Means of showing relative standard of living positions" refers to methods of comparing the user's data with national or regional averages and showing the results to the user.

[0803] The present invention is a system that provides financial planning by centrally managing expenses from various payment methods. The system collects income and expenditure data and can predict future expenses and create savings plans based on the data. It also has the function of comparing each user's spending patterns with national or regional averages.

[0804] server

[0805] The server automatically collects expenditure data sent from the user's device using various payment methods (QR code, credit card, bank account, etc.). The received data is then properly formatted and stored in a database. The server receives the collected expenditure data in real time and processes it to store it in the database. It also has an AI engine that generates a financial plan based on income and expenditure data and proposes it to the user. This AI engine has the function of visually displaying the generated financial plan on the user's device. It also has the function of comparing the user's expenditure data with national and regional averages to show the user's relative standard of living.

[0806] Terminal

[0807] When a payment is completed, the user's device (such as a smartphone or PC) sends the payment information to the server. Any income or expenses manually entered by the user are also sent to the server. The device is equipped with an interface for manually entering income and expenses. Furthermore, the user's device has the ability to visually display the financial plan and future savings plan generated by the AI ​​engine. Users can refer to this information to understand their financial future and take appropriate action.

[0808] User

[0809] Users make purchases in their daily lives using the usual payment methods. They make expenditures using QR codes, credit cards, and bank accounts, and the data is automatically sent to the server via their device. If manual input is required, they enter the data through the app. When users plan specific life events (marriage, childbirth, home purchase, etc.), they can enter that information into the app. The AI ​​engine analyzes this information and generates an appropriate financial plan. Users can refer to this plan to prepare for their future financial needs.

[0810] Specific examples

[0811] Example 1: User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily expenditure, and the proportion of expenditure in each category is also updated.

[0812] Example 2: If User B plans to get married next year, he or she enters that information into the app and the device sends it to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending.

[0813] Example 3: User C wants to compare his / her standard of living with the national average, so he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0814] Prompt Sentence Examples

[0815] "Please send data on purchasing home appliances worth 5,000 yen and retrieve financial plans and living standards comparisons for user ID 2 from the server."

[0816] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0817] Step 1:

[0818] User makes payment:

[0819] A user completes a payment using a QR code, credit card, bank account, etc. At that time, data about the payment (such as the amount spent, date and time, category, etc.) is generated.

[0820] Input: User payment behavior, expenditure data (expense amount, date and time, category, etc.)

[0821] Output: Expense data

[0822] Step 2:

[0823] The terminal sends the payment information to the server:

[0824] The terminal sends the user-completed payment information to the server, which is then sent to the server through an API endpoint.

[0825] Input: Expense Data

[0826] Output: Sending spending data to the server

[0827] What happens: The device app collects spending data and issues an HTTP POST request to send it to the server's API endpoint.

[0828] Step 3:

[0829] The server receives and stores the spending data:

[0830] The server receives the expenditure data sent from the terminal, converts it into an appropriate format, and stores it in a database.

[0831] Input: Spending data sent to the server

[0832] Output: Spending data stored in a database

[0833] Specific operation: The server receives the expenditure data and inserts it into the expenditure table in the database, while formatting and validating the data.

[0834] Step 4:

[0835] Collecting manually entered data:

[0836] Users manually enter their income and expenses through the terminal app, and the data is sent to the server.

[0837] Input: Manually entered income and expenditure data

[0838] Output: Send income and expenditure data to the server

[0839] Specific behavior: The device app collects income and expenditure data through the user interface and issues an HTTP POST request to send it to the server.

[0840] Step 5:

[0841] Generate a financial plan:

[0842] The AI ​​engine on the server uses collected income and expenditure data to predict future spending and create savings plans.

[0843] Input: Income data, expenditure data in the database

[0844] Output: Financial plan data

[0845] How it works: The AI ​​engine analyzes income and expense data and uses predictive algorithms to generate a financial plan.

[0846] Step 6:

[0847] View the generated financial plan:

[0848] A terminal receives the generated financial plan data from the server and visually displays it on a user interface.

[0849] Input: Financial plan data received from the server

[0850] Output: Financial plan displayed on the terminal

[0851] Specific behavior: The terminal app receives data from the server and displays it in the user interface in text and graph format.

[0852] Step 7:

[0853] Comparison of living standards:

[0854] The user requests a standard of living comparison function, and the server compares the collected expenditure data with the national or regional average. The results are displayed in the user interface.

[0855] Input: Standard of Living Comparison Request, User Expenditure Data

[0856] Output: Results compared to national or regional averages

[0857] What it does: The server retrieves the user's spending data and compares it with national or regional averages. The results are received by the device app and displayed visually.

[0858] To realize this application example, the user, terminal, and server cooperate to collect, manage, and analyze data, and provide the results to the user.

[0859] 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.

[0860] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[0861] 1. Data collection and centralized management

[0862] server:

[0863] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[0864] Device:

[0865] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[0866] User:

[0867] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[0868] 2. Financial Planning

[0869] server:

[0870] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[0871] Device:

[0872] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[0873] 3. Incorporating an Emotional Engine

[0874] server:

[0875] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends.

[0876] Device:

[0877] The user device has the ability to transmit emotional data obtained from the emotion engine to the server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[0878] User:

[0879] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[0880] 4. Adjust your plan based on emotions

[0881] server:

[0882] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[0883] Device:

[0884] The user terminal displays the tailored financial plan and provides emotion-based advice and suggestions.

[0885] 5. Comparison of national living standards

[0886] server:

[0887] The server anonymizes the spending data collected from all users and aggregates it into national data. It calculates national and regional averages and generates a baseline against which each user's spending data can be compared. This data provides users with a reference for their relative standard of living.

[0888] Device:

[0889] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[0890] Specific examples

[0891] Example 1:

[0892] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[0893] Example 2:

[0894] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. Additionally, if User B's current emotional state is "stress," suggestions for reducing that risk are also provided.

[0895] Example 3:

[0896] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[0897] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, it is possible to provide a more personalized financial plan that is more suited to the user.

[0898] The processing flow will be explained below.

[0899] 1. Data collection and centralized management

[0900] Step 1:

[0901] User: Makes a payment using a QR code at a store, etc.

[0902] Step 2:

[0903] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[0904] Step 3:

[0905] Terminal: In the background, the acquired payment information is sent to the server.

[0906] Step 4:

[0907] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[0908] Step 5:

[0909] User: If necessary, enter extra income or manual expenditure information through the terminal.

[0910] Step 6:

[0911] Terminal: Sends manually entered data to the server.

[0912] Step 7:

[0913] Server: Manually entered data is also stored in the database.

[0914] 2. Financial Planning

[0915] Step 1:

[0916] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[0917] Step 2:

[0918] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[0919] Step 3:

[0920] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[0921] Step 4:

[0922] Server: Save the generated financial plan in the database and associate it with the user ID.

[0923] Step 5:

[0924] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[0925] 3. Incorporating an Emotional Engine

[0926] Step 1:

[0927] User: Expresses emotions by voice or text input. If the device is equipped with a biometric sensor, emotional data is automatically acquired.

[0928] Step 2:

[0929] Terminal: Collects emotion data in real time and sends it to the server.

[0930] Step 3:

[0931] Server: Stores the emotion data analyzed by the emotion engine in a database.

[0932] 4. Adjust your plan based on emotions

[0933] Step 1:

[0934] Server: Based on the emotional data obtained from the emotion engine, the AI ​​engine adjusts the financial plan.

[0935] Step 2:

[0936] Server: Save the new financial plan, reflecting the user's emotional state, in the database.

[0937] Step 3:

[0938] On the device: When users open the app, they are presented with a tailored financial plan, along with emotional advice and recommendations.

[0939] 5. Comparison of national living standards

[0940] Step 1:

[0941] Server: Spending data collected from all users is anonymized and aggregated into national data.

[0942] Step 2:

[0943] Server: Calculates national and regional average spending data, including average spending by category.

[0944] Step 3:

[0945] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[0946] Step 4:

[0947] Server: When a user makes a request, it generates a comparison result and sends it to the terminal.

[0948] Step 5:

[0949] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[0950] Specific examples

[0951] Example 1: Managing QR code payments

[0952] Step 1:

[0953] User: Makes a QR code payment at the store (3,000 yen).

[0954] Step 2:

[0955] Terminal: Get payment information.

[0956] Step 3:

[0957] Terminal: Sends payment information to the server.

[0958] Step 4:

[0959] Server: Stores payment information in a database.

[0960] Example 2: Generating a Financial Plan

[0961] Step 1:

[0962] User: Enters next year's wedding plans into the app.

[0963] Step 2:

[0964] Terminal: Sends home configuration information to the server.

[0965] Step 3:

[0966] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[0967] Step 4:

[0968] Server: The generated plan is saved as user data.

[0969] Step 5:

[0970] Device: Shows users savings plans and spending forecasts.

[0971] Example 3: Comparing national standards of living

[0972] Step 1:

[0973] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[0974] Step 2:

[0975] User: Selects Living Standard Comparison from the app.

[0976] Step 3:

[0977] Terminal: Sends a request to the server.

[0978] Step 4:

[0979] Server: Compare user spending data with national averages.

[0980] Step 5:

[0981] Server: Sends the comparison results to the terminal.

[0982] Step 6:

[0983] Device: Displays the comparison of the user's standard of living with the national average.

[0984] Example 4: Adjusting your financial plan based on emotions

[0985] Step 1:

[0986] User: Enters "I've been feeling stressed lately" using voice input in the app.

[0987] Step 2:

[0988] Terminal: Sends emotion data to the server.

[0989] Step 3:

[0990] Server: Analyzes the received emotion data and stores it in a database.

[0991] Step 4:

[0992] Server (AI engine): Adjusts the current financial plan based on the emotional data, for example adding advice to avoid risks.

[0993] Step 5:

[0994] Device: Presents emotion-based adjustment plans to users.

[0995] The above are the specific processing steps of the program for implementing the present invention. The present invention allows users to manage their expenses and income in an integrated manner and obtain a highly personalized financial plan. Furthermore, by taking emotional data into consideration, flexible proposals tailored to the user's financial behavior are provided.

[0996] Example 2

[0997] 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."

[0998] Traditional household financial planning tools are unable to centrally manage spending data based on multiple payment methods and income sources, making it difficult to grasp a household's overall financial situation. They also fail to take into account the user's emotional state when predicting future spending and creating savings plans, making it difficult to provide personalized plans that are appropriate for each user. Furthermore, it is impossible to compare each user's spending patterns with national or regional averages, making it difficult to evaluate their relative standard of living.

[0999] 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.

[1000] In this invention, the server includes a means for centrally managing expenditure data generated by various different payment methods, a means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, a means for comparing each user's expenditure pattern with the national average or regional average, and a means for collecting user emotional data and adjusting a financial plan based on the user's emotional state. This allows for comprehensive management of user expenditure data, enabling future economic planning based on income and expenditure data, and providing personalized plans that take emotional state into consideration. Furthermore, by comparing each user's expenditure pattern with the national average or regional average, a relative evaluation of living standards is possible.

[1001] "Spending Data" refers to information about spending made by a user using various payment methods.

[1002] "Centralized management" refers to aggregating expenditure data obtained from multiple different payment methods and managing it in a single database or system.

[1003] "Income data" refers to information regarding various types of income earned by a user.

[1004] "Future expenditure forecasting" refers to forecasting future expenditures based on current and past income and expenditure data.

[1005] A "savings plan" is a specific plan for how to increase savings based on the user's future financial goals.

[1006] "Spending patterns" refers to data that indicates the characteristics and tendencies of a user's spending.

[1007] "National average" refers to the average value of various expenditures across the country.

[1008] "Regional average" refers to the average value of various expenditures in a particular region.

[1009] "Emotional data" refers to information about a user's emotional state, such as data obtained from voice input, text input, or biometric sensors.

[1010] An "emotion engine" is a system or service for analyzing emotional data obtained from voice input, text input, or biometric sensors.

[1011] A "financial plan" is a plan that presents a future economic plan based on a user's income and expenditure data.

[1012] A "life event" refers to an important event in a user's life (e.g., marriage, childbirth, home purchase, etc.) for which the associated costs need to be predicted and planned.

[1013] "Personalization" refers to providing plans and services that are optimized for individual users.

