system

Abstract

We provide the system. [Solution] A means of collecting information from multiple financial accounts and storing it in an integrated database, A means of analyzing collected data and recognizing spending patterns, A means of proposing the optimal savings and investment plan to the user based on the analysis results, A means of simulating the risks and returns of potential investments, An artificial intelligence chatbot that automatically generates answers to user questions, A means of evaluating the user's progress toward achieving their goals and providing feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, it is common for an individual to manage multiple financial accounts. However, along with this, there is a problem that it is difficult to comprehensively grasp the transaction information of each account and maintain the overall balance of income and expenditure. In particular, for users with multiple income sources and expenditure routes, manual recording and analysis are inefficient and may lead to incorrect decision-making. Furthermore, in order to make effective savings and investment plans, real-time data analysis and individual feedback are important, but there is a lack of comprehensive means to achieve this.

Means for Solving the Problems

[0005] To solve the above problems, this invention provides a means for centrally collecting information from multiple financial accounts and storing it in an integrated database. By utilizing the information in this database, it is possible to automatically analyze spending patterns and propose optimal savings and investment plans based on the user's characteristics. Furthermore, it has a function to simulate the risk and return of potential investments and provides this information to the user in visual data. In addition, an artificial intelligence chatbot function can answer user questions in real time, evaluate the user's progress toward their goals, and provide feedback. In this way, users can manage their assets more efficiently and accurately.

[0006] "Financial account information" refers to information including transactions with various financial institutions owned by the user, such as bank accounts, credit cards, and investment accounts, and related data.

[0007] A "database" is a system for organizing and managing various types of data in an electronically stored and searchable format, and in this invention, it is used to centrally manage data including financial account information.

[0008] "Spending patterns" refer to characteristic trends that describe what items a user spends money on, how frequently, and in what amounts.

[0009] A "savings plan" is a financial accumulation plan optimized according to the user's income, expenses, and savings goals.

[0010] An "investment plan" is a detailed plan outlining how a user's assets will be allocated to financial products and methods in order to generate expected profits.

[0011] "Simulation" is a technique that uses a computational model to test data in order to predict results under specific conditions, and in this invention, it is used to predict the performance of investment candidates.

[0012] An "artificial intelligence chatbot" is a program that automatically provides real-time answers to user questions using natural language.

[0013] "Feedback" refers to information that a system provides in response to a user's actions and results, offering evaluations and suggestions for improvement. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] In implementing this invention, a system is constructed to securely and efficiently collect users' financial account information from various financial institutions and manage it centrally. The system consists of user terminals, backend servers, and a network connecting them.

[0036] A user's terminal is a device used by individual consumers and serves as an interface to the system. Users first create an account and register necessary financial account information. Authentication is performed using protocols such as OAuth to ensure security. The terminal has the function of sending this information to the server.

[0037] The server is the central processing unit of the system. It receives data from terminals, stores it in a database, and then processes and analyzes it. The server uses machine learning algorithms to analyze each user's transaction data and identify spending patterns. Based on this analysis, it proposes the optimal savings and investment plan for each user. The proposal will be tailored to the user's income, spending, and risk tolerance.

[0038] The server also predicts the risk and return of potential investments using methods such as Monte Carlo simulation, and presents the results visually to the user. This information can be used by the user as a reference when making investment decisions.

[0039] Furthermore, the server is equipped with an artificial intelligence chatbot function that automatically generates answers to user questions from terminals. This provides support 24 / 7, whenever users need it. This function uses natural language processing technology to provide information and solve problems through dialogue with users.

[0040] Furthermore, the server evaluates the user's progress toward achieving their goals and provides feedback based on that evaluation. Users can receive this feedback on a dashboard via their device and use it to improve their asset management.

[0041] For example, if a user wants to improve their monthly budget management, the server analyzes the user's spending history, visualizes unnecessary spending by category, and then proposes a monthly budget that aligns with their annual savings goal. In this way, the present invention supports efficient and effective personal asset management.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user creates an account on their device and registers their financial institution account information. The user enters basic information such as their name, email address, and password, and completes the linking of their financial account after going through a secure authentication process.

[0045] Step 2:

[0046] The device sends the user's financial account information to the server. This process uses standard authentication protocols such as OAuth to ensure the data is securely delivered to the server.

[0047] Step 3:

[0048] The server stores the received account information in the database and verifies that the data has been registered correctly. During this process, it performs data formatting standardization and duplicate checks.

[0049] Step 4:

[0050] The server periodically collects transaction data from various financial institutions using APIs. The collected data is updated in the database in real time.

[0051] Step 5:

[0052] The server uses machine learning algorithms to analyze spending patterns from transaction data. The analysis results are categorized and stored in a database as the user's spending history.

[0053] Step 6:

[0054] The server generates optimal savings and investment plans based on analyzed spending patterns and income information. These plans take into account the user's risk tolerance and constitute customized recommendations.

[0055] Step 7:

[0056] The server sends the generated plan to the terminal and displays it on a dashboard for the user to view or select. The user then makes asset management decisions based on this information.

[0057] Step 8:

[0058] When a user wants to evaluate potential investments, they send a request to the server via their device. The server then runs a Monte Carlo simulation to generate data on predicted risk and return.

[0059] Step 9:

[0060] The server sends simulation results to the terminal, providing visualized information to assist in selecting investment options. This information is presented clearly using graphs and charts.

[0061] Step 10:

[0062] When a user asks a question, the device sends the content to the server, where the AI ​​chatbot uses natural language processing technology to analyze the question and generate an appropriate answer.

[0063] Step 11:

[0064] The server sends the generated response to the terminal, providing real-time feedback to the user. The user then decides on further actions based on this response.

[0065] Step 12:

[0066] The server periodically evaluates the user's asset management status and notifies the terminal of the progress toward the goal as feedback information. The user then uses this feedback to make adjustments toward achieving the goal.

[0067] (Example 1)

[0068] 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."

[0069] In modern society, it is extremely difficult for individuals to comprehensively manage information on their multiple financial assets. Furthermore, advanced analytical capabilities are required to understand spending patterns and propose optimal savings and investment plans. Moreover, accurately predicting the risks and expected returns of potential investments and identifying unnecessary spending is a significant burden for individuals. There is a need for a system that efficiently solves these asset management problems and provides users with easily actionable plans.

[0070] 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.

[0071] In this invention, the server includes means for aggregating and storing multiple asset account information in an integrated information storage device, means for analyzing the aggregated information and identifying consumption patterns, and means for presenting the user with an optimal savings plan and investment plan based on the analysis results. This makes it possible for individual users to centrally manage their asset information and easily obtain an optimal asset management plan.

[0072] "Asset account information" refers to data related to financial institutions owned by a specific individual or legal entity, including deposit accounts, credit accounts, investment accounts, etc.

[0073] An "information storage device" refers to a recording medium used to store and manage accumulated data, and includes formats such as databases and cloud storage.

[0074] "Consumption patterns" refer to the results of an analysis of spending trends and habits in a specific individual or group, and include information such as how much money is spent in which categories.

[0075] A "savings plan" refers to a plan developed by an individual or corporation to achieve future financial goals, and includes monthly savings amounts and methods for allocating funds.

[0076] An "investment plan" refers to a plan that outlines how to manage capital to maximize returns, and includes investment allocation to specific financial assets and setting risk tolerance levels.

[0077] "Risk" refers to the uncertainty and potential volatility associated with investments in specific financial assets, including the expected range of losses and risk factors.

[0078] "Profit" refers to the expected return on investment, and includes the return on the principal investment and the rate of return.

[0079] "Artificial intelligence dialogue means" refers to technologies that use artificial intelligence to respond to user inquiries in text or voice, and includes natural language processing and machine learning models.

[0080] "Goal achievement status" refers to the current progress towards the user's personal financial goals, and includes the percentage of achievement and the estimated time to achieve them.

[0081] "Advice" refers to suggestions or recommendations that indicate the best course of action in a particular situation, and includes financial information and strategies.

[0082] In carrying out the present invention, the system is constructed using servers, terminals, and a network.

[0083] Users access the system using their individual devices and input their personal asset data. The devices encrypt the input information and send it to the server using a secure protocol (e.g., SSL / TLS communication using OAuth). The devices are equipped with interface functions to facilitate user operation.

[0084] The server is the heart of the system, receiving asset data sent from terminals and securely storing it in the database. The database provides a storage environment that enables efficient data management and analysis. The server also uses machine learning algorithms (e.g., Python's scikit-learn library) to analyze user consumption patterns.

[0085] As a concrete example, suppose a user wants to "review their spending this month and find out which categories they can save money in." In response to this request, the server analyzes past transaction data and visualizes unnecessary spending by category. Furthermore, the server generates a savings and investment plan tailored to the user's income and risk tolerance, and sends the results to the device.

[0086] The server applies Monte Carlo simulations to predict the risks and returns of investment plans and presents the results visually. This allows users to obtain useful information for making investment decisions.

[0087] Furthermore, the server utilizes a generative AI model and an artificial intelligence chatbot function to automatically generate answers to user questions. This enables 24 / 7 support. An example of a prompt is, "Analyze the user's spending trends based on their transaction data from the past three months and suggest easy ways to save money."

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

[0089] Step 1:

[0090] Users log in to the system via a terminal and enter their asset information. This input data includes bank account numbers, credit card information, and other financial account details. The terminal encrypts this information and transmits it to the server using a secure protocol. The input is the user's financial information, and the output is encrypted data.

[0091] Step 2:

[0092] The server receives financial data sent from the terminal and stores it in a database. For database storage, the information is first parsed and organized into appropriate fields. The input is encrypted data, and the output is structured data stored in the database.

[0093] Step 3:

[0094] The server uses machine learning algorithms to analyze user transaction data stored in the database. It uses Python libraries (e.g., scikit-learn) to identify consumption patterns through cluster analysis. The input is structured transaction data, and the output is the analysis of consumption patterns.

[0095] Step 4:

[0096] Based on the analysis of consumption patterns, the server generates optimal savings and investment plans that take into account the user's income, expenses, and risk tolerance. This plan generation also includes trend predictions from historical data. The input is the analysis of consumption patterns, and the output is the optimized savings and investment plans.

[0097] Step 5:

[0098] The server applies Monte Carlo simulations to evaluate the risk and return of the generated investment plans. It performs numerous simulations using Python modules and generates evaluation charts based on these results. The input is the generated investment plan, and the output is visual data of the risk and return evaluation results.

[0099] Step 6:

[0100] The server utilizes a generative AI model to generate answers based on questions submitted by the user from the terminal. Here, natural language processing techniques are used to generate the optimal answer in response to the user's prompt. The input is the user's question, and the output is the AI-generated answer.

[0101] Step 7:

[0102] The server evaluates the user's progress toward their goals and generates feedback. It compares and analyzes the user's set goals with current data to calculate a quantified degree of achievement. The inputs are current asset information and goal data, and the output is the evaluated degree of achievement and feedback.

[0103] (Application Example 1)

[0104] 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."

[0105] In modern society, there is a need for consumers to manage their assets effectively based on a centralized system of financial information. However, there is no system that can grasp spending trends in the history of electronic transactions used daily by users and propose appropriate savings and asset management plans based on that information. Furthermore, there is a lack of support that is linked to intelligent dialogue devices that can answer user questions immediately and effectively, making the realization of efficient and effective personal asset management a challenge.

[0106] 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.

[0107] In this invention, the server includes means for collecting and integrating multiple financial information, means for analyzing the collected information and recognizing financial behavior patterns, and intelligent dialogue device means for automatically generating responses to information requests from users. This makes it possible to visualize financial behavior based on the user's daily electronic transaction history and propose specific savings and asset management plans.

[0108] "Financial information" refers to all information related to financial activities, and specifically includes bank account information, credit card history, and data related to asset management.

[0109] An "information management device" is a device that centrally stores and manages collected financial information, and retrieves and updates data as needed.

[0110] "Financial behavior patterns" are behavioral patterns identified based on the results of an analysis of the user's income and expenditure trends and characteristics.

[0111] An "intelligent dialogue device" is an automated response device that uses natural language processing technology to respond to inquiries and requests from users.

[0112] "Spending trends" refer to the trends obtained by visualizing and analyzing users' financial behavior in their daily transactions and consumption, categorized by type.

[0113] A "savings plan and asset management plan" proposes specific guidelines and strategies for effectively managing assets and setting optimal savings goals, based on the user's financial situation.

[0114] "Electronic transactions" refer to all financial transactions conducted via the internet, including online payments and asset transfers.

[0115] To implement this invention, the user first accesses the system using their terminal and registers multiple pieces of financial information. The terminal authenticates the user using the OAuth protocol and processes the data securely. After authentication, the financial information collected from the terminal is transmitted to an integrated information management device and stored in a database.

[0116] The server uses machine learning algorithms implemented in languages ​​such as Python and R to analyze the financial information in this database. These algorithms model the user's financial behavior and understand their spending trends. The analysis results are presented to the user visually and displayed on their device through an interface built with React Native.

[0117] The server also functions as an intelligent dialogue device. This device uses natural language processing technology based on Google Cloud's Dialogflow to automatically generate responses to user inquiries. When a user enters prompts such as "Tell me about my savings plan" or "Tell me about my spending trends this month," the server can understand the content and return detailed feedback and advice to the terminal.

