system

Abstract

Provide a system. 【Solution means】 Means for integrating financial institution information and electronic payment information, Means for analyzing income and expenditure based on the financial institution information and the electronic payment information, Means for visualizing the analyzed income and expenditure and displaying it on a terminal, Means for predicting future expenditures using machine learning based on past income and expenditure data, Means for generating proposals for related asset formation based on the selection of the user's area of interest, Means for creating a specific asset management plan based on the asset goals set by the user, Means for evaluating a plurality of investment options including risks and providing information for the user to make an optimal choice, A system including the above.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern society, many individuals are forced to make complex decisions in daily income and expenditure management and future asset formation. However, it is difficult for individuals with limited economic knowledge and time to instantly grasp the overall picture of income and expenditure and formulate an efficient and effective asset formation plan. In such a situation, it is required to provide optimal support for asset management according to individual needs. 【Means for Solving the Problems】 【0005】 This invention integrates information obtained from financial institutions and electronic payment information, analyzes the user's income and expenses based on this data, and presents it in an easy-to-understand manner through visualization. Furthermore, it can predict future expenditures using machine learning based on past data and dynamically provide asset building suggestions tailored to the user's selected areas of interest. In addition, it provides a system that supports users in making optimal decisions and efficiently managing their assets by creating a concrete plan based on the user's asset goals, evaluating its progress, and updating the plan accordingly. 【0006】 "Financial institution information" refers to data such as account balances and transaction history managed by financial institutions such as banks and credit unions. 【0007】 "Electronic payment information" refers to data related to payments made electronically, such as transaction history from cashless payment apps or credit cards. 【0008】 "Income and expenditure analysis" is the process of analyzing income and expenditure data to understand the overall flow of money. 【0009】 "Visualization" is a technique that displays numerical data and analysis results as graphs and charts to make them easier to understand visually. 【0010】 "Machine learning" is a technology in which computer programs learn patterns based on past data and use that information to make predictions and decisions about the future. 【0011】 "Asset building" is the process of increasing assets through saving and investing to achieve future financial stability. 【0012】 "Dynamic delivery" refers to a method of generating and presenting optimal information in real time in response to user input and actions. 【0013】 "Asset goals" refer to the financial results or benchmarks that a user aims to achieve. 【0014】 "Plan updating" refers to modifying an existing plan to make it more effective based on current evaluations and circumstances. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. 【Embodiments for Carrying Out the Invention】 【0016】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0019】 In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0021】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0022】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0023】 [First Embodiment] 【0024】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0025】 As shown in Figure 1, the 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. 【0026】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0027】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0028】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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. 【0029】 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0030】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0031】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0032】 As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30. 【0033】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0034】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0035】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0036】 The household budget management system of the present invention is a tool to support users in managing their daily income and expenses and building assets. Specific embodiments of each component are described below. 【0037】 Data collection and integration 【0038】 The server uses APIs to retrieve financial data such as account information and transaction history from financial institutions authorized by the user. It also collects spending data from cashless payment apps and integrates this data to provide a foundation for understanding the user's overall income and expenses. 【0039】 Data analysis and visualization 【0040】 The server analyzes the collected data and calculates the total amount for each expenditure category (e.g., food, transportation, entertainment). This makes it easier to understand the user's income and expenditure patterns. 【0041】 By displaying the analysis results in graphs and charts on the device, users can easily understand them visually and check their spending trends. 【0042】 Future spending forecasts 【0043】 The server feeds historical income and expenditure data into a machine learning algorithm to predict future spending. This allows users to know in advance how much their expenses might increase in the following month. 【0044】 The device notifies the user of the prediction results and advises them on planned financial management. 【0045】 Proposal for asset building 【0046】 Users can select their areas of interest in the app's settings screen. Based on these selections, the server dynamically generates suggestions, such as how to save for travel or how to manage assets for education expenses. 【0047】 The system displays suggestions optimized for the device, allowing users to consider specific actions based on those suggestions. 【0048】 Asset management 【0049】 Users can set annual and monthly savings goals. For example, they can enter a specific goal such as "Save 500,000 yen in one year." 【0050】 The server creates savings plans and investment strategies based on those goals. This makes the specific steps to achieving them visible. 【0051】 The server periodically evaluates asset progress, updates the plan as needed, and notifies the user via the terminal. 【0052】 Specific example 【0053】 For example, suppose a user sets a goal of saving 100,000 yen per year for travel. The server analyzes the user's current spending habits, calculates the surplus from their monthly income and expenses, and creates a savings plan. It also displays points for saving money and investment options on the user's device, providing advice to help them achieve their goal. In this way, the system aims to intelligently guide users' financial behavior and support effective wealth building. 【0054】 The following describes the processing flow. 【0055】 Step 1: 【0056】 The user opens a dedicated application and enters authentication information to connect to banks and electronic payment services. Once authentication is complete, the server retrieves financial data and transaction history through the APIs of each service. 【0057】 Step 2: 【0058】 The server analyzes the acquired data and calculates total amounts for each major expenditure category (food, housing, transportation, etc.). It also analyzes income and expenditure trends over time to understand the overall financial landscape. 【0059】 Step 3: 【0060】 The server converts the analysis results into graphs and charts using visualization tools. This creates a revenue and expenditure report that users can intuitively understand. 【0061】 Step 4: 【0062】 The device displays a visualized income and expense report to the user. Through this information, the user can visually check their financial situation and identify problems and areas for improvement in their spending. 【0063】 Step 5: 【0064】 The server uses machine learning algorithms based on past revenue and expenditure data to predict future spending. This allows it to calculate projected spending for the next month and warn of potential overspending in advance. 【0065】 Step 6: 【0066】 The device provides users with advice that includes projected spending and future savings goals. This helps users to be more mindful of their spending. 【0067】 Step 7: 【0068】 Users set financial goals (e.g., saving for travel or education) within the app. This records the specific amount and deadline for achieving those goals. 【0069】 Step 8: 【0070】 The server proposes specific savings and investment methods based on the user's set asset goals. These proposals are realistic and achievable, taking into account the user's income and expenses. 【0071】 Step 9: 【0072】 The device presents the user with asset building proposals and implementation steps. Based on this, the user can make their own decisions and put the optimal plan into action. 【0073】 Step 10: 【0074】 The server periodically evaluates the user's savings progress and checks whether they are achieving their asset goals. It updates advice as needed and notifies the user of the latest information via their terminal. 【0075】 (Example 1) 【0076】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0077】 In modern household financial management, users need to manually manage information from numerous financial institutions and payment methods to build wealth and forecast expenses. This process is complex and time-consuming, and integrating and analyzing individual data requires specialized knowledge. Furthermore, predicting future expenses and planning wealth building is difficult, and finding appropriate investment options is not easy. Therefore, there is a need to solve these challenges and achieve efficient and effective household financial management. 【0078】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0079】 In this invention, the server includes means for collecting information from financial institutions and electronic payment means via information acquisition means, means for integrating and cleansing the information, and means for analyzing expenditure costs. This enables users to centrally manage financial data from diverse sources and automatically perform income and expenditure analysis and forecasting, thereby supporting informed asset building and optimal investment choices. 【0080】 "Information acquisition methods" refer to processes and technologies for securely and efficiently collecting data from financial institutions and electronic payment systems. 【0081】 "Integration and cleansing" refers to the process of unifying information collected from multiple data sources, removing duplication and inconsistencies, and improving accuracy and consistency. 【0082】 "Analyzing spending costs" refers to the process of analyzing users' spending trends by category based on collected data, and deriving necessary information. 【0083】 "Dynamic suggestion generation" refers to a function that mechanically adjusts and provides optimal asset formation and investment strategies according to the user's interests and asset goals. 【0084】 "Regular evaluation and automatic updates" refers to a function that continuously monitors the progress toward set asset targets and updates the management plan based on the results. 【0085】 The household finance management system of this invention consists of a server, terminals, and users, and provides automated data management and asset building support. Details are provided below. 【0086】 The server first collects data from user-authorized financial institutions and electronic payment methods using data acquisition tools. This process utilizes technologies that securely and quickly acquire information using API access. The acquired data is integrated using the Python Pandas library to remove duplicates and inconsistencies. This data integration and cleansing process improves the reliability of the data. 【0087】 Next, the server analyzes the integrated data and categorizes user spending. This analysis extracts patterns from past data to understand spending trends. At this point, the data is organized in JSON format and prepared for visual display. 【0088】 The device visualizes the analyzed data using the JavaScript® D3.js library. Users can view these graphs and charts on an intuitive dashboard screen. 【0089】 Furthermore, the server uses TENSORFLOW® to train machine learning models based on past income and expenditure data to predict future spending. This technology helps users reduce uncertainty about future finances and promotes planned wealth building. 【0090】 Furthermore, users can receive personalized asset building suggestions using AI models generated on the server. Based on this, users can consider savings and investment options in specific areas. This information is generated using prompts such as: "Based on the following user's income and expenditure data, please create a savings plan to save 100,000 yen per year for travel." 【0091】 This system continuously supports users in achieving their financial goals by providing real-time evaluations and plan updates. For example, if a user sets a goal of "saving 100,000 yen per year for travel," the server analyzes their current spending habits and suggests a monthly savings target. Based on this information, the user can plan and execute specific actions. 【0092】 In this way, this system efficiently manages users' daily financial activities and supports them in planning for future asset building. 【0093】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0094】 Step 1: 【0095】 The server collects data from financial institutions and electronic payment systems through information acquisition mechanisms. It retrieves account information and transaction history using financial institution APIs authorized by the user. In this process, the input is raw data obtained from the API, and the output is financial data converted into a unified format. User data is securely retrieved using OAuth authentication. 【0096】 Step 2: 【0097】 The server integrates and cleanses the collected data. The input is the financial data obtained in step 1, and the output is the integrated dataset with duplicates and inconsistencies removed. The accuracy of the data is improved by cleaning the data using the Python Pandas library, removing duplicate entries, and performing categorization. 【0098】 Step 3: 【0099】 The server analyzes the integrated data to calculate total spending by category. The input is cleansed data, and the output is an analysis showing total spending for each category. This analysis helps users understand their own spending patterns by extracting historical data patterns and clarifying spending trends. 【0100】 Step 4: 【0101】 The device generates and displays visual graphs and charts based on the analysis results. The input is the analysis results sent from the server, and the output is the graphics displayed on the device's dashboard. By using the JavaScript D3.js library, intuitive and easy-to-understand visual feedback is provided. 【0102】 Step 5: 【0103】 The server trains a machine learning model based on historical income and expenditure data to predict future spending. The input is historical income and expenditure data, and the output is predicted future spending data. By building the model and training the data using TensorFlow, highly accurate predictions become possible. 【0104】 Step 6: 【0105】 The terminal receives predicted spending results from the server and notifies the user. The input is the predicted result, and the output is the notification message provided to the user. This allows the user to understand future spending trends in advance and use this information for asset management. 【0106】 Step 7: 【0107】 The server generates asset building suggestions using a generative AI model based on the user's interests. The input is the user's areas of interest and current financial data, and the output is specific suggestions for asset building. A prompt such as "Based on the following user income and expenditure data, please create a savings plan to save 100,000 yen per year for travel" is used, and personalized advice is provided to the user. 【0108】 (Application Example 1) 【0109】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0110】 Modern consumers engage in a vast number of financial transactions and electronic payments daily, making it difficult to grasp the overall picture of their income and expenses and manage their assets effectively. In particular, current systems do not adequately support accurately predicting future spending or providing real-time investment and savings advice. Therefore, there is a growing need for a household financial management system that enables users to efficiently build wealth and achieve their goals. 【0111】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0112】 In this invention, the server includes means for accumulating information on financial institutions and electronic payments, means for analyzing income and expenses based on the information on financial institutions and electronic payments, and means for providing individual savings methods and investment advice in real time at the time of settlement. This enables users to instantly understand their income and expense situation and effectively manage their assets while planning future spending. 【0113】 "Financial institution information" refers to data, including account information and transaction history, obtained from financial institutions such as banks and credit unions. 【0114】 "Electronic payment information" refers to information including transaction data and history related to cashless payments. 【0115】 "Means of analyzing income and expenses" refer to the technologies and methods used to analyze income and expenses based on acquired financial data and electronic payment data, and to calculate the results. 【0116】 "Means of visualizing and displaying on a terminal" refers to technologies and methods that display analyzed data in the form of graphs, charts, and other formats on the user's terminal to make it easier to understand. 【0117】 "A method of predicting future spending using machine learning" refers to a method of predicting future spending trends using machine learning algorithms based on past income and expenditure data. 【0118】 "Means for generating asset building proposals" refers to methods for providing optimal savings and investment plans based on the user's areas of interest. 【0119】 "Means of creating an asset management plan" refers to the techniques and methods for constructing specific procedures and plans to achieve the asset goals set by the user. 【0120】 "A method for providing individual savings strategies and investment advice in real time at the time of payment" refers to a method of providing users with specific advice on saving or investing at the moment a payment is made. 【0121】 The household budget management system based on this invention is implemented with a server, user terminals, and users. The server obtains information from financial institutions and electronic payment services via APIs and collects this data with the user's permission. The collected data is analyzed and visualized using software such as pandas and matplotlib. Users can view this visualized information through their terminals and check their income and expenditure trends. 【0122】 Furthermore, the server uses historical income and expenditure data to build machine learning algorithms and predict future spending. This prediction function allows users to understand how much they can expect to spend in the following month, enabling more planned financial management. 【0123】 Furthermore, based on the asset goals set by the user, the server generates and provides a specific asset management plan. Advice on saving and investing can be provided in real time according to the user's areas of interest, and this information is communicated to the user as in-app notifications using Swift or Kotlin. 【0124】 As a concrete example, consider a case where a user sets a goal of "saving 100,000 yen per year for travel." The server analyzes the user's current spending habits, calculates the surplus from monthly income and expenses, and develops a savings plan. In this way, the user can understand areas for saving and take concrete actions toward achieving their goal while considering investment options. By inputting this prompt into the generating AI model, it is possible to obtain advice such as, "Based on the savings goal set by the user, please suggest what asset management approach would be most effective going forward given the current spending trends." 