system

Abstract

We provide the system. [Solution] A means of analyzing collected financial data and evaluating an individual's financial situation based on their income, expenses, debts, and insurance contracts, A means to construct risk-managed investment proposals and automatically generate optimal insurance proposals and wealth accumulation plans, A means for dynamically generating personalized financial proposals using a generative AI model and providing prompt statements to support decision-making, A means for notifying the user device of the generated proposal and for executing and monitoring it based on the user's approval, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many individuals are unable to effectively manage their income and expenses, resulting in problems such as wasteful spending and the inability to make appropriate financial plans for the future. There is also a risk of missing out on optimal insurance contracts and investment opportunities due to a lack of financial literacy. Such situations threaten the economic stability of individuals and may lead to insufficient preparation for future life events.

Means for Solving the Problems

[0005] This invention provides a means for analyzing collected financial data and evaluating a user's financial situation based on their income, expenses, loans, and insurance contracts. This enables the construction of a risk-managed investment portfolio and the automatic generation of optimal insurance and savings plans. It also includes means for notifying the user of the generated suggestions and for their implementation and tracking based on user approval. Furthermore, it includes features for identifying unnecessary spending based on the analyzed financial data, providing real-time alerts for savings plans, and ensuring personal financial stability by dynamically adjusting financial strategies based on the user's life event information.

[0006] "Financial data" refers to the collective information of an individual's income, expenses, loans, insurance policies, and related transaction information.

[0007] "Analysis" is the process of identifying patterns and trends based on collected data and evaluating the information.

[0008] "Financial status" refers to the overall state of an individual's assets, liabilities, income, and expenses.

[0009] "Risk management" refers to the strategic planning and execution of measures to minimize the risks anticipated in asset management.

[0010] An "investment portfolio" is a collection of assets consisting of multiple investment products held by an individual.

[0011] An "insurance plan" is a collection of insurance contracts based on different conditions and coverage details.

[0012] A "savings plan" is the process of systematically accumulating funds towards future goals and objectives.

[0013] "User terminal" refers to an electronic device used by an end user to receive information and services.

[0014] "Execution" means starting and completing specific transactions and procedures based on the generated proposals.

[0015] "Tracking" is a process of continuously monitoring the progress and results of implemented transactions and procedures.

[0016] "Wasteful expenditure" refers to expenditures with little necessity or transactions with low cost-effectiveness.

[0017] "Alert" is a function that notifies important events and information in real time.

[0018] "Life event" refers to important events that affect an individual's life and finances, such as marriage, childbirth, and job transfer.

[0019] "Financial strategy" is an action plan designed to achieve an individual's short-term and long-term financial goals.

Brief Description of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] The system of the present invention includes an AI agent for analyzing an individual's financial data and proposing an optimal financial plan. The following elements are implemented as specific embodiments of the invention.

[0042] Implementation of the data collection and analysis module

[0043] The server collects transaction data using authentication information from financial institutions provided by the user. This data relates to the user's income, expenses, loans, and insurance policies. The server analyzes this data to assess the user's financial situation. Machine learning algorithms are used to identify income and expenditure patterns and calculate financial indicators.

[0044] Implementation of the proposal generation module

[0045] Based on the analysis results, the server constructs an optimal investment portfolio for the user and generates optimized insurance contract plans and automated savings plans. These suggestions emphasize risk management and are adjusted based on the user's risk tolerance. The server also dynamically optimizes the financial plan by taking into account the user's life event information.

[0046] Notification and execution of the executable module

[0047] The terminal notifies the user of proposals sent from the server. The user reviews the proposals through the terminal and decides whether to approve or reject them. For proposals approved by the user, the server automatically proceeds with the process. For example, it may complete the process for a new insurance contract or adjust investment allocations.

[0048] Implementation of monitoring and alerting functions

[0049] The server tracks the progress of implemented financial plans and alerts the user as circumstances change. For example, it immediately notifies the user if unexpected expenses occur or if the investment environment changes, prompting them to take appropriate action.

[0050] Specific example

[0051] For example, when a user is considering new car insurance, the system analyzes their current policy and compares it to other plans available on the market. The server takes into account the user's mileage, place of residence, age, etc., and recommends a more cost-effective plan. The user can review the suggestions on their device and, if they like them, switch their policy with a single button click. The server automates the contract process, saving the user money.

[0052] Thus, this invention streamlines personal financial management and supports the achievement of economic freedom. Each module works in conjunction to meet the user's needs.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] The user enters their financial institution authentication information into the terminal and sends it to the server. This includes bank account and credit card information. The terminal securely transmits this information to the server.

[0056] Step 2:

[0057] Based on the authentication information received from the user, the server uses APIs to retrieve relevant financial data from various institutions. This data includes income, expenses, loan balances, and insurance policy details.

[0058] Step 3:

[0059] The server analyzes the collected financial data and applies algorithms to evaluate the user's income and expenditure patterns and asset status. A user risk profile is also generated, and data is evaluated to help prepare for future life events.

[0060] Step 4:

[0061] Based on the analysis results, the server optimizes investment portfolios, reviews insurance policies, and creates savings plans. The server proposes the optimal combination of investment options for the user from a variety of choices.

[0062] Step 5:

[0063] The server sends the generated recommended plan to the terminal. The terminal notifies the user and provides an interface where they can view detailed information. The user reviews the plan and considers the proposal.

[0064] Step 6:

[0065] The user approves or rejects the proposed plan. If approved, the server automatically initiates the necessary procedures, such as concluding a new insurance contract or adjusting investment allocations.

[0066] Step 7:

[0067] The server continuously tracks progress after the approved plan has been implemented. If an unexpected event occurs or market conditions change, it immediately sends an alert to the user, prompting them to make necessary revisions.

[0068] This series of processes streamlines users' financial management, leading to reduced unnecessary spending and more effective use of assets.

[0069] (Example 1)

[0070] 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."

[0071] Because an individual's financial information is diverse, efficiently analyzing it and formulating appropriate financial strategies is difficult. Furthermore, there is a lack of systems to dynamically adjust financial plans in response to individual life events and market fluctuations. Additionally, there is a need for a means to quickly and accurately process procedures after a user approves a proposal.

[0072] 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.

[0073] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial situation, means for identifying income and expenditure patterns using machine learning algorithms and predicting future trends, and means for continuously monitoring implemented financial plans and issuing warnings to the user as needed. This enables the development of comprehensive financial strategies based on an individual's financial information and dynamic adjustments to those plans.

[0074] "Financial information" refers to data related to an individual's income, expenses, credit, and contracts.

[0075] "Financial status" refers to the criteria used to evaluate an economic state, including income, expenses, savings, and liabilities.

[0076] A "machine learning algorithm" is a computational method for automatically analyzing and predicting patterns and trends in data.

[0077] A "revenue and expenditure pattern" is a trend that shows the flow of income and expenses over a certain period of time.

[0078] "Risk management" is a strategy for minimizing losses by analyzing uncertainties related to investments and contracts.

[0079] "Life event information" refers to information related to important events in an individual's life, such as marriage or the birth of a child.

[0080] A "warning" is the act of notifying a user of the possibility of a specific economic event occurring.

[0081] "Prediction" refers to estimating future events or trends based on past data.

[0082] The system of this invention is designed to efficiently analyze an individual's financial information and to formulate and implement an optimal financial strategy. The system primarily functions through three parties: a server, a terminal, and a user.

[0083] First, the server collects financial information using authentication credentials provided by the user. This information consists of data on the user's income, expenses, credit, and contracts. The server uses a secure API to retrieve this information. The collected data is then analyzed using machine learning libraries such as TENSORFLOW® and Scikit-learn. This makes it possible to identify past income and expenditure patterns and predict future trends.

[0084] Next, the server evaluates the user's financial situation and, based on the results, automatically generates an optimal investment portfolio, contract plan, and automated savings plan with risk management in place. These proposals are then notified to the user's device. Through the device, the user can review the proposals in detail and decide to approve or reject them. For approved proposals, the server automates the necessary procedures and executes them quickly.

[0085] Furthermore, the server continuously monitors the executed financial plan and responds immediately to unexpected expenses and changes in the economic environment. When changes occur, it alerts the user and prompts them to take appropriate action. This process ensures that the user's financial activities are always optimized and waste is minimized.

[0086] For example, if a user is considering new car insurance, the server analyzes their current policy and compares it to potential market options. It then recommends a more efficient plan, taking into account factors such as mileage and location. The user can review the proposal on their device and switch to the new policy with a single click. The server automates the policy change process, reducing the user's effort.

[0087] An example of a prompt to the generating AI model would be: "Please suggest the optimal investment portfolio to improve my current financial situation. My annual income is 5 million yen, and my average monthly expenses are 300,000 yen. My risk tolerance is moderate, and I am considering new investment opportunities." In this way, the system provided by the present invention streamlines personal financial management and supports the achievement of financial freedom.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server receives authentication information from the user regarding their financial institution. Using this as input, the server accesses the financial institution's API to retrieve the user's latest transaction data. This data includes income, expenses, credit status, and contract details. The retrieved data is stored in a database on the server.

[0091] Step 2:

[0092] The server analyzes the collected transaction data. Using the data stored in the database as input, it applies machine learning algorithms to identify revenue and expenditure patterns and predict future trends. Libraries such as TensorFlow are used for this purpose. As output, various indicators showing the user's financial situation and trend analysis results are generated.

[0093] Step 3:

[0094] The server generates an optimal financial strategy based on the analysis results. It receives the user's risk tolerance and life event information as input and automatically creates investment configurations, contract plans, and savings plans. This allows the user to receive suggestions tailored to their individual financial goals. The generated suggestions are stored on the server.

[0095] Step 4:

[0096] The server sends the generated financial strategy to the terminal. The terminal receives it and notifies the user. The user can review the proposal on the terminal and examine the details. In this step, the specific proposal is displayed on the terminal screen, and an interface is provided that the user can interact with.

[0097] Step 5:

[0098] The user approves or rejects the proposal via their device. If approved, the server initiates an automated process based on the proposal. Specifically, it submits the necessary documents online for opening a new investment account or changing an existing contract, and then proceeds with the process. Once the process is complete, the server sends the user a completion notification.

[0099] Step 6:

[0100] The server continuously monitors the executed financial plan. If market fluctuations or unexpected expenses occur, the server detects them and alerts the user. Specifically, it performs regular scans of financial information and immediately reports any anomalies or changes in trends to the user. This allows the user to take quick and appropriate action.

[0101] (Application Example 1)

[0102] 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."

[0103] In modern personal financial management, developing optimal financial strategies that address diverse financial transactions and life events is complex and extremely difficult for the average user. Furthermore, existing methods fail to adequately address real-time financial monitoring, risk management, and the provision of personalized financial plans. There is also a need to efficiently generate individualized financial recommendations to support user decision-making.

[0104] 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.

[0105] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs risk-managed investment proposals and automatically generates optimal insurance proposals and wealth-building plans; and a server that dynamically generates personalized financial proposals using a generation AI model and provides prompt statements to support decision-making. This enables users to automatically receive efficient and risk-aware financial plans tailored to their own financial situation.

[0106] "Financial data" refers to financial information about an individual, such as their income, expenses, debts, and insurance policies.

[0107] "Assessing financial status" involves analyzing collected financial data to understand an individual's income, expenses, and debt situation.

[0108] An "investment proposal" is a plan that presents an individual with the optimal financial management plan, taking risk management into consideration.

[0109] "Insurance proposal" means presenting the most suitable insurance contract according to an individual's circumstances and needs.

[0110] A "wealth accumulation plan" is a proposal of optimal savings and asset building methods aimed at ensuring an individual's future financial stability.

[0111] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to automatically generate personalized information and suggestions.

[0112] A "prompt message" is a set of instructions or suggestions provided by a generative AI model to assist the user in making decisions.

[0113] To realize this invention, the system mainly includes the following elements: The server collects personal financial data via the financial institution's API, and organizes and analyzes the ingested data using data processing libraries such as Pandas and NumPy. Based on the analysis results, it evaluates the individual's income, expenses, and debt situation.

[0114] Taking risk management into consideration, the server uses machine learning algorithms such as Scikit-learn and TensorFlow to generate optimal investment proposals, insurance proposals, and wealth accumulation plans. In this process, the generating AI model dynamically forms financial proposals tailored to the user's individual circumstances and creates prompt messages to present them to the user.

[0115] The user's device receives notifications of suggestions from the server, and the user can review and approve the suggestions themselves. Furthermore, real-time communication platforms such as Firebase are used to quickly notify the user of the progress and revisions to the suggestions. For example, if a user adopts specific saving measures to optimize their monthly spending, the system continuously tracks the progress of those measures and makes adjustment suggestions as needed.

[0116] This approach allows individuals to manage their finances more effectively and to obtain specific and personalized solutions to their financial challenges.