[1014] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[1015] 1. Data collection and centralized management

[1016] server:

[1017] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then appropriately formatted and stored in a database such as MySQL or PostgreSQL.

[1018] Device:

[1019] Once a payment is completed, the device (such as a smartphone or PC) sends the payment information to the server. The data is also sent to the server regarding expenses and income manually entered by the user. The data is transmitted securely using the HTTPS protocol.

[1020] User:

[1021] Users go about their daily lives making purchases using their usual payment methods, and when manual input is required, they enter that data through the app, for example, cash expenditures or salary income.

[1022] 2. Financial Planning

[1023] server:

[1024] The AI ​​engine (such as TensorFlow or PyTorch) on the server analyzes the income and expenditure data collected daily. Based on the results of this analysis, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. Statistical algorithms are used for the analysis to extract past data patterns and trends. For example, if a user plans to purchase a home in the future, the system can estimate the cost and suggest an appropriate savings plan.

[1025] Device:

[1026] The device displays a financial plan and future savings plan generated by an AI engine, and users can open the app to get a concrete understanding of their financial future through various graphs and charts.

[1027] 3. Incorporating an Emotional Engine

[1028] server:

[1029] The server incorporates an emotion engine (e.g., emotion recognition API) to recognize the user's emotions and obtains emotion data from the user's voice input (voice recognition technology), text input, or biometric sensors (health monitoring devices). This data is analyzed and stored in a database. The analysis results are used for next planning based on the user's emotional patterns.

[1030] Device:

[1031] The device has the ability to transmit emotional data in real time to a server, which can then be sent via a RESTful API using scripts written in Python or Ruby, allowing financial plans to be adjusted to reflect the user's current emotional state.

[1032] User:

[1033] Users can express their emotions through voice or text input. Furthermore, if the device is equipped with a built-in biometric sensor, emotional data can be automatically acquired. For example, the emotion engine can analyze heart rate data to determine whether stress levels are high.

[1034] 4. Adjust your plan based on emotions

[1035] server:

[1036] The server uses an AI engine to adjust the financial plan based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest low-risk savings and investment plans. This adjustment is made using machine learning libraries such as Scikit-learn.

[1037] Device:

[1038] The device displays an adjusted financial plan and offers emotion-based advice and suggestions: for example, when stress levels are high, a notification will appear suggesting relaxation techniques to the user.

[1039] 5. Comparison of national living standards

[1040] server:

[1041] The server anonymizes all users' spending data and aggregates it into large datasets for analysis. The aggregation process is performed using Hadoop, Apache Spark, or similar tools. This allows for calculation of national and regional averages, generating a baseline for comparison with users' spending data.

[1042] Device:

[1043] The device has the function of displaying the comparison results sent from the server. Users can request a comparison of their living standards through the app and see the results of how their living standards compare with the national and regional averages. The results are displayed as graphs and heat maps.

[1044] Specific examples

[1045] Example 1:

[1046] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server using Python's requests library, and the server saves the information in the database. This amount is reflected in the daily expenditure, and the expenditure percentage for each category is also updated.

[1047] Example 2:

[1048] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. At the same time, if User B's current emotional state is "stressed," it also provides suggestions to reduce the risk.

[1049] Example 3:

[1050] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1051] Example prompts to be input to the generative AI model:

[1052] Generate a personalized financial plan based on the user's spending and emotional data. If the emotional state is stressed, provide advice that includes a savings plan to mitigate risk.

[1053] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, more personalized financial plans can be provided.

[1054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1055] Step 1: Data collection

[1056] Input: User's payment information (QR code, credit card, manual entry, etc.)

[1057] How it works: When a user makes a payment, the terminal captures the payment information (payment amount, date and time, payment method, etc.).

[1058] Specific operation: When a user makes a purchase of 3,000 yen using a QR code, the information is recorded in the device's app.

[1059] Step 2: Send data

[1060] Input: Captured payment information

[1061] How it works: The terminal sends the acquired payment information to the server using the HTTPS protocol. The data is structured in JSON format.

[1062] What happens: The device sends an HTTP POST request to the server, sending JSON data containing payment information.

[1063] Step 3: Receiving and storing data

[1064] Input: Payment information sent from the terminal

[1065] How it works: The server receives the incoming payment information, converts it into the appropriate format, and stores it in a database.

[1066] What it does: The server stores the received payment information in the "Expenses" table in the MySQL database.

[1067] Step 4: Generate a financial plan

[1068] Input: Income and expenditure data retrieved from the database

[1069] How it works: An AI engine on the server analyzes income and expenditure data and creates future spending forecasts and savings plans.

[1070] How it works: The AI ​​engine analyzes trends based on spending data from the past six months and suggests savings goals for the next year.

[1071] Step 5: Submit your plan

[1072] Input: Generated financial plan

[1073] Operation: The server sends the generated financial plan to the terminal. The data is structured in JSON format.

[1074] Specific operation: The server sends the generated financial plan in JSON format to the terminal as an HTTP response.

[1075] Step 6: View your plan

[1076] Input: Financial plan sent from server

[1077] How it works: The device visually displays the financial plan it receives.

[1078] What it does: The app displays a financial plan as graphs and charts for the user to review.

[1079] Step 7: Obtaining emotion data

[1080] Input: User emotion input (voice, text, biometric sensors)

[1081] Operation: The device acquires the user's emotion data.

[1082] Specific operation: When the user speaks "I'm stressed," the speech is converted into text and recorded.

[1083] Step 8: Sending Emotion Data

[1084] Input: Captured emotion data

[1085] Operation: The device transmits the acquired emotion data to the server in real time.

[1086] How it works: The device sends its emotional state and its intensity to the server using an HTTP POST request.

[1087] Step 9: Analyze the sentiment data

[1088] Input: Emotion data sent from the device

[1089] How it works: The server analyzes the emotion data using the emotion engine, and the analysis results are stored in a database.

[1090] Specific operation: The server analyzes heart rate data from the biometric sensor and determines the user's emotional state as "stressed."

[1091] Step 10: Adjust your plan

[1092] Input: Parsed emotion data

[1093] How it works: The server adjusts financial plans based on emotional data. For example, if the user is stressed, it suggests a plan with less risk.

[1094] Specific operation: The AI ​​engine selects and re-proposes safe investment plans based on stress levels.

[1095] Step 11: Submit your adjustment plan

[1096] Input: Coordinated Financial Plan

[1097] Operation: The server sends the re-adjusted financial plan to the terminal.

[1098] Specific operation: The server sends the adjusted plan in JSON format to the terminal and returns it as an HTTP response.

[1099] Step 12: View the adjustment plan

[1100] Input: Adjustment plan sent from the server

[1101] How it works: The terminal visually displays your adjusted financial plan.

[1102] What happens: The device displays the adjusted plan as a graph or chart in the app UI for the user to review.

[1103] Step 13: Compare to the national average

[1104] Input: Spending data collected from all users

[1105] How it works: The server anonymizes and aggregates spending data from all users to calculate national and regional averages.

[1106] What it does: The server aggregates the data using Hadoop and calculates the average standard of living for each region.

[1107] Step 14: Send and display comparison results

[1108] Input: Comparison results with the national average

[1109] How it works: The server sends the results of the comparison with the national average to the device, which then displays the results.

[1110] What it does: The device visualizes the comparison results received from the server as a heat map, allowing the user to check their living standards.

[1111] The above is the content of the specific processing steps of this system.

[1112] (Application example 2)

[1113] 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."

[1114] Conventional financial planning systems collect income and expenditure data and propose future expenditure forecasts and savings plans, but do not consider the user's emotions when planning. While they may provide comparisons of spending patterns with national or regional averages, they do not provide personalized financial plans based on the user's emotional data. This makes it difficult to provide appropriate planning that reflects the user's actual emotional state, making it difficult to propose stress-reducing and reasonable savings plans.

[1115] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1116] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with the national average or regional average, means for collecting user emotion data and providing a personalized financial plan based thereon, means for centrally managing payment data and comparing living standards, and means for presenting the user with a financial plan created based on their emotions. This makes it possible to provide a reasonable savings plan or a personalized financial plan that takes into account the user's emotional state.

[1117] "Expense Data" is a record of expenses incurred through various different payment methods.

[1118] A "single-source management mechanism" is a mechanism for integrating and managing data generated from different payment methods.

[1119] "Income data" is a record of all income earned by a household or individual.

[1120] "Expense forecasting" is the estimation of future costs based on collected data.

[1121] A "savings plan" is a specific plan for the amount of savings needed to achieve future goals and how to save.

[1122] "Expenditure patterns" are data that indicate how much an individual or household spends on what items.

[1123] The "national average" is the value calculated by averaging all data within Japan.

[1124] The "regional average" is a value calculated by averaging data for a specific region.

[1125] "Emotion data" is data that indicates the user's emotional state and is obtained from voice, text input, biometric sensors, and the like.

[1126] A "personalized financial plan" is a financial plan created based on a user's individual income, expenses, and emotional state.

[1127] "Life events" refer to important personal or family events such as marriage, childbirth, or home purchase.

[1128] An "emotion engine" is a system or software for recognizing and analyzing a user's emotions.

[1129] A "financial plan" is a future financial strategy or plan based on income and expenses.

[1130] A "server" is a computer system that collects, stores, analyzes data, and provides necessary information.

[1131] A "user terminal" is a device used by a user, such as a mobile phone or a personal computer.

[1132] This invention combines a system that centrally manages spending via various payment methods and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[1133] 1. Data collection and centralized management

[1134] server:

[1135] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (e.g., QR code, credit card, manual entry, etc.). The received data is properly formatted and stored in a database. A high-performance server is desirable as the hardware to be used.

[1136] Device:

[1137] The terminal is a smartphone, PC, etc., and once the payment is completed, the payment information is sent to the server. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1138] User:

[1139] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1140] 2. Financial Planning

[1141] server:

[1142] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1143] Device:

[1144] The device has the ability to display financial plans and future savings plans generated by an AI engine, allowing users to gain a concrete understanding of their financial future and take action accordingly.

[1145] 3. Incorporating an Emotional Engine

[1146] server:

[1147] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends. The software used utilizes an emotion recognition API.

[1148] Device:

[1149] The device has the ability to transmit emotional data obtained from the emotion engine to a server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[1150] User:

[1151] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[1152] 4. Adjust your plan based on emotions

[1153] server:

[1154] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[1155] Device:

[1156] The device displays a tailored financial plan and offers emotionally-driven advice and recommendations.

[1157] Specific examples

[1158] Example 1:

[1159] If a user is feeling stressed about their recent high spending, the emotion recognition engine will recognize that emotion and send it along with their payment data to the AI ​​engine, which will then generate a reasonable savings plan to reduce stress and present it to the user.

[1160] Example prompt sentence:

[1161] "I'm worried about my recent expenses."

[1162] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1163] Step 1:

[1164] The terminal receives payment information from the user as input and sends it to the server through an API endpoint. Once the payment information is entered, the terminal converts it into a proprietary format so that the spending data is properly stored on the server.

[1165] Step 2:

[1166] The server receives the expenditure data sent from the device and stores it in a database. The server checks the integrity of the data upon receiving it and standardizes the format. This process allows for real-time collection of expenditure data and centralized management.

[1167] Step 3:

[1168] The device receives the user's emotional state as input. When the user expresses their emotion through voice or text input, the device sends the data to the emotion recognition engine. It also receives data from biometric sensors, if available. The emotion recognition engine analyzes the emotional data and identifies the emotional state.

[1169] Step 4:

[1170] The server receives the emotion data sent from the emotion recognition engine and stores it in a database. This data, along with existing income and expenditure data, is input into the AI ​​engine for analysis. The AI ​​engine takes the emotion data into account and generates a customized financial plan.

[1171] Step 5:

[1172] The server then sends the generated financial plan to the device. The AI ​​engine combines emotional and economic data to output a plan that includes reasonable savings plans and low-risk investment suggestions. This plan is personalized to reflect the user's emotional state.

[1173] Step 6:

[1174] The terminal receives the financial plan sent from the server and displays it on the screen. The user can check the details of the plan on the screen and receive advice based on their financial situation and emotional state.

[1175] Step 7:

[1176] The server calculates national or regional averages based on the expenditure data collected from all users and generates comparison results with each user's expenditure patterns. This comparison data is analyzed and stored together with the users' expenditure data to show the relative positions of the users' standard of living.

