system

Abstract

We provide the system. [Solution] A means of collecting and obtaining users' financial information, An analytical means for analyzing acquired financial information, A generation means for generating financial advice based on analysis results, A means of providing the generated financial advice to the user, 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 method for controlling a persona chatbot, which is 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 in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 For personal financial management, various information is scattered. For example, bank accounts, investment information, insurance contracts, etc. exist on different platforms respectively. Therefore, it is difficult to manage these in a unified manner and obtain effective financial advice. In addition, it requires specialized knowledge for an individual to accurately grasp their own financial situation and make a long-term financial plan, which is a laborious task for many people. 【Means for Solving the Problems】 【0005】 This invention provides a system that collects and analyzes a user's financial information to provide the user with optimized financial advice. To this end, the system includes a collection means for acquiring information from financial institutions based on the user's permission, an analysis means for analyzing the acquired financial information, a generation means for generating financial advice based on the analysis results, and a provision means for providing the generated advice. This system allows the user to centrally manage multiple pieces of financial information and create an accurate financial plan. 【0006】 "Collection means" refers to means that have the function of acquiring various financial information of users from external sources and importing it into the system. 【0007】 "Analysis tools" refer to methods for analyzing collected financial information and performing calculations and evaluations to understand the user's economic situation. 【0008】 A "generation method" is a means that has the function of creating useful financial advice for the user based on the analysis results. 【0009】 "Means of delivery" refers to means that have the function of presenting generated financial advice to the user in an easy-to-understand format. 【0010】 "Communication means" refers to a network interface that allows for the exchange of data with external financial institutions or platforms, and is a means of sending and receiving information. [Brief explanation of the drawing] 【0011】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0012】 Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings. 【0013】 First, let's explain the terminology used in the following explanation. 【0014】 In the following embodiments, the numbered 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. 【0015】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0016】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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. 【0017】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0018】 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." 【0019】 [First Embodiment] 【0020】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0021】 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. 【0022】 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). 【0023】 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. 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0028】 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. 【0029】 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. 【0030】 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. 【0031】 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". 【0032】 In implementing this invention, the process begins with a user accessing the system using their own device and logging in. The device sends the authentication information entered by the user to the server and manages the process of verifying the user's identity. Once the server authenticates the user, a session is started, and an interface requesting permission from the user to collect financial information is displayed on the device. 【0033】 Users prepare to provide information from various financial institutions to the server by granting permission for data collection on their devices. Based on the user's permission, the server efficiently collects the necessary data via the financial institutions' APIs through security protocols. This data includes bank account transaction history, credit card statements, investment portfolio information, and insurance policy details, and is organized on the server. 【0034】 The acquired data is input into an analysis module by the server and analyzed in detail using AI algorithms. The analysis module visualizes the user's financial situation, understands income and expenditure patterns, and the status of assets and liabilities, and then generates specific economic indicators such as investment risk and insurance review. These analysis results are useful for future predictions and the detection of anomaly patterns. 【0035】 Next, the generation method creates optimal financial advice based on the analysis results. This advice is tailored to the user's specific financial situation and includes points for saving money, efficient risk diversification methods, and appropriate insurance review suggestions. 【0036】 Ultimately, the generated advice is sent to the user's device and displayed in a visually easy-to-understand format. An interactive dashboard is used, allowing users to click on details of each piece of advice to obtain further information and actionable guidance. Specific examples include suggestions for reducing monthly fixed expenses or guidelines for balancing investment portfolios. This advice enables users to manage their finances more effectively and healthily. 【0037】 The following describes the processing flow. 【0038】 Step 1: 【0039】 The user logs into the system using their device. Logging in requires entering a user ID and password, or authentication information such as biometric authentication. 【0040】 The terminal retrieves user input information and sends it to the server via a secure communication channel. 【0041】 The server uses the received authentication information to verify it against the database and authenticate the user. If authentication is successful, a session is started. 【0042】 Step 2: 【0043】 The device displays an interface requesting the user's permission to collect and use various financial data. 【0044】 The user selects the financial institution and type of data to be collected, and then clicks the "Allow" button. 【0045】 Based on the user's specifications, the server accesses the API of the selected financial institution and obtains the necessary access keys and tokens. 【0046】 Step 3: 【0047】 The server collects specified financial data from financial institutions via an API. This data includes transaction history, asset information, and liability summaries. 【0048】 The collected data is formatted and stored in a temporary database on the server. 【0049】 Step 4: 【0050】 The server analyzes the data. The analysis module starts up, and the AI ​​algorithm evaluates the user's financial situation. 【0051】 The analysis includes pattern analysis of income and expenditure, categorization of assets and liabilities, and assessment of investment risk, generating detailed economic indicators. 【0052】 Step 5: 【0053】 The server uses the generated analysis results to create optimal financial advice for the user. 【0054】 The generation system is activated, and the advice is converted into easily understandable language using natural language generation. This includes suggestions for saving money and revisions to investment strategies. 【0055】 Step 6: 【0056】 The server sends generated advice to the terminal. The communication is encrypted to ensure security. 【0057】 The device receives advice and displays it to the user in an easy-to-understand format, such as an interactive dashboard or graph. 【0058】 Step 7: 【0059】 Users can review the advice they receive and, if necessary, take action based on the guidelines provided. This allows them to revise and implement their long-term financial plans. 【0060】 (Example 1) 【0061】 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." 【0062】 In today's information technology landscape, efficiently and effectively managing personal finances is a crucial challenge. However, manual data collection and analysis are time-consuming and prone to errors. Furthermore, integrating and managing information from multiple financial institutions is cumbersome and burdensome for users. Therefore, there is a need for a system that allows users to easily and securely collect financial information and receive rational advice. 【0063】 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. 【0064】 In this invention, the server includes authentication means for authenticating using user input information, communication means for collecting information from various data sources based on user permission, analysis means for analyzing the collected information and generating analysis results, generation means for generating advice based on the analysis results using a generated AI model, and provision means for visually displaying and providing the generated advice to the user. This enables users to safely and quickly collect and analyze their financial information and receive appropriate financial advice. 【0065】 An "authentication method" is a mechanism for verifying a user's identity based on the information they enter. 【0066】 "Communication means" refers to a function that, with the user's permission, collects necessary information from data sources. 【0067】 "Analysis means" refers to a function that analyzes collected information and evaluates the user's financial situation. 【0068】 A "generation method" is a system that uses an AI model to create useful advice for the user from the analysis results. 【0069】 "Means of delivery" refers to an interface that visually and clearly conveys the generated advice to the user. 【0070】 A "generative AI model" is an algorithm that extracts patterns from large amounts of data and generates optimal advice for the user. 【0071】 This invention is a system for efficiently collecting and analyzing users' financial information. Users access the system using their own terminals and perform personal authentication. The terminals transmit the authentication information entered by the user to the server using an encryption protocol. The server compares this information with a database and authenticates the user. 【0072】 After successful authentication, the server displays an interface to the terminal requesting permission to collect financial information. This interface allows the user to decide which financial institutions to collect data from. Based on the user's permission, the server collects information using the APIs of the specified data sources. Specifically, it obtains bank transaction data, credit card statements, and other similar information through secure communication methods. 【0073】 The collected data is sent to the server's analysis module, where a generated AI model is applied. The AI ​​model analyzes the user's income, expenses, assets, and liabilities based on the data to assess their financial situation. From this assessment, the server generates optimal financial advice. 【0074】 The generated advice is presented to the user's device in a visually easy-to-understand format. This includes various interactive elements, allowing the user to click on each piece of advice to delve deeper and obtain more detailed information. 【0075】 By receiving this advice, users can take concrete actions to improve their financial situation. For example, in response to specific questions such as "How can I reduce my monthly expenses?", the generative AI model will suggest ways to reduce spending, allowing users to make optimal decisions based on that. 【0076】 An example of a prompt message is, "Analyze your spending over the past three months and suggest ways to reduce your entertainment expenses." In this way, a server-based AI model can provide concrete solutions to the problems the user faces. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 User Authentication 【0080】 Input: The user enters their authentication information (user ID, password) into the input fields on the device. 【0081】 Specific operation: The terminal encrypts the entered authentication information using the SSL / TLS protocol and sends it to the server. 【0082】 Data processing: The server receives encrypted authentication information and compares it with user information in the database. 【0083】 Output: If authentication is successful, the server generates a session ID and returns it to the terminal. If it fails, an error message is returned. 【0084】 Step 2: 【0085】 Presentation of authorization interface 【0086】 Input: User information with an authenticated session ID. 【0087】 Specific operation: The server creates a list of financial data that the user can access and sends it to the terminal. 【0088】 Output: The user terminal displays a list of available financial institutions and an interface requesting permission for data collection from each institution. 【0089】 Step 3: 【0090】 Data collection permission 【0091】 Input: The user selects the financial institution that they are allowed to collect data from via the interface. 【0092】 Specific action: The terminal sends the user's selection to the server. 【0093】 Data processing: The server associates the API key of the selected financial institution with the user's authorization and generates the token necessary for secure communication. 【0094】 Output: A list of financial institutions authorized to collect information. 【0095】 Step 4: 【0096】 Data collection 【0097】 Input: A list of authorized financial institutions and their associated API keys. 【0098】 Specific operation: The server accesses financial institutions via an API and retrieves data within a specified range. 【0099】 Data processing: The acquired data undergoes format conversion and data cleansing to prepare it for analysis. 【0100】 Output: A categorized financial dataset. 【0101】 Step 5: 【0102】 Data analysis 【0103】 Input: A well-organized dataset obtained from a financial institution. 【0104】 Specific operation: The server inputs data into the analysis module and performs a detailed analysis using a generated AI model. 【0105】 Data processing: AI algorithms evaluate income trends, spending patterns, and investment risks to generate economic indicators. 【0106】 Output: A detailed financial report based on the analysis results. 【0107】 Step 6: 【0108】 Generating and providing advice 【0109】 Input: Financial report generated by AI analysis. 【0110】 Specific operation: The server uses a generated AI model to create optimal advice for the user. The advice is converted into text and dashboard formats. 【0111】 Data processing: Generates asset allocation strategies and spending reduction suggestions based on the user's financial data. 【0112】 Output: Visualized advice and suggested action plans sent to the terminal. 【0113】 (Application Example 1) 【0114】 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." 【0115】 In today's world, many users find it difficult to efficiently manage complex financial information and predict and plan for future expenses. Furthermore, conventional asset management often fails to detect abnormal spending patterns, and users lack access to appropriate advice for sound asset management. 【0116】 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. 【0117】 In this invention, the server includes acquisition means for collecting user asset information, analysis means for analyzing the collected asset information, and prediction means for predicting future spending trends using the user's past spending information. This enables users to efficiently manage complex financial information and predict future spending. It also allows for the detection of abnormal spending patterns and the provision of appropriate asset management advice. 【0118】 "Means of acquisition" refers to a system or device for collecting user asset information. 【0119】 "Analysis tools" refer to systems or devices that analyze collected asset information and use the results to understand the user's financial situation. 【0120】 "Generation means" refers to a system or device that generates asset management advice for users based on analysis results. 【0121】 "Means of provision" refers to a system or device for presenting the generated asset management advice to the user. 【0122】 A "predictive means" is a system or device that predicts future spending trends based on a user's past spending information. 【0123】 "Detection means" refers to a system or device that monitors a user's spending patterns and identifies abnormal patterns. 【0124】 A "planning tool" is a system or device for forming a user's long-term asset management plan. 【0125】 "Communication means" refers to a system or device for collecting necessary information from external financial institutions, etc., based on the user's consent. 【0126】 The system for implementing the invention consists of a user terminal such as a smartphone and a server operating in the backend. The system's program collects, analyzes, and generates and provides management advice on asset information. The terminal allows users to log in via an application on the terminal, and secure login is provided through OAuth authentication. 【0127】 The server uses the Django framework as its program execution environment and retrieves data from financial institutions via APIs. The retrieved data is securely stored in Firebase, and TENSORFLOW® is used as the analysis tool to analyze the user's financial data in real time. This analysis uses a prediction tool to forecast future spending trends and detect abnormal spending patterns. The analysis results are generated by a generation tool to create advice for asset management, which is then presented to the user through a terminal application via a delivery tool. 