[0117] An example of a prompt message is: "Generate optimal savings and spending reduction suggestions based on the user's income and expenditure data. Provide a risk-managed plan that takes into account the stability of income and the types of expenses."

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server collects transaction data using authentication credentials via the user's financial institution's API. This data includes information on the user's income, expenses, loans, and insurance policies. The input data is organized as a dataframe using Pandas, ensuring data consistency.

[0121] Step 2:

[0122] The server analyzes users' income and expenditure patterns using Scikit-learn's machine learning algorithms based on organized financial data. This analysis evaluates each user's financial situation. The input data consists of income and expenditure figures, and the output is an evaluation of the user's financial status. Specifically, it identifies similar spending patterns through clustering.

[0123] Step 3:

[0124] Based on the analysis results, the server uses a generative AI model to generate optimal investment and insurance proposals for the user. Here, the proposals are customized with a focus on risk management. The input includes the analysis results, and the output is a personalized financial proposal prompt. For example, a prompt such as "Consider insurance to mitigate risk for this spending pattern" might be generated.

[0125] Step 4:

[0126] The server notifies the user's device of the generated suggestions. Using Firebase-based real-time notification technology, users can instantly view these suggestions on their smartphones. The input is the generated prompt message, and the output is the notification to the user.

[0127] Step 5:

[0128] The user reviews the proposal on their device and approves it if necessary. Once the user approves the proposal, the server automatically initiates the process. Specifically, API calls are made to modify the investment portfolio or process new insurance contracts based on the user's instructions. The input is the user's approval, and the output is confirmation information of the executed procedure.

[0129] Step 6:

[0130] The server continuously monitors the progress of executed financial plans and notifies the user of the progress via the Firebase Realtime Database. Inputs are data on the procedures performed, and outputs are notifications regarding the progress. For example, it can display the savings progress rate towards the target month and year, and send an alert early if there is a risk of not meeting the goal.

[0131] 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.

[0132] This invention provides a system that offers more personalized advice by combining a conventional system that analyzes an individual's financial data and makes optimal financial proposals with an emotion engine that recognizes and analyzes the user's emotions. In embodiments of this invention, the following elements specifically function.

[0133] Implementation of the data collection and analysis module

[0134] The server receives authentication information from users via their terminals and retrieves real-time data on income, expenses, loans, and insurance policies from bank and insurance company APIs. This data is analyzed on the server and used to assess the user's financial health.

[0135] Implementation of the emotion recognition module

[0136] The device collects emotional data through the user's facial expressions, voice, and text communication. An emotion engine on the server analyzes this data to understand the user's emotional state in real time. This emotional data is used to customize financial proposals.

[0137] Implementation of the proposal generation module

[0138] The server uses both financial and emotional data to generate risk-managed investment portfolios, optimized insurance plans, and automated savings plans. These recommendations are adjusted according to the user's emotional state; for example, conservative recommendations are given when the user is under high stress.

[0139] Implementation of notification and interaction modules

[0140] The device notifies the user of suggestions generated from the server. The notification uses a tone and language tailored to the user's emotional state, providing information in a way that is easily accepted. The user can review the suggestions on the device and decide whether to approve or reject them.

[0141] Execution of the execution and monitoring module

[0142] If the user approves the proposal, the server automatically performs the necessary procedures, including concluding new insurance contracts and changing investment allocations. After execution, the server continuously tracks progress and sends alerts to the user as needed.

[0143] Specific example

[0144] For example, if a user is feeling anxious about their loan repayment plan, the device detects this emotion from the user's facial expressions and tone of voice. The server then creates a loan refinancing proposal and offers options to gradually adjust the plan to reduce stress. This proposal is displayed on the device in gentle language to help the user make a decision with confidence.

[0145] Thus, this invention, which combines an emotional engine, enables more personalized financial advice while taking into account the user's emotional needs. This improves the user experience and facilitates the achievement of financial freedom.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user uses a terminal to enter their financial institution's authentication information and sends it to the server. This grants the server access to retrieve the user's banking transactions and insurance contract information.

[0149] Step 2:

[0150] The server collects data on income, expenses, loan balances, and insurance policies from banks and insurance companies via APIs. This collected data is then analyzed to provide foundational information for understanding the user's financial situation.

[0151] Step 3:

[0152] The device uses its built-in camera and microphone to detect the user's facial expressions and voice tone in real time. Emotion recognition software analyzes this data and sends the user's emotional state to a server.

[0153] Step 4:

[0154] The server integrates emotional data obtained by the emotion engine with financial data. Based on this, it generates optimal investment plans, insurance options, and savings plans for the user. This includes adjustments such as selecting lower-risk options if the user's stress level is high.

[0155] Step 5:

[0156] The server sends the generated suggestions to the terminal and notifies the user. When notifying, it selects a communication style based on sentiment analysis and provides information in a way that is easily accepted by the user.

[0157] Step 6:

[0158] Users view proposals through their devices and, if necessary, send approval or rejection instructions to the server. The interface provided is designed to ensure that users do not feel uneasy.

[0159] Step 7:

[0160] If the user approves, the server will automatically initiate the process according to the proposal. This may involve the creation of a new insurance contract or adjustment of the investment portfolio, and the server will continuously monitor the progress.

[0161] Step 8:

[0162] The server tracks the results of executed plans and re-evaluates them if necessary. When circumstances change or unexpected events occur, it sends appropriate alerts to the user to prompt action.

[0163] These steps enable a system with an emotion engine to provide nuanced financial management that responds to the user's emotions.

[0164] (Example 2)

[0165] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0166] Traditional financial management systems lack the ability to provide advice that considers both an individual's financial situation and emotional state simultaneously. This has resulted in a failure to adequately address users' psychological needs and make it difficult to provide optimal financial recommendations. Furthermore, providing information without considering the user's emotions can, in some cases, cause stress to the user.

[0167] 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.

[0168] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial health, means for collecting emotional information and analyzing the user's emotional state in real time, and means for generating financial recommendations tailored based on both financial and emotional information. This makes it possible to provide users with emotionally sensitive and financially optimized advice.

[0169] "Financial information" refers to data on an individual's income, expenses, debts, and insurance policies.

[0170] "Emotional information" refers to data about the emotional state obtained from the user's facial expressions, voice, and text communication.

[0171] "Financial soundness" refers to an indicator that assesses an individual's ability to maintain a balance between income and expenses and to remain financially stable.

[0172] An "asset management plan" refers to a plan that proposes the optimal investment strategy based on an individual's risk profile.

[0173] An "insurance strategy" refers to a method for providing optimized insurance contracts based on individual needs.

[0174] A "savings plan" refers to a method of automatically accumulating funds to achieve an individual's future financial goals.

[0175] A "communication terminal" refers to a device used by a user to receive or input information.

[0176] This invention is a system that integrates individual users' financial and emotional information to provide optimized financial advice.

[0177] The server first uses the authentication information of the financial institution received from the user via the terminal to obtain the individual's financial information through APIs of banks and insurance companies. This information includes data on the user's income, expenses, debts, and insurance policies. The server analyzes this data and uses a generative AI model to assess the user's financial health.

[0178] Meanwhile, the device acquires the user's facial expressions and voice data, and also collects emotional information from the user's text communication. The device sends this emotional information to the server in real time. The emotion engine implemented on the server uses this information to analyze the user's emotional state and helps generate optimal suggestions.

[0179] In generating recommendations, the server integrates both financial and emotional information to construct risk-managed asset management plans, optimized insurance strategies, and automated savings plans. These recommendations are tailored to the user's current emotional state.

[0180] For example, if a user is worried about loan repayment, the device can recognize their emotions from their facial expressions and tone of voice. Based on this, the server can create a loan refinancing proposal and adjust the proposal step by step. This proposal is displayed on the device in gentle language, allowing the user to make a decision with confidence.

[0181] Another example of a prompt message is asking the generative AI model, "When a user is feeling stressed, what kind of financial advice should you suggest? Please explain with specific examples."

[0182] This system aims to provide personalized financial advice that takes user emotions into consideration, thereby improving the user experience and facilitating the achievement of financial freedom.

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The server receives authentication information from the user via the terminal. Input includes user ID, password, and other authentication data. Using this authentication information, the server accesses the APIs of banks and insurance companies to retrieve data on income, expenses, liabilities, and insurance policies. The output is the collected financial information, which serves as the basis for analysis. Specifically, the server makes API calls and retrieves the necessary data.

[0186] Step 2:

[0187] The device uses its camera and microphone to collect the user's facial expressions, voice, and text communication. The input consists of real-time captured visual and audio data. The device sends this data to a server as emotion data. The output is data indicating the user's emotional state, and in terms of specific actions, algorithms such as facial recognition and voice analysis are executed.

[0188] Step 3:

[0189] The server integrates and analyzes the financial information obtained in Step 1 and the emotional information obtained in Step 2. The input consists of both of these data. Using a generative AI model, it performs an analysis based on financial health and emotional state to generate optimal asset management plans, insurance strategies, and savings plans. The output is proposed financial advice, which involves querying databases and performing calculations by the AI ​​model.

[0190] Step 4:

[0191] The terminal notifies the user of suggestions sent from the server. The input is the generated suggestion information. The output is an emotionally sensitive, tailored notification message displayed on the user's screen. Specifically, this involves the message being displayed through the user interface, and the user reviewing its content.

[0192] Step 5:

[0193] The user approves or rejects the proposal. The input is the proposal displayed on the terminal, and the user makes a selection. Based on the user's decision, the server executes the necessary procedures. The output is the completed procedures reflecting the user's decision, as well as the progress monitoring results. Specifically, the conclusion of insurance contracts and adjustments to asset management are automated.

[0194] (Application Example 2)

[0195] 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 device 14 will be referred to as the "terminal."

[0196] Traditional financial evaluation systems generate generic recommendations based solely on an individual's financial data, failing to consider the user's emotional state. This makes personalized recommendations difficult to accept and implement. Furthermore, there is a lack of methods to optimize user spending behavior based on emotions. This results in inefficient recommendations that ignore users' emotions and stress levels, ultimately diminishing the effectiveness of financial management.

[0197] 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.

[0198] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs a risk-managed investment portfolio and automatically generates optimal insurance schemes and savings plans; and a server that recognizes and analyzes the user's emotional state through facial expressions, voice, and text communication. This makes it possible to provide personalized financial suggestions and spending advice adapted to the user's emotional state in real time.

[0199] "Financial data" refers to information about an individual's income, expenses, debts, and insurance policies, and is used to generate financial assessments and proposals.

[0200] "Financial status" refers to an individual's economic condition as assessed based on their financial data, and includes indicators such as profitability and the health of spending.

[0201] "Investment portfolio" refers to a combination of investments designed with risk management in mind, and is a plan formed with the aim of achieving optimal investment results.

[0202] "Emotional state" refers to the psychological state recognized from a series of facial expressions, voice, and text displayed by the user, enabling real-time sentiment analysis.

[0203] "Spending advice" refers to suggestions about spending that are recommended to users based on emotional and financial data, and is a personalized approach aimed at saving and optimizing spending.

[0204] To implement this invention, the server has the function of collecting and analyzing financial data. Specifically, it obtains data from financial institutions' APIs using authentication information provided from a communication terminal. This allows the server to centrally manage information on an individual's income, expenses, debts, and insurance contracts, and to comprehensively evaluate their financial situation.

[0205] Furthermore, the terminal is equipped with emotion recognition software that can capture the user's emotions in real time through facial expressions, voice, and text. This emotion data is analyzed by an emotion engine on the server and used to evaluate the user's psychological state. This emotional state is used to customize financial proposals; for example, if the user is in a high-stress state, low-risk investments or savings proposals will be made.

[0206] Furthermore, the suggestion generation software operates on the server, automatically generating personalized investment portfolios and savings advice based on analyzed financial and sentiment data. These suggestions are communicated to the user via their device, with information presented in a tone and language appropriate to their emotional state. Users can review these suggestions and approve or reject them.

[0207] For example, if a user is shopping online while feeling stressed, the system will immediately detect this and advise them to reconsider their purchase. For instance, an alert might appear stating, "This purchase may negatively impact your current savings plan. Perhaps you should take a moment to reconsider?"

[0208] An example of a prompt to a generative AI model is, "Can you suggest an algorithm that helps provide the best advice based on emotional data when a user considers spending?" This prompt allows the AI ​​to integrate emotional and financial data to generate advice tailored to the user's needs.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server receives authentication information from the user via a communication terminal and retrieves data on income, expenses, debt, and insurance contracts from financial institutions' APIs. In this process, the user's authentication information is used as input, and financial data is collected as output. The server analyzes this data to generate basic information for evaluating an individual's financial performance.

[0212] Step 2:

[0213] The device collects emotional data in real time through the user's facial expressions, voice, and text communication. Inputs include the user's facial images, voice, and text messages, and output is the analyzed emotional state. The device processes this data using emotion recognition software and sends it to a server.