[0118] As a concrete example, if a user wants to review their spending and make their leisure activities more efficient, they can access the system on their device and input "I want to know the details of my entertainment expenses." In this case, the server analyzes spending trends in the entertainment category and suggests specific saving methods and new savings plans to the user. In this way, this invention helps users accurately understand their personal financial situation and appropriately create future financial plans.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user logs into the system using a terminal and registers their financial information (e.g., bank account information and credit card history). The terminal uses the OAuth protocol to authenticate the user for security purposes. The input consists of the user's identification information and financial account information, which is then sent to the server.

[0122] Step 2:

[0123] The server integrates the received financial information and stores it in a database. The input is financial information from the terminal, and the output is an entry into the database in an integrated format. The data is stored in an information management device and used for other analysis processes.

[0124] Step 3:

[0125] The server executes machine learning algorithms to analyze financial information in the database. The input is stored financial information, and the system processes and models the data to identify trends in financial behavior. The output is the user's financial behavior patterns.

[0126] Step 4:

[0127] The user enters a prompt message through the terminal, such as "Tell me about this month's spending trends." The terminal sends this prompt message to the server. The input is a prompt message generated by the user.

[0128] Step 5:

[0129] The server uses Dialogflow to parse the prompt text and perform the necessary data calculations. It then generates and responds with information tailored to the user's request. The input consists of the user's prompt text and the resulting financial behavior pattern, while the output is text containing feedback and advice for the user.

[0130] Step 6:

[0131] The server sends the generated feedback to the user's device through an interface built with React Native, displaying it visually. Based on this output, the user can create concrete asset management and savings plans. The input is feedback information from the server, and the output is visualized information on the user's device.

[0132] 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.

[0133] This invention provides a system that incorporates an emotion engine into a financial account management system to provide personalized feedback tailored to the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion engine, and a network.

[0134] When a user accesses the system using a device, they first create an account and register their financial account information. The user enters basic information such as their name and links their financial account after going through an authentication process. The device then sends this information to the server.

[0135] The server stores financial account information received from users in a database and periodically collects transaction data. Using this data, the server analyzes the user's spending patterns using machine learning and generates optimal savings and investment plans. These plans are then presented to the user on their device in an individually customized format.

[0136] The system is equipped with an emotion engine that analyzes the user's emotional state based on user behavior, input data, and sensor information. The emotion engine uses this information to adjust feedback and suggestions according to the user's current emotions.

[0137] For example, if a user is feeling stressed, the emotion engine will detect this and prioritize suggesting lower-risk investment plans. Conversely, if the system determines that the user is feeling very secure, it may present higher-risk but potentially higher-return investment options. In this way, the emotion engine, based on the user's emotions, works in conjunction with other system functions to support decision-making.

[0138] Furthermore, the system uses income and expense data collected from multiple users to generate community-based advice, and the server's AI chatbot explains any points of confusion users may have. This deepens users' understanding and enables better financial management.

[0139] Through these functions, the present invention enables asset management that takes user emotions into account, reducing user mental stress while supporting efficient asset building.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user accesses the device and enters information to create an account. This includes their name, email address, and password. The user then completes an authentication process to link their financial institution account.

[0143] Step 2:

[0144] The terminal securely transmits information provided by the user to the server and requests that login information and financial account link information be stored in the database.

[0145] Step 3:

[0146] The server stores user information in a database and collects user financial transaction data from various financial institutions at specified intervals using an API. This data includes transaction details and balance information.

[0147] Step 4:

[0148] The server applies machine learning algorithms to analyze the collected transaction data. Here, it identifies user spending patterns and categorizes them.

[0149] Step 5:

[0150] Based on the analysis results, the server generates savings and investment plans tailored to the user's income, expenses, and risk tolerance, and sends them to the user's device. The user can then make decisions based on these plans.

[0151] Step 6:

[0152] When a user inputs emotion-related information (e.g., text messages or sensor data) via their device, the device sends this information to the server's emotion engine.

[0153] Step 7:

[0154] The emotion engine installed on the server analyzes the received information and diagnoses the user's emotional state. This analysis may utilize natural language processing and speech / image recognition technologies.

[0155] Step 8:

[0156] The server takes the diagnosed emotional state into account and adjusts the existing plan or generates an alternative suggestion. For example, if stress is detected, it will change to a lower-risk suggestion.

[0157] Step 9:

[0158] The server sends emotionally-adjusted plans and advice to the user's device, allowing the user to choose how to manage their assets based on these. This enables the user to make rational decisions that align with their emotional state.

[0159] Step 10:

[0160] If a user has questions or concerns about the system, they can send those questions from their device to the server's AI chatbot function.

[0161] Step 11:

[0162] The server's AI chatbot analyzes questions using natural language processing and automatically generates appropriate answers. These answers are then sent back to the terminal and presented to the user.

[0163] Step 12:

[0164] Users can also refer to community-based advice and success stories from other users, and the server collects and provides this information as needed. This makes it possible to receive advice from diverse perspectives.

[0165] (Example 2)

[0166] 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".

[0167] Traditional financial management systems simply offer data-driven suggestions without considering the user's emotions or psychological state. This can lead to investment and savings choices that don't align with the user's feelings, hindering efficient financial management and stress reduction. Furthermore, few systems can integrate and manage multiple financial information streams, forcing users to manage each piece of information individually.

[0168] 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.

[0169] In this invention, the server includes means for collecting multiple financial account information and storing it in an integrated information recording device; means for analyzing the collected data and recognizing spending trends; means for proposing optimal savings and investment plans to the user based on the analysis results; means for evaluating the user's emotional state using an emotion analysis device; and means for adjusting the suggested feedback based on the evaluated emotional state. This enables personalized feedback based on the user's emotional state, resulting in more efficient and less stressful financial management.

[0170] "Financial account information" refers to data on a user's assets and liabilities managed by a financial institution.

[0171] An "information recording device" refers to a device or system that can securely store data in digital format and retrieve it as needed.

[0172] "Spending trends" refer to analytical results that show patterns and behaviors of how users spend their money.

[0173] A "savings plan" refers to a strategic financial preservation plan established to efficiently increase the user's assets.

[0174] An "investment plan" refers to the investment strategy and specific action plan necessary to increase the user's assets.

[0175] An "emotion analysis device" refers to a system or device that evaluates a user's emotions or psychological state using a database or algorithm.

[0176] "Feedback" is a general term for advice and information that a system provides to a user, and specifically refers to evaluations and suggestions.

[0177] This invention is a system that provides personalized financial management based on the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion analysis device, and a network connecting them.

[0178] Users access the system via a terminal and register their financial account information. The terminal transmits the information received from the user to the server. The server collects the user's financial data and stores it in an integrated information storage device. The data is retrieved using financial APIs and secure communication protocols. The server analyzes the collected data and recognizes spending trends using libraries such as Python's pandas and scikit-learn.

[0179] The emotion analysis device evaluates the user's emotional state based on their usage patterns and input data. This utilizes natural language processing and image recognition technologies. Based on the analysis results, the server generates feedback that reflects the user's emotional state.

[0180] For example, if a user is experiencing stress, the system detects this through an emotion analyzer and prioritizes recommending low-risk savings plans. On the other hand, if the system determines that the user is feeling secure, it can suggest higher-risk but potentially higher-return investment options. In this way, personalized recommendations are developed for each user.

[0181] An example of a prompt might be: "Please suggest a safe and low-risk savings plan to plan for college tuition." In response to such a prompt, the system will suggest the best plan for the user.

[0182] This invention enables financial management that takes user emotions into account, reducing mental stress while supporting efficient asset building.

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

[0184] Step 1:

[0185] The user enters basic information such as their name and email address using an application on their device and links their financial account information. The entered information is securely encrypted by the device and sent to the server. The output is encrypted user information data.

[0186] Step 2:

[0187] The server decrypts the encrypted user information data received from the terminal and stores it in a database. Next, it periodically collects transaction data from each user's financial account using a financial API. The output of this step is an integrated set of user transaction data.

[0188] Step 3:

[0189] The server analyzes integrated transaction data using machine learning algorithms. Here, the data is preprocessed using the Python pandas library, and clustering and classification are performed using scikit-learn. The input is an integrated set of transaction data, and the output is an analysis showing spending trends and characteristics.

[0190] Step 4:

[0191] The server uses an emotion analysis device to analyze behavioral data and input data transmitted from the user's terminal. This analysis employs natural language processing and image recognition technologies. The input is the user's behavioral data, and the output is a result indicating the user's emotional state.

[0192] Step 5:

[0193] The server generates savings and investment plans best suited to the user based on analyzed spending trends and emotional states. A generative AI model may be used for this generation. The output is a customized financial plan proposal.

[0194] Step 6:

[0195] The server sends the generated financial plan to the terminal, which the user can view and review. The terminal displays risks and returns visually, allowing the user to obtain detailed information. The output is the visualized plan information presented to the end user.

[0196] Step 7:

[0197] Users can use their devices to ask questions to the AI ​​chatbot about the presented plans. The server receives the user's questions, uses a generative AI model to generate appropriate answers, and sends them back to the device. The output is an automatically generated answer to the user's question.

[0198] Step 8:

[0199] The server uses data collected from all users to generate community-based advice, which is then displayed on each user's individual screen. The input is anonymized data from other users, and the output is advice based on collective insights. This process allows users to gain insights based on the experiences of others.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0202] In today's financial system, users are expected to effectively manage their financial situation and plan for the future. However, the complexity of diverse financial information and the emotionally driven decision-making process make it difficult to select appropriate investment and savings strategies. Therefore, there is a need for systems that provide more personalized financial advice while taking into account the user's emotional state.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes means for collecting multiple financial information and storing it on an integrated recording medium; means for analyzing the collected information and recognizing spending patterns; means for proposing optimal savings and investment plans to the user based on the analysis results; means for simulating the risks and returns of potential investments; and means for estimating the user's emotional state and adjusting savings and spending suggestions based on those emotions. This enables users to engage in more rational and personalized financial activities without being influenced by their emotions.

[0205] "Financial information" refers to all data related to a user's financial activities, including various transaction information related to bank accounts, credit cards, investment accounts, etc.

[0206] "Recording medium" refers to a device or system for storing and managing information, and includes database systems and cloud storage.

[0207] "Spending patterns" refer to patterns in how users spend their money, and include factors such as the frequency, amount, and category of purchases.

[0208] A "savings plan" is a guideline for systematically saving money for the future, and it includes regular savings amounts and financial goals.

[0209] An "investment plan" refers to a specific policy or strategy for increasing assets through investment, and this includes the selection of investment targets and the timing of investments.

[0210] "Risk" refers to an indicator that shows the uncertainty and potential for loss associated with a particular investment.

[0211] "Yield" refers to the return earned from an investment, and is expressed as a ratio to the amount invested.

[0212] "Intelligent conversation software" is a program that uses artificial intelligence technology to analyze human language and generate natural-sounding dialogues.

[0213] "Emotional state" refers to a user's psychological or emotional condition, which is a factor that influences their behavior and reactions.

[0214] "Rational and personalized financial activities" means rational and planned asset management that is optimized based on the individual needs and circumstances of the user.

[0215] The system implementing this invention consists of a user's device, a server device, an emotion engine, and a network environment. The user's device includes smartphones, tablets, and personal computers. The server device has advanced data processing and analysis capabilities, and in particular uses machine learning frameworks such as TENSORFLOW® to analyze financial data.

[0216] First, the user's device transmits information from multiple financial accounts to a storage medium. This information is stored on a server and analyzed to understand the user's spending patterns. Based on the analyzed data, the server presents the user with optimal savings and investment plans. The server also simulates the risks and returns of potential investments and displays the results visually on the user's device.

[0217] A key feature of this system is that it utilizes an emotion engine to estimate the user's emotional state and adjust its suggestions accordingly. For example, if a user is feeling stressed, the system can suggest a safer savings method. This assessment of emotional state is achieved through the analysis of sensor information and user behavior data.

[0218] Furthermore, the system incorporates intelligent conversational software that automatically generates answers to user questions. This process may utilize natural language processing libraries such as NLTK. This enables intuitive communication between the user and the system, aiding user understanding.

[0219] As a concrete example, imagine a situation where, before a user purchases an expensive electronic product in the afternoon, the emotion engine detects the user's excited state and suggests they reconsider the purchase. In this case, the system could display a prompt such as, "You seem a little excited right now. Perhaps you should reconsider your purchase to see if you really need it."

[0220] An example of a prompt to the generating AI model might be: "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions." This would allow the user to engage in rational financial activities that are not driven by emotions.

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The user enters information for multiple financial accounts using a terminal. This information is transmitted to a server via the network. The server receives this information and stores it on an integrated storage medium. This ensures that the user's asset information is centrally managed.

[0224] Step 2:

[0225] The server analyzes stored financial information to recognize the user's spending patterns. Specifically, it uses machine learning algorithms to process data from transaction history, classifying which items account for the most and how much spending is concentrated in each category. This identifies the user's current spending habits.

[0226] Step 3:

[0227] The server generates optimal savings and investment plans for the user based on the analysis results. The generated plans use simulations based on profitability and risk assessments to output predicted probabilities. These plans are presented on the terminal, allowing the user to understand specific savings and investment directions.

[0228] Step 4:

[0229] The user's device collects their emotional state via an emotion engine. The collected data is sent to a server, which estimates the user's emotional state based on their behavior and input data. Based on this information, the server rewrites investment and savings suggestions to better suit the user's emotions.

[0230] Step 5:

[0231] The server uses intelligent conversational software to answer user questions. Specifically, the server analyzes the question content through natural language processing and performs data calculations to generate appropriate feedback. By responding to the user's terminal in a human-like manner, it resolves the user's questions.

