system

Abstract

We provide the system. [Solution] A means of receiving financial data collected from users, A means of analyzing collected financial data and generating an optimal life plan tailored to the user's attributes and behavior, A means of providing the generated life plan to the user, A means of monitoring users' daily spending and providing real-time feedback based on that spending information, A system that includes this.

Description

Technical Field

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[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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] <00​​​​​​​This invention is a system that generates and provides users with an optimal life plan based on their individual attributes and behaviors by analyzing financial data collected from them. This system monitors the user's daily spending and provides real-time feedback based on that data. Furthermore, it supports the achievement of financial goals by automatically proposing financial plans, asset management, and investment portfolios that take into account the user's long-term life events.

[0006] "Financial data" refers to all information regarding a user's income, expenses, and assets.

[0007] A "life plan" is a plan designed to support a user's current and future financial stability, and is created based on the user's financial situation.

[0008] "Feedback" refers to advice and suggestions provided in response to a user's actions and circumstances, and is intended to support their economic decision-making.

[0009] "Life events" refer to important events in a user's life, such as marriage, children's education, buying a house, and retirement—events that are planned and expected in the future.

[0010] A "financial plan" is a plan that outlines the steps a user needs to take to achieve their long-term financial goals, taking into account the balance between income, expenses, savings, and investments.

[0011] "Asset management" refers to activities aimed at efficiently managing a user's assets and obtaining the maximum possible return.

[0012] An "investment portfolio" refers to a collection of investment products owned by a user, structured to optimize risk and return. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the 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, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention constructs a system that effectively utilizes users' financial information to support the achievement of their financial goals. This system is activated when a user inputs financial data from an individual terminal. The terminal securely transmits this data to a server. The server analyzes the received data and uses machine learning algorithms to generate an optimal life plan based on the user's income, expenses, and financial attributes.

[0035] Specifically, the server analyzes the user's past spending patterns and provides feedback to the user via the terminal to help reduce wasteful spending. For example, if the user inputs their monthly income and expenses and it is determined that they have many unnecessary expenses, the server will make specific suggestions on how to reduce those expenses.

[0036] Furthermore, the server takes into account future life events set by the user, evaluates their economic impact, and automatically generates an appropriate financial plan. This plan is presented to the user via the terminal, and the user can then decide on specific actions based on it.

[0037] Furthermore, the server constantly retrieves the user's latest income and expense data and uses that information to propose asset management and investment portfolios. This allows users to flexibly respond to fluctuating market conditions while maximizing their returns.

[0038] Thus, the system of the present invention helps users make wiser financial decisions by combining real-time data analysis with personalized life planning.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user enters their financial data (income, expenses, asset information, and future life events) into the terminal. The terminal encodes the entered data and sends it to the server via a secure protocol.

[0042] Step 2:

[0043] The server stores the financial data received from the terminal into a database. During this process, it checks for inconsistencies and missing data and performs data cleansing to prepare the input data for analysis.

[0044] Step 3:

[0045] The server inputs the organized data into a machine learning algorithm to analyze the user's past income and expenditure patterns and spending trends. Based on this analysis, it understands the user's lifestyle and consumption patterns.

[0046] Step 4:

[0047] The server generates an optimal life plan for the user. This plan optimizes income and expenses based on past data analysis results and combines this with a long-term economic plan that addresses future life events.

[0048] Step 5:

[0049] The server generates savings suggestions and investment advice in real time, along with the generated life plan. This includes asset management strategies and investment portfolio suggestions that take risk diversification into account.

[0050] Step 6:

[0051] The server sends this life plan and related suggestions to the terminal. The terminal presents it to the user in an appropriate interface, providing a foundation for the user to review the plan and decide on actions.

[0052] Step 7:

[0053] Once a user begins using the service, the device continuously records their spending. The device keeps the user's information up-to-date by sending new spending data to the server.

[0054] Step 8:

[0055] The server reflects the user's latest spending data and adjusts life plans and advice as needed. This ensures that the user always has a foundation for making optimal financial decisions.

[0056] (Example 1)

[0057] 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."

[0058] In today's economic environment, it is a challenging task for individuals to accurately understand their own financial situation and effectively achieve their long-term economic goals. In particular, balancing income and expenses, and developing asset management and investment strategies to respond to fluctuating market conditions, is a significant burden for many individuals. There is a need for systems that address this challenge and support users in making wise financial decisions.

[0059] 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.

[0060] In this invention, the server includes means for receiving economic information collected from the user, means for analyzing the collected economic information and generating an optimal life plan tailored to the user's individual attributes and behavior, and means for providing the generated life plan to the user and presenting it in an intuitively understandable format. This enables the user to grasp their own economic situation in real time and to plan and execute a plan that meets their individual needs.

[0061] A "user" is an entity that uses the system to register, manage, and receive personal financial information.

[0062] "Economic information" refers to data on income, expenses, savings, investments, and assets, and is important information for representing an individual's financial situation.

[0063] A "server" refers to a computer system that receives information from users, analyzes it, and generates and provides the results.

[0064] A "life plan" refers to a specific action plan regarding income, expenses, savings, and investments, created to support an individual in achieving their financial goals.

[0065] "Individual attributes" refer to information that represents the unique characteristics of each individual user, such as their economic situation, goals, and behavioral patterns.

[0066] An "intuitively understandable format" refers to a method of presentation where generated information or plans are visually displayed, allowing the content to be grasped at a glance.

[0067] "Asset management" refers to management methods for optimally utilizing the assets owned by a user.

[0068] An "investment strategy" refers to a plan for determining asset allocation and investment policies, taking into account risk and return.

[0069] This invention is a system that effectively manages personal financial information and provides support for achieving goals. This system consists of three main components: the user, the terminal, and the server.

[0070] Users input financial information such as income, expenses, and savings via their devices. These devices refer to general information processing equipment such as smartphones and computers. The devices transmit the input information to the server using secure communication protocols.

[0071] The server uses a Python-based machine learning library (e.g., Scikit-learn) to analyze the received economic information. This analysis includes analyzing the user's past spending patterns and attribute data. Based on this, an optimal lifestyle plan is generated that is tailored to the user's behavior. This lifestyle plan is intuitively represented using graphs and charts and fed back to the user's device.

[0072] For example, if the system determines that entertainment expenses need to be reduced based on the user's monthly spending data, it will provide specific suggestions on how to do so. These suggestions might take the form of, "Let's think of ways to reduce next month's leisure budget by 20%."

[0073] Furthermore, the server automatically generates financial plans for long-term life events set by the user, such as purchasing a home or preparing for education expenses. Each time the user's income and expenditure data is updated, the server uses the new data to analyze and adjust its asset management and investment strategy suggestions. This process enables real-time data updates, allowing for flexible adaptation to fluctuating market conditions.

[0074] An example of a prompt message might be a request such as, "Based on the user's income and spending patterns, please provide suggestions for eliminating waste."

[0075] This system supports users' financial decision-making and promotes better goal achievement by providing real-time data analysis and personalized life planning.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users input their financial information using their devices. Specifically, they enter their monthly income, expenses, and savings goals. The input data is converted into a server-friendly format, such as JSON or CSV. The converted data is then sent to the server using a secure protocol.

[0079] Step 2:

[0080] The server verifies the integrity of the input data based on the economic information received from the terminal. This involves checking the data format and detecting outliers. During this process, data cleansing is performed, and any errors or incomplete items are automatically corrected or omitted. This ensures that clean data is obtained that does not affect subsequent data analysis.

[0081] Step 3:

[0082] The server analyzes the data, ensuring its integrity. Specifically, it uses machine learning models (e.g., models using Scikit-learn) to analyze user spending patterns and income fluctuations. The input is the user's historical economic data, and the output includes trend analysis based on user attributes and areas where spending can be reduced.

[0083] Step 4:

[0084] The server generates an optimal lifestyle plan based on the analysis results. Utilizing a generation AI model, it formulates a plan that takes into account the user's specific attributes and long-term goals. The output includes a detailed lifestyle plan and suggestions for saving money, including visual representations such as graphs and charts.

[0085] Step 5:

[0086] The server sends the generated life plan to the terminal. The terminal displays the received information in a way that the user can intuitively understand. This display often uses an interactive graphical user interface. Based on this information, the user evaluates their financial behavior and adjusts the life plan as needed.

[0087] Step 6:

[0088] The server regularly receives user feedback and new economic information, and reanalyzes the model. This allows it to propose adaptive plans and investment strategies to users that are in line with the latest market trends and economic conditions. The generative AI model continues to learn from the feedback, improving its accuracy and usefulness.

[0089] (Application Example 1)

[0090] 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."

[0091] In recent years, individual consumers have engaged in a variety of financial transactions, which has led to a demand for efficient asset management and optimized consumption. However, it is not easy for consumers to grasp the details of their own spending and investments and design an accurate life plan. In particular, providing users with plans that aim for a prosperous future while curbing wasteful spending is a challenging task.

[0092] 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.

[0093] In this invention, the server includes means for receiving financial data collected from the user, means for analyzing the collected financial data and generating an optimal lifestyle plan tailored to the user's group and activities, and means for providing the user with savings suggestions to curb wasteful spending based on payment information. This enables the user to optimize their spending and manage their assets effectively.

[0094] A "user" refers to an individual who provides financial information and receives support regarding life planning and asset management.

[0095] "Financial data" refers to the collection of information about income, expenses, assets, and investments provided by users.

[0096] A "server" refers to a central data processing unit that receives users' financial data, analyzes it, and makes recommendations.

[0097] A "life plan" refers to information that outlines efficient consumption and asset management strategies, generated based on the user's income and expenses and future goals.

[0098] "Payment information" refers to a portion of financial data, including details about purchases and transactions made by a user.

[0099] "Savings suggestions to curb wasteful spending" refers to specific advice and measures provided to reduce unnecessary expenses by analyzing the user's spending habits.

[0100] To realize this invention, a system is needed that collects financial data from a user's device and transmits it to a server. The device should have an easy-to-use interface for the user and allow them to input financial information such as income, expenses, and asset status.

[0101] The server receives financial data sent by users and utilizes machine learning algorithms as a means of data analysis. These algorithms can use data analysis programming languages ​​such as Python and R to analyze users' past spending patterns and attribute data, and generate optimized life plans. Furthermore, scalable, real-time data processing is enabled by utilizing cloud infrastructure such as Amazon Web Services (AWS®) and Google Cloud Platform.

[0102] The server provides users with generated life plans and asset management suggestions via their devices. Specific methods of delivery include presenting information clearly through regularly updated dashboards and notifications. For example, if there's a possibility of exceeding the budget at the end of the month, the server immediately notifies the user and offers saving suggestions.

[0103] Users can optimize their daily spending habits based on feedback from the server. For example, specific savings suggestions might be made, such as, "Cutting your monthly coffee spending by 2,000 yen will free up enough money for one trip per year." This is possible because the machine learning model used is a generative AI model that understands context from the user's past spending data. By using prompts such as, "Analyze the user's recent spending patterns, identify areas of wasteful spending, and suggest specific savings," the generative AI model can provide customized suggestions for each user.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user uses a terminal to input financial information such as income, expenses, and asset status. Based on this input, the terminal formats the data and sends it to the server using a secure protocol.

