system

Abstract

Provide a system. 【Solution means】 Means for collecting the user's financial data and safely storing it in a data storage device, Means for analyzing the collected financial data and performing a comparative analysis of different financial products, Means for generating a notification of asset management activities based on the user's schedule information, Means for providing customized educational materials based on the user's learning level, Means for displaying a future asset plan in 3D visualization based on the user's investment objectives, Means for anonymizing the collected data and ensuring information security, Means for presenting real-time investment analysis results and assisting in optimizing transactions, Means for enabling the user to virtually experience a lifestyle using the visualized asset plan, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern users with busy lives, there are problems such as lack of knowledge for asset management, time constraints, and dissatisfaction with unfair advice based on biased information from financial institutions. There is also anxiety about user privacy protection, which has become an obstacle to asset management. Therefore, there is a need for a system that can perform asset management efficiently and with peace of mind using objective and fair information.

Means for Solving the Problems

[0005] This invention provides a means for securely collecting and analyzing users' financial data and fairly comparing multiple financial products. Furthermore, it supports efficient asset management by reminding users of important asset management events based on their schedules. It also aims to improve users' knowledge of asset management by providing learning content tailored to their individual knowledge levels. In addition, it enhances user motivation by visualizing future asset management simulations in 3D. Through data anonymization and encryption, a high level of security is ensured, allowing users to use the system with peace of mind.

[0006] "Financial data" refers to information related to a user's assets, including bank account information, investment information, and loan information.

[0007] A "database" is a structured collection of data used to systematically manage and access information.

[0008] Comparative analysis is the process of evaluating multiple financial products from various perspectives to determine their relative value and performance.

[0009] A "reminder" is a function that notifies the user of the due date for a specific event or task.

[0010] "Learning content" refers to educational materials and information aimed at improving users' knowledge, and includes articles, videos, infographics, and other similar content.

[0011] "3D simulation" is a technology that uses computer technology to visualize the results of asset management in three dimensions.

[0012] "Anonymization" is a data processing technique that transforms personal information in a way that makes it impossible to identify a specific individual.

[0013] "Security" is a collection of technologies and methods used to protect information from unauthorized access and data breaches. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] [[ID=十七]]It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] As an embodiment of the present invention, a system is provided that allows busy users to efficiently manage their assets by using AI technology. This system consists of the interaction of a server, a terminal, and a user.

[0036] The server first collects financial data in real time based on the financial institution account information provided by the user. The accumulated financial data is encrypted and securely stored in a database. Based on this data, the server analyzes multiple financial products and evaluates their performance and risk, enabling comparative analysis of different financial products.

[0037] Users can access the system using a terminal and enter their own schedules. Based on this schedule information, the terminal generates reminders and notifies users of important events related to asset management. This feature allows users to take the necessary actions for asset management at the appropriate time.

[0038] Furthermore, the server assesses the user's knowledge level and provides customized learning content accordingly. For example, a user who is a beginner in asset management will be provided with materials containing basic information, while an advanced user will be provided with advanced information on the latest market trends.

[0039] Furthermore, the server performs asset management simulations based on the investment goals set by the user. The simulation results are sent to the terminal and visually displayed using 3D graphics, allowing the user to intuitively understand their future asset situation. For example, upon successful investment, the user can experience a 3D simulation of the lifestyle achievable with those assets.

[0040] Finally, the server uses advanced security protocols to anonymize data and protect users' personal information. This allows us to provide safe and reliable asset management support while alleviating privacy concerns.

[0041] Thus, the present invention is implemented as a system that enables personalized asset management support for individual users and efficiently solves problems such as lack of knowledge and time. A concrete example is a system in which a server evaluates the user's asset situation, proposes an optimal stock portfolio, the user checks the information via a terminal, and executes transactions according to the reminders.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users log in to the system via a terminal and enter their financial institution account information and life events. The server receives this information securely and stores it in a database.

[0045] Step 2:

[0046] The server uses financial institutions' APIs to collect users' latest financial data. The collected data is stored in a database in an encrypted state.

[0047] Step 3:

[0048] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. This allows for comparative analysis of different financial products.

[0049] Step 4:

[0050] Users use the device to enter their schedules and set up events and tasks related to asset management. The device generates reminders based on the entered schedule information.

[0051] Step 5:

[0052] The device notifies the user of asset management events at appropriate times based on their schedule. This ensures that the user does not forget to take important actions.

[0053] Step 6:

[0054] The server provides quizzes to assess the user's knowledge level and generates customized learning content based on the results. Content tailored to the user is delivered via the device.

[0055] Step 7:

[0056] The server takes into account the investment goals and simulation parameters set by the user and runs a future asset management simulation. The simulation results are sent to the terminal as 3D graphics.

[0057] Step 8:

[0058] The terminal visually displays the 3D simulation results sent from the server. This allows users to intuitively understand future asset management results and track their progress towards their goals.

[0059] Step 9:

[0060] The server periodically anonymizes all data within the system and implements advanced security protocols to protect user privacy. It monitors for signs of unauthorized access and ensures data security.

[0061] (Example 1)

[0062] 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."

[0063] Busy users face challenges such as a lack of time and knowledge when managing their assets, as well as the need to address the security and privacy concerns of financial data. Furthermore, it is necessary to support users' investment decisions through personalized information provision and intuitive asset management simulations.

[0064] 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.

[0065] In this invention, the server includes means for obtaining financial institution account information from users and collecting financial data in real time, means for encrypting the financial data and storing it in a database, and means for performing comparative analysis using an algorithm to evaluate the risk and performance of financial products. This enables users to manage their assets efficiently and securely.

[0066] "Financial data" refers to information about a user's account balance and transaction history obtained from a financial institution.

[0067] "Encryption" is a technique that transforms data using a specific protocol, so that only authorized individuals can decrypt that data.

[0068] A "database" is a structured data storage system for systematically storing financial data and related information, and for efficiently retrieving it as needed.

[0069] Comparative analysis is the process of evaluating the characteristics of multiple financial products and comparing them relatively from the perspectives of risk and performance.

[0070] A "reminder" is an alert function that specifies the time and content to notify the user of a particular asset management event.

[0071] "Knowledge level" is a measure that indicates the amount of information and understanding a user has regarding asset management.

[0072] "3D graphics" are visual representations generated in three-dimensional space, and are a technology that allows users to visually perceive information.

[0073] "Anonymization" is a process that modifies data so that individual users cannot be identified, thereby protecting privacy.

[0074] A "machine learning model" is an algorithm or framework used to learn regularities and patterns from data and perform predictions and classifications.

[0075] A "notification service" is a communication function that sends alerts and information to the user's device to prompt necessary actions.

[0076] This invention provides an easy-to-use and highly secure asset management support system by utilizing AI technology and advanced data protection measures. The system consists of three components: a server, a terminal, and a user.

[0077] The server collects financial data in real time based on financial institution account information provided by the user. This is done by using interface API technology to retrieve data directly from the financial institution's system. This data is encrypted using AES-256 encryption technology and securely stored in a secure database (e.g., a NoSQL database).

[0078] The server uses accumulated financial data to execute algorithms that evaluate the performance and risk of financial products, employing the Python language and machine learning libraries. This allows users to compare and analyze multiple financial products.

[0079] The device plays a crucial role in improving the user's asset management experience. Users can input schedule information through a mobile application developed with React Native. This schedule information is synchronized with the Google® Calendar API, and reminders are sent to the user via Firebase Cloud Messaging. In this way, users can make asset management decisions that align with their schedules.

[0080] Furthermore, the server analyzes the user's knowledge level and delivers appropriate learning content to the device. This process utilizes Learning Management System (LMS) technology, and the content provided is customized according to the user's level of understanding.

[0081] For asset management simulations, calculation results performed on the server are sent to the terminal as 3D graphics. This visualization uses Unity or Three.js technology, allowing users to intuitively understand their future asset situation. For example, it can effectively address visual questions such as, "What will my lifestyle be like in three years with this investment?"

[0082] Ultimately, the server uses TLS, an advanced security protocol, to protect communications and safeguard user data and privacy. A concrete example would be a process where the server suggests an optimal stock portfolio based on the user's asset situation, allows the user to review it on their device, and executes trades according to reminders.

[0083] This system can also be used as a backend for analysis and recommendations by inputting a prompt message such as "Please suggest an optimal portfolio based on my personal investment goals" into an AI model. This allows users to manage their assets more efficiently and with greater peace of mind.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server receives financial institution account information provided by the user as input and accesses the financial institution's API based on that information. The API provides financial data such as the user's account balance and transaction history as output. Upon receiving this data, the server encrypts it in AES-256 format using encryption technology such as OpenSSL. The encrypted data is stored in a secure database.

[0087] Step 2:

[0088] The server takes financial data stored in the database as input and performs analysis using the Python language and machine learning libraries. Specifically, it applies algorithms to evaluate the risk and performance of financial products. As a result of the evaluation, comparative evaluation data between financial products is output. This data serves as a basis for users to decide which financial product to choose.

[0089] Step 3:

[0090] The device receives schedule information entered by the user and synchronizes that schedule with the calendar using the Google Calendar API. The synchronized data is used to generate reminders, and the device uses Firebase Cloud Messaging to notify the user of these reminders. This ensures that important events in asset management are communicated to the user in a timely manner.

[0091] Step 4:

[0092] The server takes the user's past learning history and behavioral data as input and analyzes the user's knowledge level using a learning management system. Based on the analysis results, it generates customized learning content, such as basic materials for beginners and specialized information for advanced users. This content is then delivered to the user through their device.

[0093] Step 5:

[0094] The server uses the user's investment goals and current asset information as input to perform an asset management simulation. This simulation calculates future investment scenarios and generates output visualized as 3D graphics. The terminal displays these graphics to the user, helping them to intuitively understand their future asset situation.

[0095] Step 6:

[0096] The server uses data masking technology to anonymize the data handled across the entire system. This process transforms personally identifiable information from the original data, and the anonymized data is securely used for analysis and storage that may be made publicly available. This ensures user privacy.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern society, asset management has become an important element of many people's lives. However, due to busy lifestyles and a lack of expertise in asset management, many users find it difficult to manage their assets effectively. In addition, there is a lack of means to experience investment risk management, investment advice optimized for individual users, and visualized asset planning. As a result, users often find it difficult to intuitively understand and plan their investments.

[0100] 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.

[0101] This invention includes a server that collects user financial data and securely stores it in a data storage device, a server that presents real-time investment analysis results and assists in optimizing transactions, and a server that allows users to virtually experience a lifestyle using a visualized asset plan. This makes it possible for busy users to easily learn about asset management and implement appropriate investment strategies. Furthermore, by providing visual information, users can intuitively understand complex asset plans.

[0102] "Financial data" refers to numerical information and records related to a user's asset management, including information about banking transactions and investment portfolios.

[0103] A "data storage device" is an electronic recording medium or system for securely storing collected financial data.

[0104] "Analysis" is a method of evaluating the characteristics and risks of various financial products based on collected financial data, and then comparing them to derive the optimal investment strategy for the user.

[0105] "Schedule information" refers to date and time information that users use to manage their schedules and activities, and includes appointments related to asset management activities.

[0106] A "notification" is a message or alert sent to inform a user of important activities or deadlines related to asset management.

[0107] "Learning level" is an indicator that shows the user's level of knowledge and understanding, and appropriate educational materials are provided based on this level.

[0108] "Educational materials" refer to documents and content provided to users to help them acquire knowledge about asset management and finance.

[0109] "Investment objectives" refer to the specific goals or objectives that a user hopes to achieve through asset management.

[0110] "Three-dimensional visualization" is a display technology that uses three-dimensional graphics to represent investment simulations and plans, facilitating intuitive understanding for users.

[0111] "Information security" refers to the technologies and protocols used to protect users' personal financial data from unauthorized access and data breaches.

[0112] "Real-time analysis results" refer to the results of analysis performed as soon as financial data is acquired, and the information is presented to the user immediately.

[0113] "Transaction optimization" is a process that uses users' financial data to help them make the most effective investment decisions.

[0114] "Virtual lifestyle experience" is a feature that allows users to virtually experience a life scenario after successful investment through three-dimensional visualization.

[0115] To realize this invention, the system is built primarily on the interaction between servers, terminals, and users. This system combines diverse technologies to streamline asset management.

[0116] The server first collects financial data in real time using authentication information from financial institutions provided by the user. The collected data is protected by advanced information encryption technology before being stored in data storage devices. Next, the server analyzes the financial data using a generative AI model and, based on the results, provides the user with appropriate investment recommendations and risk assessments. This analysis utilizes the latest algorithms and data processing technologies.

[0117] The application installed on the device is used by users to input their schedule information and receive important notifications related to asset management. Furthermore, it provides a three-dimensional visualization based on the user's asset status, allowing users to virtually experience their lifestyle through an intuitive interface on the device.

[0118] Users can improve their asset management skills using educational materials provided from the server via their devices. These materials are customized to the user's learning level, allowing beginners to learn progressively from basic to advanced topics.

[0119] For example, if a user is considering investing in a new market, the system analyzes market data in real time and presents specific investment proposals. Through a visualized asset plan, the user can clearly envision their future life if successful. An example of a prompt in this case might be: "Propose a new investment portfolio, assess the risk level, and visualize your future asset situation with a 3D simulation."

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server receives authentication information from the user for financial institutions and uses that information to collect financial data in real time. The input is the user's authentication information, and using this information, the server sends requests to external financial institution APIs to obtain financial transaction data. The output is the collected financial data.