【0128】 The device provides an interactive dashboard built with React Native, through which users can view detailed information and plan their asset management. For example, monthly spending on cafes is visualized in a graph, and the AI ​​issues an alert saying, "You've spent a lot on food this month, would you like to review your savings plan?" 【0129】 An example of a prompt to input into the generating AI model would be: "Predict future spending trends based on the user's payment data from the past three months and generate savings advice." 【0130】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0131】 Step 1: 【0132】 The user logs into the application using their device. The input includes the user's authentication information, which the server uses to verify the user's identity via OAuth authentication. If authentication is successful, a session is started. The output shows that the user is logged in. 【0133】 Step 2: 【0134】 Once the user grants permission for data collection, the device sends this intention to the server. The server uses acquisition methods to collect the user's asset information via the financial institution's API. The input includes the user's authorization information. This allows bank account transaction history and credit card statements to be securely stored on the server. As output, the necessary asset information is stored on the server. 【0135】 Step 3: 【0136】 The server receives the collected data and uses TensorFlow to analyze the asset information. The input includes specific asset data such as transaction history and usage details. Based on this data, data processing is performed, such as analyzing spending patterns, making it possible to understand the financial situation. The output is the analyzed asset information. 【0137】 Step 4: 【0138】 The server generates asset management advice using the analysis results. Analysis results are provided as input, and specific advice is generated by running an AI model. The output is asset management advice based on the user's current situation. 【0139】 Step 5: 【0140】 The generated asset management advice is transmitted from the server to the terminal via a delivery mechanism. The terminal visualizes and displays this information in an interactive dashboard built with React Native. Through this, users can view specific advice and future forecasts. The output is a visual and interactive financial dashboard. 【0141】 Step 6: 【0142】 Based on the advice and forecasts provided, users develop long-term asset management plans. The server assists users in developing these plans using planning tools. The input information includes various pieces of advice and forecast data obtained in advance. As an output, users can finalize their planned asset management action plan. 【0143】 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. 【0144】 This invention enables the provision of more personalized financial advice that takes into account the user's emotional state by integrating an emotion engine into existing financial management systems. First, the process begins when the user logs into the system using a terminal. The interface displayed on the terminal requests the user's permission and prepares to collect emotional data. 【0145】 The device is equipped with emotion recognition sensors that analyze the user's emotions in real time through their facial expressions and tone of voice. This emotion data is transmitted to a server via a collection mechanism and processed along with financial data. The server activates an emotion engine to analyze the emotions the user expressed upon login and identify emotional stress levels and anxiety levels. 【0146】 The results of this sentiment analysis are fed back into the evaluation of financial data through the analysis tools. For example, if a user is in a highly stressed state, the system may suggest low-risk investment methods or present savings plans to provide further peace of mind. Conversely, if the user is relaxed, it may offer advice that includes more challenging investment options. In this way, the generating tools create sentiment-based advice, and the providing tools display the advice in a format that suits the user. 【0147】 The advice displayed on the user's device is delivered through an interactive dashboard, allowing users to gain insights based on the emotional data behind the advice. For example, if a user is feeling pressured to spend, the system can pinpoint areas where savings can be made, prompting quick action. Furthermore, by analyzing the user's emotions from their feedback, if feelings such as a desire to avoid being defeated are detected, positive financial options within those constraints will be highlighted. 【0148】 In this way, by using an emotion engine, it is possible to provide financial advice that is integrated with the user's emotions, contributing to better decision-making. 【0149】 The following describes the processing flow. 【0150】 Step 1: 【0151】 Users log in to the system using their device. During login, they use their user ID and password, and biometric authentication if necessary. 【0152】 The terminal sends the entered authentication information to the server to authenticate the user and establish a session. 【0153】 Step 2: 【0154】 The device activates an emotion recognition sensor and collects the user's facial expressions and voice tone in real time through the camera and microphone. 【0155】 Prepare to store emotional data in temporary storage for analysis. 【0156】 Step 3: 【0157】 Users configure permission settings on their device for the collection of financial data. They select the financial institutions and types of data they wish to collect. 【0158】 The server, with permission, uses financial institution APIs to collect users' financial data. 【0159】 Step 4: 【0160】 The server receives emotional and financial data and uses an emotion engine to analyze the user's emotional state, determining stress levels and emotional tendencies. 【0161】 The analytical method takes sentiment data into consideration, evaluates financial data in detail, and calculates specific economic indicators. 【0162】 Step 5: 【0163】 The server generates financial advice that reflects the user's emotional state based on the analysis results. 【0164】 The generation method incorporates user emotion data into the scenario and formats the advice content into easy-to-understand natural language. 【0165】 Step 6: 【0166】 The server encrypts the generated advice and sends it to the terminal. 【0167】 The device decodes the information it receives and presents it to the user in a visually easy-to-understand dashboard. 【0168】 Step 7: 【0169】 Users view the dashboard and review the advice provided. They gain opportunities to adjust their financial behavior through specific advice based on their emotional state. 【0170】 If necessary, users will take the suggested actions and incorporate them into their long-term financial plans. 【0171】 (Example 2) 【0172】 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." 【0173】 Traditional financial management systems can analyze a user's financial information and provide advice, but they have struggled to provide personalized advice that takes into account the user's emotional state. Therefore, a challenge exists in situations where emotional factors such as stress and anxiety influence decision-making, making it difficult for users to receive effective financial advice. 【0174】 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. 【0175】 In this invention, the server includes information gathering means for acquiring the user's financial information, emotion gathering means for acquiring the user's emotional state using sensors, and data analysis means for integrating and analyzing the acquired emotional state and financial information. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0176】 "Information gathering means" refers to the hardware and software configuration necessary to collect user financial information, specifically a system that acquires information via databases or APIs. 【0177】 "Emotional gathering means" is a general term for sensors and related software used to measure and acquire a user's emotional state, and includes functions to analyze facial expressions and voice tone using cameras and microphones. 【0178】 "Data analysis means" refers to software algorithms or AI models that integrate and analyze acquired user emotional states and financial information, and possess the ability to evaluate and predict data. 【0179】 "Advice generation means" refers to the process and algorithms for generating financial advice to be provided to users based on analyzed information, and enables personalized suggestions tailored to emotional states through the generation AI model. 【0180】 "Delivery methods" refer to the components, including user interfaces and dashboards, used to present generated financial advice to users, and are designed to allow users to easily understand and interact with the information. 【0181】 "Communication methods" refer to technologies including communication protocols and network interfaces that enable a system to acquire information from external databases or financial institutions, and have mechanisms that allow for secure data transfer. 【0182】 This invention is a system that provides personalized financial advice that takes into account the user's emotional state. The user accesses this system using a terminal equipped with a camera and microphone, which function as a means of collecting emotions. This makes it possible to analyze the user's facial expressions and tone of voice in real time. 【0183】 Emotional data is sent from the terminal to the server. The server acquires financial data through information gathering methods and integrates and processes it with the emotional data. This processing uses data analysis methods with AI models to evaluate the user's stress level and anxiety state. 【0184】 Furthermore, an advice generation system is activated based on the analyzed data to generate optimal financial advice for the user. The generating AI model uses prompts such as "Please suggest the optimal investment strategy that reflects the user's emotional state" to provide suggestions tailored to the user's individual circumstances. 【0185】 As a result, the server sends this generated advice back to the terminal and displays it through a dashboard. This dashboard is designed so that users can intuitively understand the advice and take the necessary actions. For example, if a user is determined to be in a high-stress state, they may be presented with "low-risk investments" or "money-saving plans that provide peace of mind." 【0186】 This allows users to obtain information that supports appropriate financial decision-making based on their emotional state. 【0187】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0188】 Step 1: 【0189】 The user logs into the system using a terminal. The input here is the user's login information, and the output is the login authentication result. The terminal grants access based on the user's authentication information and prepares to begin sentiment data collection. 【0190】 Step 2: 【0191】 The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time as a means of emotion collection. The input for this step is the user's voice and video data, and the output is digital data indicating the emotional state. Emotion recognition software analyzes facial features and voice characteristics to detect stress levels and other emotional indicators. 【0192】 Step 3: 【0193】 The device sends the collected emotional data to the server. The input is emotional state data, and the output is the result of uploading the data to the server. The data is transmitted via a secure protocol, and the server temporarily stores the data. 【0194】 Step 4: 【0195】 The server uses information gathering tools to acquire other financial data and integrate it with sentiment data. The input for this step is sentiment data and financial information, and the output is an integrated dataset. The server analyzes this data with an AI model to evaluate the user's overall situation. 【0196】 Step 5: 【0197】 The server uses data analysis tools to analyze the user's emotional state and generates optimal financial advice using a generative AI model. The input is an integrated dataset, and the output is emotion-based financial advice. The prompt used is "Please suggest an optimal investment strategy that reflects the user's emotional state." 【0198】 Step 6: 【0199】 The server sends the generated advice to the terminal, which displays it using an interactive dashboard. The input is the generated advice, and the output is the content displayed in the interface presented to the user. The user can then make financial decisions based on this information. 【0200】 (Application Example 2) 【0201】 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". 【0202】 Traditional financial management systems provide advice based solely on the user's financial information, which hinders the provision of personalized advice that takes into account the user's emotional state. Advice provided without considering the user's emotional state is less likely to encourage appropriate action, thus necessitating a new mechanism to improve the quality of decision-making. 【0203】 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. 【0204】 In this invention, the server includes emotion recognition means for collecting emotion data to recognize the user's emotional state, analysis integration means for comprehensively analyzing the emotion data acquired by the emotion recognition means and financial information, and generation means for generating financial advice that takes the emotional state into consideration based on the analysis results by the analysis integration means. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0205】 "Collection means" refers to a device or mechanism for obtaining financial information from a user. 【0206】 "Analysis means" refers to a device or system that has the function of analyzing acquired financial information and understanding the user's financial situation and trends. 【0207】 An "emotion recognition means" is a device or mechanism for collecting emotional data from a user's facial expressions and tone of voice to recognize the user's emotional state. 【0208】 "Analysis integration means" refers to a device or system that has the function of comprehensively analyzing emotional data acquired by emotion recognition means and financial information acquired by collection means. 【0209】 "Generation means" refers to a device or mechanism for creating financial advice that takes into account the user's emotional state, based on the analysis results from the analysis integration means. 【0210】 "Providing means" refers to a device or mechanism for presenting financial advice created by the generating means to the user. 【0211】 "Planning tools" refer to devices or methods for developing long-term financial plans and providing guidance to users. 【0212】 "Communication means" refers to devices or technologies used to collect information from financial institutions with the user's permission. 【0213】 This invention begins with the user logging into the system using a terminal. The terminal is equipped with a camera and microphone to recognize the user's emotional state in real time, allowing the emotion recognition system to analyze the user's facial expressions and tone of voice. This emotional data is then transmitted from the terminal to a server in the cloud. 【0214】 The server comprehensively analyzes emotional data acquired by emotion recognition tools and user financial information collected using communication tools. Specifically, it analyzes the data using AI frameworks such as TensorFlow and PyTorch to generate financial advice that takes the user's emotional state into account. 【0215】 The information obtained from the analysis is provided to the user through a generation mechanism. This allows the user to receive personalized financial advice tailored to their emotional state. The delivery mechanism displays the financial advice in an interactive dashboard format, and the user can also gain insights into the emotional data that underlies the advice. 【0216】 This system will suggest "installment payment options for peace of mind" if the user feels anxious while shopping. Conversely, if the user is relaxed, it will offer an incentive such as "10% off for buying now." Examples of prompts include "Analyze the user's current emotional state and suggest an appropriate payment method" and "Provide the user with purchase advice that promotes relaxation." 【0217】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0218】 Step 1: 【0219】 The user logs into the system using their device. This login process sends the user ID to the server. This allows the system to record the user's past financial and emotional data. 【0220】 Step 2: 【0221】 The device's camera and microphone activate to capture the user's emotional state. Specifically, the camera captures the user's facial expressions and the microphone records their voice, collecting emotional data. This data is then sent to a server as input data to run an emotion analysis algorithm. 【0222】 Step 3: 【0223】 The server receives emotion data sent from the terminal. The server processes the emotion data and analyzes it using generative AI models such as TensorFlow to identify the user's emotional state (e.g., stress level or relaxation level). This analysis extracts emotional characteristics and provides output to proceed to the next stage. 【0224】 Step 4: 【0225】 The server integrates and analyzes the sentiment analysis results and financial data obtained through communication. Using this input, the server performs data calculations with an analysis integration mechanism to generate a financial advice portfolio based on the user's emotional state. This portfolio includes financial options best suited to the user's current emotions. 