system

Abstract

We provide the system. [Solution] A means of collecting users' financial transaction data and securely storing it in a database, A means for analyzing the aforementioned financial transaction data and understanding the user's income and expenditure patterns, A means of analyzing a user's insurance policy and providing the optimal insurance plan, A means of constructing an investment portfolio based on the user's risk tolerance and market data, A means for automatically generating an optimal savings plan based on the user's income and expenses, A means to monitor users' daily spending in real time, detect anomalies, and send alerts, A system that includes this.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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】 For users with limited income or low financial literacy, daily income and expenditure management, selection of appropriate insurance contracts, and construction of an investment portfolio considering risk diversification are complex, and there is a problem that it is difficult to achieve optimal financial management leading to economic stability. Also, wasteful expenditures and inappropriate insurance contracts pose a problem in that they hinder asset formation and savings plans. 【Means for Solving the Problems】 【0005】 This invention provides a system for collecting and securely managing users' financial transaction data. Using the collected data, it analyzes income and expenditure patterns and proposes optimal insurance contracts and investment portfolios to the user. Furthermore, by automatically generating savings plans and monitoring daily expenses in real time, it can improve the user's financial situation and reduce unnecessary spending. This makes it possible for even users with low financial literacy to easily achieve financial stability. 【0006】 "Financial transaction data" refers to data related to a user's income, expenses, and transactions with financial institutions. 【0007】 A "database" refers to an information structure used to organize, securely store, and manage collected information. 【0008】 "Insurance contract" refers to the terms of the life insurance, non-life insurance, and other insurance products that a user has purchased. 【0009】 "Risk tolerance" refers to an indicator that assesses the range of risk a user is willing to accept in their investments. 【0010】 An "investment portfolio" refers to the balance of an investment strategy that is composed of a combination of multiple asset classes. 【0011】 A "savings plan" refers to a savings goal set based on the user's income and expenses, and a specific plan to achieve that goal. 【0012】 "Real-time monitoring" refers to the process of instantly tracking and evaluating user spending. 【0013】 An "alert" refers to a warning from a system that notifies the user of unusual spending or budget overruns. [Brief explanation of the drawing] 【0014】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It 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 the data processing system in Application Example 2 when an emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0015】 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. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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. 【0018】 In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a tagged storage 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. 【0020】 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). 【0021】 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." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 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. 【0025】 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). 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 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". 【0035】 This invention aims to build a system that efficiently manages financial data and provides users with optimal financial advice. This system is realized through the interaction of servers, terminals, and users. 【0036】 First, the server collects the user's financial transaction data. This is done automatically through bank accounts, credit cards, and expense management apps. This data is securely stored in a database using encryption technology. 【0037】 Next, the server analyzes the collected data. It calculates income, expenses, and savings rates, visualizing the user's financial activity. It also analyzes the user's risk tolerance and past financial behavior to suggest appropriate insurance policies and investment portfolios. 【0038】 In this process, the server scrutinizes insurance contract details, detecting unnecessary overlaps and unnecessarily covered policies to create an optimized insurance plan. When creating investment portfolios, it combines market trends and risk analysis to calculate a balanced asset allocation. 【0039】 The device displays information on the user's device and serves as a real-time household budget monitor. When it detects spending exceeding the budget or fraudulent transactions, it immediately sends an alert to the user and suggests specific measures to reduce spending. 【0040】 As a concrete example, suppose a user wants to increase their savings with some leeway at the end of the month. The server reviews his monthly spending data and identifies that the combined cost of food and transportation is causing him to exceed his budget. Therefore, it suggests to the user via the terminal that he can save 5,000 yen per month by using public transportation during his commute. It also determines that his insurance needs to be reviewed, so the server optimizes his coverage and presents him with a plan that meets his needs while keeping costs down. 【0041】 This system allows users to centrally manage their financial information and achieve effective asset management and life planning. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The server collects financial transaction data from multiple financial institutions and user accounts via APIs. This includes users' bank account history, credit card transactions, and recurring income information. The collected data is encrypted and securely stored in a database. 【0045】 Step 2: 【0046】 The server analyzes the collected data in the database. It calculates totals for each user spending category and identifies patterns. It also calculates the user's income, expenses, and monthly balance to assess their financial situation. 【0047】 Step 3: 【0048】 The server analyzes and scrutinizes the user's insurance policy. If it detects unnecessary or duplicate coverage, it generates suggestions for optimization and sends them to the user's device. This allows the user to review their policy and reduce their costs. 【0049】 Step 4: 【0050】 The server analyzes market data, taking into account the user's risk tolerance and preferences. Based on this, it creates an investment portfolio tailored to the user and adjusts the asset allocation. The portfolio information is sent to the terminal and shared with the user. 【0051】 Step 5: 【0052】 The server calculates the amount a user can save based on their income and expenses, and automatically generates savings goals and plans. These plans are divided into short-term, medium-term, and long-term goals, and the user is notified periodically. 【0053】 Step 6: 【0054】 The device monitors the user's spending in real time. If unusual spending occurs or the budget is exceeded, an alert is immediately issued, notifying the user of unnecessary spending and opportunities for savings. This information is sent to the user's device as a push notification. 【0055】 (Example 1) 【0056】 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." 【0057】 In modern financial management, it is a challenging task for individuals to efficiently and comprehensively understand their own financial situation and select optimal asset management and insurance policies. In particular, there is a need for secure collection and analysis of financial transaction data, reduction of unnecessary insurance policies, optimization of investments, and flexible response to savings goals. However, there are insufficient systems to effectively achieve these goals. 【0058】 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. 【0059】 In this invention, the server includes means for collecting and securely storing the user's financial information, means for analyzing the user's income and expenditure trends, and means for evaluating and optimizing the user's insurance contracts. This makes it possible to provide asset management and financial advice tailored to each individual user. 【0060】 "User financial information" refers to personal financial information, including data on banking transactions, credit card usage, and spending management. 【0061】 A "computing device" is a hardware device such as a computer or server that has the function of processing and analyzing data. 【0062】 "Data collection methods" refer to the processes and technologies used to acquire and integrate data from multiple sources. 【0063】 "Analysis means" refers to algorithms and methods for processing collected data and converting it into meaningful information. 【0064】 "Risk tolerance" is a criterion that indicates the extent to which an individual can accept financial risk. 【0065】 "Investment allocation" is a strategy that determines how users invest in different types of assets. 【0066】 A "generative AI model" is an artificial intelligence technology that learns from past data and makes predictions and suggestions for new data. 【0067】 A "warning" is a notification that the system sends to the user when it detects an anomaly. 【0068】 "Asset management" refers to activities that include planning and execution to properly manage and increase a user's assets. 【0069】 "Financial advice" refers to the activity of providing guidance based on a user's financial situation. 【0070】 This invention constructs a system that provides centralized management of financial information and optimal financial advice through interaction between servers, terminals, and users. Its configuration is described below. 【0071】 First, the server collects the user's financial information. The server efficiently gathers the user's financial transaction data using bank APIs, credit card APIs, and expense management app APIs. This data is securely stored in a dedicated database on the computing device using AES encryption technology, ensuring a high level of security. 【0072】 Next, the server analyzes the collected financial information. This analysis process calculates the user's income, expenses, savings rate, and other factors. Furthermore, using a generative AI model, it analyzes the user's risk tolerance and proposes insurance policies and investment allocations tailored to their individual financial situation. This enables more personalized advice. 【0073】 The device displays this information on the user's device. The device monitors household finances in real time and immediately sends a warning to the user if it detects unusual transactions or spending that exceeds the budget. It also notifies the user of specific spending reduction measures suggested by a generated AI model and presents actionable improvement plans. 【0074】 As a concrete example, consider a user who wants to review their monthly expenses. The server analyzes past spending data and identifies that the user is spending a lot on eating out. The terminal then presents the user with specific saving suggestions, such as, "You can save 10,000 yen per month by cooking at home twice a week to reduce your food expenses." Furthermore, examples of prompts could include inputs like, "Please suggest an optimization of my insurance policy and investment allocation." 【0075】 In this way, users can achieve asset management and life planning tailored to their individual financial circumstances. This system incorporates advanced data security and financial advice functions utilizing machine learning, aiming to comprehensively address the challenges of modern financial management. 【0076】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0077】 Step 1: 【0078】 The server collects user financial transaction data through bank APIs, credit card APIs, and expense management app APIs. It receives user transaction history data obtained from each API as input, verifies data integrity, and eliminates incomplete data. After verification, the data is encrypted using AES encryption technology and securely stored in the database. This ensures stable and secure storage of financial data. 【0079】 Step 2: 【0080】 The server analyzes the collected data. Specifically, it decodes encrypted financial transaction data as input and performs calculations to determine the user's income, expenses, and savings rate. This analysis visualizes the user's economic activity and predicts future spending patterns. As a result, a concise income and expenditure analysis report is output for each user. 【0081】 Step 3: 【0082】 The server further refines the analysis results using a generative AI model. As input, it incorporates the analyzed income and expenditure data and past financial behavior into the AI ​​model to assess the user's risk tolerance. Based on this assessment, it proposes appropriate insurance contracts and investment allocations. As output, an optimized insurance plan and investment strategy are generated. 【0083】 Step 4: 【0084】 The terminal displays analysis results and advice obtained from the server to the user. Using financial report information received from the server as input, it presents the data in a graphical format on the user's device. Specifically, it monitors the household's financial situation in real time and immediately issues an alert if it detects fraudulent transactions or spending exceeding the budget. 【0085】 Step 5: 【0086】 Users improve their lifestyles based on advice sent through their devices. By using prompts, for example, by entering "Please give me suggestions for reducing my next spending," they can obtain new savings ideas from the AI ​​model. This allows users to implement improvements concretely and make progress towards their savings goals. 【0087】 (Application Example 1) 【0088】 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." 【0089】 In modern society, users are exposed to a wide range of financial services and data on a daily basis, making their management and analysis extremely complex. As a result, many users are hindered from efficient asset management, the selection of optimal insurance plans, and achieving their savings goals. They also often miss opportunities to monitor their daily spending and save. It is necessary to improve this situation and enable users to more effectively understand their financial situation and take appropriate financial actions. 【0090】 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. 【0091】 This invention includes a server that collects and securely stores the user's financial transaction data in a database, a server that notifies the user of personalized savings advice to help them achieve their goals, and a server that generates customized advice to improve the user's financial behavior. This enables the user to gain a comprehensive understanding of their asset situation and select and implement appropriate financial strategies. 【0092】 "Financial transaction data" refers to information about transactions conducted by users, including data related to bank accounts and credit cards. 【0093】 "Methods for securely storing data in a database" refer to a system that encrypts acquired financial transaction data and prevents unauthorized access from external sources, thereby ensuring its secure storage. 【0094】 "Means for understanding users' income and expenditure patterns" refers to a function that analyzes income sources and expenditure items and uses that to identify trends in users' economic activities. 【0095】 "Methods for providing the optimal insurance plan" refer to methods for analyzing the user's insurance contract details and efficiently reducing costs while maintaining adequate coverage. 【0096】 "Constructing an investment portfolio" is the process of calculating an investment allocation that maximizes returns while diversifying assets based on the user's risk tolerance and market trends. 【0097】 "A method for automatically generating an optimal savings plan" is a method that takes into account the user's current income and expenses and automatically formulates a specific strategy to achieve savings goals. 【0098】 "A means of monitoring in real time, detecting anomalies, and sending alerts" refers to a system that constantly monitors a user's spending and immediately notifies them if there is any unusual activity. 【0099】 A "means of notifying users of personalized savings advice" refers to a function that informs users of specific and effective ways to save money based on their past spending and future plans. 【0100】 A "means of generating customized advice to improve financial behavior" is a system that provides suggestions for achieving better financial conditions, tailored to the user's individual economic situation and goals. 【0101】 To implement this invention, it is necessary to build a system that efficiently manages users' financial transaction data and provides personalized asset management advice. The server collects users' financial transaction data and securely stores it in a database. Specifically, it uses financial APIs to retrieve data related to bank accounts and credit cards, encrypts it, and stores it in the database. 【0102】 The server uses Python to analyze financial data and understand the user's income and spending patterns. Machine learning algorithms are used to identify income and spending trends. Furthermore, it uses the user's risk tolerance and market data to construct an optimal investment portfolio. 【0103】 The user terminal is developed using React Native and runs on smartphones. This terminal monitors the user's spending in real time and immediately sends alerts if any anomalies are detected. Furthermore, it has a function to notify users of specific saving advice based on their savings goals and spending habits. This helps users improve their daily financial behavior and manage their assets efficiently. 【0104】 For example, if a user wants to increase their savings for a summer vacation trip, the server will use this information to generate advice such as, "You can save 10,000 yen per month by reducing the number of times you eat out by one per week." 【0105】 An example of a prompt would be, "Please give me specific advice on how to reduce this month's expenses. The target person is interested in improving their household finances and has been eating out a lot lately." Based on this prompt, the generative AI model provides effective advice. 【0106】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0107】 Step 1: 【0108】 The server retrieves the user's financial transaction data via a financial API. The input is the user's authentication information, which is securely transmitted using an encrypted protocol to retrieve data from various banks and credit cards. The output is the raw financial transaction data returned to the server in JSON format. The server then encrypts this data and stores it in a database. 