[1177] Step 8:

[1178] When a user requests a comparison of living standards, the terminal displays the results of the comparison sent from the server on the screen. Users can compare their expenditure data with the national and regional averages and visually check their own living standards.

[1179] Examples of specific operations and inputs and outputs:

[1180] The device recognizes the user's voice input, "I'm worried about my recent high expenses," and sends it to the server. The server uses an emotion recognition engine to output the emotion data "stress" from this input data. The server then inputs this emotion data along with the expenditure data into an AI engine, which generates a personalized financial plan including a reasonable savings plan and sends it to the device. The user can review this plan and use it to help with future financial activities.

[1181] 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.

[1182] 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.

[1183] 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.

[1184] [Third embodiment]

[1185] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1186] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1187] 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).

[1188] 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.

[1189] 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.

[1190] 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).

[1191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1192] 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.

[1193] 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.

[1194] 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.

[1195] 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.

[1196] 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."

[1197] The present invention is a system that centrally manages expenditures via various payment methods and provides a financial plan based on the household's economic situation, and is implemented in the following form: Cooperation between a server, a terminal, and a user is required to efficiently collect expenditure data and income data and to perform predictions and comparisons.

[1198] 1. Data collection and centralized management

[1199] server:

[1200] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[1201] Device:

[1202] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1203] User:

[1204] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1205] 2. Financial Planning

[1206] server:

[1207] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1208] Device:

[1209] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[1210] 3. Comparison of national living standards

[1211] server:

[1212] The server anonymizes, aggregates, and analyzes the spending data collected from all users. It calculates national and regional averages and generates a benchmark to compare with each user's spending data. This data provides users with a reference for their relative standard of living.

[1213] Device:

[1214] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[1215] Specific examples

[1216] Example 1:

[1217] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[1218] Example 2:

[1219] If User B plans to get married in the next fiscal year, he or she can enter that information into the app, and the device will send it to the server. The AI ​​will analyze the data, predict wedding-related expenses, and generate an appropriate savings plan. User B's device will then visually display this savings plan and predicted spending.

[1220] Example 3:

[1221] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1222] The above is an embodiment of the present invention, which enables a user to efficiently manage their household finances and make financial plans for the future.

[1223] The processing flow will be explained below.

[1224] 1. Data collection and centralized management

[1225] Step 1:

[1226] User: Makes a payment using a QR code at a store, etc.

[1227] Step 2:

[1228] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[1229] Step 3:

[1230] Terminal: In the background, the acquired payment information is sent to the server.

[1231] Step 4:

[1232] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[1233] Step 5:

[1234] User: If necessary, enter extra income or manual expenditure information through the terminal.

[1235] Step 6:

[1236] Terminal: Sends manually entered data to the server.

[1237] Step 7:

[1238] Server: Manually entered data is also stored in the database.

[1239] 2. Financial Planning

[1240] Step 1:

[1241] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[1242] Step 2:

[1243] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[1244] Step 3:

[1245] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[1246] Step 4:

[1247] Server: Save the generated financial plan in the database and associate it with the user ID.

[1248] Step 5:

[1249] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[1250] 3. Comparison of national living standards

[1251] Step 1:

[1252] Server: Spending data collected from all users is anonymized and aggregated into national data.

[1253] Step 2:

[1254] Server: Calculates national and regional average spending data, including average spending by category.

[1255] Step 3:

[1256] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[1257] Step 4:

[1258] Server: When requested by the user, generates the comparison result and sends it to the terminal.

[1259] Step 5:

[1260] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[1261] Specific examples

[1262] Example 1: Managing QR code payments

[1263] Step 1:

[1264] User: Makes a QR code payment at the store (3,000 yen).

[1265] Step 2:

[1266] Terminal: Get payment information.

[1267] Step 3:

[1268] Terminal: Sends payment information to the server.

[1269] Step 4:

[1270] Server: Stores payment information in a database.

[1271] Example 2: Generating a Financial Plan

[1272] Step 1:

[1273] User: Enters next year's wedding plans into the app.

[1274] Step 2:

[1275] Terminal: Sends home configuration information to the server.

[1276] Step 3:

[1277] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[1278] Step 4:

[1279] Server: The generated plan is saved as user data.

[1280] Step 5:

[1281] Device: Shows users savings plans and spending forecasts.

[1282] Example 3: Comparing national standards of living

[1283] Step 1:

[1284] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[1285] Step 2:

[1286] User: Selects Living Standard Comparison from the app.

[1287] Step 3:

[1288] Terminal: Sends a request to the server.

[1289] Step 4:

[1290] Server: Compare user spending data with national averages.

[1291] Step 5:

[1292] Server: Sends the comparison results to the terminal.

[1293] Step 6:

[1294] Device: Displays the comparison of the user's standard of living with the national average.

[1295] The above are the specific processing steps of the program for carrying out the present invention.

[1296] Example 1

[1297] 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."

[1298] Current household management systems are cumbersome in collecting expenditure and income data, and it is difficult to centrally manage data from different payment methods. Furthermore, it is not easy to predict future expenditures or create savings plans from the collected data, and the benchmarks for comparing living standards with those of the nation or region are unclear. There is a need to address these issues and provide a household management system that is user-friendly.

[1299] 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.

[1300] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with national or regional averages, means for receiving payment information transmitted from the user's terminal, formatting it into an appropriate data format, and storing it in a database, means for analyzing the collected income and expenditure data, visualizing the user's financial situation, and proposing future expenditure forecasts and savings plans, and means for anonymizing the aggregated expenditure data, calculating national or regional averages, and generating standards for comparison with each user's expenditure data, thereby enabling users to efficiently manage their household finances and create future economic plans.

[1301] "Expense data" refers to financial information relating to purchases and service usage made by a user using various payment methods.

[1302] "Income Data" refers to information about the income or earnings earned by a User.

[1303] "Centralized management means" refers to a means for uniformly collecting and managing expenditure data obtained from various payment methods.

[1304] "Future expenditure forecasting" refers to predicting future expenditure amounts and spending patterns based on collected data.

[1305] A "savings plan" is a detailed plan for a user to effectively save money according to their future goals and needs.

[1306] "Spending patterns" refer to a user's past payment history and spending behavior tendencies.

[1307] The "national average" refers to data on average expenditures and income across Japan.

[1308] "Regional averages" refer to data on average expenditures and incomes in specific regions of Japan.

[1309] "Database" means a data storage system for properly managing and storing received expenditure and income data.

[1310] An "AI engine" is an engine that uses artificial intelligence technology to analyze collected data and predict future economic conditions.

[1311] "Anonymization" means removing information that identifies a user personally and processing the data so that it cannot be linked to a specific individual.

[1312] "Analysis" refers to the process of extracting information and finding meaning from collected data using statistical methods and algorithms.

[1313] An "API endpoint" is an interface for sending and receiving data between different systems.

[1314] "Notification" refers to a means of providing information to inform the user of the analysis results and the generated plan.

[1315] This invention is a system that manages expenditures by various payment methods in an integrated manner and provides a financial plan based on the household's economic situation. Cooperation between the server, terminals, and users is required to efficiently collect expenditure and income data and to make predictions and comparisons.

[1316] 1. Data collection and centralized management

[1317] server:

[1318] The server provides an API endpoint to receive spending data sent from users' devices using various payment methods (QR code, credit card, manual entry, etc.). The hardware used includes a high-performance server computer, and the software includes a database management system (DBMS) and an API server. The received data is then properly formatted and stored in the database.

[1319] Device:

[1320] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to a server. The specific software used can be a mobile application or a web browser. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1321] User:

[1322] Users go about their daily routine using their usual payment methods to make purchases, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1323] 2. Financial Planning

[1324] server:

[1325] The AI ​​engine located on the server analyzes income and expenditure data collected daily. The software used includes machine learning libraries and statistical analysis tools. Based on the analysis results, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1326] Device:

[1327] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. Users can check this information through the app, gain a concrete understanding of their financial future, and take action based on that information.

[1328] 3. Comparison of national living standards

[1329] server:

[1330] The server anonymizes, aggregates, and analyzes the spending data collected from all users. The software used includes data anonymization tools and statistical analysis software. It calculates national and regional averages and generates a benchmark for comparison with each user's spending data. This data provides users with a reference for their relative standard of living.

[1331] Device:

[1332] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[1333] Specific examples

[1334] Example 1:

[1335] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server via a secure HTTP request, and the server stores the information in a database. At the same time, this amount is reflected in User A's daily spending in real time.

[1336] Example 2:

[1337] If User B plans to get married in the next fiscal year, he or she enters that information into the app. The device sends this information to the server, where an AI engine analyzes it to predict wedding-related expenses and generate an appropriate savings plan. The prediction and savings plan are then visually displayed on User B's device.

[1338] Example 3:

[1339] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device then displays the results in a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1340] Prompt Sentence Examples

[1341] "Please forecast our wedding expenses for the next year and suggest a savings plan."

[1342] "I want to know how my spending compares to the national average."

[1343] "I want to record payments using QR codes and perform spending analysis."

[1344] The above is an embodiment of the present invention. This system allows users to efficiently manage their household finances and make financial plans for the future.

[1345] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1346] Step 1: Data collection

[1347] Terminal: When a user makes a purchase, they use a QR code or credit card to pay and capture the payment information. Specifically, for example, a smartphone app automatically records payment details (date, time, location, amount, category, etc.). It receives payment information as input and generates organized payment data as output.

[1348] Step 2: Submit your payment information

[1349] Terminal: Sends the acquired payment information to the server via a secure HTTP request. The process runs in the background, and the information is sent transparently to the user. It receives payment data as input and generates data to be sent to the server as output.

[1350] Step 3: Receive and store payment information

[1351] Server: Receives payment information sent from the terminal at the API endpoint. After receiving the information, it formats it into the appropriate data format and stores it in the database. Specifically, it analyzes the received data and inserts it into the appropriate table. It receives data sent from the terminal as input and stores it in the database as output.

[1352] Step 4: Manage manually entered data

[1353] User: When a user spends cash or uses other payment methods, they manually enter that information through the app. For example, if a user pays for a 500 yen lunch with cash, they enter the details into the app. It takes the expenditure details as input and generates the manually entered data as output.

[1354] Terminal: Sends manually entered expenditure information to the server. Specifically, when the user completes the input, they press the send button or the information is sent automatically. It receives manually entered expenditure data as input and generates data to be sent to the server as output.

[1355] Step 5: Analyze your income and expense data

[1356] Server: Analyzes collected income and expenditure data and visualizes the user's financial situation. The AI ​​engine performs the analysis using statistical analysis and machine learning algorithms. Specific operations include generating graphs and dashboards and calculating the balance between income and expenditure. It receives integrated income and expenditure data as input and generates analysis results as output.

[1357] Step 6: Generate a financial plan

[1358] Server: The AI ​​engine generates predicted spending and savings plans based on the user's goals (e.g., marriage, home purchase, etc.). Specifically, it predicts future spending trends based on past spending data and proposes savings plans based on the results. It receives the user's goal information and past data as input and generates a financial plan as output.

[1359] Step 7: Present and communicate your plan

[1360] Device: Displays the generated financial plan and notifications to the user. The user can check this information at any time through the app. Specifically, the latest financial status report is automatically pushed once a week. The device receives the generated plan data as input and generates notification data for the user as output.

[1361] Step 8: Compare living standards across countries

[1362] Server: Anonymizes the spending data collected from all users and calculates regional and national averages. Specifically, it removes information that identifies individual users and statistically processes the data. It receives the collected spending data as input and produces aggregate results as output.

[1363] Step 9: Expressing the basis of comparison

[1364] Server: Generates a comparison standard based on each user's expenditure data and the national average. Specifically, it calculates how many percentage points higher or lower a user's expenditure is than the national average. It receives statistical data and user data as input and generates a comparison standard as output.

[1365] Step 10: View the comparison results

[1366] Device: Visually presents the comparison results sent from the server to the user. For example, the app uses graphs and tables to help users intuitively understand their living standards. It receives the comparison results from the server as input and generates the data to be presented visually as output.

[1367] The above is the specific processing flow of this system. This configuration allows users to efficiently manage their household finances and make future economic plans.

[1368] (Application example 1)

[1369] 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."