[0107] Step 2:

[0108] The server stores the financial data received from the terminals in a database. This data is managed in a way that allows for user identification and is used for subsequent analysis.

[0109] Step 3:

[0110] The server uses machine learning algorithms to analyze historical financial data stored in the database. This analysis generates models of the user's spending patterns and future wealth building. The input is financial data, and the output is an optimized life plan and asset management proposal.

[0111] Step 4:

[0112] The generated life plan and asset management suggestions are sent from the server to the terminal. The terminal receives this and displays it to the user as visual feedback in the form of a dashboard or notification.

[0113] Step 5:

[0114] Based on feedback provided from the device, users review their daily spending habits. In this process, a "generative AI model" uses prompts to offer additional savings suggestions based on the user's behavior, thereby encouraging them to optimize their spending.

[0115] 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.

[0116] This invention combines an emotional engine with a system that utilizes users' financial data to support the achievement of their economic goals. In addition to providing life plans that take into account the user's financial situation and lifestyle, this system enables more personalized support by providing feedback based on the user's emotions.

[0117] First, the user inputs financial data using a terminal. The terminal securely transmits this data to a server, which stores the data in a database and analyzes it using machine learning algorithms. In this process, a life plan is generated that takes into account the user's income and expenditure patterns and life events.

[0118] The emotion engine recognizes the user's emotional state in real time based on user input and data collected from sensors on the device. The emotion engine integrates this emotional information with other financial data to generate advice that helps the user maintain a positive mindset or reduce stress and anxiety.

[0119] For example, if the emotion engine determines that a user is in a high-stress state, the server sends suggestions to the device for short-term savings or actions that promote relaxation. On the other hand, if the system determines that the user is in a positive state, it provides advice that encourages a more proactive investment strategy or a review of plans toward future goals.

[0120] This system allows users to receive a more holistic and adaptive life plan that reflects not only their financial data but also their emotional state. This can encourage smarter financial decision-making and contribute to improving users' quality of life.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The user enters their financial data (income, expenses, and asset information) through the device. Once the user has finished entering the data, the device encrypts the data and sends it to the server.

[0124] Step 2:

[0125] The server receives this data and stores it in the database. It then checks for omissions and inconsistencies and performs data cleansing as needed to ensure the data is properly organized.

[0126] Step 3:

[0127] The server uses machine learning algorithms to analyze the user's financial data. It analyzes past income and spending patterns to generate an optimal life plan for the user.

[0128] Step 4:

[0129] Emotional data is collected from the user's device. This data is obtained from various sources, such as biosensors and entered text. The device then sends this data to a server.

[0130] Step 5:

[0131] The emotion engine installed on the server analyzes the received emotional data. It determines the user's current emotional state and reflects the results in providing a life plan.

[0132] Step 6:

[0133] The server adjusts the life plan according to the user's emotions. If the user is under high stress, it creates a plan that minimizes risk; if they are in a positive state, it creates a more challenging plan.

[0134] Step 7:

[0135] The server generates an integrated life plan and additional advice based on emotions, and sends it to the device.

[0136] Step 8:

[0137] The device displays the life plan and advice it receives to the user. Based on this, the user adjusts their daily economic activities and makes economic decisions that take emotional well-being into consideration.

[0138] (Example 2)

[0139] 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".

[0140] Managing personal finances in the modern era requires more than just understanding numerical data; it demands comprehensive support that takes into account an individual's emotional state. However, conventional systems are specialized in analyzing financial data and lack feedback based on the user's emotional state. As a result, users do not receive effective advice to cope with stress and anxiety, making financial decision-making difficult. Therefore, there is a need to integrate users' financial data with their emotional state to provide more personalized advice.

[0141] 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.

[0142] In this invention, the server includes means for receiving economic information, means for analyzing economic information and generating an optimal life plan, means for providing the life plan, and means for recognizing the user's emotional state and providing advice. This enables comprehensive support based on both the user's financial information and emotional state.

[0143] "Economic information" refers to numerical data about a user's income, expenses, savings, investments, etc., and is collected to understand the flow of money.

[0144] "Analysis" is the process of using statistical methods and algorithms to derive patterns and trends behind collected data.

[0145] "Life planning" refers to a plan for achieving long-term economic goals, proposed based on the user's financial situation and lifestyle.

[0146] "Providing" refers to the act of supporting decision-making by presenting generated or analyzed information or advice to the user.

[0147] "Emotional state" refers to the user's mental and emotional condition, which includes feelings such as happiness, stress, and anxiety.

[0148] "Advice" refers to specific suggestions that provide guidance or recommendations for action based on the user's specific situation or goals.

[0149] This invention is a system that provides personalized life planning and advice based on the user's financial information and emotional state. At the heart of this system are the following roles played by the server, terminal, and user:

[0150] First, the user uses a device to input financial information about their income and expenses. The device encrypts the entered data and securely transmits it to the server. A dedicated application is installed on the device, allowing the user to easily input and manage their data.

[0151] The server stores the received economic information in a database and analyzes the collected data using machine learning algorithms. The software used in this process includes, for example, analysis scripts developed in Python, which extract income and expenditure patterns from the data and generate life plans. The generated life plans are tailored to the user's individual needs, taking into account both short-term and long-term goals.

[0152] On the other hand, the device is also equipped with an emotion engine. This emotion engine has the function of analyzing the user's emotional state in real time from input data and sensor information on the device. The analysis results are sent to a server, integrated with economic data, and then reflected as feedback tailored to the user's emotional state.

[0153] For example, if a user is experiencing high levels of stress, the server can use a generative AI model to generate prompts such as, "Try these simple money-saving methods. Taking a rest on certain days can also be effective," and suggest them to the user through their device. In this way, by supporting the user from both economic and emotional perspectives, it is possible to support healthier and more effective decision-making.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The user enters financial information into the terminal. This data includes income, expenses, and savings goals. The terminal formats and encrypts the input data before sending it to the server. In this step, the input is the user's financial information, and the output is in encrypted data format.

[0157] Step 2:

[0158] The server stores the financial data received from the terminal in a database. The stored data is analyzed using a Python analysis script. Specifically, the server runs a machine learning algorithm to identify the user's income and expenditure patterns and generate an optimal financial plan. The input for this step is encrypted data sent to the server, and the output is a financial plan customized for the user.

[0159] Step 3:

[0160] The device collects information about the user's emotional state through other sensors. The emotion engine analyzes this information and determines the user's emotional state in real time. The input for this step is sensor information related to emotions, and the output is the analyzed emotional state of the user.

[0161] Step 4:

[0162] The server integrates the user's life plan and emotional state. Using a generative AI model, it generates prompt messages appropriate to the user's current emotions and financial situation. For example, if the user is showing high stress levels, it will generate advice suggesting short-term saving methods. The input for this step is the user's life plan and emotional state, and the output is the generated prompt message.

[0163] Step 5:

[0164] The terminal presents the user with prompt messages received from the server. It displays suggestions through the application interface and uses notification features to alert the user as needed. The input for this step is the prompt message from the server, and the output is specific advice to support the user's decision-making.

[0165] (Application Example 2)

[0166] 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".

[0167] In modern society, users are surrounded by a wide variety of financial information, but managing this information appropriately and creating effective life plans and asset management strategies is not easy. Furthermore, since daily emotional states influence economic decision-making, there is a need for support in enabling users to conduct economic activities while taking their emotional state into account. Conventional systems have the challenge of being unable to provide life plans and feedback that take this emotional information into account.

[0168] 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.

[0169] In this invention, the server includes means for receiving financial and emotional information collected from the user, means for analyzing the collected financial and emotional information and generating an optimal life plan tailored to the user's characteristics and behavior, and means for providing the generated life plan to the user and generating payment advice to support its implementation. As a result, the user can obtain a life plan that comprehensively considers their financial and emotional information, receive appropriate feedback according to their emotional state, and make better economic decisions.

[0170] A "user" is the entity that utilizes this system, providing financial and emotional information, and receiving life plans and feedback.

[0171] "Financial information" refers to data about a user's economic activities, such as income, expenses, savings, and investments, and is information used to form financial plans.

[0172] "Emotional information" refers to data about a user's current emotional state, which the system collects and analyzes as a factor that influences daily decision-making.

[0173] A "life plan" refers to long-term or short-term goals and action plans generated based on a user's financial and emotional information, and serves as the foundation for supporting the user in achieving their goals.

[0174] "Payment advice" refers to specific action guidelines and suggestions provided to support economic decision-making, taking into account the user's financial situation and emotional state.

[0175] "Feedback" refers to information or instructions provided to users in real time, which are used to comprehensively assess their performance and emotional state in order to encourage appropriate action.

[0176] A "server" refers to a computer system that receives, stores, and analyzes users' financial and emotional information, and provides the results to the users.

[0177] In the system that realizes this invention, the user first uses a terminal to input their financial information and daily sentiment information. The terminal securely transmits this information to a cloud server. The server performs analysis based on the collected financial and sentiment information. This analysis can use machine learning algorithms, specifically the Python TENSORFLOW® library. For collecting sentiment information, Google's ML Kit is used to analyze emotions in real time using the smartphone's camera and microphone.

[0178] The server integrates this data and generates a personalized life plan tailored to the user's characteristics and behavior. Furthermore, the generated plan is sent back to the device and presented to the user. At this time, payment advice and feedback are provided based on the user's emotional state. For example, if the system determines that the user is stressed, advice to reduce spending will be displayed. On the other hand, if the user is in a positive emotional state, suggestions for new investments will be made.

[0179] These system processes can run on AWS cloud services, enabling scalable data processing while maintaining a high level of security.

[0180] For example, in a scenario where a user says, "My spending has increased recently, and I'm feeling stressed," the system first recognizes this emotion, extracts areas where savings can be made from past spending data, and presents them to the user. In another use case, where a user says, "I'm feeling good today and considering investments," the system can present new investment options.

[0181] Examples of prompts include the following:

[0182] Use the user's emotional state and financial data to design appropriate purchase advice. For example, consider how to suggest saving techniques when the user is stressed, and investment opportunities when they are feeling positive.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The user inputs financial and sentiment information using a device. The inputted financial information includes data such as income, expenses, and savings. Sentiment information is captured in real time using the smartphone's camera and microphone. The output here is a set of the user's financial and sentiment data.

[0186] Step 2:

[0187] The terminal securely transmits this financial and emotional information to a cloud server. Data encryption technology is used for transmission. The server verifies the integrity of the received data and stores it in a database. The input is encoded information, and the output is data stored on the server.

[0188] Step 3:

[0189] The server executes machine learning algorithms to analyze stored financial and sentiment data. TensorFlow is used for data analysis, extracting user characteristics and patterns. The input is data from a database, and the output is the analysis results.

[0190] Step 4:

[0191] The server generates a life plan tailored to the user's characteristics and behavior based on the analysis results. This plan includes a life plan that considers income and expenditure balance, as well as feedback appropriate to the user's emotional state. The input is the analysis results, and the output is a specific life plan.

[0192] Step 5:

[0193] The generated life plan and advice are sent to the device and presented to the user. The device displays the information in a way that is appropriate for the user and recommends the next action. The input here is the life plan, and the output is feedback and advice to the user.