【0125】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0126】 Step 1: 【0127】 The server first obtains user permission to retrieve information from APIs of financial institutions and electronic payment services. This operation uses user authentication information (input) to retrieve account information and transaction history (output) via REST APIs. The retrieved data is stored in a database and used for subsequent data analysis. 【0128】 Step 2: 【0129】 The server integrates the acquired financial data and electronic payment data and performs data cleaning. Here, the input data is raw transaction history, and duplicate data is removed and the data is converted into a standardized format (output). This results in a well-organized dataset that can be used for analysis. 【0130】 Step 3: 【0131】 The server uses pandas to analyze income and expenses using the prepared data. It categorizes income and expenses, calculating totals and trends. This analysis takes integrated financial data as input and generates income and expense data (output) for each category. This allows users to understand their own spending patterns. 【0132】 Step 4: 【0133】 The server uses matplotlib to convert the analysis results into graphs and charts, generating visualization data. At this stage, the analyzed income and expenditure data is the input, and the resulting graphed visual data (output) is provided to the user via the terminal. 【0134】 Step 5: 【0135】 The server uses a Scikit-learn machine learning model to predict future spending based on past income and expense data. It generates predicted spending data for the next month (output) from the income and expense data (input) and uses this to notify the user of their future financial situation. 【0136】 Step 6: 【0137】 Based on the asset goals set by the user, the server develops an asset management plan. The inputs are the user's goal settings and analyzed income and expenditure data, and the output provides specific savings and investment plans. This allows the user to obtain concrete means of wealth creation. 【0138】 Step 7: 【0139】 The server prompts the running AI model as needed, generating advice on saving and investing. The input consists of the user's current spending trends and goals, and the output is personalized advice. This advice is communicated to the terminal in real time to support the user's decision-making. 【0140】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0141】 This invention combines a household finance management system, designed to support users in managing their daily income and expenses and building wealth, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below. 【0142】 Data collection and analysis 【0143】 The server collects the user's financial institution information and electronic payment information through an API interface. This allows it to obtain detailed information about the user's income and expenses and generate integrated analytical data. 【0144】 Utilizing the Emotion Engine 【0145】 The device activates an emotion engine when the user uses an app, recognizing emotions by analyzing the user's facial expressions and tone of voice. As a result of this emotion recognition, emotional states such as stress and feelings of security are quantified. 【0146】 Emotion-based asset building proposals 【0147】 The server dynamically adjusts asset building recommendations based on recognized user emotion data. For example, if a user is feeling stressed, it will offer low-risk products to provide a sense of security. 【0148】 Data visualization and feedback 【0149】 The device visualizes and displays adjusted asset building suggestions based on analyzed income and expenditure information and user sentiment data. The presented information is represented as graphs and charts, which users can view and use to make decisions tailored to their own situation. 【0150】 Automatic updates for asset management 【0151】 The server periodically monitors the user's emotions and goal achievement status, and automatically updates the asset management plan as needed. This process ensures that the plan is always adapted to the user's current state. 【0152】 Specific example 【0153】 For example, suppose a user sets a goal of saving 200,000 yen per year, and emotional data indicating stress is detected while using the app. In this case, the server suggests low-risk fixed deposits and highly liquid savings options, displaying them on the device to provide the user with a sense of security. Based on the advice provided, the user can effectively manage their assets while mitigating stress. In this way, the system provides flexible asset management support that takes emotional states into account. 【0154】 The following describes the processing flow. 【0155】 Step 1: 【0156】 The user opens a dedicated application and enters their financial institution account information and electronic payment service authentication information. Once authentication is complete, the server retrieves income and expenditure data from each service via API. 【0157】 Step 2: 【0158】 The server analyzes the collected income and expenditure data, classifying daily, weekly, and monthly income and expenses, and calculating total expenses for specific categories as needed. This information is used to understand the user's financial situation. 【0159】 Step 3: 【0160】 The device activates an emotion engine and processes camera images and audio data while the user is using the app. It analyzes facial expressions and voice tone to quantify the user's emotional state (e.g., stress, relaxation). 【0161】 Step 4: 【0162】 The server integrates information analyzed from income and expenditure data with user sentiment data to appropriately adjust asset building suggestions. If emotions indicate stress, it prioritizes including low-risk products and safe options that offer short-term profits. 【0163】 Step 5: 【0164】 The device presents users with visualized spending analysis results and emotionally-adjusted asset building suggestions. The visualizations are presented in graph and chart formats, allowing users to easily make optimal decisions tailored to their current emotional state. 【0165】 Step 6: 【0166】 Users make economic decisions based on the information displayed. For example, if the emotional engine determines that the user is stressed, they can choose the low-risk option presented, allowing them to manage their assets with peace of mind. 【0167】 Step 7: 【0168】 The server periodically monitors the user's emotional data and financial status, and evaluates the progress toward set asset goals. Based on the evaluation results, it automatically updates the asset management plan if necessary and notifies the user at the next time they use the service. 【0169】 Through this process, the system provides flexible and effective asset management that takes emotional states into consideration. 【0170】 (Example 2) 【0171】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0172】 In modern times, personal financial management has become increasingly complex, requiring effective management of income and expenses and planning for future wealth creation. Furthermore, it is crucial to respond quickly to investment risks and market fluctuations, and to support asset management that takes into account the user's emotions and psychological state. However, conventional systems struggle with flexible asset management that takes user emotions into account, and still have issues with forecasting accuracy and the timeliness of information provision. 【0173】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0174】 In this invention, the server includes means for aggregating information on financial institutions and electronic transactions, means for performing financial analysis based on the information on financial institutions and electronic transactions, and means for visualizing the analyzed financial information and presenting it on a display device. This makes it possible to provide timely and appropriate asset building advice while taking into account the user's emotional state. 【0175】 "Financial institution information" refers to data such as account balances, transaction history, and transfer information provided by financial institutions such as banks and credit unions. 【0176】 "Electronic transaction information" refers to transaction history and expenditure data related to credit card payments and online payment services. 【0177】 "Financial analysis" is a method of analyzing collected income and expenditure data to evaluate the balance of income and expenses and assets. 【0178】 "Visualization" refers to presenting data to users in an easy-to-understand manner using visual means such as graphs and charts. 【0179】 "Historical data" refers to information on past transaction history and income / expense status, which is used to make future predictions. 【0180】 "Machine learning" is a technology in which computer systems learn patterns and trends from data and make predictions and decisions based on new data. 【0181】 "Areas of interest" refers to specific fields or categories related to asset management and investment that a user is particularly interested in. 【0182】 "Asset allocation advice" refers to providing information that suggests appropriate investment methods and asset allocations based on the user's financial situation and market conditions. 【0183】 "Emotional data" refers to quantified information that indicates a user's psychological state, analyzed from their facial expressions, tone of voice, and other factors. 【0184】 An "asset management plan" is a long-term asset growth strategy formulated according to the user's financial goals and risk tolerance. 【0185】 This invention is a system that supports personal financial management and enables asset building that takes emotional states into consideration. The specific implementation method is described below. 【0186】 The server uses APIs from financial institutions to retrieve account information and transaction history in order to comprehensively manage users' financial information. For this data collection, it utilizes database management software to efficiently store the information and further analyzes income and expenditure information using data analysis tools. The server leverages programming languages ​​such as Python and R to implement machine learning models and predict future consumption patterns based on historical data. 【0187】 The device has a function to perform emotion analysis when the user uses the application. In this process, software equipped with an emotion engine is used to analyze the user's facial expressions and voice characteristics in real time, and the resulting emotion data is quantified. This makes it possible to dynamically adjust the asset building suggestions according to the user's psychological state. 【0188】 Users receive results from the server in a visualized format on their devices. This includes visual representations using graphs and charts, presenting information in a user-friendly format. Based on this, users can review appropriate asset management plans and aim to achieve their financial goals. 【0189】 As a concrete example, if a user sets a goal of "saving 200,000 yen per year," the server recommends low-risk investment products based on emotional data such as stress detected during app use. This recommendation is displayed on the device, and the user can use it as a basis for decision-making. Utilizing a generative AI model, an example of a prompt message could be, "I've been feeling stressed lately, so please suggest safe asset building options." Thus, flexible asset management support that takes emotional states into account is a key feature of this system. 【0190】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0191】 Step 1: 【0192】 The server retrieves account information and transaction history from the user's financial institution using an API. The input is financial data from the API, and the output is integrated financial information stored in the server's database. This allows the server to comprehensively manage the user's income and expenditure data. Specifically, this involves setting a regular data retrieval schedule and normalizing the data format. 【0193】 Step 2: 【0194】 The device activates its emotion engine when a user application is launched. Input is user facial and voice data acquired through the camera and microphone, and output is a quantified emotion score. The device uses facial recognition technology and voice analysis to detect the user's stress level and sense of security. This process allows for the quantification of the user's psychological state. 【0195】 Step 3: 【0196】 The server uses a machine learning model to predict future spending based on income and expenditure data from the database and sentiment scores from the terminal. The input is past income and expenditure data and current sentiment data, and the output is the predicted income and expenditure trend. The server applies a generative AI model to make income and expenditure predictions that reflect the emotional state. This makes it easier for users to understand their future financial situation. 【0197】 Step 4: 【0198】 The server generates optimal asset management suggestions for the user based on their emotional score. The input is the predicted income / expense trend and emotional score, and the output is the asset management plan provided to the user. The AI ​​model performs a risk assessment, and if stress levels are high, safety-oriented products are suggested. This process ensures that suggestions are made in a way that reduces user anxiety. 【0199】 Step 5: 【0200】 The terminal receives asset formation proposals from the server, visualizes them, and presents them to the user. The input is the operational plan received from the server, and the output is visualized graphs and charts. The terminal displays information in a visually easy-to-understand format, allowing the user to see their financial situation at a glance. This specific operation enables the user to interact with the information. 【0201】 Step 6: 【0202】 The server continuously monitors the user's emotional state and progress toward achieving financial goals, updating the asset management plan as needed. Inputs are the latest income and expense data and emotional data, acquired periodically, while output is the updated investment plan. This enables flexible planning tailored to the user's current situation, ensuring optimal asset management at all times. 【0203】 (Application Example 2) 【0204】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0205】 In modern society, personal financial management and wealth building are crucial, but there are insufficient means to do so efficiently. Furthermore, there are no systems that can propose flexible wealth building strategies that take into account the user's emotional state. Therefore, there is a growing need for a system that supports comprehensive financial management and wealth building, including emotional well-being. 【0206】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0207】 In this invention, the server includes means for accumulating financial institution data and electronic payment data, means for analyzing income and expenses and displaying them on the device, and means for recognizing emotional states using an emotion analysis engine and dynamically adjusting asset formation suggestions based on those emotional states. This enables suggestions tailored to the user's emotions and economic situation, allowing the user to manage their assets optimally based on their own circumstances. 【0208】 "Financial institution data" refers to transaction history and account information managed by financial institutions such as banks and credit unions. 【0209】 "Electronic payment data" refers to transaction history information using electronic money, credit cards, etc. 【0210】 "Methods for analyzing income and expenses" refer to methods for understanding economic conditions by analyzing income and expenditure patterns based on financial and settlement data. 【0211】 An "emotion analysis engine" refers to technology that recognizes emotions from a user's facial expressions and voice, and quantifies or categorizes those emotions. 【0212】 "Means of dynamically adjusting asset building proposals" refers to methods of optimizing asset management and investment proposals in real time according to the user's emotional state and financial situation. 【0213】 An "asset management plan" refers to a strategy or plan created to efficiently achieve asset growth and preservation based on the user's goals. 【0214】 The system realizing this invention is a comprehensive system for supporting users' income and expense management and asset building, and includes multiple data processing modules. The server collects data from financial institutions and electronic payment systems via APIs. This generates integrated statistical data detailing the user's income and expenses. Database management systems and data analysis algorithms are used in this process. 【0215】 Next, the device activates an emotion analysis engine to recognize the user's emotional state through facial recognition and voice analysis. Specifically, "OpenFace" is used for facial recognition and "DeepSpeech" for voice analysis. This data is sent to a server and used to dynamically adjust the asset building suggestions. 【0216】 Specifically, the server updates asset management plans based on sentiment data and presents users with appropriate investment options. These suggestions are customized after risk assessment and displayed to the user in a visually easy-to-understand format on their device. Libraries such as "Matplotlib" are used for data visualization during this process. 【0217】 For example, suppose a user has set a goal of keeping their monthly expenses under 50,000 yen, and emotional data indicating a sense of security is detected while using the app. In this case, the server will suggest an investment fund that is somewhat riskier but offers good returns, and display it on the device. This process makes it possible to provide support that is always adapted to the user's latest situation. 【0218】 Examples of prompts for generative AI models: 【0219】 "We will provide users with financial and emotional data. Based on this, please generate asset building proposals that meet the following conditions: prioritize safety while considering high returns in the short term." 【0220】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0221】 Step 1: 【0222】 The user enters authentication information for their financial institution and electronic payment system. The server collects this information via an API and retrieves income and expenditure history from a financial database. Based on this data, statistical analysis is performed to generate detailed income and expenditure information for the user. The input consists of authentication information and historical transaction data, and the output is the analyzed income and expenditure data. 【0223】 Step 2: 【0224】 The device uses the smartphone's camera and microphone, with the user's permission, to recognize facial expressions and voice in real time. This data is sent to an emotion analysis engine, which quantifies and identifies the user's emotional state. The input is real-time facial and voice data, and the output is quantified emotion data. 【0225】 Step 3: 【0226】 The server integrates income / expense data and sentiment data to generate dynamic asset building recommendations. Here, a generative AI model is used to customize investment options based on risk assessment and the user's emotional state. The inputs are income / expense data and sentiment data, and the output is a customized investment recommendation. 【0227】 Step 4: 【0228】 The terminal visualizes and presents the generated asset building proposals to the user. Specifically, it displays information in graph and chart formats to aid user understanding. Libraries such as "Matplotlib" are used here. The input is investment proposal data, and the output is visualized information. 【0229】 Step 5: 【0230】 The user reviews the presented proposals and selects an investment action. The server updates the asset management plan based on the selected action and saves the data for the next evaluation. The input is the user's selected investment action, and the output is the updated asset management plan. This process is repeated regularly, allowing for support that is always adapted to the user's latest financial and emotional state. 