[1370] In today's society, there are many different payment methods, and the resulting expenditure data is managed in a decentralized manner. This makes it difficult to grasp a household's financial situation in a unified manner and create an appropriate financial plan. It is also difficult for users to compare their own standard of living with the national or regional average. A system that can solve these problems is needed.

[1371] 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.

[1372] In this invention, the server includes: means for centrally managing expenditure data generated by various different payment methods; means for collecting income and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon; means for comparing each user's expenditure pattern with national or regional averages; means for automatically collecting expenditure data from QR codes, credit cards, bank accounts, etc.; means for manually inputting income and expenditures; means for visually displaying the generated financial plan on the user's terminal; and means for comparing the user's expenditure data with national or regional average data to present the user's relative standard of living. This enables centralized management of expenditure data, a detailed understanding of the household's economic situation, and the provision of an appropriate financial plan through predictions and comparisons.

[1373] "Various different payment methods" refers to multiple different payment methods, such as QR codes, credit cards, and bank accounts.

[1374] "Expense data" refers to information about a user's purchases and payments, including information such as the amount, date and time, and category of each transaction.

[1375] "Income Data" refers to information related to a user's income, including information such as salary, bonuses, and investment returns.

[1376] "Centralized management means" refers to a method of integrating data collected from multiple sources and managing it in a unified format.

[1377] "Means for predicting future expenditures and formulating savings plans" refers to methods for predicting future expenditures and formulating savings plans based on collected data.

[1378] "Means for comparison to national or regional averages" refers to a method for comparing a user's data with national or regional average data to calculate relative position.

[1379] "Automatic collection methods" refers to methods of automatically obtaining data from QR codes, credit cards, bank accounts, etc.

[1380] "Means for manual income and expense entry" refers to the methods by which a user manually enters income and expense information into an application or device.

[1381] "Visual display means" refers to a method of presenting information to a user in the form of text, graphs, charts, etc.

[1382] "Means of showing relative standard of living positions" refers to methods of comparing the user's data with national or regional averages and showing the results to the user.

[1383] The present invention is a system that provides financial planning by centrally managing expenses from various payment methods. The system collects income and expenditure data and can predict future expenses and create savings plans based on the data. It also has the function of comparing each user's spending patterns with national or regional averages.

[1384] server

[1385] The server automatically collects expenditure data sent from the user's device using various payment methods (QR code, credit card, bank account, etc.). The received data is then properly formatted and stored in a database. The server receives the collected expenditure data in real time and processes it to store it in the database. It also has an AI engine that generates a financial plan based on income and expenditure data and proposes it to the user. This AI engine has the function of visually displaying the generated financial plan on the user's device. It also has the function of comparing the user's expenditure data with national and regional averages to show the user's relative standard of living.

[1386] Terminal

[1387] When a payment is completed, the user's device (such as a smartphone or PC) sends the payment information to the server. Any income or expenses manually entered by the user are also sent to the server. The device is equipped with an interface for manually entering income and expenses. Furthermore, the user's device has the ability to visually display the financial plan and future savings plan generated by the AI ​​engine. Users can refer to this information to understand their financial future and take appropriate action.

[1388] User

[1389] Users make purchases in their daily lives using the usual payment methods. They make expenditures using QR codes, credit cards, and bank accounts, and the data is automatically sent to the server via their device. If manual input is required, they enter the data through the app. When users plan specific life events (marriage, childbirth, home purchase, etc.), they can enter that information into the app. The AI ​​engine analyzes this information and generates an appropriate financial plan. Users can refer to this plan to prepare for their future financial needs.

[1390] Specific examples

[1391] Example 1: User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily expenditure, and the proportion of expenditure in each category is also updated.

[1392] Example 2: If User B plans to get married next year, he or she enters that information into the app and the device sends it to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending.

[1393] Example 3: User C wants to compare his / her standard of living with the national average, so he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1394] Prompt Sentence Examples

[1395] "Please send data on purchasing home appliances worth 5,000 yen and retrieve financial plans and living standards comparisons for user ID 2 from the server."

[1396] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1397] Step 1:

[1398] User makes payment:

[1399] A user completes a payment using a QR code, credit card, bank account, etc. At that time, data about the payment (such as the amount spent, date and time, category, etc.) is generated.

[1400] Input: User payment behavior, expenditure data (expense amount, date and time, category, etc.)

[1401] Output: Expense data

[1402] Step 2:

[1403] The terminal sends the payment information to the server:

[1404] The terminal sends the user-completed payment information to the server, which is then sent to the server through an API endpoint.

[1405] Input: Expense Data

[1406] Output: Sending spending data to the server

[1407] What happens: The device app collects spending data and issues an HTTP POST request to send it to the server's API endpoint.

[1408] Step 3:

[1409] The server receives and stores the spending data:

[1410] The server receives the expenditure data sent from the terminal, converts it into an appropriate format, and stores it in a database.

[1411] Input: Spending data sent to the server

[1412] Output: Spending data stored in a database

[1413] Specific operation: The server receives the expenditure data and inserts it into the expenditure table in the database, while formatting and validating the data.

[1414] Step 4:

[1415] Collecting manually entered data:

[1416] Users manually enter their income and expenses through the terminal app, and the data is sent to the server.

[1417] Input: Manually entered income and expenditure data

[1418] Output: Send income and expenditure data to the server

[1419] Specific behavior: The device app collects income and expenditure data through the user interface and issues an HTTP POST request to send it to the server.

[1420] Step 5:

[1421] Generate a financial plan:

[1422] The AI ​​engine on the server uses collected income and expenditure data to predict future spending and create savings plans.

[1423] Input: Income data, expenditure data in the database

[1424] Output: Financial plan data

[1425] How it works: The AI ​​engine analyzes income and expense data and uses predictive algorithms to generate a financial plan.

[1426] Step 6:

[1427] View the generated financial plan:

[1428] A terminal receives the generated financial plan data from the server and visually displays it on a user interface.

[1429] Input: Financial plan data received from the server

[1430] Output: Financial plan displayed on the terminal

[1431] Specific behavior: The terminal app receives data from the server and displays it in the user interface in text and graph format.

[1432] Step 7:

[1433] Comparison of living standards:

[1434] The user requests a standard of living comparison function, and the server compares the collected expenditure data with the national or regional average. The results are displayed in the user interface.

[1435] Input: Standard of Living Comparison Request, User Expenditure Data

[1436] Output: Results compared to national or regional averages

[1437] What it does: The server retrieves the user's spending data and compares it with national or regional averages. The results are received by the device app and displayed visually.

[1438] To realize this application example, the user, terminal, and server cooperate to collect, manage, and analyze data, and provide the results to the user.

[1439] 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.

[1440] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[1441] 1. Data collection and centralized management

[1442] server:

[1443] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[1444] Device:

[1445] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1446] User:

[1447] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1448] 2. Financial Planning

[1449] server:

[1450] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1451] Device:

[1452] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[1453] 3. Incorporating an Emotional Engine

[1454] server:

[1455] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends.

[1456] Device:

[1457] The user device has the ability to transmit emotional data obtained from the emotion engine to the server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[1458] User:

[1459] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[1460] 4. Adjust your plan based on emotions

[1461] server:

[1462] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[1463] Device:

[1464] The user terminal displays the tailored financial plan and provides emotion-based advice and suggestions.

[1465] 5. Comparison of national living standards

[1466] server:

[1467] The server anonymizes the spending data collected from all users and aggregates it into national data. It calculates national and regional averages and generates a baseline against which each user's spending data can be compared. This data provides users with a reference for their relative standard of living.

[1468] Device:

[1469] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[1470] Specific examples

[1471] Example 1:

[1472] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[1473] Example 2:

[1474] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. Additionally, if User B's current emotional state is "stress," suggestions for reducing that risk are also provided.

[1475] Example 3:

[1476] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1477] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, it is possible to provide a more personalized financial plan that is more suited to the user.

[1478] The processing flow will be explained below.

[1479] 1. Data collection and centralized management

[1480] Step 1:

[1481] User: Makes a payment using a QR code at a store, etc.

[1482] Step 2:

[1483] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[1484] Step 3:

[1485] Terminal: In the background, the acquired payment information is sent to the server.

[1486] Step 4:

[1487] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[1488] Step 5:

[1489] User: If necessary, enter extra income or manual expenditure information through the terminal.

[1490] Step 6:

[1491] Terminal: Sends manually entered data to the server.

[1492] Step 7:

[1493] Server: Manually entered data is also stored in the database.

[1494] 2. Financial Planning

[1495] Step 1:

[1496] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[1497] Step 2:

[1498] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[1499] Step 3:

[1500] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[1501] Step 4:

[1502] Server: Save the generated financial plan in the database and associate it with the user ID.

[1503] Step 5:

[1504] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[1505] 3. Incorporating an Emotional Engine

[1506] Step 1:

[1507] User: Expresses emotions by voice or text input. If the device is equipped with a biometric sensor, emotional data is automatically acquired.

[1508] Step 2:

[1509] Terminal: Collects emotion data in real time and sends it to the server.

[1510] Step 3:

[1511] Server: Stores the emotion data analyzed by the emotion engine in a database.

[1512] 4. Adjust your plan based on emotions

[1513] Step 1:

[1514] Server: Based on the emotional data obtained from the emotion engine, the AI ​​engine adjusts the financial plan.

[1515] Step 2:

[1516] Server: Save the new financial plan, reflecting the user's emotional state, in the database.

[1517] Step 3:

[1518] On the device: When users open the app, they are presented with a tailored financial plan, along with emotional advice and recommendations.

[1519] 5. Comparison of national living standards

[1520] Step 1:

[1521] Server: Spending data collected from all users is anonymized and aggregated into national data.

[1522] Step 2:

[1523] Server: Calculates national and regional average spending data, including average spending by category.

[1524] Step 3:

[1525] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[1526] Step 4:

[1527] Server: When a user makes a request, it generates a comparison result and sends it to the terminal.

[1528] Step 5:

[1529] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[1530] Specific examples

[1531] Example 1: Managing QR code payments

[1532] Step 1:

[1533] User: Makes a QR code payment at the store (3,000 yen).

[1534] Step 2:

[1535] Terminal: Get payment information.

[1536] Step 3:

[1537] Terminal: Sends payment information to the server.

[1538] Step 4:

[1539] Server: Stores payment information in a database.

[1540] Example 2: Generating a Financial Plan

[1541] Step 1:

[1542] User: Enters next year's wedding plans into the app.

[1543] Step 2:

[1544] Terminal: Sends home configuration information to the server.

[1545] Step 3:

[1546] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[1547] Step 4:

[1548] Server: The generated plan is saved as user data.

[1549] Step 5:

[1550] Device: Shows users savings plans and spending forecasts.

[1551] Example 3: Comparing national standards of living

[1552] Step 1:

[1553] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[1554] Step 2:

[1555] User: Selects Living Standard Comparison from the app.

[1556] Step 3:

[1557] Terminal: Sends a request to the server.

[1558] Step 4:

[1559] Server: Compare user spending data with national averages.

[1560] Step 5:

[1561] Server: Sends the comparison results to the terminal.

[1562] Step 6:

[1563] Device: Displays the comparison of the user's standard of living with the national average.

[1564] Example 4: Adjusting your financial plan based on emotions

[1565] Step 1:

[1566] User: Enters "I've been feeling stressed lately" using voice input in the app.

[1567] Step 2:

[1568] Terminal: Sends emotion data to the server.

[1569] Step 3:

[1570] Server: Analyzes the received emotion data and stores it in a database.

[1571] Step 4:

[1572] Server (AI engine): Adjusts the current financial plan based on the emotional data, for example adding advice to avoid risks.

[1573] Step 5:

[1574] Device: Presents emotion-based adjustment plans to users.

[1575] The above are the specific processing steps of the program for implementing the present invention. The present invention allows users to manage their expenses and income in an integrated manner and obtain a highly personalized financial plan. Furthermore, by taking emotional data into consideration, flexible proposals tailored to the user's financial behavior are provided.

[1576] Example 2

[1577] 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."

[1578] Traditional household financial planning tools are unable to centrally manage spending data based on multiple payment methods and income sources, making it difficult to grasp a household's overall financial situation. They also fail to take into account the user's emotional state when predicting future spending and creating savings plans, making it difficult to provide personalized plans that are appropriate for each user. Furthermore, it is impossible to compare each user's spending patterns with national or regional averages, making it difficult to evaluate their relative standard of living.