[0194] Step 6:

[0195] The user makes decisions based on the feedback provided, and in some cases, inputs new financial data or confirms sentiment information. This loop allows the system to continuously monitor the user's state and update its advice. The input is the user's actions, and the output is the updated feedback.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention constructs a system that effectively utilizes users' financial information to support the achievement of their financial goals. This system is initiated when a user inputs financial data from an individual terminal. The terminal securely transmits this data to a server. The server analyzes the received data and uses machine learning algorithms to generate an optimal life plan based on the user's income, expenses, and financial attributes.

[0213] Specifically, the server analyzes the user's past spending patterns and provides feedback to the user via the terminal to help reduce wasteful spending. For example, if the user inputs their monthly income and expenses and it is determined that they have many unnecessary expenses, the server will make specific suggestions on how to reduce those expenses.

[0214] Furthermore, the server takes into account future life events set by the user, evaluates their economic impact, and automatically generates an appropriate financial plan. This plan is presented to the user via the terminal, and the user can then decide on specific actions based on it.

[0215] Furthermore, the server constantly retrieves the user's latest income and expense data and uses that information to propose asset management and investment portfolios. This allows users to flexibly respond to fluctuating market conditions while maximizing their returns.

[0216] Thus, the system of the present invention helps users make wiser financial decisions by combining real-time data analysis with personalized life planning.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The user enters their financial data (income, expenses, asset information, and future life events) into the terminal. The terminal encodes the entered data and sends it to the server via a secure protocol.

[0220] Step 2:

[0221] The server stores the financial data received from the terminal into a database. During this process, it checks for inconsistencies and missing data and performs data cleansing to prepare the input data for analysis.

[0222] Step 3:

[0223] The server inputs the organized data into a machine learning algorithm to analyze the user's past income and expenditure patterns and spending trends. Based on this analysis, it understands the user's lifestyle and consumption patterns.

[0224] Step 4:

[0225] The server generates an optimal life plan for the user. This plan optimizes income and expenses based on past data analysis results and combines this with a long-term economic plan that addresses future life events.

[0226] Step 5:

[0227] The server generates savings suggestions and investment advice in real time, along with the generated life plan. This includes asset management strategies and investment portfolio suggestions that take risk diversification into account.

[0228] Step 6:

[0229] The server sends this life plan and related suggestions to the terminal. The terminal presents it to the user in an appropriate interface, providing a foundation for the user to review the plan and decide on actions.

[0230] Step 7:

[0231] Once a user begins using the service, the device continuously records their spending. The device keeps the user's information up-to-date by sending new spending data to the server.

[0232] Step 8:

[0233] The server reflects the user's latest spending data and adjusts life plans and advice as needed. This ensures that the user always has a foundation for making optimal financial decisions.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0236] In today's economic environment, it is a challenging task for individuals to accurately understand their own financial situation and effectively achieve their long-term economic goals. In particular, balancing income and expenses, and developing asset management and investment strategies to respond to fluctuating market conditions, is a significant burden for many individuals. There is a need for systems that address this challenge and support users in making wise financial decisions.

[0237] 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.

[0238] In this invention, the server includes means for receiving economic information collected from the user, means for analyzing the collected economic information and generating an optimal life plan tailored to the user's individual attributes and behavior, and means for providing the generated life plan to the user and presenting it in an intuitively understandable format. This enables the user to grasp their own economic situation in real time and to plan and execute a plan that meets their individual needs.

[0239] A "user" is an entity that uses the system to register, manage, and receive personal financial information.

[0240] "Economic information" refers to data on income, expenses, savings, investments, and assets, and is important information for representing an individual's financial situation.

[0241] A "server" refers to a computer system that receives information from users, analyzes it, and generates and provides the results.

[0242] A "life plan" refers to a specific action plan regarding income, expenses, savings, and investments, created to support an individual in achieving their financial goals.

[0243] "Individual attributes" refer to information that represents the unique characteristics of each individual user, such as their economic situation, goals, and behavioral patterns.

[0244] An "intuitively understandable format" refers to a method of presentation where generated information or plans are visually displayed, allowing the content to be grasped at a glance.

[0245] "Asset management" refers to management methods for optimally utilizing the assets owned by a user.

[0246] An "investment strategy" refers to a plan for determining asset allocation and investment policies, taking into account risk and return.

[0247] This invention is a system that effectively manages personal financial information and provides support for achieving goals. This system consists of three main components: the user, the terminal, and the server.

[0248] Users input financial information such as income, expenses, and savings via their devices. These devices refer to general information processing equipment such as smartphones and computers. The devices transmit the input information to the server using secure communication protocols.

[0249] The server uses a Python-based machine learning library (e.g., Scikit-learn) to analyze the received economic information. This analysis includes analyzing the user's past spending patterns and attribute data. Based on this, an optimal lifestyle plan is generated that is tailored to the user's behavior. This lifestyle plan is intuitively represented using graphs and charts and fed back to the user's device.

[0250] For example, if the system determines that entertainment expenses need to be reduced based on the user's monthly spending data, it will provide specific suggestions on how to do so. These suggestions might take the form of, "Let's think of ways to reduce next month's leisure budget by 20%."

[0251] Furthermore, the server automatically generates financial plans for long-term life events set by the user, such as purchasing a home or preparing for education expenses. Each time the user's income and expenditure data is updated, the server uses the new data to analyze and adjust its asset management and investment strategy suggestions. This process enables real-time data updates, allowing for flexible adaptation to fluctuating market conditions.

[0252] An example of a prompt message might be a request such as, "Based on the user's income and spending patterns, please provide suggestions for eliminating waste."

[0253] This system supports users' financial decision-making and promotes better goal achievement by providing real-time data analysis and personalized life planning.

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] Users input their financial information using their devices. Specifically, they input monthly income, expenses, and savings goals. The input data is converted into a server-friendly format, such as JSON or CSV. The converted data is then sent to the server using a secure protocol.

[0257] Step 2:

[0258] The server verifies the integrity of the input data based on the economic information received from the terminal. This involves checking the data format and detecting outliers. During this process, data cleansing is performed, and any errors or incomplete items are automatically corrected or omitted. This ensures that clean data is obtained that does not affect subsequent data analysis.

[0259] Step 3:

[0260] The server analyzes the data, ensuring its integrity. Specifically, it uses machine learning models (e.g., models using Scikit-learn) to analyze user spending patterns and income fluctuations. The input is the user's historical economic data, and the output includes trend analysis based on user attributes and areas where spending can be reduced.

[0261] Step 4:

[0262] The server generates an optimal lifestyle plan based on the analysis results. Utilizing a generation AI model, it formulates a plan that takes into account the user's specific attributes and long-term goals. The output includes a detailed lifestyle plan and suggestions for saving money, including visual representations such as graphs and charts.

[0263] Step 5:

[0264] The server sends the generated life plan to the terminal. The terminal displays the received information in a way that the user can intuitively understand. This display often uses an interactive graphical user interface. Based on this information, the user evaluates their financial behavior and adjusts the life plan as needed.

[0265] Step 6:

[0266] The server regularly receives user feedback and new economic information, and reanalyzes the model. This allows it to propose adaptive plans and investment strategies to users that are in line with the latest market trends and economic conditions. The generative AI model continues to learn from the feedback, improving its accuracy and usefulness.

[0267] (Application Example 1)

[0268] 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."

[0269] In recent years, individual consumers have engaged in a variety of financial transactions, which has led to a demand for efficient asset management and optimized consumption. However, it is not easy for consumers to grasp the details of their own spending and investments and design an accurate life plan. In particular, providing users with plans that aim for a prosperous future while curbing wasteful spending is a challenging task.

[0270] 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.

[0271] In this invention, the server includes means for receiving financial data collected from the user, means for analyzing the collected financial data and generating an optimal lifestyle plan tailored to the user's group and activities, and means for providing the user with savings suggestions to curb wasteful spending based on payment information. This enables the user to optimize their spending and manage their assets effectively.

[0272] A "user" refers to an individual who provides financial information and receives support regarding life planning and asset management.

[0273] "Financial data" refers to the collection of information about income, expenses, assets, and investments provided by users.

[0274] A "server" refers to a central data processing unit that receives users' financial data, analyzes it, and makes recommendations.

[0275] A "life plan" refers to information that outlines efficient consumption and asset management strategies, generated based on the user's income and expenses and future goals.

[0276] "Payment information" refers to a portion of financial data, including details about purchases and transactions made by a user.

[0277] "Savings suggestions to curb wasteful spending" refers to specific advice and measures provided to reduce unnecessary expenses by analyzing the user's spending habits.

[0278] To realize this invention, a system is needed that collects financial data from a user's device and transmits it to a server. The device should have an easy-to-use interface for the user and allow them to input financial information such as income, expenses, and asset status.

[0279] The server receives financial data sent by users and utilizes machine learning algorithms as a means of data analysis. These algorithms can use data analysis programming languages ​​such as Python and R to analyze users' past spending patterns and attribute data, and generate optimized life plans. Furthermore, scalable, real-time data processing is enabled by utilizing cloud infrastructure such as Amazon Web Services (AWS) and Google Cloud Platform.

[0280] The server provides users with generated life plans and asset management suggestions via their devices. Specific methods of delivery include presenting information clearly through regularly updated dashboards and notifications. For example, if there's a possibility of exceeding the budget at the end of the month, the server immediately notifies the user and offers saving suggestions.

[0281] Users can optimize their daily spending habits based on feedback from the server. For example, specific savings suggestions might be made, such as, "Cutting your monthly coffee spending by 2,000 yen will free up enough money for one trip per year." This is possible because the machine learning model used is a generative AI model that understands context from the user's past spending data. By using prompts such as, "Analyze the user's recent spending patterns, identify areas of wasteful spending, and suggest specific savings," the generative AI model can provide customized suggestions for each user.

[0282] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0283] Step 1:

[0284] The user uses the terminal to input financial information such as income, expenses, and asset status. Based on this input, the terminal formats the data and sends it to the server using a secure protocol.

[0285] Step 2:

[0286] The server stores the financial data received from the terminal in a database. This data is managed in a form that can be identified for each user and is used for subsequent analysis.

[0287] Step 3:

[0288] The server uses a machine learning algorithm to analyze the past financial data stored in the database. This analysis generates models related to the user's spending patterns and future asset formation. The input is financial data, and the output is an optimized life plan and asset management proposal.

[0289] Step 4:

[0290] The generated life plan and asset management proposals are sent by the server to the terminal. The terminal receives this and displays it to the user as visual feedback in the form of a dashboard or notifications.

[0291] Step 5:

[0292] The user reviews their daily consumption behavior based on the feedback provided by the terminal. In this process, the "generative AI model" uses prompt sentences to make additional savings proposals based on the user's behavior. This encourages the user to optimize their spending.

[0293] 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.

[0294] This invention combines an emotional engine with a system that utilizes users' financial data to support the achievement of their economic goals. In addition to providing life plans that take into account the user's financial situation and lifestyle, this system enables more personalized support by providing feedback based on the user's emotions.

[0295] First, the user inputs financial data using a terminal. The terminal securely transmits this data to a server, which stores the data in a database and analyzes it using machine learning algorithms. In this process, a life plan is generated that takes into account the user's income and expenditure patterns and life events.

[0296] The emotion engine recognizes the user's emotional state in real time based on user input and data collected from sensors on the device. The emotion engine integrates this emotional information with other financial data to generate advice that helps the user maintain a positive mindset or reduce stress and anxiety.