[0123] Step 2:

[0124] The server encrypts the collected financial data using advanced information encryption technology and securely stores it in a data storage device. The input is financial data, which the server processes using an encryption library, outputs encrypted data, and stores it in the storage device.

[0125] Step 3:

[0126] The server analyzes stored financial data and performs investment analysis using a generated AI model. The input is encrypted financial data, which the server decrypts and passes to a data analysis algorithm for analysis. The output is the analysis result, which includes investment suggestions and risk assessments.

[0127] Step 4:

[0128] The terminal presents the analysis results received from the server to the user. The input is the analysis results generated by the server, which the terminal displays through the user interface. The output is the information displayed to the user.

[0129] Step 5:

[0130] The user enters schedule information related to asset management into the terminal, and the terminal generates reminders for future asset management events that the user needs. The input is the user's schedule information, and the terminal uses a reminder management module to schedule notifications and output the next notification event.

[0131] Step 6:

[0132] The terminal uses three-dimensional visualization technology to visually present the asset plan from the server to the user. The input is asset plan data, which the terminal renders as a 3D model using a graphics engine and displays to the user. The output is a visualized 3D simulation.

[0133] Step 7:

[0134] Users refer to educational materials displayed on their devices and acquire knowledge about asset management tailored to their learning level. The input is customized educational materials, which users view and use to deepen their understanding as output.

[0135] Step 8:

[0136] The server collects data to improve the overall system performance based on user input and feedback. This input consists of user operation logs and feedback data, which the server analyzes to generate suggestions for system improvements.

[0137] 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.

[0138] One embodiment of the present invention provides a system that recognizes user emotions and optimizes the asset management process. This system includes a server, a terminal, an emotion engine, and a user interface.

[0139] The server collects financial data based on account information from financial institutions provided by users and securely stores it in a database. The data is encrypted and its security is ensured. The financial data is analyzed, and the server performs comparative analysis of different financial products.

[0140] Furthermore, users can input schedule information via the device and set reminders for asset management events. The device uses this information to manage reminders so that users can take timely action.

[0141] One of the features of this invention is the use of an emotion engine. The emotion engine analyzes the user's emotional state using user input data, interaction history, and real-time sensor data (for example, acquired through a microphone or camera).

[0142] Once the user's emotional state is analyzed, the server adjusts its investment advice based on this information. For example, if the server detects that the user is stressed, it will prioritize suggesting lower-risk investment strategies. Conversely, if the user is excited, it can present higher-risk options that also offer the potential for greater returns.

[0143] Furthermore, learning content is adjusted based on the user's emotions. For example, when a user shows interest, more advanced information and content about new financial products are provided, while if they are deemed tired, basic content is presented to maintain their motivation.

[0144] For example, when a user accesses the system using their device after a long day of work, the emotion engine detects the user's fatigue, and the system provides learning content that is concise and relaxing. In this way, optimal asset management support tailored to the user's state is achieved.

[0145] This configuration allows users to have a more personalized asset management experience, improving the efficiency and effectiveness of asset management. The server, terminal, and emotion engine work together to provide a high level of adaptability throughout the system.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] Users log in to the system via a terminal and enter their financial institution account information. The server receives this information, securely collects the financial data, and stores it in a database.

[0149] Step 2:

[0150] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. Based on this, it performs comparative analysis of the financial products offered to users.

[0151] Step 3:

[0152] Users enter their schedule information from their device and set reminders for asset management events. The device manages this schedule information and notifies the user of the events.

[0153] Step 4:

[0154] The emotion engine uses sensors attached to the device to analyze the user's voice and facial expressions to determine their emotional state. The emotion engine then sends this information to a server.

[0155] Step 5:

[0156] The server adjusts investment advice based on the user's emotional state, as received from the emotion engine. For example, if the user is feeling stressed, it will recommend safe investment options.

[0157] Step 6:

[0158] The server customizes learning content based on the user's emotional state. If the user is relaxed, it provides detailed content; if they are not focused, it presents concise information.

[0159] Step 7:

[0160] The device displays pre-configured advice and learning content sent from the server to the user. Based on this information, the user makes their next investment decisions.

[0161] Step 8:

[0162] Once the system-wide processing is complete, the server anonymizes user data and implements security measures. This ensures that the service is provided securely while maintaining user privacy.

[0163] (Example 2)

[0164] 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".

[0165] Traditional financial systems have not adequately provided dynamic asset management advice or customized learning materials tailored to users' emotional states and circumstances. Therefore, it has been difficult to provide services that meet individual user needs and emotional states, posing challenges in improving the user experience and streamlining asset management.

[0166] 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.

[0167] In this invention, the server includes means for collecting user information and securely storing it in a storage device, means for analyzing the collected information and performing comparative analysis of different financial products, and means for providing personalized learning materials by analyzing the user's emotional state. This makes it possible to provide optimal asset management advice tailored to the user's emotional state and needs, and to realize an individualized learning experience.

[0168] A "user" is an individual or legal entity that uses the system to provide financial information and receive services.

[0169] "Information" refers to all data necessary for the operation of the system, including financial institution identification information, user schedule information, emotional status, and so on.

[0170] A "storage device" is a physical or virtual medium for storing data, capable of storing and retrieving information.

[0171] "Analysis" is the process of performing an analysis based on collected information, tailored to a specific purpose, to reveal trends and characteristics of the data.

[0172] "Financial products" refer to products and services that users are interested in for investment or asset management purposes, and these include stocks, bonds, and mutual funds.

[0173] Comparative analysis is an analytical method used to compare the risks, returns, and other characteristics of different financial products in order to find the optimal choice.

[0174] "Emotional state" refers to the user's psychological or emotional condition, which the system evaluates and uses to customize the service.

[0175] "Learning materials" include information, educational materials, and content provided to users to improve their asset management and financial knowledge.

[0176] "Anonymization" is a process that removes elements that could identify an individual from collected information in order to protect privacy.

[0177] This invention is a system for personalizing a user's financial asset management. This system includes a server, terminals, an emotion engine, and an interface that connects them. Specific embodiments thereof are described below.

[0178] The server receives financial account information provided by users and collects necessary data from financial institutions. The collected financial information, including transaction history and account balance information, is securely encrypted and stored in storage. Python libraries (e.g., Pandas, NumPy) can be used for data processing and analysis. This is effective for data cleaning and calculating basic statistics.

[0179] The terminal functions as an interface with the user, where the user can input schedule information. Based on this, the terminal generates reminders for important asset management events and notifies the user. Standard applications or email notification systems can be used as the platform for notifications.

[0180] The emotion engine is responsible for analyzing the user's emotional state in real time. It uses data from user interactions with the device and from sensors equipped on the device (e.g., camera and microphone). Open-source libraries (e.g., OpenCV, TENSORFLOW®) are used for facial recognition and voice emotion analysis.

[0181] The server adjusts investment advice based on the analyzed user's emotions. Specifically, if the user is stressed, it suggests a low-risk investment strategy; if the user is proactive, it suggests options that involve risk but offer the potential for return. These suggestions are communicated through the user's digital interface, providing immediate feedback.

[0182] Furthermore, learning materials are personalized based on the user's emotional state. Users who show interest are provided with detailed and rich content, while those who appear fatigued are shown concise information. In this way, users can obtain information tailored to their own motivation.

[0183] For example, if a user accesses the system using their device after a long day of work, the emotion engine will sense the user's fatigue and provide concise learning content that helps them relax. In this way, optimal asset management support tailored to the user's state at any given time is provided.

[0184] An example of a prompt for a generative AI model is: "The user has entered financial information using their device. Use the emotion engine to analyze the user's emotional state and, based on the results, provide appropriate investment advice and learning content."

[0185] This system allows users to receive support for asset management tailored to their needs, thereby improving the efficiency and effectiveness of their asset management.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] The user enters their financial institution account information into the terminal. The entered information includes identification information necessary for linking with the financial institution. The terminal that receives this information sends it to the server. The server uses this identification information to collect the user's financial data from each financial institution. Using encryption technology, the collected data is encrypted according to a predetermined protocol and securely stored in storage.

[0189] Step 2:

[0190] The server analyzes financial data stored in memory. This analysis uses the Python library Pandas for data preprocessing and NumPy for numerical calculations. The data is then subjected to statistical methods and machine learning models to calculate the risk and return of financial products. This provides the user with foundational data for optimal investment strategies. The analysis results are output as advantages and risk profiles for different financial products.

[0191] Step 3:

[0192] Users input their schedule information via their device. This input includes the dates of important asset management events. The device uses this information to set reminders and notify the user at the appropriate time. This reminder information is synchronized to a server using a cloud service, making it manageable across different devices.

[0193] Step 4:

[0194] The device collects interaction and sensor data to infer the user's emotions. Voice and facial expression data are acquired through the microphone and camera. The emotion engine uses this data and TensorFlow to analyze the user's emotional state. The analysis results output emotional states such as stress, fatigue, and interest.

[0195] Step 5:

[0196] The server generates investment advice based on analysis results obtained from the emotion engine. If the server determines that the user is experiencing stress, it will suggest investment plans with reduced risk. These suggestions are presented to the user via email or app notifications, based on the user's investment schedule. This advice is based on the user's emotional state and the latest financial market data.

[0197] Step 6:

[0198] The server also adjusts the learning content according to the user's emotional state. For example, if the user is feeling fatigued, simple and easy-to-understand content is selected. If the user is interested, detailed educational materials on new financial products are provided. The device displays this learning content and helps the user actively engage in learning.

[0199] (Application Example 2)

[0200] 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".

[0201] Existing systems that manage assets and make payments without considering the user's emotional state have the challenge of not being able to effectively provide optimal asset management suggestions and payment recommendations for the user. Furthermore, ignoring the impact of the user's emotional state, such as stress and fatigue, on asset management and consumption behavior may lead to decreased user satisfaction and inefficient asset management.

[0202] 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.

[0203] In this invention, the server includes means for collecting user financial data and securely storing it in a database; means for analyzing the collected financial data and performing comparative analysis of different financial products; means for analyzing the user's emotional state and adjusting asset management advice based on the analysis results; and means for adjusting and recommending payment methods according to the user's emotional state. This makes it possible to provide asset management advice and payment recommendations tailored to the user's emotional state.

[0204] "Financial data" refers to information about a user's assets and transaction records, including deposit balances, spending history, and investment status.

[0205] "Emotional state" refers to the user's mental and emotional state, including psychological elements such as stress, excitement, and fatigue.

[0206] "Asset management advice" refers to information that provides suggestions and strategies for users to efficiently manage and increase their assets.

[0207] "Payment method" refers to the means and processes used by users when making purchases or transactions, and includes cash, credit cards, electronic money, etc.

[0208] A "database" is an information management system built to systematically store financial data and user information so that it can be used for later searching and analysis.

[0209] Comparative analysis is a method for evaluating the characteristics of different financial products and services to determine which one best suits the user's needs.

[0210] "Recommendation" is the act of presenting a user with the most appropriate option based on specific conditions or circumstances.

[0211] This invention is embodied as a system that optimizes asset management and electronic payments based on the user's emotional state. This system consists of a server, a terminal, and an emotion engine.

[0212] The server collects users' financial data and securely stores it in a database. The financial data is encrypted and protected by security measures. The server analyzes the collected financial data and performs comparative analysis of different financial products on behalf of the user.

[0213] The device manages the user's schedule information and provides reminders related to asset management. These reminders are designed to help users take timely action.

[0214] The emotion engine analyzes the user's emotional state using data collected from the device's camera and microphone. Once the user's emotional state is identified, the server adjusts investment advice based on this information. For example, if the user is feeling stressed, the server will recommend a low-risk investment strategy. In electronic payments, the payment method is adjusted based on the emotion engine's analysis, providing suggestions that are best suited to the user's state.

[0215] For example, if a user accesses the system using their device after work, the emotion engine will sense the user's fatigue level. The server will then provide concise and easy-to-understand asset management content, while simultaneously recommending convenient payment options. In this way, users can obtain financial choices optimized for their own emotional state.

[0216] An example of a prompt using a generative AI model is, "Please provide an algorithm that analyzes which payment method a user is most likely to choose based on their current emotional state." This prompt helps model decision-making based on the user's emotional state.

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] The server collects financial data through the user's financial institution account information. This data includes deposit balances, spending history, and investment status, and is securely stored in a database. The input is the user's financial institution account information, and the output is encrypted financial data. Throughout this process, the server uses data encryption technology to protect the data and ensure security.

[0220] Step 2:

[0221] The user uses the device, and the emotion engine analyzes their emotional state based on real-time data obtained from the camera and microphone. The input is the user's voice and facial expression data, and the output is the analyzed emotional state. At this stage, the emotion engine uses a pre-trained model to execute an emotion recognition algorithm and identify emotions such as stress and joy.

[0222] Step 3:

[0223] The server analyzes the user's emotional state, obtained from the emotion engine, and pre-collected financial data to generate optimal investment advice. The input here is the user's emotional state and financial data, and the output is optimized investment advice. The server uses this information to assess risk and adjust the recommendations.

[0224] Step 4:

[0225] The user enters schedule information via the device, and based on this, reminders for asset management events are generated. The input is the user's schedule information, and the output is the set reminders. The device manages this information and sends notifications to the user at the appropriate time.

[0226] Step 5:

[0227] The server recommends a suitable payment method to the terminal, taking into account the user's emotional state. The input is the user's emotional state and payment history, and the output is the recommended payment option. Here, the server performs data analysis to make suggestions that reduce the hassle and stress of payment.

[0228] Step 6:

[0229] The server processes the transaction and completes the payment according to the payment method selected by the user. The input is the payment option selected by the user, and the output is a confirmation of transaction completion. The server securely processes the transaction via the payment network and stores all data encrypted.