【0109】 Step 2: 【0110】 The server analyzes financial transaction data obtained using a Python program. The input is stored transaction data. Using this data, it runs an algorithm to analyze income and expenditure patterns and understand the user's monthly cash flow. The output is the analyzed income and expenditure pattern data. 【0111】 Step 3: 【0112】 The server analyzes the user's insurance policy information. The input is the user's insurance policy data, and it uses this data to apply an algorithm that proposes the optimal insurance plan. The output is the proposed insurance plan, which is best suited to the user. 【0113】 Step 4: 【0114】 The server uses the user's risk tolerance and market data as input to construct an investment portfolio using Python and machine learning libraries. The output is a suggestion of an optimized asset allocation for the user. 【0115】 Step 5: 【0116】 The server generates a savings plan for the user. The input is the income and expenditure pattern information obtained from the previous analysis. Based on this information, it runs an algorithm that automatically generates a specific savings plan for the user's goals. The output is an overview of the savings plan. 【0117】 Step 6: 【0118】 The device monitors the user's spending data in real time. The input is the user's daily transaction data, and an anomaly detection algorithm detects spending that exceeds normal limits. The output is an alert notification regarding the detected anomaly. The device immediately informs the user of this. 【0119】 Step 7: 【0120】 This system uses a generative AI model to generate customized advice to improve the user's financial behavior. The input is information about the user's goals and lifestyle. The prompt "Please give me specific advice on how to reduce my spending this month" is sent to the generative AI, and specific advice is output. This advice is then notified to the user. 【0121】 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. 【0122】 This invention provides an advanced system that integrates a user's emotional state into their financial management. Specifically, by incorporating an emotion engine in addition to conventional methods of analyzing a user's financial data, it enables personalized advice based on the user's emotions. 【0123】 The server stores financial transaction data collected from users in a database. This data relates to income, expenses, and other financial activities and forms the basis for understanding the user's economic behavior. The server then provides an interface to obtain the user's emotional state through an emotion engine. The emotion engine uses technologies such as speech recognition, facial recognition, and text analysis to recognize the user's emotions and output them as quantified emotion data. 【0124】 The server analyzes and correlates financial and emotional data to generate optimal financial advice for the user. This advice is tailored to how the user's emotional state affects their financial behavior. For example, it might recommend low-risk investment strategies to users experiencing stress, thereby supporting their emotional well-being. 【0125】 The device displays this advice on the user's device and provides information that quickly responds to changes in the user's emotional state. To respond to emotional changes in real time, the emotion engine regularly checks updated data and provides alerts and suggestions as needed. 【0126】 As a concrete example, suppose a user is planning a loan and the emotion engine detects their anxiety. To alleviate the user's stress, the server reviews their spending, reassessss the amount of loan they can afford to repay, and provides advice through the device such as, "You can reduce your mental burden by lowering your weekly payments." 【0127】 In this way, the system can comprehensively manage users' emotions and financial data, enabling more personalized financial management. 【0128】 The following describes the processing flow. 【0129】 Step 1: 【0130】 The server collects financial transaction data from users' devices and financial institutions via APIs. This data includes detailed transaction history and income information and is securely stored in a database. 【0131】 Step 2: 【0132】 The server activates the emotion engine and collects data to recognize the user's emotions. It infers the user's emotional state in real time from voice input, facial analysis via camera, and entered text. 【0133】 Step 3: 【0134】 The server integrates and analyzes the financial and emotional data it collects. This data is cross-referenced to assess the user's current financial situation and emotional health, and to understand their overall financial status. 【0135】 Step 4: 【0136】 The server generates financial advice based on the user's emotional state. For example, if the user is feeling anxious, it will recommend a safe investment strategy, and if the situation is challenging, it will add supportive comments. 【0137】 Step 5: 【0138】 The device displays emotion-based financial advice on the user's device. The advice is delivered in a format that matches the user's current emotional state, improving its understanding and acceptance. 【0139】 Step 6: 【0140】 The device continuously monitors changes in the user's emotions and alerts the user when significant changes are detected. It also informs the user when supportive actions should be taken, if necessary. 【0141】 Step 7: 【0142】 Users act according to the advice provided. For example, they might review their spending and adjust their savings to achieve both emotional and financial stability. User feedback is also sent to the server and used to improve the accuracy of future advice. 【0143】 (Example 2) 【0144】 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 will be referred to as the "terminal." 【0145】 Traditional financial management systems provide advice based solely on a user's economic behavior, without considering their emotional state. This makes them inadequate in dealing with situations involving emotional stress and fluctuations. There is a need for more personalized advice that takes into account the impact of a user's emotional state on their financial behavior. 【0146】 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. 【0147】 This invention includes a server that collects and securely stores user financial behavior data and emotional state data; a server that analyzes the financial behavior data and emotional state data to understand the user's economic behavior patterns and their emotional impact; and a server that evaluates the user's emotional state using voice analysis, facial expression analysis, and text analysis. This makes it possible to provide more appropriate and personalized financial advice in real time, taking into account the user's emotional state. 【0148】 "Financial behavioral data" is a general term for information that shows a user's income, expenses, savings, investment activities, and other financial activities. 【0149】 "Emotional state data" refers to information that quantifies a user's emotional changes and psychological state, obtained through methods such as voice analysis, facial expression analysis, and text analysis. 【0150】 "Generative technology" refers to the process of automatically creating appropriate advice and information for users based on input data using artificial intelligence technology. 【0151】 "Voice analysis" is a technology that records a user's spoken words, analyzes their content and emotions, and infers their emotional state. 【0152】 "Facial expression analysis" is a technology that captures a user's facial expressions using a camera or other device, analyzes them, and then infers the user's emotional state. 【0153】 "Text analysis" is a technology that reads and analyzes emotions from text written by users. 【0154】 "Personalized advice" refers to advice optimized for a specific user, created based on the user's individual financial situation and emotional state. 【0155】 "Real-time delivery" refers to a process where advice is provided immediately in response to changes in the user's emotions and financial situation. 【0156】 In this invention, the server is responsible for collecting user financial behavior data and emotional state data and securely storing them in a database. Financial behavior data is obtained from the user's bank account transaction history and manually entered income and expense information, while emotional state data is obtained through voice analysis, facial expression analysis, text analysis, etc. This makes it possible to reveal the correlation between the user's economic activities and emotions. 【0157】 The server uses common SQL database software as its database management system during this process. It also utilizes speech recognition technology, facial recognition cameras, and natural language processing tools for analyzing emotional states. Specifically, it uses a common speech recognition API for speech analysis and a common facial recognition API for facial expression analysis. Based on this, the user's emotions are quantified and recorded. 【0158】 The server then integrates financial behavior data and emotional state data and analyzes this information. This analysis uses the Python programming language and its data analysis library, Pandas. The goal of the analysis is to understand how user emotions influence economic activity. The analysis results will highlight areas for improvement to enable users to conduct economic activities efficiently while maintaining a stable mental state. 【0159】 Furthermore, the server uses a generative AI model to generate personalized advice for the user based on the analysis results. For example, a general natural language generation model could be used as this AI generative model. This allows users to obtain appropriate information to make financial decisions while taking their current emotional state into consideration. 【0160】 The device displays advice received from the server to the user. Here, the device refers to common information display devices such as smartphones, tablets, and PCs. The device updates the advice in real time according to the user's emotional state and financial situation, and issues warnings as needed. The device, which is always in the user's possession, provides new information via push notifications through the application. 【0161】 For example, if the emotion engine detects that a user is experiencing high levels of stress, it might offer specific suggestions such as, "We recommend you cut back on your current spending. Prioritize long-term mental well-being and consider a low-risk investment strategy." 【0162】 Furthermore, an example of a prompt sentence is the following input sentence provided to a generative AI model: "Suggest an appropriate investment strategy when the user is feeling stressed." This prompt sentence is used by the generative AI model to generate appropriate advice. 【0163】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0164】 Step 1: 【0165】 The server collects users' financial behavior data. It receives user bank account transaction history and manually entered income / expense information as input and stores it in a database. Specifically, users retrieve transaction history via financial institution APIs and automatically add it to the database. The output is an integrated financial behavior dataset, which is used for subsequent analysis. 【0166】 Step 2: 【0167】 The server collects data on the user's emotional state. Inputs include audio data, facial image data, and text data. Audio data and facial image data are analyzed using voice analysis and facial recognition technologies, while text data is analyzed using natural language processing technologies. Specifically, emotional data is collected using the microphone and camera while the user is operating the application, and this data is analyzed and quantified. The output is data representing the user's emotions in numerical form. 【0168】 Step 3: 【0169】 The server integrates and analyzes financial behavior data and emotional state data. The data obtained in steps 1 and 2 is used as input. Python and Pandas are used to combine the datasets and analyze the impact of users' emotional states on economic activity using statistical models. Specifically, regression analysis and clustering are performed to evaluate correlations and trends. The output generates a report on the emotional impact on users' economic behavior. 【0170】 Step 4: 【0171】 The server generates personalized advice using a generative AI model. The analysis results from step 3 are provided as input. Prompts are used with the generative AI model to generate user-appropriate advice in natural language. Specifically, the AI ​​outputs an answer based on prompts such as, "What investment strategy should I recommend when I'm under high stress?" The output provides investment and financial advice that takes the user's emotional state into consideration. 【0172】 Step 5: 【0173】 The terminal displays the advice received from the server to the user. The input is the advice generated in step 4. Specifically, the application on the smartphone or tablet notifies the user of the advice in real time. If the user's situation changes, the application updates the advice in real time and sends appropriate alerts. The output is the provision of up-to-date financial advice to the user based on the latest information. 【0174】 (Application Example 2) 【0175】 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". 【0176】 In recent years, many users lead daily lives closely intertwined with complex financial activities, yet few systems adequately consider the influence of emotions on economic behavior. Most financial management systems primarily rely on analysis based on economic data, making it difficult to provide personalized strategies that take emotional factors into account. This makes it challenging for users to make optimal financial decisions while maintaining emotional stability. 【0177】 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. 【0178】 This invention includes a server that collects user financial activity data and securely stores it in a database, an emotion analysis means that analyzes the user's emotional state and generates emotion data, and a means that correlates and analyzes the financial data and emotion data to generate personalized financial advice. This enables comprehensive management of the user's economic behavior and emotional state, allowing for optimal financial decisions in a mentally stable state. 【0179】 "User financial activity data" refers to a collection of information related to a user's income, expenses, and other financial transactions in their daily life. 【0180】 "Means of securely storing data in a database" refers to storage technology that implements appropriate security protocols to protect acquired data from unauthorized external access and tampering. 【0181】 "User emotional state" refers to information that indicates the psychological state a user is feeling at a specific point in time, and is estimated through voice recognition, facial recognition, and text analysis. 【0182】 "Emotional data" refers to data that quantifies or represents a user's emotional state, expressing the type and intensity of their emotions. 【0183】 "Sentiment analysis tools" refer to a collection of algorithms and technologies that analyze user-generated data such as voice, images, and text, and use that data to infer and quantify the user's emotional state. 【0184】 "Methods for correlating and analyzing financial data and emotional data" refers to technologies that integrate financial activity data and emotional data, analyze their correlations, and then evaluate the influence of emotions on users' economic behavior. 【0185】 "Means of generating personalized financial advice" refers to the technologies and processes used to recommend the most suitable financial strategies and consumer behaviors based on a user's individual circumstances and emotional state. 【0186】 The system for implementing this invention consists of a server, a terminal, and a user. The server is responsible for collecting the user's financial activity data and storing it in a secure database. This utilizes a software platform that employs technologies such as speech recognition, facial recognition, and text analysis. 【0187】 The server uses these technologies to analyze the user's emotional state and generates and stores quantified emotional data. This emotional data is then processed in conjunction with financial data to evaluate the impact of emotions on the user's economic behavior. 【0188】 The device provides users with analysis results from the server and personalized financial advice. An intuitive interface has been developed to make it easy for users to access this information using their smartphones. It responds to changes in emotions in real time, providing users with the most appropriate financial advice. 【0189】 As a concrete example, when users manage their daily expenses, they can receive advice that takes their mental health into consideration, based on emotional analysis. For instance, if a user is stressed, a suggestion such as, "How about visiting a cafe today to relax, within reasonable limits?" might appear on the screen. 【0190】 The use of generative AI models to generate prompts is also being considered. For example, using prompts such as "Please come up with appropriate spending management suggestions for when the user is stressed" could enable the provision of more effective advice. 【0191】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0192】 Step 1: 【0193】 The server collects user financial activity data through an interface. It receives transaction history from financial institutions and manual input from users, and securely stores it in a database. Here, encryption technology is used to prevent unauthorized access to the data. Inputs are transaction history and manual input data, and outputs are data stored in an encrypted database. 【0194】 Step 2: 【0195】 The server analyzes the user's emotional state using emotion analysis methods that employ speech recognition, facial recognition, and text analysis. It receives voice data, image data, and text data provided by the user as input, and processes these through an algorithm to quantify the emotional state. The output is the quantified emotion data. 【0196】 Step 3: 【0197】 The server integrates and correlates accumulated financial and emotional data for analysis. Using financial and emotional data as input, it evaluates the correlation between the two sets of data using machine learning algorithms. This process calculates the influence of emotions on users' economic behavior. The output is analytical data showing the correlations. 【0198】 Step 4: 【0199】 The server generates personalized financial advice tailored to the user based on the analysis results. It takes emotional state and financial analysis data as input and uses a generative AI model to create advice. This process uses prompts such as, "Please suggest appropriate spending management when the user is stressed." The output is financial advice designed to improve the user experience. 【0200】 Step 5: 【0201】 The terminal visually presents financial advice sent from the server to the user. It performs activities to display the information in a user-friendly format through a smartphone application. The input is financial advice from the server, and the output is the visual interface on the terminal. 【0202】 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. 