【0231】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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. 【0232】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0233】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0234】 [Second Embodiment] 【0235】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0236】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0237】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0238】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0239】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0240】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0241】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0242】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0243】 The specific processing program 56 is an example of a "program" relating to the technology of this 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. 【0244】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0245】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0246】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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". 【0247】 The household budget management system of the present invention is a tool to support users in managing their daily income and expenses and building assets. Specific embodiments of each component are described below. 【0248】 Data collection and integration 【0249】 The server uses APIs to retrieve financial data such as account information and transaction history from financial institutions authorized by the user. It also collects spending data from cashless payment apps and integrates this data to provide a foundation for understanding the user's overall income and expenses. 【0250】 Data analysis and visualization 【0251】 The server analyzes the collected data and calculates the total amount for each expenditure category (e.g., food, transportation, entertainment). This makes it easier to understand the user's income and expenditure patterns. 【0252】 By displaying the analysis results in graphs and charts on the device, users can easily understand them visually and check their spending trends. 【0253】 Future spending forecasts 【0254】 The server feeds historical income and expenditure data into a machine learning algorithm to predict future spending. This allows users to know in advance how much their expenses might increase in the following month. 【0255】 The device notifies the user of the prediction results and advises them on planned financial management. 【0256】 Proposal for asset building 【0257】 Users can select their areas of interest in the app's settings screen. Based on these selections, the server dynamically generates suggestions, such as how to save for travel or how to manage assets for education expenses. 【0258】 The system displays suggestions optimized for the device, allowing users to consider specific actions based on those suggestions. 【0259】 Asset management 【0260】 Users can set annual and monthly savings goals. For example, they can enter a specific goal such as "Save 500,000 yen in one year." 【0261】 The server creates savings plans and investment strategies based on those goals. This makes the specific steps to achieving them visible. 【0262】 The server periodically evaluates asset progress, updates the plan as needed, and notifies the user via the terminal. 【0263】 Specific example 【0264】 For example, suppose a user sets a goal of saving 100,000 yen per year for travel. The server analyzes the user's current spending habits, calculates the surplus from their monthly income and expenses, and creates a savings plan. It also displays points for saving money and investment options on the user's device, providing advice to help them achieve their goal. In this way, the system aims to intelligently guide users' financial behavior and support effective wealth building. 【0265】 The following describes the processing flow. 【0266】 Step 1: 【0267】 The user opens a dedicated application and enters authentication information to connect to banks and electronic payment services. Once authentication is complete, the server retrieves financial data and transaction history through the APIs of each service. 【0268】 Step 2: 【0269】 The server analyzes the acquired data and calculates total amounts for each major expenditure category (food, housing, transportation, etc.). It also analyzes income and expenditure trends over time to understand the overall financial landscape. 【0270】 Step 3: 【0271】 The server converts the analysis results into graphs and charts using visualization tools. This creates a revenue and expenditure report that users can intuitively understand. 【0272】 Step 4: 【0273】 The terminal presents the user with a visualized income and expenditure report. Through this information, the user can visually check their financial situation and recognize issues and areas for improvement in their spending. 【0274】 Step 5: 【0275】 The server applies a machine learning algorithm based on past income and expenditure data to predict future spending. This calculates the expected expenditure amount for the next month and warns in advance of potential overspending. 【0276】 Step 6: 【0277】 The terminal presents the user with advice including the predicted expenditure and future savings goals. This enables the user to spend with planning in mind. 【0278】 Step 7: 【0279】 The user sets asset goals (e.g., saving for travel funds or education funds) within the app. This records the specific amount and deadline for achievement. 【0280】 Step 8: 【0281】 The server proposes specific savings and investment methods according to the asset goals set by the user. The proposal takes into account the user's income and expenditure situation and is realistic and achievable. 【0282】 Step 9: 【0283】 The terminal presents the user with proposals for asset formation and the implementation procedures. Based on this, the user can translate the optimal plan into action according to their own judgment. 【0284】 Step 10: 【0285】 The server regularly evaluates the progress of the user's savings and checks the achievement status of the asset goals. Updates the advice as necessary and informs the user of the latest information through the terminal. 【0286】 (Example 1) 【0287】 Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal". 【0288】 In modern household management, users need to manually manage information from many financial institutions and payment methods and conduct asset formation and expenditure prediction. This work is complex and time-consuming, and specialized knowledge is required for the integration and analysis of individual data. Furthermore, it is difficult to make future expenditure predictions and asset formation plans, and it is not easy to find appropriate investment options. Therefore, it is required to solve these problems and realize efficient and effective household management. 【0289】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0290】 In this invention, the server includes means for collecting information from financial institutions and electronic payment means via information acquisition means, means for performing integration and cleansing, and means for analyzing expenditure costs. As a result, users can centrally manage financial data from various information sources, automatically analyze and predict income and expenditure, and support asset formation and optimal investment selection based on information. 【0291】 "Information acquisition means" refers to a process or technology for safely and efficiently collecting data from financial institutions and electronic payment means. 【0292】 "Integration and cleansing" refers to a process of unifying information collected from multiple data sources, removing duplicates and inconsistencies, and improving accuracy and consistency. 【0293】 "Analyzing expenditure costs" refers to the work of classifying and analyzing the user's expenditure trends by category based on the collected data and deriving necessary information. 【0294】 "Dynamic suggestion generation" refers to a function that mechanically adjusts and provides optimal asset formation and investment strategies according to the user's interests and asset goals. 【0295】 "Regular evaluation and automatic updates" refers to a function that continuously monitors the progress toward set asset targets and updates the management plan based on the results. 【0296】 The household finance management system of this invention consists of a server, terminals, and users, and provides automated data management and asset building support. Details are provided below. 【0297】 The server first collects data from user-authorized financial institutions and electronic payment methods using data acquisition tools. This process utilizes technologies that securely and quickly acquire information using API access. The acquired data is integrated using the Python Pandas library to remove duplicates and inconsistencies. This data integration and cleansing process improves the reliability of the data. 【0298】 Next, the server analyzes the integrated data and categorizes user spending. This analysis extracts patterns from past data to understand spending trends. At this point, the data is organized in JSON format and prepared for visual display. 【0299】 The device visualizes the analyzed data using the JavaScript D3.js library. Users can view these graphs and charts on an intuitive dashboard screen. 【0300】 Furthermore, the server uses TensorFlow to train machine learning models based on past income and expenditure data to predict future spending. This technology helps users reduce uncertainty about future finances and promotes planned wealth building. 【0301】 Furthermore, the user can receive proposals for asset formation according to their interests using the generative AI model in the server. Based on this, the user can consider savings and investment options in a specific field. This detail is generated using prompt sentences such as the following: "Based on the following user income and expenditure data, please formulate a savings plan to save 100,000 yen annually for travel." 【0302】 This system continuously supports the user in achieving their asset goals by performing real-time evaluation and plan updates. For example, when the user sets the goal of "wanting to save 100,000 yen annually for travel," the server analyzes the current expenditure situation and proposes the monthly savings amount that can be achieved. The user can plan and execute specific actions based on this information. 【0303】 In this way, this system efficiently manages the user's daily financial activities and supports the planning for future asset formation. 【0304】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0305】 Step 1: 【0306】 The server collects data from financial institutions and electronic payment means via information acquisition means. It acquires account information and transaction history using the financial institution API permitted by the user. In this process, the input is the raw data obtained from the API, and the output is the financial data converted into a unified format. The user's data is securely acquired using OAuth authentication. 【0307】 Step 2: 【0308】 The server integrates and cleanses the collected data. The input is the financial data obtained in step 1, and the output is the integrated dataset with duplicates and inconsistencies removed. The accuracy of the data is improved by cleaning the data using the Python Pandas library, removing duplicate entries, and performing categorization. 【0309】 Step 3: 【0310】 The server analyzes the integrated data to calculate total spending by category. The input is cleansed data, and the output is an analysis showing total spending for each category. This analysis helps users understand their own spending patterns by extracting historical data patterns and clarifying spending trends. 【0311】 Step 4: 【0312】 The device generates and displays visual graphs and charts based on the analysis results. The input is the analysis results sent from the server, and the output is the graphics displayed on the device's dashboard. By using the JavaScript D3.js library, intuitive and easy-to-understand visual feedback is provided. 【0313】 Step 5: 【0314】 The server trains a machine learning model based on historical income and expenditure data to predict future spending. The input is historical income and expenditure data, and the output is predicted future spending data. By building the model and training the data using TensorFlow, highly accurate predictions become possible. 【0315】 Step 6: 【0316】 The terminal receives predicted spending results from the server and notifies the user. The input is the predicted result, and the output is the notification message provided to the user. This allows the user to understand future spending trends in advance and use this information for asset management. 【0317】 Step 7: 【0318】 The server generates asset building suggestions using a generative AI model based on the user's interests. The input is the user's areas of interest and current financial data, and the output is specific suggestions for asset building. A prompt such as "Based on the following user income and expenditure data, please create a savings plan to save 100,000 yen per year for travel" is used, and personalized advice is provided to the user. 【0319】 (Application Example 1) 【0320】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0321】 Modern consumers engage in a vast number of financial transactions and electronic payments daily, making it difficult to grasp the overall picture of their income and expenses and manage their assets effectively. In particular, current systems do not adequately support accurately predicting future spending or providing real-time investment and savings advice. Therefore, there is a growing need for a household financial management system that enables users to efficiently build wealth and achieve their goals. 【0322】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0323】 In this invention, the server includes means for accumulating information on financial institutions and electronic payments, means for analyzing income and expenses based on the information on financial institutions and electronic payments, and means for providing individual savings methods and investment advice in real time at the time of settlement. This enables users to instantly understand their income and expense situation and effectively manage their assets while planning future spending. 【0324】 "Financial institution information" refers to data, including account information and transaction history, obtained from financial institutions such as banks and credit unions. 【0325】 "Electronic payment information" refers to information including transaction data and history related to cashless payments. 【0326】 "Means of analyzing income and expenses" refer to the technologies and methods used to analyze income and expenses based on acquired financial data and electronic payment data, and to calculate the results. 【0327】 "Means of visualizing and displaying on a terminal" refers to technologies and methods that display analyzed data in the form of graphs, charts, and other formats on the user's terminal to make it easier to understand. 【0328】 "A method of predicting future spending using machine learning" refers to a method of predicting future spending trends using machine learning algorithms based on past income and expenditure data. 【0329】 "Means for generating asset building proposals" refers to methods for providing optimal savings and investment plans based on the user's areas of interest. 【0330】 "Means of creating an asset management plan" refers to the techniques and methods for constructing specific procedures and plans to achieve the asset goals set by the user. 【0331】 "A method for providing individual savings strategies and investment advice in real time at the time of payment" refers to a method of providing users with specific advice on saving or investing at the moment a payment is made. 【0332】 The household budget management system based on this invention is implemented with a server, user terminals, and users. The server obtains information from financial institutions and electronic payment services via APIs and collects this data with the user's permission. The collected data is analyzed and visualized using software such as pandas and matplotlib. Users can view this visualized information through their terminals and check their income and expenditure trends. 【0333】 Furthermore, the server uses historical income and expenditure data to build machine learning algorithms and predict future spending. This prediction function allows users to understand how much they can expect to spend in the following month, enabling more planned financial management. 【0334】 Furthermore, based on the asset goals set by the user, the server generates and provides a specific asset management plan. Advice on saving and investing can be provided in real time according to the user's areas of interest, and this information is communicated to the user as in-app notifications using Swift or Kotlin. 【0335】 As a concrete example, consider a case where a user sets a goal of "saving 100,000 yen per year for travel." The server analyzes the user's current spending habits, calculates the surplus from monthly income and expenses, and develops a savings plan. In this way, the user can understand areas for saving and take concrete actions toward achieving their goal while considering investment options. By inputting this prompt into the generating AI model, it is possible to obtain advice such as, "Based on the savings goal set by the user, please suggest what asset management approach would be most effective going forward given the current spending trends." 【0336】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0337】 Step 1: 【0338】 The server first obtains user permission to retrieve information from APIs of financial institutions and electronic payment services. This operation uses user authentication information (input) to retrieve account information and transaction history (output) via REST APIs. The retrieved data is stored in a database and used for subsequent data analysis. 【0339】 Step 2: 【0340】 The server integrates the acquired financial data and electronic payment data and performs data cleaning. Here, the input data is raw transaction history, and duplicate data is removed and the data is converted into a standardized format (output). This results in a well-organized dataset that can be used for analysis. 【0341】 Step 3: 【0342】 The server uses pandas to analyze income and expenses using the prepared data. It categorizes income and expenses, calculating totals and trends. This analysis takes integrated financial data as input and generates income and expense data (output) for each category. This allows users to understand their own spending patterns. 【0343】 Step 4: 【0344】 The server uses matplotlib to convert the analysis results into graphs and charts, generating visualization data. At this stage, the analyzed income and expenditure data is the input, and the resulting graphed visual data (output) is provided to the user via the terminal. 【0345】 Step 5: 【0346】 The server uses a Scikit-learn machine learning model to predict future spending based on past income and expense data. It generates predicted spending data for the next month (output) from the income and expense data (input) and uses this to notify the user of their future financial situation. 【0347】 Step 6: 【0348】 Based on the asset goals set by the user, the server develops an asset management plan. The inputs are the user's goal settings and analyzed income and expenditure data, and the output provides specific savings and investment plans. This allows the user to obtain concrete means of wealth creation. 【0349】 Step 7: 【0350】 The server prompts the running AI model as needed, generating advice on saving and investing. The input consists of the user's current spending trends and goals, and the output is personalized advice. This advice is communicated to the terminal in real time to support the user's decision-making. 【0351】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0352】 This invention combines a household finance management system, designed to support users in managing their daily income and expenses and building wealth, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below. 【0353】 Data collection and analysis 【0354】 The server collects the user's financial institution information and electronic payment information through an API interface. This allows it to obtain detailed information about the user's income and expenses and generate integrated analytical data. 