【0226】 Step 5: 【0227】 The server generates financial advice tailored to the user's emotional state based on integrated analysis results. This advice, including an interpretation of the underlying emotional data, is prepared to be presented to the user as a visually easy-to-understand, interactive dashboard. 【0228】 Step 6: 【0229】 The terminal displays an interactive dashboard sent from the server to the user. The user can then make financial choices that suit their emotional state by following the advice received, thereby improving the quality of their decision-making. 【0230】 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. 【0231】 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. 【0232】 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. 【0233】 [Second Embodiment] 【0234】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0235】 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. 【0236】 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). 【0237】 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. 【0238】 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. 【0239】 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). 【0240】 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. 【0241】 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. 【0242】 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. 【0243】 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. 【0244】 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. 【0245】 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". 【0246】 In implementing this invention, the process begins with a user accessing the system using their own device and logging in. The device sends the authentication information entered by the user to the server and manages the process of verifying the user's identity. Once the server authenticates the user, a session is started, and an interface requesting permission from the user to collect financial information is displayed on the device. 【0247】 Users prepare to provide information from various financial institutions to the server by granting permission for data collection on their devices. Based on the user's permission, the server efficiently collects the necessary data via the financial institutions' APIs through security protocols. This data includes bank account transaction history, credit card statements, investment portfolio information, and insurance policy details, and is organized on the server. 【0248】 The acquired data is input into an analysis module by the server and analyzed in detail using AI algorithms. The analysis module visualizes the user's financial situation, understands income and expenditure patterns, and the status of assets and liabilities, and then generates specific economic indicators such as investment risk and insurance review. These analysis results are useful for future predictions and the detection of anomaly patterns. 【0249】 Next, the generation method creates optimal financial advice based on the analysis results. This advice is tailored to the user's specific financial situation and includes points for saving money, efficient risk diversification methods, and appropriate insurance review suggestions. 【0250】 Ultimately, the generated advice is sent to the user's device and displayed in a visually easy-to-understand format. An interactive dashboard is used, allowing users to click on details of each piece of advice to obtain further information and actionable guidance. Specific examples include suggestions for reducing monthly fixed expenses or guidelines for balancing investment portfolios. This advice enables users to manage their finances more effectively and healthily. 【0251】 The following describes the processing flow. 【0252】 Step 1: 【0253】 The user logs into the system using their device. Logging in requires entering a user ID and password, or authentication information such as biometric authentication. 【0254】 The terminal retrieves user input information and sends it to the server via a secure communication channel. 【0255】 The server uses the received authentication information to verify it against the database and authenticate the user. If authentication is successful, a session is started. 【0256】 Step 2: 【0257】 The device displays an interface requesting the user's permission to collect and use various financial data. 【0258】 The user selects the financial institution and type of data to be collected, and then clicks the "Allow" button. 【0259】 Based on the user's specifications, the server accesses the API of the selected financial institution and obtains the necessary access keys and tokens. 【0260】 Step 3: 【0261】 The server collects specified financial data from financial institutions via an API. This data includes transaction history, asset information, and liability summaries. 【0262】 The collected data is formatted and stored in a temporary database on the server. 【0263】 Step 4: 【0264】 The server analyzes the data. The analysis module starts up, and the AI ​​algorithm evaluates the user's financial situation. 【0265】 The analysis includes pattern analysis of income and expenditure, categorization of assets and liabilities, and assessment of investment risk, generating detailed economic indicators. 【0266】 Step 5: 【0267】 The server uses the generated analysis results to create optimal financial advice for the user. 【0268】 The generation system is activated, and the advice is converted into easily understandable language using natural language generation. This includes suggestions for saving money and revisions to investment strategies. 【0269】 Step 6: 【0270】 The server sends generated advice to the terminal. The communication is encrypted to ensure security. 【0271】 The device receives advice and displays it to the user in an easy-to-understand format, such as an interactive dashboard or graph. 【0272】 Step 7: 【0273】 Users can review the advice they receive and, if necessary, take action based on the guidelines provided. This allows them to revise and implement their long-term financial plans. 【0274】 (Example 1) 【0275】 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." 【0276】 In today's information technology landscape, efficiently and effectively managing personal finances is a crucial challenge. However, manual data collection and analysis are time-consuming and prone to errors. Furthermore, integrating and managing information from multiple financial institutions is cumbersome and burdensome for users. Therefore, there is a need for a system that allows users to easily and securely collect financial information and receive rational advice. 【0277】 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. 【0278】 In this invention, the server includes an authentication means for performing authentication using the user's input information, a communication means for collecting information from various data sources based on the user's permission, an analysis means for analyzing the collected information to generate an analysis result, a generation means for generating advice based on the analysis result using a generated AI model, and a provision means for visually displaying the generated advice and providing it to the user. Thereby, the user can safely and quickly collect and analyze their own financial information and receive appropriate financial advice. 【0279】 The "authentication means" is a mechanism for verifying the identity based on the information input by the user. 【0280】 The "communication means" is a function for collecting necessary information from the data source with the user's permission. 【0281】 The "analysis means" is a function for analyzing the collected information and evaluating the user's financial situation. 【0282】 The "generation means" is a mechanism for creating useful advice for the user from the analysis result using an AI model. 【0283】 The "provision means" is an interface for visually and clearly conveying the generated advice to the user. 【0284】 The "generated AI model" is an algorithm for extracting patterns from a large amount of data and generating optimal advice for the user. 【0285】 This invention is a system for efficiently collecting and analyzing the user's financial information. The user accesses the system using their own terminal and performs personal authentication. The terminal encrypts the authentication information input by the user and transmits it to the server using an encryption protocol. The server collates it with the database to authenticate the user. 【0286】 After successful authentication, the server displays an interface on the terminal to request permission for financial information collection. Through this interface, the user can determine from which financial institutions to collect data. Based on the user's permission, the server uses the APIs of the specified data providers to collect information. Specifically, it obtains data such as bank transaction data and credit card details through secure communication means. 【0287】 The collected data is sent to the server's analysis module, and a generative AI model is applied. The AI model analyzes the user's income, expenses, assets, and liabilities based on the data and evaluates the financial situation. From this evaluation result, the server generates optimal financial advice. 【0288】 The generated advice is provided to the user's terminal in a visually understandable form. This includes various interactive elements, and the user can click on each piece of advice to dig deeper to obtain detailed information. 【0289】 By receiving these pieces of advice, the user can take specific actions to improve their financial situation. For example, for specific questions such as "how to reduce monthly expenses", the generative AI model proposes methods for reducing expenses, and the user can make an optimal decision based on this. 【0290】 An example of a prompt sentence is "Analyze the expenses in the past three months and propose methods for reducing entertainment expenses." In this way, for the problems faced by the user, the generative AI model via the server can provide specific solutions. 【0291】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0292】 Step 1: 【0293】 User authentication 【0294】 Input: The user inputs authentication information (user ID, password) on the terminal in the input field. 【0295】 Specific operation: The terminal encrypts the input authentication information using the SSL / TLS protocol and sends it to the server. 【0296】 Data processing: The server receives the encrypted authentication information and compares it with the user information in the database. 【0297】 Output: If authentication is successful, the server generates a session ID and returns it to the terminal. If it fails, an error message is returned. 【0298】 Step 2: 【0299】 Presentation of permission interface 【0300】 Input: User information with an authenticated session ID. 【0301】 Specific operation: The server creates a list of financial data accessible to the user and sends it to the terminal. 【0302】 Output: The user terminal displays a list of available financial institutions and an interface requesting permission for each data collection. 【0303】 Step 3: 【0304】 Permission for data collection 【0305】 Input: The user selects the financial institutions that permit data collection in the interface. 【0306】 Specific operation: The terminal sends the user's selection to the server. 【0307】 Data calculation: The server associates the API key of the selected financial institution with the user's permission and generates a token required for secure communication. 【0308】 Output: A list of financial institutions authorized to collect information. 【0309】 Step 4: 【0310】 Data collection 【0311】 Input: A list of authorized financial institutions and their associated API keys. 【0312】 Specific operation: The server accesses financial institutions via an API and retrieves data within a specified range. 【0313】 Data processing: The acquired data undergoes format conversion and data cleansing to prepare it for analysis. 【0314】 Output: A categorized financial dataset. 【0315】 Step 5: 【0316】 Data analysis 【0317】 Input: A well-organized dataset obtained from a financial institution. 【0318】 Specific operation: The server inputs data into the analysis module and performs a detailed analysis using a generated AI model. 【0319】 Data processing: AI algorithms evaluate income trends, spending patterns, and investment risks to generate economic indicators. 【0320】 Output: A detailed financial report based on the analysis results. 【0321】 Step 6: 【0322】 Generating and providing advice 【0323】 Input: Financial report generated by AI analysis. 【0324】 Specific operation: The server uses a generated AI model to create optimal advice for the user. The advice is converted into text and dashboard formats. 【0325】 Data processing: Generates asset allocation strategies and spending reduction suggestions based on the user's financial data. 【0326】 Output: Visualized advice and suggested action plans sent to the terminal. 【0327】 (Application Example 1) 【0328】 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." 【0329】 In today's world, many users find it difficult to efficiently manage complex financial information and predict and plan for future expenses. Furthermore, conventional asset management often fails to detect abnormal spending patterns, and users lack access to appropriate advice for sound asset management. 【0330】 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. 【0331】 In this invention, the server includes acquisition means for collecting user asset information, analysis means for analyzing the collected asset information, and prediction means for predicting future spending trends using the user's past spending information. This enables users to efficiently manage complex financial information and predict future spending. It also allows for the detection of abnormal spending patterns and the provision of appropriate asset management advice. 【0332】 "Means of acquisition" refers to a system or device for collecting user asset information. 【0333】 "Analysis tools" refer to systems or devices that analyze collected asset information and use the results to understand the user's financial situation. 【0334】 "Generation means" refers to a system or device that generates asset management advice for users based on analysis results. 【0335】 "Means of provision" refers to a system or device for presenting the generated asset management advice to the user. 【0336】 A "predictive means" is a system or device that predicts future spending trends based on a user's past spending information. 【0337】 "Detection means" refers to a system or device that monitors a user's spending patterns and identifies abnormal patterns. 【0338】 A "planning tool" is a system or device for forming a user's long-term asset management plan. 【0339】 "Communication means" refers to a system or device for collecting necessary information from external financial institutions, etc., based on the user's consent. 【0340】 The system for implementing the invention consists of a user terminal such as a smartphone and a server operating in the backend. The system's program collects, analyzes, and generates and provides management advice on asset information. The terminal allows users to log in via an application on the terminal, and secure login is provided through OAuth authentication. 【0341】 The server uses the Django framework as its program execution environment and retrieves data from financial institutions via APIs. The retrieved data is securely stored in Firebase, and TensorFlow is used as the analysis tool to analyze the user's financial data in real time. This analysis uses a prediction tool to forecast future spending trends and detect abnormal spending patterns. The analysis results are generated by a generation tool to create advice for asset management, which is then presented to the user through a terminal application via a delivery tool. 【0342】 The device provides an interactive dashboard built with React Native, through which users can view detailed information and plan their asset management. For example, monthly spending on cafes is visualized in a graph, and the AI ​​issues an alert saying, "You've spent a lot on food this month, would you like to review your savings plan?" 【0343】 An example of a prompt to input into the generating AI model would be: "Predict future spending trends based on the user's payment data from the past three months and generate savings advice." 【0344】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0345】 Step 1: 【0346】 The user logs into the application using their device. The input includes the user's authentication information, which the server uses to verify the user's identity via OAuth authentication. If authentication is successful, a session is started. The output shows that the user is logged in. 【0347】 Step 2: 【0348】 Once the user grants permission for data collection, the device sends this intention to the server. The server uses acquisition methods to collect the user's asset information via the financial institution's API. The input includes the user's authorization information. This allows bank account transaction history and credit card statements to be securely stored on the server. As output, the necessary asset information is stored on the server. 【0349】 Step 3: 【0350】 The server receives the collected data and uses TensorFlow to analyze the asset information. The input includes specific asset data such as transaction history and usage details. Based on this data, data processing is performed, such as analyzing spending patterns, making it possible to understand the financial situation. The output is the analyzed asset information. 【0351】 Step 4: 【0352】 The server generates asset management advice using the analysis results. Analysis results are provided as input, and specific advice is generated by running an AI model. The output is asset management advice based on the user's current situation. 【0353】 Step 5: 【0354】 The generated asset management advice is transmitted from the server to the terminal via a delivery mechanism. The terminal visualizes and displays this information in an interactive dashboard built with React Native. Through this, users can view specific advice and future forecasts. The output is a visual and interactive financial dashboard. 