[0214] Step 3:

[0215] The server receives the financial data obtained in Step 1 and the emotional data obtained in Step 2, and integrates and analyzes both. The inputs are financial data and emotional data, and the output is the result of the integrated analysis. The server uses proposal generation software to create personalized investment configurations and savings advice based on the user's current financial situation and emotional state.

[0216] Step 4:

[0217] The server sends the generated proposal to the terminal, which then notifies the user. The input is proposal data from the server, and the output is a customized notification displayed on the user's communication terminal. The terminal provides information using a tone and language appropriate to the user's emotional state, allowing the user to accept or reject the proposal.

[0218] Step 5:

[0219] When the user reviews and approves the proposal on their terminal, the server launches an executable module and proceeds with the necessary procedures. The input is the user's approval information, and the output is a notification that the execution procedure has been completed. The server then continues to monitor the transaction and progress, and sends notifications to the user if necessary.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] The system of the present invention includes an AI agent for analyzing an individual's financial data and proposing an optimal financial plan. The following elements are implemented as specific embodiments of the invention.

[0237] Implementation of the data collection and analysis module

[0238] The server collects transaction data using authentication information from financial institutions provided by the user. This data relates to the user's income, expenses, loans, and insurance policies. The server analyzes this data to assess the user's financial situation. Machine learning algorithms are used to identify income and expenditure patterns and calculate financial indicators.

[0239] Implementation of the proposal generation module

[0240] Based on the analysis results, the server constructs an optimal investment portfolio for the user and generates optimized insurance contract plans and automated savings plans. These suggestions emphasize risk management and are adjusted based on the user's risk tolerance. The server also dynamically optimizes the financial plan by taking into account the user's life event information.

[0241] Notification and execution of the executable module

[0242] The terminal notifies the user of proposals sent from the server. The user reviews the proposals through the terminal and decides whether to approve or reject them. For proposals approved by the user, the server automatically proceeds with the process. For example, it may complete the process for a new insurance contract or adjust investment allocations.

[0243] Implementation of monitoring and alerting functions

[0244] The server tracks the progress of implemented financial plans and alerts the user as circumstances change. For example, it immediately notifies the user if unexpected expenses occur or if the investment environment changes, prompting them to take appropriate action.

[0245] Specific example

[0246] For example, when a user is considering new car insurance, the system analyzes their current policy and compares it to other plans available on the market. The server takes into account the user's mileage, place of residence, age, etc., and recommends a more cost-effective plan. The user can review the suggestions on their device and, if they like them, switch their policy with a single button click. The server automates the contract process, saving the user money.

[0247] Thus, this invention streamlines personal financial management and supports the achievement of economic freedom. Each module works in conjunction to meet the user's needs.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user enters their financial institution authentication information into the terminal and sends it to the server. This includes bank account and credit card information. The terminal securely transmits this information to the server.

[0251] Step 2:

[0252] Based on the authentication information received from the user, the server uses APIs to retrieve relevant financial data from various institutions. This data includes income, expenses, loan balances, and insurance policy details.

[0253] Step 3:

[0254] The server analyzes the collected financial data and applies algorithms to evaluate the user's income and expenditure patterns and asset status. A user risk profile is also generated, and data is evaluated to help prepare for future life events.

[0255] Step 4:

[0256] Based on the analysis results, the server optimizes investment portfolios, reviews insurance policies, and creates savings plans. The server proposes the optimal combination of investment options for the user from a variety of choices.

[0257] Step 5:

[0258] The server sends the generated recommended plan to the terminal. The terminal notifies the user and provides an interface where they can view detailed information. The user reviews the plan and considers the proposal.

[0259] Step 6:

[0260] The user approves or rejects the proposed plan. If approved, the server automatically initiates the necessary procedures, such as concluding a new insurance contract or adjusting investment allocations.

[0261] Step 7:

[0262] The server continuously tracks progress after the approved plan has been implemented. If an unexpected event occurs or market conditions change, it immediately sends an alert to the user, prompting them to make necessary revisions.

[0263] This series of processes streamlines users' financial management, leading to reduced unnecessary spending and more effective use of assets.

[0264] (Example 1)

[0265] 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".

[0266] Because an individual's financial information is diverse, efficiently analyzing it and formulating appropriate financial strategies is difficult. Furthermore, there is a lack of systems to dynamically adjust financial plans in response to individual life events and market fluctuations. Additionally, there is a need for a means to quickly and accurately process procedures after a user approves a proposal.

[0267] 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.

[0268] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial situation, means for identifying income and expenditure patterns using machine learning algorithms and predicting future trends, and means for continuously monitoring implemented financial plans and issuing warnings to the user as needed. This enables the development of comprehensive financial strategies based on an individual's financial information and dynamic adjustments to those plans.

[0269] "Financial information" refers to data related to an individual's income, expenses, credit, and contracts.

[0270] "Financial status" refers to the criteria used to evaluate an economic state, including income, expenses, savings, and liabilities.

[0271] A "machine learning algorithm" is a computational method for automatically analyzing and predicting patterns and trends in data.

[0272] A "revenue and expenditure pattern" is a trend that shows the flow of income and expenses over a certain period of time.

[0273] "Risk management" is a strategy for minimizing losses by analyzing uncertainties related to investments and contracts.

[0274] "Life event information" refers to information related to important events in an individual's life, such as marriage or the birth of a child.

[0275] A "warning" is the act of notifying a user of the possibility of a specific economic event occurring.

[0276] "Prediction" refers to estimating future events or trends based on past data.

[0277] The system of this invention is designed to efficiently analyze an individual's financial information and to formulate and implement an optimal financial strategy. The system primarily functions through three parties: a server, a terminal, and a user.

[0278] First, the server collects financial information using authentication credentials provided by the user. This information consists of data on the user's income, expenses, credit, and contracts. The server uses a secure API to retrieve this information. The collected data is then analyzed using machine learning libraries such as TensorFlow and Scikit-learn. This makes it possible to identify past income and expenditure patterns and predict future trends.

[0279] Next, the server evaluates the user's financial situation and, based on the results, automatically generates an optimal investment portfolio, contract plan, and automated savings plan with risk management in place. These proposals are then notified to the user's device. Through the device, the user can review the proposals in detail and decide to approve or reject them. For approved proposals, the server automates the necessary procedures and executes them quickly.

[0280] Furthermore, the server continuously monitors the executed financial plan and responds immediately to unexpected expenses and changes in the economic environment. When changes occur, it alerts the user and prompts them to take appropriate action. This process ensures that the user's financial activities are always optimized and waste is minimized.

[0281] As a specific example, when a user is considering a new auto insurance, the server analyzes the current contract and compares it with potential market options. Then, considering conditions such as mileage and place of residence, it recommends a more efficient plan. The user can confirm the proposal on the terminal and switch the contract to a new one with a single button. The server automates the contract change procedure, reducing the user's effort.

[0282] Examples of prompt texts for the generative AI model include "Please propose an optimal investment portfolio to improve my current financial situation. My annual income is 5 million yen, and my average monthly expenditure is 300,000 yen. My risk tolerance is moderate, and I am considering new investment opportunities." In this way, the system provided by the present invention improves personal financial management and supports the achievement of economic freedom.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The server receives the authentication information of the financial institution from the user. Using this as input, the server accesses the API of the financial institution and obtains the latest transaction data of the user. This data includes income, expenditure, credit status, and contract details. The obtained data is stored in the database within the server.

[0286] Step 2:

[0287] The server analyzes the collected transaction data. Using the data stored in the database as input, it applies a machine learning algorithm to identify the income and expenditure patterns and predict future trends. At this time, libraries such as TensorFlow are used. As output, various indicators indicating the user's financial situation and the results of trend analysis are generated.

[0288] Step 3:

[0289] The server generates an optimal financial strategy based on the analysis results. It receives the user's risk tolerance and life event information as input and automatically creates investment configurations, contract plans, and savings plans. This allows the user to receive suggestions tailored to their individual financial goals. The generated suggestions are stored on the server.

[0290] Step 4:

[0291] The server sends the generated financial strategy to the terminal. The terminal receives it and notifies the user. The user can review the proposal on the terminal and examine the details. In this step, the specific proposal is displayed on the terminal screen, and an interface is provided that the user can interact with.

[0292] Step 5:

[0293] The user approves or rejects the proposal via their device. If approved, the server initiates an automated process based on the proposal. Specifically, it submits the necessary documents online for opening a new investment account or changing an existing contract, and then proceeds with the process. Once the process is complete, the server sends the user a completion notification.

[0294] Step 6:

[0295] The server continuously monitors the executed financial plan. If market fluctuations or unexpected expenses occur, the server detects them and alerts the user. Specifically, it performs regular scans of financial information and immediately reports any anomalies or changes in trends to the user. This allows the user to take quick and appropriate action.

[0296] (Application Example 1)

[0297] 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."

[0298] In modern personal financial management, developing optimal financial strategies that address diverse financial transactions and life events is complex and extremely difficult for the average user. Furthermore, existing methods fail to adequately address real-time financial monitoring, risk management, and the provision of personalized financial plans. There is also a need to efficiently generate individualized financial recommendations to support user decision-making.

[0299] 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.

[0300] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs risk-managed investment proposals and automatically generates optimal insurance proposals and wealth-building plans; and a server that dynamically generates personalized financial proposals using a generation AI model and provides prompt statements to support decision-making. This enables users to automatically receive efficient and risk-aware financial plans tailored to their own financial situation.

[0301] "Financial data" refers to financial information about an individual, such as their income, expenses, debts, and insurance policies.

[0302] "Assessing financial status" involves analyzing collected financial data to understand an individual's income, expenses, and debt situation.

[0303] An "investment proposal" is a plan that presents an individual with the optimal financial management plan, taking risk management into consideration.

[0304] "Insurance proposal" means presenting the most suitable insurance contract according to an individual's circumstances and needs.

[0305] A "wealth accumulation plan" is a proposal of optimal savings and asset building methods aimed at ensuring an individual's future financial stability.

[0306] The "generative AI model" refers to an algorithm or system that automatically generates individualized information and proposals using artificial intelligence.

[0307] The "prompt text" is the text of instructions and proposals provided by the generative AI model to assist the user in making decisions.

[0308] To implement this invention, the system mainly includes the following elements. The server collects an individual's financial data via the API of a financial institution and organizes and analyzes the captured data using data processing libraries such as Pandas and Numpy. As a result of the analysis, the server evaluates the individual's income and expenditure and debt situation.

[0309] Taking risk management into consideration, the server uses machine learning algorithms such as Scikit-learn and TensorFlow to generate optimal investment proposals, insurance proposals, and wealth management plans. At this time, through the generative AI model, a financial proposal corresponding to the user's individual situation is dynamically formed, and a prompt text for providing it to the user is created.

[0310] The user's terminal is notified of the proposals from the server, and the user can check and approve the content of the proposals. Also, by utilizing a real-time communication platform such as Firebase, the execution status and revision information of the proposals are quickly notified to the user. For example, in the case where the user adopts a specific cost-saving measure to optimize monthly expenses, the system continuously tracks the implementation progress and makes adjustment proposals as necessary.

[0311] With this approach, an individual can more effectively manage their own finances. Also, specific and individualized solutions can be obtained for the economic issues faced by the user.

[0312] An example of a prompt message is: "Generate optimal savings and spending reduction suggestions based on the user's income and expenditure data. Provide a risk-managed plan that takes into account the stability of income and the types of expenses."

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server collects transaction data using authentication credentials via the user's financial institution's API. This data includes information on the user's income, expenses, loans, and insurance policies. The input data is organized as a dataframe using Pandas, ensuring data consistency.

[0316] Step 2:

[0317] The server analyzes users' income and expenditure patterns using Scikit-learn's machine learning algorithms based on organized financial data. This analysis evaluates each user's financial situation. The input data consists of income and expenditure figures, and the output is an evaluation of the user's financial status. Specifically, it identifies similar spending patterns through clustering.

[0318] Step 3:

[0319] Based on the analysis results, the server uses a generative AI model to generate optimal investment and insurance proposals for the user. Here, the proposals are customized with a focus on risk management. The input includes the analysis results, and the output is a personalized financial proposal prompt. For example, a prompt such as "Consider insurance to mitigate risk for this spending pattern" might be generated.

[0320] Step 4:

[0321] The server notifies the user's device of the generated suggestions. Using Firebase-based real-time notification technology, users can instantly view these suggestions on their smartphones. The input is the generated prompt message, and the output is the notification to the user.

[0322] Step 5:

[0323] The user reviews the proposal on their device and approves it if necessary. Once the user approves the proposal, the server automatically initiates the process. Specifically, API calls are made to modify the investment portfolio or process new insurance contracts based on the user's instructions. The input is the user's approval, and the output is confirmation information of the executed procedure.

[0324] Step 6:

[0325] The server continuously monitors the progress of executed financial plans and notifies the user of the progress via the Firebase Realtime Database. Inputs are data on the procedures performed, and outputs are notifications regarding the progress. For example, it can display the savings progress rate towards the target month and year, and send an alert early if there is a risk of not meeting the goal.