[0297] For example, if the emotion engine determines that a user is in a high-stress state, the server sends suggestions to the device for short-term savings or actions that promote relaxation. On the other hand, if the system determines that the user is in a positive state, it provides advice that encourages a more proactive investment strategy or a review of plans toward future goals.

[0298] This system allows users to receive a more holistic and adaptive life plan that reflects not only their financial data but also their emotional state. This can encourage smarter financial decision-making and contribute to improving users' quality of life.

[0299] The following describes the process flow.

[0300] Step 1:

[0301] The user inputs their financial data (income, expenditure, asset information) through the terminal. When the user completes the input, the terminal encrypts the data and sends it to the server.

[0302] Step 2:

[0303] The server receives this data and stores it in the database. By checking for deficiencies and inconsistencies and performing data cleansing as necessary, the data is properly organized.

[0304] Step 3:

[0305] The server analyzes the user's financial data using a machine learning algorithm. By analyzing past income and expenditure patterns, an optimal life plan is generated for the user.

[0306] Step 4:

[0307] Emotion data is collected from the user's terminal. This is obtained from various sources such as biosensors and input text. The terminal sends this data to the server.

[0308] Step 5:

[0309] The emotion engine installed on the server analyzes the received emotion data. The current emotional state of the user is determined, and the result is reflected in the provision of the life plan.

[0310] Step 6:

[0311] The server adjusts the life plan according to the user's emotions. If the stress is high, a plan to reduce risk is constructed, and if the state is positive, a more challenging plan is constructed.

[0312] Step 7:

[0313] The server generates an integrated life plan and additional advice based on emotions, and sends it to the device.

[0314] Step 8:

[0315] The device displays the life plan and advice it receives to the user. Based on this, the user adjusts their daily economic activities and makes economic decisions that take emotional well-being into consideration.

[0316] (Example 2)

[0317] 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".

[0318] Managing personal finances in the modern era requires more than just understanding numerical data; it demands comprehensive support that takes into account an individual's emotional state. However, conventional systems are specialized in analyzing financial data and lack feedback based on the user's emotional state. As a result, users do not receive effective advice to cope with stress and anxiety, making financial decision-making difficult. Therefore, there is a need to integrate users' financial data with their emotional state to provide more personalized advice.

[0319] 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.

[0320] In this invention, the server includes means for receiving economic information, means for analyzing economic information and generating an optimal life plan, means for providing the life plan, and means for recognizing the user's emotional state and providing advice. This enables comprehensive support based on both the user's financial information and emotional state.

[0321] "Economic information" refers to numerical data about a user's income, expenses, savings, investments, etc., and is collected to understand the flow of money.

[0322] "Analysis" is the process of using statistical methods and algorithms to derive patterns and trends behind collected data.

[0323] "Life planning" refers to a plan for achieving long-term economic goals, proposed based on the user's financial situation and lifestyle.

[0324] "Providing" refers to the act of supporting decision-making by presenting generated or analyzed information or advice to the user.

[0325] "Emotional state" refers to the user's mental and emotional condition, which includes feelings such as happiness, stress, and anxiety.

[0326] "Advice" refers to specific suggestions that provide guidance or recommendations for action based on the user's specific situation or goals.

[0327] This invention is a system that provides personalized life planning and advice based on the user's financial information and emotional state. At the heart of this system are the following roles played by the server, terminal, and user:

[0328] First, the user uses a device to input financial information about their income and expenses. The device encrypts the entered data and securely transmits it to the server. A dedicated application is installed on the device, allowing the user to easily input and manage their data.

[0329] The server stores the received economic information in a database and analyzes the collected data using machine learning algorithms. The software used in this process includes, for example, analysis scripts developed in Python, which extract income and expenditure patterns from the data and generate life plans. The generated life plans are tailored to the user's individual needs, taking into account both short-term and long-term goals.

[0330] On the other hand, the device is also equipped with an emotion engine. This emotion engine has the function of analyzing the user's emotional state in real time from input data and sensor information on the device. The analysis results are sent to a server, integrated with economic data, and then reflected as feedback tailored to the user's emotional state.

[0331] For example, if a user is experiencing high levels of stress, the server can use a generative AI model to generate prompts such as, "Try these simple money-saving methods. Taking a rest on certain days can also be effective," and suggest them to the user through their device. In this way, by supporting the user from both economic and emotional perspectives, it is possible to support healthier and more effective decision-making.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The user enters financial information into the terminal. This data includes income, expenses, and savings goals. The terminal formats and encrypts the input data before sending it to the server. In this step, the input is the user's financial information, and the output is in encrypted data format.

[0335] Step 2:

[0336] The server stores the financial data received from the terminal in a database. The stored data is analyzed using a Python analysis script. Specifically, the server runs a machine learning algorithm to identify the user's income and expenditure patterns and generate an optimal financial plan. The input for this step is encrypted data sent to the server, and the output is a financial plan customized for the user.

[0337] Step 3:

[0338] The device collects information about the user's emotional state through other sensors. The emotion engine analyzes this information and determines the user's emotional state in real time. The input for this step is sensor information related to emotions, and the output is the analyzed emotional state of the user.

[0339] Step 4:

[0340] The server integrates the user's life plan and emotional state. Using a generative AI model, it generates prompt messages appropriate to the user's current emotions and financial situation. For example, if the user is showing high stress levels, it will generate advice suggesting short-term saving methods. The input for this step is the user's life plan and emotional state, and the output is the generated prompt message.

[0341] Step 5:

[0342] The terminal presents the user with prompt messages received from the server. It displays suggestions through the application interface and uses notification features to alert the user as needed. The input for this step is the prompt message from the server, and the output is specific advice to support the user's decision-making.

[0343] (Application Example 2)

[0344] 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."

[0345] In modern society, users are surrounded by a wide variety of financial information, but managing this information appropriately and creating effective life plans and asset management strategies is not easy. Furthermore, since daily emotional states influence economic decision-making, there is a need for support in enabling users to conduct economic activities while taking their emotional state into account. Conventional systems have the challenge of being unable to provide life plans and feedback that take this emotional information into account.

[0346] 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.

[0347] In this invention, the server includes means for receiving financial and emotional information collected from the user, means for analyzing the collected financial and emotional information and generating an optimal life plan tailored to the user's characteristics and behavior, and means for providing the generated life plan to the user and generating payment advice to support its implementation. As a result, the user can obtain a life plan that comprehensively considers their financial and emotional information, receive appropriate feedback according to their emotional state, and make better economic decisions.

[0348] A "user" is the entity that utilizes this system, providing financial and emotional information, and receiving life plans and feedback.

[0349] "Financial information" refers to data on a user's economic activities, such as income, expenses, savings, and investments, and is used to form financial plans.

[0350] "Emotional information" refers to data about a user's current emotional state, which the system collects and analyzes as a factor that influences daily decision-making.

[0351] A "life plan" refers to long-term or short-term goals and action plans generated based on a user's financial and emotional information, and serves as the foundation for supporting the user in achieving their goals.

[0352] "Payment advice" refers to specific action guidelines and suggestions provided to support economic decision-making, taking into account the user's financial situation and emotional state.

[0353] "Feedback" refers to information or instructions provided to users in real time, which are used to comprehensively assess their performance and emotional state in order to encourage appropriate action.

[0354] A "server" refers to a computer system that receives, stores, and analyzes users' financial and emotional information, and provides the results to the users.

[0355] In the system that implements this invention, the user first uses a terminal to input their financial information and daily sentiment information. The terminal securely transmits this information to a cloud server. The server performs analysis based on the collected financial and sentiment information. This analysis can utilize machine learning algorithms, specifically the TensorFlow library in Python. For collecting sentiment information, Google's ML Kit is used to analyze emotions in real time using the smartphone's camera and microphone.

[0356] The server integrates this data and generates a personalized life plan tailored to the user's characteristics and behavior. Furthermore, the generated plan is sent back to the device and presented to the user. At this time, payment advice and feedback are provided based on the user's emotional state. For example, if the system determines that the user is stressed, advice to reduce spending will be displayed. On the other hand, if the user is in a positive emotional state, suggestions for new investments will be made.

[0357] These system processes can run on AWS cloud services, enabling scalable data processing while maintaining a high level of security.

[0358] For example, in a scenario where a user says, "My spending has increased recently, and I'm feeling stressed," the system first recognizes this emotion, extracts areas where savings can be made from past spending data, and presents them to the user. In another use case, where a user says, "I'm feeling good today and considering investments," the system can present new investment options.

[0359] Examples of prompts include the following:

[0360] Use the user's emotional state and financial data to design appropriate purchase advice. For example, consider how to suggest saving tips when the user is stressed, and investment opportunities when they are feeling positive.

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The user inputs financial and sentiment information using a device. The inputted financial information includes data such as income, expenses, and savings. Sentiment information is captured in real time using the smartphone's camera and microphone. The output here is a set of the user's financial and sentiment data.

[0364] Step 2:

[0365] The terminal securely transmits this financial and emotional information to the cloud server. Data encryption technology is used for transmission. The server verifies the integrity of the received data and stores it in a database. The input is encoded information, and the output is data stored on the server.

[0366] Step 3:

[0367] The server executes machine learning algorithms to analyze stored financial and sentiment data. TensorFlow is used for data analysis, extracting user characteristics and patterns. The input is data from a database, and the output is the analysis results.

[0368] Step 4:

[0369] The server generates a life plan tailored to the user's characteristics and behavior based on the analysis results. This plan includes a life plan that considers income and expenditure balance, as well as feedback appropriate to the user's emotional state. The input is the analysis results, and the output is a specific life plan.

[0370] Step 5:

[0371] The generated life plan and advice are sent to the device and presented to the user. The device displays the information in a way that is appropriate for the user and recommends the next action. The input here is the life plan, and the output is feedback and advice to the user.

[0372] Step 6:

[0373] The user makes decisions based on the feedback provided, and in some cases, inputs new financial data or confirms sentiment information. This loop allows the system to continuously monitor the user's state and update its advice. The input is the user's actions, and the output is the updated feedback.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] [Third Embodiment]

[0378] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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".

[0390] This invention constructs a system that effectively utilizes users' financial information to support the achievement of their financial goals. This system is initiated when a user inputs financial data from an individual terminal. The terminal securely transmits this data to a server. The server analyzes the received data and uses machine learning algorithms to generate an optimal life plan based on the user's income, expenses, and financial attributes.

[0391] Specifically, the server analyzes the user's past spending patterns and provides feedback to the user via the terminal to help reduce wasteful spending. For example, if the user inputs their monthly income and expenses and it is determined that they have many unnecessary expenses, the server will make specific suggestions on how to reduce those expenses.

[0392] Furthermore, the server takes into account future life events set by the user, evaluates their economic impact, and automatically generates an appropriate financial plan. This plan is presented to the user via the terminal, and the user can then decide on specific actions based on it.

[0393] Furthermore, the server constantly retrieves the user's latest income and expense data and uses that information to propose asset management and investment portfolios. This allows users to flexibly respond to fluctuating market conditions while maximizing their returns.

[0394] Thus, the system of the present invention helps users make wiser financial decisions by combining real-time data analysis with personalized life planning.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The user enters their financial data (income, expenses, asset information, and future life events) into the terminal. The terminal encodes the entered data and sends it to the server via a secure protocol.