[1579] 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.

[1580] In this invention, the server includes a means for centrally managing expenditure data generated by various different payment methods, a means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, a means for comparing each user's expenditure pattern with the national average or regional average, and a means for collecting user emotional data and adjusting a financial plan based on the user's emotional state. This allows for comprehensive management of user expenditure data, enabling future economic planning based on income and expenditure data, and providing personalized plans that take emotional state into consideration. Furthermore, by comparing each user's expenditure pattern with the national average or regional average, a relative evaluation of living standards is possible.

[1581] "Spending Data" refers to information about spending made by a user using various payment methods.

[1582] "Centralized management" refers to aggregating expenditure data obtained from multiple different payment methods and managing it in a single database or system.

[1583] "Income data" refers to information regarding various types of income earned by a user.

[1584] "Future expenditure forecasting" refers to forecasting future expenditures based on current and past income and expenditure data.

[1585] A "savings plan" is a specific plan for how to increase savings based on the user's future financial goals.

[1586] "Spending patterns" refers to data that indicates the characteristics and tendencies of a user's spending.

[1587] "National average" refers to the average value of various expenditures across the country.

[1588] "Regional average" refers to the average value of various expenditures in a particular region.

[1589] "Emotional data" refers to information about a user's emotional state, such as data obtained from voice input, text input, or biometric sensors.

[1590] An "emotion engine" is a system or service for analyzing emotional data obtained from voice input, text input, or biometric sensors.

[1591] A "financial plan" is a plan that presents a future economic plan based on a user's income and expenditure data.

[1592] A "life event" refers to an important event in a user's life (e.g., marriage, childbirth, home purchase, etc.) for which the associated costs need to be predicted and planned.

[1593] "Personalization" refers to providing plans and services that are optimized for individual users.

[1594] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[1595] 1. Data collection and centralized management

[1596] server:

[1597] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then appropriately formatted and stored in a database such as MySQL or PostgreSQL.

[1598] Device:

[1599] Once a payment is completed, the device (such as a smartphone or PC) sends the payment information to the server. The data is also sent to the server regarding expenses and income manually entered by the user. The data is transmitted securely using the HTTPS protocol.

[1600] User:

[1601] Users go about their daily lives making purchases using their usual payment methods, and when manual input is required, they enter that data through the app, for example, cash expenditures or salary income.

[1602] 2. Financial Planning

[1603] server:

[1604] The AI ​​engine (such as TensorFlow or PyTorch) on the server analyzes the income and expenditure data collected daily. Based on the results of this analysis, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. Statistical algorithms are used for the analysis to extract past data patterns and trends. For example, if a user plans to purchase a home in the future, the system can estimate the cost and suggest an appropriate savings plan.

[1605] Device:

[1606] The device displays a financial plan and future savings plan generated by an AI engine, and users can open the app to get a concrete understanding of their financial future through various graphs and charts.

[1607] 3. Incorporating an Emotional Engine

[1608] server:

[1609] The server incorporates an emotion engine (e.g., emotion recognition API) to recognize the user's emotions and obtains emotion data from the user's voice input (voice recognition technology), text input, or biometric sensors (health monitoring devices). This data is analyzed and stored in a database. The analysis results are used for next planning based on the user's emotional patterns.

[1610] Device:

[1611] The device has the ability to transmit emotional data in real time to a server, which can then be sent via a RESTful API using scripts written in Python or Ruby, allowing financial plans to be adjusted to reflect the user's current emotional state.

[1612] User:

[1613] Users can express their emotions through voice or text input. Furthermore, if the device is equipped with a built-in biometric sensor, emotional data can be automatically acquired. For example, the emotion engine can analyze heart rate data to determine whether stress levels are high.

[1614] 4. Adjust your plan based on emotions

[1615] server:

[1616] The server uses an AI engine to adjust the financial plan based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest low-risk savings and investment plans. This adjustment is made using machine learning libraries such as Scikit-learn.

[1617] Device:

[1618] The device displays an adjusted financial plan and offers emotion-based advice and suggestions: for example, when stress levels are high, a notification will appear suggesting relaxation techniques to the user.

[1619] 5. Comparison of national living standards

[1620] server:

[1621] The server anonymizes all users' spending data and aggregates it into large datasets for analysis. The aggregation process is performed using Hadoop, Apache Spark, or similar tools. This allows for calculation of national and regional averages, generating a baseline for comparison with users' spending data.

[1622] Device:

[1623] The device has the function of displaying the comparison results sent from the server. Users can request a comparison of their living standards through the app and see the results of how their living standards compare with the national and regional averages. The results are displayed as graphs and heat maps.

[1624] Specific examples

[1625] Example 1:

[1626] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server using Python's requests library, and the server saves the information in the database. This amount is reflected in the daily expenditure, and the expenditure percentage for each category is also updated.

[1627] Example 2:

[1628] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. At the same time, if User B's current emotional state is "stressed," it also provides suggestions to reduce the risk.

[1629] Example 3:

[1630] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1631] Example prompts to be input to the generative AI model:

[1632] Generate a personalized financial plan based on the user's spending and emotional data. If the emotional state is stressed, provide advice that includes a savings plan to mitigate risk.

[1633] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, more personalized financial plans can be provided.

[1634] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1635] Step 1: Data collection

[1636] Input: User's payment information (QR code, credit card, manual entry, etc.)

[1637] How it works: When a user makes a payment, the terminal captures the payment information (payment amount, date and time, payment method, etc.).

[1638] Specific operation: When a user makes a purchase of 3,000 yen using a QR code, the information is recorded in the device's app.

[1639] Step 2: Send data

[1640] Input: Captured payment information

[1641] How it works: The terminal sends the acquired payment information to the server using the HTTPS protocol. The data is structured in JSON format.

[1642] What happens: The device sends an HTTP POST request to the server, sending JSON data containing payment information.

[1643] Step 3: Receiving and storing data

[1644] Input: Payment information sent from the terminal

[1645] How it works: The server receives the incoming payment information, converts it into the appropriate format, and stores it in a database.

[1646] What it does: The server stores the received payment information in the "Expenses" table in the MySQL database.

[1647] Step 4: Generate a financial plan

[1648] Input: Income and expenditure data retrieved from the database

[1649] How it works: An AI engine on the server analyzes income and expenditure data and creates future spending forecasts and savings plans.

[1650] How it works: The AI ​​engine analyzes trends based on spending data from the past six months and suggests savings goals for the next year.

[1651] Step 5: Submit your plan

[1652] Input: Generated financial plan

[1653] Operation: The server sends the generated financial plan to the terminal. The data is structured in JSON format.

[1654] Specific operation: The server sends the generated financial plan in JSON format to the terminal as an HTTP response.

[1655] Step 6: View your plan

[1656] Input: Financial plan sent from server

[1657] How it works: The device visually displays the financial plan it receives.

[1658] What it does: The app displays a financial plan as graphs and charts for the user to review.

[1659] Step 7: Obtaining emotion data

[1660] Input: User emotion input (voice, text, biometric sensors)

[1661] Operation: The device acquires the user's emotion data.

[1662] Specific operation: When the user speaks "I'm stressed," the speech is converted into text and recorded.

[1663] Step 8: Sending Emotion Data

[1664] Input: Captured emotion data

[1665] Operation: The device transmits the acquired emotion data to the server in real time.

[1666] How it works: The device sends its emotional state and its intensity to the server using an HTTP POST request.

[1667] Step 9: Analyze the sentiment data

[1668] Input: Emotion data sent from the device

[1669] How it works: The server analyzes the emotion data using the emotion engine, and the analysis results are stored in a database.

[1670] Specific operation: The server analyzes heart rate data from the biometric sensor and determines the user's emotional state as "stressed."

[1671] Step 10: Adjust your plan

[1672] Input: Parsed emotion data

[1673] How it works: The server adjusts financial plans based on emotional data. For example, if the user is stressed, it suggests a plan with less risk.

[1674] Specific operation: The AI ​​engine selects and re-proposes safe investment plans based on stress levels.

[1675] Step 11: Submit your adjustment plan

[1676] Input: Coordinated Financial Plan

[1677] Operation: The server sends the re-adjusted financial plan to the terminal.

[1678] Specific operation: The server sends the adjusted plan in JSON format to the terminal and returns it as an HTTP response.

[1679] Step 12: View the adjustment plan

[1680] Input: Adjustment plan sent from the server

[1681] How it works: The terminal visually displays your adjusted financial plan.

[1682] What happens: The device displays the adjusted plan as a graph or chart in the app UI for the user to review.

[1683] Step 13: Compare to the national average

[1684] Input: Spending data collected from all users

[1685] How it works: The server anonymizes and aggregates spending data from all users to calculate national and regional averages.

[1686] What it does: The server aggregates the data using Hadoop and calculates the average standard of living for each region.

[1687] Step 14: Send and display comparison results

[1688] Input: Comparison results with the national average

[1689] How it works: The server sends the results of the comparison with the national average to the device, which then displays the results.

[1690] What it does: The device visualizes the comparison results received from the server as a heat map, allowing the user to check their living standards.

[1691] The above is the content of the specific processing steps of this system.

[1692] (Application example 2)

[1693] 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."

[1694] Conventional financial planning systems collect income and expenditure data and propose future expenditure forecasts and savings plans, but do not consider the user's emotions when planning. While they may provide comparisons of spending patterns with national or regional averages, they do not provide personalized financial plans based on the user's emotional data. This makes it difficult to provide appropriate planning that reflects the user's actual emotional state, making it difficult to propose stress-reducing and reasonable savings plans.

[1695] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1696] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with the national average or regional average, means for collecting user emotion data and providing a personalized financial plan based thereon, means for centrally managing payment data and comparing living standards, and means for presenting the user with a financial plan created based on their emotions. This makes it possible to provide a reasonable savings plan or a personalized financial plan that takes into account the user's emotional state.

[1697] "Expense Data" is a record of expenses incurred through various different payment methods.

[1698] A "single-source management mechanism" is a mechanism for integrating and managing data generated from different payment methods.

[1699] "Income data" is a record of all income earned by a household or individual.

[1700] "Expense forecasting" is the estimation of future costs based on collected data.

[1701] A "savings plan" is a specific plan for the amount of savings needed to achieve future goals and how to save.

[1702] "Expenditure patterns" are data that indicate how much an individual or household spends on what items.

[1703] The "national average" is the value calculated by averaging all data within Japan.

[1704] The "regional average" is a value calculated by averaging data for a specific region.

[1705] "Emotion data" is data that indicates the user's emotional state and is obtained from voice, text input, biometric sensors, and the like.

[1706] A "personalized financial plan" is a financial plan created based on a user's individual income, expenses, and emotional state.

[1707] "Life events" refer to important personal or family events such as marriage, childbirth, or home purchase.

[1708] An "emotion engine" is a system or software for recognizing and analyzing a user's emotions.

[1709] A "financial plan" is a future financial strategy or plan based on income and expenses.

[1710] A "server" is a computer system that collects, stores, analyzes data, and provides necessary information.

[1711] A "user terminal" is a device used by a user, such as a mobile phone or a personal computer.

[1712] This invention combines a system that centrally manages spending via various payment methods and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[1713] 1. Data collection and centralized management

[1714] server:

[1715] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (e.g., QR code, credit card, manual entry, etc.). The received data is properly formatted and stored in a database. A high-performance server is desirable as the hardware to be used.

[1716] Device:

[1717] The terminal is a smartphone, PC, etc., and once the payment is completed, the payment information is sent to the server. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1718] User:

[1719] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1720] 2. Financial Planning

[1721] server:

[1722] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1723] Device:

[1724] The device has the ability to display financial plans and future savings plans generated by an AI engine, allowing users to gain a concrete understanding of their financial future and take action accordingly.

[1725] 3. Incorporating an Emotional Engine

[1726] server:

[1727] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends. The software used utilizes an emotion recognition API.

[1728] Device:

[1729] The device has the ability to transmit emotional data obtained from the emotion engine to a server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[1730] User:

[1731] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[1732] 4. Adjust your plan based on emotions

[1733] server:

[1734] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[1735] Device:

[1736] The device displays a tailored financial plan and offers emotionally-driven advice and recommendations.