[0232] Step 6:

[0233] Ultimately, the generative AI model is used to generate prompts that provide expert advice, including overall system improvements. This enables users to make better financial decisions. For example, a prompt might read, "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions."

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] In implementing this invention, a system is constructed to securely and efficiently collect users' financial account information from various financial institutions and manage it centrally. The system consists of user terminals, backend servers, and a network connecting them.

[0251] A user's terminal is a device used by individual consumers and serves as an interface to the system. Users first create an account and register necessary financial account information. Authentication is performed using protocols such as OAuth to ensure security. The terminal has the function of sending this information to the server.

[0252] The server is the central processing unit of the system. It receives data from terminals, stores it in a database, and then processes and analyzes it. The server uses machine learning algorithms to analyze each user's transaction data and identify spending patterns. Based on this analysis, it proposes the optimal savings and investment plan for each user. The proposal will be tailored to the user's income, spending, and risk tolerance.

[0253] The server also predicts the risk and return of potential investments using methods such as Monte Carlo simulation, and presents the results visually to the user. This information can be used by the user as a reference when making investment decisions.

[0254] Furthermore, the server is equipped with an artificial intelligence chatbot function that automatically generates answers to user questions from terminals. This provides support 24 / 7, whenever users need it. This function uses natural language processing technology to provide information and solve problems through dialogue with users.

[0255] Furthermore, the server evaluates the user's progress toward achieving their goals and provides feedback based on that evaluation. Users can receive this feedback on a dashboard via their device and use it to improve their asset management.

[0256] For example, if a user wants to improve their monthly budget management, the server analyzes the user's spending history, visualizes unnecessary spending by category, and then proposes a monthly budget that aligns with their annual savings goal. In this way, the present invention supports efficient and effective personal asset management.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user creates an account on their device and registers their financial institution account information. The user enters basic information such as their name, email address, and password, and completes the linking of their financial account after going through a secure authentication process.

[0260] Step 2:

[0261] The device sends the user's financial account information to the server. This process uses standard authentication protocols such as OAuth to ensure the data is securely delivered to the server.

[0262] Step 3:

[0263] The server stores the received account information in the database and verifies that the data has been registered correctly. During this process, it performs data formatting standardization and duplicate checks.

[0264] Step 4:

[0265] The server periodically collects transaction data from various financial institutions using APIs. The collected data is updated in the database in real time.

[0266] Step 5:

[0267] The server uses machine learning algorithms to analyze spending patterns from transaction data. The analysis results are categorized and stored in a database as the user's spending history.

[0268] Step 6:

[0269] The server generates optimal savings and investment plans based on analyzed spending patterns and income information. These plans take into account the user's risk tolerance and constitute customized recommendations.

[0270] Step 7:

[0271] The server sends the generated plan to the terminal and displays it on a dashboard for the user to view or select. The user then makes asset management decisions based on this information.

[0272] Step 8:

[0273] When a user wants to evaluate potential investments, they send a request to the server via their device. The server then runs a Monte Carlo simulation to generate data on predicted risk and return.

[0274] Step 9:

[0275] The server sends simulation results to the terminal, providing visualized information to assist in selecting investment options. This information is presented clearly using graphs and charts.

[0276] Step 10:

[0277] When a user asks a question, the device sends the content to the server, where the AI ​​chatbot uses natural language processing technology to analyze the question and generate an appropriate answer.

[0278] Step 11:

[0279] The server sends the generated response to the terminal, providing real-time feedback to the user. The user then decides on further actions based on this response.

[0280] Step 12:

[0281] The server periodically evaluates the user's asset management status and notifies the terminal of the progress toward the goal as feedback information. The user then uses this feedback to make adjustments toward achieving the goal.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0284] In modern society, it is very difficult for an individual to integrally manage a plurality of financial asset information he / she has. Also, in order to grasp consumption patterns and present an optimal savings plan and investment plan, a high level of analytical ability is required. Furthermore, accurately predicting the risks and expected profits of investment candidates and identifying wasteful expenditures is also a heavy burden for an individual. There is a need for a system that can efficiently solve such problems related to asset management and enable a user to obtain an easily executable plan.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0286] In this invention, the server includes means for integrating a plurality of asset account information and storing it in an integrated information storage device, means for analyzing the integrated information and identifying consumption patterns, and means for presenting an optimal savings plan and investment plan to the user based on the analysis result. Thereby, each individual user can manage his / her asset information in a unified manner and easily obtain an optimal asset operation plan.

[0287] "Asset account information" refers to data related to financial institutions owned by a specific individual or corporation, and includes deposit accounts, credit accounts, investment accounts, etc.

[0288] "Information storage device" refers to a recording medium for storing and managing integrated data, and includes forms such as databases and cloud storage.

[0289] "Consumption pattern" refers to the result of analyzing the expenditure trends and habits of a specific individual or group, and includes information such as in which category how much cost occurs.

[0290] A "savings plan" refers to a plan developed by an individual or corporation to achieve future financial goals, and includes monthly savings amounts and methods for allocating funds.

[0291] An "investment plan" refers to a plan that outlines how to manage capital to maximize returns, and includes investment allocation to specific financial assets and setting risk tolerance levels.

[0292] "Risk" refers to the uncertainty and potential volatility associated with investments in specific financial assets, including the expected range of losses and risk factors.

[0293] "Profit" refers to the expected return on investment, and includes the return on the principal investment and the rate of return.

[0294] "Artificial intelligence dialogue means" refers to technologies that use artificial intelligence to respond to user inquiries in text or voice, and includes natural language processing and machine learning models.

[0295] "Goal achievement status" refers to the current progress towards the user's personal financial goals, and includes the percentage of achievement and the estimated time to achieve them.

[0296] "Advice" refers to suggestions or recommendations that indicate the best course of action in a particular situation, and includes financial information and strategies.

[0297] In carrying out the present invention, the system is constructed using servers, terminals, and a network.

[0298] Users access the system using their individual devices and input their personal asset data. The devices encrypt the input information and send it to the server using a secure protocol (e.g., SSL / TLS communication using OAuth). The devices are equipped with interface functions to facilitate user operation.

[0299] The server is the heart of the system, receiving asset data sent from terminals and securely storing it in the database. The database provides a storage environment that enables efficient data management and analysis. The server also uses machine learning algorithms (e.g., Python's scikit-learn library) to analyze user consumption patterns.

[0300] As a concrete example, suppose a user wants to "review their spending this month and find out which categories they can save money in." In response to this request, the server analyzes past transaction data and visualizes unnecessary spending by category. Furthermore, the server generates a savings and investment plan tailored to the user's income and risk tolerance, and sends the results to the device.

[0301] The server applies Monte Carlo simulations to predict the risks and returns of investment plans and presents the results visually. This allows users to obtain useful information for making investment decisions.

[0302] Furthermore, the server utilizes a generative AI model and an artificial intelligence chatbot function to automatically generate answers to user questions. This enables 24 / 7 support. An example of a prompt is, "Analyze the user's spending trends based on their transaction data from the past three months and suggest easy ways to save money."

[0303] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0304] Step 1:

[0305] Users log in to the system via a terminal and enter their asset information. This input data includes bank account numbers, credit card information, and other financial account details. The terminal encrypts this information and transmits it to the server using a secure protocol. The input is the user's financial information, and the output is encrypted data.

[0306] Step 2:

[0307] The server receives the financial data sent from the terminal and stores it in the database. For database storage, the information is first parsed and organized into appropriate fields. The input is encrypted data, and the output is structured data stored in the database.

[0308] Step 3:

[0309] The server uses a machine learning algorithm to analyze the user's transaction data stored in the database. Using Python libraries (e.g., scikit-learn), consumption patterns are identified through cluster analysis. The input is structured transaction data, and the output is the analysis result of consumption patterns.

[0310] Step 4:

[0311] Based on the analysis result of consumption patterns, the server generates an optimal savings plan and investment plan considering the user's income, expenditure, and risk tolerance. This plan generation also includes trend prediction from past data. The input is the analysis result of consumption patterns, and the output is the optimized savings and investment plan.

[0312] Step 5:

[0313] The server applies Monte Carlo simulation to evaluate the risk and return of the generated investment plan. Using a Python module, a large number of simulations are performed, and based on this, an evaluation chart is generated. The input is the generated investment plan, and the output is visual data of the evaluation results of risk and return.

[0314] Step 6:

[0315] The server utilizes a generative AI model to generate answers based on questions submitted by the user from the terminal. Here, natural language processing techniques are used to generate the optimal answer in response to the user's prompt. The input is the user's question, and the output is the AI-generated answer.

[0316] Step 7:

[0317] The server evaluates the user's progress toward their goals and generates feedback. It compares and analyzes the user's set goals with current data to calculate a quantified degree of achievement. The inputs are current asset information and goal data, and the output is the evaluated degree of achievement and feedback.

[0318] (Application Example 1)

[0319] 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."

[0320] In modern society, there is a need for consumers to manage their assets effectively based on a centralized system of financial information. However, there is no system that can grasp spending trends in the history of electronic transactions used daily by users and propose appropriate savings and asset management plans based on that information. Furthermore, there is a lack of support that is linked to intelligent dialogue devices that can answer user questions immediately and effectively, making the realization of efficient and effective personal asset management a challenge.

[0321] 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.

[0322] In this invention, the server includes means for collecting and integrating multiple financial information, means for analyzing the collected information and recognizing financial behavior patterns, and intelligent dialogue device means for automatically generating responses to information requests from users. This makes it possible to visualize financial behavior based on the user's daily electronic transaction history and propose specific savings and asset management plans.

[0323] "Financial information" refers to all information related to financial activities, and specifically includes bank account information, credit card history, and data related to asset management.

[0324] An "information management device" is a device that centrally stores and manages collected financial information, and retrieves and updates data as needed.

[0325] "Financial behavior patterns" are behavioral patterns identified based on the results of an analysis of the user's income and expenditure trends and characteristics.

[0326] An "intelligent dialogue device" is an automated response device that uses natural language processing technology to respond to inquiries and requests from users.

[0327] "Spending trends" refer to the trends obtained by visualizing and analyzing users' financial behavior in their daily transactions and consumption, categorized by type.

[0328] A "savings plan and asset management plan" proposes specific guidelines and strategies for effectively managing assets and setting optimal savings goals, based on the user's financial situation.

[0329] "Electronic transactions" refer to all financial transactions conducted via the internet, including online payments and asset transfers.

[0330] To implement this invention, the user first accesses the system using their terminal and registers multiple pieces of financial information. The terminal authenticates the user using the OAuth protocol and processes the data securely. After authentication, the financial information collected from the terminal is transmitted to an integrated information management device and stored in a database.

[0331] The server uses machine learning algorithms implemented in languages ​​such as Python and R to analyze the financial information in this database. These algorithms model the user's financial behavior and understand their spending trends. The analysis results are presented to the user visually and displayed on their device through an interface built with React Native.

[0332] The server also functions as an intelligent dialogue device. This device uses natural language processing technology based on Google Cloud's Dialogflow to automatically generate responses to user inquiries. When a user enters prompts such as "Tell me about my savings plan" or "Tell me about my spending trends this month," the server can understand the content and return detailed feedback and advice to the terminal.

[0333] As a concrete example, if a user wants to review their spending and make their leisure activities more efficient, they can access the system on their device and input "I want to know the details of my entertainment expenses." In this case, the server analyzes spending trends in the entertainment category and suggests specific saving methods and new savings plans to the user. In this way, this invention helps users accurately understand their personal financial situation and appropriately create future financial plans.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The user logs into the system using a terminal and registers their financial information (e.g., bank account information and credit card history). The terminal uses the OAuth protocol to authenticate the user for security purposes. The input consists of the user's identification information and financial account information, which is then sent to the server.

[0337] Step 2:

[0338] The server integrates the received financial information and stores it in a database. The input is financial information from the terminal, and the output is an entry into the database in an integrated format. The data is stored in an information management device and used for other analysis processes.

[0339] Step 3:

[0340] The server executes machine learning algorithms to analyze financial information in the database. The input is stored financial information, and the system processes and models the data to identify trends in financial behavior. The output is the user's financial behavior patterns.

[0341] Step 4:

[0342] The user enters a prompt message through the terminal, such as "Tell me about this month's spending trends." The terminal sends this prompt message to the server. The input is a prompt message generated by the user.

[0343] Step 5:

[0344] The server uses Dialogflow to parse the prompt text and perform the necessary data calculations. It then generates and responds with information tailored to the user's request. The input consists of the user's prompt text and the resulting financial behavior pattern, while the output is text containing feedback and advice for the user.

[0345] Step 6:

[0346] The server sends the generated feedback to the user's device through an interface built with React Native, displaying it visually. Based on this output, the user can create concrete asset management and savings plans. The input is feedback information from the server, and the output is visualized information on the user's device.

[0347] 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.

[0348] This invention provides a system that incorporates an emotion engine into a financial account management system to provide personalized feedback tailored to the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion engine, and a network.

[0349] When a user accesses the system using a device, they first create an account and register their financial account information. The user enters basic information such as their name and links their financial account after going through an authentication process. The device then sends this information to the server.

[0350] The server stores financial account information received from users in a database and periodically collects transaction data. Using this data, the server analyzes the user's spending patterns using machine learning and generates optimal savings and investment plans. These plans are then presented to the user on their device in an individually customized format.