[0398] Step 2:

[0399] The server stores the financial data received from the terminal into a database. During this process, it checks for inconsistencies and missing data and performs data cleansing to prepare the input data for analysis.

[0400] Step 3:

[0401] The server inputs the organized data into a machine learning algorithm to analyze the user's past income and expenditure patterns and spending trends. Based on this analysis, it understands the user's lifestyle and consumption patterns.

[0402] Step 4:

[0403] The server generates an optimal life plan for the user. This plan optimizes income and expenses based on past data analysis results and combines this with a long-term economic plan that addresses future life events.

[0404] Step 5:

[0405] The server generates savings suggestions and investment advice in real time, along with the generated life plan. This includes asset management strategies and investment portfolio suggestions that take risk diversification into account.

[0406] Step 6:

[0407] The server sends this life plan and related suggestions to the terminal. The terminal presents it to the user in an appropriate interface, providing a foundation for the user to review the plan and decide on actions.

[0408] Step 7:

[0409] Once a user begins using the service, the device continuously records their spending. The device keeps the user's information up-to-date by sending new spending data to the server.

[0410] Step 8:

[0411] The server reflects the user's latest spending data and adjusts life plans and advice as needed. This ensures that the user always has a foundation for making optimal financial decisions.

[0412] (Example 1)

[0413] 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."

[0414] In today's economic environment, it is a challenging task for individuals to accurately understand their own financial situation and effectively achieve their long-term economic goals. In particular, balancing income and expenses, and developing asset management and investment strategies to respond to fluctuating market conditions, is a significant burden for many individuals. There is a need for systems that address this challenge and support users in making wise financial decisions.

[0415] 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.

[0416] In this invention, the server includes means for receiving economic information collected from the user, means for analyzing the collected economic information and generating an optimal life plan tailored to the user's individual attributes and behavior, and means for providing the generated life plan to the user and presenting it in an intuitively understandable format. This enables the user to grasp their own economic situation in real time and to plan and execute a plan that meets their individual needs.

[0417] A "user" is an entity that uses the system to register, manage, and receive personal financial information.

[0418] "Economic information" refers to data on income, expenses, savings, investments, and assets, and is important information for representing an individual's financial situation.

[0419] A "server" refers to a computer system that receives information from users, analyzes it, and generates and provides the results.

[0420] A "life plan" refers to a specific action plan regarding income, expenses, savings, and investments, created to support an individual in achieving their financial goals.

[0421] "Individual attributes" refer to information that represents the unique characteristics of each individual user, such as their economic situation, goals, and behavioral patterns.

[0422] An "intuitively understandable format" refers to a method of presentation where generated information or plans are visually displayed, allowing the content to be grasped at a glance.

[0423] "Asset management" refers to management methods for optimally utilizing the assets owned by a user.

[0424] An "investment strategy" refers to a plan for determining asset allocation and investment policies, taking into account risk and return.

[0425] This invention is a system that effectively manages personal financial information and provides support for achieving goals. This system consists of three main components: the user, the terminal, and the server.

[0426] Users input financial information such as income, expenses, and savings via their devices. These devices refer to general information processing equipment such as smartphones and computers. The devices transmit the input information to the server using secure communication protocols.

[0427] The server uses a Python-based machine learning library (e.g., Scikit-learn) to analyze the received economic information. This analysis includes analyzing the user's past spending patterns and attribute data. Based on this, an optimal lifestyle plan is generated that is tailored to the user's behavior. This lifestyle plan is intuitively represented using graphs and charts and fed back to the user's device.

[0428] For example, if the system determines that entertainment expenses need to be reduced based on the user's monthly spending data, it will provide specific suggestions on how to do so. These suggestions might take the form of, "Let's think of ways to reduce next month's leisure budget by 20%."

[0429] Furthermore, the server automatically generates financial plans for long-term life events set by the user, such as purchasing a home or preparing for education expenses. Each time the user's income and expenditure data is updated, the server uses the new data to analyze and adjust its asset management and investment strategy suggestions. This process enables real-time data updates, allowing for flexible adaptation to fluctuating market conditions.

[0430] An example of a prompt message might be a request such as, "Based on the user's income and spending patterns, please provide suggestions for eliminating waste."

[0431] This system supports users' financial decision-making and promotes better goal achievement by providing real-time data analysis and personalized life planning.

[0432] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0433] Step 1:

[0434] Users input their financial information using their devices. Specifically, they input monthly income, expenses, and savings goals. The input data is converted into a server-friendly format, such as JSON or CSV. The converted data is then sent to the server using a secure protocol.

[0435] Step 2:

[0436] The server verifies the integrity of the input data based on the economic information received from the terminal. This involves checking the data format and detecting outliers. During this process, data cleansing is performed, and any errors or incomplete items are automatically corrected or omitted. This ensures that clean data is obtained that does not affect subsequent data analysis.

[0437] Step 3:

[0438] The server analyzes the data, ensuring its integrity. Specifically, it uses machine learning models (e.g., models using Scikit-learn) to analyze user spending patterns and income fluctuations. The input is the user's historical economic data, and the output includes trend analysis based on user attributes and areas where spending can be reduced.

[0439] Step 4:

[0440] The server generates an optimal lifestyle plan based on the analysis results. Utilizing a generation AI model, it formulates a plan that takes into account the user's specific attributes and long-term goals. The output includes a detailed lifestyle plan and suggestions for saving money, including visual representations such as graphs and charts.

[0441] Step 5:

[0442] The server sends the generated life plan to the terminal. The terminal displays the received information in a way that the user can intuitively understand. This display often uses an interactive graphical user interface. Based on this information, the user evaluates their financial behavior and adjusts the life plan as needed.

[0443] Step 6:

[0444] The server regularly receives user feedback and new economic information, and reanalyzes the model. This allows it to propose adaptive plans and investment strategies to users that are in line with the latest market trends and economic conditions. The generative AI model continues to learn from the feedback, improving its accuracy and usefulness.

[0445] (Application Example 1)

[0446] 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."

[0447] In recent years, individual consumers have engaged in a variety of financial transactions, which has led to a demand for efficient asset management and optimized consumption. However, it is not easy for consumers to grasp the details of their own spending and investments and design an accurate life plan. In particular, providing users with plans that aim for a prosperous future while curbing wasteful spending is a challenging task.

[0448] 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.

[0449] In this invention, the server includes means for receiving financial data collected from the user, means for analyzing the collected financial data and generating an optimal lifestyle plan tailored to the user's group and activities, and means for providing the user with savings suggestions to curb wasteful spending based on payment information. This enables the user to optimize their spending and manage their assets effectively.

[0450] A "user" refers to an individual who provides financial information and receives support regarding life planning and asset management.

[0451] "Financial data" refers to the collection of information about income, expenses, assets, and investments provided by users.

[0452] A "server" refers to a central data processing unit that receives users' financial data, analyzes it, and makes recommendations.

[0453] A "life plan" refers to information that outlines efficient consumption and asset management strategies, generated based on the user's income and expenses and future goals.

[0454] "Payment information" refers to a portion of financial data, including details about purchases and transactions made by a user.

[0455] "Savings suggestions to curb wasteful spending" refers to specific advice and measures provided to reduce unnecessary expenses by analyzing the user's spending habits.

[0456] To realize this invention, a system is needed that collects financial data from a user's device and transmits it to a server. The device should have an easy-to-use interface for the user and allow them to input financial information such as income, expenses, and asset status.

[0457] The server receives financial data sent by users and utilizes machine learning algorithms as a means of data analysis. These algorithms can use data analysis programming languages ​​such as Python and R to analyze users' past spending patterns and attribute data, and generate optimized life plans. Furthermore, scalable, real-time data processing is enabled by utilizing cloud infrastructure such as Amazon Web Services (AWS) and Google Cloud Platform.

[0458] The server provides users with generated life plans and asset management suggestions via their devices. Specific methods of delivery include presenting information clearly through regularly updated dashboards and notifications. For example, if there's a possibility of exceeding the budget at the end of the month, the server immediately notifies the user and offers saving suggestions.

[0459] Users can optimize their daily spending habits based on feedback from the server. For example, specific savings suggestions might be made, such as, "Cutting your monthly coffee spending by 2,000 yen will free up enough money for one trip per year." This is possible because the machine learning model used is a generative AI model that understands context from the user's past spending data. By using prompts such as, "Analyze the user's recent spending patterns, identify areas of wasteful spending, and suggest specific savings," the generative AI model can provide customized suggestions for each user.

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The user uses a terminal to input financial information such as income, expenses, and asset status. Based on this input, the terminal formats the data and sends it to the server using a secure protocol.

[0463] Step 2:

[0464] The server stores the financial data received from the terminals in a database. This data is managed in a way that allows for user identification and is used for subsequent analysis.

[0465] Step 3:

[0466] The server uses machine learning algorithms to analyze historical financial data stored in the database. This analysis generates models of the user's spending patterns and future wealth building. The input is financial data, and the output is an optimized life plan and asset management proposal.

[0467] Step 4:

[0468] The generated life plan and asset management suggestions are sent from the server to the terminal. The terminal receives this and displays it to the user as visual feedback in the form of a dashboard or notification.

[0469] Step 5:

[0470] Based on feedback provided by the device, users review their daily spending habits. In this process, a "generative AI model" utilizes prompts to offer additional savings suggestions based on the user's behavior, thereby encouraging them to optimize their spending.

[0471] 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.

[0472] This invention combines an emotional engine with a system that utilizes users' financial data to support the achievement of their economic goals. In addition to providing life plans that take into account the user's financial situation and lifestyle, this system enables more personalized support by providing feedback based on the user's emotions.

[0473] First, the user inputs financial data using a terminal. The terminal securely transmits this data to a server, which stores the data in a database and analyzes it using machine learning algorithms. In this process, a life plan is generated that takes into account the user's income and expenditure patterns and life events.

[0474] The emotion engine recognizes the user's emotional state in real time based on user input and data collected from sensors on the device. The emotion engine integrates this emotional information with other financial data to generate advice that helps the user maintain a positive mindset or reduce stress and anxiety.

[0475] For example, if the emotion engine determines that a user is in a high-stress state, the server sends suggestions to the device for short-term savings or actions that promote relaxation. On the other hand, if the system determines that the user is in a positive state, it provides advice that encourages a more proactive investment strategy or a review of plans toward future goals.

[0476] This system allows users to receive a more holistic and adaptive life plan that reflects not only their financial data but also their emotional state. This can encourage smarter financial decision-making and contribute to improving users' quality of life.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The user enters their financial data (income, expenses, and asset information) through the device. Once the user has finished entering the data, the device encrypts the data and sends it to the server.

[0480] Step 2:

[0481] The server receives this data and stores it in the database. It then checks for omissions and inconsistencies and performs data cleansing as needed to ensure the data is properly organized.

[0482] Step 3:

[0483] The server uses machine learning algorithms to analyze the user's financial data. It analyzes past income and spending patterns to generate an optimal life plan for the user.

[0484] Step 4:

[0485] Emotional data is collected from the user's device. This data is obtained from various sources, such as biosensors and entered text. The device then sends this data to a server.

[0486] Step 5:

[0487] The emotion engine installed on the server analyzes the received emotional data. It determines the user's current emotional state and reflects the results in providing a life plan.