[0326] 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.

[0327] This invention provides a system that offers more personalized advice by combining a conventional system that analyzes an individual's financial data and makes optimal financial proposals with an emotion engine that recognizes and analyzes the user's emotions. In embodiments of this invention, the following elements specifically function.

[0328] Implementation of the data collection and analysis module

[0329] The server receives authentication information from users via their terminals and retrieves real-time data on income, expenses, loans, and insurance policies from bank and insurance company APIs. This data is analyzed on the server and used to assess the user's financial health.

[0330] Implementation of the emotion recognition module

[0331] The device collects emotional data through the user's facial expressions, voice, and text communication. An emotion engine on the server analyzes this data to understand the user's emotional state in real time. This emotional data is used to customize financial proposals.

[0332] Implementation of the proposal generation module

[0333] The server uses both financial and emotional data to generate risk-managed investment portfolios, optimized insurance plans, and automated savings plans. These recommendations are adjusted according to the user's emotional state; for example, conservative recommendations are given when the user is under high stress.

[0334] Implementation of notification and interaction modules

[0335] The device notifies the user of suggestions generated from the server. The notification uses a tone and language tailored to the user's emotional state, providing information in a way that is easily accepted. The user can review the suggestions on the device and decide whether to approve or reject them.

[0336] Execution of the execution and monitoring module

[0337] If the user approves the proposal, the server automatically performs the necessary procedures, including concluding new insurance contracts and changing investment allocations. After execution, the server continuously tracks progress and sends alerts to the user as needed.

[0338] Specific example

[0339] For example, if a user is feeling anxious about their loan repayment plan, the device detects this emotion from the user's facial expressions and tone of voice. The server then creates a loan refinancing proposal and offers options to gradually adjust the plan to reduce stress. This proposal is displayed on the device in gentle language to help the user make a decision with confidence.

[0340] Thus, this invention, which combines an emotional engine, enables more personalized financial advice while taking into account the user's emotional needs. This improves the user experience and facilitates the achievement of financial freedom.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user uses a terminal to enter their financial institution's authentication information and sends it to the server. This grants the server access to retrieve the user's banking transactions and insurance contract information.

[0344] Step 2:

[0345] The server collects data on income, expenses, loan balances, and insurance policies from banks and insurance companies via APIs. This collected data is then analyzed to provide foundational information for understanding the user's financial situation.

[0346] Step 3:

[0347] The device uses its built-in camera and microphone to detect the user's facial expressions and voice tone in real time. Emotion recognition software analyzes this data and sends the user's emotional state to a server.

[0348] Step 4:

[0349] The server integrates emotional data obtained by the emotion engine with financial data. Based on this, it generates optimal investment plans, insurance options, and savings plans for the user. This includes adjustments such as selecting lower-risk options if the user's stress level is high.

[0350] Step 5:

[0351] The server sends the generated suggestions to the terminal and notifies the user. When notifying, it selects a communication style based on sentiment analysis and provides information in a way that is easily accepted by the user.

[0352] Step 6:

[0353] Users view proposals through their devices and, if necessary, send approval or rejection instructions to the server. The interface provided is designed to ensure that users do not feel uneasy.

[0354] Step 7:

[0355] If the user approves, the server will automatically initiate the process according to the proposal. This may involve the creation of a new insurance contract or adjustment of the investment portfolio, and the server will continuously monitor the progress.

[0356] Step 8:

[0357] The server tracks the results of executed plans and re-evaluates them if necessary. When circumstances change or unexpected events occur, it sends appropriate alerts to the user to prompt action.

[0358] These steps enable a system with an emotion engine to provide nuanced financial management that responds to the user's emotions.

[0359] (Example 2)

[0360] 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".

[0361] Traditional financial management systems lack the ability to provide advice that considers both an individual's financial situation and emotional state simultaneously. This has resulted in a failure to adequately address users' psychological needs and make it difficult to provide optimal financial recommendations. Furthermore, providing information without considering the user's emotions can, in some cases, cause stress to the user.

[0362] 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.

[0363] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial health, means for collecting emotional information and analyzing the user's emotional state in real time, and means for generating financial recommendations tailored based on both financial and emotional information. This makes it possible to provide users with emotionally sensitive and financially optimized advice.

[0364] "Financial information" refers to data on an individual's income, expenses, debts, and insurance policies.

[0365] "Emotional information" refers to data about the emotional state obtained from the user's facial expressions, voice, and text communication.

[0366] "Financial soundness" refers to an indicator that assesses an individual's ability to maintain a balance between income and expenses and to remain financially stable.

[0367] An "asset management plan" refers to a plan that proposes the optimal investment strategy based on an individual's risk profile.

[0368] An "insurance strategy" refers to a method for providing optimized insurance contracts based on individual needs.

[0369] A "savings plan" refers to a method of automatically accumulating funds to achieve an individual's future financial goals.

[0370] A "communication terminal" refers to a device used by a user to receive or input information.

[0371] This invention is a system that integrates individual users' financial and emotional information to provide optimized financial advice.

[0372] The server first uses the authentication information of the financial institution received from the user via the terminal to obtain the individual's financial information through APIs of banks and insurance companies. This information includes data on the user's income, expenses, debts, and insurance policies. The server analyzes this data and uses a generative AI model to assess the user's financial health.

[0373] Meanwhile, the device acquires the user's facial expressions and voice data, and also collects emotional information from the user's text communication. The device sends this emotional information to the server in real time. The emotion engine implemented on the server uses this information to analyze the user's emotional state and helps generate optimal suggestions.

[0374] In generating recommendations, the server integrates both financial and emotional information to construct risk-managed asset management plans, optimized insurance strategies, and automated savings plans. These recommendations are tailored to the user's current emotional state.

[0375] For example, if a user is worried about loan repayment, the device can recognize their emotions from their facial expressions and tone of voice. Based on this, the server can create a loan refinancing proposal and adjust the proposal step by step. This proposal is displayed on the device in gentle language, allowing the user to make a decision with confidence.

[0376] Another example of a prompt message is asking the generative AI model, "When a user is feeling stressed, what kind of financial advice should you suggest? Please explain with specific examples."

[0377] This system aims to provide personalized financial advice that takes user emotions into consideration, thereby improving the user experience and facilitating the achievement of financial freedom.

[0378] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0379] Step 1:

[0380] The server receives authentication information from the user via the terminal. Input includes user ID, password, and other authentication data. Using this authentication information, the server accesses the APIs of banks and insurance companies to retrieve data on income, expenses, liabilities, and insurance policies. The output is the collected financial information, which serves as the basis for analysis. Specifically, the server makes API calls and retrieves the necessary data.

[0381] Step 2:

[0382] The device uses its camera and microphone to collect the user's facial expressions, voice, and text communication. The input consists of real-time captured visual and audio data. The device sends this data to a server as emotion data. The output is data indicating the user's emotional state, and in terms of specific actions, algorithms such as facial recognition and voice analysis are executed.

[0383] Step 3:

[0384] The server integrates and analyzes the financial information obtained in Step 1 and the emotional information obtained in Step 2. The input consists of both of these data. Using a generative AI model, it performs an analysis based on financial health and emotional state to generate optimal asset management plans, insurance strategies, and savings plans. The output is proposed financial advice, which involves querying databases and performing calculations by the AI ​​model.

[0385] Step 4:

[0386] The terminal notifies the user of suggestions sent from the server. The input is the generated suggestion information. The output is an emotionally sensitive, tailored notification message displayed on the user's screen. Specifically, this involves the message being displayed through the user interface, and the user reviewing its content.

[0387] Step 5:

[0388] The user approves or rejects the proposal. The input is the proposal displayed on the terminal, and the user makes a selection. Based on the user's decision, the server executes the necessary procedures. The output is the completed procedures reflecting the user's decision, as well as the progress monitoring results. Specifically, the conclusion of insurance contracts and adjustments to asset management are automated.

[0389] (Application Example 2)

[0390] 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."

[0391] Traditional financial evaluation systems generate generic recommendations based solely on an individual's financial data, failing to consider the user's emotional state. This makes personalized recommendations difficult to accept and implement. Furthermore, there is a lack of methods to optimize user spending behavior based on emotions. This results in inefficient recommendations that ignore users' emotions and stress levels, ultimately diminishing the effectiveness of financial management.

[0392] 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.

[0393] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs a risk-managed investment portfolio and automatically generates optimal insurance schemes and savings plans; and a server that recognizes and analyzes the user's emotional state through facial expressions, voice, and text communication. This makes it possible to provide personalized financial suggestions and spending advice adapted to the user's emotional state in real time.

[0394] "Financial data" refers to information about an individual's income, expenses, debts, and insurance policies, and is used to generate financial assessments and proposals.

[0395] "Financial status" refers to an individual's economic condition as assessed based on their financial data, and includes indicators such as profitability and the health of spending.

[0396] "Investment portfolio" refers to a combination of investments designed with risk management in mind, and is a plan formed with the aim of achieving optimal investment results.

[0397] "Emotional state" refers to the psychological state recognized from a series of facial expressions, voice, and text displayed by the user, enabling real-time sentiment analysis.

[0398] "Spending advice" refers to suggestions about spending that are recommended to users based on emotional and financial data, and is a personalized approach aimed at saving and optimizing spending.

[0399] To implement this invention, the server has the function of collecting and analyzing financial data. Specifically, it obtains data from financial institutions' APIs using authentication information provided from a communication terminal. This allows the server to centrally manage information on an individual's income, expenses, debts, and insurance contracts, and to comprehensively evaluate their financial situation.

[0400] Furthermore, the terminal is equipped with emotion recognition software that can capture the user's emotions in real time through facial expressions, voice, and text. This emotion data is analyzed by an emotion engine on the server and used to evaluate the user's psychological state. This emotional state is used to customize financial proposals; for example, if the user is in a high-stress state, low-risk investments or savings proposals will be made.

[0401] Furthermore, the suggestion generation software operates on the server, automatically generating personalized investment portfolios and savings advice based on analyzed financial and sentiment data. These suggestions are communicated to the user via their device, with information presented in a tone and language appropriate to their emotional state. Users can review these suggestions and approve or reject them.

[0402] For example, if a user is shopping online while feeling stressed, the system will immediately detect this and advise them to reconsider their purchase. For instance, an alert might appear stating, "This purchase may negatively impact your current savings plan. Perhaps you should take a moment to reconsider?"

[0403] An example of a prompt to a generative AI model is, "Can you suggest an algorithm that helps provide the best advice based on emotional data when a user considers spending?" This prompt allows the AI ​​to integrate emotional and financial data to generate advice tailored to the user's needs.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The server receives authentication information from the user via a communication terminal and retrieves data on income, expenses, debt, and insurance contracts from financial institutions' APIs. In this process, the user's authentication information is used as input, and financial data is collected as output. The server analyzes this data to generate basic information for evaluating an individual's financial performance.

[0407] Step 2:

[0408] The device collects emotional data in real time through the user's facial expressions, voice, and text communication. Inputs include the user's facial images, voice, and text messages, and output is the analyzed emotional state. The device processes this data using emotion recognition software and sends it to a server.

[0409] Step 3:

[0410] The server receives the financial data obtained in Step 1 and the emotional data obtained in Step 2, and integrates and analyzes both. The inputs are financial data and emotional data, and the output is the result of the integrated analysis. The server uses proposal generation software to create personalized investment configurations and savings advice based on the user's current financial situation and emotional state.

[0411] Step 4:

[0412] The server sends the generated proposal to the terminal, which then notifies the user. The input is proposal data from the server, and the output is a customized notification displayed on the user's communication terminal. The terminal provides information using a tone and language appropriate to the user's emotional state, allowing the user to accept or reject the proposal.

[0413] Step 5:

[0414] When the user reviews and approves the proposal on their terminal, the server launches an executable module and proceeds with the necessary procedures. The input is the user's approval information, and the output is a notification that the execution procedure has been completed. The server then continues to monitor the transaction and progress, and sends notifications to the user if necessary.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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".

[0431] The system of the present invention includes an AI agent for analyzing an individual's financial data and proposing an optimal financial plan. The following elements are implemented as specific embodiments of the invention.

[0432] Implementation of the data collection and analysis module

[0433] The server collects transaction data using authentication information from financial institutions provided by the user. This data relates to the user's income, expenses, loans, and insurance policies. The server analyzes this data to assess the user's financial situation. Machine learning algorithms are used to identify income and expenditure patterns and calculate financial indicators.

[0434] Implementation of the proposal generation module

[0435] Based on the analysis results, the server constructs an optimal investment portfolio for the user and generates optimized insurance contract plans and automated savings plans. These suggestions emphasize risk management and are adjusted based on the user's risk tolerance. The server also dynamically optimizes the financial plan by taking into account the user's life event information.