[0351] The system is equipped with an emotion engine that analyzes the user's emotional state based on user behavior, input data, and sensor information. The emotion engine uses this information to adjust feedback and suggestions according to the user's current emotions.

[0352] For example, if a user is feeling stressed, the emotion engine will detect this and prioritize suggesting lower-risk investment plans. Conversely, if the system determines that the user is feeling very secure, it may present higher-risk but potentially higher-return investment options. In this way, the emotion engine, based on the user's emotions, works in conjunction with other system functions to support decision-making.

[0353] Furthermore, the system uses income and expense data collected from multiple users to generate community-based advice, and the server's AI chatbot explains any points of confusion users may have. This deepens users' understanding and enables better financial management.

[0354] Through these functions, the present invention enables asset management that takes user emotions into account, reducing user mental stress while supporting efficient asset building.

[0355] The following describes the processing flow.

[0356] Step 1:

[0357] The user accesses the device and enters information to create an account. This includes their name, email address, and password. The user then completes an authentication process to link their financial institution account.

[0358] Step 2:

[0359] The terminal securely transmits information provided by the user to the server and requests that login information and financial account link information be stored in the database.

[0360] Step 3:

[0361] The server stores user information in a database and collects user financial transaction data from various financial institutions at specified intervals using an API. This data includes transaction details and balance information.

[0362] Step 4:

[0363] The server applies machine learning algorithms to analyze the collected transaction data. Here, it identifies user spending patterns and categorizes them.

[0364] Step 5:

[0365] Based on the analysis results, the server generates savings and investment plans tailored to the user's income, expenses, and risk tolerance, and sends them to the user's device. The user can then make decisions based on these plans.

[0366] Step 6:

[0367] When a user inputs emotion-related information (e.g., text messages or sensor data) via their device, the device sends this information to the server's emotion engine.

[0368] Step 7:

[0369] The emotion engine installed on the server analyzes the received information and diagnoses the user's emotional state. This analysis may utilize natural language processing and speech / image recognition technologies.

[0370] Step 8:

[0371] The server takes the diagnosed emotional state into account and adjusts the existing plan or generates an alternative suggestion. For example, if stress is detected, it will change to a lower-risk suggestion.

[0372] Step 9:

[0373] The server sends emotionally-adjusted plans and advice to the user's device, allowing the user to choose how to manage their assets based on these. This enables the user to make rational decisions that align with their emotional state.

[0374] Step 10:

[0375] If a user has questions or concerns about the system, they can send those questions from their device to the server's AI chatbot function.

[0376] Step 11:

[0377] The server's AI chatbot analyzes questions using natural language processing and automatically generates appropriate answers. These answers are then sent back to the terminal and presented to the user.

[0378] Step 12:

[0379] Users can also refer to community-based advice and success stories from other users, and the server collects and provides this information as needed. This makes it possible to receive advice from diverse perspectives.

[0380] (Example 2)

[0381] 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".

[0382] Traditional financial management systems simply offer data-driven suggestions without considering the user's emotions or psychological state. This can lead to investment and savings choices that don't align with the user's feelings, hindering efficient financial management and stress reduction. Furthermore, few systems can integrate and manage multiple financial information streams, forcing users to manage each piece of information individually.

[0383] 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.

[0384] In this invention, the server includes means for collecting multiple financial account information and storing it in an integrated information recording device; means for analyzing the collected data and recognizing spending trends; means for proposing optimal savings and investment plans to the user based on the analysis results; means for evaluating the user's emotional state using an emotion analysis device; and means for adjusting the suggested feedback based on the evaluated emotional state. This enables personalized feedback based on the user's emotional state, resulting in more efficient and less stressful financial management.

[0385] "Financial account information" refers to data on a user's assets and liabilities managed by a financial institution.

[0386] An "information recording device" refers to a device or system that can securely store data in digital format and retrieve it as needed.

[0387] "Spending trends" refer to analytical results that show patterns and behaviors of how users spend their money.

[0388] A "savings plan" refers to a strategic financial preservation plan established to efficiently increase the user's assets.

[0389] An "investment plan" refers to the investment strategy and specific action plan necessary to increase the user's assets.

[0390] An "emotion analysis device" refers to a system or device that evaluates a user's emotions or psychological state using a database or algorithm.

[0391] "Feedback" is a general term for advice and information that a system provides to a user, and specifically refers to evaluations and suggestions.

[0392] This invention is a system that provides personalized financial management based on the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion analysis device, and a network connecting them.

[0393] Users access the system via a terminal and register their financial account information. The terminal transmits the information received from the user to the server. The server collects the user's financial data and stores it in an integrated information storage device. The data is retrieved using financial APIs and secure communication protocols. The server analyzes the collected data and recognizes spending trends using libraries such as Python's pandas and scikit-learn.

[0394] The emotion analysis device evaluates the user's emotional state based on their usage patterns and input data. This utilizes natural language processing and image recognition technologies. Based on the analysis results, the server generates feedback that reflects the user's emotional state.

[0395] For example, if a user is experiencing stress, the system detects this through an emotion analyzer and prioritizes recommending low-risk savings plans. On the other hand, if the system determines that the user is feeling secure, it can suggest higher-risk but potentially higher-return investment options. In this way, personalized recommendations are developed for each user.

[0396] An example of a prompt might be: "Please suggest a safe and low-risk savings plan to plan for college tuition." In response to such a prompt, the system will suggest the best plan for the user.

[0397] This invention enables financial management that takes user emotions into account, reducing mental stress while supporting efficient asset building.

[0398] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0399] Step 1:

[0400] The user enters basic information such as their name and email address using an application on their device and links their financial account information. The entered information is securely encrypted by the device and sent to the server. The output is encrypted user information data.

[0401] Step 2:

[0402] The server decrypts the encrypted user information data received from the terminal and stores it in a database. Next, it periodically collects transaction data from each user's financial account using a financial API. The output of this step is an integrated set of user transaction data.

[0403] Step 3:

[0404] The server analyzes integrated transaction data using machine learning algorithms. Here, the data is preprocessed using the Python pandas library, and clustering and classification are performed using scikit-learn. The input is an integrated set of transaction data, and the output is an analysis showing spending trends and characteristics.

[0405] Step 4:

[0406] The server uses an emotion analysis device to analyze behavioral data and input data transmitted from the user's terminal. This analysis employs natural language processing and image recognition technologies. The input is the user's behavioral data, and the output is a result indicating the user's emotional state.

[0407] Step 5:

[0408] The server generates savings and investment plans best suited to the user based on analyzed spending trends and emotional states. A generative AI model may be used for this generation. The output is a customized financial plan proposal.

[0409] Step 6:

[0410] The server sends the generated financial plan to the terminal, which the user can view and review. The terminal displays risks and returns visually, allowing the user to obtain detailed information. The output is the visualized plan information presented to the end user.

[0411] Step 7:

[0412] Users can use their devices to ask questions to the AI ​​chatbot about the presented plans. The server receives the user's questions, uses a generative AI model to generate appropriate answers, and sends them back to the device. The output is an automatically generated answer to the user's question.

[0413] Step 8:

[0414] The server uses data collected from all users to generate community-based advice, which is then displayed on each user's individual screen. The input is anonymized data from other users, and the output is advice based on collective insights. This process allows users to gain insights based on the experiences of others.

[0415] (Application Example 2)

[0416] 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."

[0417] In today's financial system, users are expected to effectively manage their financial situation and plan for the future. However, the complexity of diverse financial information and the emotionally driven decision-making process make it difficult to select appropriate investment and savings strategies. Therefore, there is a need for systems that provide more personalized financial advice while taking into account the user's emotional state.

[0418] 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.

[0419] In this invention, the server includes means for collecting multiple financial information and storing it on an integrated recording medium; means for analyzing the collected information and recognizing spending patterns; means for proposing optimal savings and investment plans to the user based on the analysis results; means for simulating the risks and returns of potential investments; and means for estimating the user's emotional state and adjusting savings and spending suggestions based on those emotions. This enables users to engage in more rational and personalized financial activities without being influenced by their emotions.

[0420] "Financial information" refers to all data related to a user's financial activities, including various transaction information related to bank accounts, credit cards, investment accounts, etc.

[0421] "Recording medium" refers to a device or system for storing and managing information, and includes database systems and cloud storage.

[0422] "Spending patterns" refer to patterns in how users spend their money, and include factors such as the frequency, amount, and category of purchases.

[0423] A "savings plan" is a guideline for systematically saving money for the future, and it includes regular savings amounts and financial goals.

[0424] An "investment plan" refers to a specific policy or strategy for increasing assets through investment, and this includes the selection of investment targets and the timing of investments.

[0425] "Risk" refers to an indicator that shows the uncertainty and potential for loss associated with a particular investment.

[0426] "Yield" refers to the return earned from an investment, and is expressed as a ratio to the amount invested.

[0427] "Intelligent conversation software" is a program that uses artificial intelligence technology to analyze human language and generate natural-sounding dialogues.

[0428] "Emotional state" refers to a user's psychological or emotional condition, which is a factor that influences their behavior and reactions.

[0429] "Rational and personalized financial activities" means rational and planned asset management that is optimized based on the individual needs and circumstances of the user.

[0430] The system implementing this invention consists of a user's device, a server device, an emotion engine, and a network environment. The user's device includes smartphones, tablets, and personal computers. The server device has advanced data processing and analysis capabilities, and in particular uses machine learning frameworks such as TensorFlow to analyze financial data.

[0431] First, the user's device transmits information from multiple financial accounts to a storage medium. This information is stored on a server and analyzed to understand the user's spending patterns. Based on the analyzed data, the server presents the user with optimal savings and investment plans. The server also simulates the risks and returns of potential investments and displays the results visually on the user's device.

[0432] A key feature of this system is that it utilizes an emotion engine to estimate the user's emotional state and adjust its suggestions accordingly. For example, if a user is feeling stressed, the system can suggest a safer savings method. This assessment of emotional state is achieved through the analysis of sensor information and user behavior data.

[0433] Furthermore, the system incorporates intelligent conversational software that automatically generates answers to user questions. This process may utilize natural language processing libraries such as NLTK. This enables intuitive communication between the user and the system, aiding user understanding.

[0434] As a concrete example, imagine a situation where, before a user purchases an expensive electronic product in the afternoon, the emotion engine detects the user's excited state and suggests they reconsider the purchase. In this case, the system could display a prompt such as, "You seem a little excited right now. Perhaps you should reconsider your purchase to see if you really need it."

[0435] An example of a prompt to the generating AI model might be: "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions." This would allow the user to engage in rational financial activities that are not driven by emotions.

[0436] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0437] Step 1:

[0438] The user enters information for multiple financial accounts using a terminal. This information is transmitted to a server via the network. The server receives this information and stores it on an integrated storage medium. This ensures that the user's asset information is centrally managed.

[0439] Step 2:

[0440] The server analyzes stored financial information to recognize the user's spending patterns. Specifically, it uses machine learning algorithms to process data from transaction history, classifying which items account for the most and how much spending is concentrated in each category. This identifies the user's current spending habits.

[0441] Step 3:

[0442] The server generates optimal savings and investment plans for the user based on the analysis results. The generated plans use simulations based on profitability and risk assessments to output predicted probabilities. These plans are presented on the terminal, allowing the user to understand specific savings and investment directions.

[0443] Step 4:

[0444] The user's device collects their emotional state via an emotion engine. The collected data is sent to a server, which estimates the user's emotional state based on their behavior and input data. Based on this information, the server rewrites investment and savings suggestions to better suit the user's emotions.

[0445] Step 5:

[0446] The server uses intelligent conversational software to answer user questions. Specifically, the server analyzes the question content through natural language processing and performs data calculations to generate appropriate feedback. By responding to the user's terminal in a human-like manner, it resolves the user's questions.

[0447] Step 6:

[0448] Ultimately, the generative AI model is used to generate prompts that provide expert advice, including overall system improvements. This enables users to make better financial decisions. For example, a prompt might read, "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions."

[0449] 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.

[0450] 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.

[0451] 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.

[0452] [Third Embodiment]

[0453] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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".

[0465] In implementing this invention, a system is constructed to securely and efficiently collect users' financial account information from various financial institutions and manage it centrally. The system consists of user terminals, backend servers, and a network connecting them.

[0466] A user's terminal is a device used by individual consumers and serves as an interface to the system. Users first create an account and register necessary financial account information. Authentication is performed using protocols such as OAuth to ensure security. The terminal has the function of sending this information to the server.

[0467] The server is the central processing unit of the system. It receives data from terminals, stores it in a database, and then processes and analyzes it. The server uses machine learning algorithms to analyze each user's transaction data and identify spending patterns. Based on this analysis, it proposes the optimal savings and investment plan for each user. The proposal will be tailored to the user's income, spending, and risk tolerance.

[0468] The server also predicts the risk and return of potential investments using methods such as Monte Carlo simulation, and presents the results visually to the user. This information can be used by the user as a reference when making investment decisions.

[0469] Furthermore, the server is equipped with an artificial intelligence chatbot function that automatically generates answers to user questions from terminals. This provides support 24 / 7, whenever users need it. This function uses natural language processing technology to provide information and solve problems through dialogue with users.

[0470] Furthermore, the server evaluates the user's progress toward achieving their goals and provides feedback based on that evaluation. Users can receive this feedback on a dashboard via their device and use it to improve their asset management.