【0355】 Step 6: 【0356】 Based on the advice and forecasts provided, users develop long-term asset management plans. The server assists users in developing these plans using planning tools. The input information includes various pieces of advice and forecast data obtained in advance. As an output, users can finalize their planned asset management action plan. 【0357】 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. 【0358】 This invention enables the provision of more personalized financial advice that takes into account the user's emotional state by integrating an emotion engine into existing financial management systems. First, the process begins when the user logs into the system using a terminal. The interface displayed on the terminal requests the user's permission and prepares to collect emotional data. 【0359】 The device is equipped with emotion recognition sensors that analyze the user's emotions in real time through their facial expressions and tone of voice. This emotion data is transmitted to a server via a collection mechanism and processed along with financial data. The server activates an emotion engine to analyze the emotions the user expressed upon login and identify emotional stress levels and anxiety levels. 【0360】 The results of this sentiment analysis are fed back into the evaluation of financial data through the analysis tools. For example, if a user is in a highly stressed state, the system may suggest low-risk investment methods or present savings plans to provide further peace of mind. Conversely, if the user is relaxed, it may offer advice that includes more challenging investment options. In this way, the generating tools create sentiment-based advice, and the providing tools display the advice in a format that suits the user. 【0361】 The advice displayed on the user's device is delivered through an interactive dashboard, allowing users to gain insights based on the emotional data behind the advice. For example, if a user is feeling pressured to spend, the system can pinpoint areas where savings can be made, prompting quick action. Furthermore, by analyzing the user's emotions from their feedback, if feelings such as a desire to avoid being defeated are detected, positive financial options within those constraints will be highlighted. 【0362】 In this way, by using an emotion engine, it is possible to provide financial advice that is integrated with the user's emotions, contributing to better decision-making. 【0363】 The following describes the processing flow. 【0364】 Step 1: 【0365】 Users log in to the system using their device. During login, they use their user ID and password, and biometric authentication if necessary. 【0366】 The terminal sends the entered authentication information to the server to authenticate the user and establish a session. 【0367】 Step 2: 【0368】 The device activates an emotion recognition sensor and collects the user's facial expressions and voice tone in real time through the camera and microphone. 【0369】 Prepare to store emotional data in temporary storage for analysis. 【0370】 Step 3: 【0371】 Users configure permission settings on their device for the collection of financial data. They select the financial institutions and types of data they wish to collect. 【0372】 The server, with permission, uses financial institution APIs to collect users' financial data. 【0373】 Step 4: 【0374】 The server receives emotional and financial data and uses an emotion engine to analyze the user's emotional state, determining stress levels and emotional tendencies. 【0375】 The analytical method takes sentiment data into consideration, evaluates financial data in detail, and calculates specific economic indicators. 【0376】 Step 5: 【0377】 The server generates financial advice that reflects the user's emotional state based on the analysis results. 【0378】 The generation method incorporates user emotion data into the scenario and formats the advice content into easy-to-understand natural language. 【0379】 Step 6: 【0380】 The server encrypts the generated advice and sends it to the terminal. 【0381】 The device decodes the information it receives and presents it to the user in a visually easy-to-understand dashboard. 【0382】 Step 7: 【0383】 Users view the dashboard and review the advice provided. They gain opportunities to adjust their financial behavior through specific advice based on their emotional state. 【0384】 If necessary, users will take the suggested actions and incorporate them into their long-term financial plans. 【0385】 (Example 2) 【0386】 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". 【0387】 Traditional financial management systems can analyze a user's financial information and provide advice, but they have struggled to provide personalized advice that takes into account the user's emotional state. Therefore, a challenge exists in situations where emotional factors such as stress and anxiety influence decision-making, making it difficult for users to receive effective financial advice. 【0388】 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. 【0389】 In this invention, the server includes information gathering means for acquiring the user's financial information, emotion gathering means for acquiring the user's emotional state using sensors, and data analysis means for integrating and analyzing the acquired emotional state and financial information. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0390】 "Information gathering means" refers to the hardware and software configuration necessary to collect user financial information, specifically a system that acquires information via databases or APIs. 【0391】 "Emotional gathering means" is a general term for sensors and related software used to measure and acquire a user's emotional state, and includes functions to analyze facial expressions and voice tone using cameras and microphones. 【0392】 "Data analysis means" refers to software algorithms or AI models that integrate and analyze acquired user emotional states and financial information, and possess the ability to evaluate and predict data. 【0393】 "Advice generation means" refers to the process and algorithms for generating financial advice to be provided to users based on analyzed information, and enables personalized suggestions tailored to emotional states through the generation AI model. 【0394】 "Delivery methods" refer to the components, including user interfaces and dashboards, used to present generated financial advice to users, and are designed to allow users to easily understand and interact with the information. 【0395】 "Communication methods" refer to technologies including communication protocols and network interfaces that enable a system to acquire information from external databases or financial institutions, and have mechanisms that allow for secure data transfer. 【0396】 This invention is a system that provides personalized financial advice that takes into account the user's emotional state. The user accesses this system using a terminal equipped with a camera and microphone, which function as a means of collecting emotions. This makes it possible to analyze the user's facial expressions and tone of voice in real time. 【0397】 Emotional data is sent from the terminal to the server. The server acquires financial data through information gathering methods and integrates and processes it with the emotional data. This processing uses data analysis methods with AI models to evaluate the user's stress level and anxiety state. 【0398】 Furthermore, an advice generation system is activated based on the analyzed data to generate optimal financial advice for the user. The generating AI model uses prompts such as "Please suggest the optimal investment strategy that reflects the user's emotional state" to provide suggestions tailored to the user's individual circumstances. 【0399】 As a result, the server sends this generated advice back to the terminal and displays it through a dashboard. This dashboard is designed so that users can intuitively understand the advice and take the necessary actions. For example, if a user is determined to be in a high-stress state, they may be presented with "low-risk investments" or "money-saving plans that provide peace of mind." 【0400】 This allows users to obtain information that supports appropriate financial decision-making based on their emotional state. 【0401】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0402】 Step 1: 【0403】 The user logs into the system using a terminal. The input here is the user's login information, and the output is the login authentication result. The terminal grants access based on the user's authentication information and prepares to begin sentiment data collection. 【0404】 Step 2: 【0405】 The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time as a means of emotion collection. The input for this step is the user's voice and video data, and the output is digital data indicating the emotional state. Emotion recognition software analyzes facial features and voice characteristics to detect stress levels and other emotional indicators. 【0406】 Step 3: 【0407】 The device sends the collected emotional data to the server. The input is emotional state data, and the output is the result of uploading the data to the server. The data is transmitted via a secure protocol, and the server temporarily stores the data. 【0408】 Step 4: 【0409】 The server uses information gathering tools to acquire other financial data and integrate it with sentiment data. The input for this step is sentiment data and financial information, and the output is an integrated dataset. The server analyzes this data with an AI model to evaluate the user's overall situation. 【0410】 Step 5: 【0411】 The server uses data analysis tools to analyze the user's emotional state and generates optimal financial advice using a generative AI model. The input is an integrated dataset, and the output is emotion-based financial advice. The prompt used is "Please suggest an optimal investment strategy that reflects the user's emotional state." 【0412】 Step 6: 【0413】 The server sends the generated advice to the terminal, which displays it using an interactive dashboard. The input is the generated advice, and the output is the content displayed in the interface presented to the user. The user can then make financial decisions based on this information. 【0414】 (Application Example 2) 【0415】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0416】 Traditional financial management systems provide advice based solely on the user's financial information, which hinders the provision of personalized advice that takes into account the user's emotional state. Advice provided without considering the user's emotional state is less likely to encourage appropriate action, thus necessitating a new mechanism to improve the quality of decision-making. 【0417】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0418】 In this invention, the server includes emotion recognition means for collecting emotion data to recognize the user's emotional state, analysis integration means for comprehensively analyzing the emotion data acquired by the emotion recognition means and financial information, and generation means for generating financial advice that takes the emotional state into consideration based on the analysis results by the analysis integration means. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0419】 "Collection means" refers to a device or mechanism for obtaining financial information from a user. 【0420】 "Analysis means" refers to a device or system that has the function of analyzing acquired financial information and understanding the user's financial situation and trends. 【0421】 An "emotion recognition means" is a device or mechanism for collecting emotional data from a user's facial expressions and tone of voice to recognize the user's emotional state. 【0422】 "Analysis integration means" refers to a device or system that has the function of comprehensively analyzing emotional data acquired by emotion recognition means and financial information acquired by collection means. 【0423】 "Generation means" refers to a device or mechanism for creating financial advice that takes into account the user's emotional state, based on the analysis results from the analysis integration means. 【0424】 "Providing means" refers to a device or mechanism for presenting financial advice created by the generating means to the user. 【0425】 "Planning tools" refer to devices or methods for developing long-term financial plans and providing guidance to users. 【0426】 "Communication means" refers to devices or technologies used to collect information from financial institutions with the user's permission. 【0427】 This invention begins with the user logging into the system using a terminal. The terminal is equipped with a camera and microphone to recognize the user's emotional state in real time, allowing the emotion recognition system to analyze the user's facial expressions and tone of voice. This emotional data is then transmitted from the terminal to a server in the cloud. 【0428】 The server comprehensively analyzes emotional data acquired by emotion recognition tools and user financial information collected using communication tools. Specifically, it analyzes the data using AI frameworks such as TensorFlow and PyTorch to generate financial advice that takes the user's emotional state into account. 【0429】 The information obtained from the analysis is provided to the user through a generation mechanism. This allows the user to receive personalized financial advice tailored to their emotional state. The delivery mechanism displays the financial advice in an interactive dashboard format, and the user can also gain insights into the emotional data that underlies the advice. 【0430】 This system will suggest "installment payment options for peace of mind" if the user feels anxious while shopping. Conversely, if the user is relaxed, it will offer an incentive such as "10% off for buying now." Examples of prompts include "Analyze the user's current emotional state and suggest an appropriate payment method" and "Provide the user with purchase advice that promotes relaxation." 【0431】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0432】 Step 1: 【0433】 The user logs into the system using their device. This login process sends the user ID to the server. This allows the system to record the user's past financial and emotional data. 【0434】 Step 2: 【0435】 The device's camera and microphone activate to capture the user's emotional state. Specifically, the camera captures the user's facial expressions and the microphone records their voice, collecting emotional data. This data is then sent to a server as input data to run an emotion analysis algorithm. 【0436】 Step 3: 【0437】 The server receives emotion data sent from the terminal. The server processes the emotion data and analyzes it using generative AI models such as TensorFlow to identify the user's emotional state (e.g., stress level or relaxation level). This analysis extracts emotional characteristics and provides output to proceed to the next stage. 【0438】 Step 4: 【0439】 The server integrates and analyzes the sentiment analysis results and financial data obtained through communication. Using this input, the server performs data calculations with an analysis integration mechanism to generate a financial advice portfolio based on the user's emotional state. This portfolio includes financial options best suited to the user's current emotions. 【0440】 Step 5: 【0441】 The server generates financial advice tailored to the user's emotional state based on integrated analysis results. This advice, including an interpretation of the underlying emotional data, is prepared to be presented to the user as a visually easy-to-understand, interactive dashboard. 【0442】 Step 6: 【0443】 The terminal displays an interactive dashboard sent from the server to the user. The user can then make financial choices that suit their emotional state by following the advice received, thereby improving the quality of their decision-making. 【0444】 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. 【0445】 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. 【0446】 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. 【0447】 [Third Embodiment] 【0448】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0449】 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. 【0450】 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). 【0451】 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. 【0452】 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. 【0453】 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). 【0454】 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. 【0455】 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. 【0456】 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. 【0457】 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. 【0458】 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. 【0459】 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". 【0460】 In implementing this invention, the process begins with a user accessing the system using their own device and logging in. The device sends the authentication information entered by the user to the server and manages the process of verifying the user's identity. Once the server authenticates the user, a session is started, and an interface requesting permission from the user to collect financial information is displayed on the device. 【0461】 Users prepare to provide information from various financial institutions to the server by granting permission for data collection on their devices. Based on the user's permission, the server efficiently collects the necessary data via the financial institutions' APIs through security protocols. This data includes bank account transaction history, credit card statements, investment portfolio information, and insurance policy details, and is organized on the server. 