【0203】 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. 【0204】 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. 【0205】 [Second Embodiment] 【0206】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0207】 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. 【0208】 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). 【0209】 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. 【0210】 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. 【0211】 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). 【0212】 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. 【0213】 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. 【0214】 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. 【0215】 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. 【0216】 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. 【0217】 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". 【0218】 This invention aims to build a system that efficiently manages financial data and provides users with optimal financial advice. This system is realized through the interaction of servers, terminals, and users. 【0219】 First, the server collects the user's financial transaction data. This is done automatically through bank accounts, credit cards, and expense management apps. This data is securely stored in a database using encryption technology. 【0220】 Next, the server analyzes the collected data. It calculates income, expenses, and savings rates, visualizing the user's financial activity. It also analyzes the user's risk tolerance and past financial behavior to suggest appropriate insurance policies and investment portfolios. 【0221】 In this process, the server scrutinizes insurance contract details, detecting unnecessary overlaps and unnecessarily covered policies to create an optimized insurance plan. When creating investment portfolios, it combines market trends and risk analysis to calculate a balanced asset allocation. 【0222】 The device displays information on the user's device and serves as a real-time household budget monitor. When it detects spending exceeding the budget or fraudulent transactions, it immediately sends an alert to the user and suggests specific measures to reduce spending. 【0223】 As a concrete example, suppose a user wants to increase their savings with some leeway at the end of the month. The server reviews his monthly spending data and identifies that the combined cost of food and transportation is causing him to exceed his budget. Therefore, it suggests to the user via the terminal that he can save 5,000 yen per month by using public transportation during his commute. It also determines that his insurance needs to be reviewed, so the server optimizes his coverage and presents him with a plan that meets his needs while keeping costs down. 【0224】 This system allows users to centrally manage their financial information and achieve effective asset management and life planning. 【0225】 The following describes the processing flow. 【0226】 Step 1: 【0227】 The server collects financial transaction data from multiple financial institutions and user accounts via APIs. This includes users' bank account history, credit card transactions, and recurring income information. The collected data is encrypted and securely stored in a database. 【0228】 Step 2: 【0229】 The server analyzes the collected data in the database. It calculates totals for each user spending category and identifies patterns. It also calculates the user's income, expenses, and monthly balance to assess their financial situation. 【0230】 Step 3: 【0231】 The server analyzes and scrutinizes the user's insurance policy. If it detects unnecessary or duplicate coverage, it generates suggestions for optimization and sends them to the user's device. This allows the user to review their policy and reduce their costs. 【0232】 Step 4: 【0233】 The server analyzes market data, taking into account the user's risk tolerance and preferences. Based on this, it creates an investment portfolio tailored to the user and adjusts the asset allocation. The portfolio information is sent to the terminal and shared with the user. 【0234】 Step 5: 【0235】 The server calculates the amount a user can save based on their income and expenses, and automatically generates savings goals and plans. These plans are divided into short-term, medium-term, and long-term goals, and the user is notified periodically. 【0236】 Step 6: 【0237】 The device monitors the user's spending in real time. If unusual spending occurs or the budget is exceeded, an alert is immediately issued, notifying the user of unnecessary spending and opportunities for savings. This information is sent to the user's device as a push notification. 【0238】 (Example 1) 【0239】 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 glasses 214 will be referred to as the "terminal." 【0240】 In modern financial management, it is a challenging task for individuals to efficiently and comprehensively understand their own financial situation and select optimal asset management and insurance policies. In particular, there is a need for secure collection and analysis of financial transaction data, reduction of unnecessary insurance policies, optimization of investments, and flexible response to savings goals. However, there are insufficient systems to effectively achieve these goals. 【0241】 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. 【0242】 In this invention, the server includes means for collecting and securely storing the user's financial information, means for analyzing the user's income and expenditure trends, and means for evaluating and optimizing the user's insurance contracts. This makes it possible to provide asset management and financial advice tailored to each individual user. 【0243】 "User financial information" refers to personal financial information, including data on banking transactions, credit card usage, and spending management. 【0244】 A "computing device" is a hardware device such as a computer or server that has the function of processing and analyzing data. 【0245】 "Data collection methods" refer to the processes and technologies used to acquire and integrate data from multiple sources. 【0246】 "Analysis means" refers to algorithms and methods for processing collected data and converting it into meaningful information. 【0247】 "Risk tolerance" is a criterion that indicates the extent to which an individual can accept financial risk. 【0248】 "Investment allocation" is a strategy that determines how users invest in different types of assets. 【0249】 A "generative AI model" is an artificial intelligence technology that learns from past data and makes predictions and suggestions for new data. 【0250】 A "warning" is a notification that the system sends to the user when it detects an anomaly. 【0251】 "Asset management" refers to activities that include planning and execution to properly manage and increase a user's assets. 【0252】 "Financial advice" refers to the activity of providing guidance based on a user's financial situation. 【0253】 This invention constructs a system that provides centralized management of financial information and optimal financial advice through interaction between servers, terminals, and users. Its configuration is described below. 【0254】 First, the server collects the user's financial information. The server efficiently gathers the user's financial transaction data using bank APIs, credit card APIs, and expense management app APIs. This data is securely stored in a dedicated database on the computing device using AES encryption technology, ensuring a high level of security. 【0255】 Next, the server analyzes the collected financial information. This analysis process calculates the user's income, expenses, savings rate, and other factors. Furthermore, using a generative AI model, it analyzes the user's risk tolerance and proposes insurance policies and investment allocations tailored to their individual financial situation. This enables more personalized advice. 【0256】 The device displays this information on the user's device. The device monitors household finances in real time and immediately sends a warning to the user if it detects unusual transactions or spending that exceeds the budget. It also notifies the user of specific spending reduction measures suggested by a generated AI model and presents actionable improvement plans. 【0257】 As a concrete example, consider a user who wants to review their monthly expenses. The server analyzes past spending data and identifies that the user is spending a lot on eating out. The terminal then presents the user with specific saving suggestions, such as, "You can save 10,000 yen per month by cooking at home twice a week to reduce your food expenses." Furthermore, examples of prompts could include inputs like, "Please suggest an optimization of my insurance policy and investment allocation." 【0258】 In this way, users can achieve asset management and life planning tailored to their individual financial circumstances. This system incorporates advanced data security and financial advice functions utilizing machine learning, aiming to comprehensively address the challenges of modern financial management. 【0259】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0260】 Step 1: 【0261】 The server collects user financial transaction data through bank APIs, credit card APIs, and expense management app APIs. It receives user transaction history data obtained from each API as input, verifies data integrity, and eliminates incomplete data. After verification, the data is encrypted using AES encryption technology and securely stored in the database. This ensures stable and secure storage of financial data. 【0262】 Step 2: 【0263】 The server analyzes the collected data. Specifically, it decodes encrypted financial transaction data as input and performs calculations to determine the user's income, expenses, and savings rate. This analysis visualizes the user's economic activity and predicts future spending patterns. As a result, a concise income and expenditure analysis report is output for each user. 【0264】 Step 3: 【0265】 The server further refines the analysis results using a generative AI model. As input, it incorporates the analyzed income and expenditure data and past financial behavior into the AI ​​model to assess the user's risk tolerance. Based on this assessment, it proposes appropriate insurance contracts and investment allocations. As output, an optimized insurance plan and investment strategy are generated. 【0266】 Step 4: 【0267】 The terminal displays analysis results and advice obtained from the server to the user. Using financial report information received from the server as input, it presents the data in a graphical format on the user's device. Specifically, it monitors the household's financial situation in real time and immediately issues an alert if it detects fraudulent transactions or spending exceeding the budget. 【0268】 Step 5: 【0269】 Users improve their lifestyles based on advice sent through their devices. By using prompts, for example, by entering "Please give me suggestions for reducing my next spending," they can obtain new savings ideas from the AI ​​model. This allows users to implement improvements concretely and make progress towards their savings goals. 【0270】 (Application Example 1) 【0271】 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." 【0272】 In modern society, users are exposed to a wide range of financial services and data on a daily basis, making their management and analysis extremely complex. As a result, many users are hindered from efficient asset management, the selection of optimal insurance plans, and achieving their savings goals. They also often miss opportunities to monitor their daily spending and save. It is necessary to improve this situation and enable users to more effectively understand their financial situation and take appropriate financial actions. 【0273】 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. 【0274】 This invention includes a server that collects and securely stores the user's financial transaction data in a database, a server that notifies the user of personalized savings advice to help them achieve their goals, and a server that generates customized advice to improve the user's financial behavior. This enables the user to gain a comprehensive understanding of their asset situation and select and implement appropriate financial strategies. 【0275】 "Financial transaction data" refers to information about transactions conducted by users, including data related to bank accounts and credit cards. 【0276】 "Methods for securely storing data in a database" refer to a system that encrypts acquired financial transaction data and prevents unauthorized access from external sources, thereby ensuring its secure storage. 【0277】 "Means for understanding users' income and expenditure patterns" refers to a function that analyzes income sources and expenditure items and uses that to identify trends in users' economic activities. 【0278】 "Methods for providing the optimal insurance plan" refer to methods for analyzing the user's insurance contract details and efficiently reducing costs while maintaining adequate coverage. 【0279】 "Constructing an investment portfolio" is the process of calculating an investment allocation that maximizes returns while diversifying assets based on the user's risk tolerance and market trends. 【0280】 "A method for automatically generating an optimal savings plan" is a method that takes into account the user's current income and expenses and automatically formulates a specific strategy to achieve savings goals. 【0281】 "A means of monitoring in real time, detecting anomalies, and sending alerts" refers to a system that constantly monitors a user's spending and immediately notifies them if there is any unusual activity. 【0282】 A "means of notifying users of personalized savings advice" refers to a function that informs users of specific and effective ways to save money based on their past spending and future plans. 【0283】 A "means of generating customized advice to improve financial behavior" is a system that provides suggestions for achieving better financial conditions, tailored to the user's individual economic situation and goals. 【0284】 To implement this invention, it is necessary to build a system that efficiently manages users' financial transaction data and provides individual asset management advice. The server collects users' financial transaction data and securely stores it in a database. Specifically, it uses financial APIs to obtain data related to bank accounts and credit cards, encrypts this data, and saves it in the database. 【0285】 The server uses Python to analyze financial data and understand users' income and expenditure patterns. By utilizing machine learning algorithms, it can identify income and expenditure trends. Furthermore, it constructs an optimal investment portfolio using the users' risk tolerance and market data. 【0286】 The user terminal is developed using React Native and operates on smartphones. This terminal monitors users' expenditures in real-time and immediately sends alerts if any anomalies are detected. Additionally, it has a function to notify specific savings advice based on users' savings goals and expenditure trends. This enables users to improve their daily financial behaviors and achieve efficient asset management. 【0287】 As a specific example, when a user wants to increase savings for a summer vacation trip, the server generates advice such as "By reducing the number of dining-outs by once a week, it is possible to save 10,000 yen per month" based on this information. 【0288】 An example of a prompt sentence is "Please tell me specific advice to reduce this month's expenses. The target person is interested in improving household finances and has been dining out frequently recently." Based on this prompt, the generative AI model provides effective advice. 【0289】 The flow of the specific process in Application Example 1 will be described using Figure 12. 【0290】 Step 1: 【0291】 The server retrieves the user's financial transaction data via a financial API. The input is the user's authentication information, which is securely transmitted using an encrypted protocol to retrieve data from various banks and credit cards. The output is the raw financial transaction data returned to the server in JSON format. The server then encrypts this data and stores it in a database. 【0292】 Step 2: 【0293】 The server analyzes financial transaction data obtained using a Python program. The input is stored transaction data. Using this data, it runs an algorithm to analyze income and expenditure patterns and understand the user's monthly cash flow. The output is the analyzed income and expenditure pattern data. 【0294】 Step 3: 【0295】 The server analyzes the user's insurance policy information. The input is the user's insurance policy data, and it uses this data to apply an algorithm that proposes the optimal insurance plan. The output is the proposed insurance plan, which is best suited to the user. 【0296】 Step 4: 【0297】 The server uses the user's risk tolerance and market data as input to construct an investment portfolio using Python and machine learning libraries. The output is a suggestion of an optimized asset allocation for the user. 【0298】 Step 5: 【0299】 The server generates a savings plan for the user. The input is the income and expenditure pattern information obtained from the previous analysis. Based on this information, it runs an algorithm that automatically generates a specific savings plan for the user's goals. The output is an overview of the savings plan. 【0300】 Step 6: 【0301】 The device monitors the user's spending data in real time. The input is the user's daily transaction data, and an anomaly detection algorithm detects spending that exceeds normal limits. The output is an alert notification regarding the detected anomaly. The device immediately informs the user of this. 【0302】 Step 7: 【0303】 This system uses a generative AI model to generate customized advice to improve the user's financial behavior. The input is information about the user's goals and lifestyle. The prompt "Please give me specific advice on how to reduce my spending this month" is sent to the generative AI, and specific advice is output. This advice is then notified to the user. 【0304】 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. 【0305】 This invention provides an advanced system that integrates a user's emotional state into their financial management. Specifically, by incorporating an emotion engine in addition to conventional methods of analyzing a user's financial data, it enables personalized advice based on the user's emotions. 【0306】 The server stores financial transaction data collected from users in a database. This data relates to income, expenses, and other financial activities and forms the basis for understanding the user's economic behavior. The server then provides an interface to obtain the user's emotional state through an emotion engine. The emotion engine uses technologies such as speech recognition, facial recognition, and text analysis to recognize the user's emotions and output them as quantified emotion data. 