[0488] Step 6:

[0489] The server adjusts the life plan according to the user's emotions. If the user is under high stress, it creates a plan that minimizes risk; if they are in a positive state, it creates a more challenging plan.

[0490] Step 7:

[0491] The server generates an integrated life plan and additional advice based on emotions, and sends it to the device.

[0492] Step 8:

[0493] The device displays the life plan and advice it receives to the user. Based on this, the user adjusts their daily economic activities and makes economic decisions that take emotional well-being into consideration.

[0494] (Example 2)

[0495] 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."

[0496] Managing personal finances in the modern era requires more than just understanding numerical data; it demands comprehensive support that takes into account an individual's emotional state. However, conventional systems are specialized in analyzing financial data and lack feedback based on the user's emotional state. As a result, users do not receive effective advice to cope with stress and anxiety, making financial decision-making difficult. Therefore, there is a need to integrate users' financial data with their emotional state to provide more personalized advice.

[0497] 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.

[0498] In this invention, the server includes means for receiving economic information, means for analyzing economic information and generating an optimal life plan, means for providing the life plan, and means for recognizing the user's emotional state and providing advice. This enables comprehensive support based on both the user's financial information and emotional state.

[0499] "Economic information" refers to numerical data about a user's income, expenses, savings, investments, etc., and is collected to understand the flow of money.

[0500] "Analysis" is the process of using statistical methods and algorithms to derive patterns and trends behind collected data.

[0501] "Life planning" refers to a plan for achieving long-term economic goals, proposed based on the user's financial situation and lifestyle.

[0502] "Providing" refers to the act of supporting decision-making by presenting generated or analyzed information or advice to the user.

[0503] "Emotional state" refers to the user's mental and emotional condition, which includes feelings such as happiness, stress, and anxiety.

[0504] "Advice" refers to specific suggestions that provide guidance or recommendations for action based on the user's specific situation or goals.

[0505] This invention is a system that provides personalized life planning and advice based on the user's financial information and emotional state. At the heart of this system are the following roles played by the server, terminal, and user:

[0506] First, the user uses a device to input financial information about their income and expenses. The device encrypts the entered data and securely transmits it to the server. A dedicated application is installed on the device, allowing the user to easily input and manage their data.

[0507] The server stores the received economic information in a database and analyzes the collected data using machine learning algorithms. The software used in this process includes, for example, analysis scripts developed in Python, which extract income and expenditure patterns from the data and generate life plans. The generated life plans are tailored to the user's individual needs, taking into account both short-term and long-term goals.

[0508] On the other hand, the device is also equipped with an emotion engine. This emotion engine has the function of analyzing the user's emotional state in real time from input data and sensor information on the device. The analysis results are sent to a server, integrated with economic data, and then reflected as feedback tailored to the user's emotional state.

[0509] For example, if a user is experiencing high levels of stress, the server can use a generative AI model to generate prompts such as, "Try these simple money-saving methods. Taking a rest on certain days can also be effective," and suggest them to the user through their device. In this way, by supporting the user from both economic and emotional perspectives, it is possible to support healthier and more effective decision-making.

[0510] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0511] Step 1:

[0512] The user enters financial information into the terminal. This data includes income, expenses, and savings goals. The terminal formats and encrypts the input data before sending it to the server. In this step, the input is the user's financial information, and the output is in encrypted data format.

[0513] Step 2:

[0514] The server stores the financial data received from the terminal in a database. The stored data is analyzed using a Python analysis script. Specifically, the server runs a machine learning algorithm to identify the user's income and expenditure patterns and generate an optimal financial plan. The input for this step is encrypted data sent to the server, and the output is a financial plan customized for the user.

[0515] Step 3:

[0516] The device collects information about the user's emotional state through other sensors. The emotion engine analyzes this information and determines the user's emotional state in real time. The input for this step is sensor information related to emotions, and the output is the analyzed emotional state of the user.

[0517] Step 4:

[0518] The server integrates the user's life plan and emotional state. Using a generative AI model, it generates prompt messages appropriate to the user's current emotions and financial situation. For example, if the user is showing high stress levels, it will generate advice suggesting short-term saving methods. The input for this step is the user's life plan and emotional state, and the output is the generated prompt message.

[0519] Step 5:

[0520] The terminal presents the user with prompt messages received from the server. It displays suggestions through the application interface and uses notification features to alert the user as needed. The input for this step is the prompt message from the server, and the output is specific advice to support the user's decision-making.

[0521] (Application Example 2)

[0522] 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."

[0523] In modern society, users are surrounded by a wide variety of financial information, but managing this information appropriately and creating effective life plans and asset management strategies is not easy. Furthermore, since daily emotional states influence economic decision-making, there is a need for support in enabling users to conduct economic activities while taking their emotional state into account. Conventional systems have the challenge of being unable to provide life plans and feedback that take this emotional information into account.

[0524] 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.

[0525] In this invention, the server includes means for receiving financial and emotional information collected from the user, means for analyzing the collected financial and emotional information and generating an optimal life plan tailored to the user's characteristics and behavior, and means for providing the generated life plan to the user and generating payment advice to support its implementation. As a result, the user can obtain a life plan that comprehensively considers their financial and emotional information, receive appropriate feedback according to their emotional state, and make better economic decisions.

[0526] A "user" is the entity that utilizes this system, providing financial and emotional information, and receiving life plans and feedback.

[0527] "Financial information" refers to data on a user's economic activities, such as income, expenses, savings, and investments, and is used to form financial plans.

[0528] "Emotional information" refers to data about a user's current emotional state, which the system collects and analyzes as a factor that influences daily decision-making.

[0529] A "life plan" refers to long-term or short-term goals and action plans generated based on a user's financial and emotional information, and serves as the foundation for supporting the user in achieving their goals.

[0530] "Payment advice" refers to specific action guidelines and suggestions provided to support economic decision-making, taking into account the user's financial situation and emotional state.

[0531] "Feedback" refers to information or instructions provided to users in real time, which are used to comprehensively assess their performance and emotional state in order to encourage appropriate action.

[0532] A "server" refers to a computer system that receives, stores, and analyzes users' financial and emotional information, and provides the results to the users.

[0533] In the system that implements this invention, the user first uses a terminal to input their financial information and daily sentiment information. The terminal securely transmits this information to a cloud server. The server performs analysis based on the collected financial and sentiment information. This analysis can utilize machine learning algorithms, specifically the TensorFlow library in Python. For collecting sentiment information, Google's ML Kit is used to analyze emotions in real time using the smartphone's camera and microphone.

[0534] The server integrates this data and generates a personalized life plan tailored to the user's characteristics and behavior. Furthermore, the generated plan is sent back to the device and presented to the user. At this time, payment advice and feedback are provided based on the user's emotional state. For example, if the system determines that the user is stressed, advice to reduce spending will be displayed. On the other hand, if the user is in a positive emotional state, suggestions for new investments will be made.

[0535] These system processes can run on AWS cloud services, enabling scalable data processing while maintaining a high level of security.

[0536] For example, in a scenario where a user says, "My spending has increased recently, and I'm feeling stressed," the system first recognizes this emotion, extracts areas where savings can be made from past spending data, and presents them to the user. In another use case, where a user says, "I'm feeling good today and considering investments," the system can present new investment options.

[0537] Examples of prompts include the following:

[0538] Use the user's emotional state and financial data to design appropriate purchase advice. For example, consider how to suggest saving tips when the user is stressed, and investment opportunities when they are feeling positive.

[0539] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0540] Step 1:

[0541] The user inputs financial and sentiment information using a device. The inputted financial information includes data such as income, expenses, and savings. Sentiment information is captured in real time using the smartphone's camera and microphone. The output here is a set of the user's financial and sentiment data.

[0542] Step 2:

[0543] The terminal securely transmits this financial and emotional information to the cloud server. Data encryption technology is used for transmission. The server verifies the integrity of the received data and stores it in a database. The input is encoded information, and the output is data stored on the server.

[0544] Step 3:

[0545] The server executes machine learning algorithms to analyze stored financial and sentiment data. TensorFlow is used for data analysis, extracting user characteristics and patterns. The input is data from a database, and the output is the analysis results.

[0546] Step 4:

[0547] The server generates a life plan tailored to the user's characteristics and behavior based on the analysis results. This plan includes a life plan that considers income and expenditure balance, as well as feedback appropriate to the user's emotional state. The input is the analysis results, and the output is a specific life plan.

[0548] Step 5:

[0549] The generated life plan and advice are sent to the device and presented to the user. The device displays the information in a way that is appropriate for the user and recommends the next action. The input here is the life plan, and the output is feedback and advice to the user.

[0550] Step 6:

[0551] The user makes decisions based on the feedback provided, and in some cases, inputs new financial data or confirms sentiment information. This loop allows the system to continuously monitor the user's state and update its advice. The input is the user's actions, and the output is the updated feedback.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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".

[0569] This invention constructs a system that effectively utilizes users' financial information to support the achievement of their financial goals. This system is initiated when a user inputs financial data from an individual terminal. The terminal securely transmits this data to a server. The server analyzes the received data and uses machine learning algorithms to generate an optimal life plan based on the user's income, expenses, and financial attributes.

[0570] Specifically, the server analyzes the user's past spending patterns and provides feedback to the user via the terminal to help reduce wasteful spending. For example, if the user inputs their monthly income and expenses and it is determined that they have many unnecessary expenses, the server will make specific suggestions on how to reduce those expenses.

[0571] Furthermore, the server takes into account future life events set by the user, evaluates their economic impact, and automatically generates an appropriate financial plan. This plan is presented to the user via the terminal, and the user can then decide on specific actions based on it.

[0572] Furthermore, the server constantly retrieves the user's latest income and expense data and uses that information to propose asset management and investment portfolios. This allows users to flexibly respond to fluctuating market conditions while maximizing their returns.

[0573] Thus, the system of the present invention helps users make wiser financial decisions by combining real-time data analysis with personalized life planning.

[0574] The following describes the processing flow.

[0575] Step 1:

[0576] The user enters their financial data (income, expenses, asset information, and future life events) into the terminal. The terminal encodes the entered data and sends it to the server via a secure protocol.

[0577] Step 2:

[0578] The server stores the financial data received from the terminal into a database. During this process, it checks for inconsistencies and missing data and performs data cleansing to prepare the input data for analysis.

[0579] Step 3:

[0580] The server inputs the organized data into a machine learning algorithm to analyze the user's past income and expenditure patterns and spending trends. Based on this analysis, it understands the user's lifestyle and consumption patterns.

[0581] Step 4:

[0582] The server generates an optimal life plan for the user. This plan optimizes income and expenses based on past data analysis results and combines this with a long-term economic plan that addresses future life events.

[0583] Step 5:

[0584] The server generates savings suggestions and investment advice in real time, along with the generated life plan. This includes asset management strategies and investment portfolio suggestions that take risk diversification into account.

[0585] Step 6:

[0586] The server sends this life plan and related suggestions to the terminal. The terminal presents it to the user in an appropriate interface, providing a foundation for the user to review the plan and decide on actions.

[0587] Step 7:

[0588] Once a user begins using the service, the device continuously records their spending. The device keeps the user's information up-to-date by sending new spending data to the server.

[0589] Step 8:

[0590] The server reflects the user's latest spending data and adjusts life plans and advice as needed. This ensures that the user always has a foundation for making optimal financial decisions.