[0436] Notification and execution of the executable module

[0437] The terminal notifies the user of proposals sent from the server. The user reviews the proposals through the terminal and decides whether to approve or reject them. For proposals approved by the user, the server automatically proceeds with the process. For example, it may complete the process for a new insurance contract or adjust investment allocations.

[0438] Implementation of monitoring and alerting functions

[0439] The server tracks the progress of implemented financial plans and alerts the user as circumstances change. For example, it immediately notifies the user if unexpected expenses occur or if the investment environment changes, prompting them to take appropriate action.

[0440] Specific example

[0441] For example, when a user is considering new car insurance, the system analyzes their current policy and compares it to other plans available on the market. The server takes into account the user's mileage, place of residence, age, etc., and recommends a more cost-effective plan. The user can review the suggestions on their device and, if they like them, switch their policy with a single button click. The server automates the contract process, saving the user money.

[0442] Thus, this invention streamlines personal financial management and supports the achievement of economic freedom. Each module works in conjunction to meet the user's needs.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The user enters their financial institution authentication information into the terminal and sends it to the server. This includes bank account and credit card information. The terminal securely transmits this information to the server.

[0446] Step 2:

[0447] Based on the authentication information received from the user, the server uses APIs to retrieve relevant financial data from various institutions. This data includes income, expenses, loan balances, and insurance policy details.

[0448] Step 3:

[0449] The server analyzes the collected financial data and applies algorithms to evaluate the user's income and expenditure patterns and asset status. A user risk profile is also generated, and data is evaluated to help prepare for future life events.

[0450] Step 4:

[0451] Based on the analysis results, the server optimizes investment portfolios, reviews insurance policies, and creates savings plans. The server proposes the optimal combination of investment options for the user from a variety of choices.

[0452] Step 5:

[0453] The server sends the generated recommended plan to the terminal. The terminal notifies the user and provides an interface where they can view detailed information. The user reviews the plan and considers the proposal.

[0454] Step 6:

[0455] The user approves or rejects the proposed plan. If approved, the server automatically initiates the necessary procedures, such as concluding a new insurance contract or adjusting investment allocations.

[0456] Step 7:

[0457] The server continuously tracks progress after the approved plan has been implemented. If an unexpected event occurs or market conditions change, it immediately sends an alert to the user, prompting them to make necessary revisions.

[0458] This series of processes streamlines users' financial management, leading to reduced unnecessary spending and more effective use of assets.

[0459] (Example 1)

[0460] 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."

[0461] Because an individual's financial information is diverse, efficiently analyzing it and formulating appropriate financial strategies is difficult. Furthermore, there is a lack of systems to dynamically adjust financial plans in response to individual life events and market fluctuations. Additionally, there is a need for a means to quickly and accurately process procedures after a user approves a proposal.

[0462] 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.

[0463] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial situation, means for identifying income and expenditure patterns using machine learning algorithms and predicting future trends, and means for continuously monitoring implemented financial plans and issuing warnings to the user as needed. This enables the development of comprehensive financial strategies based on an individual's financial information and dynamic adjustments to those plans.

[0464] "Financial information" refers to data related to an individual's income, expenses, credit, and contracts.

[0465] "Financial status" refers to the criteria used to evaluate an economic state, including income, expenses, savings, and liabilities.

[0466] A "machine learning algorithm" is a computational method for automatically analyzing and predicting patterns and trends in data.

[0467] A "revenue and expenditure pattern" is a trend that shows the flow of income and expenses over a certain period of time.

[0468] "Risk management" is a strategy for minimizing losses by analyzing uncertainties related to investments and contracts.

[0469] "Life event information" refers to information related to important events in an individual's life, such as marriage or the birth of a child.

[0470] A "warning" is the act of notifying a user of the possibility of a specific economic event occurring.

[0471] "Prediction" refers to estimating future events or trends based on past data.

[0472] The system of this invention is designed to efficiently analyze an individual's financial information and to formulate and implement an optimal financial strategy. The system primarily functions through three parties: a server, a terminal, and a user.

[0473] First, the server collects financial information using authentication credentials provided by the user. This information consists of data on the user's income, expenses, credit, and contracts. The server uses a secure API to retrieve this information. The collected data is then analyzed using machine learning libraries such as TensorFlow and Scikit-learn. This makes it possible to identify past income and expenditure patterns and predict future trends.

[0474] Next, the server evaluates the user's financial situation and, based on the results, automatically generates an optimal investment portfolio, contract plan, and automated savings plan with risk management in place. These proposals are then notified to the user's device. Through the device, the user can review the proposals in detail and decide to approve or reject them. For approved proposals, the server automates the necessary procedures and executes them quickly.

[0475] Furthermore, the server continuously monitors the executed financial plan and responds immediately to unexpected expenses and changes in the economic environment. When changes occur, it alerts the user and prompts them to take appropriate action. This process ensures that the user's financial activities are always optimized and waste is minimized.

[0476] For example, if a user is considering new car insurance, the server analyzes their current policy and compares it to potential market options. It then recommends a more efficient plan, taking into account factors such as mileage and location. The user can review the proposal on their device and switch to the new policy with a single click. The server automates the policy change process, reducing the user's effort.

[0477] An example of a prompt to the generating AI model would be: "Please suggest the optimal investment portfolio to improve my current financial situation. My annual income is 5 million yen, and my average monthly expenses are 300,000 yen. My risk tolerance is moderate, and I am considering new investment opportunities." In this way, the system provided by the present invention streamlines personal financial management and supports the achievement of financial freedom.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The server receives authentication information from the user regarding their financial institution. Using this as input, the server accesses the financial institution's API to retrieve the user's latest transaction data. This data includes income, expenses, credit status, and contract details. The retrieved data is stored in a database on the server.

[0481] Step 2:

[0482] The server analyzes the collected transaction data. Using the data stored in the database as input, it applies machine learning algorithms to identify revenue and expenditure patterns and predict future trends. Libraries such as TensorFlow are used for this purpose. As output, various indicators showing the user's financial situation and trend analysis results are generated.

[0483] Step 3:

[0484] The server generates an optimal financial strategy based on the analysis results. It receives the user's risk tolerance and life event information as input and automatically creates investment configurations, contract plans, and savings plans. This allows the user to receive suggestions tailored to their individual financial goals. The generated suggestions are stored on the server.

[0485] Step 4:

[0486] The server sends the generated financial strategy to the terminal. The terminal receives it and notifies the user. The user can review the proposal on the terminal and examine the details. In this step, the specific proposal is displayed on the terminal screen, and an interface is provided that the user can interact with.

[0487] Step 5:

[0488] The user approves or rejects the proposal via their device. If approved, the server initiates an automated process based on the proposal. Specifically, it submits the necessary documents online for opening a new investment account or changing an existing contract, and then proceeds with the process. Once the process is complete, the server sends the user a completion notification.

[0489] Step 6:

[0490] The server continuously monitors the executed financial plan. If market fluctuations or unexpected expenses occur, the server detects them and alerts the user. Specifically, it performs regular scans of financial information and immediately reports any anomalies or changes in trends to the user. This allows the user to take quick and appropriate action.

[0491] (Application Example 1)

[0492] 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."

[0493] In modern personal financial management, developing optimal financial strategies that address diverse financial transactions and life events is complex and extremely difficult for the average user. Furthermore, existing methods fail to adequately address real-time financial monitoring, risk management, and the provision of personalized financial plans. There is also a need to efficiently generate individualized financial recommendations to support user decision-making.

[0494] 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.

[0495] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs risk-managed investment proposals and automatically generates optimal insurance proposals and wealth-building plans; and a server that dynamically generates personalized financial proposals using a generation AI model and provides prompt statements to support decision-making. This enables users to automatically receive efficient and risk-aware financial plans tailored to their own financial situation.

[0496] "Financial data" refers to financial information about an individual, such as their income, expenses, debts, and insurance policies.

[0497] "Assessing financial status" involves analyzing collected financial data to understand an individual's income, expenses, and debt situation.

[0498] An "investment proposal" is a plan that presents an individual with the optimal financial management plan, taking risk management into consideration.

[0499] "Insurance proposal" means presenting the most suitable insurance contract according to an individual's circumstances and needs.

[0500] A "wealth accumulation plan" is a proposal of optimal savings and asset building methods aimed at ensuring an individual's future financial stability.

[0501] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to automatically generate personalized information and suggestions.

[0502] A "prompt message" is a set of instructions or suggestions provided by a generative AI model to assist the user in making decisions.

[0503] To realize this invention, the system mainly includes the following elements: The server collects personal financial data via the financial institution's API, and organizes and analyzes the ingested data using data processing libraries such as Pandas and NumPy. Based on the analysis results, it evaluates the individual's income, expenses, and debt situation.

[0504] Taking risk management into consideration, the server uses machine learning algorithms such as Scikit-learn and TensorFlow to generate optimal investment proposals, insurance proposals, and wealth accumulation plans. In this process, the generating AI model dynamically forms financial proposals tailored to the user's individual circumstances and creates prompt messages to present them to the user.

[0505] The user's device receives notifications of suggestions from the server, and the user can review and approve the suggestions themselves. Furthermore, real-time communication platforms such as Firebase are used to quickly notify the user of the progress and revisions to the suggestions. For example, if a user adopts specific saving measures to optimize their monthly spending, the system continuously tracks the progress of those measures and makes adjustment suggestions as needed.

[0506] This approach allows individuals to manage their finances more effectively and to obtain specific and personalized solutions to their financial challenges.

[0507] An example of a prompt message is: "Generate optimal savings and spending reduction suggestions based on the user's income and expenditure data. Provide a risk-managed plan that takes into account the stability of income and the types of expenses."

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The server collects transaction data using authentication credentials via the user's financial institution's API. This data includes information on the user's income, expenses, loans, and insurance policies. The input data is organized as a dataframe using Pandas, ensuring data consistency.

[0511] Step 2:

[0512] The server analyzes users' income and expenditure patterns using Scikit-learn's machine learning algorithms based on organized financial data. This analysis evaluates each user's financial situation. The input data consists of income and expenditure figures, and the output is an evaluation of the user's financial status. Specifically, it identifies similar spending patterns through clustering.

[0513] Step 3:

[0514] Based on the analysis results, the server uses a generative AI model to generate optimal investment and insurance proposals for the user. Here, the proposals are customized with a focus on risk management. The input includes the analysis results, and the output is a personalized financial proposal prompt. For example, a prompt such as "Consider insurance to mitigate risk for this spending pattern" might be generated.

[0515] Step 4:

[0516] The server notifies the user's device of the generated suggestions. Using Firebase-based real-time notification technology, users can instantly view these suggestions on their smartphones. The input is the generated prompt message, and the output is the notification to the user.

[0517] Step 5:

[0518] The user reviews the proposal on their device and approves it if necessary. Once the user approves the proposal, the server automatically initiates the process. Specifically, API calls are made to modify the investment portfolio or process new insurance contracts based on the user's instructions. The input is the user's approval, and the output is confirmation information of the executed procedure.

[0519] Step 6:

[0520] The server continuously monitors the progress of executed financial plans and notifies the user of the progress via the Firebase Realtime Database. Inputs are data on the procedures performed, and outputs are notifications regarding the progress. For example, it can display the savings progress rate towards the target month and year, and send an alert early if there is a risk of not meeting the goal.

[0521] 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.

[0522] This invention provides a system that offers more personalized advice by combining a conventional system that analyzes an individual's financial data and makes optimal financial proposals with an emotion engine that recognizes and analyzes the user's emotions. In embodiments of this invention, the following elements specifically function.

[0523] Implementation of the data collection and analysis module

[0524] The server receives authentication information from users via their terminals and retrieves real-time data on income, expenses, loans, and insurance policies from bank and insurance company APIs. This data is analyzed on the server and used to assess the user's financial health.

[0525] Implementation of the emotion recognition module

[0526] The device collects emotional data through the user's facial expressions, voice, and text communication. An emotion engine on the server analyzes this data to understand the user's emotional state in real time. This emotional data is used to customize financial proposals.

[0527] Implementation of the proposal generation module

[0528] The server uses both financial and emotional data to generate risk-managed investment portfolios, optimized insurance plans, and automated savings plans. These recommendations are adjusted according to the user's emotional state; for example, conservative recommendations are given when the user is under high stress.

[0529] Implementation of notification and interaction modules

[0530] The device notifies the user of suggestions generated from the server. The notification uses a tone and language tailored to the user's emotional state, providing information in a way that is easily accepted. The user can review the suggestions on the device and decide whether to approve or reject them.

[0531] Execution of the execution and monitoring module

[0532] If the user approves the proposal, the server automatically performs the necessary procedures, including concluding new insurance contracts and changing investment allocations. After execution, the server continuously tracks progress and sends alerts to the user as needed.

[0533] Specific example

[0534] For example, if a user is feeling anxious about their loan repayment plan, the device detects this emotion from the user's facial expressions and tone of voice. The server then creates a loan refinancing proposal and offers options to gradually adjust the plan to reduce stress. This proposal is displayed on the device in gentle language to help the user make a decision with confidence.