【0462】 The acquired data is input into an analysis module by the server and analyzed in detail using AI algorithms. The analysis module visualizes the user's financial situation, understands income and expenditure patterns, and the status of assets and liabilities, and then generates specific economic indicators such as investment risk and insurance review. These analysis results are useful for future predictions and the detection of anomaly patterns. 【0463】 Next, the generation method creates optimal financial advice based on the analysis results. This advice is tailored to the user's specific financial situation and includes points for saving money, efficient risk diversification methods, and appropriate insurance review suggestions. 【0464】 Ultimately, the generated advice is sent to the user's device and displayed in a visually easy-to-understand format. An interactive dashboard is used, allowing users to click on details of each piece of advice to obtain further information and actionable guidance. Specific examples include suggestions for reducing monthly fixed expenses or guidelines for balancing investment portfolios. This advice enables users to manage their finances more effectively and healthily. 【0465】 The following describes the processing flow. 【0466】 Step 1: 【0467】 The user logs into the system using their device. Logging in requires entering a user ID and password, or authentication information such as biometric authentication. 【0468】 The terminal retrieves user input information and sends it to the server via a secure communication channel. 【0469】 The server uses the received authentication information to verify it against the database and authenticate the user. If authentication is successful, a session is started. 【0470】 Step 2: 【0471】 The device displays an interface requesting the user's permission to collect and use various financial data. 【0472】 The user selects the financial institution and type of data to be collected, and then clicks the "Allow" button. 【0473】 Based on the user's specifications, the server accesses the API of the selected financial institution and obtains the necessary access keys and tokens. 【0474】 Step 3: 【0475】 The server collects specified financial data from financial institutions via an API. This data includes transaction history, asset information, and liability summaries. 【0476】 The collected data is formatted and stored in a temporary database on the server. 【0477】 Step 4: 【0478】 The server analyzes the data. The analysis module starts up, and the AI ​​algorithm evaluates the user's financial situation. 【0479】 The analysis includes pattern analysis of income and expenditure, categorization of assets and liabilities, and assessment of investment risk, generating detailed economic indicators. 【0480】 Step 5: 【0481】 The server uses the generated analysis results to create optimal financial advice for the user. 【0482】 The generation system is activated, and the advice is converted into easily understandable language using natural language generation. This includes suggestions for saving money and revisions to investment strategies. 【0483】 Step 6: 【0484】 The server sends generated advice to the terminal. The communication is encrypted to ensure security. 【0485】 The device receives advice and displays it to the user in an easy-to-understand format, such as an interactive dashboard or graph. 【0486】 Step 7: 【0487】 Users can review the advice they receive and, if necessary, take action based on the guidelines provided. This allows them to revise and implement their long-term financial plans. 【0488】 (Example 1) 【0489】 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." 【0490】 In today's information technology landscape, efficiently and effectively managing personal finances is a crucial challenge. However, manual data collection and analysis are time-consuming and prone to errors. Furthermore, integrating and managing information from multiple financial institutions is cumbersome and burdensome for users. Therefore, there is a need for a system that allows users to easily and securely collect financial information and receive rational advice. 【0491】 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. 【0492】 In this invention, the server includes authentication means for authenticating using user input information, communication means for collecting information from various data sources based on user permission, analysis means for analyzing the collected information and generating analysis results, generation means for generating advice based on the analysis results using a generated AI model, and provision means for visually displaying and providing the generated advice to the user. This enables users to safely and quickly collect and analyze their financial information and receive appropriate financial advice. 【0493】 An "authentication method" is a mechanism for verifying a user's identity based on the information they enter. 【0494】 "Communication means" refers to a function that, with the user's permission, collects necessary information from data sources. 【0495】 "Analysis means" refers to a function that analyzes collected information and evaluates the user's financial situation. 【0496】 A "generation method" is a system that uses an AI model to create useful advice for the user from the analysis results. 【0497】 "Means of delivery" refers to an interface that visually and clearly conveys the generated advice to the user. 【0498】 A "generative AI model" is an algorithm that extracts patterns from large amounts of data and generates optimal advice for the user. 【0499】 This invention is a system for efficiently collecting and analyzing users' financial information. Users access the system using their own terminals and perform personal authentication. The terminals transmit the authentication information entered by the user to the server using an encryption protocol. The server compares this information with a database and authenticates the user. 【0500】 After successful authentication, the server displays an interface to the terminal requesting permission to collect financial information. This interface allows the user to decide which financial institutions to collect data from. Based on the user's permission, the server collects information using the APIs of the specified data sources. Specifically, it obtains bank transaction data, credit card statements, and other similar information through secure communication methods. 【0501】 The collected data is sent to the server's analysis module, where a generated AI model is applied. The AI ​​model analyzes the user's income, expenses, assets, and liabilities based on the data to assess their financial situation. From this assessment, the server generates optimal financial advice. 【0502】 The generated advice is presented to the user's device in a visually easy-to-understand format. This includes various interactive elements, allowing the user to click on each piece of advice to delve deeper and obtain more detailed information. 【0503】 By receiving this advice, users can take concrete actions to improve their financial situation. For example, in response to specific questions such as "How can I reduce my monthly expenses?", the generative AI model will suggest ways to reduce spending, allowing users to make optimal decisions based on that. 【0504】 An example of a prompt message is, "Analyze your spending over the past three months and suggest ways to reduce your entertainment expenses." In this way, a server-based AI model can provide concrete solutions to the problems the user faces. 【0505】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0506】 Step 1: 【0507】 User Authentication 【0508】 Input: The user enters their authentication information (user ID, password) into the input fields on the device. 【0509】 Specific operation: The terminal encrypts the entered authentication information using the SSL / TLS protocol and sends it to the server. 【0510】 Data processing: The server receives encrypted authentication information and compares it with user information in the database. 【0511】 Output: If authentication is successful, the server generates a session ID and returns it to the terminal. If it fails, an error message is returned. 【0512】 Step 2: 【0513】 Presentation of authorization interface 【0514】 Input: User information with an authenticated session ID. 【0515】 Specific operation: The server creates a list of financial data that the user can access and sends it to the terminal. 【0516】 Output: The user terminal displays a list of available financial institutions and an interface requesting permission for data collection from each institution. 【0517】 Step 3: 【0518】 Data collection permission 【0519】 Input: The user selects the financial institution that they are allowed to collect data from via the interface. 【0520】 Specific action: The terminal sends the user's selection to the server. 【0521】 Data processing: The server associates the API key of the selected financial institution with the user's authorization and generates the token necessary for secure communication. 【0522】 Output: A list of financial institutions authorized to collect information. 【0523】 Step 4: 【0524】 Data collection 【0525】 Input: A list of authorized financial institutions and their associated API keys. 【0526】 Specific operation: The server accesses financial institutions via an API and retrieves data within a specified range. 【0527】 Data processing: The acquired data undergoes format conversion and data cleansing to prepare it for analysis. 【0528】 Output: A categorized financial dataset. 【0529】 Step 5: 【0530】 Data analysis 【0531】 Input: A well-organized dataset obtained from a financial institution. 【0532】 Specific operation: The server inputs data into the analysis module and performs a detailed analysis using a generated AI model. 【0533】 Data processing: AI algorithms evaluate income trends, spending patterns, and investment risks to generate economic indicators. 【0534】 Output: A detailed financial report based on the analysis results. 【0535】 Step 6: 【0536】 Generating and providing advice 【0537】 Input: Financial report generated by AI analysis. 【0538】 Specific operation: The server uses a generated AI model to create optimal advice for the user. The advice is converted into text and dashboard formats. 【0539】 Data processing: Generates asset allocation strategies and spending reduction suggestions based on the user's financial data. 【0540】 Output: Visualized advice and suggested action plans sent to the terminal. 【0541】 (Application Example 1) 【0542】 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." 【0543】 In today's world, many users find it difficult to efficiently manage complex financial information and predict and plan for future expenses. Furthermore, conventional asset management often fails to detect abnormal spending patterns, and users lack access to appropriate advice for sound asset management. 【0544】 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. 【0545】 In this invention, the server includes acquisition means for collecting user asset information, analysis means for analyzing the collected asset information, and prediction means for predicting future spending trends using the user's past spending information. This enables users to efficiently manage complex financial information and predict future spending. It also allows for the detection of abnormal spending patterns and the provision of appropriate asset management advice. 【0546】 "Means of acquisition" refers to a system or device for collecting user asset information. 【0547】 "Analysis tools" refer to systems or devices that analyze collected asset information and use the results to understand the user's financial situation. 【0548】 "Generation means" refers to a system or device that generates asset management advice for users based on analysis results. 【0549】 "Means of provision" refers to a system or device for presenting the generated asset management advice to the user. 【0550】 A "predictive means" is a system or device that predicts future spending trends based on a user's past spending information. 【0551】 "Detection means" refers to a system or device that monitors a user's spending patterns and identifies abnormal patterns. 【0552】 A "planning tool" is a system or device for forming a user's long-term asset management plan. 【0553】 "Communication means" refers to a system or device for collecting necessary information from external financial institutions, etc., based on the user's consent. 【0554】 The system for implementing the invention consists of a user terminal such as a smartphone and a server operating in the backend. The system's program collects, analyzes, and generates and provides management advice on asset information. The terminal allows users to log in via an application on the terminal, and secure login is provided through OAuth authentication. 【0555】 The server uses the Django framework as its program execution environment and retrieves data from financial institutions via APIs. The retrieved data is securely stored in Firebase, and TensorFlow is used as the analysis tool to analyze the user's financial data in real time. This analysis uses a prediction tool to forecast future spending trends and detect abnormal spending patterns. The analysis results are generated by a generation tool to create advice for asset management, which is then presented to the user through a terminal application via a delivery tool. 【0556】 The device provides an interactive dashboard built with React Native, through which users can view detailed information and plan their asset management. For example, monthly spending on cafes is visualized in a graph, and the AI ​​issues an alert saying, "You've spent a lot on food this month, would you like to review your savings plan?" 【0557】 An example of a prompt to input into the generating AI model would be: "Predict future spending trends based on the user's payment data from the past three months and generate savings advice." 【0558】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0559】 Step 1: 【0560】 The user logs into the application using their device. The input includes the user's authentication information, which the server uses to verify the user's identity via OAuth authentication. If authentication is successful, a session is started. The output shows that the user is logged in. 【0561】 Step 2: 【0562】 Once the user grants permission for data collection, the device sends this intention to the server. The server uses acquisition methods to collect the user's asset information via the financial institution's API. The input includes the user's authorization information. This allows bank account transaction history and credit card statements to be securely stored on the server. As output, the necessary asset information is stored on the server. 【0563】 Step 3: 【0564】 The server receives the collected data and uses TensorFlow to analyze the asset information. The input includes specific asset data such as transaction history and usage details. Based on this data, data processing is performed, such as analyzing spending patterns, making it possible to understand the financial situation. The output is the analyzed asset information. 【0565】 Step 4: 【0566】 The server generates asset management advice using the analysis results. Analysis results are provided as input, and specific advice is generated by running an AI model. The output is asset management advice based on the user's current situation. 【0567】 Step 5: 【0568】 The generated asset management advice is transmitted from the server to the terminal via a delivery mechanism. The terminal visualizes and displays this information in an interactive dashboard built with React Native. Through this, users can view specific advice and future forecasts. The output is a visual and interactive financial dashboard. 【0569】 Step 6: 【0570】 Based on the advice and forecasts provided, users develop long-term asset management plans. The server assists users in developing these plans using planning tools. The input information includes various pieces of advice and forecast data obtained in advance. As an output, users can finalize their planned asset management action plan. 【0571】 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. 【0572】 This invention enables the provision of more personalized financial advice that takes into account the user's emotional state by integrating an emotion engine into existing financial management systems. First, the process begins when the user logs into the system using a terminal. The interface displayed on the terminal requests the user's permission and prepares to collect emotional data. 【0573】 The device is equipped with emotion recognition sensors that analyze the user's emotions in real time through their facial expressions and tone of voice. This emotion data is transmitted to a server via a collection mechanism and processed along with financial data. The server activates an emotion engine to analyze the emotions the user expressed upon login and identify emotional stress levels and anxiety levels. 【0574】 The results of this sentiment analysis are fed back into the evaluation of financial data through the analysis tools. For example, if a user is in a highly stressed state, the system may suggest low-risk investment methods or present savings plans to provide further peace of mind. Conversely, if the user is relaxed, it may offer advice that includes more challenging investment options. In this way, the generating tools create sentiment-based advice, and the providing tools display the advice in a format that suits the user. 【0575】 The advice displayed on the user's device is delivered through an interactive dashboard, allowing users to gain insights based on the emotional data behind the advice. For example, if a user is feeling pressured to spend, the system can pinpoint areas where savings can be made, prompting quick action. Furthermore, by analyzing the user's emotions from their feedback, if feelings such as a desire to avoid being defeated are detected, positive financial options within those constraints will be highlighted. 