system

Abstract

We provide the system. [Solution] Methods for collecting users' financial data in an anonymized form, Means for obtaining the latest market information and financial product data from external data sources, A means to analyze a user's asset allocation and generate an optimal asset management strategy, A means of managing user schedules and life events and providing operational notifications, A means of providing learning content tailored to the user's knowledge level, A system including means for generating 3D simulations based on the user's asset management goals.

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] Modern users, especially those with busy occupations, often lack sufficient time and knowledge regarding asset management. Also, there is a problem that advice provided by financial institutions tends to be biased towards their own products, making it difficult to obtain fair information. Furthermore, there is also a resistance to disclosing one's asset situation to others, making it difficult to grasp one's own situation. As a result, there are problems that it is difficult to have a successful image of asset management and difficult to maintain motivation.

Means for Solving the Problems

[0005] This invention provides a system for securely collecting users' financial data in an anonymized form and obtaining the latest market information and financial product data. This enables the system to analyze the user's asset allocation and generate an optimal asset management strategy. Furthermore, by managing the user's schedule and life events, it provides timely notifications regarding investment management. It also provides learning content tailored to the user's knowledge level to improve their understanding of asset management. Finally, it generates a 3D simulation based on the user's asset management goals, visually representing future success and thus maintaining motivation.

[0006] "User financial data" refers to information about financial assets and liabilities held by an individual or organization, including data related to transactions and investments.

[0007] "Anonymization" is the process of removing or transforming personally identifiable information to make it impossible to identify the original individual.

[0008] "Market information" refers to information about the current state and forecasts of the market, such as price trends, trading volume, indices, interest rates, and economic indicators in financial markets.

[0009] "Financial product data" refers to information such as value, risk, liquidity, and yield related to financial products such as stocks, bonds, mutual funds, and derivatives.

[0010] "Asset allocation" refers to the process of determining the proportion of investment in each financial instrument or asset class that makes up an investment portfolio, as well as the proportions themselves.

[0011] An "investment strategy" is a plan that outlines the policies for selecting, buying, selling, and holding assets in order to achieve specific investment objectives.

[0012] "Schedule management" is the process of efficiently organizing planned events, tasks, and activities, and optimally allocating time according to the objective.

[0013] A "life event" is an event that has a significant impact on an individual's or family's assets or finances, and includes events such as marriage, buying a home, having children, and retirement.

[0014] "Learning content" is a general term for educational materials and teaching aids designed to acquire, understand, and apply specific knowledge.

[0015] A "3D simulation" is a virtual model that uses three-dimensional computer graphics to simulate real-world situations and allows for a visual experience. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention relates to an asset management advice system based on a method for anonymizing users' financial data and securely transmitting it to a server. This system securely manages users' asset information and acquires and analyzes the latest market information to propose the optimal asset management strategy for the user.

[0038] The server receives asset information submitted by the user and obtains the latest market data from various financial institutions. Based on this data, the server analyzes the collected information and generates asset management strategies tailored to each user's unique risk tolerance and investment goals. The generated strategies are then sent to the user's terminal for notification. Upon receiving this notification, the user can review the proposed strategy and adjust their asset allocation as needed.

[0039] Furthermore, the terminal manages the user's schedule information and has a function to remind users of important timings for operational advice received from the server. By entering life events and other appointments into the terminal, users can manage important events that are useful for asset management without missing any.

[0040] Furthermore, the server has a function that provides learning content tailored to the user's knowledge level. This allows users to effectively improve the knowledge necessary for asset management.

[0041] The 3D simulation function provides a tool for users to visually experience future scenarios based on their investment goals. The server generates predictive scenarios for the user's goals and sends them to the terminal. The terminal interactively displays the 3D simulation received from the server, making it easier for users to concretely visualize their future success in investment management.

[0042] As a concrete example, suppose User A regularly invests in stocks as part of their asset management. The user enters their schedule into a terminal, and the server analyzes market information to provide the next most effective investment timing. If the user accepts the advice provided, the terminal sends a reminder at that time, allowing the user to make the investment. This system enables the user to achieve efficient asset management and aim for reliable wealth building.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user enters asset information into the terminal. The terminal then transmits the entered information to the server using a secure communication protocol.

[0046] Step 2:

[0047] The server anonymizes the asset information it receives and securely stores it in a database. It also obtains the latest market information and financial product data from trusted external data sources.