【0307】 The server analyzes by associating financial data with emotional data and generates optimal financial advice for users. This advice is adjusted considering how the emotional state affects the user's economic behavior. For example, for a user feeling stressed, it supports mental stability by recommending a low-risk investment strategy. 【0308】 The terminal displays this advice on the user's device and provides information that quickly responds to changes in the user's emotional state. To respond to real-time emotional changes, it periodically checks the updated data of the emotion engine and gives alerts and suggestions as necessary. 【0309】 As a specific example, suppose a user is making a loan plan and the emotion engine detects their anxious emotion. To reduce the user's stress, the server reviews the expenses and re-evaluates the repayable loan amount, and provides advice such as "Reducing the repayment amount per week can relieve the mental burden" through the terminal. 【0310】 In this way, the system can comprehensively manage the user's emotions and financial data and achieve more personalized financial management. 【0311】 The following explains the processing flow. 【0312】 Step 1: 【0313】 The server collects financial transaction data from the user's device and financial institutions through APIs. This data includes detailed transaction histories and income information and is securely stored in a database. 【0314】 Step 2: 【0315】 The server activates the emotion engine and collects data for recognizing the user's emotions. It estimates the user's emotional state in real time from voice input, face analysis by camera, and the input text. 【0316】 Step 3: 【0317】 The server integrates and analyzes the financial and emotional data it collects. This data is cross-referenced to assess the user's current financial situation and emotional health, and to understand their overall financial status. 【0318】 Step 4: 【0319】 The server generates financial advice based on the user's emotional state. For example, if the user is feeling anxious, it will recommend a safe investment strategy, and if the situation is challenging, it will add supportive comments. 【0320】 Step 5: 【0321】 The device displays emotion-based financial advice on the user's device. The advice is delivered in a format that matches the user's current emotional state, improving its understanding and acceptance. 【0322】 Step 6: 【0323】 The device continuously monitors changes in the user's emotions and alerts the user when significant changes are detected. It also informs the user when supportive actions should be taken, if necessary. 【0324】 Step 7: 【0325】 Users act according to the advice provided. For example, they might review their spending and adjust their savings to achieve both emotional and financial stability. User feedback is also sent to the server and used to improve the accuracy of future advice. 【0326】 (Example 2) 【0327】 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". 【0328】 Traditional financial management systems provide advice based solely on a user's economic behavior, without considering their emotional state. This makes them inadequate in dealing with situations involving emotional stress and fluctuations. There is a need for more personalized advice that takes into account the impact of a user's emotional state on their financial behavior. 【0329】 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. 【0330】 This invention includes a server that collects and securely stores user financial behavior data and emotional state data; a server that analyzes the financial behavior data and emotional state data to understand the user's economic behavior patterns and their emotional impact; and a server that evaluates the user's emotional state using voice analysis, facial expression analysis, and text analysis. This makes it possible to provide more appropriate and personalized financial advice in real time, taking into account the user's emotional state. 【0331】 "Financial behavioral data" is a general term for information that shows a user's income, expenses, savings, investment activities, and other financial activities. 【0332】 "Emotional state data" refers to information that quantifies a user's emotional changes and psychological state, obtained through methods such as voice analysis, facial expression analysis, and text analysis. 【0333】 "Generative technology" refers to the process of automatically creating appropriate advice and information for users based on input data using artificial intelligence technology. 【0334】 "Voice analysis" is a technology that records a user's spoken words, analyzes their content and emotions, and infers their emotional state. 【0335】 "Facial expression analysis" is a technology that captures a user's facial expressions using a camera or other device, analyzes them, and then infers the user's emotional state. 【0336】 "Text analysis" is a technology that reads and analyzes emotions from text written by users. 【0337】 "Personalized advice" refers to advice optimized for a specific user, created based on the user's individual financial situation and emotional state. 【0338】 "Real-time delivery" refers to a process where advice is provided immediately in response to changes in the user's emotions and financial situation. 【0339】 In this invention, the server is responsible for collecting user financial behavior data and emotional state data and securely storing them in a database. Financial behavior data is obtained from the user's bank account transaction history and manually entered income and expense information, while emotional state data is obtained through voice analysis, facial expression analysis, text analysis, etc. This makes it possible to reveal the correlation between the user's economic activities and emotions. 【0340】 The server uses common SQL database software as its database management system during this process. It also utilizes speech recognition technology, facial recognition cameras, and natural language processing tools for analyzing emotional states. Specifically, it uses a common speech recognition API for speech analysis and a common facial recognition API for facial expression analysis. Based on this, the user's emotions are quantified and recorded. 【0341】 The server then integrates financial behavior data and emotional state data and analyzes this information. This analysis uses the Python programming language and its data analysis library, Pandas. The goal of the analysis is to understand how user emotions influence economic activity. The analysis results will highlight areas for improvement to enable users to conduct economic activities efficiently while maintaining a stable mental state. 【0342】 Furthermore, the server uses a generative AI model to generate personalized advice for the user based on the analysis results. For example, a general natural language generation model could be used as this AI generative model. This allows users to obtain appropriate information to make financial decisions while taking their current emotional state into consideration. 【0343】 The device displays advice received from the server to the user. Here, the device refers to common information display devices such as smartphones, tablets, and PCs. The device updates the advice in real time according to the user's emotional state and financial situation, and issues warnings as needed. The device, which is always in the user's possession, provides new information via push notifications through the application. 【0344】 For example, if the emotion engine detects that a user is experiencing high levels of stress, it might offer specific suggestions such as, "We recommend you cut back on your current spending. Prioritize long-term mental well-being and consider a low-risk investment strategy." 【0345】 Furthermore, an example of a prompt sentence is the following input sentence provided to a generative AI model: "Suggest an appropriate investment strategy when the user is feeling stressed." This prompt sentence is used by the generative AI model to generate appropriate advice. 【0346】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0347】 Step 1: 【0348】 The server collects users' financial behavior data. It receives user bank account transaction history and manually entered income / expense information as input and stores it in a database. Specifically, users retrieve transaction history via financial institution APIs and automatically add it to the database. The output is an integrated financial behavior dataset, which is used for subsequent analysis. 【0349】 Step 2: 【0350】 The server collects data on the user's emotional state. Inputs include audio data, facial image data, and text data. Audio data and facial image data are analyzed using voice analysis and facial recognition technologies, while text data is analyzed using natural language processing technologies. Specifically, emotional data is collected using the microphone and camera while the user is operating the application, and this data is analyzed and quantified. The output is data representing the user's emotions in numerical form. 【0351】 Step 3: 【0352】 The server integrates and analyzes financial behavior data and emotional state data. The data obtained in steps 1 and 2 is used as input. Python and Pandas are used to combine the datasets and analyze the impact of users' emotional states on economic activity using statistical models. Specifically, regression analysis and clustering are performed to evaluate correlations and trends. The output generates a report on the emotional impact on users' economic behavior. 【0353】 Step 4: 【0354】 The server generates personalized advice using a generative AI model. The analysis results from step 3 are provided as input. Prompts are used with the generative AI model to generate user-appropriate advice in natural language. Specifically, the AI ​​outputs an answer based on prompts such as, "What investment strategy should I recommend when I'm under high stress?" The output provides investment and financial advice that takes the user's emotional state into consideration. 【0355】 Step 5: 【0356】 The terminal displays the advice received from the server to the user. The input is the advice generated in step 4. Specifically, the application on the smartphone or tablet notifies the user of the advice in real time. If the user's situation changes, the application updates the advice in real time and sends appropriate alerts. The output is the provision of up-to-date financial advice to the user based on the latest information. 【0357】 (Application Example 2) 【0358】 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." 【0359】 In recent years, many users lead daily lives closely intertwined with complex financial activities, yet few systems adequately consider the influence of emotions on economic behavior. Most financial management systems primarily rely on analysis based on economic data, making it difficult to provide personalized strategies that take emotional factors into account. This makes it challenging for users to make optimal financial decisions while maintaining emotional stability. 【0360】 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. 【0361】 This invention includes a server that collects user financial activity data and securely stores it in a database, an emotion analysis means that analyzes the user's emotional state and generates emotion data, and a means that correlates and analyzes the financial data and emotion data to generate personalized financial advice. This enables comprehensive management of the user's economic behavior and emotional state, allowing for optimal financial decisions in a mentally stable state. 【0362】 "User financial activity data" refers to a collection of information related to a user's income, expenses, and other financial transactions in their daily life. 【0363】 "Means of securely storing data in a database" refers to storage technology that implements appropriate security protocols to protect acquired data from unauthorized external access and tampering. 【0364】 "User emotional state" refers to information that indicates the psychological state a user is feeling at a specific point in time, and is estimated through voice recognition, facial recognition, and text analysis. 【0365】 "Emotional data" refers to data that quantifies or represents a user's emotional state, expressing the type and intensity of their emotions. 【0366】 "Sentiment analysis tools" refer to a collection of algorithms and technologies that analyze user-generated data such as voice, images, and text, and use that data to infer and quantify the user's emotional state. 【0367】 "Methods for correlating and analyzing financial data and emotional data" refers to technologies that integrate financial activity data and emotional data, analyze their correlations, and then evaluate the influence of emotions on users' economic behavior. 【0368】 "Means of generating personalized financial advice" refers to the technologies and processes used to recommend the most suitable financial strategies and consumer behaviors based on a user's individual circumstances and emotional state. 【0369】 The system for implementing this invention consists of a server, a terminal, and a user. The server is responsible for collecting the user's financial activity data and storing it in a secure database. This utilizes a software platform that employs technologies such as speech recognition, facial recognition, and text analysis. 【0370】 The server uses these technologies to analyze the user's emotional state and generates and stores quantified emotional data. This emotional data is then processed in conjunction with financial data to evaluate the impact of emotions on the user's economic behavior. 【0371】 The device provides users with analysis results from the server and personalized financial advice. An intuitive interface has been developed to make it easy for users to access this information using their smartphones. It responds to changes in emotions in real time, providing users with the most appropriate financial advice. 【0372】 As a concrete example, when users manage their daily expenses, they can receive advice that takes their mental health into consideration, based on emotional analysis. For instance, if a user is stressed, a suggestion such as, "How about visiting a cafe today to relax, within reasonable limits?" might appear on the screen. 【0373】 The use of generative AI models to generate prompts is also being considered. For example, using prompts such as "Please come up with appropriate spending management suggestions for when the user is stressed" could enable the provision of more effective advice. 【0374】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0375】 Step 1: 【0376】 The server collects user financial activity data through an interface. It receives transaction history from financial institutions and manual input from users, and securely stores it in a database. Here, encryption technology is used to prevent unauthorized access to the data. Inputs are transaction history and manual input data, and outputs are data stored in an encrypted database. 【0377】 Step 2: 【0378】 The server analyzes the user's emotional state using emotion analysis methods that employ speech recognition, facial recognition, and text analysis. It receives voice data, image data, and text data provided by the user as input, and processes these through an algorithm to quantify the emotional state. The output is the quantified emotion data. 【0379】 Step 3: 【0380】 The server integrates and correlates accumulated financial and emotional data for analysis. Using financial and emotional data as input, it evaluates the correlation between the two sets of data using machine learning algorithms. This process calculates the influence of emotions on users' economic behavior. The output is analytical data showing the correlations. 【0381】 Step 4: 【0382】 The server generates personalized financial advice tailored to the user based on the analysis results. It takes emotional state and financial analysis data as input and uses a generative AI model to create advice. This process uses prompts such as, "Please suggest appropriate spending management when the user is stressed." The output is financial advice designed to improve the user experience. 【0383】 Step 5: 【0384】 The terminal visually presents financial advice sent from the server to the user. It performs activities to display the information in a user-friendly format through a smartphone application. The input is financial advice from the server, and the output is the visual interface on the terminal. 【0385】 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. 【0386】 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. 【0387】 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. 【0388】 [Third Embodiment] 【0389】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0390】 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. 【0391】 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). 【0392】 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. 【0393】 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. 【0394】 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). 【0395】 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. 【0396】 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. 【0397】 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. 【0398】 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. 【0399】 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. 【0400】 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". 【0401】 This invention aims to build a system that efficiently manages financial data and provides users with optimal financial advice. This system is realized through the interaction of servers, terminals, and users. 【0402】 First, the server collects the user's financial transaction data. This is done automatically through bank accounts, credit cards, and expense management apps. This data is securely stored in a database using encryption technology. 【0403】 Next, the server analyzes the collected data. It calculates income, expenses, and savings rates, visualizing the user's financial activity. It also analyzes the user's risk tolerance and past financial behavior to suggest appropriate insurance policies and investment portfolios. 【0404】 In this process, the server scrutinizes insurance contract details, detecting unnecessary overlaps and unnecessarily covered policies to create an optimized insurance plan. When creating investment portfolios, it combines market trends and risk analysis to calculate a balanced asset allocation. 【0405】 The device displays information on the user's device and serves as a real-time household budget monitor. When it detects spending exceeding the budget or fraudulent transactions, it immediately sends an alert to the user and suggests specific measures to reduce spending. 【0406】 As a concrete example, suppose a user wants to increase their savings with some leeway at the end of the month. The server reviews his monthly spending data and identifies that the combined cost of food and transportation is causing him to exceed his budget. Therefore, it suggests to the user via the terminal that he can save 5,000 yen per month by using public transportation during his commute. It also determines that his insurance needs to be reviewed, so the server optimizes his coverage and presents him with a plan that meets his needs while keeping costs down. 