【0576】 In this way, by using an emotion engine, it is possible to provide financial advice that is integrated with the user's emotions, contributing to better decision-making. 【0577】 The following describes the processing flow. 【0578】 Step 1: 【0579】 Users log in to the system using their device. During login, they use their user ID and password, and biometric authentication if necessary. 【0580】 The terminal sends the entered authentication information to the server to authenticate the user and establish a session. 【0581】 Step 2: 【0582】 The device activates an emotion recognition sensor and collects the user's facial expressions and voice tone in real time through the camera and microphone. 【0583】 Prepare to store emotional data in temporary storage for analysis. 【0584】 Step 3: 【0585】 Users configure permission settings on their device for the collection of financial data. They select the financial institutions and types of data they wish to collect. 【0586】 The server, with permission, uses financial institution APIs to collect users' financial data. 【0587】 Step 4: 【0588】 The server receives emotional and financial data and uses an emotion engine to analyze the user's emotional state, determining stress levels and emotional tendencies. 【0589】 The analytical method takes sentiment data into consideration, evaluates financial data in detail, and calculates specific economic indicators. 【0590】 Step 5: 【0591】 The server generates financial advice that reflects the user's emotional state based on the analysis results. 【0592】 The generation method incorporates user emotion data into the scenario and formats the advice content into easy-to-understand natural language. 【0593】 Step 6: 【0594】 The server encrypts the generated advice and sends it to the terminal. 【0595】 The device decodes the information it receives and presents it to the user in a visually easy-to-understand dashboard. 【0596】 Step 7: 【0597】 Users view the dashboard and review the advice provided. They gain opportunities to adjust their financial behavior through specific advice based on their emotional state. 【0598】 If necessary, users will take the suggested actions and incorporate them into their long-term financial plans. 【0599】 (Example 2) 【0600】 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." 【0601】 Traditional financial management systems can analyze a user's financial information and provide advice, but they have struggled to provide personalized advice that takes into account the user's emotional state. Therefore, a challenge exists in situations where emotional factors such as stress and anxiety influence decision-making, making it difficult for users to receive effective financial advice. 【0602】 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. 【0603】 In this invention, the server includes information gathering means for acquiring the user's financial information, emotion gathering means for acquiring the user's emotional state using sensors, and data analysis means for integrating and analyzing the acquired emotional state and financial information. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0604】 "Information gathering means" refers to the hardware and software configuration necessary to collect user financial information, specifically a system that acquires information via databases or APIs. 【0605】 "Emotional gathering means" is a general term for sensors and related software used to measure and acquire a user's emotional state, and includes functions to analyze facial expressions and voice tone using cameras and microphones. 【0606】 "Data analysis means" refers to software algorithms or AI models that integrate and analyze acquired user emotional states and financial information, and possess the ability to evaluate and predict data. 【0607】 "Advice generation means" refers to the process and algorithms for generating financial advice to be provided to users based on analyzed information, and enables personalized suggestions tailored to emotional states through the generation AI model. 【0608】 "Delivery methods" refer to the components, including user interfaces and dashboards, used to present generated financial advice to users, and are designed to allow users to easily understand and interact with the information. 【0609】 "Communication methods" refer to technologies including communication protocols and network interfaces that enable a system to acquire information from external databases or financial institutions, and have mechanisms that allow for secure data transfer. 【0610】 This invention is a system that provides personalized financial advice that takes into account the user's emotional state. The user accesses this system using a terminal equipped with a camera and microphone, which function as a means of collecting emotions. This makes it possible to analyze the user's facial expressions and tone of voice in real time. 【0611】 Emotional data is sent from the terminal to the server. The server acquires financial data through information gathering methods and integrates and processes it with the emotional data. This processing uses data analysis methods with AI models to evaluate the user's stress level and anxiety state. 【0612】 Furthermore, an advice generation system is activated based on the analyzed data to generate optimal financial advice for the user. The generating AI model uses prompts such as "Please suggest the optimal investment strategy that reflects the user's emotional state" to provide suggestions tailored to the user's individual circumstances. 【0613】 As a result, the server sends this generated advice back to the terminal and displays it through a dashboard. This dashboard is designed so that users can intuitively understand the advice and take the necessary actions. For example, if a user is determined to be in a high-stress state, they may be presented with "low-risk investments" or "money-saving plans that provide peace of mind." 【0614】 This allows users to obtain information that supports appropriate financial decision-making based on their emotional state. 【0615】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0616】 Step 1: 【0617】 The user logs into the system using a terminal. The input here is the user's login information, and the output is the login authentication result. The terminal grants access based on the user's authentication information and prepares to begin sentiment data collection. 【0618】 Step 2: 【0619】 The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time as a means of emotion collection. The input for this step is the user's voice and video data, and the output is digital data indicating the emotional state. Emotion recognition software analyzes facial features and voice characteristics to detect stress levels and other emotional indicators. 【0620】 Step 3: 【0621】 The device sends the collected emotional data to the server. The input is emotional state data, and the output is the result of uploading the data to the server. The data is transmitted via a secure protocol, and the server temporarily stores the data. 【0622】 Step 4: 【0623】 The server uses information gathering tools to acquire other financial data and integrate it with sentiment data. The input for this step is sentiment data and financial information, and the output is an integrated dataset. The server analyzes this data with an AI model to evaluate the user's overall situation. 【0624】 Step 5: 【0625】 The server uses data analysis tools to analyze the user's emotional state and generates optimal financial advice using a generative AI model. The input is an integrated dataset, and the output is emotion-based financial advice. The prompt used is "Please suggest an optimal investment strategy that reflects the user's emotional state." 【0626】 Step 6: 【0627】 The server sends the generated advice to the terminal, which displays it using an interactive dashboard. The input is the generated advice, and the output is the content displayed in the interface presented to the user. The user can then make financial decisions based on this information. 【0628】 (Application Example 2) 【0629】 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." 【0630】 Traditional financial management systems provide advice based solely on the user's financial information, which hinders the provision of personalized advice that takes into account the user's emotional state. Advice provided without considering the user's emotional state is less likely to encourage appropriate action, thus necessitating a new mechanism to improve the quality of decision-making. 【0631】 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. 【0632】 In this invention, the server includes emotion recognition means for collecting emotion data to recognize the user's emotional state, analysis integration means for comprehensively analyzing the emotion data acquired by the emotion recognition means and financial information, and generation means for generating financial advice that takes the emotional state into consideration based on the analysis results by the analysis integration means. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0633】 "Collection means" refers to a device or mechanism for obtaining financial information from a user. 【0634】 "Analysis means" refers to a device or system that has the function of analyzing acquired financial information and understanding the user's financial situation and trends. 【0635】 An "emotion recognition means" is a device or mechanism for collecting emotional data from a user's facial expressions and tone of voice to recognize the user's emotional state. 【0636】 "Analysis integration means" refers to a device or system that has the function of comprehensively analyzing emotional data acquired by emotion recognition means and financial information acquired by collection means. 【0637】 "Generation means" refers to a device or mechanism for creating financial advice that takes into account the user's emotional state, based on the analysis results from the analysis integration means. 【0638】 "Providing means" refers to a device or mechanism for presenting financial advice created by the generating means to the user. 【0639】 "Planning tools" refer to devices or methods for developing long-term financial plans and providing guidance to users. 【0640】 "Communication means" refers to devices or technologies used to collect information from financial institutions with the user's permission. 【0641】 This invention begins with the user logging into the system using a terminal. The terminal is equipped with a camera and microphone to recognize the user's emotional state in real time, allowing the emotion recognition system to analyze the user's facial expressions and tone of voice. This emotional data is then transmitted from the terminal to a server in the cloud. 【0642】 The server comprehensively analyzes emotional data acquired by emotion recognition tools and user financial information collected using communication tools. Specifically, it analyzes the data using AI frameworks such as TensorFlow and PyTorch to generate financial advice that takes the user's emotional state into account. 【0643】 The information obtained from the analysis is provided to the user through a generation mechanism. This allows the user to receive personalized financial advice tailored to their emotional state. The delivery mechanism displays the financial advice in an interactive dashboard format, and the user can also gain insights into the emotional data that underlies the advice. 【0644】 This system will suggest "installment payment options for peace of mind" if the user feels anxious while shopping. Conversely, if the user is relaxed, it will offer an incentive such as "10% off for buying now." Examples of prompts include "Analyze the user's current emotional state and suggest an appropriate payment method" and "Provide the user with purchase advice that promotes relaxation." 【0645】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0646】 Step 1: 【0647】 The user logs into the system using their device. This login process sends the user ID to the server. This allows the system to record the user's past financial and emotional data. 【0648】 Step 2: 【0649】 The device's camera and microphone activate to capture the user's emotional state. Specifically, the camera captures the user's facial expressions and the microphone records their voice, collecting emotional data. This data is then sent to a server as input data to run an emotion analysis algorithm. 【0650】 Step 3: 【0651】 The server receives emotion data sent from the terminal. The server processes the emotion data and analyzes it using generative AI models such as TensorFlow to identify the user's emotional state (e.g., stress level or relaxation level). This analysis extracts emotional characteristics and provides output to proceed to the next stage. 【0652】 Step 4: 【0653】 The server integrates and analyzes the sentiment analysis results and financial data obtained through communication. Using this input, the server performs data calculations with an analysis integration mechanism to generate a financial advice portfolio based on the user's emotional state. This portfolio includes financial options best suited to the user's current emotions. 【0654】 Step 5: 【0655】 The server generates financial advice tailored to the user's emotional state based on integrated analysis results. This advice, including an interpretation of the underlying emotional data, is prepared to be presented to the user as a visually easy-to-understand, interactive dashboard. 【0656】 Step 6: 【0657】 The terminal displays an interactive dashboard sent from the server to the user. The user can then make financial choices that suit their emotional state by following the advice received, thereby improving the quality of their decision-making. 【0658】 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. 【0659】 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. 【0660】 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. 【0661】 [Fourth Embodiment] 【0662】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0663】 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. 【0664】 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). 【0665】 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. 【0666】 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. 【0667】 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). 【0668】 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. 【0669】 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. 【0670】 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. 【0671】 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. 【0672】 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. 【0673】 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. 【0674】 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". 【0675】 In implementing this invention, the process begins with a user accessing the system using their own device and logging in. The device sends the authentication information entered by the user to the server and manages the process of verifying the user's identity. Once the server authenticates the user, a session is started, and an interface requesting permission from the user to collect financial information is displayed on the device. 【0676】 Users prepare to provide information from various financial institutions to the server by granting permission for data collection on their devices. Based on the user's permission, the server efficiently collects the necessary data via the financial institutions' APIs through security protocols. This data includes bank account transaction history, credit card statements, investment portfolio information, and insurance policy details, and is organized on the server. 【0677】 The acquired data is input into an analysis module by the server and analyzed in detail using AI algorithms. The analysis module visualizes the user's financial situation, understands income and expenditure patterns, and the status of assets and liabilities, and then generates specific economic indicators such as investment risk and insurance review. These analysis results are useful for future predictions and the detection of anomaly patterns. 【0678】 Next, the generation method creates optimal financial advice based on the analysis results. This advice is tailored to the user's specific financial situation and includes points for saving money, efficient risk diversification methods, and appropriate insurance review suggestions. 【0679】 Ultimately, the generated advice is sent to the user's device and displayed in a visually easy-to-understand format. An interactive dashboard is used, allowing users to click on details of each piece of advice to obtain further information and actionable guidance. Specific examples include suggestions for reducing monthly fixed expenses or guidelines for balancing investment portfolios. This advice enables users to manage their finances more effectively and healthily. 【0680】 The following describes the processing flow. 【0681】 Step 1: 【0682】 The user logs into the system using their device. Logging in requires entering a user ID and password, or authentication information such as biometric authentication. 【0683】 The terminal retrieves user input information and sends it to the server via a secure communication channel. 【0684】 The server uses the received authentication information to verify it against the database and authenticate the user. If authentication is successful, a session is started. 【0685】 Step 2: 【0686】 The device displays an interface requesting the user's permission to collect and use various financial data. 【0687】 The user selects the financial institution and type of data to be collected, and then clicks the "Allow" button. 【0688】 Based on the user's specifications, the server accesses the API of the selected financial institution and obtains the necessary access keys and tokens. 