[0048] Step 3:

[0049] The server uses collected market information and user asset information to perform risk and return analysis. Based on the analysis results, it generates an asset management strategy that is suitable for the user's investment goals and risk tolerance.

[0050] Step 4:

[0051] The operational strategy generated by the server is sent to the terminal, and the terminal notifies the user of the proposed operational strategy. The user reviews the proposal and revise their asset allocation as needed.

[0052] Step 5:

[0053] Users input life events and schedule information into the device. The device manages the entered information and is configured to remind users of important investment timings based on operational strategies received from the server.

[0054] Step 6:

[0055] The server assesses the user's knowledge level and selects appropriate learning content based on that assessment. The learning content is then sent to the user's device, and the user improves their knowledge of asset management through the provided content.

[0056] Step 7:

[0057] The server generates a 3D simulation of future investment results based on the user's asset management goals. This simulation is sent to the terminal, which interactively displays the 3D simulation to the user, visualizing a concrete vision of future success.

[0058] (Example 1)

[0059] 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."

[0060] Traditional asset management systems have suffered from problems such as reduced investment efficiency for users due to insufficient security of users' financial data, lack of access to the latest market information, and inadequate provision of individually optimized asset management strategies. Furthermore, the lack of content to support user knowledge enhancement and visualization of future forecasts made it difficult for users to make effective asset management decisions.

[0061] 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.

[0062] In this invention, the server includes means for anonymizing and collecting users' financial data, means for acquiring the latest market information, and means for generating and providing optimal asset management strategies that differ for each user. This makes it possible to provide individually optimized strategies based on the latest market information while ensuring the security of users' financial data. Furthermore, it deepens users' understanding of asset management and supports more effective investment decision-making by providing educational content tailored to the user's knowledge level and visualizing future predictions through three-dimensional simulations.

[0063] "Financial data" refers to information about a user's asset status and investment activities, specifically including deposit balances, stock and bond holdings, and investment goals.

[0064] "Anonymization" refers to a technology that processes data in a way that prevents individuals from being identified, thereby protecting privacy.

[0065] "Market information" refers to the latest data on stock prices, exchange rates, commodity prices, etc., in financial markets, and serves as fundamental information for formulating asset management strategies.

[0066] An "asset management strategy" refers to a plan regarding asset allocation and investment timing, which is formulated based on the user's risk tolerance and investment goals.

[0067] "Schedule" refers to the management information for appointments related to the user's daily life and asset management, and plays a role in ensuring that important events are not missed.

[0068] "Life events" refer to significant events in a user's life that may influence decisions regarding asset management.

[0069] "Notifications" refer to means of informing users of important information, and are provided in the form of push notifications, etc.

[0070] "Educational content" refers to learning materials provided to improve users' knowledge of asset management, and includes lessons, quizzes, and other similar materials.

[0071] "Three-dimensional simulation" is a technology that visually represents future scenarios based on the user's asset management goals, providing an interactive experience.

[0072] A "generative model" refers to an algorithm or machine learning technique used to generate optimal asset management strategies based on available data.

[0073] A "central processing unit" refers to a central device used for data analysis and asset management strategy generation, and typically possesses the capability to handle large-scale calculations.

[0074] "Device" refers to electronic devices used by users to access the system, and includes smartphones, personal computers, and other similar devices.

[0075] This invention is a system that provides individually customized asset management strategies while anonymizing and securely managing users' financial information. This system is built through the interaction of a server, a terminal, and the user.

[0076] The server utilizes SSL / TLS as a highly secure protocol to receive anonymized financial data sent by users. The server organizes and stores the data using various database systems (e.g., MySQL®). Subsequently, the server leverages data analysis libraries such as Pandas and NumPy to generate asset management strategies based on the user's risk profile and market data. This involves the use of generative AI models and information obtained from diverse data sources. Furthermore, the generated investment strategy is notified to the user's terminal.

[0077] The terminal presents the operational strategy transmitted from the server to the user, providing a visually easy-to-understand interface. The terminal also manages the user's schedule and sends important notifications via reminders at specific times. This enables the user to make investment decisions efficiently and at the right time.

[0078] Users can access learning content about asset management using their devices. This content is tailored to the user's knowledge level and is delivered through an e-learning platform (e.g., a general education management system). Users can also experience server-generated 3D simulations on their devices. This allows users to visually understand future asset management scenarios and develop a clearer vision for their investment strategy.