[0230] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0231] Data generation model 58 is a type of 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 those described above. 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 shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0232] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0233] [Second Embodiment]

[0234] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0235] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0236] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0237] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0238] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0239] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0240] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0241] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0242] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0243] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0244] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0245] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0246] As an embodiment of the present invention, a system is provided that allows busy users to efficiently manage their assets by using AI technology. This system consists of the interaction of a server, a terminal, and a user.

[0247] The server first collects financial data in real time based on the financial institution account information provided by the user. The accumulated financial data is encrypted and securely stored in a database. Based on this data, the server analyzes multiple financial products and evaluates their performance and risk, enabling comparative analysis of different financial products.

[0248] Users can access the system using a terminal and enter their own schedules. Based on this schedule information, the terminal generates reminders and notifies users of important events related to asset management. This feature allows users to take the necessary actions for asset management at the appropriate time.

[0249] Furthermore, the server assesses the user's knowledge level and provides customized learning content accordingly. For example, a user who is a beginner in asset management will be provided with materials containing basic information, while an advanced user will be provided with advanced information on the latest market trends.

[0250] Furthermore, the server performs asset management simulations based on the investment goals set by the user. The simulation results are sent to the terminal and visually displayed using 3D graphics, allowing the user to intuitively understand their future asset situation. For example, upon successful investment, the user can experience a 3D simulation of the lifestyle achievable with those assets.

[0251] Finally, the server uses advanced security protocols to anonymize data and protect users' personal information. This allows us to provide safe and reliable asset management support while alleviating privacy concerns.

[0252] Thus, the present invention is implemented as a system that enables personalized asset management support for individual users and efficiently solves problems such as lack of knowledge and time. A concrete example is a system in which a server evaluates the user's asset situation, proposes an optimal stock portfolio, the user checks the information via a terminal, and executes transactions according to the reminders.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] Users log in to the system via a terminal and enter their financial institution account information and life events. The server receives this information securely and stores it in a database.

[0256] Step 2:

[0257] The server uses financial institutions' APIs to collect users' latest financial data. The collected data is stored in a database in an encrypted state.

[0258] Step 3:

[0259] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. This allows for comparative analysis of different financial products.

[0260] Step 4:

[0261] Users use the device to enter their schedules and set up events and tasks related to asset management. The device generates reminders based on the entered schedule information.

[0262] Step 5:

[0263] The device notifies the user of asset management events at appropriate times based on their schedule. This ensures that the user does not forget to take important actions.

[0264] Step 6:

[0265] The server provides quizzes to assess the user's knowledge level and generates customized learning content based on the results. Content tailored to the user is delivered via the device.

[0266] Step 7:

[0267] The server takes into account the investment goals and simulation parameters set by the user and runs a future asset management simulation. The simulation results are sent to the terminal as 3D graphics.

[0268] Step 8:

[0269] The terminal visually displays the 3D simulation results sent from the server. This allows users to intuitively understand future asset management results and track their progress towards their goals.

[0270] Step 9:

[0271] The server periodically anonymizes all data within the system and implements advanced security protocols to protect user privacy. It monitors for signs of unauthorized access and ensures data security.

[0272] (Example 1)

[0273] 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."

[0274] Busy users face challenges such as a lack of time and knowledge when managing their assets, as well as the need to address the security and privacy concerns of financial data. Furthermore, it is necessary to support users' investment decisions through personalized information provision and intuitive asset management simulations.

[0275] 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.

[0276] In this invention, the server includes means for acquiring account information of a financial institution from a user and collecting financial data in real time, means for encrypting the financial data and storing it in a database, and means for performing comparative analysis using an algorithm for evaluating the risks and performance of financial products. As a result, the user can efficiently and safely manage assets.

[0277] "Financial data" refers to information regarding the user's account balance and transaction history obtained from a financial institution.

[0278] "Encryption" is a technology that uses a specific protocol to convert data so that only authorized persons can decrypt the data.

[0279] "Database" is a structured data storage system for systematically storing financial data and related information and efficiently retrieving it as needed.

[0280] "Comparative analysis" is an operation of evaluating the characteristics of multiple financial products and relatively comparing them from the perspectives of risk and performance.

[0281] "Reminder" is an alert function that specifies time and content and is set to notify the user of specific asset management events.

[0282] "Knowledge level" is a measure indicating the information and understanding degree that the user has regarding asset management.

[0283] "3D graphics" is a visual representation generated in a three-dimensional space and is a technology that enables the user to visually confirm information.

[0284] "Anonymization" is a process for protecting privacy by changing data so that individual users cannot be identified.

[0285] A "machine learning model" is an algorithm or framework that learns rules and patterns from data for prediction and classification.

[0286] A "notification service" is a communication function that sends alerts and information to a user's terminal to prompt necessary actions.

[0287] The present invention provides an asset management support system that is easy to operate and highly secure by using AI technology and advanced data protection means. The components of this system include a server, a terminal, and a user.

[0288] The server collects financial data in real time based on the account information of financial institutions provided by the user. For this, data is directly obtained from the systems of financial institutions by using interface API technology. This data is encrypted using AES-256 encryption technology and securely stored in a secure database (for example, a NoSQL database).

[0289] Based on the accumulated financial data, the server executes an algorithm for performing performance and risk evaluations of financial products using the Python language and machine learning libraries. As a result, the user can compare and analyze multiple financial products.

[0290] The terminal plays an important role in improving the user's asset management experience. The user can input schedule information through a mobile application developed with React Native. This schedule information is synchronized with the Google Calendar API, and reminders are sent to the user via Firebase Cloud Messaging. Thus, the user can make asset management decisions according to their schedule.

[0291] Furthermore, the server analyzes the user's knowledge level and delivers appropriate learning content to the device. This process utilizes Learning Management System (LMS) technology, and the content provided is customized according to the user's level of understanding.

[0292] For asset management simulations, calculation results performed on the server are sent to the terminal as 3D graphics. This visualization uses Unity or Three.js technology, allowing users to intuitively understand their future asset situation. For example, it can effectively address visual questions such as, "What will my lifestyle be like in three years with this investment?"

[0293] Ultimately, the server uses TLS, an advanced security protocol, to protect communications and safeguard user data and privacy. A concrete example would be a process where the server suggests an optimal stock portfolio based on the user's asset situation, allows the user to review it on their device, and executes trades according to reminders.

[0294] This system can also be used as a backend for analysis and recommendations by inputting a prompt message such as "Please suggest an optimal portfolio based on my personal investment goals" into an AI model. This allows users to manage their assets more efficiently and with greater peace of mind.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The server receives financial institution account information provided by the user as input and accesses the financial institution's API based on that information. The API provides financial data such as the user's account balance and transaction history as output. Upon receiving this data, the server encrypts it in AES-256 format using encryption technology such as OpenSSL. The encrypted data is stored in a secure database.

[0298] Step 2:

[0299] The server takes financial data stored in the database as input and performs analysis using the Python language and machine learning libraries. Specifically, it applies algorithms to evaluate the risk and performance of financial products. As a result of the evaluation, comparative evaluation data between financial products is output. This data serves as a basis for users to decide which financial product to choose.

[0300] Step 3:

[0301] The device receives schedule information entered by the user and synchronizes that schedule with the calendar using the Google Calendar API. The synchronized data is used to generate reminders, and the device uses Firebase Cloud Messaging to notify the user of these reminders. This ensures that important events in asset management are communicated to the user in a timely manner.

[0302] Step 4:

[0303] The server takes the user's past learning history and behavioral data as input and analyzes the user's knowledge level using a learning management system. Based on the analysis results, it generates customized learning content, such as basic materials for beginners and specialized information for advanced users. This content is then delivered to the user through their device.

[0304] Step 5:

[0305] The server uses the user's investment goals and current asset information as input to perform an asset management simulation. This simulation calculates future investment scenarios and generates output visualized as 3D graphics. The terminal displays these graphics to the user, helping them to intuitively understand their future asset situation.

[0306] Step 6:

[0307] The server uses data masking technology to anonymize the data handled throughout the system. In this process, information that can identify individuals is converted from the original data, and the anonymized data can be safely used for external public analysis and storage. This ensures the privacy of users.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In modern society, asset management has become an important element in the lives of many people. However, due to busy lives and a lack of specialized knowledge in asset management, many users find it difficult to conduct effective asset management. In addition, there is a lack of means for investment risk management, providing investment advice optimized for individual users, and experiencing a visualized asset plan. As a result, users often have difficulty in intuitive understanding and formulating plans

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0312] In this invention, the server includes means for collecting the user's financial data and safely storing it in a data storage device, means for presenting real-time investment analysis results and assisting in optimizing transactions, and means for enabling the user to virtually experience a lifestyle using a visualized asset plan. This enables busy users to easily learn asset management and execute appropriate investment strategies. In addition, by providing visual information, users can intuitively understand complex asset plans.

[0313] "Financial data" refers to numerical information and records related to the user's asset management, including information on bank transactions and investment portfolios.

[0314] A "data storage device" is an electronic recording medium or system for securely storing collected financial data.

[0315] "Analysis" is a method of evaluating the characteristics and risks of various financial products based on collected financial data, and then comparing them to derive the optimal investment strategy for the user.

[0316] "Schedule information" refers to date and time information that users use to manage their schedules and activities, and includes appointments related to asset management activities.

[0317] A "notification" is a message or alert sent to inform a user of important activities or deadlines related to asset management.

[0318] "Learning level" is an indicator that shows the user's level of knowledge and understanding, and appropriate educational materials are provided based on this level.

[0319] "Educational materials" refer to documents and content provided to users to help them acquire knowledge about asset management and finance.

[0320] "Investment objectives" refer to the specific goals or objectives that a user hopes to achieve through asset management.

[0321] "Three-dimensional visualization" is a display technology that uses three-dimensional graphics to represent investment simulations and plans, facilitating intuitive understanding for users.

[0322] "Information security" refers to the technologies and protocols used to protect users' personal financial data from unauthorized access and data breaches.

[0323] "Real-time analysis results" refer to the results of analysis performed as soon as financial data is acquired, and the information is presented to the user immediately.

[0324] "Transaction optimization" is a process that uses users' financial data to help them make the most effective investment decisions.

[0325] "Virtual lifestyle experience" is a feature that allows users to virtually experience a life scenario after successful investment through three-dimensional visualization.

[0326] To realize this invention, the system is built primarily on the interaction between servers, terminals, and users. This system combines diverse technologies to streamline asset management.

[0327] The server first collects financial data in real time using authentication information from financial institutions provided by the user. The collected data is protected by advanced information encryption technology before being stored in data storage devices. Next, the server analyzes the financial data using a generative AI model and, based on the results, provides the user with appropriate investment recommendations and risk assessments. This analysis utilizes the latest algorithms and data processing technologies.

[0328] The application installed on the device is used by users to input their schedule information and receive important notifications related to asset management. Furthermore, it provides a three-dimensional visualization based on the user's asset status, allowing users to virtually experience their lifestyle through an intuitive interface on the device.

[0329] Users can improve their asset management skills using educational materials provided from the server via their devices. These materials are customized to the user's learning level, allowing beginners to learn gradually from basic to advanced topics.

[0330] For example, if a user is considering investing in a new market, the system analyzes market data in real time and presents specific investment proposals. Through a visualized asset plan, the user can clearly envision their future life if successful. An example of a prompt in this case might be: "Propose a new investment portfolio, assess the risk level, and visualize your future asset situation with a 3D simulation."

[0331] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0332] Step 1:

[0333] The server receives authentication information from the user for financial institutions and uses that information to collect financial data in real time. The input is the user's authentication information, and using this information, the server sends requests to external financial institution APIs to obtain financial transaction data. The output is the collected financial data.

[0334] Step 2:

[0335] The server encrypts the collected financial data using advanced information encryption technology and securely stores it in a data storage device. The input is financial data, which the server processes using an encryption library, outputs encrypted data, and stores it in the storage device.

[0336] Step 3:

[0337] The server analyzes stored financial data and performs investment analysis using a generated AI model. The input is encrypted financial data, which the server decrypts and passes to a data analysis algorithm for analysis. The output is the analysis results, which include investment suggestions and risk assessments.

[0338] Step 4:

[0339] The terminal presents the analysis results received from the server to the user. The input is the analysis results generated by the server, which the terminal displays through the user interface. The output is the information displayed to the user.

[0340] Step 5:

[0341] The user enters schedule information related to asset management into the terminal, and the terminal generates reminders for future asset management events that the user needs. The input is the user's schedule information, and the terminal uses a reminder management module to schedule notifications and output the next notification event.

[0342] Step 6:

[0343] The terminal uses three-dimensional visualization technology to visually present the asset plan from the server to the user. The input is asset plan data, which the terminal renders as a 3D model using a graphics engine and displays to the user. The output is a visualized 3D simulation.

[0344] Step 7:

[0345] Users refer to educational materials displayed on their devices and acquire knowledge about asset management tailored to their learning level. The input is customized educational materials, which users view and use to deepen their understanding as output.

[0346] Step 8:

[0347] The server collects data to improve the overall system performance based on user input and feedback. This input consists of user operation logs and feedback data, which the server analyzes to generate suggestions for system improvements.

[0348] 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.

[0349] One embodiment of the present invention provides a system that recognizes user emotions and optimizes the asset management process. This system includes a server, a terminal, an emotion engine, and a user interface.

[0350] The server collects financial data based on account information from financial institutions provided by users and securely stores it in a database. The data is encrypted and its security is ensured. The financial data is analyzed, and the server performs comparative analysis of different financial products.