[0471] For example, if a user wants to improve their monthly budget management, the server analyzes the user's spending history, visualizes unnecessary spending by category, and then proposes a monthly budget that aligns with their annual savings goal. In this way, the present invention supports efficient and effective personal asset management.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user creates an account on their device and registers their financial institution account information. The user enters basic information such as their name, email address, and password, and completes the linking of their financial account after going through a secure authentication process.

[0475] Step 2:

[0476] The device sends the user's financial account information to the server. This process uses standard authentication protocols such as OAuth to ensure the data is securely delivered to the server.

[0477] Step 3:

[0478] The server stores the received account information in the database and verifies that the data has been registered correctly. During this process, it performs data formatting standardization and duplicate checks.

[0479] Step 4:

[0480] The server periodically collects transaction data from various financial institutions using APIs. The collected data is updated in the database in real time.

[0481] Step 5:

[0482] The server uses machine learning algorithms to analyze spending patterns from transaction data. The analysis results are categorized and stored in a database as the user's spending history.

[0483] Step 6:

[0484] The server generates optimal savings and investment plans based on analyzed spending patterns and income information. These plans take into account the user's risk tolerance and constitute customized recommendations.

[0485] Step 7:

[0486] The server sends the generated plan to the terminal and displays it on a dashboard for the user to view or select. The user then makes asset management decisions based on this information.

[0487] Step 8:

[0488] When a user wants to evaluate potential investments, they send a request to the server via their device. The server then runs a Monte Carlo simulation to generate data on predicted risk and return.

[0489] Step 9:

[0490] The server sends simulation results to the terminal, providing visualized information to assist in selecting investment options. This information is presented clearly using graphs and charts.

[0491] Step 10:

[0492] When a user asks a question, the device sends the content to the server, where the AI ​​chatbot uses natural language processing technology to analyze the question and generate an appropriate answer.

[0493] Step 11:

[0494] The server sends the generated response to the terminal, providing real-time feedback to the user. The user then decides on further actions based on this response.

[0495] Step 12:

[0496] The server periodically evaluates the user's asset management status and notifies the terminal of the progress toward the goal as feedback information. The user then uses this feedback to make adjustments toward achieving the goal.

[0497] (Example 1)

[0498] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In modern society, it is extremely difficult for individuals to comprehensively manage information on their multiple financial assets. Furthermore, advanced analytical capabilities are required to understand spending patterns and propose optimal savings and investment plans. Moreover, accurately predicting the risks and expected returns of potential investments and identifying unnecessary spending is a significant burden for individuals. There is a need for a system that efficiently solves these asset management problems and provides users with easily actionable plans.

[0500] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0501] In this invention, the server includes means for aggregating and storing multiple asset account information in an integrated information storage device, means for analyzing the aggregated information and identifying consumption patterns, and means for presenting the user with an optimal savings plan and investment plan based on the analysis results. This makes it possible for individual users to centrally manage their asset information and easily obtain an optimal asset management plan.

[0502] "Asset account information" refers to data related to financial institutions owned by a specific individual or legal entity, including deposit accounts, credit accounts, investment accounts, etc.

[0503] An "information storage device" refers to a recording medium used to store and manage accumulated data, and includes formats such as databases and cloud storage.

[0504] "Consumption patterns" refer to the results of an analysis of spending trends and habits in a specific individual or group, and include information such as how much money is spent in which categories.

[0505] A "savings plan" refers to a plan developed by an individual or corporation to achieve future financial goals, and includes monthly savings amounts and methods for allocating funds.

[0506] An "investment plan" refers to a plan that outlines how to manage capital to maximize returns, and includes investment allocation to specific financial assets and setting risk tolerance levels.

[0507] "Risk" refers to the uncertainty and potential volatility associated with investments in specific financial assets, including the expected range of losses and risk factors.

[0508] "Profit" refers to the expected return on investment, and includes the return on the principal investment and the rate of return.

[0509] "Artificial intelligence dialogue means" refers to technologies that use artificial intelligence to respond to user inquiries in text or voice, and includes natural language processing and machine learning models.

[0510] "Goal achievement status" refers to the current progress towards the user's personal financial goals, and includes the percentage of achievement and the estimated time to achieve them.

[0511] "Advice" refers to suggestions or recommendations that indicate the best course of action in a particular situation, and includes financial information and strategies.

[0512] In carrying out the present invention, the system is constructed using servers, terminals, and a network.

[0513] Users access the system using their individual devices and input their personal asset data. The devices encrypt the input information and send it to the server using a secure protocol (e.g., SSL / TLS communication using OAuth). The devices are equipped with interface functions to facilitate user operation.

[0514] The server is the heart of the system, receiving asset data sent from terminals and securely storing it in the database. The database provides a storage environment that enables efficient data management and analysis. The server also uses machine learning algorithms (e.g., Python's scikit-learn library) to analyze user consumption patterns.

[0515] As a concrete example, suppose a user wants to "review their spending this month and find out which categories they can save money in." In response to this request, the server analyzes past transaction data and visualizes unnecessary spending by category. Furthermore, the server generates a savings and investment plan tailored to the user's income and risk tolerance, and sends the results to the device.

[0516] The server applies Monte Carlo simulations to predict the risks and returns of investment plans and presents the results visually. This allows users to obtain useful information for making investment decisions.

[0517] Furthermore, the server utilizes a generative AI model and an artificial intelligence chatbot function to automatically generate answers to user questions. This enables 24 / 7 support. An example of a prompt is, "Analyze the user's spending trends based on their transaction data from the past three months and suggest easy ways to save money."

[0518] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0519] Step 1:

[0520] Users log in to the system via a terminal and enter their asset information. This input data includes bank account numbers, credit card information, and other financial account details. The terminal encrypts this information and transmits it to the server using a secure protocol. The input is the user's financial information, and the output is encrypted data.

[0521] Step 2:

[0522] The server receives financial data sent from the terminal and stores it in a database. For database storage, the information is first parsed and organized into appropriate fields. The input is encrypted data, and the output is structured data stored in the database.

[0523] Step 3:

[0524] The server uses machine learning algorithms to analyze user transaction data stored in the database. It uses Python libraries (e.g., scikit-learn) to identify consumption patterns through cluster analysis. The input is structured transaction data, and the output is the analysis of consumption patterns.

[0525] Step 4:

[0526] Based on the analysis of consumption patterns, the server generates optimal savings and investment plans that take into account the user's income, expenses, and risk tolerance. This plan generation also includes trend predictions from historical data. The input is the analysis of consumption patterns, and the output is the optimized savings and investment plans.

[0527] Step 5:

[0528] The server applies Monte Carlo simulations to evaluate the risk and return of the generated investment plans. It performs numerous simulations using Python modules and generates evaluation charts based on these results. The input is the generated investment plan, and the output is visual data of the risk and return evaluation results.

[0529] Step 6:

[0530] The server utilizes a generative AI model to generate answers based on questions submitted by the user from the terminal. Here, natural language processing techniques are used to generate the optimal answer in response to the user's prompt. The input is the user's question, and the output is the AI-generated answer.

[0531] Step 7:

[0532] The server evaluates the user's progress toward their goals and generates feedback. It compares and analyzes the user's set goals with current data to calculate a quantified degree of achievement. The inputs are current asset information and goal data, and the output is the evaluated degree of achievement and feedback.

[0533] (Application Example 1)

[0534] 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."

[0535] In modern society, there is a need for consumers to manage their assets effectively based on a centralized system of financial information. However, there is no system that can grasp spending trends in the history of electronic transactions used daily by users and propose appropriate savings and asset management plans based on that information. Furthermore, there is a lack of support that is linked to intelligent dialogue devices that can answer user questions immediately and effectively, making the realization of efficient and effective personal asset management a challenge.

[0536] 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.

[0537] In this invention, the server includes means for collecting and integrating multiple financial information, means for analyzing the collected information and recognizing financial behavior patterns, and intelligent dialogue device means for automatically generating responses to information requests from users. This makes it possible to visualize financial behavior based on the user's daily electronic transaction history and propose specific savings and asset management plans.

[0538] "Financial information" refers to all information related to financial activities, and specifically includes bank account information, credit card history, and data related to asset management.

[0539] An "information management device" is a device that centrally stores and manages collected financial information, and retrieves and updates data as needed.

[0540] "Financial behavior patterns" are behavioral patterns identified based on the results of an analysis of the user's income and expenditure trends and characteristics.

[0541] An "intelligent dialogue device" is an automated response device that uses natural language processing technology to respond to inquiries and requests from users.

[0542] "Spending trends" refer to the trends obtained by visualizing and analyzing users' financial behavior in their daily transactions and consumption, categorized by type.

[0543] A "savings plan and asset management plan" proposes specific guidelines and strategies for effectively managing assets and setting optimal savings goals, based on the user's financial situation.

[0544] "Electronic transactions" refer to all financial transactions conducted via the internet, including online payments and asset transfers.

[0545] To implement this invention, the user first accesses the system using their terminal and registers multiple pieces of financial information. The terminal authenticates the user using the OAuth protocol and processes the data securely. After authentication, the financial information collected from the terminal is transmitted to an integrated information management device and stored in a database.

[0546] The server uses machine learning algorithms implemented in languages ​​such as Python and R to analyze the financial information in this database. These algorithms model the user's financial behavior and understand their spending trends. The analysis results are presented to the user visually and displayed on their device through an interface built with React Native.

[0547] The server also functions as an intelligent dialogue device. This device uses natural language processing technology based on Google Cloud's Dialogflow to automatically generate responses to user inquiries. When a user enters prompts such as "Tell me about my savings plan" or "Tell me about my spending trends this month," the server can understand the content and return detailed feedback and advice to the terminal.

[0548] As a concrete example, if a user wants to review their spending and make their leisure activities more efficient, they can access the system on their device and input "I want to know the details of my entertainment expenses." In this case, the server analyzes spending trends in the entertainment category and suggests specific saving methods and new savings plans to the user. In this way, this invention helps users accurately understand their personal financial situation and appropriately create future financial plans.

[0549] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0550] Step 1:

[0551] The user logs into the system using a terminal and registers their financial information (e.g., bank account information and credit card history). The terminal uses the OAuth protocol to authenticate the user for security purposes. The input consists of the user's identification information and financial account information, which is then sent to the server.

[0552] Step 2:

[0553] The server integrates the received financial information and stores it in a database. The input is financial information from the terminal, and the output is an entry into the database in an integrated format. The data is stored in an information management device and used for other analysis processes.

[0554] Step 3:

[0555] The server executes machine learning algorithms to analyze financial information in the database. The input is stored financial information, and the system processes and models the data to identify trends in financial behavior. The output is the user's financial behavior patterns.

[0556] Step 4:

[0557] The user enters a prompt message through the terminal, such as "Tell me about this month's spending trends." The terminal sends this prompt message to the server. The input is a prompt message generated by the user.

[0558] Step 5:

[0559] The server uses Dialogflow to parse the prompt text and perform the necessary data calculations. It then generates and responds with information tailored to the user's request. The input consists of the user's prompt text and the resulting financial behavior pattern, while the output is text containing feedback and advice for the user.

[0560] Step 6:

[0561] The server sends the generated feedback to the user's device through an interface built with React Native, displaying it visually. Based on this output, the user can create concrete asset management and savings plans. The input is feedback information from the server, and the output is visualized information on the user's device.

[0562] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0563] This invention provides a system that incorporates an emotion engine into a financial account management system to provide personalized feedback tailored to the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion engine, and a network.

[0564] When a user accesses the system using a device, they first create an account and register their financial account information. The user enters basic information such as their name and links their financial account after going through an authentication process. The device then sends this information to the server.

[0565] The server stores financial account information received from users in a database and periodically collects transaction data. Using this data, the server analyzes the user's spending patterns using machine learning and generates optimal savings and investment plans. These plans are then presented to the user on their device in an individually customized format.

[0566] The system is equipped with an emotion engine that analyzes the user's emotional state based on user behavior, input data, and sensor information. The emotion engine uses this information to adjust feedback and suggestions according to the user's current emotions.

[0567] For example, if a user is feeling stressed, the emotion engine will detect this and prioritize suggesting lower-risk investment plans. Conversely, if the system determines that the user is feeling very secure, it may present higher-risk but potentially higher-return investment options. In this way, the emotion engine, based on the user's emotions, works in conjunction with other system functions to support decision-making.

[0568] Furthermore, the system uses income and expense data collected from multiple users to generate community-based advice, and the server's AI chatbot explains any points of confusion users may have. This deepens users' understanding and enables better financial management.

[0569] Through these functions, the present invention enables asset management that takes user emotions into account, reducing user mental stress while supporting efficient asset building.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] The user accesses the device and enters information to create an account. This includes their name, email address, and password. The user then completes an authentication process to link their financial institution account.

[0573] Step 2:

[0574] The terminal securely transmits information provided by the user to the server and requests that login information and financial account link information be stored in the database.

[0575] Step 3:

[0576] The server stores user information in a database and collects user financial transaction data from various financial institutions at specified intervals using an API. This data includes transaction details and balance information.

[0577] Step 4:

[0578] The server applies machine learning algorithms to analyze the collected transaction data. Here, it identifies user spending patterns and categorizes them.

[0579] Step 5:

[0580] Based on the analysis results, the server generates savings and investment plans tailored to the user's income, expenses, and risk tolerance, and sends them to the user's device. The user can then make decisions based on these plans.

[0581] Step 6:

[0582] When a user inputs emotion-related information (e.g., text messages or sensor data) via their device, the device sends this information to the server's emotion engine.