【0355】 Utilizing the Emotion Engine 【0356】 The device activates an emotion engine when the user uses an app, recognizing emotions by analyzing the user's facial expressions and tone of voice. As a result of this emotion recognition, emotional states such as stress and feelings of security are quantified. 【0357】 Emotion-based asset building proposals 【0358】 The server dynamically adjusts asset building recommendations based on recognized user emotion data. For example, if a user is feeling stressed, it will offer low-risk products to provide a sense of security. 【0359】 Data visualization and feedback 【0360】 The device visualizes and displays adjusted asset building suggestions based on analyzed income and expenditure information and user sentiment data. The presented information is represented as graphs and charts, which users can view and use to make decisions tailored to their own situation. 【0361】 Automatic updates for asset management 【0362】 The server periodically monitors the user's emotions and goal achievement status, and automatically updates the asset management plan as needed. This process ensures that the plan is always adapted to the user's current state. 【0363】 Specific example 【0364】 For example, suppose a user sets a goal of saving 200,000 yen per year, and emotional data indicating stress is detected while using the app. In this case, the server suggests low-risk fixed deposits and highly liquid savings options, displaying them on the device to provide the user with a sense of security. Based on the advice provided, the user can effectively manage their assets while mitigating stress. In this way, the system provides flexible asset management support that takes emotional states into account. 【0365】 The following describes the processing flow. 【0366】 Step 1: 【0367】 The user opens a dedicated application and enters their financial institution account information and electronic payment service authentication information. Once authentication is complete, the server retrieves income and expenditure data from each service via API. 【0368】 Step 2: 【0369】 The server analyzes the collected income and expenditure data, classifying daily, weekly, and monthly income and expenses, and calculating total expenses for specific categories as needed. This information is used to understand the user's financial situation. 【0370】 Step 3: 【0371】 The device activates an emotion engine and processes camera images and audio data while the user is using the app. It analyzes facial expressions and voice tone to quantify the user's emotional state (e.g., stress, relaxation). 【0372】 Step 4: 【0373】 The server integrates information analyzed from income and expenditure data with user sentiment data to appropriately adjust asset building suggestions. If emotions indicate stress, it prioritizes including low-risk products and safe options that offer short-term profits. 【0374】 Step 5: 【0375】 The device presents users with visualized spending analysis results and emotionally-adjusted asset building suggestions. The visualizations are presented in graph and chart formats, allowing users to easily make optimal decisions tailored to their current emotional state. 【0376】 Step 6: 【0377】 Users make economic decisions based on the information displayed. For example, if the emotional engine determines that the user is stressed, they can choose the low-risk option presented, allowing them to manage their assets with peace of mind. 【0378】 Step 7: 【0379】 The server periodically monitors the user's emotional data and financial status, and evaluates the progress toward set asset goals. Based on the evaluation results, it automatically updates the asset management plan if necessary and notifies the user at the next time they use the service. 【0380】 Through this process, the system provides flexible and effective asset management that takes emotional states into consideration. 【0381】 (Example 2) 【0382】 Next, we will describe Example 2. 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". 【0383】 In modern times, personal financial management has become increasingly complex, requiring effective management of income and expenses and planning for future wealth creation. Furthermore, it is crucial to respond quickly to investment risks and market fluctuations, and to support asset management that takes into account the user's emotions and psychological state. However, conventional systems struggle with flexible asset management that takes user emotions into account, and still have issues with forecasting accuracy and the timeliness of information provision. 【0384】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0385】 In this invention, the server includes means for aggregating information on financial institutions and electronic transactions, means for performing financial analysis based on the information on financial institutions and electronic transactions, and means for visualizing the analyzed financial information and presenting it on a display device. This makes it possible to provide timely and appropriate asset building advice while taking into account the user's emotional state. 【0386】 "Financial institution information" refers to data such as account balances, transaction history, and transfer information provided by financial institutions such as banks and credit unions. 【0387】 "Electronic transaction information" refers to transaction history and expenditure data related to credit card payments and online payment services. 【0388】 "Financial analysis" is a method of analyzing collected income and expenditure data to evaluate the balance of income and expenses and assets. 【0389】 "Visualization" refers to presenting data to users in an easy-to-understand manner using visual means such as graphs and charts. 【0390】 "Historical data" refers to information on past transaction history and income / expense status, which is used to make future predictions. 【0391】 "Machine learning" is a technology in which computer systems learn patterns and trends from data and make predictions and decisions based on new data. 【0392】 "Areas of interest" refers to specific fields or categories related to asset management and investment that a user is particularly interested in. 【0393】 "Asset allocation advice" refers to providing information that suggests appropriate investment methods and asset allocations based on the user's financial situation and market conditions. 【0394】 "Emotional data" refers to quantified information that indicates a user's psychological state, analyzed from their facial expressions, tone of voice, and other factors. 【0395】 An "asset management plan" is a long-term asset growth strategy formulated according to the user's financial goals and risk tolerance. 【0396】 This invention is a system that supports personal financial management and enables asset building that takes emotional states into consideration. The specific implementation method is described below. 【0397】 The server uses APIs from financial institutions to retrieve account information and transaction history in order to comprehensively manage users' financial information. For this data collection, it utilizes database management software to efficiently store the information and further analyzes income and expenditure information using data analysis tools. The server leverages programming languages ​​such as Python and R to implement machine learning models and predict future consumption patterns based on historical data. 【0398】 The device has a function to perform emotion analysis when the user uses the application. In this process, software equipped with an emotion engine is used to analyze the user's facial expressions and voice characteristics in real time, and the resulting emotion data is quantified. This makes it possible to dynamically adjust the asset building suggestions according to the user's psychological state. 【0399】 Users receive results from the server in a visualized format on their devices. This includes visual representations using graphs and charts, presenting information in a user-friendly format. Based on this, users can review appropriate asset management plans and aim to achieve their financial goals. 【0400】 As a concrete example, if a user sets a goal of "saving 200,000 yen per year," the server recommends low-risk investment products based on emotional data such as stress detected during app use. This recommendation is displayed on the device, and the user can use it as a basis for decision-making. Utilizing a generative AI model, an example of a prompt message could be, "I've been feeling stressed lately, so please suggest safe asset building options." Thus, flexible asset management support that takes emotional states into account is a key feature of this system. 【0401】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0402】 Step 1: 【0403】 The server retrieves account information and transaction history from the user's financial institution using an API. The input is financial data from the API, and the output is integrated financial information stored in the server's database. This allows the server to comprehensively manage the user's income and expenditure data. Specifically, this involves setting a regular data retrieval schedule and normalizing the data format. 【0404】 Step 2: 【0405】 The device activates its emotion engine when a user application is launched. Input is user facial and voice data acquired through the camera and microphone, and output is a quantified emotion score. The device uses facial recognition technology and voice analysis to detect the user's stress level and sense of security. This process allows for the quantification of the user's psychological state. 【0406】 Step 3: 【0407】 The server uses a machine learning model to predict future spending based on income and expenditure data from the database and sentiment scores from the terminal. The input is past income and expenditure data and current sentiment data, and the output is the predicted income and expenditure trend. The server applies a generative AI model to make income and expenditure predictions that reflect the emotional state. This makes it easier for users to understand their future financial situation. 【0408】 Step 4: 【0409】 The server generates optimal asset management suggestions for the user based on their emotional score. The input is the predicted income / expense trend and emotional score, and the output is the asset management plan provided to the user. The AI ​​model performs a risk assessment, and if stress levels are high, safety-oriented products are suggested. This process ensures that suggestions are made in a way that reduces user anxiety. 【0410】 Step 5: 【0411】 The terminal receives asset formation proposals from the server, visualizes them, and presents them to the user. The input is the operational plan received from the server, and the output is visualized graphs and charts. The terminal displays information in a visually easy-to-understand format, allowing the user to see their financial situation at a glance. This specific operation enables the user to interact with the information. 【0412】 Step 6: 【0413】 The server continuously monitors the user's emotional state and progress toward achieving financial goals, updating the asset management plan as needed. Inputs are the latest income and expense data and emotional data, acquired periodically, while output is the updated investment plan. This enables flexible planning tailored to the user's current situation, ensuring optimal asset management at all times. 【0414】 (Application Example 2) 【0415】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0416】 In modern society, personal financial management and wealth building are crucial, but there are insufficient means to do so efficiently. Furthermore, there are no systems that can propose flexible wealth building strategies that take into account the user's emotional state. Therefore, there is a growing need for a system that supports comprehensive financial management and wealth building, including emotional well-being. 【0417】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0418】 In this invention, the server includes means for accumulating financial institution data and electronic payment data, means for analyzing income and expenses and displaying them on the device, and means for recognizing emotional states using an emotion analysis engine and dynamically adjusting asset formation suggestions based on those emotional states. This enables suggestions tailored to the user's emotions and economic situation, allowing the user to manage their assets optimally based on their own circumstances. 【0419】 "Financial institution data" refers to transaction history and account information managed by financial institutions such as banks and credit unions. 【0420】 "Electronic payment data" refers to transaction history information using electronic money, credit cards, etc. 【0421】 "Methods for analyzing income and expenses" refer to methods for understanding economic conditions by analyzing income and expenditure patterns based on financial and settlement data. 【0422】 An "emotion analysis engine" refers to technology that recognizes emotions from a user's facial expressions and voice, and quantifies or categorizes those emotions. 【0423】 "Means of dynamically adjusting asset building proposals" refers to methods of optimizing asset management and investment proposals in real time according to the user's emotional state and financial situation. 【0424】 An "asset management plan" refers to a strategy or plan created to efficiently achieve asset growth and preservation based on the user's goals. 【0425】 The system realizing this invention is a comprehensive system for supporting users' income and expense management and asset building, and includes multiple data processing modules. The server collects data from financial institutions and electronic payment systems via APIs. This generates integrated statistical data detailing the user's income and expenses. Database management systems and data analysis algorithms are used in this process. 【0426】 Next, the device activates an emotion analysis engine to recognize the user's emotional state through facial recognition and voice analysis. Specifically, "OpenFace" is used for facial recognition and "DeepSpeech" for voice analysis. This data is sent to a server and used to dynamically adjust the asset building suggestions. 【0427】 Specifically, the server updates asset management plans based on sentiment data and presents users with appropriate investment options. These suggestions are customized after risk assessment and displayed to the user in a visually easy-to-understand format on their device. Libraries such as "Matplotlib" are used for data visualization during this process. 【0428】 For example, suppose a user has set a goal of keeping their monthly expenses under 50,000 yen, and emotional data indicating a sense of security is detected while using the app. In this case, the server will suggest an investment fund that is somewhat riskier but offers good returns, and display it on the device. This process makes it possible to provide support that is always adapted to the user's latest situation. 【0429】 Examples of prompts for generative AI models: 【0430】 "We will provide users with financial and emotional data. Based on this, please generate asset building proposals that meet the following conditions: prioritize safety while considering high returns in the short term." 【0431】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0432】 Step 1: 【0433】 The user enters authentication information for their financial institution and electronic payment system. The server collects this information via an API and retrieves income and expenditure history from a financial database. Based on this data, statistical analysis is performed to generate detailed income and expenditure information for the user. The input consists of authentication information and historical transaction data, and the output is the analyzed income and expenditure data. 【0434】 Step 2: 【0435】 The device uses the smartphone's camera and microphone, with the user's permission, to recognize facial expressions and voice in real time. This data is sent to an emotion analysis engine, which quantifies and identifies the user's emotional state. The input is real-time facial and voice data, and the output is quantified emotion data. 【0436】 Step 3: 【0437】 The server integrates income / expense data and sentiment data to generate dynamic asset building recommendations. Here, a generative AI model is used to customize investment options based on risk assessment and the user's emotional state. The inputs are income / expense data and sentiment data, and the output is a customized investment recommendation. 【0438】 Step 4: 【0439】 The terminal visualizes and presents the generated asset building proposals to the user. Specifically, it displays information in graph and chart formats to aid user understanding. Libraries such as "Matplotlib" are used here. The input is investment proposal data, and the output is visualized information. 【0440】 Step 5: 【0441】 The user reviews the presented proposals and selects an investment action. The server updates the asset management plan based on the selected action and saves the data for the next evaluation. The input is the user's selected investment action, and the output is the updated asset management plan. This process is repeated regularly, allowing for support that is always adapted to the user's latest financial and emotional state. 【0442】 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0443】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0444】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0445】 [Third Embodiment] 【0446】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0447】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0448】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0449】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0450】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0451】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0452】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0453】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0454】 The specific processing program 56 is an example of a "program" relating to the technology of this 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. 【0455】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0456】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0457】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0458】 The household budget management system of the present invention is a tool to support users in managing their daily income and expenses and building assets. Specific embodiments of each component are described below. 【0459】 Data collection and integration 【0460】 The server uses APIs to retrieve financial data such as account information and transaction history from financial institutions authorized by the user. It also collects spending data from cashless payment apps and integrates this data to provide a foundation for understanding the user's overall income and expenses. 【0461】 Data analysis and visualization 【0462】 The server analyzes the collected data and calculates the total amount for each expenditure category (e.g., food, transportation, entertainment). This makes it easier to understand the user's income and expenditure patterns. 【0463】 By displaying the analysis results in graphs and charts on the device, users can easily understand them visually and check their spending trends. 【0464】 Future spending forecasts 【0465】 The server feeds historical income and expenditure data into a machine learning algorithm to predict future spending. This allows users to know in advance how much their expenses might increase in the following month. 【0466】 The device notifies the user of the prediction results and advises them on planned financial management. 【0467】 Proposal for asset building 【0468】 Users can select their areas of interest in the app's settings screen. Based on these selections, the server dynamically generates suggestions, such as how to save for travel or how to manage assets for education expenses. 【0469】 The system displays suggestions optimized for the device, allowing users to consider specific actions based on those suggestions. 【0470】 Asset management 【0471】 Users can set annual and monthly savings goals. For example, they can enter a specific goal such as "Save 500,000 yen in one year." 【0472】 The server creates savings plans and investment strategies based on those goals. This makes the specific steps to achieving them visible. 【0473】 The server periodically evaluates asset progress, updates the plan as needed, and notifies the user via the terminal. 