【0689】 Step 3: 【0690】 The server collects specified financial data from financial institutions via an API. This data includes transaction history, asset information, and liability summaries. 【0691】 The collected data is formatted and stored in a temporary database on the server. 【0692】 Step 4: 【0693】 The server analyzes the data. The analysis module starts up, and the AI ​​algorithm evaluates the user's financial situation. 【0694】 The analysis includes pattern analysis of income and expenditure, categorization of assets and liabilities, and assessment of investment risk, generating detailed economic indicators. 【0695】 Step 5: 【0696】 The server uses the generated analysis results to create optimal financial advice for the user. 【0697】 The generation system is activated, and the advice is converted into easily understandable language using natural language generation. This includes suggestions for saving money and revisions to investment strategies. 【0698】 Step 6: 【0699】 The server sends generated advice to the terminal. The communication is encrypted to ensure security. 【0700】 The device receives advice and displays it to the user in an easy-to-understand format, such as an interactive dashboard or graph. 【0701】 Step 7: 【0702】 Users can review the advice they receive and, if necessary, take action based on the guidelines provided. This allows them to revise and implement their long-term financial plans. 【0703】 (Example 1) 【0704】 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". 【0705】 In today's information technology landscape, efficiently and effectively managing personal finances is a crucial challenge. However, manual data collection and analysis are time-consuming and prone to errors. Furthermore, integrating and managing information from multiple financial institutions is cumbersome and burdensome for users. Therefore, there is a need for a system that allows users to easily and securely collect financial information and receive rational advice. 【0706】 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. 【0707】 In this invention, the server includes authentication means for authenticating using user input information, communication means for collecting information from various data sources based on user permission, analysis means for analyzing the collected information and generating analysis results, generation means for generating advice based on the analysis results using a generated AI model, and provision means for visually displaying and providing the generated advice to the user. This enables users to safely and quickly collect and analyze their financial information and receive appropriate financial advice. 【0708】 An "authentication method" is a mechanism for verifying a user's identity based on the information they enter. 【0709】 "Communication means" refers to a function that, with the user's permission, collects necessary information from data sources. 【0710】 "Analysis means" refers to a function that analyzes collected information and evaluates the user's financial situation. 【0711】 A "generation method" is a system that uses an AI model to create useful advice for the user from the analysis results. 【0712】 "Means of delivery" refers to an interface that visually and clearly conveys the generated advice to the user. 【0713】 A "generative AI model" is an algorithm that extracts patterns from large amounts of data and generates optimal advice for the user. 【0714】 This invention is a system for efficiently collecting and analyzing users' financial information. Users access the system using their own terminals and perform personal authentication. The terminals transmit the authentication information entered by the user to the server using an encryption protocol. The server compares this information with a database and authenticates the user. 【0715】 After successful authentication, the server displays an interface to the terminal requesting permission to collect financial information. This interface allows the user to decide which financial institutions to collect data from. Based on the user's permission, the server collects information using the APIs of the specified data sources. Specifically, it obtains bank transaction data, credit card statements, and other similar information through secure communication methods. 【0716】 The collected data is sent to the server's analysis module, where a generated AI model is applied. The AI ​​model analyzes the user's income, expenses, assets, and liabilities based on the data to assess their financial situation. From this assessment, the server generates optimal financial advice. 【0717】 The generated advice is presented to the user's device in a visually easy-to-understand format. This includes various interactive elements, allowing the user to click on each piece of advice to delve deeper and obtain more detailed information. 【0718】 By receiving this advice, users can take concrete actions to improve their financial situation. For example, in response to specific questions such as "How can I reduce my monthly expenses?", the generative AI model will suggest ways to reduce spending, allowing users to make optimal decisions based on that. 【0719】 An example of a prompt message is, "Analyze your spending over the past three months and suggest ways to reduce your entertainment expenses." In this way, a server-based AI model can provide concrete solutions to the problems the user faces. 【0720】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0721】 Step 1: 【0722】 User Authentication 【0723】 Input: The user enters their authentication information (user ID, password) into the input fields on the device. 【0724】 Specific operation: The terminal encrypts the entered authentication information using the SSL / TLS protocol and sends it to the server. 【0725】 Data processing: The server receives encrypted authentication information and compares it with user information in the database. 【0726】 Output: If authentication is successful, the server generates a session ID and returns it to the terminal. If it fails, an error message is returned. 【0727】 Step 2: 【0728】 Presentation of authorization interface 【0729】 Input: User information with an authenticated session ID. 【0730】 Specific operation: The server creates a list of financial data that the user can access and sends it to the terminal. 【0731】 Output: The user terminal displays a list of available financial institutions and an interface requesting permission for data collection from each institution. 【0732】 Step 3: 【0733】 Data collection permission 【0734】 Input: The user selects the financial institution that they are allowed to collect data from via the interface. 【0735】 Specific action: The terminal sends the user's selection to the server. 【0736】 Data processing: The server associates the API key of the selected financial institution with the user's authorization and generates the token necessary for secure communication. 【0737】 Output: A list of financial institutions authorized to collect information. 【0738】 Step 4: 【0739】 Data collection 【0740】 Input: A list of authorized financial institutions and their associated API keys. 【0741】 Specific operation: The server accesses financial institutions via an API and retrieves data within a specified range. 【0742】 Data processing: The acquired data undergoes format conversion and data cleansing to prepare it for analysis. 【0743】 Output: A categorized financial dataset. 【0744】 Step 5: 【0745】 Data analysis 【0746】 Input: A well-organized dataset obtained from a financial institution. 【0747】 Specific operation: The server inputs data into the analysis module and performs a detailed analysis using a generated AI model. 【0748】 Data processing: AI algorithms evaluate income trends, spending patterns, and investment risks to generate economic indicators. 【0749】 Output: A detailed financial report based on the analysis results. 【0750】 Step 6: 【0751】 Generating and providing advice 【0752】 Input: Financial report generated by AI analysis. 【0753】 Specific operation: The server uses a generated AI model to create optimal advice for the user. The advice is converted into text and dashboard formats. 【0754】 Data processing: Generates asset allocation strategies and spending reduction suggestions based on the user's financial data. 【0755】 Output: Visualized advice and suggested action plans sent to the terminal. 【0756】 (Application Example 1) 【0757】 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". 【0758】 In today's world, many users find it difficult to efficiently manage complex financial information and predict and plan for future expenses. Furthermore, conventional asset management often fails to detect abnormal spending patterns, and users lack access to appropriate advice for sound asset management. 【0759】 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. 【0760】 In this invention, the server includes acquisition means for collecting user asset information, analysis means for analyzing the collected asset information, and prediction means for predicting future spending trends using the user's past spending information. This enables users to efficiently manage complex financial information and predict future spending. It also allows for the detection of abnormal spending patterns and the provision of appropriate asset management advice. 【0761】 "Means of acquisition" refers to a system or device for collecting user asset information. 【0762】 "Analysis tools" refer to systems or devices that analyze collected asset information and use the results to understand the user's financial situation. 【0763】 "Generation means" refers to a system or device that generates asset management advice for users based on analysis results. 【0764】 "Means of provision" refers to a system or device for presenting the generated asset management advice to the user. 【0765】 A "predictive means" is a system or device that predicts future spending trends based on a user's past spending information. 【0766】 "Detection means" refers to a system or device that monitors a user's spending patterns and identifies abnormal patterns. 【0767】 A "planning tool" is a system or device for forming a user's long-term asset management plan. 【0768】 "Communication means" refers to a system or device for collecting necessary information from external financial institutions, etc., based on the user's consent. 【0769】 The system for implementing the invention consists of a user terminal such as a smartphone and a server operating in the backend. The system's program collects, analyzes, and generates and provides management advice on asset information. The terminal allows users to log in via an application on the terminal, and secure login is provided through OAuth authentication. 【0770】 The server uses the Django framework as its program execution environment and retrieves data from financial institutions via APIs. The retrieved data is securely stored in Firebase, and TensorFlow is used as the analysis tool to analyze the user's financial data in real time. This analysis uses a prediction tool to forecast future spending trends and detect abnormal spending patterns. The analysis results are generated by a generation tool to create advice for asset management, which is then presented to the user through a terminal application via a delivery tool. 【0771】 The device provides an interactive dashboard built with React Native, through which users can view detailed information and plan their asset management. For example, monthly spending on cafes is visualized in a graph, and the AI ​​issues an alert saying, "You've spent a lot on food this month, would you like to review your savings plan?" 【0772】 An example of a prompt to input into the generating AI model would be: "Predict future spending trends based on the user's payment data from the past three months and generate savings advice." 【0773】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0774】 Step 1: 【0775】 The user logs into the application using their device. The input includes the user's authentication information, which the server uses to verify the user's identity via OAuth authentication. If authentication is successful, a session is started. The output shows that the user is logged in. 【0776】 Step 2: 【0777】 Once the user grants permission for data collection, the device sends this intention to the server. The server uses acquisition methods to collect the user's asset information via the financial institution's API. The input includes the user's authorization information. This allows bank account transaction history and credit card statements to be securely stored on the server. As output, the necessary asset information is stored on the server. 【0778】 Step 3: 【0779】 The server receives the collected data and uses TensorFlow to analyze the asset information. The input includes specific asset data such as transaction history and usage details. Based on this data, data processing is performed, such as analyzing spending patterns, making it possible to understand the financial situation. The output is the analyzed asset information. 【0780】 Step 4: 【0781】 The server generates asset management advice using the analysis results. Analysis results are provided as input, and specific advice is generated by running an AI model. The output is asset management advice based on the user's current situation. 【0782】 Step 5: 【0783】 The generated asset management advice is transmitted from the server to the terminal via a delivery mechanism. The terminal visualizes and displays this information in an interactive dashboard built with React Native. Through this, users can view specific advice and future forecasts. The output is a visual and interactive financial dashboard. 【0784】 Step 6: 【0785】 Based on the advice and forecasts provided, users develop long-term asset management plans. The server assists users in developing these plans using planning tools. The input information includes various pieces of advice and forecast data obtained in advance. As an output, users can finalize their planned asset management action plan. 【0786】 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. 【0787】 This invention enables the provision of more personalized financial advice that takes into account the user's emotional state by integrating an emotion engine into existing financial management systems. First, the process begins when the user logs into the system using a terminal. The interface displayed on the terminal requests the user's permission and prepares to collect emotional data. 【0788】 The device is equipped with emotion recognition sensors that analyze the user's emotions in real time through their facial expressions and tone of voice. This emotion data is transmitted to a server via a collection mechanism and processed along with financial data. The server activates an emotion engine to analyze the emotions the user expressed upon login and identify emotional stress levels and anxiety levels. 【0789】 The results of this sentiment analysis are fed back into the evaluation of financial data through the analysis tools. For example, if a user is in a highly stressed state, the system may suggest low-risk investment methods or present savings plans to provide further peace of mind. Conversely, if the user is relaxed, it may offer advice that includes more challenging investment options. In this way, the generating tools create sentiment-based advice, and the providing tools display the advice in a format that suits the user. 【0790】 The advice displayed on the user's device is delivered through an interactive dashboard, allowing users to gain insights based on the emotional data behind the advice. For example, if a user is feeling pressured to spend, the system can pinpoint areas where savings can be made, prompting quick action. Furthermore, by analyzing the user's emotions from their feedback, if feelings such as a desire to avoid being defeated are detected, positive financial options within those constraints will be highlighted. 【0791】 In this way, by using an emotion engine, it is possible to provide financial advice that is integrated with the user's emotions, contributing to better decision-making. 【0792】 The following describes the processing flow. 【0793】 Step 1: 【0794】 Users log in to the system using their device. During login, they use their user ID and password, and biometric authentication if necessary. 【0795】 The terminal sends the entered authentication information to the server to authenticate the user and establish a session. 【0796】 Step 2: 【0797】 The device activates an emotion recognition sensor and collects the user's facial expressions and voice tone in real time through the camera and microphone. 【0798】 Prepare to store emotional data in temporary storage for analysis. 【0799】 Step 3: 【0800】 Users configure permission settings on their device for the collection of financial data. They select the financial institutions and types of data they wish to collect. 【0801】 The server, with permission, uses financial institution APIs to collect users' financial data. 【0802】 Step 4: 【0803】 The server receives emotional and financial data and uses an emotion engine to analyze the user's emotional state, determining stress levels and emotional tendencies. 【0804】 The analytical method takes sentiment data into consideration, evaluates financial data in detail, and calculates specific economic indicators. 【0805】 Step 5: 【0806】 The server generates financial advice that reflects the user's emotional state based on the analysis results. 【0807】 The generation method incorporates user emotion data into the scenario and formats the advice content into easy-to-understand natural language. 【0808】 Step 6: 【0809】 The server encrypts the generated advice and sends it to the terminal. 【0810】 The device decodes the information it receives and presents it to the user in a visually easy-to-understand dashboard. 【0811】 Step 7: 【0812】 Users view the dashboard and review the advice provided. They gain opportunities to adjust their financial behavior through specific advice based on their emotional state. 【0813】 If necessary, users will take the suggested actions and incorporate them into their long-term financial plans. 