[0583] Step 7:

[0584] The emotion engine installed on the server analyzes the received information and diagnoses the user's emotional state. This analysis may utilize natural language processing and speech / image recognition technologies.

[0585] Step 8:

[0586] The server takes the diagnosed emotional state into account and adjusts the existing plan or generates an alternative suggestion. For example, if stress is detected, it will change to a lower-risk suggestion.

[0587] Step 9:

[0588] The server sends emotionally-adjusted plans and advice to the user's device, allowing the user to choose how to manage their assets based on these. This enables the user to make rational decisions that align with their emotional state.

[0589] Step 10:

[0590] If a user has questions or concerns about the system, they can send those questions from their device to the server's AI chatbot function.

[0591] Step 11:

[0592] The server's AI chatbot analyzes questions using natural language processing and automatically generates appropriate answers. These answers are then sent back to the terminal and presented to the user.

[0593] Step 12:

[0594] Users can also refer to community-based advice and success stories from other users, and the server collects and provides this information as needed. This makes it possible to receive advice from diverse perspectives.

[0595] (Example 2)

[0596] 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."

[0597] Traditional financial management systems simply offer data-driven suggestions without considering the user's emotions or psychological state. This can lead to investment and savings choices that don't align with the user's feelings, hindering efficient financial management and stress reduction. Furthermore, few systems can integrate and manage multiple financial information streams, forcing users to manage each piece of information individually.

[0598] 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.

[0599] In this invention, the server includes means for collecting multiple financial account information and storing it in an integrated information recording device; means for analyzing the collected data and recognizing spending trends; means for proposing optimal savings and investment plans to the user based on the analysis results; means for evaluating the user's emotional state using an emotion analysis device; and means for adjusting the suggested feedback based on the evaluated emotional state. This enables personalized feedback based on the user's emotional state, resulting in more efficient and less stressful financial management.

[0600] "Financial account information" refers to data on a user's assets and liabilities managed by a financial institution.

[0601] An "information recording device" refers to a device or system that can securely store data in digital format and retrieve it as needed.

[0602] "Spending trends" refer to analytical results that show patterns and behaviors of how users spend their money.

[0603] A "savings plan" refers to a strategic financial preservation plan established to efficiently increase the user's assets.

[0604] An "investment plan" refers to the investment strategy and specific action plan necessary to increase the user's assets.

[0605] An "emotion analysis device" refers to a system or device that evaluates a user's emotions or psychological state using a database or algorithm.

[0606] "Feedback" is a general term for advice and information that a system provides to a user, and specifically refers to evaluations and suggestions.

[0607] This invention is a system that provides personalized financial management based on the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion analysis device, and a network connecting them.

[0608] Users access the system via a terminal and register their financial account information. The terminal transmits the information received from the user to the server. The server collects the user's financial data and stores it in an integrated information storage device. The data is retrieved using financial APIs and secure communication protocols. The server analyzes the collected data and recognizes spending trends using libraries such as Python's pandas and scikit-learn.

[0609] The emotion analysis device evaluates the user's emotional state based on their usage patterns and input data. This utilizes natural language processing and image recognition technologies. Based on the analysis results, the server generates feedback that reflects the user's emotional state.

[0610] For example, if a user is experiencing stress, the system detects this through an emotion analyzer and prioritizes recommending low-risk savings plans. On the other hand, if the system determines that the user is feeling secure, it can suggest higher-risk but potentially higher-return investment options. In this way, personalized recommendations are developed for each user.

[0611] An example of a prompt might be: "Please suggest a safe and low-risk savings plan to plan for college tuition." In response to such a prompt, the system will suggest the best plan for the user.

[0612] This invention enables financial management that takes user emotions into account, reducing mental stress while supporting efficient asset building.

[0613] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0614] Step 1:

[0615] The user enters basic information such as their name and email address using an application on their device and links their financial account information. The entered information is securely encrypted by the device and sent to the server. The output is encrypted user information data.

[0616] Step 2:

[0617] The server decrypts the encrypted user information data received from the terminal and stores it in a database. Next, it periodically collects transaction data from each user's financial account using a financial API. The output of this step is an integrated set of user transaction data.

[0618] Step 3:

[0619] The server analyzes integrated transaction data using machine learning algorithms. Here, the data is preprocessed using the Python pandas library, and clustering and classification are performed using scikit-learn. The input is an integrated set of transaction data, and the output is an analysis showing spending trends and characteristics.

[0620] Step 4:

[0621] The server uses an emotion analysis device to analyze behavioral data and input data transmitted from the user's terminal. This analysis employs natural language processing and image recognition technologies. The input is the user's behavioral data, and the output is a result indicating the user's emotional state.

[0622] Step 5:

[0623] The server generates savings and investment plans best suited to the user based on analyzed spending trends and emotional states. A generative AI model may be used for this generation. The output is a customized financial plan proposal.

[0624] Step 6:

[0625] The server sends the generated financial plan to the terminal, which the user can view and review. The terminal displays risks and returns visually, allowing the user to obtain detailed information. The output is the visualized plan information presented to the end user.

[0626] Step 7:

[0627] Users can use their devices to ask questions to the AI ​​chatbot about the presented plans. The server receives the user's questions, uses a generative AI model to generate appropriate answers, and sends them back to the device. The output is an automatically generated answer to the user's question.

[0628] Step 8:

[0629] The server uses data collected from all users to generate community-based advice, which is then displayed on each user's individual screen. The input is anonymized data from other users, and the output is advice based on collective insights. This process allows users to gain insights based on the experiences of others.

[0630] (Application Example 2)

[0631] 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."

[0632] In today's financial system, users are expected to effectively manage their financial situation and plan for the future. However, the complexity of diverse financial information and the emotionally driven decision-making process make it difficult to select appropriate investment and savings strategies. Therefore, there is a need for systems that provide more personalized financial advice while taking into account the user's emotional state.

[0633] 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.

[0634] In this invention, the server includes means for collecting multiple financial information and storing it on an integrated recording medium; means for analyzing the collected information and recognizing spending patterns; means for proposing optimal savings and investment plans to the user based on the analysis results; means for simulating the risks and returns of potential investments; and means for estimating the user's emotional state and adjusting savings and spending suggestions based on those emotions. This enables users to engage in more rational and personalized financial activities without being influenced by their emotions.

[0635] "Financial information" refers to all data related to a user's financial activities, including various transaction information related to bank accounts, credit cards, investment accounts, etc.

[0636] "Recording medium" refers to a device or system for storing and managing information, and includes database systems and cloud storage.

[0637] "Spending patterns" refer to patterns in how users spend their money, and include factors such as the frequency, amount, and category of purchases.

[0638] A "savings plan" is a guideline for systematically saving money for the future, and it includes regular savings amounts and financial goals.

[0639] An "investment plan" refers to a specific policy or strategy for increasing assets through investment, and this includes the selection of investment targets and the timing of investments.

[0640] "Risk" refers to an indicator that shows the uncertainty and potential for loss associated with a particular investment.

[0641] "Yield" refers to the return earned from an investment, and is expressed as a ratio to the amount invested.

[0642] "Intelligent conversation software" is a program that uses artificial intelligence technology to analyze human language and generate natural-sounding dialogues.

[0643] "Emotional state" refers to a user's psychological or emotional condition, which is a factor that influences their behavior and reactions.

[0644] "Rational and personalized financial activities" means rational and planned asset management that is optimized based on the individual needs and circumstances of the user.

[0645] The system implementing this invention consists of a user's device, a server device, an emotion engine, and a network environment. The user's device includes smartphones, tablets, and personal computers. The server device has advanced data processing and analysis capabilities, and in particular uses machine learning frameworks such as TensorFlow to analyze financial data.

[0646] First, the user's device transmits information from multiple financial accounts to a storage medium. This information is stored on a server and analyzed to understand the user's spending patterns. Based on the analyzed data, the server presents the user with optimal savings and investment plans. The server also simulates the risks and returns of potential investments and displays the results visually on the user's device.

[0647] A key feature of this system is that it utilizes an emotion engine to estimate the user's emotional state and adjust its suggestions accordingly. For example, if a user is feeling stressed, the system can suggest a safer savings method. This assessment of emotional state is achieved through the analysis of sensor information and user behavior data.

[0648] Furthermore, the system incorporates intelligent conversational software that automatically generates answers to user questions. This process may utilize natural language processing libraries such as NLTK. This enables intuitive communication between the user and the system, aiding user understanding.

[0649] As a concrete example, imagine a situation where, before a user purchases an expensive electronic product in the afternoon, the emotion engine detects the user's excited state and suggests they reconsider the purchase. In this case, the system could display a prompt such as, "You seem a little excited right now. Perhaps you should reconsider your purchase to see if you really need it."

[0650] An example of a prompt to the generating AI model might be: "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions." This would allow the user to engage in rational financial activities that are not driven by emotions.

[0651] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0652] Step 1:

[0653] The user enters information for multiple financial accounts using a terminal. This information is transmitted to a server via the network. The server receives this information and stores it on an integrated storage medium. This ensures that the user's asset information is centrally managed.

[0654] Step 2:

[0655] The server analyzes stored financial information to recognize the user's spending patterns. Specifically, it uses machine learning algorithms to process data from transaction history, classifying which items account for the most and how much spending is concentrated in each category. This identifies the user's current spending habits.

[0656] Step 3:

[0657] The server generates optimal savings and investment plans for the user based on the analysis results. The generated plans use simulations based on profitability and risk assessments to output predicted probabilities. These plans are presented on the terminal, allowing the user to understand specific savings and investment directions.

[0658] Step 4:

[0659] The user's device collects their emotional state via an emotion engine. The collected data is sent to a server, which estimates the user's emotional state based on their behavior and input data. Based on this information, the server rewrites investment and savings suggestions to better suit the user's emotions.

[0660] Step 5:

[0661] The server uses intelligent conversational software to answer user questions. Specifically, the server analyzes the question content through natural language processing and performs data calculations to generate appropriate feedback. By responding to the user's terminal in a human-like manner, it resolves the user's questions.

[0662] Step 6:

[0663] Ultimately, the generative AI model is used to generate prompts that provide expert advice, including overall system improvements. This enables users to make better financial decisions. For example, a prompt might read, "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions."

[0664] 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.

[0665] 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.

[0666] 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.

[0667] [Fourth Embodiment]

[0668] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0669] 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.

[0670] 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).

[0671] 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.

[0672] 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.

[0673] 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).

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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".

[0681] In implementing this invention, a system is constructed to securely and efficiently collect users' financial account information from various financial institutions and manage it centrally. The system consists of user terminals, backend servers, and a network connecting them.

[0682] A user's terminal is a device used by individual consumers and serves as an interface to the system. Users first create an account and register necessary financial account information. Authentication is performed using protocols such as OAuth to ensure security. The terminal has the function of sending this information to the server.

[0683] The server is the central processing unit of the system. It receives data from terminals, stores it in a database, and then processes and analyzes it. The server uses machine learning algorithms to analyze each user's transaction data and identify spending patterns. Based on this analysis, it proposes the optimal savings and investment plan for each user. The proposal will be tailored to the user's income, spending, and risk tolerance.

[0684] The server also predicts the risk and return of potential investments using methods such as Monte Carlo simulation, and presents the results visually to the user. This information can be used by the user as a reference when making investment decisions.

[0685] Furthermore, the server is equipped with an artificial intelligence chatbot function that automatically generates answers to user questions from terminals. This provides support 24 / 7, whenever users need it. This function uses natural language processing technology to provide information and solve problems through dialogue with users.

[0686] Furthermore, the server evaluates the user's progress toward achieving their goals and provides feedback based on that evaluation. Users can receive this feedback on a dashboard via their device and use it to improve their asset management.

[0687] For example, if a user wants to improve their monthly budget management, the server analyzes the user's spending history, visualizes unnecessary spending by category, and then proposes a monthly budget that aligns with their annual savings goal. In this way, the present invention supports efficient and effective personal asset management.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The user creates an account on their device and registers their financial institution account information. The user enters basic information such as their name, email address, and password, and completes the linking of their financial account after going through a secure authentication process.

[0691] Step 2:

[0692] The device sends the user's financial account information to the server. This process uses standard authentication protocols such as OAuth to ensure the data is securely delivered to the server.

[0693] Step 3:

[0694] The server stores the received account information in the database and verifies that the data has been registered correctly. During this process, it performs data formatting standardization and duplicate checks.

[0695] Step 4:

[0696] The server periodically collects transaction data from various financial institutions using APIs. The collected data is updated in the database in real time.

[0697] Step 5:

[0698] The server uses machine learning algorithms to analyze spending patterns from transaction data. The analysis results are categorized and stored in a database as the user's spending history.

[0699] Step 6:

[0700] The server generates optimal savings and investment plans based on analyzed spending patterns and income information. These plans take into account the user's risk tolerance and constitute customized recommendations.

[0701] Step 7:

[0702] The server sends the generated plan to the terminal and displays it on a dashboard for the user to view or select. The user then makes asset management decisions based on this information.

[0703] Step 8:

[0704] When a user wants to evaluate potential investments, they send a request to the server via their device. The server then runs a Monte Carlo simulation to generate data on predicted risk and return.

[0705] Step 9:

[0706] The server sends simulation results to the terminal, providing visualized information to assist in selecting investment options. This information is presented clearly using graphs and charts.

[0707] Step 10:

[0708] When a user asks a question, the device sends the content to the server, where the AI ​​chatbot uses natural language processing technology to analyze the question and generate an appropriate answer.