【0474】 Specific example 【0475】 For example, suppose a user sets a goal of saving 100,000 yen per year for travel. The server analyzes the user's current spending habits, calculates the surplus from their monthly income and expenses, and creates a savings plan. It also displays points for saving money and investment options on the user's device, providing advice to help them achieve their goal. In this way, the system aims to intelligently guide users' financial behavior and support effective wealth building. 【0476】 The following describes the processing flow. 【0477】 Step 1: 【0478】 The user opens a dedicated application and enters authentication information to connect to banks and electronic payment services. Once authentication is complete, the server retrieves financial data and transaction history through the APIs of each service. 【0479】 Step 2: 【0480】 The server analyzes the acquired data and calculates total amounts for each major expenditure category (food, housing, transportation, etc.). It also analyzes income and expenditure trends over time to understand the overall financial landscape. 【0481】 Step 3: 【0482】 The server converts the analysis results into graphs and charts using visualization tools. This creates a revenue and expenditure report that users can intuitively understand. 【0483】 Step 4: 【0484】 The device displays a visualized income and expense report to the user. Through this information, the user can visually check their financial situation and identify problems and areas for improvement in their spending. 【0485】 Step 5: 【0486】 The server uses machine learning algorithms based on past revenue and expenditure data to predict future spending. This allows it to calculate projected spending for the next month and warn of potential overspending in advance. 【0487】 Step 6: 【0488】 The device provides users with advice that includes projected spending and future savings goals. This helps users to be more mindful of their spending. 【0489】 Step 7: 【0490】 Users set financial goals (e.g., saving for travel or education) within the app. This records the specific amount and deadline for achieving those goals. 【0491】 Step 8: 【0492】 The server proposes specific savings and investment methods based on the user's set asset goals. These proposals are realistic and achievable, taking into account the user's income and expenses. 【0493】 Step 9: 【0494】 The device presents the user with asset building proposals and implementation steps. Based on this, the user can make their own decisions and put the optimal plan into action. 【0495】 Step 10: 【0496】 The server periodically evaluates the user's savings progress and checks whether they are achieving their asset goals. It updates advice as needed and notifies the user of the latest information via their terminal. 【0497】 (Example 1) 【0498】 Next, we will describe Example 1. 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." 【0499】 In modern household financial management, users need to manually manage information from numerous financial institutions and payment methods to build wealth and forecast expenses. This process is complex and time-consuming, and integrating and analyzing individual data requires specialized knowledge. Furthermore, predicting future expenses and planning wealth building is difficult, and finding appropriate investment options is not easy. Therefore, there is a need to solve these challenges and achieve efficient and effective household financial management. 【0500】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0501】 In this invention, the server includes means for collecting information from financial institutions and electronic payment means via information acquisition means, means for integrating and cleansing the information, and means for analyzing expenditure costs. This enables users to centrally manage financial data from diverse sources and automatically perform income and expenditure analysis and forecasting, thereby supporting informed asset building and optimal investment choices. 【0502】 "Information acquisition methods" refer to processes and technologies for securely and efficiently collecting data from financial institutions and electronic payment systems. 【0503】 "Integration and cleansing" refers to the process of unifying information collected from multiple data sources, removing duplication and inconsistencies, and improving accuracy and consistency. 【0504】 "Analyzing spending costs" refers to the process of analyzing users' spending trends by category based on collected data, and deriving necessary information. 【0505】 "Dynamic suggestion generation" refers to a function that mechanically adjusts and provides optimal asset formation and investment strategies according to the user's interests and asset goals. 【0506】 "Regular evaluation and automatic updates" refers to a function that continuously monitors the progress toward set asset targets and updates the management plan based on the results. 【0507】 The household finance management system of this invention consists of a server, terminals, and users, and provides automated data management and asset building support. Details are provided below. 【0508】 The server first collects data from user-authorized financial institutions and electronic payment methods using data acquisition tools. This process utilizes technologies that securely and quickly acquire information using API access. The acquired data is integrated using the Python Pandas library to remove duplicates and inconsistencies. This data integration and cleansing process improves the reliability of the data. 【0509】 Next, the server analyzes the integrated data and categorizes user spending. This analysis extracts patterns from past data to understand spending trends. At this point, the data is organized in JSON format and prepared for visual display. 【0510】 The device visualizes the analyzed data using the JavaScript D3.js library. Users can view these graphs and charts on an intuitive dashboard screen. 【0511】 Furthermore, the server uses TensorFlow to train machine learning models based on past income and expenditure data to predict future spending. This technology helps users reduce uncertainty about future finances and promotes planned wealth building. 【0512】 Furthermore, users can receive personalized asset building suggestions using AI models generated on the server. Based on this, users can consider savings and investment options in specific areas. This information is generated using prompts such as: "Based on the following user's income and expenditure data, please create a savings plan to save 100,000 yen per year for travel." 【0513】 This system continuously supports users in achieving their financial goals by providing real-time evaluations and plan updates. For example, if a user sets a goal of "saving 100,000 yen per year for travel," the server analyzes their current spending habits and suggests a monthly savings target. Based on this information, the user can plan and execute specific actions. 【0514】 In this way, this system efficiently manages users' daily financial activities and supports them in planning for future asset building. 【0515】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0516】 Step 1: 【0517】 The server collects data from financial institutions and electronic payment systems through information acquisition mechanisms. It retrieves account information and transaction history using financial institution APIs authorized by the user. In this process, the input is raw data obtained from the API, and the output is financial data converted into a unified format. User data is securely retrieved using OAuth authentication. 【0518】 Step 2: 【0519】 The server integrates and cleanses the collected data. The input is the financial data obtained in step 1, and the output is the integrated dataset with duplicates and inconsistencies removed. The accuracy of the data is improved by cleaning the data using the Python Pandas library, removing duplicate entries, and performing categorization. 【0520】 Step 3: 【0521】 The server analyzes the integrated data to calculate total spending by category. The input is cleansed data, and the output is an analysis showing total spending for each category. This analysis helps users understand their own spending patterns by extracting historical data patterns and clarifying spending trends. 【0522】 Step 4: 【0523】 The device generates and displays visual graphs and charts based on the analysis results. The input is the analysis results sent from the server, and the output is the graphics displayed on the device's dashboard. By using the JavaScript D3.js library, intuitive and easy-to-understand visual feedback is provided. 【0524】 Step 5: 【0525】 The server trains a machine learning model based on historical income and expenditure data to predict future spending. The input is historical income and expenditure data, and the output is predicted future spending data. By building the model and training the data using TensorFlow, highly accurate predictions become possible. 【0526】 Step 6: 【0527】 The terminal receives predicted spending results from the server and notifies the user. The input is the predicted result, and the output is the notification message provided to the user. This allows the user to understand future spending trends in advance and use this information for asset management. 【0528】 Step 7: 【0529】 The server generates asset building suggestions using a generative AI model based on the user's interests. The input is the user's areas of interest and current financial data, and the output is specific suggestions for asset building. A prompt such as "Based on the following user income and expenditure data, please create a savings plan to save 100,000 yen per year for travel" is used, and personalized advice is provided to the user. 【0530】 (Application Example 1) 【0531】 Next, we will explain Application Example 1. In the following explanation, 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." 【0532】 Modern consumers engage in a vast number of financial transactions and electronic payments daily, making it difficult to grasp the overall picture of their income and expenses and manage their assets effectively. In particular, current systems do not adequately support accurately predicting future spending or providing real-time investment and savings advice. Therefore, there is a growing need for a household financial management system that enables users to efficiently build wealth and achieve their goals. 【0533】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0534】 In this invention, the server includes means for accumulating information on financial institutions and electronic payments, means for analyzing income and expenses based on the information on financial institutions and electronic payments, and means for providing individual savings methods and investment advice in real time at the time of settlement. This enables users to instantly understand their income and expense situation and effectively manage their assets while planning future spending. 【0535】 "Financial institution information" refers to data, including account information and transaction history, obtained from financial institutions such as banks and credit unions. 【0536】 "Electronic payment information" refers to information including transaction data and history related to cashless payments. 【0537】 "Means of analyzing income and expenses" refer to the technologies and methods used to analyze income and expenses based on acquired financial data and electronic payment data, and to calculate the results. 【0538】 "Means of visualizing and displaying on a terminal" refers to technologies and methods that display analyzed data in the form of graphs, charts, and other formats on the user's terminal to make it easier to understand. 【0539】 "A method of predicting future spending using machine learning" refers to a method of predicting future spending trends using machine learning algorithms based on past income and expenditure data. 【0540】 "Means for generating asset building proposals" refers to methods for providing optimal savings and investment plans based on the user's areas of interest. 【0541】 "Means of creating an asset management plan" refers to the techniques and methods for constructing specific procedures and plans to achieve the asset goals set by the user. 【0542】 "A method for providing individual savings strategies and investment advice in real time at the time of payment" refers to a method of providing users with specific advice on saving or investing at the moment a payment is made. 【0543】 The household budget management system based on this invention is implemented with a server, user terminals, and users. The server obtains information from financial institutions and electronic payment services via APIs and collects this data with the user's permission. The collected data is analyzed and visualized using software such as pandas and matplotlib. Users can view this visualized information through their terminals and check their income and expenditure trends. 【0544】 Furthermore, the server uses historical income and expenditure data to build machine learning algorithms and predict future spending. This prediction function allows users to understand how much they can expect to spend in the following month, enabling more planned financial management. 【0545】 Furthermore, based on the asset goals set by the user, the server generates and provides a specific asset management plan. Advice on saving and investing can be provided in real time according to the user's areas of interest, and this information is communicated to the user as in-app notifications using Swift or Kotlin. 【0546】 As a concrete example, consider a case where a user sets a goal of "saving 100,000 yen per year for travel." The server analyzes the user's current spending habits, calculates the surplus from monthly income and expenses, and develops a savings plan. In this way, the user can understand areas for saving and take concrete actions toward achieving their goal while considering investment options. By inputting this prompt into the generating AI model, it is possible to obtain advice such as, "Based on the savings goal set by the user, please suggest what asset management approach would be most effective going forward given the current spending trends." 【0547】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0548】 Step 1: 【0549】 The server first obtains user permission to retrieve information from APIs of financial institutions and electronic payment services. This operation uses user authentication information (input) to retrieve account information and transaction history (output) via REST APIs. The retrieved data is stored in a database and used for subsequent data analysis. 【0550】 Step 2: 【0551】 The server integrates the acquired financial data and electronic payment data and performs data cleaning. Here, the input data is raw transaction history, and duplicate data is removed and the data is converted into a standardized format (output). This results in a well-organized dataset that can be used for analysis. 【0552】 Step 3: 【0553】 The server uses pandas to analyze income and expenses using the prepared data. It categorizes income and expenses, calculating totals and trends. This analysis takes integrated financial data as input and generates income and expense data (output) for each category. This allows users to understand their own spending patterns. 【0554】 Step 4: 【0555】 The server uses matplotlib to convert the analysis results into graphs and charts, generating visualization data. At this stage, the analyzed income and expenditure data is the input, and the resulting graphed visual data (output) is provided to the user via the terminal. 【0556】 Step 5: 【0557】 The server uses a Scikit-learn machine learning model to predict future spending based on past income and expense data. It generates predicted spending data for the next month (output) from the income and expense data (input) and uses this to notify the user of their future financial situation. 【0558】 Step 6: 【0559】 Based on the asset goals set by the user, the server develops an asset management plan. The inputs are the user's goal settings and analyzed income and expenditure data, and the output provides specific savings and investment plans. This allows the user to obtain concrete means of wealth creation. 【0560】 Step 7: 【0561】 The server prompts the running AI model as needed, generating advice on saving and investing. The input consists of the user's current spending trends and goals, and the output is personalized advice. This advice is communicated to the terminal in real time to support the user's decision-making. 【0562】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0563】 This invention combines a household finance management system, designed to support users in managing their daily income and expenses and building wealth, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below. 【0564】 Data collection and analysis 【0565】 The server collects the user's financial institution information and electronic payment information through an API interface. This allows it to obtain detailed information about the user's income and expenses and generate integrated analytical data. 【0566】 Utilizing the Emotion Engine 【0567】 The device activates an emotion engine when the user uses an app, recognizing emotions by analyzing the user's facial expressions and tone of voice. As a result of this emotion recognition, emotional states such as stress and feelings of security are quantified. 【0568】 Emotion-based asset building proposals 【0569】 The server dynamically adjusts asset building recommendations based on recognized user emotion data. For example, if a user is feeling stressed, it will offer low-risk products to provide a sense of security. 【0570】 Data visualization and feedback 【0571】 The device visualizes and displays adjusted asset building suggestions based on analyzed income and expenditure information and user sentiment data. The presented information is represented as graphs and charts, which users can view and use to make decisions tailored to their own situation. 【0572】 Automatic updates for asset management 【0573】 The server periodically monitors the user's emotions and goal achievement status, and automatically updates the asset management plan as needed. This process ensures that the plan is always adapted to the user's current state. 【0574】 Specific example 【0575】 For example, suppose a user sets a goal of saving 200,000 yen per year, and emotional data indicating stress is detected while using the app. In this case, the server suggests low-risk fixed deposits and highly liquid savings options, displaying them on the device to provide the user with a sense of security. Based on the advice provided, the user can effectively manage their assets while mitigating stress. In this way, the system provides flexible asset management support that takes emotional states into account. 【0576】 The following describes the processing flow. 【0577】 Step 1: 【0578】 The user opens a dedicated application and enters their financial institution account information and electronic payment service authentication information. Once authentication is complete, the server retrieves income and expenditure data from each service via API. 【0579】 Step 2: 【0580】 The server analyzes the collected income and expenditure data, classifying daily, weekly, and monthly income and expenses, and calculating total expenses for specific categories as needed. This information is used to understand the user's financial situation. 【0581】 Step 3: 【0582】 The device activates an emotion engine and processes camera images and audio data while the user is using the app. It analyzes facial expressions and voice tone to quantify the user's emotional state (e.g., stress, relaxation). 【0583】 Step 4: 【0584】 The server integrates information analyzed from income and expenditure data with user sentiment data to appropriately adjust asset building suggestions. If emotions indicate stress, it prioritizes including low-risk products and safe options that offer short-term profits. 【0585】 Step 5: 【0586】 The device presents users with visualized spending analysis results and emotionally-adjusted asset building suggestions. The visualizations are presented in graph and chart formats, allowing users to easily make optimal decisions tailored to their current emotional state. 【0587】 Step 6: 【0588】 Users make economic decisions based on the information displayed. For example, if the emotional engine determines that the user is stressed, they can choose the low-risk option presented, allowing them to manage their assets with peace of mind. 