【0407】 This system allows users to centrally manage their financial information and achieve effective asset management and life planning. 【0408】 The following describes the processing flow. 【0409】 Step 1: 【0410】 The server collects financial transaction data from multiple financial institutions and user accounts via APIs. This includes users' bank account history, credit card transactions, and recurring income information. The collected data is encrypted and securely stored in a database. 【0411】 Step 2: 【0412】 The server analyzes the collected data in the database. It calculates totals for each user spending category and identifies patterns. It also calculates the user's income, expenses, and monthly balance to assess their financial situation. 【0413】 Step 3: 【0414】 The server analyzes and scrutinizes the user's insurance policy. If it detects unnecessary or duplicate coverage, it generates suggestions for optimization and sends them to the user's device. This allows the user to review their policy and reduce their costs. 【0415】 Step 4: 【0416】 The server analyzes market data, taking into account the user's risk tolerance and preferences. Based on this, it creates an investment portfolio tailored to the user and adjusts the asset allocation. The portfolio information is sent to the terminal and shared with the user. 【0417】 Step 5: 【0418】 The server calculates the amount a user can save based on their income and expenses, and automatically generates savings goals and plans. These plans are divided into short-term, medium-term, and long-term goals, and the user is notified periodically. 【0419】 Step 6: 【0420】 The device monitors the user's spending in real time. If unusual spending occurs or the budget is exceeded, an alert is immediately issued, notifying the user of unnecessary spending and opportunities for savings. This information is sent to the user's device as a push notification. 【0421】 (Example 1) 【0422】 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." 【0423】 In modern financial management, it is a challenging task for individuals to efficiently and comprehensively understand their own financial situation and select optimal asset management and insurance policies. In particular, there is a need for secure collection and analysis of financial transaction data, reduction of unnecessary insurance policies, optimization of investments, and flexible response to savings goals. However, there are insufficient systems to effectively achieve these goals. 【0424】 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. 【0425】 In this invention, the server includes means for collecting and securely storing the user's financial information, means for analyzing the user's income and expenditure trends, and means for evaluating and optimizing the user's insurance contracts. This makes it possible to provide asset management and financial advice tailored to each individual user. 【0426】 "User financial information" refers to personal financial information, including data on banking transactions, credit card usage, and spending management. 【0427】 A "computing device" is a hardware device such as a computer or server that has the function of processing and analyzing data. 【0428】 "Data collection methods" refer to the processes and technologies used to acquire and integrate data from multiple sources. 【0429】 "Analysis means" refers to algorithms and methods for processing collected data and converting it into meaningful information. 【0430】 "Risk tolerance" is a criterion that indicates the extent to which an individual can accept financial risk. 【0431】 "Investment allocation" is a strategy that determines how users invest in different types of assets. 【0432】 A "generative AI model" is an artificial intelligence technology that learns from past data and makes predictions and suggestions for new data. 【0433】 A "warning" is a notification that the system sends to the user when it detects an anomaly. 【0434】 "Asset management" refers to activities that include planning and execution to properly manage and increase a user's assets. 【0435】 "Financial advice" refers to the activity of providing guidance based on a user's financial situation. 【0436】 This invention constructs a system that provides centralized management of financial information and optimal financial advice through interaction between servers, terminals, and users. Its configuration is described below. 【0437】 First, the server collects the user's financial information. The server efficiently gathers the user's financial transaction data using bank APIs, credit card APIs, and expense management app APIs. This data is securely stored in a dedicated database on the computing device using AES encryption technology, ensuring a high level of security. 【0438】 Next, the server analyzes the collected financial information. This analysis process calculates the user's income, expenses, savings rate, and other factors. Furthermore, using a generative AI model, it analyzes the user's risk tolerance and proposes insurance policies and investment allocations tailored to their individual financial situation. This enables more personalized advice. 【0439】 The device displays this information on the user's device. The device monitors household finances in real time and immediately sends a warning to the user if it detects unusual transactions or spending that exceeds the budget. It also notifies the user of specific spending reduction measures suggested by a generated AI model and presents actionable improvement plans. 【0440】 As a concrete example, consider a user who wants to review their monthly expenses. The server analyzes past spending data and identifies that the user is spending a lot on eating out. The terminal then presents the user with specific saving suggestions, such as, "You can save 10,000 yen per month by cooking at home twice a week to reduce your food expenses." Furthermore, examples of prompts could include inputs like, "Please suggest an optimization of my insurance policy and investment allocation." 【0441】 In this way, users can achieve asset management and life planning tailored to their individual financial circumstances. This system incorporates advanced data security and financial advice functions utilizing machine learning, aiming to comprehensively address the challenges of modern financial management. 【0442】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0443】 Step 1: 【0444】 The server collects user financial transaction data through bank APIs, credit card APIs, and expense management app APIs. It receives user transaction history data obtained from each API as input, verifies data integrity, and eliminates incomplete data. After verification, the data is encrypted using AES encryption technology and securely stored in the database. This ensures stable and secure storage of financial data. 【0445】 Step 2: 【0446】 The server analyzes the collected data. Specifically, it decodes encrypted financial transaction data as input and performs calculations to determine the user's income, expenses, and savings rate. This analysis visualizes the user's economic activity and predicts future spending patterns. As a result, a concise income and expenditure analysis report is output for each user. 【0447】 Step 3: 【0448】 The server further refines the analysis results using a generative AI model. As input, it incorporates the analyzed income and expenditure data and past financial behavior into the AI ​​model to assess the user's risk tolerance. Based on this assessment, it proposes appropriate insurance contracts and investment allocations. As output, an optimized insurance plan and investment strategy are generated. 【0449】 Step 4: 【0450】 The terminal displays analysis results and advice obtained from the server to the user. Using financial report information received from the server as input, it presents the data in a graphical format on the user's device. Specifically, it monitors the household's financial situation in real time and immediately issues an alert if it detects fraudulent transactions or spending exceeding the budget. 【0451】 Step 5: 【0452】 Users improve their lifestyles based on advice sent through their devices. By using prompts, for example, by entering "Please give me suggestions for reducing my next spending," they can obtain new savings ideas from the AI ​​model. This allows users to implement improvements concretely and make progress towards their savings goals. 【0453】 (Application Example 1) 【0454】 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." 【0455】 In modern society, users are exposed to a wide range of financial services and data on a daily basis, making their management and analysis extremely complex. As a result, many users are hindered from efficient asset management, the selection of optimal insurance plans, and achieving their savings goals. They also often miss opportunities to monitor their daily spending and save. It is necessary to improve this situation and enable users to more effectively understand their financial situation and take appropriate financial actions. 【0456】 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. 【0457】 This invention includes a server that collects and securely stores the user's financial transaction data in a database, a server that notifies the user of personalized savings advice to help them achieve their goals, and a server that generates customized advice to improve the user's financial behavior. This enables the user to gain a comprehensive understanding of their asset situation and select and implement appropriate financial strategies. 【0458】 "Financial transaction data" refers to information about transactions conducted by users, including data related to bank accounts and credit cards. 【0459】 "Methods for securely storing data in a database" refer to a system that encrypts acquired financial transaction data and prevents unauthorized access from external sources, thereby ensuring its secure storage. 【0460】 "Means for understanding users' income and expenditure patterns" refers to a function that analyzes income sources and expenditure items and uses that to identify trends in users' economic activities. 【0461】 "Methods for providing the optimal insurance plan" refer to methods for analyzing the user's insurance contract details and efficiently reducing costs while maintaining adequate coverage. 【0462】 "Constructing an investment portfolio" is the process of calculating an investment allocation that maximizes returns while diversifying assets based on the user's risk tolerance and market trends. 【0463】 "A method for automatically generating an optimal savings plan" is a method that takes into account the user's current income and expenses and automatically formulates a specific strategy to achieve savings goals. 【0464】 "A means of monitoring in real time, detecting anomalies, and sending alerts" refers to a system that constantly monitors a user's spending and immediately notifies them if there is any unusual activity. 【0465】 A "means of notifying users of personalized savings advice" refers to a function that informs users of specific and effective ways to save money based on their past spending and future plans. 【0466】 A "means of generating customized advice to improve financial behavior" is a system that provides suggestions for achieving better financial conditions, tailored to the user's individual economic situation and goals. 【0467】 To implement this invention, it is necessary to build a system that efficiently manages users' financial transaction data and provides personalized asset management advice. The server collects users' financial transaction data and securely stores it in a database. Specifically, it uses financial APIs to retrieve data related to bank accounts and credit cards, encrypts it, and stores it in the database. 【0468】 The server uses Python to analyze financial data and understand the user's income and spending patterns. Machine learning algorithms are used to identify income and spending trends. Furthermore, it uses the user's risk tolerance and market data to construct an optimal investment portfolio. 【0469】 The user terminal is developed using React Native and runs on smartphones. This terminal monitors the user's spending in real time and immediately sends alerts if any anomalies are detected. Furthermore, it has a function to notify users of specific saving advice based on their savings goals and spending habits. This helps users improve their daily financial behavior and manage their assets efficiently. 【0470】 For example, if a user wants to increase their savings for a summer vacation trip, the server will use this information to generate advice such as, "You can save 10,000 yen per month by reducing the number of times you eat out by one per week." 【0471】 An example of a prompt would be, "Please give me specific advice on how to reduce this month's expenses. The target person is interested in improving their household finances and has been eating out a lot lately." Based on this prompt, the generative AI model provides effective advice. 【0472】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0473】 Step 1: 【0474】 The server retrieves the user's financial transaction data via a financial API. The input is the user's authentication information, which is securely transmitted using an encrypted protocol to retrieve data from various banks and credit cards. The output is the raw financial transaction data returned to the server in JSON format. The server then encrypts this data and stores it in a database. 【0475】 Step 2: 【0476】 The server analyzes financial transaction data obtained using a Python program. The input is stored transaction data. Using this data, it runs an algorithm to analyze income and expenditure patterns and understand the user's monthly cash flow. The output is the analyzed income and expenditure pattern data. 【0477】 Step 3: 【0478】 The server analyzes the user's insurance policy information. The input is the user's insurance policy data, and it uses this data to apply an algorithm that proposes the optimal insurance plan. The output is the proposed insurance plan, which is best suited to the user. 【0479】 Step 4: 【0480】 The server uses the user's risk tolerance and market data as input to construct an investment portfolio using Python and machine learning libraries. The output is a suggestion of an optimized asset allocation for the user. 【0481】 Step 5: 【0482】 The server generates a savings plan for the user. The input is the income and expenditure pattern information obtained from the previous analysis. Based on this information, it runs an algorithm that automatically generates a specific savings plan for the user's goals. The output is an overview of the savings plan. 【0483】 Step 6: 【0484】 The device monitors the user's spending data in real time. The input is the user's daily transaction data, and an anomaly detection algorithm detects spending that exceeds normal limits. The output is an alert notification regarding the detected anomaly. The device immediately informs the user of this. 【0485】 Step 7: 【0486】 This system uses a generative AI model to generate customized advice to improve the user's financial behavior. The input is information about the user's goals and lifestyle. The prompt "Please give me specific advice on how to reduce my spending this month" is sent to the generative AI, and specific advice is output. This advice is then notified to the user. 【0487】 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. 【0488】 This invention provides an advanced system that integrates a user's emotional state into their financial management. Specifically, by incorporating an emotion engine in addition to conventional methods of analyzing a user's financial data, it enables personalized advice based on the user's emotions. 【0489】 The server stores financial transaction data collected from users in a database. This data relates to income, expenses, and other financial activities and forms the basis for understanding the user's economic behavior. The server then provides an interface to obtain the user's emotional state through an emotion engine. The emotion engine uses technologies such as speech recognition, facial recognition, and text analysis to recognize the user's emotions and output them as quantified emotion data. 【0490】 The server analyzes and correlates financial and emotional data to generate optimal financial advice for the user. This advice is tailored to how the user's emotional state affects their financial behavior. For example, it might recommend low-risk investment strategies to users experiencing stress, thereby supporting their emotional well-being. 【0491】 The device displays this advice on the user's device and provides information that quickly responds to changes in the user's emotional state. To respond to emotional changes in real time, the emotion engine regularly checks updated data and provides alerts and suggestions as needed. 【0492】 As a concrete example, suppose a user is planning a loan and the emotion engine detects their anxiety. To alleviate the user's stress, the server reviews their spending, reassessss the amount of loan they can afford to repay, and provides advice through the device such as, "You can reduce your mental burden by lowering your weekly payments." 【0493】 In this way, the system can comprehensively manage users' emotions and financial data, enabling more personalized financial management. 【0494】 The following describes the processing flow. 【0495】 Step 1: 【0496】 The server collects financial transaction data from users' devices and financial institutions via APIs. This data includes detailed transaction history and income information and is securely stored in a database. 【0497】 Step 2: 【0498】 The server activates the emotion engine and collects data to recognize the user's emotions. It infers the user's emotional state in real time from voice input, facial analysis via camera, and entered text. 【0499】 Step 3: 【0500】 The server integrates and analyzes the financial and emotional data it collects. This data is cross-referenced to assess the user's current financial situation and emotional health, and to understand their overall financial status. 【0501】 Step 4: 【0502】 The server generates financial advice based on the user's emotional state. For example, if the user is feeling anxious, it will recommend a safe investment strategy, and if the situation is challenging, it will add supportive comments. 【0503】 Step 5: 【0504】 The device displays emotion-based financial advice on the user's device. The advice is delivered in a format that matches the user's current emotional state, improving its understanding and acceptance. 【0505】 Step 6: 【0506】 The device continuously monitors changes in the user's emotions and alerts the user when significant changes are detected. It also informs the user when supportive actions should be taken, if necessary. 【0507】 Step 7: 【0508】 Users act according to the advice provided. For example, they might review their spending and adjust their savings to achieve both emotional and financial stability. User feedback is also sent to the server and used to improve the accuracy of future advice. 【0509】 (Example 2) 【0510】 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." 