[1737] Specific examples

[1738] Example 1:

[1739] If a user is feeling stressed about their recent high spending, the emotion recognition engine will recognize that emotion and send it along with their payment data to the AI ​​engine, which will then generate a reasonable savings plan to reduce stress and present it to the user.

[1740] Example prompt sentence:

[1741] "I'm worried about my recent expenses."

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] The terminal receives payment information from the user as input and sends it to the server through an API endpoint. Once the payment information is entered, the terminal converts it into a proprietary format so that the spending data is properly stored on the server.

[1745] Step 2:

[1746] The server receives the expenditure data sent from the device and stores it in a database. The server checks the integrity of the data upon receiving it and standardizes the format. This process allows for real-time collection of expenditure data and centralized management.

[1747] Step 3:

[1748] The device receives the user's emotional state as input. When the user expresses their emotion through voice or text input, the device sends the data to the emotion recognition engine. It also receives data from biometric sensors, if available. The emotion recognition engine analyzes the emotional data and identifies the emotional state.

[1749] Step 4:

[1750] The server receives the emotion data sent from the emotion recognition engine and stores it in a database. This data, along with existing income and expenditure data, is input into the AI ​​engine for analysis. The AI ​​engine takes the emotion data into account and generates a customized financial plan.

[1751] Step 5:

[1752] The server then sends the generated financial plan to the device. The AI ​​engine combines emotional and economic data to output a plan that includes reasonable savings plans and low-risk investment suggestions. This plan is personalized to reflect the user's emotional state.

[1753] Step 6:

[1754] The terminal receives the financial plan sent from the server and displays it on the screen. The user can check the details of the plan on the screen and receive advice based on their financial situation and emotional state.

[1755] Step 7:

[1756] The server calculates national or regional averages based on the expenditure data collected from all users and generates comparison results with each user's expenditure patterns. This comparison data is analyzed and stored together with the users' expenditure data to show the relative positions of the users' standard of living.

[1757] Step 8:

[1758] When a user requests a comparison of living standards, the terminal displays the results of the comparison sent from the server on the screen. Users can compare their expenditure data with the national and regional averages and visually check their own living standards.

[1759] Examples of specific operations and inputs and outputs:

[1760] The device recognizes the user's voice input, "I'm worried about my recent high expenses," and sends it to the server. The server uses an emotion recognition engine to output the emotion data "stress" from this input data. The server then inputs this emotion data along with the expenditure data into an AI engine, which generates a personalized financial plan including a reasonable savings plan and sends it to the device. The user can review this plan and use it to help with future financial activities.

[1761] 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.

[1762] 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.

[1763] 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.

[1764] [Fourth embodiment]

[1765] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1766] 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.

[1767] 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).

[1768] 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.

[1769] 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.

[1770] 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).

[1771] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1772] 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.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] 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.

[1777] 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."

[1778] The present invention is a system that centrally manages expenditures via various payment methods and provides a financial plan based on the household's economic situation, and is implemented in the following form: Cooperation between a server, a terminal, and a user is required to efficiently collect expenditure data and income data and to perform predictions and comparisons.

[1779] 1. Data collection and centralized management

[1780] server:

[1781] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[1782] Device:

[1783] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1784] User:

[1785] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1786] 2. Financial Planning

[1787] server:

[1788] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1789] Device:

[1790] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[1791] 3. Comparison of national living standards

[1792] server:

[1793] The server anonymizes, aggregates, and analyzes the spending data collected from all users. It calculates national and regional averages and generates a benchmark to compare with each user's spending data. This data provides users with a reference for their relative standard of living.

[1794] Device:

[1795] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[1796] Specific examples

[1797] Example 1:

[1798] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[1799] Example 2:

[1800] If User B plans to get married in the next fiscal year, he or she can enter that information into the app, and the device will send it to the server. The AI ​​will analyze the data, predict wedding-related expenses, and generate an appropriate savings plan. User B's device will then visually display this savings plan and predicted spending.

[1801] Example 3:

[1802] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1803] The above is an embodiment of the present invention, which enables a user to efficiently manage their household finances and make financial plans for the future.

[1804] The processing flow will be explained below.

[1805] 1. Data collection and centralized management

[1806] Step 1:

[1807] User: Makes a payment using a QR code at a store, etc.

[1808] Step 2:

[1809] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[1810] Step 3:

[1811] Terminal: In the background, the acquired payment information is sent to the server.

[1812] Step 4:

[1813] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[1814] Step 5:

[1815] User: If necessary, enter extra income or manual expenditure information through the terminal.

[1816] Step 6:

[1817] Terminal: Sends manually entered data to the server.

[1818] Step 7:

[1819] Server: Manually entered data is also stored in the database.

[1820] 2. Financial Planning

[1821] Step 1:

[1822] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[1823] Step 2:

[1824] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[1825] Step 3:

[1826] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[1827] Step 4:

[1828] Server: Save the generated financial plan in the database and associate it with the user ID.

[1829] Step 5:

[1830] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[1831] 3. Comparison of national living standards

[1832] Step 1:

[1833] Server: Spending data collected from all users is anonymized and aggregated into national data.

[1834] Step 2:

[1835] Server: Calculates national and regional average spending data, including average spending by category.

[1836] Step 3:

[1837] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[1838] Step 4:

[1839] Server: When requested by the user, generates the comparison result and sends it to the terminal.

[1840] Step 5:

[1841] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[1842] Specific examples

[1843] Example 1: Managing QR code payments

[1844] Step 1:

[1845] User: Makes a QR code payment at the store (3,000 yen).

[1846] Step 2:

[1847] Terminal: Get payment information.

[1848] Step 3:

[1849] Terminal: Sends payment information to the server.

[1850] Step 4:

[1851] Server: Stores payment information in a database.

[1852] Example 2: Generating a Financial Plan

[1853] Step 1:

[1854] User: Enters next year's wedding plans into the app.

[1855] Step 2:

[1856] Terminal: Sends home configuration information to the server.

[1857] Step 3:

[1858] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[1859] Step 4:

[1860] Server: The generated plan is saved as user data.

[1861] Step 5:

[1862] Device: Shows users savings plans and spending forecasts.

[1863] Example 3: Comparing national standards of living

[1864] Step 1:

[1865] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[1866] Step 2:

[1867] User: Selects Living Standard Comparison from the app.

[1868] Step 3:

[1869] Terminal: Sends a request to the server.

[1870] Step 4:

[1871] Server: Compare user spending data with national averages.

[1872] Step 5:

[1873] Server: Sends the comparison results to the terminal.

[1874] Step 6:

[1875] Device: Displays the comparison of the user's standard of living with the national average.

[1876] The above are the specific processing steps of the program for carrying out the present invention.

[1877] Example 1

[1878] 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."

[1879] Current household management systems are cumbersome in collecting expenditure and income data, and it is difficult to centrally manage data from different payment methods. Furthermore, it is not easy to predict future expenditures or create savings plans from the collected data, and the benchmarks for comparing living standards with those of the nation or region are unclear. There is a need to address these issues and provide a household management system that is user-friendly.

[1880] 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.

[1881] In this invention, the server includes means for centrally managing expenditure data generated by various different payment methods, means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, means for comparing each user's expenditure pattern with national or regional averages, means for receiving payment information transmitted from the user's terminal, formatting it into an appropriate data format, and storing it in a database, means for analyzing the collected income and expenditure data, visualizing the user's financial situation, and proposing future expenditure forecasts and savings plans, and means for anonymizing the aggregated expenditure data, calculating national or regional averages, and generating standards for comparison with each user's expenditure data, thereby enabling users to efficiently manage their household finances and create future economic plans.

[1882] "Expense data" refers to financial information relating to purchases and service usage made by a user using various payment methods.

[1883] "Income Data" refers to information about the income or earnings earned by a User.

[1884] "Centralized management means" refers to a means for uniformly collecting and managing expenditure data obtained from various payment methods.

[1885] "Future expenditure forecasting" refers to predicting future expenditure amounts and spending patterns based on collected data.

[1886] A "savings plan" is a detailed plan for a user to effectively save money according to their future goals and needs.

[1887] "Spending patterns" refer to a user's past payment history and spending behavior tendencies.

[1888] The "national average" refers to data on average expenditures and income across Japan.

[1889] "Regional averages" refer to data on average expenditures and incomes in specific regions of Japan.

[1890] "Database" means a data storage system for properly managing and storing received expenditure and income data.

[1891] An "AI engine" is an engine that uses artificial intelligence technology to analyze collected data and predict future economic conditions.

[1892] "Anonymization" means removing information that identifies a user personally and processing the data so that it cannot be linked to a specific individual.

[1893] "Analysis" refers to the process of extracting information and finding meaning from collected data using statistical methods and algorithms.

[1894] An "API endpoint" is an interface for sending and receiving data between different systems.

[1895] "Notification" refers to a means of providing information to inform the user of the analysis results and the generated plan.

[1896] This invention is a system that manages expenditures by various payment methods in an integrated manner and provides a financial plan based on the household's economic situation. Cooperation between the server, terminals, and users is required to efficiently collect expenditure and income data and to make predictions and comparisons.

[1897] 1. Data collection and centralized management

[1898] server:

[1899] The server provides an API endpoint to receive spending data sent from users' devices using various payment methods (QR code, credit card, manual entry, etc.). The hardware used includes a high-performance server computer, and the software includes a database management system (DBMS) and an API server. The received data is then properly formatted and stored in the database.

[1900] Device:

[1901] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to a server. The specific software used can be a mobile application or a web browser. Expenses and income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[1902] User:

[1903] Users go about their daily routine using their usual payment methods to make purchases, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[1904] 2. Financial Planning

[1905] server:

[1906] The AI ​​engine located on the server analyzes income and expenditure data collected daily. The software used includes machine learning libraries and statistical analysis tools. Based on the analysis results, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[1907] Device:

[1908] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. Users can check this information through the app, gain a concrete understanding of their financial future, and take action based on that information.

[1909] 3. Comparison of national living standards

[1910] server:

[1911] The server anonymizes, aggregates, and analyzes the spending data collected from all users. The software used includes data anonymization tools and statistical analysis software. It calculates national and regional averages and generates a benchmark for comparison with each user's spending data. This data provides users with a reference for their relative standard of living.

[1912] Device:

[1913] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[1914] Specific examples

[1915] Example 1:

[1916] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server via a secure HTTP request, and the server stores the information in a database. At the same time, this amount is reflected in User A's daily spending in real time.

[1917] Example 2:

[1918] If User B plans to get married in the next fiscal year, he or she enters that information into the app. The device sends this information to the server, where an AI engine analyzes it to predict wedding-related expenses and generate an appropriate savings plan. The prediction and savings plan are then visually displayed on User B's device.

[1919] Example 3:

[1920] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device then displays the results in a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1921] Prompt Sentence Examples

[1922] "Please forecast our wedding expenses for the next year and suggest a savings plan."

[1923] "I want to know how my spending compares to the national average."

[1924] "I want to record payments using QR codes and perform spending analysis."

[1925] The above is an embodiment of the present invention. This system allows users to efficiently manage their household finances and make financial plans for the future.

[1926] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1927] Step 1: Data collection

[1928] Terminal: When a user makes a purchase, they use a QR code or credit card to pay and capture the payment information. Specifically, for example, a smartphone app automatically records payment details (date, time, location, amount, category, etc.). It receives payment information as input and generates organized payment data as output.

[1929] Step 2: Submit your payment information

[1930] Terminal: Sends the acquired payment information to the server via a secure HTTP request. The process runs in the background, and the information is sent transparently to the user. It receives payment data as input and generates data to be sent to the server as output.

[1931] Step 3: Receive and store payment information

[1932] Server: Receives payment information sent from the terminal at the API endpoint. After receiving the information, it formats it into the appropriate data format and stores it in the database. Specifically, it analyzes the received data and inserts it into the appropriate table. It receives data sent from the terminal as input and stores it in the database as output.

[1933] Step 4: Manage manually entered data

[1934] User: When a user spends cash or uses other payment methods, they manually enter that information through the app. For example, if a user pays for a 500 yen lunch with cash, they enter the details into the app. It takes the expenditure details as input and generates the manually entered data as output.