[0535] Thus, this invention, which combines an emotional engine, enables more personalized financial advice while taking into account the user's emotional needs. This improves the user experience and facilitates the achievement of financial freedom.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The user uses a terminal to enter their financial institution's authentication information and sends it to the server. This grants the server access to retrieve the user's banking transactions and insurance contract information.

[0539] Step 2:

[0540] The server collects data on income, expenses, loan balances, and insurance policies from banks and insurance companies via APIs. This collected data is then analyzed to provide foundational information for understanding the user's financial situation.

[0541] Step 3:

[0542] The device uses its built-in camera and microphone to detect the user's facial expressions and voice tone in real time. Emotion recognition software analyzes this data and sends the user's emotional state to a server.

[0543] Step 4:

[0544] The server integrates emotional data obtained by the emotion engine with financial data. Based on this, it generates optimal investment plans, insurance options, and savings plans for the user. This includes adjustments such as selecting lower-risk options if the user's stress level is high.

[0545] Step 5:

[0546] The server sends the generated suggestions to the terminal and notifies the user. When notifying, it selects a communication style based on sentiment analysis and provides information in a way that is easily accepted by the user.

[0547] Step 6:

[0548] Users view proposals through their devices and, if necessary, send approval or rejection instructions to the server. The interface provided is designed to ensure that users do not feel uneasy.

[0549] Step 7:

[0550] If the user approves, the server will automatically initiate the process according to the proposal. This may involve the creation of a new insurance contract or adjustment of the investment portfolio, and the server will continuously monitor the progress.

[0551] Step 8:

[0552] The server tracks the results of executed plans and re-evaluates them if necessary. When circumstances change or unexpected events occur, it sends appropriate alerts to the user to prompt action.

[0553] These steps enable a system with an emotion engine to provide nuanced financial management that responds to the user's emotions.

[0554] (Example 2)

[0555] 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."

[0556] Traditional financial management systems lack the ability to provide advice that considers both an individual's financial situation and emotional state simultaneously. This has resulted in a failure to adequately address users' psychological needs and make it difficult to provide optimal financial recommendations. Furthermore, providing information without considering the user's emotions can, in some cases, cause stress to the user.

[0557] 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.

[0558] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial health, means for collecting emotional information and analyzing the user's emotional state in real time, and means for generating financial recommendations tailored based on both financial and emotional information. This makes it possible to provide users with emotionally sensitive and financially optimized advice.

[0559] "Financial information" refers to data on an individual's income, expenses, debts, and insurance policies.

[0560] "Emotional information" refers to data about the emotional state obtained from the user's facial expressions, voice, and text communication.

[0561] "Financial soundness" refers to an indicator that assesses an individual's ability to maintain a balance between income and expenses and to remain financially stable.

[0562] An "asset management plan" refers to a plan that proposes the optimal investment strategy based on an individual's risk profile.

[0563] An "insurance strategy" refers to a method for providing optimized insurance contracts based on individual needs.

[0564] A "savings plan" refers to a method of automatically accumulating funds to achieve an individual's future financial goals.

[0565] A "communication terminal" refers to a device used by a user to receive or input information.

[0566] This invention is a system that integrates individual users' financial and emotional information to provide optimized financial advice.

[0567] The server first uses the authentication information of the financial institution received from the user via the terminal to obtain the individual's financial information through APIs of banks and insurance companies. This information includes data on the user's income, expenses, debts, and insurance policies. The server analyzes this data and uses a generative AI model to assess the user's financial health.

[0568] Meanwhile, the device acquires the user's facial expressions and voice data, and also collects emotional information from the user's text communication. The device sends this emotional information to the server in real time. The emotion engine implemented on the server uses this information to analyze the user's emotional state and helps generate optimal suggestions.

[0569] In generating recommendations, the server integrates both financial and emotional information to construct risk-managed asset management plans, optimized insurance strategies, and automated savings plans. These recommendations are tailored to the user's current emotional state.

[0570] For example, if a user is worried about loan repayment, the device can recognize their emotions from their facial expressions and tone of voice. Based on this, the server can create a loan refinancing proposal and adjust the proposal step by step. This proposal is displayed on the device in gentle language, allowing the user to make a decision with confidence.

[0571] Another example of a prompt message is asking the generative AI model, "When a user is feeling stressed, what kind of financial advice should you suggest? Please explain with specific examples."

[0572] This system aims to provide personalized financial advice that takes user emotions into consideration, thereby improving the user experience and facilitating the achievement of financial freedom.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The server receives authentication information from the user via the terminal. Input includes user ID, password, and other authentication data. Using this authentication information, the server accesses the APIs of banks and insurance companies to retrieve data on income, expenses, liabilities, and insurance policies. The output is the collected financial information, which serves as the basis for analysis. Specifically, the server makes API calls and retrieves the necessary data.

[0576] Step 2:

[0577] The device uses its camera and microphone to collect the user's facial expressions, voice, and text communication. The input consists of real-time captured visual and audio data. The device sends this data to a server as emotion data. The output is data indicating the user's emotional state, and in terms of specific actions, algorithms such as facial recognition and voice analysis are executed.

[0578] Step 3:

[0579] The server integrates and analyzes the financial information obtained in Step 1 and the emotional information obtained in Step 2. The input consists of both of these data. Using a generative AI model, it performs an analysis based on financial health and emotional state to generate optimal asset management plans, insurance strategies, and savings plans. The output is proposed financial advice, which involves querying databases and performing calculations by the AI ​​model.

[0580] Step 4:

[0581] The terminal notifies the user of suggestions sent from the server. The input is the generated suggestion information. The output is an emotionally sensitive, tailored notification message displayed on the user's screen. Specifically, this involves the message being displayed through the user interface, and the user reviewing its content.

[0582] Step 5:

[0583] The user approves or rejects the proposal. The input is the proposal displayed on the terminal, and the user makes a selection. Based on the user's decision, the server executes the necessary procedures. The output is the completed procedures reflecting the user's decision, as well as the progress monitoring results. Specifically, the conclusion of insurance contracts and adjustments to asset management are automated.

[0584] (Application Example 2)

[0585] 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."

[0586] Traditional financial evaluation systems generate generic recommendations based solely on an individual's financial data, failing to consider the user's emotional state. This makes personalized recommendations difficult to accept and implement. Furthermore, there is a lack of methods to optimize user spending behavior based on emotions. This results in inefficient recommendations that ignore users' emotions and stress levels, ultimately diminishing the effectiveness of financial management.

[0587] 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.

[0588] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs a risk-managed investment portfolio and automatically generates optimal insurance schemes and savings plans; and a server that recognizes and analyzes the user's emotional state through facial expressions, voice, and text communication. This makes it possible to provide personalized financial suggestions and spending advice adapted to the user's emotional state in real time.

[0589] "Financial data" refers to information about an individual's income, expenses, debts, and insurance policies, and is used to generate financial assessments and proposals.

[0590] "Financial status" refers to an individual's economic condition as assessed based on their financial data, and includes indicators such as profitability and the health of spending.

[0591] "Investment portfolio" refers to a combination of investments designed with risk management in mind, and is a plan formed with the aim of achieving optimal investment results.

[0592] "Emotional state" refers to the psychological state recognized from a series of facial expressions, voice, and text displayed by the user, enabling real-time sentiment analysis.

[0593] "Spending advice" refers to suggestions about spending that are recommended to users based on emotional and financial data, and is a personalized approach aimed at saving and optimizing spending.

[0594] To implement this invention, the server has the function of collecting and analyzing financial data. Specifically, it obtains data from financial institutions' APIs using authentication information provided from a communication terminal. This allows the server to centrally manage information on an individual's income, expenses, debts, and insurance contracts, and to comprehensively evaluate their financial situation.

[0595] Furthermore, the terminal is equipped with emotion recognition software that can capture the user's emotions in real time through facial expressions, voice, and text. This emotion data is analyzed by an emotion engine on the server and used to evaluate the user's psychological state. This emotional state is used to customize financial proposals; for example, if the user is in a high-stress state, low-risk investments or savings proposals will be made.

[0596] Furthermore, the suggestion generation software operates on the server, automatically generating personalized investment portfolios and savings advice based on analyzed financial and sentiment data. These suggestions are communicated to the user via their device, with information presented in a tone and language appropriate to their emotional state. Users can review these suggestions and approve or reject them.

[0597] For example, if a user is shopping online while feeling stressed, the system will immediately detect this and advise them to reconsider their purchase. For instance, an alert might appear stating, "This purchase may negatively impact your current savings plan. Perhaps you should take a moment to reconsider?"

[0598] An example of a prompt to a generative AI model is, "Can you suggest an algorithm that helps provide the best advice based on emotional data when a user considers spending?" This prompt allows the AI ​​to integrate emotional and financial data to generate advice tailored to the user's needs.

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The server receives authentication information from the user via a communication terminal and retrieves data on income, expenses, debt, and insurance contracts from financial institutions' APIs. In this process, the user's authentication information is used as input, and financial data is collected as output. The server analyzes this data to generate basic information for evaluating an individual's financial performance.

[0602] Step 2:

[0603] The device collects emotional data in real time through the user's facial expressions, voice, and text communication. Inputs include the user's facial images, voice, and text messages, and output is the analyzed emotional state. The device processes this data using emotion recognition software and sends it to a server.

[0604] Step 3:

[0605] The server receives the financial data obtained in Step 1 and the emotional data obtained in Step 2, and integrates and analyzes both. The inputs are financial data and emotional data, and the output is the result of the integrated analysis. The server uses proposal generation software to create personalized investment configurations and savings advice based on the user's current financial situation and emotional state.

[0606] Step 4:

[0607] The server sends the generated proposal to the terminal, which then notifies the user. The input is proposal data from the server, and the output is a customized notification displayed on the user's communication terminal. The terminal provides information using a tone and language appropriate to the user's emotional state, allowing the user to accept or reject the proposal.

[0608] Step 5:

[0609] When the user reviews and approves the proposal on their terminal, the server launches an executable module and proceeds with the necessary procedures. The input is the user's approval information, and the output is a notification that the execution procedure has been completed. The server then continues to monitor the transaction and progress, and sends notifications to the user if necessary.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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".

[0627] The system of the present invention includes an AI agent for analyzing an individual's financial data and proposing an optimal financial plan. The following elements are implemented as specific embodiments of the invention.

[0628] Implementation of the data collection and analysis module

[0629] The server collects transaction data using authentication information from financial institutions provided by the user. This data relates to the user's income, expenses, loans, and insurance policies. The server analyzes this data to assess the user's financial situation. Machine learning algorithms are used to identify income and expenditure patterns and calculate financial indicators.

[0630] Implementation of the proposal generation module

[0631] Based on the analysis results, the server constructs an optimal investment portfolio for the user and generates optimized insurance contract plans and automated savings plans. These suggestions emphasize risk management and are adjusted based on the user's risk tolerance. The server also dynamically optimizes the financial plan by taking into account the user's life event information.

[0632] Notification and execution of the executable module

[0633] The terminal notifies the user of proposals sent from the server. The user reviews the proposals through the terminal and decides whether to approve or reject them. For proposals approved by the user, the server automatically proceeds with the process. For example, it may complete the process for a new insurance contract or adjust investment allocations.

[0634] Implementation of monitoring and alerting functions

[0635] The server tracks the progress of implemented financial plans and alerts the user as circumstances change. For example, it immediately notifies the user if unexpected expenses occur or if the investment environment changes, prompting them to take appropriate action.

[0636] Specific example

[0637] For example, when a user is considering new car insurance, the system analyzes their current policy and compares it to other plans available on the market. The server takes into account the user's mileage, place of residence, age, etc., and recommends a more cost-effective plan. The user can review the suggestions on their device and, if they like them, switch their policy with a single button click. The server automates the contract process, saving the user money.

[0638] Thus, this invention streamlines personal financial management and supports the achievement of economic freedom. Each module works in conjunction to meet the user's needs.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The user enters their financial institution authentication information into the terminal and sends it to the server. This includes bank account and credit card information. The terminal securely transmits this information to the server.

[0642] Step 2:

[0643] Based on the authentication information received from the user, the server uses APIs to retrieve relevant financial data from various institutions. This data includes income, expenses, loan balances, and insurance policy details.

[0644] Step 3:

[0645] The server analyzes the collected financial data and applies algorithms to evaluate the user's income and expenditure patterns and asset status. A user risk profile is also generated, and data is evaluated to help prepare for future life events.

[0646] Step 4:

[0647] Based on the analysis results, the server optimizes investment portfolios, reviews insurance policies, and creates savings plans. The server proposes the optimal combination of investment options for the user from a variety of choices.

[0648] Step 5:

[0649] The server sends the generated recommended plan to the terminal. The terminal notifies the user and provides an interface where they can view detailed information. The user reviews the plan and considers the proposal.