【0511】 Traditional financial management systems provide advice based solely on a user's economic behavior, without considering their emotional state. This makes them inadequate in dealing with situations involving emotional stress and fluctuations. There is a need for more personalized advice that takes into account the impact of a user's emotional state on their financial behavior. 【0512】 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. 【0513】 This invention includes a server that collects and securely stores user financial behavior data and emotional state data; a server that analyzes the financial behavior data and emotional state data to understand the user's economic behavior patterns and their emotional impact; and a server that evaluates the user's emotional state using voice analysis, facial expression analysis, and text analysis. This makes it possible to provide more appropriate and personalized financial advice in real time, taking into account the user's emotional state. 【0514】 "Financial behavioral data" is a general term for information that shows a user's income, expenses, savings, investment activities, and other financial activities. 【0515】 "Emotional state data" refers to information that quantifies a user's emotional changes and psychological state, obtained through methods such as voice analysis, facial expression analysis, and text analysis. 【0516】 "Generative technology" refers to the process of automatically creating appropriate advice and information for users based on input data using artificial intelligence technology. 【0517】 "Voice analysis" is a technology that records a user's spoken words, analyzes their content and emotions, and infers their emotional state. 【0518】 "Facial expression analysis" is a technology that captures a user's facial expressions using a camera or other device, analyzes them, and then infers the user's emotional state. 【0519】 "Text analysis" is a technology that reads and analyzes emotions from text written by users. 【0520】 "Personalized advice" refers to advice optimized for a specific user, created based on the user's individual financial situation and emotional state. 【0521】 "Real-time delivery" refers to a process where advice is provided immediately in response to changes in the user's emotions and financial situation. 【0522】 In this invention, the server is responsible for collecting user financial behavior data and emotional state data and securely storing them in a database. Financial behavior data is obtained from the user's bank account transaction history and manually entered income and expense information, while emotional state data is obtained through voice analysis, facial expression analysis, text analysis, etc. This makes it possible to reveal the correlation between the user's economic activities and emotions. 【0523】 The server uses common SQL database software as its database management system during this process. It also utilizes speech recognition technology, facial recognition cameras, and natural language processing tools for analyzing emotional states. Specifically, it uses a common speech recognition API for speech analysis and a common facial recognition API for facial expression analysis. Based on this, the user's emotions are quantified and recorded. 【0524】 The server then integrates financial behavior data and emotional state data and analyzes this information. This analysis uses the Python programming language and its data analysis library, Pandas. The goal of the analysis is to understand how user emotions influence economic activity. The analysis results will highlight areas for improvement to enable users to conduct economic activities efficiently while maintaining a stable mental state. 【0525】 Furthermore, the server uses a generative AI model to generate personalized advice for the user based on the analysis results. For example, a general natural language generation model could be used as this AI generative model. This allows users to obtain appropriate information to make financial decisions while taking their current emotional state into consideration. 【0526】 The device displays advice received from the server to the user. Here, the device refers to common information display devices such as smartphones, tablets, and PCs. The device updates the advice in real time according to the user's emotional state and financial situation, and issues warnings as needed. The device, which is always in the user's possession, provides new information via push notifications through the application. 【0527】 For example, if the emotion engine detects that a user is experiencing high levels of stress, it might offer specific suggestions such as, "We recommend you cut back on your current spending. Prioritize long-term mental well-being and consider a low-risk investment strategy." 【0528】 Furthermore, an example of a prompt sentence is the following input sentence provided to a generative AI model: "Suggest an appropriate investment strategy when the user is feeling stressed." This prompt sentence is used by the generative AI model to generate appropriate advice. 【0529】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0530】 Step 1: 【0531】 The server collects users' financial behavior data. It receives user bank account transaction history and manually entered income / expense information as input and stores it in a database. Specifically, users retrieve transaction history via financial institution APIs and automatically add it to the database. The output is an integrated financial behavior dataset, which is used for subsequent analysis. 【0532】 Step 2: 【0533】 The server collects data on the user's emotional state. Inputs include audio data, facial image data, and text data. Audio data and facial image data are analyzed using voice analysis and facial recognition technologies, while text data is analyzed using natural language processing technologies. Specifically, emotional data is collected using the microphone and camera while the user is operating the application, and this data is analyzed and quantified. The output is data representing the user's emotions in numerical form. 【0534】 Step 3: 【0535】 The server integrates and analyzes financial behavior data and emotional state data. The data obtained in steps 1 and 2 is used as input. Python and Pandas are used to combine the datasets and analyze the impact of users' emotional states on economic activity using statistical models. Specifically, regression analysis and clustering are performed to evaluate correlations and trends. The output generates a report on the emotional impact on users' economic behavior. 【0536】 Step 4: 【0537】 The server generates personalized advice using a generative AI model. The analysis results from step 3 are provided as input. Prompts are used with the generative AI model to generate user-appropriate advice in natural language. Specifically, the AI ​​outputs an answer based on prompts such as, "What investment strategy should I recommend when I'm under high stress?" The output provides investment and financial advice that takes the user's emotional state into consideration. 【0538】 Step 5: 【0539】 The terminal displays the advice received from the server to the user. The input is the advice generated in step 4. Specifically, the application on the smartphone or tablet notifies the user of the advice in real time. If the user's situation changes, the application updates the advice in real time and sends appropriate alerts. The output is the provision of up-to-date financial advice to the user based on the latest information. 【0540】 (Application Example 2) 【0541】 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." 【0542】 In recent years, many users lead daily lives closely intertwined with complex financial activities, yet few systems adequately consider the influence of emotions on economic behavior. Most financial management systems primarily rely on analysis based on economic data, making it difficult to provide personalized strategies that take emotional factors into account. This makes it challenging for users to make optimal financial decisions while maintaining emotional stability. 【0543】 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. 【0544】 This invention includes a server that collects user financial activity data and securely stores it in a database, an emotion analysis means that analyzes the user's emotional state and generates emotion data, and a means that correlates and analyzes the financial data and emotion data to generate personalized financial advice. This enables comprehensive management of the user's economic behavior and emotional state, allowing for optimal financial decisions in a mentally stable state. 【0545】 "User financial activity data" refers to a collection of information related to a user's income, expenses, and other financial transactions in their daily life. 【0546】 "Means of securely storing data in a database" refers to storage technology that implements appropriate security protocols to protect acquired data from unauthorized external access and tampering. 【0547】 "User emotional state" refers to information that indicates the psychological state a user is feeling at a specific point in time, and is estimated through voice recognition, facial recognition, and text analysis. 【0548】 "Emotional data" refers to data that quantifies or represents a user's emotional state, expressing the type and intensity of their emotions. 【0549】 "Sentiment analysis tools" refer to a collection of algorithms and technologies that analyze user-generated data such as voice, images, and text, and use that data to infer and quantify the user's emotional state. 【0550】 "Methods for correlating and analyzing financial data and emotional data" refers to technologies that integrate financial activity data and emotional data, analyze their correlations, and then evaluate the influence of emotions on users' economic behavior. 【0551】 "Means of generating personalized financial advice" refers to the technologies and processes used to recommend the most suitable financial strategies and consumer behaviors based on a user's individual circumstances and emotional state. 【0552】 The system for implementing this invention consists of a server, a terminal, and a user. The server is responsible for collecting the user's financial activity data and storing it in a secure database. This utilizes a software platform that employs technologies such as speech recognition, facial recognition, and text analysis. 【0553】 The server uses these technologies to analyze the user's emotional state and generates and stores quantified emotional data. This emotional data is then processed in conjunction with financial data to evaluate the impact of emotions on the user's economic behavior. 【0554】 The device provides users with analysis results from the server and personalized financial advice. An intuitive interface has been developed to make it easy for users to access this information using their smartphones. It responds to changes in emotions in real time, providing users with the most appropriate financial advice. 【0555】 As a concrete example, when users manage their daily expenses, they can receive advice that takes their mental health into consideration, based on emotional analysis. For instance, if a user is stressed, a suggestion such as, "How about visiting a cafe today to relax, within reasonable limits?" might appear on the screen. 【0556】 The use of generative AI models to generate prompts is also being considered. For example, using prompts such as "Please come up with appropriate spending management suggestions for when the user is stressed" could enable the provision of more effective advice. 【0557】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0558】 Step 1: 【0559】 The server collects user financial activity data through an interface. It receives transaction history from financial institutions and manual input from users, and securely stores it in a database. Here, encryption technology is used to prevent unauthorized access to the data. Inputs are transaction history and manual input data, and outputs are data stored in an encrypted database. 【0560】 Step 2: 【0561】 The server analyzes the user's emotional state using emotion analysis methods that employ speech recognition, facial recognition, and text analysis. It receives voice data, image data, and text data provided by the user as input, and processes these through an algorithm to quantify the emotional state. The output is the quantified emotion data. 【0562】 Step 3: 【0563】 The server integrates and correlates accumulated financial and emotional data for analysis. Using financial and emotional data as input, it evaluates the correlation between the two sets of data using machine learning algorithms. This process calculates the influence of emotions on users' economic behavior. The output is analytical data showing the correlations. 【0564】 Step 4: 【0565】 The server generates personalized financial advice tailored to the user based on the analysis results. It takes emotional state and financial analysis data as input and uses a generative AI model to create advice. This process uses prompts such as, "Please suggest appropriate spending management when the user is stressed." The output is financial advice designed to improve the user experience. 【0566】 Step 5: 【0567】 The terminal visually presents financial advice sent from the server to the user. It performs activities to display the information in a user-friendly format through a smartphone application. The input is financial advice from the server, and the output is the visual interface on the terminal. 【0568】 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. 【0569】 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. 【0570】 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. 【0571】 [Fourth Embodiment] 【0572】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0573】 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. 【0574】 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). 【0575】 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. 【0576】 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. 【0577】 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). 【0578】 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. 【0579】 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. 【0580】 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. 【0581】 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. 【0582】 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. 【0583】 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. 【0584】 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". 【0585】 This invention aims to build a system that efficiently manages financial data and provides users with optimal financial advice. This system is realized through the interaction of servers, terminals, and users. 【0586】 First, the server collects the user's financial transaction data. This is done automatically through bank accounts, credit cards, and expense management apps. This data is securely stored in a database using encryption technology. 【0587】 Next, the server analyzes the collected data. It calculates income, expenses, and savings rates, visualizing the user's financial activity. It also analyzes the user's risk tolerance and past financial behavior to suggest appropriate insurance policies and investment portfolios. 【0588】 In this process, the server scrutinizes insurance contract details, detecting unnecessary overlaps and unnecessarily covered policies to create an optimized insurance plan. When creating investment portfolios, it combines market trends and risk analysis to calculate a balanced asset allocation. 【0589】 The device displays information on the user's device and serves as a real-time household budget monitor. When it detects spending exceeding the budget or fraudulent transactions, it immediately sends an alert to the user and suggests specific measures to reduce spending. 【0590】 As a concrete example, suppose a user wants to increase their savings with some leeway at the end of the month. The server reviews his monthly spending data and identifies that the combined cost of food and transportation is causing him to exceed his budget. Therefore, it suggests to the user via the terminal that he can save 5,000 yen per month by using public transportation during his commute. It also determines that his insurance needs to be reviewed, so the server optimizes his coverage and presents him with a plan that meets his needs while keeping costs down. 【0591】 This system allows users to centrally manage their financial information and achieve effective asset management and life planning. 【0592】 The following describes the processing flow. 【0593】 Step 1: 【0594】 The server collects financial transaction data from multiple financial institutions and user accounts via APIs. This includes users' bank account history, credit card transactions, and recurring income information. The collected data is encrypted and securely stored in a database. 【0595】 Step 2: 【0596】 The server analyzes the collected data in the database. It calculates totals for each user spending category and identifies patterns. It also calculates the user's income, expenses, and monthly balance to assess their financial situation. 【0597】 Step 3: 【0598】 The server analyzes and scrutinizes the user's insurance policy. If it detects unnecessary or duplicate coverage, it generates suggestions for optimization and sends them to the user's device. This allows the user to review their policy and reduce their costs. 【0599】 Step 4: 【0600】 The server analyzes market data, taking into account the user's risk tolerance and preferences. Based on this, it creates an investment portfolio tailored to the user and adjusts the asset allocation. The portfolio information is sent to the terminal and shared with the user. 【0601】 Step 5: 【0602】 The server calculates the amount a user can save based on their income and expenses, and automatically generates savings goals and plans. These plans are divided into short-term, medium-term, and long-term goals, and the user is notified periodically. 【0603】 Step 6: 【0604】 The device monitors the user's spending in real time. If unusual spending occurs or the budget is exceeded, an alert is immediately issued, notifying the user of unnecessary spending and opportunities for savings. This information is sent to the user's device as a push notification. 【0605】 (Example 1) 【0606】 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". 【0607】 In modern financial management, it is a challenging task for individuals to efficiently and comprehensively understand their own financial situation and select optimal asset management and insurance policies. In particular, there is a need for secure collection and analysis of financial transaction data, reduction of unnecessary insurance policies, optimization of investments, and flexible response to savings goals. However, there are insufficient systems to effectively achieve these goals. 【0608】 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. 【0609】 In this invention, the server includes means for collecting and securely storing the user's financial information, means for analyzing the user's income and expenditure trends, and means for evaluating and optimizing the user's insurance contracts. This makes it possible to provide asset management and financial advice tailored to each individual user. 