[1935] Terminal: Sends manually entered expenditure information to the server. Specifically, when the user completes the input, they press the send button or the information is sent automatically. It receives manually entered expenditure data as input and generates data to be sent to the server as output.

[1936] Step 5: Analyze your income and expense data

[1937] Server: Analyzes collected income and expenditure data and visualizes the user's financial situation. The AI ​​engine performs the analysis using statistical analysis and machine learning algorithms. Specific operations include generating graphs and dashboards and calculating the balance between income and expenditure. It receives integrated income and expenditure data as input and generates analysis results as output.

[1938] Step 6: Generate a financial plan

[1939] Server: The AI ​​engine generates predicted spending and savings plans based on the user's goals (e.g., marriage, home purchase, etc.). Specifically, it predicts future spending trends based on past spending data and proposes savings plans based on the results. It receives the user's goal information and past data as input and generates a financial plan as output.

[1940] Step 7: Present and communicate your plan

[1941] Device: Displays the generated financial plan and notifications to the user. The user can check this information at any time through the app. Specifically, the latest financial status report is automatically pushed once a week. The device receives the generated plan data as input and generates notification data for the user as output.

[1942] Step 8: Compare living standards across countries

[1943] Server: Anonymizes the spending data collected from all users and calculates regional and national averages. Specifically, it removes information that identifies individual users and statistically processes the data. It receives the collected spending data as input and produces aggregate results as output.

[1944] Step 9: Expressing the basis of comparison

[1945] Server: Generates a comparison standard based on each user's expenditure data and the national average. Specifically, it calculates how many percentage points higher or lower a user's expenditure is than the national average. It receives statistical data and user data as input and generates a comparison standard as output.

[1946] Step 10: View the comparison results

[1947] Device: Visually presents the comparison results sent from the server to the user. For example, the app uses graphs and tables to help users intuitively understand their living standards. It receives the comparison results from the server as input and generates the data to be presented visually as output.

[1948] The above is the specific processing flow of this system. This configuration allows users to efficiently manage their household finances and make future economic plans.

[1949] (Application example 1)

[1950] 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."

[1951] In today's society, there are many different payment methods, and the resulting expenditure data is managed in a decentralized manner. This makes it difficult to grasp a household's financial situation in a unified manner and create an appropriate financial plan. It is also difficult for users to compare their own standard of living with the national or regional average. A system that can solve these problems is needed.

[1952] 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.

[1953] In this invention, the server includes: means for centrally managing expenditure data generated by various different payment methods; means for collecting income and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon; means for comparing each user's expenditure pattern with national or regional averages; means for automatically collecting expenditure data from QR codes, credit cards, bank accounts, etc.; means for manually inputting income and expenditures; means for visually displaying the generated financial plan on the user's terminal; and means for comparing the user's expenditure data with national or regional average data to present the user's relative standard of living. This enables centralized management of expenditure data, a detailed understanding of the household's economic situation, and the provision of an appropriate financial plan through predictions and comparisons.

[1954] "Various different payment methods" refers to multiple different payment methods, such as QR codes, credit cards, and bank accounts.

[1955] "Expense data" refers to information about a user's purchases and payments, including information such as the amount, date and time, and category of each transaction.

[1956] "Income Data" refers to information related to a user's income, including information such as salary, bonuses, and investment returns.

[1957] "Centralized management means" refers to a method of integrating data collected from multiple sources and managing it in a unified format.

[1958] "Means for predicting future expenditures and formulating savings plans" refers to methods for predicting future expenditures and formulating savings plans based on collected data.

[1959] "Means for comparison to national or regional averages" refers to a method for comparing a user's data with national or regional average data to calculate relative position.

[1960] "Automatic collection methods" refers to methods of automatically obtaining data from QR codes, credit cards, bank accounts, etc.

[1961] "Means for manual income and expense entry" refers to the methods by which a user manually enters income and expense information into an application or device.

[1962] "Visual display means" refers to a method of presenting information to a user in the form of text, graphs, charts, etc.

[1963] "Means of showing relative standard of living positions" refers to methods of comparing the user's data with national or regional averages and showing the results to the user.

[1964] The present invention is a system that provides financial planning by centrally managing expenses from various payment methods. The system collects income and expenditure data and can predict future expenses and create savings plans based on the data. It also has the function of comparing each user's spending patterns with national or regional averages.

[1965] server

[1966] The server automatically collects expenditure data sent from the user's device using various payment methods (QR code, credit card, bank account, etc.). The received data is then properly formatted and stored in a database. The server receives the collected expenditure data in real time and processes it to store it in the database. It also has an AI engine that generates a financial plan based on income and expenditure data and proposes it to the user. This AI engine has the function of visually displaying the generated financial plan on the user's device. It also has the function of comparing the user's expenditure data with national and regional averages to show the user's relative standard of living.

[1967] Terminal

[1968] When a payment is completed, the user's device (such as a smartphone or PC) sends the payment information to the server. Any income or expenses manually entered by the user are also sent to the server. The device is equipped with an interface for manually entering income and expenses. Furthermore, the user's device has the ability to visually display the financial plan and future savings plan generated by the AI ​​engine. Users can refer to this information to understand their financial future and take appropriate action.

[1969] User

[1970] Users make purchases in their daily lives using the usual payment methods. They make expenditures using QR codes, credit cards, and bank accounts, and the data is automatically sent to the server via their device. If manual input is required, they enter the data through the app. When users plan specific life events (marriage, childbirth, home purchase, etc.), they can enter that information into the app. The AI ​​engine analyzes this information and generates an appropriate financial plan. Users can refer to this plan to prepare for their future financial needs.

[1971] Specific examples

[1972] Example 1: User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily expenditure, and the proportion of expenditure in each category is also updated.

[1973] Example 2: If User B plans to get married next year, he or she enters that information into the app and the device sends it to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending.

[1974] Example 3: User C wants to compare his / her standard of living with the national average, so he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[1975] Prompt Sentence Examples

[1976] "Please send data on purchasing home appliances worth 5,000 yen and retrieve financial plans and living standards comparisons for user ID 2 from the server."

[1977] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1978] Step 1:

[1979] User makes payment:

[1980] A user completes a payment using a QR code, credit card, bank account, etc. At that time, data about the payment (such as the amount spent, date and time, category, etc.) is generated.

[1981] Input: User payment behavior, expenditure data (expense amount, date and time, category, etc.)

[1982] Output: Expense data

[1983] Step 2:

[1984] The terminal sends the payment information to the server:

[1985] The terminal sends the user-completed payment information to the server, which is then sent to the server through an API endpoint.

[1986] Input: Expense Data

[1987] Output: Sending spending data to the server

[1988] What happens: The device app collects spending data and issues an HTTP POST request to send it to the server's API endpoint.

[1989] Step 3:

[1990] The server receives and stores the spending data:

[1991] The server receives the expenditure data sent from the terminal, converts it into an appropriate format, and stores it in a database.

[1992] Input: Spending data sent to the server

[1993] Output: Spending data stored in a database

[1994] Specific operation: The server receives the expenditure data and inserts it into the expenditure table in the database, while formatting and validating the data.

[1995] Step 4:

[1996] Collecting manually entered data:

[1997] Users manually enter their income and expenses through the terminal app, and the data is sent to the server.

[1998] Input: Manually entered income and expenditure data

[1999] Output: Send income and expenditure data to the server

[2000] Specific behavior: The device app collects income and expenditure data through the user interface and issues an HTTP POST request to send it to the server.

[2001] Step 5:

[2002] Generate a financial plan:

[2003] The AI ​​engine on the server uses collected income and expenditure data to predict future spending and create savings plans.

[2004] Input: Income data, expenditure data in the database

[2005] Output: Financial plan data

[2006] How it works: The AI ​​engine analyzes income and expense data and uses predictive algorithms to generate a financial plan.

[2007] Step 6:

[2008] View the generated financial plan:

[2009] A terminal receives the generated financial plan data from the server and visually displays it on a user interface.

[2010] Input: Financial plan data received from the server

[2011] Output: Financial plan displayed on the terminal

[2012] Specific behavior: The terminal app receives data from the server and displays it in the user interface in text and graph format.

[2013] Step 7:

[2014] Comparison of living standards:

[2015] The user requests a standard of living comparison function, and the server compares the collected expenditure data with the national or regional average. The results are displayed in the user interface.

[2016] Input: Standard of Living Comparison Request, User Expenditure Data

[2017] Output: Results compared to national or regional averages

[2018] What it does: The server retrieves the user's spending data and compares it with national or regional averages. The results are received by the device app and displayed visually.

[2019] To realize this application example, the user, terminal, and server cooperate to collect, manage, and analyze data, and provide the results to the user.

[2020] 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.

[2021] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[2022] 1. Data collection and centralized management

[2023] server:

[2024] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then formatted appropriately and stored in a database.

[2025] Device:

[2026] When a user's device (such as a smartphone or PC) completes a payment, it sends the payment information to the server. Any expenses or income manually entered by the user are also sent to the server. This allows all expenditure data to be managed centrally.

[2027] User:

[2028] Users make purchases in their daily lives using their usual payment methods, and when manual input is required, they enter the data through the app, allowing all spending to be managed centrally via the device.

[2029] 2. Financial Planning

[2030] server:

[2031] The AI ​​engine installed on the server analyzes income and expenditure data collected daily. Based on the analysis results, the system visualizes the user's financial situation, predicts future expenses, and creates savings plans. For example, it focuses on specific life events (marriage, childbirth, home purchase, etc.), estimates the expenses required for those events, and suggests appropriate savings plans to the user.

[2032] Device:

[2033] The user device has the ability to display the financial plan and future savings plan generated by the AI ​​engine. By viewing this information, users can concretely understand their financial future and take action based on that information.

[2034] 3. Incorporating an Emotional Engine

[2035] server:

[2036] The server incorporates an emotion engine to recognize the user's emotions, and acquires emotion data from the user's voice, text input, or biometric sensors. This data is analyzed and stored in a database to understand the user's emotional trends.

[2037] Device:

[2038] The user device has the ability to transmit emotional data obtained from the emotion engine to the server in real time, allowing the financial plan to be adjusted to reflect the user's current emotional state.

[2039] User:

[2040] Users can express their emotions through voice or text input, and if the device is equipped with a biometric sensor, emotional data can be automatically collected.

[2041] 4. Adjust your plan based on emotions

[2042] server:

[2043] Based on the emotional data recognized by the emotion engine, the AI ​​engine can adjust the financial plan accordingly. For example, if the user is feeling stressed, it will suggest a reasonable savings plan or low-risk investment ideas.

[2044] Device:

[2045] The user terminal displays the tailored financial plan and provides emotion-based advice and suggestions.

[2046] 5. Comparison of national living standards

[2047] server:

[2048] The server anonymizes the spending data collected from all users and aggregates it into national data. It calculates national and regional averages and generates a baseline against which each user's spending data can be compared. This data provides users with a reference for their relative standard of living.

[2049] Device:

[2050] The user's device has the function to display the comparison results sent from the server. When a user makes a request to compare living standards through the app, the results of comparing their living standards with the latest national and regional averages are displayed.

[2051] Specific examples

[2052] Example 1:

[2053] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server, which stores it in the database. At the same time, this amount is reflected in User A's daily spending, and the percentage of spending in each category is also updated.

[2054] Example 2:

[2055] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. Additionally, if User B's current emotional state is "stress," suggestions for reducing that risk are also provided.

[2056] Example 3:

[2057] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[2058] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, it is possible to provide a more personalized financial plan that is more suited to the user.

[2059] The processing flow will be explained below.

[2060] 1. Data collection and centralized management

[2061] Step 1:

[2062] User: Makes a payment using a QR code at a store, etc.

[2063] Step 2:

[2064] Terminal: Once the QR code payment is completed, payment information (date and time of use, destination, amount, and payment method) is automatically obtained.

[2065] Step 3:

[2066] Terminal: In the background, the acquired payment information is sent to the server.

[2067] Step 4:

[2068] Server: Formats the received payment information and stores it in a database, where it is associated with the user ID.

[2069] Step 5:

[2070] User: If necessary, enter extra income or manual expenditure information through the terminal.

[2071] Step 6:

[2072] Terminal: Sends manually entered data to the server.

[2073] Step 7:

[2074] Server: Manually entered data is also stored in the database.