[0650] Step 6:

[0651] The user approves or rejects the proposed plan. If approved, the server automatically initiates the necessary procedures, such as concluding a new insurance contract or adjusting investment allocations.

[0652] Step 7:

[0653] The server continuously tracks progress after the approved plan has been implemented. If an unexpected event occurs or market conditions change, it immediately sends an alert to the user, prompting them to make necessary revisions.

[0654] This series of processes streamlines users' financial management, leading to reduced unnecessary spending and more effective use of assets.

[0655] (Example 1)

[0656] 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".

[0657] Because an individual's financial information is diverse, efficiently analyzing it and formulating appropriate financial strategies is difficult. Furthermore, there is a lack of systems to dynamically adjust financial plans in response to individual life events and market fluctuations. Additionally, there is a need for a means to quickly and accurately process procedures after a user approves a proposal.

[0658] 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.

[0659] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial situation, means for identifying income and expenditure patterns using machine learning algorithms and predicting future trends, and means for continuously monitoring implemented financial plans and issuing warnings to the user as needed. This enables the development of comprehensive financial strategies based on an individual's financial information and dynamic adjustments to those plans.

[0660] "Financial information" refers to data related to an individual's income, expenses, credit, and contracts.

[0661] "Financial status" refers to the criteria used to evaluate an economic state, including income, expenses, savings, and liabilities.

[0662] A "machine learning algorithm" is a computational method for automatically analyzing and predicting patterns and trends in data.

[0663] A "revenue and expenditure pattern" is a trend that shows the flow of income and expenses over a certain period of time.

[0664] "Risk management" is a strategy for minimizing losses by analyzing uncertainties related to investments and contracts.

[0665] "Life event information" refers to information related to important events in an individual's life, such as marriage or the birth of a child.

[0666] A "warning" is the act of notifying a user of the possibility of a specific economic event occurring.

[0667] "Prediction" refers to estimating future events or trends based on past data.

[0668] The system of this invention is designed to efficiently analyze an individual's financial information and to formulate and implement an optimal financial strategy. The system primarily functions through three parties: a server, a terminal, and a user.

[0669] First, the server collects financial information using authentication credentials provided by the user. This information consists of data on the user's income, expenses, credit, and contracts. The server uses a secure API to retrieve this information. The collected data is then analyzed using machine learning libraries such as TensorFlow and Scikit-learn. This makes it possible to identify past income and expenditure patterns and predict future trends.

[0670] Next, the server evaluates the user's financial situation and, based on the results, automatically generates an optimal investment portfolio, contract plan, and automated savings plan with risk management in place. These proposals are then notified to the user's device. Through the device, the user can review the proposals in detail and decide to approve or reject them. For approved proposals, the server automates the necessary procedures and executes them quickly.

[0671] Furthermore, the server continuously monitors the executed financial plan and responds immediately to unexpected expenses and changes in the economic environment. When changes occur, it alerts the user and prompts them to take appropriate action. This process ensures that the user's financial activities are always optimized and waste is minimized.

[0672] For example, if a user is considering new car insurance, the server analyzes their current policy and compares it to potential market options. It then recommends a more efficient plan, taking into account factors such as mileage and location. The user can review the proposal on their device and switch to the new policy with a single click. The server automates the policy change process, reducing the user's effort.

[0673] An example of a prompt to the generating AI model would be: "Please suggest the optimal investment portfolio to improve my current financial situation. My annual income is 5 million yen, and my average monthly expenses are 300,000 yen. My risk tolerance is moderate, and I am considering new investment opportunities." In this way, the system provided by the present invention streamlines personal financial management and supports the achievement of financial freedom.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The server receives authentication information from the user regarding their financial institution. Using this as input, the server accesses the financial institution's API to retrieve the user's latest transaction data. This data includes income, expenses, credit status, and contract details. The retrieved data is stored in a database on the server.

[0677] Step 2:

[0678] The server analyzes the collected transaction data. Using the data stored in the database as input, it applies machine learning algorithms to identify revenue and expenditure patterns and predict future trends. Libraries such as TensorFlow are used for this purpose. As output, various indicators showing the user's financial situation and trend analysis results are generated.

[0679] Step 3:

[0680] The server generates an optimal financial strategy based on the analysis results. It receives the user's risk tolerance and life event information as input and automatically creates investment configurations, contract plans, and savings plans. This allows the user to receive suggestions tailored to their individual financial goals. The generated suggestions are stored on the server.

[0681] Step 4:

[0682] The server sends the generated financial strategy to the terminal. The terminal receives it and notifies the user. The user can review the proposal on the terminal and examine the details. In this step, the specific proposal is displayed on the terminal screen, and an interface is provided that the user can interact with.

[0683] Step 5:

[0684] The user approves or rejects the proposal via their device. If approved, the server initiates an automated process based on the proposal. Specifically, it submits the necessary documents online for opening a new investment account or changing an existing contract, and then proceeds with the process. Once the process is complete, the server sends the user a completion notification.

[0685] Step 6:

[0686] The server continuously monitors the executed financial plan. If market fluctuations or unexpected expenses occur, the server detects them and alerts the user. Specifically, it performs regular scans of financial information and immediately reports any anomalies or changes in trends to the user. This allows the user to take quick and appropriate action.

[0687] (Application Example 1)

[0688] 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".

[0689] In modern personal financial management, developing optimal financial strategies that address diverse financial transactions and life events is complex and extremely difficult for the average user. Furthermore, existing methods fail to adequately address real-time financial monitoring, risk management, and the provision of personalized financial plans. There is also a need to efficiently generate individualized financial recommendations to support user decision-making.

[0690] 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.

[0691] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs risk-managed investment proposals and automatically generates optimal insurance proposals and wealth-building plans; and a server that dynamically generates personalized financial proposals using a generation AI model and provides prompt statements to support decision-making. This enables users to automatically receive efficient and risk-aware financial plans tailored to their own financial situation.

[0692] "Financial data" refers to financial information about an individual, such as their income, expenses, debts, and insurance policies.

[0693] "Assessing financial status" involves analyzing collected financial data to understand an individual's income, expenses, and debt situation.

[0694] An "investment proposal" is a plan that presents an individual with the optimal financial management plan, taking risk management into consideration.

[0695] "Insurance proposal" means presenting the most suitable insurance contract according to an individual's circumstances and needs.

[0696] A "wealth accumulation plan" is a proposal of optimal savings and asset building methods aimed at ensuring an individual's future financial stability.

[0697] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to automatically generate personalized information and suggestions.

[0698] A "prompt message" is a set of instructions or suggestions provided by a generative AI model to assist the user in making decisions.

[0699] To realize this invention, the system mainly includes the following elements: The server collects personal financial data via the financial institution's API, and organizes and analyzes the ingested data using data processing libraries such as Pandas and NumPy. Based on the analysis results, it evaluates the individual's income, expenses, and debt situation.

[0700] Taking risk management into consideration, the server uses machine learning algorithms such as Scikit-learn and TensorFlow to generate optimal investment proposals, insurance proposals, and wealth accumulation plans. In this process, the generating AI model dynamically forms financial proposals tailored to the user's individual circumstances and creates prompt messages to present them to the user.

[0701] The user's device receives notifications of suggestions from the server, and the user can review and approve the suggestions themselves. Furthermore, real-time communication platforms such as Firebase are used to quickly notify the user of the progress and revisions to the suggestions. For example, if a user adopts specific saving measures to optimize their monthly spending, the system continuously tracks the progress of those measures and makes adjustment suggestions as needed.

[0702] This approach allows individuals to manage their finances more effectively and to obtain specific and personalized solutions to their financial challenges.

[0703] An example of a prompt message is: "Generate optimal savings and spending reduction suggestions based on the user's income and expenditure data. Provide a risk-managed plan that takes into account the stability of income and the types of expenses."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The server collects transaction data using authentication credentials via the user's financial institution's API. This data includes information on the user's income, expenses, loans, and insurance policies. The input data is organized as a dataframe using Pandas, ensuring data consistency.

[0707] Step 2:

[0708] The server analyzes users' income and expenditure patterns using Scikit-learn's machine learning algorithms based on organized financial data. This analysis evaluates each user's financial situation. The input data consists of income and expenditure figures, and the output is an evaluation of the user's financial status. Specifically, it identifies similar spending patterns through clustering.

[0709] Step 3:

[0710] Based on the analysis results, the server uses a generative AI model to generate optimal investment and insurance proposals for the user. Here, the proposals are customized with a focus on risk management. The input includes the analysis results, and the output is a personalized financial proposal prompt. For example, a prompt such as "Consider insurance to mitigate risk for this spending pattern" might be generated.

[0711] Step 4:

[0712] The server notifies the user's device of the generated suggestions. Using Firebase-based real-time notification technology, users can instantly view these suggestions on their smartphones. The input is the generated prompt message, and the output is the notification to the user.

[0713] Step 5:

[0714] The user reviews the proposal on their device and approves it if necessary. Once the user approves the proposal, the server automatically initiates the process. Specifically, API calls are made to modify the investment portfolio or process new insurance contracts based on the user's instructions. The input is the user's approval, and the output is confirmation information of the executed procedure.

[0715] Step 6:

[0716] The server continuously monitors the progress of executed financial plans and notifies the user of the progress via the Firebase Realtime Database. Inputs are data on the procedures performed, and outputs are notifications regarding the progress. For example, it can display the savings progress rate towards the target month and year, and send an alert early if there is a risk of not meeting the goal.

[0717] 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.

[0718] This invention provides a system that offers more personalized advice by combining a conventional system that analyzes an individual's financial data and makes optimal financial proposals with an emotion engine that recognizes and analyzes the user's emotions. In embodiments of this invention, the following elements specifically function.

[0719] Implementation of the data collection and analysis module

[0720] The server receives authentication information from users via their terminals and retrieves real-time data on income, expenses, loans, and insurance policies from bank and insurance company APIs. This data is analyzed on the server and used to assess the user's financial health.

[0721] Implementation of the emotion recognition module

[0722] The device collects emotional data through the user's facial expressions, voice, and text communication. An emotion engine on the server analyzes this data to understand the user's emotional state in real time. This emotional data is used to customize financial proposals.

[0723] Implementation of the proposal generation module

[0724] The server uses both financial and emotional data to generate risk-managed investment portfolios, optimized insurance plans, and automated savings plans. These recommendations are adjusted according to the user's emotional state; for example, conservative recommendations are given when the user is under high stress.

[0725] Implementation of notification and interaction modules

[0726] The device notifies the user of suggestions generated from the server. The notification uses a tone and language tailored to the user's emotional state, providing information in a way that is easily accepted. The user can review the suggestions on the device and decide whether to approve or reject them.

[0727] Execution of the execution and monitoring module

[0728] If the user approves the proposal, the server automatically performs the necessary procedures, including concluding new insurance contracts and changing investment allocations. After execution, the server continuously tracks progress and sends alerts to the user as needed.

[0729] Specific example

[0730] For example, if a user is feeling anxious about their loan repayment plan, the device detects this emotion from the user's facial expressions and tone of voice. The server then creates a loan refinancing proposal and offers options to gradually adjust the plan to reduce stress. This proposal is displayed on the device in gentle language to help the user make a decision with confidence.

[0731] Thus, this invention, which combines an emotional engine, enables more personalized financial advice while taking into account the user's emotional needs. This improves the user experience and facilitates the achievement of financial freedom.

[0732] The following describes the processing flow.

[0733] Step 1:

[0734] The user uses a terminal to enter their financial institution's authentication information and sends it to the server. This grants the server access to retrieve the user's banking transactions and insurance contract information.

[0735] Step 2:

[0736] The server collects data on income, expenses, loan balances, and insurance policies from banks and insurance companies via APIs. This collected data is then analyzed to provide foundational information for understanding the user's financial situation.

[0737] Step 3:

[0738] The device uses its built-in camera and microphone to detect the user's facial expressions and voice tone in real time. Emotion recognition software analyzes this data and sends the user's emotional state to a server.

[0739] Step 4:

[0740] The server integrates emotional data obtained by the emotion engine with financial data. Based on this, it generates optimal investment plans, insurance options, and savings plans for the user. This includes adjustments such as selecting lower-risk options if the user's stress level is high.

[0741] Step 5:

[0742] The server sends the generated suggestions to the terminal and notifies the user. When notifying, it selects a communication style based on sentiment analysis and provides information in a way that is easily accepted by the user.

[0743] Step 6:

[0744] Users view proposals through their devices and, if necessary, send approval or rejection instructions to the server. The interface provided is designed to ensure that users do not feel uneasy.

[0745] Step 7:

[0746] If the user approves, the server will automatically initiate the process according to the proposal. This may involve the creation of a new insurance contract or adjustment of the investment portfolio, and the server will continuously monitor the progress.

[0747] Step 8:

[0748] The server tracks the results of executed plans and re-evaluates them if necessary. When circumstances change or unexpected events occur, it sends appropriate alerts to the user to prompt action.