[0351] Furthermore, users can input schedule information via the device and set reminders for asset management events. The device uses this information to manage reminders so that users can take timely action.

[0352] One of the features of this invention is the use of an emotion engine. The emotion engine analyzes the user's emotional state using user input data, interaction history, and real-time sensor data (for example, acquired through a microphone or camera).

[0353] Once the user's emotional state is analyzed, the server adjusts its investment advice based on this information. For example, if the server detects that the user is stressed, it will prioritize suggesting lower-risk investment strategies. Conversely, if the user is excited, it can present higher-risk options that also offer the potential for greater returns.

[0354] Furthermore, learning content is adjusted based on the user's emotions. For example, when a user shows interest, more advanced information and content about new financial products are provided, while if they are deemed tired, basic content is presented to maintain their motivation.

[0355] For example, when a user accesses the system using their device after a long day of work, the emotion engine detects the user's fatigue, and the system provides learning content that is concise and relaxing. In this way, optimal asset management support tailored to the user's state is achieved.

[0356] This configuration allows users to have a more personalized asset management experience, improving the efficiency and effectiveness of asset management. The server, terminal, and emotion engine work together to provide a high level of adaptability throughout the system.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] Users log in to the system via a terminal and enter their financial institution account information. The server receives this information, securely collects the financial data, and stores it in a database.

[0360] Step 2:

[0361] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. Based on this, it performs comparative analysis of the financial products offered to users.

[0362] Step 3:

[0363] Users enter their schedule information from their device and set reminders for asset management events. The device manages this schedule information and notifies the user of the events.

[0364] Step 4:

[0365] The emotion engine uses sensors attached to the device to analyze the user's voice and facial expressions to determine their emotional state. The emotion engine then sends this information to a server.

[0366] Step 5:

[0367] The server adjusts investment advice based on the user's emotional state, as received from the emotion engine. For example, if the user is feeling stressed, it will recommend safe investment options.

[0368] Step 6:

[0369] The server customizes learning content based on the user's emotional state. If the user is relaxed, it provides detailed content; if they are not focused, it presents concise information.

[0370] Step 7:

[0371] The device displays pre-configured advice and learning content sent from the server to the user. Based on this information, the user makes their next investment decisions.

[0372] Step 8:

[0373] Once the system-wide processing is complete, the server anonymizes user data and implements security measures. This ensures that the service is provided securely while maintaining user privacy.

[0374] (Example 2)

[0375] 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".

[0376] Traditional financial systems have not adequately provided dynamic asset management advice or customized learning materials tailored to users' emotional states and circumstances. Therefore, it has been difficult to provide services that meet individual user needs and emotional states, posing challenges in improving the user experience and streamlining asset management.

[0377] 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.

[0378] In this invention, the server includes means for collecting user information and securely storing it in a storage device, means for analyzing the collected information and performing comparative analysis of different financial products, and means for providing personalized learning materials by analyzing the user's emotional state. This makes it possible to provide optimal asset management advice tailored to the user's emotional state and needs, and to realize an individualized learning experience.

[0379] A "user" is an individual or legal entity that uses the system to provide financial information and receive services.

[0380] "Information" refers to all data necessary for the operation of the system, including financial institution identification information, user schedule information, emotional status, and so on.

[0381] A "storage device" is a physical or virtual medium for storing data, capable of storing and retrieving information.

[0382] "Analysis" is the process of performing an analysis based on collected information, tailored to a specific purpose, to reveal trends and characteristics of the data.

[0383] "Financial products" refer to products and services that users are interested in for investment or asset management purposes, and these include stocks, bonds, and mutual funds.

[0384] Comparative analysis is an analytical method used to compare the risks, returns, and other characteristics of different financial products in order to find the optimal choice.

[0385] "Emotional state" refers to the user's psychological or emotional condition, which the system evaluates and uses to customize the service.

[0386] "Learning materials" include information, educational materials, and content provided to users to improve their asset management and financial knowledge.

[0387] "Anonymization" is a process that removes elements that could identify an individual from collected information in order to protect privacy.

[0388] This invention is a system for personalizing a user's financial asset management. This system includes a server, terminals, an emotion engine, and an interface that connects them. Specific embodiments thereof are described below.

[0389] The server receives financial account information provided by users and collects necessary data from financial institutions. The collected financial information, including transaction history and account balance information, is securely encrypted and stored in storage. Python libraries (e.g., Pandas, NumPy) can be used for data processing and analysis. This is effective for data cleaning and calculating basic statistics.

[0390] The terminal functions as an interface with the user, where the user can input schedule information. Based on this, the terminal generates reminders for important asset management events and notifies the user. Standard applications or email notification systems can be used as the platform for notifications.

[0391] The emotion engine is responsible for analyzing the user's emotional state in real time. It uses data from user interactions with the device and from sensors equipped on the device (e.g., camera and microphone). Open-source libraries (e.g., OpenCV, TensorFlow) are used for facial recognition and voice emotion analysis.

[0392] The server adjusts investment advice based on the analyzed user's emotions. Specifically, if the user is stressed, it suggests a low-risk investment strategy; if the user is proactive, it suggests options that involve risk but offer the potential for return. These suggestions are communicated through the user's digital interface, providing immediate feedback.

[0393] Furthermore, learning materials are personalized based on the user's emotional state. Users who show interest are provided with detailed and rich content, while those who appear fatigued are shown concise information. In this way, users can obtain information tailored to their own motivation.

[0394] For example, if a user accesses the system using their device after a long day of work, the emotion engine will sense the user's fatigue and provide concise learning content that helps them relax. In this way, optimal asset management support tailored to the user's state at any given time is provided.

[0395] An example of a prompt for a generative AI model is: "The user has entered financial information using their device. Use the emotion engine to analyze the user's emotional state and, based on the results, provide appropriate investment advice and learning content."

[0396] This system allows users to receive support for asset management tailored to their needs, thereby improving the efficiency and effectiveness of their asset management.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The user enters their financial institution account information into the terminal. The entered information includes identification information necessary for linking with the financial institution. The terminal that receives this information sends it to the server. The server uses this identification information to collect the user's financial data from each financial institution. Using encryption technology, the collected data is encrypted according to a predetermined protocol and securely stored in storage.

[0400] Step 2:

[0401] The server analyzes financial data stored in memory. This analysis uses the Python library Pandas for data preprocessing and NumPy for numerical calculations. The data is then subjected to statistical methods and machine learning models to calculate the risk and return of financial products. This provides the user with foundational data for optimal investment strategies. The analysis results are output as advantages and risk profiles for different financial products.

[0402] Step 3:

[0403] Users input their schedule information via their device. This input includes the dates of important asset management events. The device uses this information to set reminders and notify the user at the appropriate time. This reminder information is synchronized to a server using a cloud service, making it manageable across different devices.

[0404] Step 4:

[0405] The device collects interaction and sensor data to infer the user's emotions. Voice and facial expression data are acquired through the microphone and camera. The emotion engine uses this data and TensorFlow to analyze the user's emotional state. The analysis results output emotional states such as stress, fatigue, and interest.

[0406] Step 5:

[0407] The server generates investment advice based on analysis results obtained from the emotion engine. If the server determines that the user is experiencing stress, it will suggest investment plans with reduced risk. These suggestions are presented to the user via email or app notifications, based on the user's investment schedule. This advice is based on the user's emotional state and the latest financial market data.

[0408] Step 6:

[0409] The server also adjusts the learning content according to the user's emotional state. For example, if the user is feeling fatigued, simple and easy-to-understand content is selected. If the user is interested, detailed educational materials on new financial products are provided. The device displays this learning content and helps the user actively engage in learning.

[0410] (Application Example 2)

[0411] 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."

[0412] Existing systems that manage assets and make payments without considering the user's emotional state have the challenge of not being able to effectively provide optimal asset management suggestions and payment recommendations for the user. Furthermore, ignoring the impact of the user's emotional state, such as stress and fatigue, on asset management and consumption behavior may lead to decreased user satisfaction and inefficient asset management.

[0413] 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.

[0414] In this invention, the server includes means for collecting user financial data and securely storing it in a database; means for analyzing the collected financial data and performing comparative analysis of different financial products; means for analyzing the user's emotional state and adjusting asset management advice based on the analysis results; and means for adjusting and recommending payment methods according to the user's emotional state. This makes it possible to provide asset management advice and payment recommendations tailored to the user's emotional state.

[0415] "Financial data" refers to information about a user's assets and transaction records, including deposit balances, spending history, and investment status.

[0416] "Emotional state" refers to the user's mental and emotional state, including psychological elements such as stress, excitement, and fatigue.

[0417] "Asset management advice" refers to information that provides suggestions and strategies for users to efficiently manage and increase their assets.

[0418] "Payment method" refers to the means and processes used by users when making purchases or transactions, and includes cash, credit cards, electronic money, etc.

[0419] A "database" is an information management system built to systematically store financial data and user information so that it can be used for later searching and analysis.

[0420] Comparative analysis is a method for evaluating the characteristics of different financial products and services to determine which one best suits the user's needs.

[0421] "Recommendation" is the act of presenting a user with the most appropriate option based on specific conditions or circumstances.

[0422] This invention is embodied as a system that optimizes asset management and electronic payments based on the user's emotional state. This system consists of a server, a terminal, and an emotion engine.

[0423] The server collects users' financial data and securely stores it in a database. The financial data is encrypted and protected by security measures. The server analyzes the collected financial data and performs comparative analysis of different financial products on behalf of the user.

[0424] The device manages the user's schedule information and provides reminders related to asset management. These reminders are designed to help users take timely action.

[0425] The emotion engine analyzes the user's emotional state using data collected from the device's camera and microphone. Once the user's emotional state is identified, the server adjusts investment advice based on this information. For example, if the user is feeling stressed, the server will recommend a low-risk investment strategy. In electronic payments, the payment method is adjusted based on the emotion engine's analysis, providing suggestions that are best suited to the user's state.

[0426] For example, if a user accesses the system using their device after work, the emotion engine will sense the user's fatigue level. The server will then provide concise and easy-to-understand asset management content, while simultaneously recommending convenient payment options. In this way, users can obtain financial choices optimized for their own emotional state.

[0427] An example of a prompt using a generative AI model is, "Please provide an algorithm that analyzes which payment method a user is most likely to choose based on their current emotional state." This prompt helps model decision-making based on the user's emotional state.

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The server collects financial data through the user's financial institution account information. This data includes deposit balances, spending history, and investment status, and is securely stored in a database. The input is the user's financial institution account information, and the output is encrypted financial data. Throughout this process, the server uses data encryption technology to protect the data and ensure security.

[0431] Step 2:

[0432] The user uses the device, and the emotion engine analyzes their emotional state based on real-time data obtained from the camera and microphone. The input is the user's voice and facial expression data, and the output is the analyzed emotional state. At this stage, the emotion engine uses a pre-trained model to execute an emotion recognition algorithm and identify emotions such as stress and joy.

[0433] Step 3:

[0434] The server analyzes the user's emotional state, obtained from the emotion engine, and pre-collected financial data to generate optimal investment advice. The input here is the user's emotional state and financial data, and the output is optimized investment advice. The server uses this information to assess risk and adjust the recommendations.

[0435] Step 4:

[0436] The user enters schedule information via the device, and based on this, reminders for asset management events are generated. The input is the user's schedule information, and the output is the set reminders. The device manages this information and sends notifications to the user at the appropriate time.

[0437] Step 5:

[0438] The server recommends a suitable payment method to the terminal, taking into account the user's emotional state. The input is the user's emotional state and payment history, and the output is the recommended payment option. Here, the server performs data analysis to make suggestions that reduce the hassle and stress of payment.

[0439] Step 6:

[0440] The server processes the transaction and completes the payment according to the payment method selected by the user. The input is the payment option selected by the user, and the output is a confirmation of transaction completion. The server securely processes the transaction via the payment network and stores all data encrypted.

[0441] 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.

[0442] 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 those described above. 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 shown 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.

[0443] 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.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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".

[0457] As an embodiment of the present invention, a system is provided that allows busy users to efficiently manage their assets by using AI technology. This system consists of the interaction of a server, a terminal, and a user.

[0458] The server first collects financial data in real time based on the financial institution account information provided by the user. The accumulated financial data is encrypted and securely stored in a database. Based on this data, the server analyzes multiple financial products and evaluates their performance and risk, enabling comparative analysis of different financial products.

[0459] Users can access the system using a terminal and enter their own schedules. Based on this schedule information, the terminal generates reminders and notifies users of important events related to asset management. This feature allows users to take the necessary actions for asset management at the appropriate time.

[0460] Furthermore, the server assesses the user's knowledge level and provides customized learning content accordingly. For example, a user who is a beginner in asset management will be provided with materials containing basic information, while an advanced user will be provided with advanced information on the latest market trends.

[0461] Furthermore, the server performs asset management simulations based on the investment goals set by the user. The simulation results are sent to the terminal and visually displayed using 3D graphics, allowing the user to intuitively understand their future asset situation. For example, upon successful investment, the user can experience a 3D simulation of the lifestyle achievable with those assets.

[0462] Finally, the server uses advanced security protocols to anonymize data and protect users' personal information. This allows us to provide safe and reliable asset management support while alleviating privacy concerns.

[0463] Thus, the present invention is implemented as a system that enables personalized asset management support for individual users and efficiently solves problems such as lack of knowledge and time. A concrete example is a system in which a server evaluates the user's asset situation, proposes an optimal stock portfolio, the user checks the information via a terminal, and executes transactions according to the reminders.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] Users log in to the system via a terminal and enter their financial institution account information and life events. The server receives this information securely and stores it in a database.