【0589】 Step 7: 【0590】 The server periodically monitors the user's emotional data and financial status, and evaluates the progress toward set asset goals. Based on the evaluation results, it automatically updates the asset management plan if necessary and notifies the user at the next time they use the service. 【0591】 Through this process, the system provides flexible and effective asset management that takes emotional states into consideration. 【0592】 (Example 2) 【0593】 Next, we will describe Example 2. 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." 【0594】 In modern times, personal financial management has become increasingly complex, requiring effective management of income and expenses and planning for future wealth creation. Furthermore, it is crucial to respond quickly to investment risks and market fluctuations, and to support asset management that takes into account the user's emotions and psychological state. However, conventional systems struggle with flexible asset management that takes user emotions into account, and still have issues with forecasting accuracy and the timeliness of information provision. 【0595】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0596】 In this invention, the server includes means for aggregating information on financial institutions and electronic transactions, means for performing financial analysis based on the information on financial institutions and electronic transactions, and means for visualizing the analyzed financial information and presenting it on a display device. This makes it possible to provide timely and appropriate asset building advice while taking into account the user's emotional state. 【0597】 "Financial institution information" refers to data such as account balances, transaction history, and transfer information provided by financial institutions such as banks and credit unions. 【0598】 "Electronic transaction information" refers to transaction history and expenditure data related to credit card payments and online payment services. 【0599】 "Financial analysis" is a method of analyzing collected income and expenditure data to evaluate the balance of income and expenses and assets. 【0600】 "Visualization" refers to presenting data to users in an easy-to-understand manner using visual means such as graphs and charts. 【0601】 "Historical data" refers to information on past transaction history and income / expense status, which is used to make future predictions. 【0602】 "Machine learning" is a technology in which computer systems learn patterns and trends from data and make predictions and decisions based on new data. 【0603】 "Areas of interest" refers to specific fields or categories related to asset management and investment that a user is particularly interested in. 【0604】 "Asset allocation advice" refers to providing information that suggests appropriate investment methods and asset allocations based on the user's financial situation and market conditions. 【0605】 "Emotional data" refers to quantified information that indicates a user's psychological state, analyzed from their facial expressions, tone of voice, and other factors. 【0606】 An "asset management plan" is a long-term asset growth strategy formulated according to the user's financial goals and risk tolerance. 【0607】 This invention is a system that supports personal financial management and enables asset building that takes emotional states into consideration. The specific implementation method is described below. 【0608】 The server uses APIs from financial institutions to retrieve account information and transaction history in order to comprehensively manage users' financial information. For this data collection, it utilizes database management software to efficiently store the information and further analyzes income and expenditure information using data analysis tools. The server leverages programming languages ​​such as Python and R to implement machine learning models and predict future consumption patterns based on historical data. 【0609】 The device has a function to perform emotion analysis when the user uses the application. In this process, software equipped with an emotion engine is used to analyze the user's facial expressions and voice characteristics in real time, and the resulting emotion data is quantified. This makes it possible to dynamically adjust the asset building suggestions according to the user's psychological state. 【0610】 Users receive results from the server in a visualized format on their devices. This includes visual representations using graphs and charts, presenting information in a user-friendly format. Based on this, users can review appropriate asset management plans and aim to achieve their financial goals. 【0611】 As a concrete example, if a user sets a goal of "saving 200,000 yen per year," the server recommends low-risk investment products based on emotional data such as stress detected during app use. This recommendation is displayed on the device, and the user can use it as a basis for decision-making. Utilizing a generative AI model, an example of a prompt message could be, "I've been feeling stressed lately, so please suggest safe asset building options." Thus, flexible asset management support that takes emotional states into account is a key feature of this system. 【0612】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0613】 Step 1: 【0614】 The server retrieves account information and transaction history from the user's financial institution using an API. The input is financial data from the API, and the output is integrated financial information stored in the server's database. This allows the server to comprehensively manage the user's income and expenditure data. Specifically, this involves setting a regular data retrieval schedule and normalizing the data format. 【0615】 Step 2: 【0616】 The device activates its emotion engine when a user application is launched. Input is user facial and voice data acquired through the camera and microphone, and output is a quantified emotion score. The device uses facial recognition technology and voice analysis to detect the user's stress level and sense of security. This process allows for the quantification of the user's psychological state. 【0617】 Step 3: 【0618】 The server uses a machine learning model to predict future spending based on income and expenditure data from the database and sentiment scores from the terminal. The input is past income and expenditure data and current sentiment data, and the output is the predicted income and expenditure trend. The server applies a generative AI model to make income and expenditure predictions that reflect the emotional state. This makes it easier for users to understand their future financial situation. 【0619】 Step 4: 【0620】 The server generates optimal asset management suggestions for the user based on their emotional score. The input is the predicted income / expense trend and emotional score, and the output is the asset management plan provided to the user. The AI ​​model performs a risk assessment, and if stress levels are high, safety-oriented products are suggested. This process ensures that suggestions are made in a way that reduces user anxiety. 【0621】 Step 5: 【0622】 The terminal receives asset formation proposals from the server, visualizes them, and presents them to the user. The input is the operational plan received from the server, and the output is visualized graphs and charts. The terminal displays information in a visually easy-to-understand format, allowing the user to see their financial situation at a glance. This specific operation enables the user to interact with the information. 【0623】 Step 6: 【0624】 The server continuously monitors the user's emotional state and progress toward achieving financial goals, updating the asset management plan as needed. Inputs are the latest income and expense data and emotional data, acquired periodically, while output is the updated investment plan. This enables flexible planning tailored to the user's current situation, ensuring optimal asset management at all times. 【0625】 (Application Example 2) 【0626】 Next, we will explain application example 2. In the following explanation, 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." 【0627】 In modern society, personal financial management and wealth building are crucial, but there are insufficient means to do so efficiently. Furthermore, there are no systems that can propose flexible wealth building strategies that take into account the user's emotional state. Therefore, there is a growing need for a system that supports comprehensive financial management and wealth building, including emotional well-being. 【0628】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0629】 In this invention, the server includes means for accumulating financial institution data and electronic payment data, means for analyzing income and expenses and displaying them on the device, and means for recognizing emotional states using an emotion analysis engine and dynamically adjusting asset formation suggestions based on those emotional states. This enables suggestions tailored to the user's emotions and economic situation, allowing the user to manage their assets optimally based on their own circumstances. 【0630】 "Financial institution data" refers to transaction history and account information managed by financial institutions such as banks and credit unions. 【0631】 "Electronic payment data" refers to transaction history information using electronic money, credit cards, etc. 【0632】 "Methods for analyzing income and expenses" refer to methods for understanding economic conditions by analyzing income and expenditure patterns based on financial and settlement data. 【0633】 An "emotion analysis engine" refers to technology that recognizes emotions from a user's facial expressions and voice, and quantifies or categorizes those emotions. 【0634】 "Means of dynamically adjusting asset building proposals" refers to methods of optimizing asset management and investment proposals in real time according to the user's emotional state and financial situation. 【0635】 An "asset management plan" refers to a strategy or plan created to efficiently achieve asset growth and preservation based on the user's goals. 【0636】 The system realizing this invention is a comprehensive system for supporting users' income and expense management and asset building, and includes multiple data processing modules. The server collects data from financial institutions and electronic payment systems via APIs. This generates integrated statistical data detailing the user's income and expenses. Database management systems and data analysis algorithms are used in this process. 【0637】 Next, the device activates an emotion analysis engine to recognize the user's emotional state through facial recognition and voice analysis. Specifically, "OpenFace" is used for facial recognition and "DeepSpeech" for voice analysis. This data is sent to a server and used to dynamically adjust the asset building suggestions. 【0638】 Specifically, the server updates asset management plans based on sentiment data and presents users with appropriate investment options. These suggestions are customized after risk assessment and displayed to the user in a visually easy-to-understand format on their device. Libraries such as "Matplotlib" are used for data visualization during this process. 【0639】 For example, suppose a user has set a goal of keeping their monthly expenses under 50,000 yen, and emotional data indicating a sense of security is detected while using the app. In this case, the server will suggest an investment fund that is somewhat riskier but offers good returns, and display it on the device. This process makes it possible to provide support that is always adapted to the user's latest situation. 【0640】 Examples of prompts for generative AI models: 【0641】 "We will provide users with financial and emotional data. Based on this, please generate asset building proposals that meet the following conditions: prioritize safety while considering high returns in the short term." 【0642】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0643】 Step 1: 【0644】 The user enters authentication information for their financial institution and electronic payment system. The server collects this information via an API and retrieves income and expenditure history from a financial database. Based on this data, statistical analysis is performed to generate detailed income and expenditure information for the user. The input consists of authentication information and historical transaction data, and the output is the analyzed income and expenditure data. 【0645】 Step 2: 【0646】 The device uses the smartphone's camera and microphone, with the user's permission, to recognize facial expressions and voice in real time. This data is sent to an emotion analysis engine, which quantifies and identifies the user's emotional state. The input is real-time facial and voice data, and the output is quantified emotion data. 【0647】 Step 3: 【0648】 The server integrates income / expense data and sentiment data to generate dynamic asset building recommendations. Here, a generative AI model is used to customize investment options based on risk assessment and the user's emotional state. The inputs are income / expense data and sentiment data, and the output is a customized investment recommendation. 【0649】 Step 4: 【0650】 The terminal visualizes and presents the generated asset building proposals to the user. Specifically, it displays information in graph and chart formats to aid user understanding. Libraries such as "Matplotlib" are used here. The input is investment proposal data, and the output is visualized information. 【0651】 Step 5: 【0652】 The user reviews the presented proposals and selects an investment action. The server updates the asset management plan based on the selected action and saves the data for the next evaluation. The input is the user's selected investment action, and the output is the updated asset management plan. This process is repeated regularly, allowing for support that is always adapted to the user's latest financial and emotional state. 【0653】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0654】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0655】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0656】 [Fourth Embodiment] 【0657】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0658】 As shown in Figure 7, the 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. 【0659】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0660】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0661】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0662】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0663】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0664】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0665】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0666】 The specific processing program 56 is an example of a "program" relating to the technology of this 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. 【0667】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0668】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0669】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0670】 The household budget management system of the present invention is a tool to support users in managing their daily income and expenses and building assets. Specific embodiments of each component are described below. 【0671】 Data collection and integration 【0672】 The server uses APIs to retrieve financial data such as account information and transaction history from financial institutions authorized by the user. It also collects spending data from cashless payment apps and integrates this data to provide a foundation for understanding the user's overall income and expenses. 【0673】 Data analysis and visualization 【0674】 The server analyzes the collected data and calculates the total amount for each expenditure category (e.g., food, transportation, entertainment). This makes it easier to understand the user's income and expenditure patterns. 【0675】 By displaying the analysis results in graphs and charts on the device, users can easily understand them visually and check their spending trends. 【0676】 Future spending forecasts 【0677】 The server feeds historical income and expenditure data into a machine learning algorithm to predict future spending. This allows users to know in advance how much their expenses might increase in the following month. 【0678】 The device notifies the user of the prediction results and advises them on planned financial management. 【0679】 Proposal for asset building 【0680】 Users can select their areas of interest in the app's settings screen. Based on these selections, the server dynamically generates suggestions, such as how to save for travel or how to manage assets for education expenses. 【0681】 The system displays suggestions optimized for the device, allowing users to consider specific actions based on those suggestions. 【0682】 Asset management 【0683】 Users can set annual and monthly savings goals. For example, they can enter a specific goal such as "Save 500,000 yen in one year." 【0684】 The server creates savings plans and investment strategies based on those goals. This makes the specific steps to achieving them visible. 【0685】 The server periodically evaluates asset progress, updates the plan as needed, and notifies the user via the terminal. 【0686】 Specific example 【0687】 For example, suppose a user sets a goal of saving 100,000 yen per year for travel. The server analyzes the user's current spending habits, calculates the surplus from their monthly income and expenses, and creates a savings plan. It also displays points for saving money and investment options on the user's device, providing advice to help them achieve their goal. In this way, the system aims to intelligently guide users' financial behavior and support effective wealth building. 【0688】 The following describes the processing flow. 【0689】 Step 1: 【0690】 The user opens a dedicated application and enters authentication information to connect to banks and electronic payment services. Once authentication is complete, the server retrieves financial data and transaction history through the APIs of each service. 【0691】 Step 2: 【0692】 The server analyzes the acquired data and calculates total amounts for each major expenditure category (food, housing, transportation, etc.). It also analyzes income and expenditure trends over time to understand the overall financial landscape. 【0693】 Step 3: 【0694】 The server converts the analysis results into graphs and charts using visualization tools. This creates a revenue and expenditure report that users can intuitively understand. 【0695】 Step 4: 【0696】 The device displays a visualized income and expense report to the user. Through this information, the user can visually check their financial situation and identify problems and areas for improvement in their spending. 【0697】 Step 5: 【0698】 The server uses machine learning algorithms based on past revenue and expenditure data to predict future spending. This allows it to calculate projected spending for the next month and warn of potential overspending in advance. 【0699】 Step 6: 【0700】 The device provides users with advice that includes projected spending and future savings goals. This helps users to be more mindful of their spending. 【0701】 Step 7: 【0702】 Users set financial goals (e.g., saving for travel or education) within the app. This records the specific amount and deadline for achieving those goals. 【0703】 Step 8: 【0704】 The server proposes specific savings and investment methods based on the user's set asset goals. These proposals are realistic and achievable, taking into account the user's income and expenses. 【0705】 Step 9: 【0706】 The device presents the user with asset building proposals and implementation steps. Based on this, the user can make their own decisions and put the optimal plan into action. 【0707】 Step 10: 【0708】 The server periodically evaluates the user's savings progress and checks whether they are achieving their asset goals. It updates advice as needed and notifies the user of the latest information via their terminal. 【0709】 (Example 1) 【0710】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0711】 In modern household financial management, users need to manually manage information from numerous financial institutions and payment methods to build wealth and forecast expenses. This process is complex and time-consuming, and integrating and analyzing individual data requires specialized knowledge. Furthermore, predicting future expenses and planning wealth building is difficult, and finding appropriate investment options is not easy. Therefore, there is a need to solve these challenges and achieve efficient and effective household financial management. 