[0709] Step 11:

[0710] The server sends the generated response to the terminal, providing real-time feedback to the user. The user then decides on further actions based on this response.

[0711] Step 12:

[0712] The server periodically evaluates the user's asset management status and notifies the terminal of the progress toward the goal as feedback information. The user then uses this feedback to make adjustments toward achieving the goal.

[0713] (Example 1)

[0714] 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".

[0715] In modern society, it is extremely difficult for individuals to comprehensively manage information on their multiple financial assets. Furthermore, advanced analytical capabilities are required to understand spending patterns and propose optimal savings and investment plans. Moreover, accurately predicting the risks and expected returns of potential investments and identifying unnecessary spending is a significant burden for individuals. There is a need for a system that efficiently solves these asset management problems and provides users with easily actionable plans.

[0716] 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.

[0717] In this invention, the server includes means for aggregating and storing multiple asset account information in an integrated information storage device, means for analyzing the aggregated information and identifying consumption patterns, and means for presenting the user with an optimal savings plan and investment plan based on the analysis results. This makes it possible for individual users to centrally manage their asset information and easily obtain an optimal asset management plan.

[0718] "Asset account information" refers to data related to financial institutions owned by a specific individual or legal entity, including deposit accounts, credit accounts, investment accounts, etc.

[0719] An "information storage device" refers to a recording medium used to store and manage accumulated data, and includes formats such as databases and cloud storage.

[0720] "Consumption patterns" refer to the results of an analysis of spending trends and habits in a specific individual or group, and include information such as how much money is spent in which categories.

[0721] A "savings plan" refers to a plan developed by an individual or corporation to achieve future financial goals, and includes monthly savings amounts and methods for allocating funds.

[0722] An "investment plan" refers to a plan that outlines how to manage capital to maximize returns, and includes investment allocation to specific financial assets and setting risk tolerance levels.

[0723] "Risk" refers to the uncertainty and potential volatility associated with investments in specific financial assets, including the expected range of losses and risk factors.

[0724] "Profit" refers to the expected return on investment, and includes the return on the principal investment and the rate of return.

[0725] "Artificial intelligence dialogue means" refers to technologies that use artificial intelligence to respond to user inquiries in text or voice, and includes natural language processing and machine learning models.

[0726] "Goal achievement status" refers to the current progress towards the user's personal financial goals, and includes the percentage of achievement and the estimated time to achieve them.

[0727] "Advice" refers to suggestions or recommendations that indicate the best course of action in a particular situation, and includes financial information and strategies.

[0728] In carrying out the present invention, the system is constructed using servers, terminals, and a network.

[0729] Users access the system using their individual devices and input their personal asset data. The devices encrypt the input information and send it to the server using a secure protocol (e.g., SSL / TLS communication using OAuth). The devices are equipped with interface functions to facilitate user operation.

[0730] The server is the heart of the system, receiving asset data sent from terminals and securely storing it in the database. The database provides a storage environment that enables efficient data management and analysis. The server also uses machine learning algorithms (e.g., Python's scikit-learn library) to analyze user consumption patterns.

[0731] As a concrete example, suppose a user wants to "review their spending this month and find out which categories they can save money in." In response to this request, the server analyzes past transaction data and visualizes unnecessary spending by category. Furthermore, the server generates a savings and investment plan tailored to the user's income and risk tolerance, and sends the results to the device.

[0732] The server applies Monte Carlo simulations to predict the risks and returns of investment plans and presents the results visually. This allows users to obtain useful information for making investment decisions.

[0733] Furthermore, the server utilizes a generative AI model and an artificial intelligence chatbot function to automatically generate answers to user questions. This enables 24 / 7 support. An example of a prompt is, "Analyze the user's spending trends based on their transaction data from the past three months and suggest easy ways to save money."

[0734] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0735] Step 1:

[0736] Users log in to the system via a terminal and enter their asset information. This input data includes bank account numbers, credit card information, and other financial account details. The terminal encrypts this information and transmits it to the server using a secure protocol. The input is the user's financial information, and the output is encrypted data.

[0737] Step 2:

[0738] The server receives financial data sent from the terminal and stores it in a database. For database storage, the information is first parsed and organized into appropriate fields. The input is encrypted data, and the output is structured data stored in the database.

[0739] Step 3:

[0740] The server uses machine learning algorithms to analyze user transaction data stored in the database. It uses Python libraries (e.g., scikit-learn) to identify consumption patterns through cluster analysis. The input is structured transaction data, and the output is the analysis of consumption patterns.

[0741] Step 4:

[0742] Based on the analysis of consumption patterns, the server generates optimal savings and investment plans that take into account the user's income, expenses, and risk tolerance. This plan generation also includes trend predictions from historical data. The input is the analysis of consumption patterns, and the output is the optimized savings and investment plans.

[0743] Step 5:

[0744] The server applies Monte Carlo simulations to evaluate the risk and return of the generated investment plans. It performs numerous simulations using Python modules and generates evaluation charts based on these results. The input is the generated investment plan, and the output is visual data of the risk and return evaluation results.

[0745] Step 6:

[0746] The server utilizes a generative AI model to generate answers based on questions submitted by the user from the terminal. Here, natural language processing techniques are used to generate the optimal answer in response to the user's prompt. The input is the user's question, and the output is the AI-generated answer.

[0747] Step 7:

[0748] The server evaluates the user's progress toward their goals and generates feedback. It compares and analyzes the user's set goals with current data to calculate a quantified degree of achievement. The inputs are current asset information and goal data, and the output is the evaluated degree of achievement and feedback.

[0749] (Application Example 1)

[0750] 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".

[0751] In modern society, there is a need for consumers to manage their assets effectively based on a centralized system of financial information. However, there is no system that can grasp spending trends in the history of electronic transactions used daily by users and propose appropriate savings and asset management plans based on that information. Furthermore, there is a lack of support that is linked to intelligent dialogue devices that can answer user questions immediately and effectively, making the realization of efficient and effective personal asset management a challenge.

[0752] 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.

[0753] In this invention, the server includes means for collecting and integrating multiple financial information, means for analyzing the collected information and recognizing financial behavior patterns, and intelligent dialogue device means for automatically generating responses to information requests from users. This makes it possible to visualize financial behavior based on the user's daily electronic transaction history and propose specific savings and asset management plans.

[0754] "Financial information" refers to all information related to financial activities, and specifically includes bank account information, credit card history, and data related to asset management.

[0755] An "information management device" is a device that centrally stores and manages collected financial information, and retrieves and updates data as needed.

[0756] "Financial behavior patterns" are behavioral patterns identified based on the results of an analysis of the user's income and expenditure trends and characteristics.

[0757] An "intelligent dialogue device" is an automated response device that uses natural language processing technology to respond to inquiries and requests from users.

[0758] "Spending trends" refer to the trends obtained by visualizing and analyzing users' financial behavior in their daily transactions and consumption, categorized by type.

[0759] A "savings plan and asset management plan" proposes specific guidelines and strategies for effectively managing assets and setting optimal savings goals, based on the user's financial situation.

[0760] "Electronic transactions" refer to all financial transactions conducted via the internet, including online payments and asset transfers.

[0761] To implement this invention, the user first accesses the system using their terminal and registers multiple pieces of financial information. The terminal authenticates the user using the OAuth protocol and processes the data securely. After authentication, the financial information collected from the terminal is transmitted to an integrated information management device and stored in a database.

[0762] The server uses machine learning algorithms implemented in languages ​​such as Python and R to analyze the financial information in this database. These algorithms model the user's financial behavior and understand their spending trends. The analysis results are presented to the user visually and displayed on their device through an interface built with React Native.

[0763] The server also functions as an intelligent dialogue device. This device uses natural language processing technology based on Google Cloud's Dialogflow to automatically generate responses to user inquiries. When a user enters prompts such as "Tell me about my savings plan" or "Tell me about my spending trends this month," the server can understand the content and return detailed feedback and advice to the terminal.

[0764] As a concrete example, if a user wants to review their spending and make their leisure activities more efficient, they can access the system on their device and input "I want to know the details of my entertainment expenses." In this case, the server analyzes spending trends in the entertainment category and suggests specific saving methods and new savings plans to the user. In this way, this invention helps users accurately understand their personal financial situation and appropriately create future financial plans.

[0765] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0766] Step 1:

[0767] The user logs into the system using a terminal and registers their financial information (e.g., bank account information and credit card history). The terminal uses the OAuth protocol to authenticate the user for security purposes. The input consists of the user's identification information and financial account information, which is then sent to the server.

[0768] Step 2:

[0769] The server integrates the received financial information and stores it in a database. The input is financial information from the terminal, and the output is an entry into the database in an integrated format. The data is stored in an information management device and used for other analysis processes.

[0770] Step 3:

[0771] The server executes machine learning algorithms to analyze financial information in the database. The input is stored financial information, and the system processes and models the data to identify trends in financial behavior. The output is the user's financial behavior patterns.

[0772] Step 4:

[0773] The user enters a prompt message through the terminal, such as "Tell me about this month's spending trends." The terminal sends this prompt message to the server. The input is a prompt message generated by the user.

[0774] Step 5:

[0775] The server uses Dialogflow to parse the prompt text and perform the necessary data calculations. It then generates and responds with information tailored to the user's request. The input consists of the user's prompt text and the resulting financial behavior pattern, while the output is text containing feedback and advice for the user.

[0776] Step 6:

[0777] The server sends the generated feedback to the user's device through an interface built with React Native, displaying it visually. Based on this output, the user can create concrete asset management and savings plans. The input is feedback information from the server, and the output is visualized information on the user's device.

[0778] 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.

[0779] This invention provides a system that incorporates an emotion engine into a financial account management system to provide personalized feedback tailored to the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion engine, and a network.

[0780] When a user accesses the system using a device, they first create an account and register their financial account information. The user enters basic information such as their name and links their financial account after going through an authentication process. The device then sends this information to the server.

[0781] The server stores financial account information received from users in a database and periodically collects transaction data. Using this data, the server analyzes the user's spending patterns using machine learning and generates optimal savings and investment plans. These plans are then presented to the user on their device in an individually customized format.

[0782] The system is equipped with an emotion engine that analyzes the user's emotional state based on user behavior, input data, and sensor information. The emotion engine uses this information to adjust feedback and suggestions according to the user's current emotions.

[0783] For example, if a user is feeling stressed, the emotion engine will detect this and prioritize suggesting lower-risk investment plans. Conversely, if the system determines that the user is feeling very secure, it may present higher-risk but potentially higher-return investment options. In this way, the emotion engine, based on the user's emotions, works in conjunction with other system functions to support decision-making.

[0784] Furthermore, the system uses income and expense data collected from multiple users to generate community-based advice, and the server's AI chatbot explains any points of confusion users may have. This deepens users' understanding and enables better financial management.

[0785] Through these functions, the present invention enables asset management that takes user emotions into account, reducing user mental stress while supporting efficient asset building.

[0786] The following describes the processing flow.

[0787] Step 1:

[0788] The user accesses the device and enters information to create an account. This includes their name, email address, and password. The user then completes an authentication process to link their financial institution account.

[0789] Step 2:

[0790] The terminal securely transmits information provided by the user to the server and requests that login information and financial account link information be stored in the database.

[0791] Step 3:

[0792] The server stores user information in a database and collects user financial transaction data from various financial institutions at specified intervals using an API. This data includes transaction details and balance information.

[0793] Step 4:

[0794] The server applies machine learning algorithms to analyze the collected transaction data. Here, it identifies user spending patterns and categorizes them.

[0795] Step 5:

[0796] Based on the analysis results, the server generates savings and investment plans tailored to the user's income, expenses, and risk tolerance, and sends them to the user's device. The user can then make decisions based on these plans.

[0797] Step 6:

[0798] When a user inputs emotion-related information (e.g., text messages or sensor data) via their device, the device sends this information to the server's emotion engine.

[0799] Step 7:

[0800] The emotion engine installed on the server analyzes the received information and diagnoses the user's emotional state. This analysis may utilize natural language processing and speech / image recognition technologies.

[0801] Step 8:

[0802] The server takes the diagnosed emotional state into account and adjusts the existing plan or generates an alternative suggestion. For example, if stress is detected, it will change to a lower-risk suggestion.

[0803] Step 9:

[0804] The server sends emotionally-adjusted plans and advice to the user's device, allowing the user to choose how to manage their assets based on these. This enables the user to make rational decisions that align with their emotional state.

[0805] Step 10:

[0806] If a user has questions or concerns about the system, they can send those questions from their device to the server's AI chatbot function.

[0807] Step 11:

[0808] The server's AI chatbot analyzes questions using natural language processing and automatically generates appropriate answers. These answers are then sent back to the terminal and presented to the user.

[0809] Step 12:

[0810] Users can also refer to community-based advice and success stories from other users, and the server collects and provides this information as needed. This makes it possible to receive advice from diverse perspectives.

[0811] (Example 2)

[0812] 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".

[0813] Traditional financial management systems simply offer data-driven suggestions without considering the user's emotions or psychological state. This can lead to investment and savings choices that don't align with the user's feelings, hindering efficient financial management and stress reduction. Furthermore, few systems can integrate and manage multiple financial information streams, forcing users to manage each piece of information individually.

[0814] 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.

[0815] In this invention, the server includes means for collecting multiple financial account information and storing it in an integrated information recording device; means for analyzing the collected data and recognizing spending trends; means for proposing optimal savings and investment plans to the user based on the analysis results; means for evaluating the user's emotional state using an emotion analysis device; and means for adjusting the suggested feedback based on the evaluated emotional state. This enables personalized feedback based on the user's emotional state, resulting in more efficient and less stressful financial management.