[0467] Step 2:

[0468] The server uses financial institutions' APIs to collect users' latest financial data. The collected data is stored in a database in an encrypted state.

[0469] Step 3:

[0470] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. This allows for comparative analysis of different financial products.

[0471] Step 4:

[0472] Users use the device to enter their schedules and set up events and tasks related to asset management. The device generates reminders based on the entered schedule information.

[0473] Step 5:

[0474] The device notifies the user of asset management events at appropriate times based on their schedule. This ensures that the user does not forget to take important actions.

[0475] Step 6:

[0476] The server provides quizzes to assess the user's knowledge level and generates customized learning content based on the results. Content tailored to the user is delivered via the device.

[0477] Step 7:

[0478] The server takes into account the investment goals and simulation parameters set by the user and runs a future asset management simulation. The simulation results are sent to the terminal as 3D graphics.

[0479] Step 8:

[0480] The terminal visually displays the 3D simulation results sent from the server. This allows users to intuitively understand future asset management results and track their progress towards their goals.

[0481] Step 9:

[0482] The server periodically anonymizes all data within the system and implements advanced security protocols to protect user privacy. It monitors for signs of unauthorized access and ensures data security.

[0483] (Example 1)

[0484] 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."

[0485] Busy users face challenges such as a lack of time and knowledge when managing their assets, as well as the need to address the security and privacy concerns of financial data. Furthermore, it is necessary to support users' investment decisions through personalized information provision and intuitive asset management simulations.

[0486] 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.

[0487] In this invention, the server includes means for obtaining financial institution account information from users and collecting financial data in real time, means for encrypting the financial data and storing it in a database, and means for performing comparative analysis using an algorithm to evaluate the risk and performance of financial products. This enables users to manage their assets efficiently and securely.

[0488] "Financial data" refers to information about a user's account balance and transaction history obtained from a financial institution.

[0489] "Encryption" is a technique that transforms data using a specific protocol, so that only authorized individuals can decrypt that data.

[0490] A "database" is a structured data storage system for systematically storing financial data and related information, and for efficiently retrieving it as needed.

[0491] Comparative analysis is the process of evaluating the characteristics of multiple financial products and comparing them relatively from the perspectives of risk and performance.

[0492] A "reminder" is an alert function that specifies the time and content to notify the user of a particular asset management event.

[0493] "Knowledge level" is a measure that indicates the amount of information and understanding a user has regarding asset management.

[0494] "3D graphics" are visual representations generated in three-dimensional space, and are a technology that allows users to visually perceive information.

[0495] "Anonymization" is a process that modifies data so that individual users cannot be identified, thereby protecting privacy.

[0496] A "machine learning model" is an algorithm or framework used to learn regularities and patterns from data and perform predictions and classifications.

[0497] A "notification service" is a communication function that sends alerts and information to the user's device to prompt necessary actions.

[0498] This invention provides an easy-to-use and highly secure asset management support system by utilizing AI technology and advanced data protection measures. The system consists of three components: a server, a terminal, and a user.

[0499] The server collects financial data in real time based on financial institution account information provided by the user. This is done by using interface API technology to retrieve data directly from the financial institution's system. This data is encrypted using AES-256 encryption technology and securely stored in a secure database (e.g., a NoSQL database).

[0500] The server uses accumulated financial data to execute algorithms that evaluate the performance and risk of financial products, employing the Python language and machine learning libraries. This allows users to compare and analyze multiple financial products.

[0501] The device plays a crucial role in improving the user's asset management experience. Users can input schedule information through a mobile application developed with React Native. This schedule information is synchronized with the Google Calendar API, and reminders are sent to the user via Firebase Cloud Messaging. In this way, users can make asset management decisions that align with their schedules.

[0502] Furthermore, the server analyzes the user's knowledge level and delivers appropriate learning content to the device. This process utilizes Learning Management System (LMS) technology, and the content provided is customized according to the user's level of understanding.

[0503] For asset management simulations, calculation results performed on the server are sent to the terminal as 3D graphics. This visualization uses Unity or Three.js technology, allowing users to intuitively understand their future asset situation. For example, it can effectively address visual questions such as, "What will my lifestyle be like in three years with this investment?"

[0504] Ultimately, the server uses TLS, an advanced security protocol, to protect communications and safeguard user data and privacy. A concrete example would be a process where the server suggests an optimal stock portfolio based on the user's asset situation, allows the user to review it on their device, and executes trades according to reminders.

[0505] This system can also be used as a backend for analysis and recommendations by inputting a prompt message such as "Please suggest an optimal portfolio based on my personal investment goals" into an AI model. This allows users to manage their assets more efficiently and with greater peace of mind.

[0506] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0507] Step 1:

[0508] The server receives financial institution account information provided by the user as input and accesses the financial institution's API based on that information. The API provides financial data such as the user's account balance and transaction history as output. Upon receiving this data, the server encrypts it in AES-256 format using encryption technology such as OpenSSL. The encrypted data is stored in a secure database.

[0509] Step 2:

[0510] The server takes financial data stored in the database as input and performs analysis using the Python language and machine learning libraries. Specifically, it applies algorithms to evaluate the risk and performance of financial products. As a result of the evaluation, comparative evaluation data between financial products is output. This data serves as a basis for users to decide which financial product to choose.

[0511] Step 3:

[0512] The device receives schedule information entered by the user and synchronizes that schedule with the calendar using the Google Calendar API. The synchronized data is used to generate reminders, and the device uses Firebase Cloud Messaging to notify the user of these reminders. This ensures that important events in asset management are communicated to the user in a timely manner.

[0513] Step 4:

[0514] The server takes the user's past learning history and behavioral data as input and analyzes the user's knowledge level using a learning management system. Based on the analysis results, it generates customized learning content, such as basic materials for beginners and specialized information for advanced users. This content is then delivered to the user through their device.

[0515] Step 5:

[0516] The server uses the user's investment goals and current asset information as input to perform an asset management simulation. This simulation calculates future investment scenarios and generates output visualized as 3D graphics. The terminal displays these graphics to the user, helping them to intuitively understand their future asset situation.

[0517] Step 6:

[0518] The server uses data masking technology to anonymize the data handled across the entire system. This process transforms personally identifiable information from the original data, and the anonymized data is securely used for analysis and storage that may be made publicly available. This ensures user privacy.

[0519] (Application Example 1)

[0520] 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."

[0521] In modern society, asset management has become an important element of many people's lives. However, due to busy lifestyles and a lack of expertise in asset management, many users find it difficult to manage their assets effectively. In addition, there is a lack of means to experience investment risk management, investment advice optimized for individual users, and visualized asset planning. As a result, users often find it difficult to intuitively understand and plan their investments.

[0522] 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.

[0523] This invention includes a server that collects user financial data and securely stores it in a data storage device, a server that presents real-time investment analysis results and assists in optimizing transactions, and a server that allows users to virtually experience a lifestyle using a visualized asset plan. This makes it possible for busy users to easily learn about asset management and implement appropriate investment strategies. Furthermore, by providing visual information, users can intuitively understand complex asset plans.

[0524] "Financial data" refers to numerical information and records related to a user's asset management, including information about banking transactions and investment portfolios.

[0525] A "data storage device" is an electronic recording medium or system for securely storing collected financial data.

[0526] "Analysis" is a method of evaluating the characteristics and risks of various financial products based on collected financial data, and then comparing them to derive the optimal investment strategy for the user.

[0527] "Schedule information" refers to date and time information that users use to manage their schedules and activities, and includes appointments related to asset management activities.

[0528] A "notification" is a message or alert sent to inform a user of important activities or deadlines related to asset management.

[0529] "Learning level" is an indicator that shows the user's level of knowledge and understanding, and appropriate educational materials are provided based on this level.

[0530] "Educational materials" refer to documents and content provided to users to help them acquire knowledge about asset management and finance.

[0531] "Investment objectives" refer to the specific goals or objectives that a user hopes to achieve through asset management.

[0532] "Three-dimensional visualization" is a display technology that uses three-dimensional graphics to represent investment simulations and plans, facilitating intuitive understanding for users.

[0533] "Information security" refers to the technologies and protocols used to protect users' personal financial data from unauthorized access and data breaches.

[0534] "Real-time analysis results" refer to the results of analysis performed as soon as financial data is acquired, and the information is presented to the user immediately.

[0535] "Transaction optimization" is a process that uses users' financial data to help them make the most effective investment decisions.

[0536] "Virtual lifestyle experience" is a feature that allows users to virtually experience a life scenario after successful investment through three-dimensional visualization.

[0537] To realize this invention, the system is built primarily on the interaction between servers, terminals, and users. This system combines diverse technologies to streamline asset management.

[0538] The server first collects financial data in real time using authentication information from financial institutions provided by the user. The collected data is protected by advanced information encryption technology before being stored in data storage devices. Next, the server analyzes the financial data using a generative AI model and, based on the results, provides the user with appropriate investment recommendations and risk assessments. This analysis utilizes the latest algorithms and data processing technologies.

[0539] The application installed on the device is used by users to input their schedule information and receive important notifications related to asset management. Furthermore, it provides a three-dimensional visualization based on the user's asset status, allowing users to virtually experience their lifestyle through an intuitive interface on the device.

[0540] Users can improve their asset management skills using educational materials provided from the server via their devices. These materials are customized to the user's learning level, allowing beginners to learn progressively from basic to advanced topics.

[0541] For example, if a user is considering investing in a new market, the system analyzes market data in real time and presents specific investment proposals. Through a visualized asset plan, the user can clearly envision their future life if successful. An example of a prompt in this case might be: "Propose a new investment portfolio, assess the risk level, and visualize your future asset situation with a 3D simulation."

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The server receives authentication information from the user for financial institutions and uses that information to collect financial data in real time. The input is the user's authentication information, and using this information, the server sends requests to external financial institution APIs to obtain financial transaction data. The output is the collected financial data.

[0545] Step 2:

[0546] The server encrypts the collected financial data using advanced information encryption technology and securely stores it in a data storage device. The input is financial data, which the server processes using an encryption library, outputs encrypted data, and stores it in the storage device.

[0547] Step 3:

[0548] The server analyzes stored financial data and performs investment analysis using a generated AI model. The input is encrypted financial data, which the server decrypts and passes to a data analysis algorithm for analysis. The output is the analysis result, which includes investment suggestions and risk assessments.

[0549] Step 4:

[0550] The terminal presents the analysis results received from the server to the user. The input is the analysis results generated by the server, which the terminal displays through the user interface. The output is the information displayed to the user.

[0551] Step 5:

[0552] The user enters schedule information related to asset management into the terminal, and the terminal generates reminders for future asset management events that the user needs. The input is the user's schedule information, and the terminal uses a reminder management module to schedule notifications and output the next notification event.

[0553] Step 6:

[0554] The terminal uses three-dimensional visualization technology to visually present the asset plan from the server to the user. The input is asset plan data, which the terminal renders as a 3D model using a graphics engine and displays to the user. The output is a visualized 3D simulation.

[0555] Step 7:

[0556] Users refer to educational materials displayed on their devices and acquire knowledge about asset management tailored to their learning level. The input is customized educational materials, which users view and use to deepen their understanding as output.

[0557] Step 8:

[0558] The server collects data to improve the overall system performance based on user input and feedback. This input consists of user operation logs and feedback data, which the server analyzes to generate suggestions for system improvements.

[0559] 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.

[0560] One embodiment of the present invention provides a system that recognizes user emotions and optimizes the asset management process. This system includes a server, a terminal, an emotion engine, and a user interface.

[0561] The server collects financial data based on account information from financial institutions provided by users and securely stores it in a database. The data is encrypted and its security is ensured. The financial data is analyzed, and the server performs comparative analysis of different financial products.

[0562] Furthermore, users can input schedule information via the device and set reminders for asset management events. The device uses this information to manage reminders so that users can take timely action.

[0563] One of the features of this invention is the use of an emotion engine. The emotion engine analyzes the user's emotional state using user input data, interaction history, and real-time sensor data (for example, acquired through a microphone or camera).

[0564] Once the user's emotional state is analyzed, the server adjusts its investment advice based on this information. For example, if the server detects that the user is stressed, it will prioritize suggesting lower-risk investment strategies. Conversely, if the user is excited, it can present higher-risk options that also offer the potential for greater returns.

[0565] Furthermore, learning content is adjusted based on the user's emotions. For example, when a user shows interest, more advanced information and content about new financial products are provided, while if they are deemed tired, basic content is presented to maintain their motivation.

[0566] For example, when a user accesses the system using their device after a long day of work, the emotion engine detects the user's fatigue, and the system provides learning content that is concise and relaxing. In this way, optimal asset management support tailored to the user's state is achieved.

[0567] This configuration allows users to have a more personalized asset management experience, improving the efficiency and effectiveness of asset management. The server, terminal, and emotion engine work together to provide a high level of adaptability throughout the system.

[0568] The following describes the processing flow.

[0569] Step 1:

[0570] Users log in to the system via a terminal and enter their financial institution account information. The server receives this information, securely collects the financial data, and stores it in a database.

[0571] Step 2:

[0572] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. Based on this, it performs comparative analysis of the financial products offered to users.

[0573] Step 3:

[0574] Users enter their schedule information from their device and set reminders for asset management events. The device manages this schedule information and notifies the user of the events.

[0575] Step 4:

[0576] The emotion engine uses sensors attached to the device to analyze the user's voice and facial expressions to determine their emotional state. The emotion engine then sends this information to a server.