[0079] As a concrete example, user A sets up their investment plan on a terminal, and the server analyzes market data to inform them of an appropriate asset management strategy. Once this strategy is announced, the terminal reminds the user of the timing for investment. The user can then smoothly manage their assets by utilizing the information provided by the system.

[0080] An example of a prompt for a generative AI model might be text such as, "Based on the current market conditions, please tell me the appropriate timing for stock investment for user A." This prompt is used as part of the data collection and analysis process to provide the system with a suitable strategy for the user.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user enters their financial information into the terminal. This information includes the status of their financial assets and investment goals. This input data is anonymized to protect personal information; specifically, personal information such as name and address is hashed and AES encryption is performed. The anonymized data is then sent to the server.

[0084] Step 2:

[0085] The server securely receives anonymized data sent from the terminal via the SSL / TLS protocol. The received data is stored in a database, serving as the basis for subsequent analysis. This allows the server to determine which users possess which asset data, but it does not include any personally identifiable information.

[0086] Step 3:

[0087] The server uses APIs from external financial data providers to obtain the latest market information. The retrieved market data is stored in a database and combined with user data to create an analyzable format. This allows for a clear understanding of market trends and developments.

[0088] Step 4:

[0089] The server uses data analysis libraries such as Pandas and NumPy to integrate and analyze user asset information and market data. From this analysis, an optimal asset management strategy is generated based on each user's risk tolerance and investment goals, and a generative AI model is used. This strategy is customized to reflect the user's unique conditions.

[0090] Step 5:

[0091] The server encrypts the generated operational strategy and sends it to the terminal. The terminal notifies the user of the received operational strategy and displays it in a visually easy-to-understand format. The user can review the presented operational strategy and adjust the investment plan as needed.

[0092] Step 6:

[0093] The device sets reminders for important events related to asset management based on the user's daily schedule. It provides timely reminders based on the user's specified date and time, helping users avoid missing important investment opportunities.

[0094] Step 7:

[0095] The server generates educational content tailored to the user's knowledge level and provides it through the terminal. Users can access this content at any time to deepen their understanding of asset management.

[0096] Step 8:

[0097] The server generates a three-dimensional simulation of future scenarios based on the user's asset management goals. This simulation is created using a 3D engine such as Unity, and the terminal displays it in an interactive format. The user can use this to visually confirm future asset management results.

[0098] (Application Example 1)

[0099] 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."

[0100] Traditional asset management systems struggle to provide personalized investment strategies while ensuring the anonymity of users' financial data. Furthermore, they lack the means to achieve efficient asset management by considering users' daily schedules and life events. As a result, users may miss investment opportunities or receive proposals that don't match their risk profile. Additionally, there is insufficient provision of educational content to deepen users' understanding of asset management.

[0101] 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.

[0102] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a 3D simulation based on the user's asset management goals; means for estimating important life events based on payment history and suggesting appropriate investment timing; and means for creating inputs for adjusting individual asset management strategies using a generative AI model. This enables flexible and accurate asset management tailored to the user's lifestyle and goals, promoting improved investment knowledge and successful wealth building.

[0103] "Methods for collecting users' financial data in an anonymized form" refers to a function that converts individual user information into a format that does not allow others to identify the user, and then securely aggregates that information.

[0104] "Means of obtaining the latest market information and financial product data from external data sources" refers to the function of importing the latest data reflecting market trends and the characteristics of financial products from external information providers.

[0105] "A means of analyzing a user's asset allocation and generating the optimal asset management strategy" refers to an analytical function that uses collected user asset information to formulate the most suitable investment plan for that individual.

[0106] "Means for managing user schedules and life events and providing investment-related notifications" refers to functions that record users' appointments and important events, and provide investment suggestions and reminders accordingly.

[0107] "Means of providing learning content tailored to the user's knowledge level" refers to a function that appropriately provides educational materials to deepen the user's understanding, according to their level of financial knowledge.

[0108] "Means for generating 3D simulations based on the user's asset management goals" refers to a function that generates three-dimensional images to visually reproduce the user's desired economic outcomes as a scenario.

[0109] "A means of estimating important life events based on payment history and suggesting appropriate investment timing" refers to a function that predicts future events from past payment patterns and advises on optimal investment actions.