【0712】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0713】 In this invention, the server includes means for collecting information from financial institutions and electronic payment means via information acquisition means, means for integrating and cleansing the information, and means for analyzing expenditure costs. This enables users to centrally manage financial data from diverse sources and automatically perform income and expenditure analysis and forecasting, thereby supporting informed asset building and optimal investment choices. 【0714】 "Information acquisition methods" refer to processes and technologies for securely and efficiently collecting data from financial institutions and electronic payment systems. 【0715】 "Integration and cleansing" refers to the process of unifying information collected from multiple data sources, removing duplication and inconsistencies, and improving accuracy and consistency. 【0716】 "Analyzing spending costs" refers to the process of analyzing users' spending trends by category based on collected data, and deriving necessary information. 【0717】 "Dynamic suggestion generation" refers to a function that mechanically adjusts and provides optimal asset formation and investment strategies according to the user's interests and asset goals. 【0718】 "Regular evaluation and automatic updates" refers to a function that continuously monitors the progress toward set asset targets and updates the management plan based on the results. 【0719】 The household finance management system of this invention consists of a server, terminals, and users, and provides automated data management and asset building support. Details are provided below. 【0720】 The server first collects data from user-authorized financial institutions and electronic payment methods using data acquisition tools. This process utilizes technologies that securely and quickly acquire information using API access. The acquired data is integrated using the Python Pandas library to remove duplicates and inconsistencies. This data integration and cleansing process improves the reliability of the data. 【0721】 Next, the server analyzes the integrated data and categorizes user spending. This analysis extracts patterns from past data to understand spending trends. At this point, the data is organized in JSON format and prepared for visual display. 【0722】 The device visualizes the analyzed data using the JavaScript D3.js library. Users can view these graphs and charts on an intuitive dashboard screen. 【0723】 Furthermore, the server uses TensorFlow to train machine learning models based on past income and expenditure data to predict future spending. This technology helps users reduce uncertainty about future finances and promotes planned wealth building. 【0724】 Furthermore, users can receive personalized asset building suggestions using AI models generated on the server. Based on this, users can consider savings and investment options in specific areas. This information is generated using prompts such as: "Based on the following user's income and expenditure data, please create a savings plan to save 100,000 yen per year for travel." 【0725】 This system continuously supports users in achieving their financial goals by providing real-time evaluations and plan updates. For example, if a user sets a goal of "saving 100,000 yen per year for travel," the server analyzes their current spending habits and suggests a monthly savings target. Based on this information, the user can plan and execute specific actions. 【0726】 In this way, this system efficiently manages users' daily financial activities and supports them in planning for future asset building. 【0727】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0728】 Step 1: 【0729】 The server collects data from financial institutions and electronic payment systems through information acquisition mechanisms. It retrieves account information and transaction history using financial institution APIs authorized by the user. In this process, the input is raw data obtained from the API, and the output is financial data converted into a unified format. User data is securely retrieved using OAuth authentication. 【0730】 Step 2: 【0731】 The server integrates and cleanses the collected data. The input is the financial data obtained in step 1, and the output is the integrated dataset with duplicates and inconsistencies removed. The accuracy of the data is improved by cleaning the data using the Python Pandas library, removing duplicate entries, and performing categorization. 【0732】 Step 3: 【0733】 The server analyzes the integrated data to calculate total spending by category. The input is cleansed data, and the output is an analysis showing total spending for each category. This analysis helps users understand their own spending patterns by extracting historical data patterns and clarifying spending trends. 【0734】 Step 4: 【0735】 The device generates and displays visual graphs and charts based on the analysis results. The input is the analysis results sent from the server, and the output is the graphics displayed on the device's dashboard. By using the JavaScript D3.js library, intuitive and easy-to-understand visual feedback is provided. 【0736】 Step 5: 【0737】 The server trains a machine learning model based on historical income and expenditure data to predict future spending. The input is historical income and expenditure data, and the output is predicted future spending data. By building the model and training the data using TensorFlow, highly accurate predictions become possible. 【0738】 Step 6: 【0739】 The terminal receives predicted spending results from the server and notifies the user. The input is the predicted result, and the output is the notification message provided to the user. This allows the user to understand future spending trends in advance and use this information for asset management. 【0740】 Step 7: 【0741】 The server generates asset building suggestions using a generative AI model based on the user's interests. The input is the user's areas of interest and current financial data, and the output is specific suggestions for asset building. A prompt such as "Based on the following user income and expenditure data, please create a savings plan to save 100,000 yen per year for travel" is used, and personalized advice is provided to the user. 【0742】 (Application Example 1) 【0743】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0744】 Modern consumers engage in a vast number of financial transactions and electronic payments daily, making it difficult to grasp the overall picture of their income and expenses and manage their assets effectively. In particular, current systems do not adequately support accurately predicting future spending or providing real-time investment and savings advice. Therefore, there is a growing need for a household financial management system that enables users to efficiently build wealth and achieve their goals. 【0745】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0746】 In this invention, the server includes means for accumulating information on financial institutions and electronic payments, means for analyzing income and expenses based on the information on financial institutions and electronic payments, and means for providing individual savings methods and investment advice in real time at the time of settlement. This enables users to instantly understand their income and expense situation and effectively manage their assets while planning future spending. 【0747】 "Financial institution information" refers to data, including account information and transaction history, obtained from financial institutions such as banks and credit unions. 【0748】 "Electronic payment information" refers to information including transaction data and history related to cashless payments. 【0749】 "Means of analyzing income and expenses" refer to the technologies and methods used to analyze income and expenses based on acquired financial data and electronic payment data, and to calculate the results. 【0750】 "Means of visualizing and displaying on a terminal" refers to technologies and methods that display analyzed data in the form of graphs, charts, and other formats on the user's terminal to make it easier to understand. 【0751】 "A method of predicting future spending using machine learning" refers to a method of predicting future spending trends using machine learning algorithms based on past income and expenditure data. 【0752】 "Means for generating asset building proposals" refers to methods for providing optimal savings and investment plans based on the user's areas of interest. 【0753】 "Means of creating an asset management plan" refers to the techniques and methods for constructing specific procedures and plans to achieve the asset goals set by the user. 【0754】 "A method for providing individual savings strategies and investment advice in real time at the time of payment" refers to a method of providing users with specific advice on saving or investing at the moment a payment is made. 【0755】 The household budget management system based on this invention is implemented with a server, user terminals, and users. The server obtains information from financial institutions and electronic payment services via APIs and collects this data with the user's permission. The collected data is analyzed and visualized using software such as pandas and matplotlib. Users can view this visualized information through their terminals and check their income and expenditure trends. 【0756】 Furthermore, the server uses historical income and expenditure data to build machine learning algorithms and predict future spending. This prediction function allows users to understand how much they can expect to spend in the following month, enabling more planned financial management. 【0757】 Furthermore, based on the asset goals set by the user, the server generates and provides a specific asset management plan. Advice on saving and investing can be provided in real time according to the user's areas of interest, and this information is communicated to the user as in-app notifications using Swift or Kotlin. 【0758】 As a concrete example, consider a case where a user sets a goal of "saving 100,000 yen per year for travel." The server analyzes the user's current spending habits, calculates the surplus from monthly income and expenses, and develops a savings plan. In this way, the user can understand areas for saving and take concrete actions toward achieving their goal while considering investment options. By inputting this prompt into the generating AI model, it is possible to obtain advice such as, "Based on the savings goal set by the user, please suggest what asset management approach would be most effective going forward given the current spending trends." 【0759】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0760】 Step 1: 【0761】 The server first obtains user permission to retrieve information from APIs of financial institutions and electronic payment services. This operation uses user authentication information (input) to retrieve account information and transaction history (output) via REST APIs. The retrieved data is stored in a database and used for subsequent data analysis. 【0762】 Step 2: 【0763】 The server integrates the acquired financial data and electronic payment data and performs data cleaning. Here, the input data is raw transaction history, and duplicate data is removed and the data is converted into a standardized format (output). This results in a well-organized dataset that can be used for analysis. 【0764】 Step 3: 【0765】 The server uses pandas to analyze income and expenses using the prepared data. It categorizes income and expenses, calculating totals and trends. This analysis takes integrated financial data as input and generates income and expense data (output) for each category. This allows users to understand their own spending patterns. 【0766】 Step 4: 【0767】 The server uses matplotlib to convert the analysis results into graphs and charts, generating visualization data. At this stage, the analyzed income and expenditure data is the input, and the resulting graphed visual data (output) is provided to the user via the terminal. 【0768】 Step 5: 【0769】 The server uses a Scikit-learn machine learning model to predict future spending based on past income and expense data. It generates predicted spending data for the next month (output) from the income and expense data (input) and uses this to notify the user of their future financial situation. 【0770】 Step 6: 【0771】 Based on the asset goals set by the user, the server develops an asset management plan. The inputs are the user's goal settings and analyzed income and expenditure data, and the output provides specific savings and investment plans. This allows the user to obtain concrete means of wealth creation. 【0772】 Step 7: 【0773】 The server prompts the running AI model as needed, generating advice on saving and investing. The input consists of the user's current spending trends and goals, and the output is personalized advice. This advice is communicated to the terminal in real time to support the user's decision-making. 【0774】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0775】 This invention combines a household finance management system, designed to support users in managing their daily income and expenses and building wealth, with an emotion engine that recognizes the user's emotions. An embodiment of this system will be described in detail below. 【0776】 Data collection and analysis 【0777】 The server collects the user's financial institution information and electronic payment information through an API interface. This allows it to obtain detailed information about the user's income and expenses and generate integrated analytical data. 【0778】 Utilizing the Emotion Engine 【0779】 The device activates an emotion engine when the user uses an app, recognizing emotions by analyzing the user's facial expressions and tone of voice. As a result of this emotion recognition, emotional states such as stress and feelings of security are quantified. 【0780】 Emotion-based asset building proposals 【0781】 The server dynamically adjusts asset building recommendations based on recognized user emotion data. For example, if a user is feeling stressed, it will offer low-risk products to provide a sense of security. 【0782】 Data visualization and feedback 【0783】 The device visualizes and displays adjusted asset building suggestions based on analyzed income and expenditure information and user sentiment data. The presented information is represented as graphs and charts, which users can view and use to make decisions tailored to their own situation. 【0784】 Automatic updates for asset management 【0785】 The server periodically monitors the user's emotions and goal achievement status, and automatically updates the asset management plan as needed. This process ensures that the plan is always adapted to the user's current state. 【0786】 Specific example 【0787】 For example, suppose a user sets a goal of saving 200,000 yen per year, and emotional data indicating stress is detected while using the app. In this case, the server suggests low-risk fixed deposits and highly liquid savings options, displaying them on the device to provide the user with a sense of security. Based on the advice provided, the user can effectively manage their assets while mitigating stress. In this way, the system provides flexible asset management support that takes emotional states into account. 【0788】 The following describes the processing flow. 【0789】 Step 1: 【0790】 The user opens a dedicated application and enters their financial institution account information and electronic payment service authentication information. Once authentication is complete, the server retrieves income and expenditure data from each service via API. 【0791】 Step 2: 【0792】 The server analyzes the collected income and expenditure data, classifying daily, weekly, and monthly income and expenses, and calculating total expenses for specific categories as needed. This information is used to understand the user's financial situation. 【0793】 Step 3: 【0794】 The device activates an emotion engine and processes camera images and audio data while the user is using the app. It analyzes facial expressions and voice tone to quantify the user's emotional state (e.g., stress, relaxation). 【0795】 Step 4: 【0796】 The server integrates information analyzed from income and expenditure data with user sentiment data to appropriately adjust asset building suggestions. If emotions indicate stress, it prioritizes including low-risk products and safe options that offer short-term profits. 【0797】 Step 5: 【0798】 The device presents users with visualized spending analysis results and emotionally-adjusted asset building suggestions. The visualizations are presented in graph and chart formats, allowing users to easily make optimal decisions tailored to their current emotional state. 【0799】 Step 6: 【0800】 Users make economic decisions based on the information displayed. For example, if the emotional engine determines that the user is stressed, they can choose the low-risk option presented, allowing them to manage their assets with peace of mind. 【0801】 Step 7: 【0802】 The server periodically monitors the user's emotional data and financial status, and evaluates the progress toward set asset goals. Based on the evaluation results, it automatically updates the asset management plan if necessary and notifies the user at the next time they use the service. 【0803】 Through this process, the system provides flexible and effective asset management that takes emotional states into consideration. 【0804】 (Example 2) 【0805】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0806】 In modern times, personal financial management has become increasingly complex, requiring effective management of income and expenses and planning for future wealth creation. Furthermore, it is crucial to respond quickly to investment risks and market fluctuations, and to support asset management that takes into account the user's emotions and psychological state. However, conventional systems struggle with flexible asset management that takes user emotions into account, and still have issues with forecasting accuracy and the timeliness of information provision. 【0807】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0808】 In this invention, the server includes means for aggregating information on financial institutions and electronic transactions, means for performing financial analysis based on the information on financial institutions and electronic transactions, and means for visualizing the analyzed financial information and presenting it on a display device. This makes it possible to provide timely and appropriate asset building advice while taking into account the user's emotional state. 【0809】 "Financial institution information" refers to data such as account balances, transaction history, and transfer information provided by financial institutions such as banks and credit unions. 【0810】 "Electronic transaction information" refers to transaction history and expenditure data related to credit card payments and online payment services. 【0811】 "Financial analysis" is a method of analyzing collected income and expenditure data to evaluate the balance of income and expenses and assets. 【0812】 "Visualization" refers to presenting data to users in an easy-to-understand manner using visual means such as graphs and charts. 【0813】 "Historical data" refers to information on past transaction history and income / expense status, which is used to make future predictions. 【0814】 "Machine learning" is a technology in which computer systems learn patterns and trends from data and make predictions and decisions based on new data. 【0815】 "Areas of interest" refers to specific fields or categories related to asset management and investment that a user is particularly interested in. 【0816】 "Asset allocation advice" refers to providing information that suggests appropriate investment methods and asset allocations based on the user's financial situation and market conditions. 【0817】 "Emotional data" refers to quantified information that indicates a user's psychological state, analyzed from their facial expressions, tone of voice, and other factors. 【0818】 An "asset management plan" is a long-term asset growth strategy formulated according to the user's financial goals and risk tolerance. 