[0577] Step 5:

[0578] The server adjusts investment advice based on the user's emotional state, as received from the emotion engine. For example, if the user is feeling stressed, it will recommend safe investment options.

[0579] Step 6:

[0580] The server customizes learning content based on the user's emotional state. If the user is relaxed, it provides detailed content; if they are not focused, it presents concise information.

[0581] Step 7:

[0582] The device displays pre-configured advice and learning content sent from the server to the user. Based on this information, the user makes their next investment decisions.

[0583] Step 8:

[0584] Once the system-wide processing is complete, the server anonymizes user data and implements security measures. This ensures that the service is provided securely while maintaining user privacy.

[0585] (Example 2)

[0586] 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."

[0587] Traditional financial systems have not adequately provided dynamic asset management advice or customized learning materials tailored to users' emotional states and circumstances. Therefore, it has been difficult to provide services that meet individual user needs and emotional states, posing challenges in improving the user experience and streamlining asset management.

[0588] 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.

[0589] In this invention, the server includes means for collecting user information and securely storing it in a storage device, means for analyzing the collected information and performing comparative analysis of different financial products, and means for providing personalized learning materials by analyzing the user's emotional state. This makes it possible to provide optimal asset management advice tailored to the user's emotional state and needs, and to realize an individualized learning experience.

[0590] A "user" is an individual or legal entity that uses the system to provide financial information and receive services.

[0591] "Information" refers to all data necessary for the operation of the system, including financial institution identification information, user schedule information, emotional status, and so on.

[0592] A "storage device" is a physical or virtual medium for storing data, capable of storing and retrieving information.

[0593] "Analysis" is the process of performing an analysis based on collected information, tailored to a specific purpose, to reveal trends and characteristics of the data.

[0594] "Financial products" refer to products and services that users are interested in for investment or asset management purposes, and these include stocks, bonds, and mutual funds.

[0595] Comparative analysis is an analytical method used to compare the risks, returns, and other characteristics of different financial products in order to find the optimal choice.

[0596] "Emotional state" refers to the user's psychological or emotional condition, which the system evaluates and uses to customize the service.

[0597] "Learning materials" include information, educational materials, and content provided to users to improve their asset management and financial knowledge.

[0598] "Anonymization" is a process that removes elements that could identify an individual from collected information in order to protect privacy.

[0599] This invention is a system for personalizing a user's financial asset management. This system includes a server, terminals, an emotion engine, and an interface that connects them. Specific embodiments thereof are described below.

[0600] The server receives financial account information provided by users and collects necessary data from financial institutions. The collected financial information, including transaction history and account balance information, is securely encrypted and stored in storage. Python libraries (e.g., Pandas, NumPy) can be used for data processing and analysis. This is effective for data cleaning and calculating basic statistics.

[0601] The terminal functions as an interface with the user, where the user can input schedule information. Based on this, the terminal generates reminders for important asset management events and notifies the user. Standard applications or email notification systems can be used as the platform for notifications.

[0602] The emotion engine is responsible for analyzing the user's emotional state in real time. It uses data from user interactions with the device and from sensors equipped on the device (e.g., camera and microphone). Open-source libraries (e.g., OpenCV, TensorFlow) are used for facial recognition and voice emotion analysis.

[0603] The server adjusts investment advice based on the analyzed user's emotions. Specifically, if the user is stressed, it suggests a low-risk investment strategy; if the user is proactive, it suggests options that involve risk but offer the potential for return. These suggestions are communicated through the user's digital interface, providing immediate feedback.

[0604] Furthermore, learning materials are personalized based on the user's emotional state. Users who show interest are provided with detailed and rich content, while those who appear fatigued are shown concise information. In this way, users can obtain information tailored to their own motivation.

[0605] For example, if a user accesses the system using their device after a long day of work, the emotion engine will sense the user's fatigue and provide concise learning content that helps them relax. In this way, optimal asset management support tailored to the user's state at any given time is provided.

[0606] An example of a prompt for a generative AI model is: "The user has entered financial information using their device. Use the emotion engine to analyze the user's emotional state and, based on the results, provide appropriate investment advice and learning content."

[0607] This system allows users to receive support for asset management tailored to their needs, thereby improving the efficiency and effectiveness of their asset management.

[0608] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0609] Step 1:

[0610] The user enters their financial institution account information into the terminal. The entered information includes identification information necessary for linking with the financial institution. The terminal that receives this information sends it to the server. The server uses this identification information to collect the user's financial data from each financial institution. Using encryption technology, the collected data is encrypted according to a predetermined protocol and securely stored in storage.

[0611] Step 2:

[0612] The server analyzes financial data stored in memory. This analysis uses the Python library Pandas for data preprocessing and NumPy for numerical calculations. The data is then subjected to statistical methods and machine learning models to calculate the risk and return of financial products. This provides the user with foundational data for optimal investment strategies. The analysis results are output as advantages and risk profiles for different financial products.

[0613] Step 3:

[0614] Users input their schedule information via their device. This input includes the dates of important asset management events. The device uses this information to set reminders and notify the user at the appropriate time. This reminder information is synchronized to a server using a cloud service, making it manageable across different devices.

[0615] Step 4:

[0616] The device collects interaction and sensor data to infer the user's emotions. Voice and facial expression data are acquired through the microphone and camera. The emotion engine uses this data and TensorFlow to analyze the user's emotional state. The analysis results output emotional states such as stress, fatigue, and interest.

[0617] Step 5:

[0618] The server generates investment advice based on analysis results obtained from the emotion engine. If the server determines that the user is experiencing stress, it will suggest investment plans with reduced risk. These suggestions are presented to the user via email or app notifications, based on the user's investment schedule. This advice is based on the user's emotional state and the latest financial market data.

[0619] Step 6:

[0620] The server also adjusts the learning content according to the user's emotional state. For example, if the user is feeling fatigued, simple and easy-to-understand content is selected. If the user is interested, detailed educational materials on new financial products are provided. The device displays this learning content and helps the user actively engage in learning.

[0621] (Application Example 2)

[0622] 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."

[0623] Existing systems that manage assets and make payments without considering the user's emotional state have the challenge of not being able to effectively provide optimal asset management suggestions and payment recommendations for the user. Furthermore, ignoring the impact of the user's emotional state, such as stress and fatigue, on asset management and consumption behavior may lead to decreased user satisfaction and inefficient asset management.

[0624] 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.

[0625] In this invention, the server includes means for collecting user financial data and securely storing it in a database; means for analyzing the collected financial data and performing comparative analysis of different financial products; means for analyzing the user's emotional state and adjusting asset management advice based on the analysis results; and means for adjusting and recommending payment methods according to the user's emotional state. This makes it possible to provide asset management advice and payment recommendations tailored to the user's emotional state.

[0626] "Financial data" refers to information about a user's assets and transaction records, including deposit balances, spending history, and investment status.

[0627] "Emotional state" refers to the user's mental and emotional state, including psychological elements such as stress, excitement, and fatigue.

[0628] "Asset management advice" refers to information that provides suggestions and strategies for users to efficiently manage and increase their assets.

[0629] "Payment method" refers to the means and processes used by users when making purchases or transactions, and includes cash, credit cards, electronic money, etc.

[0630] A "database" is an information management system built to systematically store financial data and user information so that it can be used for later searching and analysis.

[0631] Comparative analysis is a method for evaluating the characteristics of different financial products and services to determine which one best suits the user's needs.

[0632] "Recommendation" is the act of presenting a user with the most appropriate option based on specific conditions or circumstances.

[0633] This invention is embodied as a system that optimizes asset management and electronic payments based on the user's emotional state. This system consists of a server, a terminal, and an emotion engine.

[0634] The server collects users' financial data and securely stores it in a database. The financial data is encrypted and protected by security measures. The server analyzes the collected financial data and performs comparative analysis of different financial products on behalf of the user.

[0635] The device manages the user's schedule information and provides reminders related to asset management. These reminders are designed to help users take timely action.

[0636] The emotion engine analyzes the user's emotional state using data collected from the device's camera and microphone. Once the user's emotional state is identified, the server adjusts investment advice based on this information. For example, if the user is feeling stressed, the server will recommend a low-risk investment strategy. In electronic payments, the payment method is adjusted based on the emotion engine's analysis, providing suggestions that are best suited to the user's state.

[0637] For example, if a user accesses the system using their device after work, the emotion engine will sense the user's fatigue level. The server will then provide concise and easy-to-understand asset management content, while simultaneously recommending convenient payment options. In this way, users can obtain financial choices optimized for their own emotional state.

[0638] An example of a prompt using a generative AI model is, "Please provide an algorithm that analyzes which payment method a user is most likely to choose based on their current emotional state." This prompt helps model decision-making based on the user's emotional state.

[0639] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0640] Step 1:

[0641] The server collects financial data through the user's financial institution account information. This data includes deposit balances, spending history, and investment status, and is securely stored in a database. The input is the user's financial institution account information, and the output is encrypted financial data. Throughout this process, the server uses data encryption technology to protect the data and ensure security.

[0642] Step 2:

[0643] The user uses the device, and the emotion engine analyzes their emotional state based on real-time data obtained from the camera and microphone. The input is the user's voice and facial expression data, and the output is the analyzed emotional state. At this stage, the emotion engine uses a pre-trained model to execute an emotion recognition algorithm and identify emotions such as stress and joy.

[0644] Step 3:

[0645] The server analyzes the user's emotional state, obtained from the emotion engine, and pre-collected financial data to generate optimal investment advice. The input here is the user's emotional state and financial data, and the output is optimized investment advice. The server uses this information to assess risk and adjust the recommendations.

[0646] Step 4:

[0647] The user enters schedule information via the device, and based on this, reminders for asset management events are generated. The input is the user's schedule information, and the output is the set reminders. The device manages this information and sends notifications to the user at the appropriate time.

[0648] Step 5:

[0649] The server recommends a suitable payment method to the terminal, taking into account the user's emotional state. The input is the user's emotional state and payment history, and the output is the recommended payment option. Here, the server performs data analysis to make suggestions that reduce the hassle and stress of payment.

[0650] Step 6:

[0651] The server processes the transaction and completes the payment according to the payment method selected by the user. The input is the payment option selected by the user, and the output is a confirmation of transaction completion. The server securely processes the transaction via the payment network and stores all data encrypted.

[0652] 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.

[0653] 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 those described above. 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 shown 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.

[0654] 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.

[0655] [Fourth Embodiment]

[0656] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0657] 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.

[0658] 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).

[0659] 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.

[0660] 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.

[0661] 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).

[0662] 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.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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".

[0669] As an embodiment of the present invention, a system is provided that allows busy users to efficiently manage their assets by using AI technology. This system consists of the interaction of a server, a terminal, and a user.

[0670] The server first collects financial data in real time based on the financial institution account information provided by the user. The accumulated financial data is encrypted and securely stored in a database. Based on this data, the server analyzes multiple financial products and evaluates their performance and risk, enabling comparative analysis of different financial products.

[0671] Users can access the system using a terminal and enter their own schedules. Based on this schedule information, the terminal generates reminders and notifies users of important events related to asset management. This feature allows users to take the necessary actions for asset management at the appropriate time.

[0672] Furthermore, the server assesses the user's knowledge level and provides customized learning content accordingly. For example, a user who is a beginner in asset management will be provided with materials containing basic information, while an advanced user will be provided with advanced information on the latest market trends.

[0673] Furthermore, the server performs asset management simulations based on the investment goals set by the user. The simulation results are sent to the terminal and visually displayed using 3D graphics, allowing the user to intuitively understand their future asset situation. For example, upon successful investment, the user can experience a 3D simulation of the lifestyle achievable with those assets.

[0674] Finally, the server uses advanced security protocols to anonymize data and protect users' personal information. This allows us to provide safe and reliable asset management support while alleviating privacy concerns.

[0675] Thus, the present invention is implemented as a system that enables personalized asset management support for individual users and efficiently solves problems such as lack of knowledge and time. A concrete example is a system in which a server evaluates the user's asset situation, proposes an optimal stock portfolio, the user checks the information via a terminal, and executes transactions according to the reminders.

[0676] The following describes the processing flow.

[0677] Step 1:

[0678] Users log in to the system via a terminal and enter their financial institution account information and life events. The server receives this information securely and stores it in a database.

[0679] Step 2:

[0680] The server uses financial institutions' APIs to collect users' latest financial data. The collected data is stored in a database in an encrypted state.

[0681] Step 3:

[0682] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. This allows for comparative analysis of different financial products.

[0683] Step 4:

[0684] Users use the device to enter their schedules and set up events and tasks related to asset management. The device generates reminders based on the entered schedule information.

[0685] Step 5:

[0686] The device notifies the user of asset management events at appropriate times based on their schedule. This ensures that the user does not forget to take important actions.

[0687] Step 6:

[0688] The server provides quizzes to assess the user's knowledge level and generates customized learning content based on the results. Content tailored to the user is delivered via the device.

[0689] Step 7:

[0690] The server takes into account the investment goals and simulation parameters set by the user and runs a future asset management simulation. The simulation results are sent to the terminal as 3D graphics.

[0691] Step 8:

[0692] The terminal visually displays the 3D simulation results sent from the server. This allows users to intuitively understand future asset management results and track their progress towards their goals.

[0693] Step 9:

[0694] The server periodically anonymizes all data within the system and implements advanced security protocols to protect user privacy. It monitors for signs of unauthorized access and ensures data security.

[0695] (Example 1)

[0696] 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".

[0697] Busy users face challenges such as a lack of time and knowledge when managing their assets, as well as the need to address the security and privacy concerns of financial data. Furthermore, it is necessary to support users' investment decisions through personalized information provision and intuitive asset management simulations.