[0110] "Means of creating inputs for adjusting individual asset management strategies using generative AI models" refers to a function that utilizes artificial intelligence technology to generate data for strategic adjustments tailored to the user's investment policy.

[0111] This invention centers on sensing technology for anonymizing and collecting users' financial data and securely storing it in a database, and data analysis technology for incorporating the latest market information and generating asset management strategies based on it. The server uses data analysis languages ​​such as Python and R to generate an optimal asset management strategy based on market data and the user's risk tolerance.

[0112] The device manages the user's schedule and life events, and sends reminders at appropriate times based on those events. Reminder notifications are implemented using the smartphone's notification function. The device also provides learning content and visually displays 3D simulations. These 3D simulations utilize 3D engines such as Unity and Unreal Engine to visualize future asset management scenarios.

[0113] The server analyzes payment history data and estimates life events. In this process, machine learning models are used to predict important future events from past history, and system adjustments are made using generative AI models as needed. The generative AI models utilize prompts to provide better asset management advice tailored to the user's unique operational needs, offering individual strategic suggestions. For example, a user can register travel plans on their device and receive suggestions to liquidate their assets before spending. An example of a prompt using the generative AI model is, "Develop an investment strategy tailored to the user's travel plan and suggest the optimal timing for liquidating assets."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server receives the user's financial data and anonymizes it. It takes raw financial data provided by the user as input and transforms it using an anonymization algorithm. The output is anonymized data, which is stored in a secure database. Specifically, encryption technologies such as RSA and AES are used to make the data impossible to identify individuals.

[0117] Step 2:

[0118] The server retrieves the latest market information and financial product data from external market information providers. It takes external datasets as input via HTTP requests through APIs. The output is the latest market dataset, which is used in the data analysis layer. Specifically, it performs scheduled periodic data retrieval and on-demand retrieval as needed.

[0119] Step 3:

[0120] The server analyzes acquired market information and anonymized user asset data to generate asset management strategies. It accepts pre-processed market data and user asset data as input. As output, it generates individually tailored asset management strategies. Specifically, it uses Python or R to perform data analysis using machine learning algorithms, and then adjusts simulations and recommendations using a generated AI model.

[0121] Step 4:

[0122] The device manages the user's schedule information and life event data. It takes schedule data registered by the user as input. The output is a timeline of scheduled events, which serves as the basis for reminder logic. Specific operations include scheduling using the calendar function and setting event priorities.

[0123] Step 5:

[0124] The terminal displays asset management strategy notifications provided by the server to the user and sends reminders. It receives asset management advice data sent from the server as input. The output is a notification message directed to the user. Specifically, it uses the smartphone's push notification function to inform the user in a timely manner.

[0125] Step 6:

[0126] The server estimates life events based on the user's operation and transaction history and proposes new investment timings. It analyzes the user's transaction logs as input. Based on this input, it uses a machine learning model to predict future life events and generates new investment suggestions as output. Specifically, it performs filtering and feature extraction of historical data.

[0127] Step 7:

[0128] The server uses a generative AI model to create prompts to refine the user's individual asset management strategy. It receives the user's investment status and preferences as input, and the generative AI model generates prompts accordingly. The output is the refined prompt. Specifically, natural language processing techniques are applied to generate input sentences that improve the quality of the AI ​​model's response.

[0129] 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.

[0130] This invention combines an emotion engine with a system that supports effective asset management by utilizing users' financial data. This system recognizes users' emotions in real time and provides investment advice and learning support tailored to their psychological state, thereby realizing a more personalized experience.

[0131] The server receives anonymized financial data from users and retrieves the latest market information and financial product data from external data sources. Based on this, it analyzes the user's asset allocation and generates an optimal investment strategy. It also uses an emotion engine to analyze the user's emotional state and adjust the way the generated investment strategy is presented and its content. Specifically, if the user is feeling stressed, it can emphasize low-risk, safe options.

[0132] The device manages the user's schedule and life events and provides operational notifications that take emotional states into account. The emotion engine analyzes the user's facial expressions and tone of voice through sensor devices such as cameras and microphones to infer their emotions.

[0133] Furthermore, the device assesses the user's knowledge level and provides appropriate learning content based on the results. The emotion engine responds to the user's emotional state and presents content at the optimal timing according to the user's learning progress, thereby enhancing the user's learning effectiveness.