[0816] "Financial account information" refers to data on a user's assets and liabilities managed by a financial institution.

[0817] An "information recording device" refers to a device or system that can securely store data in digital format and retrieve it as needed.

[0818] "Spending trends" refer to analytical results that show patterns and behaviors of how users spend their money.

[0819] A "savings plan" refers to a strategic financial preservation plan established to efficiently increase the user's assets.

[0820] An "investment plan" refers to the investment strategy and specific action plan necessary to increase the user's assets.

[0821] An "emotion analysis device" refers to a system or device that evaluates a user's emotions or psychological state using a database or algorithm.

[0822] "Feedback" is a general term for advice and information that a system provides to a user, and specifically refers to evaluations and suggestions.

[0823] This invention is a system that provides personalized financial management based on the user's emotional state. The system consists of the user's terminal, a server operating in the backend, an emotion analysis device, and a network connecting them.

[0824] Users access the system via a terminal and register their financial account information. The terminal transmits the information received from the user to the server. The server collects the user's financial data and stores it in an integrated information storage device. The data is retrieved using financial APIs and secure communication protocols. The server analyzes the collected data and recognizes spending trends using libraries such as Python's pandas and scikit-learn.

[0825] The emotion analysis device evaluates the user's emotional state based on their usage patterns and input data. This utilizes natural language processing and image recognition technologies. Based on the analysis results, the server generates feedback that reflects the user's emotional state.

[0826] For example, if a user is experiencing stress, the system detects this through an emotion analyzer and prioritizes recommending low-risk savings plans. On the other hand, if the system determines that the user is feeling secure, it can suggest higher-risk but potentially higher-return investment options. In this way, personalized recommendations are developed for each user.

[0827] An example of a prompt might be: "Please suggest a safe and low-risk savings plan to plan for college tuition." In response to such a prompt, the system will suggest the best plan for the user.

[0828] This invention enables financial management that takes user emotions into account, reducing mental stress while supporting efficient asset building.

[0829] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0830] Step 1:

[0831] The user enters basic information such as their name and email address using an application on their device and links their financial account information. The entered information is securely encrypted by the device and sent to the server. The output is encrypted user information data.

[0832] Step 2:

[0833] The server decrypts the encrypted user information data received from the terminal and stores it in a database. Next, it periodically collects transaction data from each user's financial account using a financial API. The output of this step is an integrated set of user transaction data.

[0834] Step 3:

[0835] The server analyzes integrated transaction data using machine learning algorithms. Here, the data is preprocessed using the Python pandas library, and clustering and classification are performed using scikit-learn. The input is an integrated set of transaction data, and the output is an analysis showing spending trends and characteristics.

[0836] Step 4:

[0837] The server uses an emotion analysis device to analyze behavioral data and input data transmitted from the user's terminal. This analysis employs natural language processing and image recognition technologies. The input is the user's behavioral data, and the output is a result indicating the user's emotional state.

[0838] Step 5:

[0839] The server generates savings and investment plans best suited to the user based on analyzed spending trends and emotional states. A generative AI model may be used for this generation. The output is a customized financial plan proposal.

[0840] Step 6:

[0841] The server sends the generated financial plan to the terminal, which the user can view and review. The terminal displays risks and returns visually, allowing the user to obtain detailed information. The output is the visualized plan information presented to the end user.

[0842] Step 7:

[0843] Users can use their devices to ask questions to the AI ​​chatbot about the presented plans. The server receives the user's questions, uses a generative AI model to generate appropriate answers, and sends them back to the device. The output is an automatically generated answer to the user's question.

[0844] Step 8:

[0845] The server uses data collected from all users to generate community-based advice, which is then displayed on each user's individual screen. The input is anonymized data from other users, and the output is advice based on collective insights. This process allows users to gain insights based on the experiences of others.

[0846] (Application Example 2)

[0847] 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".

[0848] In today's financial system, users are expected to effectively manage their financial situation and plan for the future. However, the complexity of diverse financial information and the emotionally driven decision-making process make it difficult to select appropriate investment and savings strategies. Therefore, there is a need for systems that provide more personalized financial advice while taking into account the user's emotional state.

[0849] 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.

[0850] In this invention, the server includes means for collecting multiple financial information and storing it on an integrated recording medium; means for analyzing the collected information and recognizing spending patterns; means for proposing optimal savings and investment plans to the user based on the analysis results; means for simulating the risks and returns of potential investments; and means for estimating the user's emotional state and adjusting savings and spending suggestions based on those emotions. This enables users to engage in more rational and personalized financial activities without being influenced by their emotions.

[0851] "Financial information" refers to all data related to a user's financial activities, including various transaction information related to bank accounts, credit cards, investment accounts, etc.

[0852] "Recording medium" refers to a device or system for storing and managing information, and includes database systems and cloud storage.

[0853] "Spending patterns" refer to patterns in how users spend their money, and include factors such as the frequency, amount, and category of purchases.

[0854] A "savings plan" is a guideline for systematically saving money for the future, and it includes regular savings amounts and financial goals.

[0855] An "investment plan" refers to a specific policy or strategy for increasing assets through investment, and this includes the selection of investment targets and the timing of investments.

[0856] "Risk" refers to an indicator that shows the uncertainty and potential for loss associated with a particular investment.

[0857] "Yield" refers to the return earned from an investment, and is expressed as a ratio to the amount invested.

[0858] "Intelligent conversation software" is a program that uses artificial intelligence technology to analyze human language and generate natural-sounding dialogues.

[0859] "Emotional state" refers to a user's psychological or emotional condition, which is a factor that influences their behavior and reactions.

[0860] "Rational and personalized financial activities" means rational and planned asset management that is optimized based on the individual needs and circumstances of the user.

[0861] The system implementing this invention consists of a user's device, a server device, an emotion engine, and a network environment. The user's device includes smartphones, tablets, and personal computers. The server device has advanced data processing and analysis capabilities, and in particular uses machine learning frameworks such as TensorFlow to analyze financial data.

[0862] First, the user's device transmits information from multiple financial accounts to a storage medium. This information is stored on a server and analyzed to understand the user's spending patterns. Based on the analyzed data, the server presents the user with optimal savings and investment plans. The server also simulates the risks and returns of potential investments and displays the results visually on the user's device.

[0863] A key feature of this system is that it utilizes an emotion engine to estimate the user's emotional state and adjust its suggestions accordingly. For example, if a user is feeling stressed, the system can suggest a safer savings method. This assessment of emotional state is achieved through the analysis of sensor information and user behavior data.

[0864] Furthermore, the system incorporates intelligent conversational software that automatically generates answers to user questions. This process may utilize natural language processing libraries such as NLTK. This enables intuitive communication between the user and the system, aiding user understanding.

[0865] As a concrete example, imagine a situation where, before a user purchases an expensive electronic product in the afternoon, the emotion engine detects the user's excited state and suggests they reconsider the purchase. In this case, the system could display a prompt such as, "You seem a little excited right now. Perhaps you should reconsider your purchase to see if you really need it."

[0866] An example of a prompt to the generating AI model might be: "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions." This would allow the user to engage in rational financial activities that are not driven by emotions.

[0867] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0868] Step 1:

[0869] The user enters information for multiple financial accounts using a terminal. This information is transmitted to a server via the network. The server receives this information and stores it on an integrated storage medium. This ensures that the user's asset information is centrally managed.

[0870] Step 2:

[0871] The server analyzes stored financial information to recognize the user's spending patterns. Specifically, it uses machine learning algorithms to process data from transaction history, classifying which items account for the most and how much spending is concentrated in each category. This identifies the user's current spending habits.

[0872] Step 3:

[0873] The server generates optimal savings and investment plans for the user based on the analysis results. The generated plans use simulations based on profitability and risk assessments to output predicted probabilities. These plans are presented on the terminal, allowing the user to understand specific savings and investment directions.

[0874] Step 4:

[0875] The user's device collects their emotional state via an emotion engine. The collected data is sent to a server, which estimates the user's emotional state based on their behavior and input data. Based on this information, the server rewrites investment and savings suggestions to better suit the user's emotions.

[0876] Step 5:

[0877] The server uses intelligent conversational software to answer user questions. Specifically, the server analyzes the question content through natural language processing and performs data calculations to generate appropriate feedback. By responding to the user's terminal in a human-like manner, it resolves the user's questions.

[0878] Step 6:

[0879] Ultimately, the generative AI model is used to generate prompts that provide expert advice, including overall system improvements. This enables users to make better financial decisions. For example, a prompt might read, "Consider the user's transaction history and sentiment data to generate appropriate savings advice and sentiment-based spending suggestions."

[0880] 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.

[0881] 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.

[0882] 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 robot 414.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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."

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] The following is further disclosed regarding the embodiments described above.

[0902] (Claim 1)

[0903] A means of collecting information from multiple financial accounts and storing it in an integrated database,

[0904] A means of analyzing collected data and recognizing spending patterns,

[0905] A means of proposing the optimal savings and investment plan to the user based on the analysis results,

[0906] A means of simulating the risks and returns of potential investments,

[0907] An artificial intelligence chatbot that automatically generates answers to user questions,

[0908] A means of evaluating the user's progress toward achieving their goals and providing feedback,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1 for visually displaying the risks and returns of a particular investment plan.

[0912] (Claim 3)

[0913] The system according to claim 1, which provides community-based advice based on the user's financial information.

[0914] "Example 1"

[0915] (Claim 1)

[0916] A means for collecting and storing information from multiple asset accounts in an integrated information storage device,

[0917] A means for analyzing accumulated information and identifying consumption patterns,

[0918] A means of presenting the user with the most suitable savings and investment plan based on the analysis results,

[0919] Means for predicting the risks and benefits of potential investments,

[0920] An artificial intelligence dialogue system that automatically generates answers to user inquiries,

[0921] A means of evaluating the user's progress toward achieving their goals and providing advice,

[0922] A system that includes a means to visualize unnecessary spending by category.

[0923] (Claim 2)

[0924] The system according to claim 1, which visually represents the risks and benefits of a particular investment plan.

[0925] (Claim 3)

[0926] The system according to claim 1, which provides group-based advice based on the income and expenditure information of users.

[0927] "Application Example 1"

[0928] (Claim 1)

[0929] A means for collecting multiple financial information and storing it in an integrated information management device,

[0930] A means of analyzing collected information and recognizing financial behavior patterns,

[0931] A means of proposing the optimal savings plan and asset management plan to the user based on the analysis results,

[0932] Means for predicting the risks and benefits of potential assets,

[0933] An intelligent dialogue device means that automatically generates a response to an information request from a user,

[0934] A means of evaluating the user's goal achievement and providing feedback,

[0935] A means of analyzing the history of electronic transactions that users use on a daily basis and visually displaying spending trends for each type,

[0936] A means of proposing savings and asset management plans suitable for the user and providing continuous user support using an automated dialogue response system,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, which visually displays the risks and benefits of a specific asset management plan and suggests savings plans suitable for the user.

[0940] (Claim 3)

[0941] The system according to claim 1, which provides knowledge-sharing advice based on the user's financial information.

[0942] "Example 2 of combining an emotion engine"

[0943] (Claim 1)

[0944] A means for collecting information from multiple financial accounts and storing it in an integrated information recording device,

[0945] A means of analyzing collected data and recognizing spending trends,

[0946] A means of proposing the optimal savings and investment plan for the user based on the analysis results,

[0947] A means of evaluating the emotional state of a user using an emotion analysis device,

[0948] A means of adjusting the feedback suggested based on the assessed emotional state,

[0949] An artificial intelligence dialogue program that automatically generates answers to questions from users,

[0950] A means of evaluating the user's achievement of goals and providing feedback,

[0951] A system that includes this.

[0952] (Claim 2)

[0953] The system according to claim 1 for visually displaying the risks and returns of a particular investment plan.

[0954] (Claim 3)

[0955] The system according to claim 1, which provides community-based advice based on users' income and expenditure information.

[0956] "Application example 2 when combining with an emotional engine"

[0957] (Claim 1)

[0958] A means of collecting multiple financial information and storing it on an integrated recording medium,

[0959] A means of analyzing the collected information and recognizing spending patterns,

[0960] A means of proposing the optimal savings and investment plan for the user based on the analysis results,

[0961] A means of simulating the risks and returns of potential investments,

[0962] An intelligent conversational software means that automatically generates answers to questions from the user,

[0963] A means of evaluating the user's achievement of goals and providing feedback,

[0964] A means for estimating the user's emotional state and adjusting savings and spending suggestions based on those emotions,

[0965] A system that includes this.

[0966] (Claim 2)

[0967] The system according to claim 1 for visually displaying the risks and returns of a particular investment plan.

[0968] (Claim 3)

[0969] The system according to claim 1, which provides aggregate-based advice based on the user's income and expenditure information. [Explanation of symbols]

[0970] 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 collecting information from multiple financial accounts and storing it in an integrated database, A means of analyzing collected data and recognizing spending patterns, A means of proposing the optimal savings and investment plan to the user based on the analysis results, A means of simulating the risks and returns of potential investments, An artificial intelligence chatbot that automatically generates answers to user questions, A means of evaluating the user's progress toward achieving their goals and providing feedback, A system that includes this.

2. The system according to claim 1, which visually displays the risks and returns of a specific investment plan.

3. The system according to claim 1, which provides community-based advice based on the user's financial information.

Images