[0698] 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.

[0699] In this invention, the server includes means for obtaining financial institution account information from users and collecting financial data in real time, means for encrypting the financial data and storing it in a database, and means for performing comparative analysis using an algorithm to evaluate the risk and performance of financial products. This enables users to manage their assets efficiently and securely.

[0700] "Financial data" refers to information about a user's account balance and transaction history obtained from a financial institution.

[0701] "Encryption" is a technique that transforms data using a specific protocol, so that only authorized individuals can decrypt that data.

[0702] A "database" is a structured data storage system for systematically storing financial data and related information, and for efficiently retrieving it as needed.

[0703] Comparative analysis is the process of evaluating the characteristics of multiple financial products and comparing them relatively from the perspectives of risk and performance.

[0704] A "reminder" is an alert function that specifies the time and content to notify the user of a particular asset management event.

[0705] "Knowledge level" is a measure that indicates the amount of information and understanding a user has regarding asset management.

[0706] "3D graphics" are visual representations generated in three-dimensional space, and are a technology that allows users to visually perceive information.

[0707] "Anonymization" is a process that modifies data so that individual users cannot be identified, thereby protecting privacy.

[0708] A "machine learning model" is an algorithm or framework used to learn regularities and patterns from data and perform predictions and classifications.

[0709] A "notification service" is a communication function that sends alerts and information to the user's device to prompt necessary actions.

[0710] This invention provides an easy-to-use and highly secure asset management support system by utilizing AI technology and advanced data protection measures. The system consists of three components: a server, a terminal, and a user.

[0711] The server collects financial data in real time based on financial institution account information provided by the user. This is done by using interface API technology to retrieve data directly from the financial institution's system. This data is encrypted using AES-256 encryption technology and securely stored in a secure database (e.g., a NoSQL database).

[0712] The server uses accumulated financial data to execute algorithms that evaluate the performance and risk of financial products, employing the Python language and machine learning libraries. This allows users to compare and analyze multiple financial products.

[0713] The device plays a crucial role in improving the user's asset management experience. Users can input schedule information through a mobile application developed with React Native. This schedule information is synchronized with the Google Calendar API, and reminders are sent to the user via Firebase Cloud Messaging. In this way, users can make asset management decisions that align with their schedules.

[0714] Furthermore, the server analyzes the user's knowledge level and delivers appropriate learning content to the device. This process utilizes Learning Management System (LMS) technology, and the content provided is customized according to the user's level of understanding.

[0715] For asset management simulations, calculation results performed on the server are sent to the terminal as 3D graphics. This visualization uses Unity or Three.js technology, allowing users to intuitively understand their future asset situation. For example, it can effectively address visual questions such as, "What will my lifestyle be like in three years with this investment?"

[0716] Ultimately, the server uses TLS, an advanced security protocol, to protect communications and safeguard user data and privacy. A concrete example would be a process where the server suggests an optimal stock portfolio based on the user's asset situation, allows the user to review it on their device, and executes trades according to reminders.

[0717] This system can also be used as a backend for analysis and recommendations by inputting a prompt message such as "Please suggest an optimal portfolio based on my personal investment goals" into an AI model. This allows users to manage their assets more efficiently and with greater peace of mind.

[0718] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0719] Step 1:

[0720] The server receives financial institution account information provided by the user as input and accesses the financial institution's API based on that information. The API provides financial data such as the user's account balance and transaction history as output. Upon receiving this data, the server encrypts it in AES-256 format using encryption technology such as OpenSSL. The encrypted data is stored in a secure database.

[0721] Step 2:

[0722] The server takes financial data stored in the database as input and performs analysis using the Python language and machine learning libraries. Specifically, it applies algorithms to evaluate the risk and performance of financial products. As a result of the evaluation, comparative evaluation data between financial products is output. This data serves as a basis for users to decide which financial product to choose.

[0723] Step 3:

[0724] The device receives schedule information entered by the user and synchronizes that schedule with the calendar using the Google Calendar API. The synchronized data is used to generate reminders, and the device uses Firebase Cloud Messaging to notify the user of these reminders. This ensures that important events in asset management are communicated to the user in a timely manner.

[0725] Step 4:

[0726] The server takes the user's past learning history and behavioral data as input and analyzes the user's knowledge level using a learning management system. Based on the analysis results, it generates customized learning content, such as basic materials for beginners and specialized information for advanced users. This content is then delivered to the user through their device.

[0727] Step 5:

[0728] The server uses the user's investment goals and current asset information as input to perform an asset management simulation. This simulation calculates future investment scenarios and generates output visualized as 3D graphics. The terminal displays these graphics to the user, helping them to intuitively understand their future asset situation.

[0729] Step 6:

[0730] The server uses data masking technology to anonymize the data handled across the entire system. This process transforms personally identifiable information from the original data, and the anonymized data is securely used for analysis and storage that may be made publicly available. This ensures user privacy.

[0731] (Application Example 1)

[0732] 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".

[0733] In modern society, asset management has become an important element of many people's lives. However, due to busy lifestyles and a lack of expertise in asset management, many users find it difficult to manage their assets effectively. In addition, there is a lack of means to experience investment risk management, investment advice optimized for individual users, and visualized asset planning. As a result, users often find it difficult to intuitively understand and plan their investments.

[0734] 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.

[0735] This invention includes a server that collects user financial data and securely stores it in a data storage device, a server that presents real-time investment analysis results and assists in optimizing transactions, and a server that allows users to virtually experience a lifestyle using a visualized asset plan. This makes it possible for busy users to easily learn about asset management and implement appropriate investment strategies. Furthermore, by providing visual information, users can intuitively understand complex asset plans.

[0736] "Financial data" refers to numerical information and records related to a user's asset management, including information about banking transactions and investment portfolios.

[0737] A "data storage device" is an electronic recording medium or system for securely storing collected financial data.

[0738] "Analysis" is a method of evaluating the characteristics and risks of various financial products based on collected financial data, and then comparing them to derive the optimal investment strategy for the user.

[0739] "Schedule information" refers to date and time information that users use to manage their schedules and activities, and includes appointments related to asset management activities.

[0740] A "notification" is a message or alert sent to inform a user of important activities or deadlines related to asset management.

[0741] "Learning level" is an indicator that shows the user's level of knowledge and understanding, and appropriate educational materials are provided based on this level.

[0742] "Educational materials" refer to documents and content provided to users to help them acquire knowledge about asset management and finance.

[0743] "Investment objectives" refer to the specific goals or objectives that a user hopes to achieve through asset management.

[0744] "Three-dimensional visualization" is a display technology that uses three-dimensional graphics to represent investment simulations and plans, facilitating intuitive understanding for users.

[0745] "Information security" refers to the technologies and protocols used to protect users' personal financial data from unauthorized access and data breaches.

[0746] "Real-time analysis results" refer to the results of analysis performed as soon as financial data is acquired, and the information is presented to the user immediately.

[0747] "Transaction optimization" is a process that uses users' financial data to help them make the most effective investment decisions.

[0748] "Virtual lifestyle experience" is a feature that allows users to virtually experience a life scenario after successful investment through three-dimensional visualization.

[0749] To realize this invention, the system is built primarily on the interaction between servers, terminals, and users. This system combines diverse technologies to streamline asset management.

[0750] The server first collects financial data in real time using authentication information from financial institutions provided by the user. The collected data is protected by advanced information encryption technology before being stored in data storage devices. Next, the server analyzes the financial data using a generative AI model and, based on the results, provides the user with appropriate investment recommendations and risk assessments. This analysis utilizes the latest algorithms and data processing technologies.

[0751] The application installed on the device is used by users to input their schedule information and receive important notifications related to asset management. Furthermore, it provides a three-dimensional visualization based on the user's asset status, allowing users to virtually experience their lifestyle through an intuitive interface on the device.

[0752] Users can improve their asset management skills using educational materials provided from the server via their devices. These materials are customized to the user's learning level, allowing beginners to learn progressively from basic to advanced topics.

[0753] For example, if a user is considering investing in a new market, the system analyzes market data in real time and presents specific investment proposals. Through a visualized asset plan, the user can clearly envision their future life if successful. An example of a prompt in this case might be: "Propose a new investment portfolio, assess the risk level, and visualize your future asset situation with a 3D simulation."

[0754] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0755] Step 1:

[0756] The server receives authentication information from the user for financial institutions and uses that information to collect financial data in real time. The input is the user's authentication information, and using this information, the server sends requests to external financial institution APIs to obtain financial transaction data. The output is the collected financial data.

[0757] Step 2:

[0758] The server encrypts the collected financial data using advanced information encryption technology and securely stores it in a data storage device. The input is financial data, which the server processes using an encryption library, outputs encrypted data, and stores it in the storage device.

[0759] Step 3:

[0760] The server analyzes stored financial data and performs investment analysis using a generated AI model. The input is encrypted financial data, which the server decrypts and passes to a data analysis algorithm for analysis. The output is the analysis result, which includes investment suggestions and risk assessments.

[0761] Step 4:

[0762] The terminal presents the analysis results received from the server to the user. The input is the analysis results generated by the server, which the terminal displays through the user interface. The output is the information displayed to the user.

[0763] Step 5:

[0764] The user enters schedule information related to asset management into the terminal, and the terminal generates reminders for future asset management events that the user needs. The input is the user's schedule information, and the terminal uses a reminder management module to schedule notifications and output the next notification event.

[0765] Step 6:

[0766] The terminal uses three-dimensional visualization technology to visually present the asset plan from the server to the user. The input is asset plan data, which the terminal renders as a 3D model using a graphics engine and displays to the user. The output is a visualized 3D simulation.

[0767] Step 7:

[0768] Users refer to educational materials displayed on their devices and acquire knowledge about asset management tailored to their learning level. The input is customized educational materials, which users view and use to deepen their understanding as output.

[0769] Step 8:

[0770] The server collects data to improve the overall system performance based on user input and feedback. This input consists of user operation logs and feedback data, which the server analyzes to generate suggestions for system improvements.

[0771] 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.

[0772] One embodiment of the present invention provides a system that recognizes user emotions and optimizes the asset management process. This system includes a server, a terminal, an emotion engine, and a user interface.

[0773] The server collects financial data based on account information from financial institutions provided by users and securely stores it in a database. The data is encrypted and its security is ensured. The financial data is analyzed, and the server performs comparative analysis of different financial products.

[0774] Furthermore, users can input schedule information via the device and set reminders for asset management events. The device uses this information to manage reminders so that users can take timely action.

[0775] One of the features of this invention is the use of an emotion engine. The emotion engine analyzes the user's emotional state using user input data, interaction history, and real-time sensor data (for example, acquired through a microphone or camera).

[0776] Once the user's emotional state is analyzed, the server adjusts its investment advice based on this information. For example, if the server detects that the user is stressed, it will prioritize suggesting lower-risk investment strategies. Conversely, if the user is excited, it can present higher-risk options that also offer the potential for greater returns.

[0777] Furthermore, learning content is adjusted based on the user's emotions. For example, when a user shows interest, more advanced information and content about new financial products are provided, while if they are deemed tired, basic content is presented to maintain their motivation.

[0778] For example, when a user accesses the system using their device after a long day of work, the emotion engine detects the user's fatigue, and the system provides learning content that is concise and relaxing. In this way, optimal asset management support tailored to the user's state is achieved.

[0779] This configuration allows users to have a more personalized asset management experience, improving the efficiency and effectiveness of asset management. The server, terminal, and emotion engine work together to provide a high level of adaptability throughout the system.

[0780] The following describes the processing flow.

[0781] Step 1:

[0782] Users log in to the system via a terminal and enter their financial institution account information. The server receives this information, securely collects the financial data, and stores it in a database.

[0783] Step 2:

[0784] The server analyzes the collected financial data and evaluates the performance and risk of each financial product. Based on this, it performs comparative analysis of the financial products offered to users.

[0785] Step 3:

[0786] Users enter their schedule information from their device and set reminders for asset management events. The device manages this schedule information and notifies the user of the events.

[0787] Step 4:

[0788] The emotion engine uses sensors attached to the device to analyze the user's voice and facial expressions to determine their emotional state. The emotion engine then sends this information to a server.

[0789] Step 5:

[0790] The server adjusts investment advice based on the user's emotional state, as received from the emotion engine. For example, if the user is feeling stressed, it will recommend safe investment options.

[0791] Step 6:

[0792] The server customizes learning content based on the user's emotional state. If the user is relaxed, it provides detailed content; if they are not focused, it presents concise information.

[0793] Step 7:

[0794] The device displays pre-configured advice and learning content sent from the server to the user. Based on this information, the user makes their next investment decisions.

[0795] Step 8:

[0796] Once the system-wide processing is complete, the server anonymizes user data and implements security measures. This ensures that the service is provided securely while maintaining user privacy.

[0797] (Example 2)

[0798] 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".

[0799] Traditional financial systems have not adequately provided dynamic asset management advice or customized learning materials tailored to users' emotional states and circumstances. Therefore, it has been difficult to provide services that meet individual user needs and emotional states, posing challenges in improving the user experience and streamlining asset management.

[0800] 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.

[0801] In this invention, the server includes means for collecting user information and securely storing it in a storage device, means for analyzing the collected information and performing comparative analysis of different financial products, and means for providing personalized learning materials by analyzing the user's emotional state. This makes it possible to provide optimal asset management advice tailored to the user's emotional state and needs, and to realize an individualized learning experience.

[0802] A "user" is an individual or legal entity that uses the system to provide financial information and receive services.