[0134] In the 3D simulation function, the server generates simulations based on the user's asset management goals, and the emotion engine reflects the user's psychological state. For example, if the user is feeling anxious, the simulation can prioritize presenting stable asset growth scenarios.

[0135] As a concrete example, consider a scenario where User B manages their assets on a holiday. User B checks market trends while viewing their investment information entered into the terminal. The terminal then analyzes User B's voice and detects their stress levels. The server adjusts future investment strategies accordingly and generates suggestions strongly recommending investment in safe assets. In this way, the system of the present invention provides more user-centric asset management advice in real time, increasing confidence and peace of mind in asset management.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user enters asset information into the terminal. The terminal transmits the user's input data to the server using a secure communication method. Simultaneously, the terminal's sensors begin to monitor the user's emotions in real time.

[0139] Step 2:

[0140] The server anonymizes the transmitted asset information and stores it in a database. Next, it retrieves the latest market information and financial product data from external data sources.

[0141] Step 3:

[0142] The server analyzes acquired market information and user asset information to perform risk and return analysis. Based on this, it generates an asset management strategy that matches the user's risk tolerance and investment goals.

[0143] Step 4:

[0144] The server uses an emotion engine to analyze the user's emotional state. Based on the analysis results, it adjusts its operational policies and presentation methods to formulate asset management advice that takes the user's emotions into consideration.

[0145] Step 5:

[0146] The terminal notifies the user of operational strategies and advice from the server. If the user's emotional state falls below a certain level, the advice is presented in a gentler manner, or lower-risk options are emphasized.

[0147] Step 6:

[0148] The user inputs life events and daily schedules into the device. The device reflects these in the schedule and, based on advice generated by the server, sets up notifications for appropriate investment timing, taking into account the user's emotional state.

[0149] Step 7:

[0150] The server selects appropriate learning content based on the user's knowledge level and emotional state. It then delivers the content to the device according to a presentation schedule that takes the user's emotions into consideration.

[0151] Step 8:

[0152] The server generates a 3D simulation that reflects the user's investment goals and emotional state. The terminal receives this and displays it interactively to the user. Depending on the user's current emotional state, the aim is to enhance their sense of security by highlighting specific scenarios.

[0153] (Example 2)

[0154] 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".

[0155] Traditional asset management systems have a problem in that they only propose standardized investment policies based on the user's financial information, and do not adequately provide flexible investment support that takes into account the emotional state and learning needs of individual users. As a result, users are unable to make appropriate investment decisions when they feel stressed or anxious, and are unable to achieve sufficient asset growth.

[0156] 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.

[0157] In this invention, the server includes means for analyzing the user's emotional state and adjusting the operational policy according to that emotion, means for providing learning materials according to the user's knowledge level, and means for generating a 3D simulation based on the user's asset management goals. This enables optimal asset management support tailored to the user's psychological state and promotes effective learning.

[0158] "User financial information" refers to information about an individual's financial resources and how they are being managed, including the contents of bank accounts and investment portfolios.

[0159] "Anonymization" refers to a technical process that makes it impossible to identify a specific individual in order to protect their privacy.

[0160] "External data sources" refer to the sources of data provided by information providers outside the system, and include information on financial markets and details of financial products.

[0161] "Asset allocation" refers to the proportion of a user's assets that are distributed across different investment destinations or asset classes.

[0162] An "asset management policy" refers to an investment strategy designed to efficiently increase assets, and includes risk management and an approach tailored to investment objectives.

[0163] "Emotional state" refers to the user's psychological feelings and mental reactions, encompassing states such as stress, anxiety, and a sense of security.

[0164] "Life events" refer to important events or scheduled activities in a user's lifestyle, including changes in work, marriage, and raising children.

[0165] "Learning materials" refer to informational content provided to deepen users' understanding and knowledge, including educational materials and training materials.

[0166] "3D simulation" refers to a technology that visually and three-dimensionally represents the results and scenarios of asset management in a digital environment.

[0167] This invention relates to a system that utilizes users' financial information to provide asset management support tailored to their individual emotional states. The system works in cooperation with a server, terminals, and users to collect, analyze, and notify data.