【0610】 "User financial information" refers to personal financial information, including data on banking transactions, credit card usage, and spending management. 【0611】 A "computing device" is a hardware device such as a computer or server that has the function of processing and analyzing data. 【0612】 "Data collection methods" refer to the processes and technologies used to acquire and integrate data from multiple sources. 【0613】 "Analysis means" refers to algorithms and methods for processing collected data and converting it into meaningful information. 【0614】 "Risk tolerance" is a criterion that indicates the extent to which an individual can accept financial risk. 【0615】 "Investment allocation" is a strategy that determines how users invest in different types of assets. 【0616】 A "generative AI model" is an artificial intelligence technology that learns from past data and makes predictions and suggestions for new data. 【0617】 A "warning" is a notification that the system sends to the user when it detects an anomaly. 【0618】 "Asset management" refers to activities that include planning and execution to properly manage and increase a user's assets. 【0619】 "Financial advice" refers to the activity of providing guidance based on a user's financial situation. 【0620】 This invention constructs a system that provides centralized management of financial information and optimal financial advice through interaction between servers, terminals, and users. Its configuration is described below. 【0621】 First, the server collects the user's financial information. The server efficiently gathers the user's financial transaction data using bank APIs, credit card APIs, and expense management app APIs. This data is securely stored in a dedicated database on the computing device using AES encryption technology, ensuring a high level of security. 【0622】 Next, the server analyzes the collected financial information. This analysis process calculates the user's income, expenses, savings rate, and other factors. Furthermore, using a generative AI model, it analyzes the user's risk tolerance and proposes insurance policies and investment allocations tailored to their individual financial situation. This enables more personalized advice. 【0623】 The device displays this information on the user's device. The device monitors household finances in real time and immediately sends a warning to the user if it detects unusual transactions or spending that exceeds the budget. It also notifies the user of specific spending reduction measures suggested by a generated AI model and presents actionable improvement plans. 【0624】 As a concrete example, consider a user who wants to review their monthly expenses. The server analyzes past spending data and identifies that the user is spending a lot on eating out. The terminal then presents the user with specific saving suggestions, such as, "You can save 10,000 yen per month by cooking at home twice a week to reduce your food expenses." Furthermore, examples of prompts could include inputs like, "Please suggest an optimization of my insurance policy and investment allocation." 【0625】 In this way, users can achieve asset management and life planning tailored to their individual financial circumstances. This system incorporates advanced data security and financial advice functions utilizing machine learning, aiming to comprehensively address the challenges of modern financial management. 【0626】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0627】 Step 1: 【0628】 The server collects user financial transaction data through bank APIs, credit card APIs, and expense management app APIs. It receives user transaction history data obtained from each API as input, verifies data integrity, and eliminates incomplete data. After verification, the data is encrypted using AES encryption technology and securely stored in the database. This ensures stable and secure storage of financial data. 【0629】 Step 2: 【0630】 The server analyzes the collected data. Specifically, it decodes encrypted financial transaction data as input and performs calculations to determine the user's income, expenses, and savings rate. This analysis visualizes the user's economic activity and predicts future spending patterns. As a result, a concise income and expenditure analysis report is output for each user. 【0631】 Step 3: 【0632】 The server further refines the analysis results using a generative AI model. As input, it incorporates the analyzed income and expenditure data and past financial behavior into the AI ​​model to assess the user's risk tolerance. Based on this assessment, it proposes appropriate insurance contracts and investment allocations. As output, an optimized insurance plan and investment strategy are generated. 【0633】 Step 4: 【0634】 The terminal displays analysis results and advice obtained from the server to the user. Using financial report information received from the server as input, it presents the data in a graphical format on the user's device. Specifically, it monitors the household's financial situation in real time and immediately issues an alert if it detects fraudulent transactions or spending exceeding the budget. 【0635】 Step 5: 【0636】 Users improve their lifestyles based on advice sent through their devices. By using prompts, for example, by entering "Please give me suggestions for reducing my next spending," they can obtain new savings ideas from the AI ​​model. This allows users to implement improvements concretely and make progress towards their savings goals. 【0637】 (Application Example 1) 【0638】 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". 【0639】 In modern society, users are exposed to a wide range of financial services and data on a daily basis, making their management and analysis extremely complex. As a result, many users are hindered from efficient asset management, the selection of optimal insurance plans, and achieving their savings goals. They also often miss opportunities to monitor their daily spending and save. It is necessary to improve this situation and enable users to more effectively understand their financial situation and take appropriate financial actions. 【0640】 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. 【0641】 This invention includes a server that collects and securely stores the user's financial transaction data in a database, a server that notifies the user of personalized savings advice to help them achieve their goals, and a server that generates customized advice to improve the user's financial behavior. This enables the user to gain a comprehensive understanding of their asset situation and select and implement appropriate financial strategies. 【0642】 "Financial transaction data" refers to information about transactions conducted by users, including data related to bank accounts and credit cards. 【0643】 "Methods for securely storing data in a database" refer to a system that encrypts acquired financial transaction data and prevents unauthorized access from external sources, thereby ensuring its secure storage. 【0644】 "Means for understanding users' income and expenditure patterns" refers to a function that analyzes income sources and expenditure items and uses that to identify trends in users' economic activities. 【0645】 "Methods for providing the optimal insurance plan" refer to methods for analyzing the user's insurance contract details and efficiently reducing costs while maintaining adequate coverage. 【0646】 "Constructing an investment portfolio" is the process of calculating an investment allocation that maximizes returns while diversifying assets based on the user's risk tolerance and market trends. 【0647】 "A method for automatically generating an optimal savings plan" is a method that takes into account the user's current income and expenses and automatically formulates a specific strategy to achieve savings goals. 【0648】 "A means of monitoring in real time, detecting anomalies, and sending alerts" refers to a system that constantly monitors a user's spending and immediately notifies them if there is any unusual activity. 【0649】 A "means of notifying users of personalized savings advice" refers to a function that informs users of specific and effective ways to save money based on their past spending and future plans. 【0650】 A "means of generating customized advice to improve financial behavior" is a system that provides suggestions for achieving better financial conditions, tailored to the user's individual economic situation and goals. 【0651】 To implement this invention, it is necessary to build a system that efficiently manages users' financial transaction data and provides personalized asset management advice. The server collects users' financial transaction data and securely stores it in a database. Specifically, it uses financial APIs to retrieve data related to bank accounts and credit cards, encrypts it, and stores it in the database. 【0652】 The server uses Python to analyze financial data and understand the user's income and spending patterns. Machine learning algorithms are used to identify income and spending trends. Furthermore, it uses the user's risk tolerance and market data to construct an optimal investment portfolio. 【0653】 The user terminal is developed using React Native and runs on smartphones. This terminal monitors the user's spending in real time and immediately sends alerts if any anomalies are detected. Furthermore, it has a function to notify users of specific saving advice based on their savings goals and spending habits. This helps users improve their daily financial behavior and manage their assets efficiently. 【0654】 For example, if a user wants to increase their savings for a summer vacation trip, the server will use this information to generate advice such as, "You can save 10,000 yen per month by reducing the number of times you eat out by one per week." 【0655】 An example of a prompt would be, "Please give me specific advice on how to reduce this month's expenses. The target person is interested in improving their household finances and has been eating out a lot lately." Based on this prompt, the generative AI model provides effective advice. 【0656】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0657】 Step 1: 【0658】 The server retrieves the user's financial transaction data via a financial API. The input is the user's authentication information, which is securely transmitted using an encrypted protocol to retrieve data from various banks and credit cards. The output is the raw financial transaction data returned to the server in JSON format. The server then encrypts this data and stores it in a database. 【0659】 Step 2: 【0660】 The server analyzes financial transaction data obtained using a Python program. The input is stored transaction data. Using this data, it runs an algorithm to analyze income and expenditure patterns and understand the user's monthly cash flow. The output is the analyzed income and expenditure pattern data. 【0661】 Step 3: 【0662】 The server analyzes the user's insurance policy information. The input is the user's insurance policy data, and it uses this data to apply an algorithm that proposes the optimal insurance plan. The output is the proposed insurance plan, which is best suited to the user. 【0663】 Step 4: 【0664】 The server uses the user's risk tolerance and market data as input to construct an investment portfolio using Python and machine learning libraries. The output is a suggestion of an optimized asset allocation for the user. 【0665】 Step 5: 【0666】 The server generates a savings plan for the user. The input is the income and expenditure pattern information obtained from the previous analysis. Based on this information, it runs an algorithm that automatically generates a specific savings plan for the user's goals. The output is an overview of the savings plan. 【0667】 Step 6: 【0668】 The device monitors the user's spending data in real time. The input is the user's daily transaction data, and an anomaly detection algorithm detects spending that exceeds normal limits. The output is an alert notification regarding the detected anomaly. The device immediately informs the user of this. 【0669】 Step 7: 【0670】 This system uses a generative AI model to generate customized advice to improve the user's financial behavior. The input is information about the user's goals and lifestyle. The prompt "Please give me specific advice on how to reduce my spending this month" is sent to the generative AI, and specific advice is output. This advice is then notified to the user. 【0671】 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. 【0672】 This invention provides an advanced system that integrates a user's emotional state into their financial management. Specifically, by incorporating an emotion engine in addition to conventional methods of analyzing a user's financial data, it enables personalized advice based on the user's emotions. 【0673】 The server stores financial transaction data collected from users in a database. This data relates to income, expenses, and other financial activities and forms the basis for understanding the user's economic behavior. The server then provides an interface to obtain the user's emotional state through an emotion engine. The emotion engine uses technologies such as speech recognition, facial recognition, and text analysis to recognize the user's emotions and output them as quantified emotion data. 【0674】 The server analyzes and correlates financial and emotional data to generate optimal financial advice for the user. This advice is tailored to how the user's emotional state affects their financial behavior. For example, it might recommend low-risk investment strategies to users experiencing stress, thereby supporting their emotional well-being. 【0675】 The device displays this advice on the user's device and provides information that quickly responds to changes in the user's emotional state. To respond to emotional changes in real time, the emotion engine regularly checks updated data and provides alerts and suggestions as needed. 【0676】 As a concrete example, suppose a user is planning a loan and the emotion engine detects their anxiety. To alleviate the user's stress, the server reviews their spending, reassessss the amount of loan they can afford to repay, and provides advice through the device such as, "You can reduce your mental burden by lowering your weekly payments." 【0677】 In this way, the system can comprehensively manage users' emotions and financial data, enabling more personalized financial management. 【0678】 The following describes the processing flow. 【0679】 Step 1: 【0680】 The server collects financial transaction data from users' devices and financial institutions via APIs. This data includes detailed transaction history and income information and is securely stored in a database. 【0681】 Step 2: 【0682】 The server activates the emotion engine and collects data to recognize the user's emotions. It infers the user's emotional state in real time from voice input, facial analysis via camera, and entered text. 【0683】 Step 3: 【0684】 The server integrates and analyzes the financial and emotional data it collects. This data is cross-referenced to assess the user's current financial situation and emotional health, and to understand their overall financial status. 【0685】 Step 4: 【0686】 The server generates financial advice based on the user's emotional state. For example, if the user is feeling anxious, it will recommend a safe investment strategy, and if the situation is challenging, it will add supportive comments. 【0687】 Step 5: 【0688】 The device displays emotion-based financial advice on the user's device. The advice is delivered in a format that matches the user's current emotional state, improving its understanding and acceptance. 【0689】 Step 6: 【0690】 The device continuously monitors changes in the user's emotions and alerts the user when significant changes are detected. It also informs the user when supportive actions should be taken, if necessary. 【0691】 Step 7: 【0692】 Users act according to the advice provided. For example, they might review their spending and adjust their savings to achieve both emotional and financial stability. User feedback is also sent to the server and used to improve the accuracy of future advice. 【0693】 (Example 2) 【0694】 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". 【0695】 Traditional financial management systems provide advice based solely on a user's economic behavior, without considering their emotional state. This makes them inadequate in dealing with situations involving emotional stress and fluctuations. There is a need for more personalized advice that takes into account the impact of a user's emotional state on their financial behavior. 【0696】 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. 【0697】 This invention includes a server that collects and securely stores user financial behavior data and emotional state data; a server that analyzes the financial behavior data and emotional state data to understand the user's economic behavior patterns and their emotional impact; and a server that evaluates the user's emotional state using voice analysis, facial expression analysis, and text analysis. This makes it possible to provide more appropriate and personalized financial advice in real time, taking into account the user's emotional state. 【0698】 "Financial behavioral data" is a general term for information that shows a user's income, expenses, savings, investment activities, and other financial activities. 【0699】 "Emotional state data" refers to information that quantifies a user's emotional changes and psychological state, obtained through methods such as voice analysis, facial expression analysis, and text analysis. 【0700】 "Generative technology" refers to the process of automatically creating appropriate advice and information for users based on input data using artificial intelligence technology. 【0701】 "Voice analysis" is a technology that records a user's spoken words, analyzes their content and emotions, and infers their emotional state. 【0702】 "Facial expression analysis" is a technology that captures a user's facial expressions using a camera or other device, analyzes them, and then infers the user's emotional state. 【0703】 "Text analysis" is a technology that reads and analyzes emotions from text written by users. 【0704】 "Personalized advice" refers to advice optimized for a specific user, created based on the user's individual financial situation and emotional state. 【0705】 "Real-time delivery" refers to a process where advice is provided immediately in response to changes in the user's emotions and financial situation. 【0706】 In this invention, the server is responsible for collecting user financial behavior data and emotional state data and securely storing them in a database. Financial behavior data is obtained from the user's bank account transaction history and manually entered income and expense information, while emotional state data is obtained through voice analysis, facial expression analysis, text analysis, etc. This makes it possible to reveal the correlation between the user's economic activities and emotions. 【0707】 The server uses common SQL database software as its database management system during this process. It also utilizes speech recognition technology, facial recognition cameras, and natural language processing tools for analyzing emotional states. Specifically, it uses a common speech recognition API for speech analysis and a common facial recognition API for facial expression analysis. Based on this, the user's emotions are quantified and recorded. 