[0803] "Information" refers to all data necessary for the operation of the system, including financial institution identification information, user schedule information, emotional status, and so on.

[0804] A "storage device" is a physical or virtual medium for storing data, capable of storing and retrieving information.

[0805] "Analysis" is the process of performing an analysis based on collected information, tailored to a specific purpose, to reveal trends and characteristics of the data.

[0806] "Financial products" refer to products and services that users are interested in for investment or asset management purposes, and these include stocks, bonds, and mutual funds.

[0807] Comparative analysis is an analytical method used to compare the risks, returns, and other characteristics of different financial products in order to find the optimal choice.

[0808] "Emotional state" refers to the user's psychological or emotional condition, which the system evaluates and uses to customize the service.

[0809] "Learning materials" include information, educational materials, and content provided to users to improve their asset management and financial knowledge.

[0810] "Anonymization" is a process that removes elements that could identify an individual from collected information in order to protect privacy.

[0811] This invention is a system for personalizing a user's financial asset management. This system includes a server, terminals, an emotion engine, and an interface that connects them. Specific embodiments thereof are described below.

[0812] The server receives financial account information provided by users and collects necessary data from financial institutions. The collected financial information, including transaction history and account balance information, is securely encrypted and stored in storage. Python libraries (e.g., Pandas, NumPy) can be used for data processing and analysis. This is effective for data cleaning and calculating basic statistics.

[0813] The terminal functions as an interface with the user, where the user can input schedule information. Based on this, the terminal generates reminders for important asset management events and notifies the user. Standard applications or email notification systems can be used as the platform for notifications.

[0814] The emotion engine is responsible for analyzing the user's emotional state in real time. It uses data from user interactions with the device and from sensors equipped on the device (e.g., camera and microphone). Open-source libraries (e.g., OpenCV, TensorFlow) are used for facial recognition and voice emotion analysis.

[0815] The server adjusts investment advice based on the analyzed user's emotions. Specifically, if the user is stressed, it suggests a low-risk investment strategy; if the user is proactive, it suggests options that involve risk but offer the potential for return. These suggestions are communicated through the user's digital interface, providing immediate feedback.

[0816] Furthermore, learning materials are personalized based on the user's emotional state. Users who show interest are provided with detailed and rich content, while those who appear fatigued are shown concise information. In this way, users can obtain information tailored to their own motivation.

[0817] For example, if a user accesses the system using their device after a long day of work, the emotion engine will sense the user's fatigue and provide concise learning content that helps them relax. In this way, optimal asset management support tailored to the user's state at any given time is provided.

[0818] An example of a prompt for a generative AI model is: "The user has entered financial information using their device. Use the emotion engine to analyze the user's emotional state and, based on the results, provide appropriate investment advice and learning content."

[0819] This system allows users to receive support for asset management tailored to their needs, thereby improving the efficiency and effectiveness of their asset management.

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] The user enters their financial institution account information into the terminal. The entered information includes identification information necessary for linking with the financial institution. The terminal that receives this information sends it to the server. The server uses this identification information to collect the user's financial data from each financial institution. Using encryption technology, the collected data is encrypted according to a predetermined protocol and securely stored in storage.

[0823] Step 2:

[0824] The server analyzes financial data stored in memory. This analysis uses the Python library Pandas for data preprocessing and NumPy for numerical calculations. The data is then subjected to statistical methods and machine learning models to calculate the risk and return of financial products. This provides the user with foundational data for optimal investment strategies. The analysis results are output as advantages and risk profiles for different financial products.

[0825] Step 3:

[0826] Users input their schedule information via their device. This input includes the dates of important asset management events. The device uses this information to set reminders and notify the user at the appropriate time. This reminder information is synchronized to a server using a cloud service, making it manageable across different devices.

[0827] Step 4:

[0828] The device collects interaction and sensor data to infer the user's emotions. Voice and facial expression data are acquired through the microphone and camera. The emotion engine uses this data and TensorFlow to analyze the user's emotional state. The analysis results output emotional states such as stress, fatigue, and interest.

[0829] Step 5:

[0830] The server generates investment advice based on analysis results obtained from the emotion engine. If the server determines that the user is experiencing stress, it will suggest investment plans with reduced risk. These suggestions are presented to the user via email or app notifications, based on the user's investment schedule. This advice is based on the user's emotional state and the latest financial market data.

[0831] Step 6:

[0832] The server also adjusts the learning content according to the user's emotional state. For example, if the user is feeling fatigued, simple and easy-to-understand content is selected. If the user is interested, detailed educational materials on new financial products are provided. The device displays this learning content and helps the user actively engage in learning.

[0833] (Application Example 2)

[0834] 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".

[0835] Existing systems that manage assets and make payments without considering the user's emotional state have the challenge of not being able to effectively provide optimal asset management suggestions and payment recommendations for the user. Furthermore, ignoring the impact of the user's emotional state, such as stress and fatigue, on asset management and consumption behavior may lead to decreased user satisfaction and inefficient asset management.

[0836] 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.

[0837] In this invention, the server includes means for collecting user financial data and securely storing it in a database; means for analyzing the collected financial data and performing comparative analysis of different financial products; means for analyzing the user's emotional state and adjusting asset management advice based on the analysis results; and means for adjusting and recommending payment methods according to the user's emotional state. This makes it possible to provide asset management advice and payment recommendations tailored to the user's emotional state.

[0838] "Financial data" refers to information about a user's assets and transaction records, including deposit balances, spending history, and investment status.

[0839] "Emotional state" refers to the user's mental and emotional state, including psychological elements such as stress, excitement, and fatigue.

[0840] "Asset management advice" refers to information that provides suggestions and strategies for users to efficiently manage and increase their assets.

[0841] "Payment method" refers to the means and processes used by users when making purchases or transactions, and includes cash, credit cards, electronic money, etc.

[0842] A "database" is an information management system built to systematically store financial data and user information so that it can be used for later searching and analysis.

[0843] Comparative analysis is a method for evaluating the characteristics of different financial products and services to determine which one best suits the user's needs.

[0844] "Recommendation" is the act of presenting a user with the most appropriate option based on specific conditions or circumstances.

[0845] This invention is embodied as a system that optimizes asset management and electronic payments based on the user's emotional state. This system consists of a server, a terminal, and an emotion engine.

[0846] The server collects users' financial data and securely stores it in a database. The financial data is encrypted and protected by security measures. The server analyzes the collected financial data and performs comparative analysis of different financial products on behalf of the user.

[0847] The device manages the user's schedule information and provides reminders related to asset management. These reminders are designed to help users take timely action.

[0848] The emotion engine analyzes the user's emotional state using data collected from the device's camera and microphone. Once the user's emotional state is identified, the server adjusts investment advice based on this information. For example, if the user is feeling stressed, the server will recommend a low-risk investment strategy. In electronic payments, the payment method is adjusted based on the emotion engine's analysis, providing suggestions that are best suited to the user's state.

[0849] For example, if a user accesses the system using their device after work, the emotion engine will sense the user's fatigue level. The server will then provide concise and easy-to-understand asset management content, while simultaneously recommending convenient payment options. In this way, users can obtain financial choices optimized for their own emotional state.

[0850] An example of a prompt using a generative AI model is, "Please provide an algorithm that analyzes which payment method a user is most likely to choose based on their current emotional state." This prompt helps model decision-making based on the user's emotional state.

[0851] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0852] Step 1:

[0853] The server collects financial data through the user's financial institution account information. This data includes deposit balances, spending history, and investment status, and is securely stored in a database. The input is the user's financial institution account information, and the output is encrypted financial data. Throughout this process, the server uses data encryption technology to protect the data and ensure security.

[0854] Step 2:

[0855] The user uses the device, and the emotion engine analyzes their emotional state based on real-time data obtained from the camera and microphone. The input is the user's voice and facial expression data, and the output is the analyzed emotional state. At this stage, the emotion engine uses a pre-trained model to execute an emotion recognition algorithm and identify emotions such as stress and joy.

[0856] Step 3:

[0857] The server analyzes the user's emotional state, obtained from the emotion engine, and pre-collected financial data to generate optimal investment advice. The input here is the user's emotional state and financial data, and the output is optimized investment advice. The server uses this information to assess risk and adjust the recommendations.

[0858] Step 4:

[0859] The user enters schedule information via the device, and based on this, reminders for asset management events are generated. The input is the user's schedule information, and the output is the set reminders. The device manages this information and sends notifications to the user at the appropriate time.

[0860] Step 5:

[0861] The server recommends a suitable payment method to the terminal, taking into account the user's emotional state. The input is the user's emotional state and payment history, and the output is the recommended payment option. Here, the server performs data analysis to make suggestions that reduce the hassle and stress of payment.

[0862] Step 6:

[0863] The server processes the transaction and completes the payment according to the payment method selected by the user. The input is the payment option selected by the user, and the output is a confirmation of transaction completion. The server securely processes the transaction via the payment network and stores all data encrypted.

[0864] 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.

[0865] 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 those described above. 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 shown 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.

[0866] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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."

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] The following is further disclosed regarding the embodiments described above.

[0886] (Claim 1)

[0887] A means of collecting users' financial data and securely storing it in a database,

[0888] A means of analyzing collected financial data and conducting comparative analysis of different financial products,

[0889] A means for generating reminders for asset management events based on the user's schedule information,

[0890] A means of providing customized learning content based on the user's knowledge level,

[0891] A means of visualizing future asset management through 3D simulation based on the user's investment goals,

[0892] A means to anonymize the collected data and ensure security,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, which obtains financial institution account information from a user and collects financial data based on this information.

[0896] (Claim 3)

[0897] The system according to claim 1, which uses data encryption technology to protect financial data stored in a database.

[0898] "Example 1"

[0899] (Claim 1)

[0900] A method for obtaining financial institution account information from users and collecting financial data in real time based on this information,

[0901] A means of encrypting the collected financial data and securely storing it in a database,

[0902] A means of evaluating and comparing the risks and performance of different financial products using algorithms that analyze financial data,

[0903] A means of generating and notifying users of asset management events using schedule information entered by the user,

[0904] A means of providing and sending learning content optimized according to the user's level of understanding,

[0905] A means of visualizing and displaying future asset management simulation results in 3D graphics based on the user's investment goals,

[0906] Means to protect user data and privacy using data anonymization technologies and communication protection protocols,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which inputs acquired financial data into a machine learning model for financial product analysis and performs analysis.

[0910] (Claim 3)

[0911] The system according to claim 1, which reliably delivers reminders generated based on the user's schedule to the user using a notification service.

[0912] "Application Example 1"

[0913] (Claim 1)

[0914] A means of collecting users' financial data and securely storing it in a data storage device,

[0915] A means of analyzing collected financial data and conducting comparative analysis of different financial products,

[0916] A means for generating notifications of asset management activities based on the user's schedule information,

[0917] A means of providing customized educational materials based on the user's learning level,

[0918] A means of displaying a future asset plan in three dimensions based on the user's investment objectives,

[0919] A means to anonymize the collected data and ensure information security,

[0920] A means to provide real-time investment analysis results and support the optimization of trades,

[0921] A means for users to virtually experience their lifestyle using a visualized asset plan,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, which obtains authentication information from a financial institution from a user and collects financial data based on this information.

[0925] (Claim 3)

[0926] The system according to claim 1, which uses information encryption technology to protect financial data stored in a data storage device.

[0927] "Example 2 of combining an emotion engine"

[0928] (Claim 1)

[0929] A means of collecting user information and securely storing it in a storage device,

[0930] A means of analyzing collected information and conducting comparative analysis of different financial products,

[0931] A means for generating notifications of financial management events based on the user's schedule information,

[0932] By analyzing the user's emotional state, a means of providing personalized learning materials,

[0933] A means of adjusting investment recommendations based on the results of user sentiment analysis,

[0934] Anonymization of collected information and measures to ensure security,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, which obtains identification information of financial institutions from a user and collects financial information based on this information.

[0938] (Claim 3)

[0939] The system according to claim 1, which uses information encryption technology to protect financial information stored in a storage device.

[0940] "Application example 2 when combining with an emotional engine"

[0941] (Claim 1)

[0942] A means of collecting users' financial data and securely storing it in a database,

[0943] A means of analyzing collected financial data and conducting comparative analysis of different financial products,

[0944] A means of analyzing the user's emotional state and adjusting asset management advice based on the analysis results,

[0945] A means for generating reminders for asset management events based on the user's schedule information,

[0946] A means of providing customized learning content based on the user's knowledge level,

[0947] A means of adjusting and recommending payment methods according to the user's emotional state,

[0948] A means to anonymize the collected data and ensure security,

[0949] A system that includes this.

[0950] (Claim 2)

[0951] The system according to claim 1, which obtains financial institution account information from a user and collects financial data based on this information.

[0952] (Claim 3)

[0953] The system according to claim 1, which uses data encryption technology to protect financial data stored in a database. [Explanation of symbols]

[0954] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting users' financial data and securely storing it in a data storage device, A means of analyzing collected financial data and conducting comparative analysis of different financial products, A means for generating notifications of asset management activities based on the user's schedule information, A means of providing customized educational materials based on the user's learning level, A means of displaying a future asset plan in three dimensions based on the user's investment objectives, A means to anonymize the collected data and ensure information security, A means to provide real-time investment analysis results and support the optimization of trades, A means for users to virtually experience their lifestyle using a visualized asset plan, A system that includes this.

2. The system according to claim 1, which obtains authentication information from a financial institution from a user and collects financial data based on this information.

3. The system according to claim 1, which uses information encryption technology to protect financial data stored in a data storage device.

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