[2075] 2. Financial Planning

[2076] Step 1:

[2077] Server: Aggregates the collected income and expenditure data on a daily or weekly basis. Based on this aggregated data, basic statistics (e.g., expenditure percentage by category) are generated.

[2078] Step 2:

[2079] Server (AI engine): Uses aggregated data to predict future spending for users, taking into account past spending patterns and income data.

[2080] Step 3:

[2081] Server (AI engine): Generates specific financial plans based on family structure and life event information (marriage, childbirth, home purchase, etc.).

[2082] Step 4:

[2083] Server: Save the generated financial plan in the database and associate it with the user ID.

[2084] Step 5:

[2085] On the device: When users open the app, they're presented with an up-to-date financial plan, including future spending forecasts and savings plans.

[2086] 3. Incorporating an Emotional Engine

[2087] Step 1:

[2088] User: Expresses emotions by voice or text input. If the device is equipped with a biometric sensor, emotional data is automatically acquired.

[2089] Step 2:

[2090] Terminal: Collects emotion data in real time and sends it to the server.

[2091] Step 3:

[2092] Server: Stores the emotion data analyzed by the emotion engine in a database.

[2093] 4. Adjust your plan based on emotions

[2094] Step 1:

[2095] Server: Based on the emotional data obtained from the emotion engine, the AI ​​engine adjusts the financial plan.

[2096] Step 2:

[2097] Server: Save the new financial plan, reflecting the user's emotional state, in the database.

[2098] Step 3:

[2099] On the device: When users open the app, they are presented with a tailored financial plan, along with emotional advice and recommendations.

[2100] 5. Comparison of national living standards

[2101] Step 1:

[2102] Server: Spending data collected from all users is anonymized and aggregated into national data.

[2103] Step 2:

[2104] Server: Calculates national and regional average spending data, including average spending by category.

[2105] Step 3:

[2106] Server: Compares the user's spending data with the national average data and calculates their relative spending position.

[2107] Step 4:

[2108] Server: When a user makes a request, it generates a comparison result and sends it to the terminal.

[2109] Step 5:

[2110] On device: When users select the standard of living comparison feature from the app, the results are displayed visually, including graphs and text showing excesses and shortfalls, as well as differences from the average, for each category.

[2111] Specific examples

[2112] Example 1: Managing QR code payments

[2113] Step 1:

[2114] User: Makes a QR code payment at the store (3,000 yen).

[2115] Step 2:

[2116] Terminal: Get payment information.

[2117] Step 3:

[2118] Terminal: Sends payment information to the server.

[2119] Step 4:

[2120] Server: Stores payment information in a database.

[2121] Example 2: Generating a Financial Plan

[2122] Step 1:

[2123] User: Enters next year's wedding plans into the app.

[2124] Step 2:

[2125] Terminal: Sends home configuration information to the server.

[2126] Step 3:

[2127] Server (AI engine): Predicts wedding-related expenses and generates a savings plan.

[2128] Step 4:

[2129] Server: The generated plan is saved as user data.

[2130] Step 5:

[2131] Device: Shows users savings plans and spending forecasts.

[2132] Example 3: Comparing national standards of living

[2133] Step 1:

[2134] Server: Aggregates nationwide expenditure data and generates anonymized average data.

[2135] Step 2:

[2136] User: Selects Living Standard Comparison from the app.

[2137] Step 3:

[2138] Terminal: Sends a request to the server.

[2139] Step 4:

[2140] Server: Compare user spending data with national averages.

[2141] Step 5:

[2142] Server: Sends the comparison results to the terminal.

[2143] Step 6:

[2144] Device: Displays the comparison of the user's standard of living with the national average.

[2145] Example 4: Adjusting your financial plan based on emotions

[2146] Step 1:

[2147] User: Enters "I've been feeling stressed lately" using voice input in the app.

[2148] Step 2:

[2149] Terminal: Sends emotion data to the server.

[2150] Step 3:

[2151] Server: Analyzes the received emotion data and stores it in a database.

[2152] Step 4:

[2153] Server (AI engine): Adjusts the current financial plan based on the emotional data, for example adding advice to avoid risks.

[2154] Step 5:

[2155] Device: Presents emotion-based adjustment plans to users.

[2156] The above are the specific processing steps of the program for implementing the present invention. The present invention allows users to manage their expenses and income in an integrated manner and obtain a highly personalized financial plan. Furthermore, by taking emotional data into consideration, flexible proposals tailored to the user's financial behavior are provided.

[2157] Example 2

[2158] 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."

[2159] Traditional household financial planning tools are unable to centrally manage spending data based on multiple payment methods and income sources, making it difficult to grasp a household's overall financial situation. They also fail to take into account the user's emotional state when predicting future spending and creating savings plans, making it difficult to provide personalized plans that are appropriate for each user. Furthermore, it is impossible to compare each user's spending patterns with national or regional averages, making it difficult to evaluate their relative standard of living.

[2160] 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.

[2161] In this invention, the server includes a means for centrally managing expenditure data generated by various different payment methods, a means for collecting income data and expenditure data for each household and creating future expenditure forecasts and savings plans based thereon, a means for comparing each user's expenditure pattern with the national average or regional average, and a means for collecting user emotional data and adjusting a financial plan based on the user's emotional state. This allows for comprehensive management of user expenditure data, enabling future economic planning based on income and expenditure data, and providing personalized plans that take emotional state into consideration. Furthermore, by comparing each user's expenditure pattern with the national average or regional average, a relative evaluation of living standards is possible.

[2162] "Spending Data" refers to information about spending made by a user using various payment methods.

[2163] "Centralized management" refers to aggregating expenditure data obtained from multiple different payment methods and managing it in a single database or system.

[2164] "Income data" refers to information regarding various types of income earned by a user.

[2165] "Future expenditure forecasting" refers to forecasting future expenditures based on current and past income and expenditure data.

[2166] A "savings plan" is a specific plan for how to increase savings based on the user's future financial goals.

[2167] "Spending patterns" refers to data that indicates the characteristics and tendencies of a user's spending.

[2168] "National average" refers to the average value of various expenditures across the country.

[2169] "Regional average" refers to the average value of various expenditures in a particular region.

[2170] "Emotional data" refers to information about a user's emotional state, such as data obtained from voice input, text input, or biometric sensors.

[2171] An "emotion engine" is a system or service for analyzing emotional data obtained from voice input, text input, or biometric sensors.

[2172] A "financial plan" is a plan that presents a future economic plan based on a user's income and expenditure data.

[2173] A "life event" refers to an important event in a user's life (e.g., marriage, childbirth, home purchase, etc.) for which the associated costs need to be predicted and planned.

[2174] "Personalization" refers to providing plans and services that are optimized for individual users.

[2175] The present invention combines a system that manages expenditures via various payment methods in an integrated manner and provides financial plans based on the household's economic situation with an emotion engine that recognizes the user's emotions and takes them into consideration when making plans. Specific embodiments are described below.

[2176] 1. Data collection and centralized management

[2177] server:

[2178] The server provides an API endpoint to receive spending data sent from the user's device using various payment methods (QR code, credit card, manual entry, etc.) The received data is then appropriately formatted and stored in a database such as MySQL or PostgreSQL.

[2179] Device:

[2180] Once a payment is completed, the device (such as a smartphone or PC) sends the payment information to the server. The data is also sent to the server regarding expenses and income manually entered by the user. The data is transmitted securely using the HTTPS protocol.

[2181] User:

[2182] Users go about their daily lives making purchases using their usual payment methods, and when manual input is required, they enter that data through the app, for example, cash expenditures or salary income.

[2183] 2. Financial Planning

[2184] server:

[2185] The AI ​​engine (such as TensorFlow or PyTorch) on the server analyzes the income and expenditure data collected daily. Based on the results of this analysis, the system visualizes the user's financial situation and creates future expenditure forecasts and savings plans. Statistical algorithms are used for the analysis to extract past data patterns and trends. For example, if a user plans to purchase a home in the future, the system can estimate the cost and suggest an appropriate savings plan.

[2186] Device:

[2187] The device displays a financial plan and future savings plan generated by an AI engine, and users can open the app to get a concrete understanding of their financial future through various graphs and charts.

[2188] 3. Incorporating an Emotional Engine

[2189] server:

[2190] The server incorporates an emotion engine (e.g., emotion recognition API) to recognize the user's emotions and obtains emotion data from the user's voice input (voice recognition technology), text input, or biometric sensors (health monitoring devices). This data is analyzed and stored in a database. The analysis results are used for next planning based on the user's emotional patterns.

[2191] Device:

[2192] The device has the ability to transmit emotional data in real time to a server, which can then be sent via a RESTful API using scripts written in Python or Ruby, allowing financial plans to be adjusted to reflect the user's current emotional state.

[2193] User:

[2194] Users can express their emotions through voice or text input. Furthermore, if the device is equipped with a built-in biometric sensor, emotional data can be automatically acquired. For example, the emotion engine can analyze heart rate data to determine whether stress levels are high.

[2195] 4. Adjust your plan based on emotions

[2196] server:

[2197] The server uses an AI engine to adjust the financial plan based on the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, it will suggest low-risk savings and investment plans. This adjustment is made using machine learning libraries such as Scikit-learn.

[2198] Device:

[2199] The device displays an adjusted financial plan and offers emotion-based advice and suggestions: for example, when stress levels are high, a notification will appear suggesting relaxation techniques to the user.

[2200] 5. Comparison of national living standards

[2201] server:

[2202] The server anonymizes all users' spending data and aggregates it into large datasets for analysis. The aggregation process is performed using Hadoop, Apache Spark, or similar tools. This allows for calculation of national and regional averages, generating a baseline for comparison with users' spending data.

[2203] Device:

[2204] The device has the function of displaying the comparison results sent from the server. Users can request a comparison of their living standards through the app and see the results of how their living standards compare with the national and regional averages. The results are displayed as graphs and heat maps.

[2205] Specific examples

[2206] Example 1:

[2207] User A makes a purchase of 3,000 yen using QR code payment. The device sends the payment information to the server using Python's requests library, and the server saves the information in the database. This amount is reflected in the daily expenditure, and the expenditure percentage for each category is also updated.

[2208] Example 2:

[2209] If User B plans to get married in the next year, he or she enters that information into the app, and the device sends this information to the server. The AI ​​analyzes the data, predicts wedding-related expenses, and generates an appropriate savings plan. User B's device visually displays this savings plan and predicted spending. At the same time, if User B's current emotional state is "stressed," it also provides suggestions to reduce the risk.

[2210] Example 3:

[2211] If User C wants to compare his / her standard of living with the national average, he / she requests the comparison function through the app. The server compares User C's expenditure data with the national average and sends the results to the device. The device displays the results as a graph, allowing User C to see whether his / her expenditure is higher or lower than the national average.

[2212] Example prompts to be input to the generative AI model:

[2213] Generate a personalized financial plan based on the user's spending and emotional data. If the emotional state is stressed, provide advice that includes a savings plan to mitigate risk.

[2214] The above is an embodiment of the present invention. This embodiment allows users to efficiently manage their household finances and make financial plans for the future. Furthermore, by utilizing emotion data, more personalized financial plans can be provided.

[2215] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2216] Step 1: Data collection

[2217] Input: User's payment information (QR code, credit card, manual entry, etc.)

[2218] How it works: When a user makes a payment, the terminal captures the payment information (payment amount, date and time, payment method, etc.).

[2219] Specific operation: When a user makes a purchase of 3,000 yen using a QR code, the information is recorded in the device's app.

[2220] Step 2: Send data

[2221] Input: Captured payment information

[2222] How it works: The terminal sends the acquired payment information to the server using the HTTPS protocol. The data is structured in JSON format.

[2223] What happens: The device sends an HTTP POST request to the server, sending JSON data containing payment information.

[2224] Step 3: Receiving and storing data

[2225] Input: Payment in...

Claims

1. A means to centrally manage expenditure data generated by various different payment methods; A means for collecting income data and expenditure data of each household and making future expenditure forecasts and savings plans based thereon; A system that includes a means for comparing each user's spending patterns with national or regional averages.

2. 10. The system of claim 1, further comprising means for receiving the collected spending data in real time and storing it in a database.

3. The system according to claim 1 , further comprising means for generating a financial plan for a specific life event based on the income data and expenditure data and proposing the plan to the user.

Images