[0749] These steps enable a system with an emotion engine to provide nuanced financial management that responds to the user's emotions.

[0750] (Example 2)

[0751] 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".

[0752] Traditional financial management systems lack the ability to provide advice that considers both an individual's financial situation and emotional state simultaneously. This has resulted in a failure to adequately address users' psychological needs and make it difficult to provide optimal financial recommendations. Furthermore, providing information without considering the user's emotions can, in some cases, cause stress to the user.

[0753] 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.

[0754] In this invention, the server includes means for analyzing collected financial information and evaluating an individual's financial health, means for collecting emotional information and analyzing the user's emotional state in real time, and means for generating financial recommendations tailored based on both financial and emotional information. This makes it possible to provide users with emotionally sensitive and financially optimized advice.

[0755] "Financial information" refers to data on an individual's income, expenses, debts, and insurance policies.

[0756] "Emotional information" refers to data about the emotional state obtained from the user's facial expressions, voice, and text communication.

[0757] "Financial soundness" refers to an indicator that assesses an individual's ability to maintain a balance between income and expenses and to remain financially stable.

[0758] An "asset management plan" refers to a plan that proposes the optimal investment strategy based on an individual's risk profile.

[0759] An "insurance strategy" refers to a method for providing optimized insurance contracts based on individual needs.

[0760] A "savings plan" refers to a method of automatically accumulating funds to achieve an individual's future financial goals.

[0761] A "communication terminal" refers to a device used by a user to receive or input information.

[0762] This invention is a system that integrates individual users' financial and emotional information to provide optimized financial advice.

[0763] The server first uses the authentication information of the financial institution received from the user via the terminal to obtain the individual's financial information through APIs of banks and insurance companies. This information includes data on the user's income, expenses, debts, and insurance policies. The server analyzes this data and uses a generative AI model to assess the user's financial health.

[0764] Meanwhile, the device acquires the user's facial expressions and voice data, and also collects emotional information from the user's text communication. The device sends this emotional information to the server in real time. The emotion engine implemented on the server uses this information to analyze the user's emotional state and helps generate optimal suggestions.

[0765] In generating recommendations, the server integrates both financial and emotional information to construct risk-managed asset management plans, optimized insurance strategies, and automated savings plans. These recommendations are tailored to the user's current emotional state.

[0766] For example, if a user is worried about loan repayment, the device can recognize their emotions from their facial expressions and tone of voice. Based on this, the server can create a loan refinancing proposal and adjust the proposal step by step. This proposal is displayed on the device in gentle language, allowing the user to make a decision with confidence.

[0767] Another example of a prompt message is asking the generative AI model, "When a user is feeling stressed, what kind of financial advice should you suggest? Please explain with specific examples."

[0768] This system aims to provide personalized financial advice that takes user emotions into consideration, thereby improving the user experience and facilitating the achievement of financial freedom.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The server receives authentication information from the user via the terminal. Input includes user ID, password, and other authentication data. Using this authentication information, the server accesses the APIs of banks and insurance companies to retrieve data on income, expenses, liabilities, and insurance policies. The output is the collected financial information, which serves as the basis for analysis. Specifically, the server makes API calls and retrieves the necessary data.

[0772] Step 2:

[0773] The device uses its camera and microphone to collect the user's facial expressions, voice, and text communication. The input consists of real-time captured visual and audio data. The device sends this data to a server as emotion data. The output is data indicating the user's emotional state, and in terms of specific actions, algorithms such as facial recognition and voice analysis are executed.

[0774] Step 3:

[0775] The server integrates and analyzes the financial information obtained in Step 1 and the emotional information obtained in Step 2. The input consists of both of these data. Using a generative AI model, it performs an analysis based on financial health and emotional state to generate optimal asset management plans, insurance strategies, and savings plans. The output is proposed financial advice, which involves querying databases and performing calculations by the AI ​​model.

[0776] Step 4:

[0777] The terminal notifies the user of suggestions sent from the server. The input is the generated suggestion information. The output is an emotionally sensitive, tailored notification message displayed on the user's screen. Specifically, this involves the message being displayed through the user interface, and the user reviewing its content.

[0778] Step 5:

[0779] The user approves or rejects the proposal. The input is the proposal displayed on the terminal, and the user makes a selection. Based on the user's decision, the server executes the necessary procedures. The output is the completed procedures reflecting the user's decision, as well as the progress monitoring results. Specifically, the conclusion of insurance contracts and adjustments to asset management are automated.

[0780] (Application Example 2)

[0781] 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".

[0782] Traditional financial evaluation systems generate generic recommendations based solely on an individual's financial data, failing to consider the user's emotional state. This makes personalized recommendations difficult to accept and implement. Furthermore, there is a lack of methods to optimize user spending behavior based on emotions. This results in inefficient recommendations that ignore users' emotions and stress levels, ultimately diminishing the effectiveness of financial management.

[0783] 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.

[0784] This invention includes a server that analyzes collected financial data and evaluates an individual's financial situation based on their income, expenses, debts, and insurance contracts; a server that constructs a risk-managed investment portfolio and automatically generates optimal insurance schemes and savings plans; and a server that recognizes and analyzes the user's emotional state through facial expressions, voice, and text communication. This makes it possible to provide personalized financial suggestions and spending advice adapted to the user's emotional state in real time.

[0785] "Financial data" refers to information about an individual's income, expenses, debts, and insurance policies, and is used to generate financial assessments and proposals.

[0786] "Financial status" refers to an individual's economic condition as assessed based on their financial data, and includes indicators such as profitability and the health of spending.

[0787] "Investment portfolio" refers to a combination of investments designed with risk management in mind, and is a plan formed with the aim of achieving optimal investment results.

[0788] "Emotional state" refers to the psychological state recognized from a series of facial expressions, voice, and text displayed by the user, enabling real-time sentiment analysis.

[0789] "Spending advice" refers to suggestions about spending that are recommended to users based on emotional and financial data, and is a personalized approach aimed at saving and optimizing spending.

[0790] To implement this invention, the server has the function of collecting and analyzing financial data. Specifically, it obtains data from financial institutions' APIs using authentication information provided from a communication terminal. This allows the server to centrally manage information on an individual's income, expenses, debts, and insurance contracts, and to comprehensively evaluate their financial situation.

[0791] Furthermore, the terminal is equipped with emotion recognition software that can capture the user's emotions in real time through facial expressions, voice, and text. This emotion data is analyzed by an emotion engine on the server and used to evaluate the user's psychological state. This emotional state is used to customize financial proposals; for example, if the user is in a high-stress state, low-risk investments or savings proposals will be made.

[0792] Furthermore, the suggestion generation software operates on the server, automatically generating personalized investment portfolios and savings advice based on analyzed financial and sentiment data. These suggestions are communicated to the user via their device, with information presented in a tone and language appropriate to their emotional state. Users can review these suggestions and approve or reject them.

[0793] For example, if a user is shopping online while feeling stressed, the system will immediately detect this and advise them to reconsider their purchase. For instance, an alert might appear stating, "This purchase may negatively impact your current savings plan. Perhaps you should take a moment to reconsider?"

[0794] An example of a prompt to a generative AI model is, "Can you suggest an algorithm that helps provide the best advice based on emotional data when a user considers spending?" This prompt allows the AI ​​to integrate emotional and financial data to generate advice tailored to the user's needs.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The server receives authentication information from the user via a communication terminal and retrieves data on income, expenses, debt, and insurance contracts from financial institutions' APIs. In this process, the user's authentication information is used as input, and financial data is collected as output. The server analyzes this data to generate basic information for evaluating an individual's financial performance.

[0798] Step 2:

[0799] The device collects emotional data in real time through the user's facial expressions, voice, and text communication. Inputs include the user's facial images, voice, and text messages, and output is the analyzed emotional state. The device processes this data using emotion recognition software and sends it to a server.

[0800] Step 3:

[0801] The server receives the financial data obtained in Step 1 and the emotional data obtained in Step 2, and integrates and analyzes both. The inputs are financial data and emotional data, and the output is the result of the integrated analysis. The server uses proposal generation software to create personalized investment configurations and savings advice based on the user's current financial situation and emotional state.

[0802] Step 4:

[0803] The server sends the generated proposal to the terminal, which then notifies the user. The input is proposal data from the server, and the output is a customized notification displayed on the user's communication terminal. The terminal provides information using a tone and language appropriate to the user's emotional state, allowing the user to accept or reject the proposal.

[0804] Step 5:

[0805] When the user reviews and approves the proposal on their terminal, the server launches an executable module and proceeds with the necessary procedures. The input is the user's approval information, and the output is a notification that the execution procedure has been completed. The server then continues to monitor the transaction and progress, and sends notifications to the user if necessary.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means of analyzing collected financial data and evaluating an individual's financial situation based on their income, expenses, loans, and insurance contracts,

[0830] A means to construct a risk-managed investment portfolio and automatically generate optimal insurance and savings plans,

[0831] A means for notifying the user terminal of the generated proposal and for executing and tracking it based on the user's approval,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, which identifies unnecessary spending based on analyzed financial data and alerts the user in real time to a savings plan.

[0835] (Claim 3)

[0836] The system according to claim 1, which collects user life event information and dynamically adjusts financial strategies based on that information.

[0837] "Example 1"

[0838] (Claim 1)

[0839] A means of analyzing collected financial information and evaluating an individual's financial situation based on their income, expenses, credit, and contracts,

[0840] A method for identifying past income and expenditure patterns and predicting future trends using machine learning algorithms,

[0841] A means to construct an investment portfolio with risk management in place and to automatically generate optimal contract plans and deposit plans,

[0842] A means of notifying the user terminal of the generated proposal and proceeding with its execution and procedures based on the user's approval,

[0843] A means to continuously monitor the implemented financial plan and alert the user in response to unexpected expenses or changes in circumstances,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which identifies unnecessary spending based on analyzed financial information and notifies the user of a savings plan in real time.

[0847] (Claim 3)

[0848] The system according to claim 1, which collects information on a user's life events and dynamically adjusts a financial strategy based on that information.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A means of analyzing collected financial data and evaluating an individual's financial situation based on their income, expenses, debts, and insurance contracts,

[0852] A means to construct risk-managed investment proposals and automatically generate optimal insurance proposals and wealth accumulation plans,

[0853] A means for dynamically generating personalized financial proposals using a generative AI model and providing prompt statements to support decision-making,

[0854] A means for notifying the user device of the generated proposal and for executing and monitoring it based on the user's approval,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, which identifies unnecessary spending based on analyzed financial data and notifies the user of saving strategies in real time.

[0858] (Claim 3)

[0859] The system according to claim 1, which collects information on users' life events and dynamically adjusts financial policies based on that information.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] A device that analyzes collected financial information and assesses financial health based on an individual's income, expenses, debts, and insurance contracts,

[0863] A device that collects emotional information and analyzes the user's emotional state in real time,

[0864] A device that generates risk-managed asset management plans, optimized insurance strategies, and automated savings plans based on both financial and emotional information, and adjusts them according to the user's emotional state.

[0865] A device that notifies a communication terminal of the generated proposal and executes and monitors it based on user approval,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, which identifies the user's emotional state based on analyzed emotional information and notifies the user using appropriate language according to that state.

[0869] (Claim 3)

[0870] The system according to claim 1, which collects information on the emotional state of users and dynamically adjusts financial strategies based on that information.

[0871] "Application example 2 when combining with an emotional engine"

[0872] (Claim 1)

[0873] A means of analyzing collected financial data and evaluating an individual's financial situation based on their income, expenses, debts, and insurance contracts,

[0874] A means to construct an investment portfolio with risk management in place and to automatically generate the optimal insurance method and savings plan,

[0875] A means for notifying a communication terminal of the generated proposal and for executing and tracking it based on the user's approval,

[0876] A means of recognizing and analyzing the user's emotional state through facial expressions, voice, and text communication,

[0877] A method for providing spending advice tailored to your mood while shopping, based on emotional data,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which identifies wasteful spending based on analyzed financial and emotional data and alerts the user in real time to a savings plan.

[0881] (Claim 3)

[0882] The system according to claim 1, which adjusts the content of expenditures according to the emotional state of the user. [Explanation of Symbols]

[0883] 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

1. A means of analyzing collected financial data and evaluating an individual's financial situation based on their income, expenses, debts, and insurance contracts, A means to construct risk-managed investment proposals and automatically generate optimal insurance proposals and wealth accumulation plans, A means for dynamically generating personalized financial proposals using a generative AI model and providing prompt statements to support decision-making, A means for notifying the user device of the generated proposal and for executing and monitoring it based on the user's approval, A system that includes this.

2. The system according to claim 1, which identifies unnecessary spending based on analyzed financial data and notifies the user of saving strategies in real time.

3. The system according to claim 1, which collects information on users' life events and dynamically adjusts financial policies based on that information.

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