【0814】 (Example 2) 【0815】 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". 【0816】 Traditional financial management systems can analyze a user's financial information and provide advice, but they have struggled to provide personalized advice that takes into account the user's emotional state. Therefore, a challenge exists in situations where emotional factors such as stress and anxiety influence decision-making, making it difficult for users to receive effective financial advice. 【0817】 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. 【0818】 In this invention, the server includes information gathering means for acquiring the user's financial information, emotion gathering means for acquiring the user's emotional state using sensors, and data analysis means for integrating and analyzing the acquired emotional state and financial information. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0819】 "Information gathering means" refers to the hardware and software configuration necessary to collect user financial information, specifically a system that acquires information via databases or APIs. 【0820】 "Emotional gathering means" is a general term for sensors and related software used to measure and acquire a user's emotional state, and includes functions to analyze facial expressions and voice tone using cameras and microphones. 【0821】 "Data analysis means" refers to software algorithms or AI models that integrate and analyze acquired user emotional states and financial information, and possess the ability to evaluate and predict data. 【0822】 "Advice generation means" refers to the process and algorithms for generating financial advice to be provided to users based on analyzed information, and enables personalized suggestions tailored to emotional states through the generation AI model. 【0823】 "Delivery methods" refer to the components, including user interfaces and dashboards, used to present generated financial advice to users, and are designed to allow users to easily understand and interact with the information. 【0824】 "Communication methods" refer to technologies including communication protocols and network interfaces that enable a system to acquire information from external databases or financial institutions, and have mechanisms that allow for secure data transfer. 【0825】 This invention is a system that provides personalized financial advice that takes into account the user's emotional state. The user accesses this system using a terminal equipped with a camera and microphone, which function as a means of collecting emotions. This makes it possible to analyze the user's facial expressions and tone of voice in real time. 【0826】 Emotional data is sent from the terminal to the server. The server acquires financial data through information gathering methods and integrates and processes it with the emotional data. This processing uses data analysis methods with AI models to evaluate the user's stress level and anxiety state. 【0827】 Furthermore, an advice generation system is activated based on the analyzed data to generate optimal financial advice for the user. The generating AI model uses prompts such as "Please suggest the optimal investment strategy that reflects the user's emotional state" to provide suggestions tailored to the user's individual circumstances. 【0828】 As a result, the server sends this generated advice back to the terminal and displays it through a dashboard. This dashboard is designed so that users can intuitively understand the advice and take the necessary actions. For example, if a user is determined to be in a high-stress state, they may be presented with "low-risk investments" or "money-saving plans that provide peace of mind." 【0829】 This allows users to obtain information that supports appropriate financial decision-making based on their emotional state. 【0830】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0831】 Step 1: 【0832】 The user logs into the system using a terminal. The input here is the user's login information, and the output is the login authentication result. The terminal grants access based on the user's authentication information and prepares to begin sentiment data collection. 【0833】 Step 2: 【0834】 The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time as a means of emotion collection. The input for this step is the user's voice and video data, and the output is digital data indicating the emotional state. Emotion recognition software analyzes facial features and voice characteristics to detect stress levels and other emotional indicators. 【0835】 Step 3: 【0836】 The device sends the collected emotional data to the server. The input is emotional state data, and the output is the result of uploading the data to the server. The data is transmitted via a secure protocol, and the server temporarily stores the data. 【0837】 Step 4: 【0838】 The server uses information gathering tools to acquire other financial data and integrate it with sentiment data. The input for this step is sentiment data and financial information, and the output is an integrated dataset. The server analyzes this data with an AI model to evaluate the user's overall situation. 【0839】 Step 5: 【0840】 The server uses data analysis tools to analyze the user's emotional state and generates optimal financial advice using a generative AI model. The input is an integrated dataset, and the output is emotion-based financial advice. The prompt used is "Please suggest an optimal investment strategy that reflects the user's emotional state." 【0841】 Step 6: 【0842】 The server sends the generated advice to the terminal, which displays it using an interactive dashboard. The input is the generated advice, and the output is the content displayed in the interface presented to the user. The user can then make financial decisions based on this information. 【0843】 (Application Example 2) 【0844】 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". 【0845】 Traditional financial management systems provide advice based solely on the user's financial information, which hinders the provision of personalized advice that takes into account the user's emotional state. Advice provided without considering the user's emotional state is less likely to encourage appropriate action, thus necessitating a new mechanism to improve the quality of decision-making. 【0846】 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. 【0847】 In this invention, the server includes emotion recognition means for collecting emotion data to recognize the user's emotional state, analysis integration means for comprehensively analyzing the emotion data acquired by the emotion recognition means and financial information, and generation means for generating financial advice that takes the emotional state into consideration based on the analysis results by the analysis integration means. This makes it possible to provide personalized financial advice that takes the user's emotional state into consideration. 【0848】 "Collection means" refers to a device or mechanism for obtaining financial information from a user. 【0849】 "Analysis means" refers to a device or system that has the function of analyzing acquired financial information and understanding the user's financial situation and trends. 【0850】 An "emotion recognition means" is a device or mechanism for collecting emotional data from a user's facial expressions and tone of voice to recognize the user's emotional state. 【0851】 "Analysis integration means" refers to a device or system that has the function of comprehensively analyzing emotional data acquired by emotion recognition means and financial information acquired by collection means. 【0852】 "Generation means" refers to a device or mechanism for creating financial advice that takes into account the user's emotional state, based on the analysis results from the analysis integration means. 【0853】 "Providing means" refers to a device or mechanism for presenting financial advice created by the generating means to the user. 【0854】 "Planning tools" refer to devices or methods for developing long-term financial plans and providing guidance to users. 【0855】 "Communication means" refers to devices or technologies used to collect information from financial institutions with the user's permission. 【0856】 This invention begins with the user logging into the system using a terminal. The terminal is equipped with a camera and microphone to recognize the user's emotional state in real time, allowing the emotion recognition system to analyze the user's facial expressions and tone of voice. This emotional data is then transmitted from the terminal to a server in the cloud. 【0857】 The server comprehensively analyzes emotional data acquired by emotion recognition tools and user financial information collected using communication tools. Specifically, it analyzes the data using AI frameworks such as TensorFlow and PyTorch to generate financial advice that takes the user's emotional state into account. 【0858】 The information obtained from the analysis is provided to the user through a generation mechanism. This allows the user to receive personalized financial advice tailored to their emotional state. The delivery mechanism displays the financial advice in an interactive dashboard format, and the user can also gain insights into the emotional data that underlies the advice. 【0859】 This system will suggest "installment payment options for peace of mind" if the user feels anxious while shopping. Conversely, if the user is relaxed, it will offer an incentive such as "10% off for buying now." Examples of prompts include "Analyze the user's current emotional state and suggest an appropriate payment method" and "Provide the user with purchase advice that promotes relaxation." 【0860】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0861】 Step 1: 【0862】 The user logs into the system using their device. This login process sends the user ID to the server. This allows the system to record the user's past financial and emotional data. 【0863】 Step 2: 【0864】 The device's camera and microphone activate to capture the user's emotional state. Specifically, the camera captures the user's facial expressions and the microphone records their voice, collecting emotional data. This data is then sent to a server as input data to run an emotion analysis algorithm. 【0865】 Step 3: 【0866】 The server receives emotion data sent from the terminal. The server processes the emotion data and analyzes it using generative AI models such as TensorFlow to identify the user's emotional state (e.g., stress level or relaxation level). This analysis extracts emotional characteristics and provides output to proceed to the next stage. 【0867】 Step 4: 【0868】 The server integrates and analyzes the sentiment analysis results and financial data obtained through communication. Using this input, the server performs data calculations with an analysis integration mechanism to generate a financial advice portfolio based on the user's emotional state. This portfolio includes financial options best suited to the user's current emotions. 【0869】 Step 5: 【0870】 The server generates financial advice tailored to the user's emotional state based on integrated analysis results. This advice, including an interpretation of the underlying emotional data, is prepared to be presented to the user as a visually easy-to-understand, interactive dashboard. 【0871】 Step 6: 【0872】 The terminal displays an interactive dashboard sent from the server to the user. The user can then make financial choices that suit their emotional state by following the advice received, thereby improving the quality of their decision-making. 【0873】 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. 【0874】 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. 【0875】 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. 【0876】 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. 【0877】 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. 【0878】 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. 【0879】 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. 【0880】 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. 【0881】 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." 【0882】 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. 【0883】 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. 【0884】 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. 【0885】 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. 【0886】 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. 【0887】 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. 【0888】 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. 【0889】 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. 【0890】 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. 【0891】 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. 【0892】 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. 【0893】 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. 【0894】 The following is further disclosed regarding the embodiments described above. 【0895】 (Claim 1) 【0896】 A means of collecting and obtaining users' financial information, 【0897】 An analytical means for analyzing acquired financial information, 【0898】 A generation means for generating financial advice based on analysis results, 【0899】 A means of providing the generated financial advice to the user, 【0900】 A system that includes this. 【0901】 (Claim 2) 【0902】 A planning means for constructing a long-term financial plan, according to claim 1. 【0903】 (Claim 3) 【0904】 A communication means for collecting information from financial institutions based on the user's permission, according to claim 1. 【0905】 "Example 1" 【0906】 (Claim 1) 【0907】 Authentication means that use user input information to perform authentication, 【0908】 A communication means for collecting information from various data sources based on user permission, 【0909】 An analytical means that analyzes the collected information and generates analysis results, 【0910】 A generation method that generates advice based on analysis results using a generative AI model, 【0911】 A means of providing the generated advice to the user by visually displaying it, 【0912】 A system that includes this. 【0913】 (Claim 2) 【0914】 The system according to claim 1, a means for providing detailed information to a user through an interactive interface. 【0915】 (Claim 3) 【0916】 A system according to claim 1, for generating long-term economic indicators from analysis results. 【0917】 "Application Example 1" 【0918】 (Claim 1) 【0919】 A means of acquiring user asset information, 【0920】 Analytical methods for analyzing collected asset information, 【0921】 A generation means for generating asset management advice based on analysis results, 【0922】 A means of providing the generated asset management advice to the user, 【0923】 A prediction method that uses the user's past spending information to predict future spending trends, 【0924】 A detection means for detecting abnormal spending patterns, 【0925】 A system that includes this. 【0926】 (Claim 2) 【0927】 A system according to claim 1, which provides a planning means for forming a long-term asset management plan. 【0928】 (Claim 3) 【0929】 A communication means for collecting information from financial institutions based on the user's consent, as described in claim 1. 【0930】 "Example 2 of combining an emotion engine" 【0931】 (Claim 1) 【0932】 Information gathering means for obtaining users' financial information, 【0933】 An emotion collection method that acquires the user's emotional state using sensors, 【0934】 A data analysis method that integrates and analyzes acquired emotional states and financial information, 【0935】 An advice generation method that generates financial advice that takes emotions into account based on analysis results, 【0936】 A means of providing generated advice to the user and displaying it interactively, 【0937】 A system that includes this. 【0938】 (Claim 2) 【0939】 The system according to claim 1, which provides a planning means for constructing a long-term financial plan in accordance with the user's emotional state. 【0940】 (Claim 3) 【0941】 A communication means for collecting information from an external database based on user permission, according to claim 1. 【0942】 "Application example 2 when combining with an emotional engine" 【0943】 (Claim 1) 【0944】 A means of collecting and obtaining users' financial information, 【0945】 An analytical means for analyzing acquired financial information, 【0946】 An emotion recognition means for collecting emotion data to recognize the emotional state of a user, 【0947】 An analytical integration means that comprehensively analyzes emotional data acquired by an emotion recognition means and financial information, 【0948】 A generation means that generates financial advice that takes emotional state into consideration based on the analysis results from the analysis integration means, 【0949】 A means of providing users with generated sentiment-aware financial advice, 【0950】 A system that includes this. 【0951】 (Claim 2) 【0952】 A system according to claim 1, which provides a means for formulating a long-term financial plan. 【0953】 (Claim 3) 【0954】 A communication means for collecting information from financial institutions based on the user's permission, according to claim 1. [Explanation of symbols] 【0955】 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 and obtaining users' financial information, An analytical means for analyzing acquired financial information, A generation means for generating financial advice based on analysis results, A means of providing the generated financial advice to the user, A system that includes this. [Claim 2] The system according to claim 1, further comprising a planning means for constructing a long-term financial plan. [Claim 3] The system according to claim 1, further comprising a means of communication for collecting information from financial institutions based on the user's permission.

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