【0819】 This invention is a system that supports personal financial management and enables asset building that takes emotional states into consideration. The specific implementation method is described below. 【0820】 The server uses APIs from financial institutions to retrieve account information and transaction history in order to comprehensively manage users' financial information. For this data collection, it utilizes database management software to efficiently store the information and further analyzes income and expenditure information using data analysis tools. The server leverages programming languages ​​such as Python and R to implement machine learning models and predict future consumption patterns based on historical data. 【0821】 The device has a function to perform emotion analysis when the user uses the application. In this process, software equipped with an emotion engine is used to analyze the user's facial expressions and voice characteristics in real time, and the resulting emotion data is quantified. This makes it possible to dynamically adjust the asset building suggestions according to the user's psychological state. 【0822】 Users receive results from the server in a visualized format on their devices. This includes visual representations using graphs and charts, presenting information in a user-friendly format. Based on this, users can review appropriate asset management plans and aim to achieve their financial goals. 【0823】 As a concrete example, if a user sets a goal of "saving 200,000 yen per year," the server recommends low-risk investment products based on emotional data such as stress detected during app use. This recommendation is displayed on the device, and the user can use it as a basis for decision-making. Utilizing a generative AI model, an example of a prompt message could be, "I've been feeling stressed lately, so please suggest safe asset building options." Thus, flexible asset management support that takes emotional states into account is a key feature of this system. 【0824】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0825】 Step 1: 【0826】 The server retrieves account information and transaction history from the user's financial institution using an API. The input is financial data from the API, and the output is integrated financial information stored in the server's database. This allows the server to comprehensively manage the user's income and expenditure data. Specifically, this involves setting a regular data retrieval schedule and normalizing the data format. 【0827】 Step 2: 【0828】 The device activates its emotion engine when a user application is launched. Input is user facial and voice data acquired through the camera and microphone, and output is a quantified emotion score. The device uses facial recognition technology and voice analysis to detect the user's stress level and sense of security. This process allows for the quantification of the user's psychological state. 【0829】 Step 3: 【0830】 The server uses a machine learning model to predict future spending based on income and expenditure data from the database and sentiment scores from the terminal. The input is past income and expenditure data and current sentiment data, and the output is the predicted income and expenditure trend. The server applies a generative AI model to make income and expenditure predictions that reflect the emotional state. This makes it easier for users to understand their future financial situation. 【0831】 Step 4: 【0832】 The server generates optimal asset management suggestions for the user based on their emotional score. The input is the predicted income / expense trend and emotional score, and the output is the asset management plan provided to the user. The AI ​​model performs a risk assessment, and if stress levels are high, safety-oriented products are suggested. This process ensures that suggestions are made in a way that reduces user anxiety. 【0833】 Step 5: 【0834】 The terminal receives asset formation proposals from the server, visualizes them, and presents them to the user. The input is the operational plan received from the server, and the output is visualized graphs and charts. The terminal displays information in a visually easy-to-understand format, allowing the user to see their financial situation at a glance. This specific operation enables the user to interact with the information. 【0835】 Step 6: 【0836】 The server continuously monitors the user's emotional state and progress toward achieving financial goals, updating the asset management plan as needed. Inputs are the latest income and expense data and emotional data, acquired periodically, while output is the updated investment plan. This enables flexible planning tailored to the user's current situation, ensuring optimal asset management at all times. 【0837】 (Application Example 2) 【0838】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0839】 In modern society, personal financial management and wealth building are crucial, but there are insufficient means to do so efficiently. Furthermore, there are no systems that can propose flexible wealth building strategies that take into account the user's emotional state. Therefore, there is a growing need for a system that supports comprehensive financial management and wealth building, including emotional well-being. 【0840】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0841】 In this invention, the server includes means for accumulating financial institution data and electronic payment data, means for analyzing income and expenses and displaying them on the device, and means for recognizing emotional states using an emotion analysis engine and dynamically adjusting asset formation suggestions based on those emotional states. This enables suggestions tailored to the user's emotions and economic situation, allowing the user to manage their assets optimally based on their own circumstances. 【0842】 "Financial institution data" refers to transaction history and account information managed by financial institutions such as banks and credit unions. 【0843】 "Electronic payment data" refers to transaction history information using electronic money, credit cards, etc. 【0844】 "Methods for analyzing income and expenses" refer to methods for understanding economic conditions by analyzing income and expenditure patterns based on financial and settlement data. 【0845】 An "emotion analysis engine" refers to technology that recognizes emotions from a user's facial expressions and voice, and quantifies or categorizes those emotions. 【0846】 "Means of dynamically adjusting asset building proposals" refers to methods of optimizing asset management and investment proposals in real time according to the user's emotional state and financial situation. 【0847】 An "asset management plan" refers to a strategy or plan created to efficiently achieve asset growth and preservation based on the user's goals. 【0848】 The system realizing this invention is a comprehensive system for supporting users' income and expense management and asset building, and includes multiple data processing modules. The server collects data from financial institutions and electronic payment systems via APIs. This generates integrated statistical data detailing the user's income and expenses. Database management systems and data analysis algorithms are used in this process. 【0849】 Next, the device activates an emotion analysis engine to recognize the user's emotional state through facial recognition and voice analysis. Specifically, "OpenFace" is used for facial recognition and "DeepSpeech" for voice analysis. This data is sent to a server and used to dynamically adjust the asset building suggestions. 【0850】 Specifically, the server updates asset management plans based on sentiment data and presents users with appropriate investment options. These suggestions are customized after risk assessment and displayed to the user in a visually easy-to-understand format on their device. Libraries such as "Matplotlib" are used for data visualization during this process. 【0851】 For example, suppose a user has set a goal of keeping their monthly expenses under 50,000 yen, and emotional data indicating a sense of security is detected while using the app. In this case, the server will suggest an investment fund that is somewhat riskier but offers good returns, and display it on the device. This process makes it possible to provide support that is always adapted to the user's latest situation. 【0852】 Examples of prompts for generative AI models: 【0853】 "We will provide users with financial and emotional data. Based on this, please generate asset building proposals that meet the following conditions: prioritize safety while considering high returns in the short term." 【0854】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0855】 Step 1: 【0856】 The user enters authentication information for their financial institution and electronic payment system. The server collects this information via an API and retrieves income and expenditure history from a financial database. Based on this data, statistical analysis is performed to generate detailed income and expenditure information for the user. The input consists of authentication information and historical transaction data, and the output is the analyzed income and expenditure data. 【0857】 Step 2: 【0858】 The device uses the smartphone's camera and microphone, with the user's permission, to recognize facial expressions and voice in real time. This data is sent to an emotion analysis engine, which quantifies and identifies the user's emotional state. The input is real-time facial and voice data, and the output is quantified emotion data. 【0859】 Step 3: 【0860】 The server integrates income / expense data and sentiment data to generate dynamic asset building recommendations. Here, a generative AI model is used to customize investment options based on risk assessment and the user's emotional state. The inputs are income / expense data and sentiment data, and the output is a customized investment recommendation. 【0861】 Step 4: 【0862】 The terminal visualizes and presents the generated asset building proposals to the user. Specifically, it displays information in graph and chart formats to aid user understanding. Libraries such as "Matplotlib" are used here. The input is investment proposal data, and the output is visualized information. 【0863】 Step 5: 【0864】 The user reviews the presented proposals and selects an investment action. The server updates the asset management plan based on the selected action and saves the data for the next evaluation. The input is the user's selected investment action, and the output is the updated asset management plan. This process is repeated regularly, allowing for support that is always adapted to the user's latest financial and emotional state. 【0865】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0866】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0867】 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0868】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0869】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0870】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0871】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0872】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0873】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0874】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0875】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0876】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0877】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0878】 Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12. 【0879】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0880】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0881】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0882】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0883】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0884】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0885】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0886】 The following is further disclosed regarding the embodiments described above. 【0887】 (Claim 1) 【0888】 A means of aggregating information from financial institutions and electronic payment information, 【0889】 A means for analyzing income and expenses based on the information of the aforementioned financial institution and the information of the aforementioned electronic payment, 【0890】 A means for visualizing the analyzed income and expenses and displaying them on a terminal, 【0891】 A method for predicting future expenditures using machine learning based on past income and expenditure data, 【0892】 A means for generating relevant asset formation proposals based on the user's selection of areas of interest, 【0893】 A means of creating a specific asset management plan based on the asset goals set by the user, 【0894】 A means of evaluating multiple investment options, including their risks, and providing information to help users make the best choice. 【0895】 A system that includes this. 【0896】 (Claim 2) 【0897】 The system according to claim 1, further comprising means for dynamically changing the content of asset formation proposals according to the user's selection of areas of interest. 【0898】 (Claim 3) 【0899】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward asset goals and automatically updating the asset management plan based on the evaluation results. 【0900】 "Example 1" 【0901】 (Claim 1) 【0902】 Means for collecting information from financial institutions and electronic payment methods via information acquisition means, 【0903】 A means for integrating the collected information and performing cleansing, 【0904】 A means for analyzing expenditures based on the aforementioned integrated information and calculating total amounts by category, 【0905】 A display means for visually displaying the analysis results on a terminal, 【0906】 A method for predicting expenditures using machine learning techniques based on past income and expenditure data, 【0907】 A means of dynamically proposing asset formation based on user interests, 【0908】 A means of creating an asset management plan based on the asset goals set by the user, 【0909】 A means of providing users with information on multiple investment options while assessing the risks, 【0910】 To support users in achieving their financial goals, a means of regularly evaluating their progress and automatically updating their plans is provided. 【0911】 A system that includes this. 【0912】 (Claim 2) 【0913】 The system according to claim 1, further comprising means for dynamically adapting asset formation proposals according to the user's interests. 【0914】 (Claim 3) 【0915】 The system according to claim 1, further comprising means for dynamically adjusting the asset management plan based on achievement evaluations and notifying the user. 【0916】 "Application Example 1" 【0917】 (Claim 1) 【0918】 A means of aggregating information from financial institutions and electronic payment information, 【0919】 A means for analyzing income and expenses based on the information of the aforementioned financial institution and the information of the aforementioned electronic payment, 【0920】 A means for visualizing the analyzed income and expenses and displaying them on a terminal, 【0921】 A method for predicting future expenditures using machine learning based on past income and expenditure data, 【0922】 A means for generating relevant asset formation proposals based on the user's selection of areas of interest, 【0923】 A means of creating a specific asset management plan based on the asset goals set by the user, 【0924】 A method of providing individual savings strategies and investment advice in real time at the time of settlement, 【0925】 A system that includes this. 【0926】 (Claim 2) 【0927】 The system according to claim 1, further comprising means for dynamically changing the content of asset formation proposals according to the user's selection of areas of interest. 【0928】 (Claim 3) 【0929】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward asset goals and automatically updating the asset management plan based on the evaluation results. 【0930】 "Example 2 of combining an emotion engine" 【0931】 (Claim 1) 【0932】 A means of aggregating information from financial institutions and electronic transaction information, 【0933】 A means for conducting financial analysis based on the information of the aforementioned financial institution and the information of the aforementioned electronic transactions, 【0934】 Means for visualizing the analyzed financial information and presenting it on a display device, 【0935】 A method for predicting future consumption using machine learning based on historical data, 【0936】 A means for generating relevant asset allocation advice based on the user's selection of areas of interest, 【0937】 A means of creating a specific asset management plan based on the user's set financial goals, 【0938】 A means of evaluating multiple investment options, including their risks, and providing information to help users make the best choice. 【0939】 A means for collecting and analyzing user emotional data and adjusting investment proposals based on that emotional state, 【0940】 A system that includes this. 【0941】 (Claim 2) 【0942】 The system according to claim 1, further comprising means for dynamically changing the content of asset allocation advice according to the user's selection of areas of interest. 【0943】 (Claim 3) 【0944】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward their financial goals and their emotional state, and for automatically updating their asset management plan based on the evaluation results. 【0945】 "Application example 2 when combining with an emotional engine" 【0946】 (Claim 1) 【0947】 A means of aggregating financial institution data and electronic payment data, 【0948】 A means for analyzing income and expenses based on the data of the aforementioned financial institution and the data of the aforementioned electronic payment, 【0949】 Means for visualizing the analyzed balance and displaying it on the device, 【0950】 A method for predicting future spending using machine learning based on past economic data, 【0951】 A means for generating relevant asset formation proposals based on the user's selection of areas of interest, 【0952】 A means of creating a specific asset management plan based on the asset goals set by the user, 【0953】 A means of evaluating multiple investment options, including their risks, and providing information to help users make the best choice. 【0954】 A means of recognizing the user's emotional state using an emotion analysis engine and dynamically adjusting asset building proposals based on that emotional state, 【0955】 A means to visualize and display suggestions based on analyzed emotional data and financial information, enabling users to make decisions tailored to their own situation, 【0956】 A system that includes this. 【0957】 (Claim 2) 【0958】 The system according to claim 1, further comprising means for dynamically changing the content of asset formation proposals according to the emotional state of the user. 【0959】 (Claim 3) 【0960】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward asset goals and automatically updating the asset management plan based on the evaluation results and perceived emotional state. [Explanation of symbols] 【0961】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of aggregating information from financial institutions and electronic payment information, A means for analyzing income and expenses based on the information of the aforementioned financial institution and the information of the aforementioned electronic payment, A means for visualizing the analyzed income and expenses and displaying them on a terminal, A method for predicting future expenditures using machine learning based on past income and expenditure data, A means for generating relevant asset formation proposals based on the user's selection of areas of interest, A means of creating a specific asset management plan based on the asset goals set by the user, A means of evaluating multiple investment options, including their risks, and providing information to help users make the best choice. A system that includes this. [Claim 2] The system according to claim 1, further comprising means for dynamically changing the content of asset formation proposals according to the user's selection of areas of interest. [Claim 3] The system according to claim 1, further comprising means for periodically evaluating the user's progress toward asset goals and automatically updating the asset management plan based on the evaluation results.

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