[0591] (Example 1)

[0592] 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".

[0593] In today's economic environment, it is a challenging task for individuals to accurately understand their own financial situation and effectively achieve their long-term economic goals. In particular, balancing income and expenses, and developing asset management and investment strategies to respond to fluctuating market conditions, is a significant burden for many individuals. There is a need for systems that address this challenge and support users in making wise financial decisions.

[0594] 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.

[0595] In this invention, the server includes means for receiving economic information collected from the user, means for analyzing the collected economic information and generating an optimal life plan tailored to the user's individual attributes and behavior, and means for providing the generated life plan to the user and presenting it in an intuitively understandable format. This enables the user to grasp their own economic situation in real time and to plan and execute a plan that meets their individual needs.

[0596] A "user" is an entity that uses the system to register, manage, and receive personal financial information.

[0597] "Economic information" refers to data on income, expenses, savings, investments, and assets, and is important information for representing an individual's financial situation.

[0598] A "server" refers to a computer system that receives information from users, analyzes it, and generates and provides the results.

[0599] A "life plan" refers to a specific action plan regarding income, expenses, savings, and investments, created to support an individual in achieving their financial goals.

[0600] "Individual attributes" refer to information that represents the unique characteristics of each individual user, such as their economic situation, goals, and behavioral patterns.

[0601] An "intuitively understandable format" refers to a method of presentation where generated information or plans are visually displayed, allowing the content to be grasped at a glance.

[0602] "Asset management" refers to management methods for optimally utilizing the assets owned by a user.

[0603] An "investment strategy" refers to a plan for determining asset allocation and investment policies, taking into account risk and return.

[0604] This invention is a system that effectively manages personal financial information and provides support for achieving goals. This system consists of three main components: the user, the terminal, and the server.

[0605] Users input financial information such as income, expenses, and savings via their devices. These devices refer to general information processing equipment such as smartphones and computers. The devices transmit the input information to the server using secure communication protocols.

[0606] The server uses a Python-based machine learning library (e.g., Scikit-learn) to analyze the received economic information. This analysis includes analyzing the user's past spending patterns and attribute data. Based on this, an optimal lifestyle plan is generated that is tailored to the user's behavior. This lifestyle plan is intuitively represented using graphs and charts and fed back to the user's device.

[0607] For example, if the system determines that entertainment expenses need to be reduced based on the user's monthly spending data, it will provide specific suggestions on how to do so. These suggestions might take the form of, "Let's think of ways to reduce next month's leisure budget by 20%."

[0608] Furthermore, the server automatically generates financial plans for long-term life events set by the user, such as purchasing a home or preparing for education expenses. Each time the user's income and expenditure data is updated, the server uses the new data to analyze and adjust its asset management and investment strategy suggestions. This process enables real-time data updates, allowing for flexible adaptation to fluctuating market conditions.

[0609] An example of a prompt message might be a request such as, "Based on the user's income and spending patterns, please provide suggestions for eliminating waste."

[0610] This system supports users' financial decision-making and promotes better goal achievement by providing real-time data analysis and personalized life planning.

[0611] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0612] Step 1:

[0613] Users input their financial information using their devices. Specifically, they input monthly income, expenses, and savings goals. The input data is converted into a server-friendly format, such as JSON or CSV. The converted data is then sent to the server using a secure protocol.

[0614] Step 2:

[0615] The server verifies the integrity of the input data based on the economic information received from the terminal. This involves checking the data format and detecting outliers. During this process, data cleansing is performed, and any errors or incomplete items are automatically corrected or omitted. This ensures that clean data is obtained that does not affect subsequent data analysis.

[0616] Step 3:

[0617] The server analyzes the data, ensuring its integrity. Specifically, it uses machine learning models (e.g., models using Scikit-learn) to analyze user spending patterns and income fluctuations. The input is the user's historical economic data, and the output includes trend analysis based on user attributes and areas where spending can be reduced.

[0618] Step 4:

[0619] The server generates an optimal lifestyle plan based on the analysis results. Utilizing a generation AI model, it formulates a plan that takes into account the user's specific attributes and long-term goals. The output includes a detailed lifestyle plan and suggestions for saving money, including visual representations such as graphs and charts.

[0620] Step 5:

[0621] The server sends the generated life plan to the terminal. The terminal displays the received information in a way that the user can intuitively understand. This display often uses an interactive graphical user interface. Based on this information, the user evaluates their financial behavior and adjusts the life plan as needed.

[0622] Step 6:

[0623] The server regularly receives user feedback and new economic information, and reanalyzes the model. This allows it to propose adaptive plans and investment strategies to users that are in line with the latest market trends and economic conditions. The generative AI model continues to learn from the feedback, improving its accuracy and usefulness.

[0624] (Application Example 1)

[0625] 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".

[0626] In recent years, individual consumers have engaged in a variety of financial transactions, which has led to a demand for efficient asset management and optimized consumption. However, it is not easy for consumers to grasp the details of their own spending and investments and design an accurate life plan. In particular, providing users with plans that aim for a prosperous future while curbing wasteful spending is a challenging task.

[0627] 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.

[0628] In this invention, the server includes means for receiving financial data collected from the user, means for analyzing the collected financial data and generating an optimal lifestyle plan tailored to the user's group and activities, and means for providing the user with savings suggestions to curb wasteful spending based on payment information. This enables the user to optimize their spending and manage their assets effectively.

[0629] A "user" refers to an individual who provides financial information and receives support regarding life planning and asset management.

[0630] "Financial data" refers to the collection of information about income, expenses, assets, and investments provided by users.

[0631] A "server" refers to a central data processing unit that receives users' financial data, analyzes it, and makes recommendations.

[0632] A "life plan" refers to information that outlines efficient consumption and asset management strategies, generated based on the user's income and expenses and future goals.

[0633] "Payment information" refers to a portion of financial data, including details about purchases and transactions made by a user.

[0634] "Savings suggestions to curb wasteful spending" refers to specific advice and measures provided to reduce unnecessary expenses by analyzing the user's spending habits.

[0635] To realize this invention, a system is needed that collects financial data from a user's device and transmits it to a server. The device should have an easy-to-use interface for the user and allow them to input financial information such as income, expenses, and asset status.

[0636] The server receives financial data sent by users and utilizes machine learning algorithms as a means of data analysis. These algorithms can use data analysis programming languages ​​such as Python and R to analyze users' past spending patterns and attribute data, and generate optimized life plans. Furthermore, scalable, real-time data processing is enabled by utilizing cloud infrastructure such as Amazon Web Services (AWS) and Google Cloud Platform.

[0637] The server provides users with generated life plans and asset management suggestions via their devices. Specific methods of delivery include presenting information clearly through regularly updated dashboards and notifications. For example, if there's a possibility of exceeding the budget at the end of the month, the server immediately notifies the user and offers saving suggestions.

[0638] Users can optimize their daily spending habits based on feedback from the server. For example, specific savings suggestions might be made, such as, "Cutting your monthly coffee spending by 2,000 yen will free up enough money for one trip per year." This is possible because the machine learning model used is a generative AI model that understands context from the user's past spending data. By using prompts such as, "Analyze the user's recent spending patterns, identify areas of wasteful spending, and suggest specific savings," the generative AI model can provide customized suggestions for each user.

[0639] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0640] Step 1:

[0641] The user uses a terminal to input financial information such as income, expenses, and asset status. Based on this input, the terminal formats the data and sends it to the server using a secure protocol.

[0642] Step 2:

[0643] The server stores the financial data received from the terminals in a database. This data is managed in a way that allows for user identification and is used for subsequent analysis.

[0644] Step 3:

[0645] The server uses machine learning algorithms to analyze historical financial data stored in the database. This analysis generates models of the user's spending patterns and future wealth building. The input is financial data, and the output is an optimized life plan and asset management proposal.

[0646] Step 4:

[0647] The generated life plan and asset management suggestions are sent from the server to the terminal. The terminal receives this and displays it to the user as visual feedback in the form of a dashboard or notification.

[0648] Step 5:

[0649] Based on feedback provided by the device, users review their daily spending habits. In this process, a "generative AI model" utilizes prompts to offer additional savings suggestions based on the user's behavior, thereby encouraging them to optimize their spending.

[0650] 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.

[0651] This invention combines an emotional engine with a system that utilizes users' financial data to support the achievement of their economic goals. In addition to providing life plans that take into account the user's financial situation and lifestyle, this system enables more personalized support by providing feedback based on the user's emotions.

[0652] First, the user inputs financial data using a terminal. The terminal securely transmits this data to a server, which stores the data in a database and analyzes it using machine learning algorithms. In this process, a life plan is generated that takes into account the user's income and expenditure patterns and life events.

[0653] The emotion engine recognizes the user's emotional state in real time based on user input and data collected from sensors on the device. The emotion engine integrates this emotional information with other financial data to generate advice that helps the user maintain a positive mindset or reduce stress and anxiety.

[0654] For example, if the emotion engine determines that a user is in a high-stress state, the server sends suggestions to the device for short-term savings or actions that promote relaxation. On the other hand, if the system determines that the user is in a positive state, it provides advice that encourages a more proactive investment strategy or a review of plans toward future goals.

[0655] This system allows users to receive a more holistic and adaptive life plan that reflects not only their financial data but also their emotional state. This can encourage smarter financial decision-making and contribute to improving users' quality of life.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The user enters their financial data (income, expenses, and asset information) through the device. Once the user has finished entering the data, the device encrypts the data and sends it to the server.

[0659] Step 2:

[0660] The server receives this data and stores it in the database. It then checks for omissions and inconsistencies and performs data cleansing as needed to ensure the data is properly organized.

[0661] Step 3:

[0662] The server uses machine learning algorithms to analyze the user's financial data. It analyzes past income and spending patterns to generate an optimal life plan for the user.

[0663] Step 4:

[0664] Emotional data is collected from the user's device. This data is obtained from various sources, such as biosensors and entered text. The device then sends this data to a server.

[0665] Step 5:

[0666] The emotion engine installed on the server analyzes the received emotional data. It determines the user's current emotional state and reflects the results in providing a life plan.

[0667] Step 6:

[0668] The server adjusts the life plan according to the user's emotions. If the user is under high stress, it creates a plan that minimizes risk; if they are in a positive state, it creates a more challenging plan.

[0669] Step 7:

[0670] The server generates an integrated life plan and additional advice based on emotions, and sends it to the device.

[0671] Step 8:

[0672] The device displays the life plan and advice it receives to the user. Based on this, the user adjusts their daily economic activities and makes economic decisions that take emotional well-being into consideration.

[0673] (Example 2)

[0674] 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".

[0675] Managing personal finances in the modern era requires more than just understanding numerical data; it demands comprehensive support that takes into account an individual's emotional state. However, conventional systems are specialized in analyzing financial data and lack feedback based on the user's emotional state. As a result, users do not receive effective advice to cope with stress and anxiety, making financial decision-making difficult. Therefore, there is a need to integrate users' financial data with their emotional state to provide more personalized advice.

[0676] 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.

[0677] In this invention, the server includes means for receiving economic information, means for analyzing economic information and generating an optimal life plan, means for providing the life plan, and means for recognizing the user's emotional state and providing advice. This enables comprehensive support based on both the user's financial information and emotional state.