【0708】 The server then integrates financial behavior data and emotional state data and analyzes this information. This analysis uses the Python programming language and its data analysis library, Pandas. The goal of the analysis is to understand how user emotions influence economic activity. The analysis results will highlight areas for improvement to enable users to conduct economic activities efficiently while maintaining a stable mental state. 【0709】 Furthermore, the server uses a generative AI model to generate personalized advice for the user based on the analysis results. For example, a general natural language generation model could be used as this AI generative model. This allows users to obtain appropriate information to make financial decisions while taking their current emotional state into consideration. 【0710】 The device displays advice received from the server to the user. Here, the device refers to common information display devices such as smartphones, tablets, and PCs. The device updates the advice in real time according to the user's emotional state and financial situation, and issues warnings as needed. The device, which is always in the user's possession, provides new information via push notifications through the application. 【0711】 For example, if the emotion engine detects that a user is experiencing high levels of stress, it might offer specific suggestions such as, "We recommend you cut back on your current spending. Prioritize long-term mental well-being and consider a low-risk investment strategy." 【0712】 Furthermore, an example of a prompt sentence is the following input sentence provided to a generative AI model: "Suggest an appropriate investment strategy when the user is feeling stressed." This prompt sentence is used by the generative AI model to generate appropriate advice. 【0713】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0714】 Step 1: 【0715】 The server collects users' financial behavior data. It receives user bank account transaction history and manually entered income / expense information as input and stores it in a database. Specifically, users retrieve transaction history via financial institution APIs and automatically add it to the database. The output is an integrated financial behavior dataset, which is used for subsequent analysis. 【0716】 Step 2: 【0717】 The server collects data on the user's emotional state. Inputs include audio data, facial image data, and text data. Audio data and facial image data are analyzed using voice analysis and facial recognition technologies, while text data is analyzed using natural language processing technologies. Specifically, emotional data is collected using the microphone and camera while the user is operating the application, and this data is analyzed and quantified. The output is data representing the user's emotions in numerical form. 【0718】 Step 3: 【0719】 The server integrates and analyzes financial behavior data and emotional state data. The data obtained in steps 1 and 2 is used as input. Python and Pandas are used to combine the datasets and analyze the impact of users' emotional states on economic activity using statistical models. Specifically, regression analysis and clustering are performed to evaluate correlations and trends. The output generates a report on the emotional impact on users' economic behavior. 【0720】 Step 4: 【0721】 The server generates personalized advice using a generative AI model. The analysis results from step 3 are provided as input. Prompts are used with the generative AI model to generate user-appropriate advice in natural language. Specifically, the AI ​​outputs an answer based on prompts such as, "What investment strategy should I recommend when I'm under high stress?" The output provides investment and financial advice that takes the user's emotional state into consideration. 【0722】 Step 5: 【0723】 The terminal displays the advice received from the server to the user. The input is the advice generated in step 4. Specifically, the application on the smartphone or tablet notifies the user of the advice in real time. If the user's situation changes, the application updates the advice in real time and sends appropriate alerts. The output is the provision of up-to-date financial advice to the user based on the latest information. 【0724】 (Application Example 2) 【0725】 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". 【0726】 In recent years, many users lead daily lives closely intertwined with complex financial activities, yet few systems adequately consider the influence of emotions on economic behavior. Most financial management systems primarily rely on analysis based on economic data, making it difficult to provide personalized strategies that take emotional factors into account. This makes it challenging for users to make optimal financial decisions while maintaining emotional stability. 【0727】 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. 【0728】 This invention includes a server that collects user financial activity data and securely stores it in a database, an emotion analysis means that analyzes the user's emotional state and generates emotion data, and a means that correlates and analyzes the financial data and emotion data to generate personalized financial advice. This enables comprehensive management of the user's economic behavior and emotional state, allowing for optimal financial decisions in a mentally stable state. 【0729】 "User financial activity data" refers to a collection of information related to a user's income, expenses, and other financial transactions in their daily life. 【0730】 "Means of securely storing data in a database" refers to storage technology that implements appropriate security protocols to protect acquired data from unauthorized external access and tampering. 【0731】 "User emotional state" refers to information that indicates the psychological state a user is feeling at a specific point in time, and is estimated through voice recognition, facial recognition, and text analysis. 【0732】 "Emotional data" refers to data that quantifies or represents a user's emotional state, expressing the type and intensity of their emotions. 【0733】 "Sentiment analysis tools" refer to a collection of algorithms and technologies that analyze user-generated data such as voice, images, and text, and use that data to infer and quantify the user's emotional state. 【0734】 "Methods for correlating and analyzing financial data and emotional data" refers to technologies that integrate financial activity data and emotional data, analyze their correlations, and then evaluate the influence of emotions on users' economic behavior. 【0735】 "Means of generating personalized financial advice" refers to the technologies and processes used to recommend the most suitable financial strategies and consumer behaviors based on a user's individual circumstances and emotional state. 【0736】 The system for implementing this invention consists of a server, a terminal, and a user. The server is responsible for collecting the user's financial activity data and storing it in a secure database. This utilizes a software platform that employs technologies such as speech recognition, facial recognition, and text analysis. 【0737】 The server uses these technologies to analyze the user's emotional state and generates and stores quantified emotional data. This emotional data is then processed in conjunction with financial data to evaluate the impact of emotions on the user's economic behavior. 【0738】 The device provides users with analysis results from the server and personalized financial advice. An intuitive interface has been developed to make it easy for users to access this information using their smartphones. It responds to changes in emotions in real time, providing users with the most appropriate financial advice. 【0739】 As a concrete example, when users manage their daily expenses, they can receive advice that takes their mental health into consideration, based on emotional analysis. For instance, if a user is stressed, a suggestion such as, "How about visiting a cafe today to relax, within reasonable limits?" might appear on the screen. 【0740】 The use of generative AI models to generate prompts is also being considered. For example, using prompts such as "Please come up with appropriate spending management suggestions for when the user is stressed" could enable the provision of more effective advice. 【0741】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0742】 Step 1: 【0743】 The server collects user financial activity data through an interface. It receives transaction history from financial institutions and manual input from users, and securely stores it in a database. Here, encryption technology is used to prevent unauthorized access to the data. Inputs are transaction history and manual input data, and outputs are data stored in an encrypted database. 【0744】 Step 2: 【0745】 The server analyzes the user's emotional state using emotion analysis methods that employ speech recognition, facial recognition, and text analysis. It receives voice data, image data, and text data provided by the user as input, and processes these through an algorithm to quantify the emotional state. The output is the quantified emotion data. 【0746】 Step 3: 【0747】 The server integrates and correlates accumulated financial and emotional data for analysis. Using financial and emotional data as input, it evaluates the correlation between the two sets of data using machine learning algorithms. This process calculates the influence of emotions on users' economic behavior. The output is analytical data showing the correlations. 【0748】 Step 4: 【0749】 The server generates personalized financial advice tailored to the user based on the analysis results. It takes emotional state and financial analysis data as input and uses a generative AI model to create advice. This process uses prompts such as, "Please suggest appropriate spending management when the user is stressed." The output is financial advice designed to improve the user experience. 【0750】 Step 5: 【0751】 The terminal visually presents financial advice sent from the server to the user. It performs activities to display the information in a user-friendly format through a smartphone application. The input is financial advice from the server, and the output is the visual interface on the terminal. 【0752】 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. 【0753】 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. 【0754】 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. 【0755】 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. 【0756】 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. 【0757】 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. 【0758】 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. 【0759】 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. 【0760】 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." 【0761】 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. 【0762】 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. 【0763】 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. 【0764】 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. 【0765】 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. 【0766】 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. 【0767】 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. 【0768】 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. 【0769】 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. 【0770】 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. 【0771】 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. 【0772】 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 as being incorporated by reference. 【0773】 The following is further disclosed regarding the embodiments described above. 【0774】 (Claim 1) 【0775】 A means of collecting users' financial transaction data and securely storing it in a database, 【0776】 A means for analyzing the aforementioned financial transaction data and understanding the user's income and expenditure patterns, 【0777】 A means of analyzing a user's insurance policy and providing the optimal insurance plan, 【0778】 A means of constructing an investment portfolio based on the user's risk tolerance and market data, 【0779】 A means for automatically generating an optimal savings plan based on the user's income and expenses, 【0780】 A means to monitor users' daily spending in real time, detect anomalies, and send alerts, 【0781】 A system that includes this. 【0782】 (Claim 2) 【0783】 The system according to claim 1, comprising means for detecting duplication of users' insurance contracts and using an optimization algorithm to reduce waste. 【0784】 (Claim 3) 【0785】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward achieving their savings goals and adjusting the savings plan as necessary. 【0786】 "Example 1" 【0787】 (Claim 1) 【0788】 A means of collecting users' financial information and securely storing it on a computer, 【0789】 A means for processing the aforementioned financial information and understanding the user's income and expenditure trends, 【0790】 A means of evaluating a user's insurance policy and providing the most suitable insurance policy proposal, 【0791】 A means of constructing investment allocations based on the user's risk tolerance and market conditions, 【0792】 A means for automatically generating an optimal savings plan based on the user's income and expenses, 【0793】 A means to instantly monitor the user's daily spending, detect anomalies, and send alerts, 【0794】 A means of providing users with asset management and financial advice by utilizing generative AI models, 【0795】 A system that includes this. 【0796】 (Claim 2) 【0797】 The system according to claim 1, comprising means for detecting duplication of users' insurance contracts and using optimization processing to reduce waste. 【0798】 (Claim 3) 【0799】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward achieving their savings goals and adjusting the savings plan as necessary. 【0800】 "Application Example 1" 【0801】 (Claim 1) 【0802】 A means of collecting users' financial transaction data and securely storing it in a database, 【0803】 A means for analyzing the aforementioned financial transaction data and understanding the user's income and expenditure patterns, 【0804】 A means of analyzing a user's insurance policy and providing the optimal insurance plan, 【0805】 A means of constructing an investment portfolio based on the user's risk tolerance and market data, 【0806】 A means for automatically generating an optimal savings plan based on the user's income and expenses, 【0807】 A means to monitor users' daily spending in real time, detect anomalies, and send alerts, 【0808】 A means of notifying users of personalized savings advice to help them achieve their goals, 【0809】 A means of generating customized advice to improve users' financial behavior, 【0810】 A system that includes this. 【0811】 (Claim 2) 【0812】 The system according to claim 1, comprising means for detecting duplication of users' insurance contracts and using an optimization algorithm to reduce waste. 【0813】 (Claim 3) 【0814】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward achieving their savings goals and adjusting the savings plan as necessary. 【0815】 "Example 2 of combining an emotion engine" 【0816】 (Claim 1) 【0817】 A means of collecting and securely storing users' financial behavior data and emotional state data, 【0818】 A means for analyzing the aforementioned financial behavior data and emotional state data to understand the user's economic behavior patterns and their emotional impact, 【0819】 A means of evaluating a user's emotional state using voice analysis, facial expression analysis, and text analysis, 【0820】 Based on the analyzed data, a means of providing optimized advice to the user using generation technology, 【0821】 A means of personalizing investment and financial plans based on the user's emotional state, 【0822】 A means to respond to users' real-time emotional changes and update alerts and financial advice, 【0823】 A system that includes this. 【0824】 (Claim 2) 【0825】 The system according to claim 1, comprising means for analyzing the impact of a user's emotional state on a financial plan and adjusting the plan based on the results. 【0826】 (Claim 3) 【0827】 The system according to claim 1, comprising means for evaluating the relationship between user emotions and financial behavior using generation technology, performing continuous monitoring, and updating the advice content as necessary. 【0828】 "Application example 2 when combining with an emotional engine" 【0829】 (Claim 1) 【0830】 A means of collecting users' financial activity data and securely storing it in a database, 【0831】 A means for analyzing the aforementioned financial activity data and understanding the user's income and expenditure processes, 【0832】 A means of constructing investment strategies based on the user's risk tolerance and market data, 【0833】 A means for automatically generating an optimal savings plan based on the user's income and expenses, 【0834】 A means to monitor users' daily spending in real time, detect abnormal activity and send notifications, 【0835】 A sentiment analysis method that analyzes a user's emotional state using speech recognition, facial recognition, and text analysis, and generates sentiment data. 【0836】 A method for analyzing and correlating financial data and emotional data to generate financial advice tailored to the user's emotions, 【0837】 A means of providing spending management based on the user's emotional state and a consumption strategy that takes mental health into consideration, 【0838】 A system that includes this. 【0839】 (Claim 2) 【0840】 The system according to claim 1, comprising means for detecting duplication of users' insurance contracts and using an optimization algorithm to reduce waste. 【0841】 (Claim 3) 【0842】 The system according to claim 1, further comprising means for periodically evaluating the user's progress toward achieving their savings goals and adjusting the savings plan according to the user's emotional state. [Explanation of symbols] 【0843】 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 collecting users' financial transaction data and securely storing it in a database, A means for analyzing the aforementioned financial transaction data and understanding the user's income and expenditure patterns, A means of analyzing a user's insurance policy and providing the optimal insurance plan, A means of constructing an investment portfolio based on the user's risk tolerance and market data, A means for automatically generating an optimal savings plan based on the user's income and expenses, A means to monitor users' daily spending in real time, detect anomalies, and send alerts, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for using an optimization algorithm to detect duplication of users' insurance contracts and reduce waste. [Claim 3] The system according to claim 1, further comprising means for periodically evaluating the user's progress toward achieving their savings goals and adjusting the savings plan as necessary.

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