[0678] "Economic information" refers to numerical data about a user's income, expenses, savings, investments, etc., and is collected to understand the flow of money.

[0679] "Analysis" is the process of using statistical methods and algorithms to derive patterns and trends behind collected data.

[0680] "Life planning" refers to a plan for achieving long-term economic goals, proposed based on the user's financial situation and lifestyle.

[0681] "Providing" refers to the act of supporting decision-making by presenting generated or analyzed information or advice to the user.

[0682] "Emotional state" refers to the user's mental and emotional condition, which includes feelings such as happiness, stress, and anxiety.

[0683] "Advice" refers to specific suggestions that provide guidance or recommendations for action based on the user's specific situation or goals.

[0684] This invention is a system that provides personalized life planning and advice based on the user's financial information and emotional state. At the heart of this system are the following roles played by the server, terminal, and user:

[0685] First, the user uses a device to input financial information about their income and expenses. The device encrypts the entered data and securely transmits it to the server. A dedicated application is installed on the device, allowing the user to easily input and manage their data.

[0686] The server stores the received economic information in a database and analyzes the collected data using machine learning algorithms. The software used in this process includes, for example, analysis scripts developed in Python, which extract income and expenditure patterns from the data and generate life plans. The generated life plans are tailored to the user's individual needs, taking into account both short-term and long-term goals.

[0687] On the other hand, the device is also equipped with an emotion engine. This emotion engine has the function of analyzing the user's emotional state in real time from input data and sensor information on the device. The analysis results are sent to a server, integrated with economic data, and then reflected as feedback tailored to the user's emotional state.

[0688] For example, if a user is experiencing high levels of stress, the server can use a generative AI model to generate prompts such as, "Try these simple money-saving methods. Taking a rest on certain days can also be effective," and suggest them to the user through their device. In this way, by supporting the user from both economic and emotional perspectives, it is possible to support healthier and more effective decision-making.

[0689] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0690] Step 1:

[0691] The user enters financial information into the terminal. This data includes income, expenses, and savings goals. The terminal formats and encrypts the input data before sending it to the server. In this step, the input is the user's financial information, and the output is in encrypted data format.

[0692] Step 2:

[0693] The server stores the financial data received from the terminal in a database. The stored data is analyzed using a Python analysis script. Specifically, the server runs a machine learning algorithm to identify the user's income and expenditure patterns and generate an optimal financial plan. The input for this step is encrypted data sent to the server, and the output is a financial plan customized for the user.

[0694] Step 3:

[0695] The device collects information about the user's emotional state through other sensors. The emotion engine analyzes this information and determines the user's emotional state in real time. The input for this step is sensor information related to emotions, and the output is the analyzed emotional state of the user.

[0696] Step 4:

[0697] The server integrates the user's life plan and emotional state. Using a generative AI model, it generates prompt messages appropriate to the user's current emotions and financial situation. For example, if the user is showing high stress levels, it will generate advice suggesting short-term saving methods. The input for this step is the user's life plan and emotional state, and the output is the generated prompt message.

[0698] Step 5:

[0699] The terminal presents the user with prompt messages received from the server. It displays suggestions through the application interface and uses notification features to alert the user as needed. The input for this step is the prompt message from the server, and the output is specific advice to support the user's decision-making.

[0700] (Application Example 2)

[0701] 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".

[0702] In modern society, users are surrounded by a wide variety of financial information, but managing this information appropriately and creating effective life plans and asset management strategies is not easy. Furthermore, since daily emotional states influence economic decision-making, there is a need for support in enabling users to conduct economic activities while taking their emotional state into account. Conventional systems have the challenge of being unable to provide life plans and feedback that take this emotional information into account.

[0703] 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.

[0704] In this invention, the server includes means for receiving financial and emotional information collected from the user, means for analyzing the collected financial and emotional information and generating an optimal life plan tailored to the user's characteristics and behavior, and means for providing the generated life plan to the user and generating payment advice to support its implementation. As a result, the user can obtain a life plan that comprehensively considers their financial and emotional information, receive appropriate feedback according to their emotional state, and make better economic decisions.

[0705] A "user" is the entity that utilizes this system, providing financial and emotional information, and receiving life plans and feedback.

[0706] "Financial information" refers to data on a user's economic activities, such as income, expenses, savings, and investments, and is used to form financial plans.

[0707] "Emotional information" refers to data about a user's current emotional state, which the system collects and analyzes as a factor that influences daily decision-making.

[0708] A "life plan" refers to long-term or short-term goals and action plans generated based on a user's financial and emotional information, and serves as the foundation for supporting the user in achieving their goals.

[0709] "Payment advice" refers to specific action guidelines and suggestions provided to support economic decision-making, taking into account the user's financial situation and emotional state.

[0710] "Feedback" refers to information or instructions provided to users in real time, which are used to comprehensively assess their performance and emotional state in order to encourage appropriate action.

[0711] A "server" refers to a computer system that receives, stores, and analyzes users' financial and emotional information, and provides the results to the users.

[0712] In the system that implements this invention, the user first uses a terminal to input their financial information and daily sentiment information. The terminal securely transmits this information to a cloud server. The server performs analysis based on the collected financial and sentiment information. This analysis can utilize machine learning algorithms, specifically the TensorFlow library in Python. For collecting sentiment information, Google's ML Kit is used to analyze emotions in real time using the smartphone's camera and microphone.

[0713] The server integrates this data and generates a personalized life plan tailored to the user's characteristics and behavior. Furthermore, the generated plan is sent back to the device and presented to the user. At this time, payment advice and feedback are provided based on the user's emotional state. For example, if the system determines that the user is stressed, advice to reduce spending will be displayed. On the other hand, if the user is in a positive emotional state, suggestions for new investments will be made.

[0714] These system processes can run on AWS cloud services, enabling scalable data processing while maintaining a high level of security.

[0715] For example, in a scenario where a user says, "My spending has increased recently, and I'm feeling stressed," the system first recognizes this emotion, extracts areas where savings can be made from past spending data, and presents them to the user. In another use case, where a user says, "I'm feeling good today and considering investments," the system can present new investment options.

[0716] Examples of prompts include the following:

[0717] Use the user's emotional state and financial data to design appropriate purchase advice. For example, consider how to suggest saving tips when the user is stressed, and investment opportunities when they are feeling positive.

[0718] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0719] Step 1:

[0720] The user inputs financial and sentiment information using a device. The inputted financial information includes data such as income, expenses, and savings. Sentiment information is captured in real time using the smartphone's camera and microphone. The output here is a set of the user's financial and sentiment data.

[0721] Step 2:

[0722] The terminal securely transmits this financial and emotional information to the cloud server. Data encryption technology is used for transmission. The server verifies the integrity of the received data and stores it in a database. The input is encoded information, and the output is data stored on the server.

[0723] Step 3:

[0724] The server executes machine learning algorithms to analyze stored financial and sentiment data. TensorFlow is used for data analysis, extracting user characteristics and patterns. The input is data from a database, and the output is the analysis results.

[0725] Step 4:

[0726] The server generates a life plan tailored to the user's characteristics and behavior based on the analysis results. This plan includes a life plan that considers income and expenditure balance, as well as feedback appropriate to the user's emotional state. The input is the analysis results, and the output is a specific life plan.

[0727] Step 5:

[0728] The generated life plan and advice are sent to the device and presented to the user. The device displays the information in a way that is appropriate for the user and recommends the next action. The input here is the life plan, and the output is feedback and advice to the user.

[0729] Step 6:

[0730] The user makes decisions based on the feedback provided, and in some cases, inputs new financial data or confirms sentiment information. This loop allows the system to continuously monitor the user's state and update its advice. The input is the user's actions, and the output is the updated feedback.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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."

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] The following is further disclosed regarding the embodiments described above.

[0753] (Claim 1)

[0754] A means of receiving financial data collected from users,

[0755] A means of analyzing collected financial data and generating an optimal life plan tailored to the user's attributes and behavior,

[0756] A means of providing the generated life plan to the user,

[0757] A means of monitoring users' daily spending and providing real-time feedback based on that spending information,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, which automatically generates and provides a financial plan to the user based on the user's long-term life events.

[0761] (Claim 3)

[0762] The system according to claim 1, which automatically proposes a user's asset management and investment portfolio.

[0763] "Example 1"

[0764] (Claim 1)

[0765] A means of receiving economic information collected from users,

[0766] A means of analyzing collected economic information and generating an optimal lifestyle plan tailored to the individual user's attributes and behavior,

[0767] A means of providing the generated life plan to the user and presenting it in an intuitively understandable format,

[0768] A means of monitoring a user's daily spending and providing information immediately based on that spending data,

[0769] A means of continuously obtaining the latest income and expenditure information of users and using it for decision-making,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, which automatically generates and provides a financial plan to the user based on the user's long-term life events.

[0773] (Claim 3)

[0774] The system according to claim 1, which automatically proposes asset management and investment plans for the user.

[0775] "Application Example 1"

[0776] (Claim 1)

[0777] A means of receiving financial data collected from users,

[0778] A means of analyzing collected financial data and generating an optimal lifestyle plan tailored to the user's group and activities,

[0779] A means of providing the generated life plan to the user,

[0780] A means of monitoring users' daily consumption and providing immediate feedback based on that consumption data,

[0781] A means of providing users with savings suggestions to curb unnecessary spending based on payment information,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, which automatically generates and provides to the user an economic plan based on the user's long-term life events.

[0785] (Claim 3)

[0786] The system according to claim 1, which automatically proposes a user's financial asset management and investment strategy.

[0787] "Example 2 of combining an emotion engine"

[0788] (Claim 1)

[0789] A means of receiving economic information collected from users,

[0790] A means of analyzing collected economic information and generating an optimal lifestyle plan tailored to the user's characteristics and behavior,

[0791] A means of providing the generated life plan to the user,

[0792] A means of monitoring users' daily spending and providing real-time feedback based on that spending information,

[0793] A means of recognizing the user's emotional state and providing personalized advice based on that emotional information,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, which automatically generates and provides a financial plan to the user based on the user's long-term life events.

[0797] (Claim 3)

[0798] The system according to claim 1, which automatically proposes asset management and investment strategies for users.

[0799] "Application example 2 when combining with an emotional engine"

[0800] (Claim 1)

[0801] A means of receiving financial and emotional information collected from users,

[0802] A means of analyzing collected financial and emotional information to generate an optimal life plan tailored to the user's characteristics and behavior,

[0803] A means of providing users with a generated life plan and generating payment advice to support its implementation,

[0804] A means of monitoring users' daily spending and emotional state, and providing real-time personalized feedback based on that information,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, which automatically generates and provides a financial plan to the user based on the user's long-term life events and emotional changes.

[0808] (Claim 3)

[0809] The system according to claim 1, which automatically proposes a user's asset management and investment plan, taking into account their emotional state. [Explanation of Symbols]

[0810] 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 receiving financial data collected from users, A means of analyzing collected financial data and generating an optimal life plan tailored to the user's attributes and behavior, A means of providing the generated life plan to the user, A means of monitoring users' daily spending and providing real-time feedback based on that spending information, A system that includes this.

2. The system according to claim 1, which automatically generates and provides a financial plan to the user based on the user's long-term life events.

3. The system according to claim 1, which automatically proposes a user's asset management and investment portfolio.

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