[0168] The server is responsible for processing financial information, analyzing the user's asset allocation, and generating the optimal investment strategy. In this process, it uses a generative AI model to construct effective strategies from a large amount of market and financial product information. It also utilizes an emotion engine to adjust the investment strategy according to the user's emotional state. Furthermore, the server collects the latest financial information in real time from external data sources and uses this information to propose the most effective investment method for the user.

[0169] Meanwhile, the device functions as the user's interface, transmitting financial information and emotional states to the server. The device uses sensors such as cameras and microphones to analyze the user's facial expressions and tone of voice, collecting emotional data in real time. The collected data is encrypted and sent to the server in a privacy-preserving manner. The device also provides learning materials to the user, supporting them in deepening their knowledge of asset management.

[0170] Through this system, users can input their financial information into a terminal and create a plan that reflects their investment stance and life events. The system features a 3D simulation function that visually presents the user's long-term asset growth scenario. This allows for discussion and review, and consideration of the next steps.

[0171] As a concrete example, consider a scenario where a user checks market trends using a device. If the device detects the user's voice and determines that their stress levels are high, it retrieves and displays advice from the server recommending investment in safe assets. The generating AI model constructs an investment strategy using the prompt message, "Consider the user's current investment portfolio and emotional state, and propose an investment strategy that minimizes risk." Through this mechanism, users can achieve asset management that reflects their own emotional state and gain a sense of security.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user enters their financial information into the terminal. This data includes bank account information and investment portfolios. The terminal encrypts this information and prepares it for processing in a privacy-enhanced manner. The entered information is anonymized and formatted into a dataset for processing.

[0175] Step 2:

[0176] The device uses a camera and microphone to monitor the user's facial expressions and voice tone in real time and collect emotional data. The collected data is analyzed to identify the user's current emotional state. The results of the emotional analysis are output as an index representing the user's psychological state and sent to the server.

[0177] Step 3:

[0178] The server receives financial information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to analyze asset allocation and construct an optimal investment strategy. Specifically, the model uses the prompt message, "Consider the user's current investment portfolio and sentiment state, and propose an investment strategy that minimizes risk." The resulting investment strategy is then adjusted to reflect characteristics such as the degree of risk.

[0179] Step 4:

[0180] The server retrieves the latest market information from external data sources to verify the validity of operational policies. Data processing here includes information collection via external APIs and periodic data updates. Market information is added to operational policies, enabling strategies that take real-time market changes into account.

[0181] Step 5:

[0182] The server generates operational policies tailored to the user's emotional state, along with a 3D simulation that includes market information. The simulation visualizes multiple asset growth scenarios and presents them in an easy-to-understand format. The generated simulation results are sent to the terminal as material to support the user's decision-making.

[0183] Step 6:

[0184] The terminal presents the user with operational policies and simulation results received from the server. Specifically, it visually represents the situation through a graphical UI. It assists the user in reviewing the information and deciding on the next steps.

[0185] (Application Example 2)

[0186] 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".

[0187] Current asset management systems fail to consider the user's emotional state, making it difficult to provide optimal advice. Furthermore, they cannot reflect individual lifestyles or emotions in user spending management, potentially leading to inefficient asset management. There is a need to solve these problems and realize more personalized asset management support.

[0188] 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.

[0189] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a three-dimensional simulation based on the user's asset management goals; means for analyzing the user's emotional state in real time using emotion analysis technology and adjusting the method of presenting the investment strategy accordingly; and means for analyzing the user's spending patterns and providing advice to reduce unnecessary spending. This enables more personalized asset management support and spending management based on the user's emotions and lifestyle.

[0190] "User financial data" refers to financial information such as the user's assets, liabilities, income, and expenses, which is anonymized and analyzed by the system.

[0191] "Market information" refers to external information that affects the value of financial assets, such as the latest economic trends, financial market data, stock indices, and interest rates.

[0192] "Financial product data" refers to detailed information about products such as investment trusts, stocks, and bond options offered by various financial institutions.

[0193] "Asset allocation" refers to a strategy for how a user's assets will be distributed across different investment targets.

[0194] "3D simulation" is a function that visually simulates the asset management process in three dimensions based on the user's asset management goals.

[0195] "Emotion analysis technology" is a technology that analyzes a user's facial expressions, voice tone, etc., and evaluates their emotional state in real time.

[0196] "Adjusting the presentation method of investment strategies" refers to customizing the way and content of asset management strategies are presented to users based on sentiment analysis results.

[0197] "Analyzing spending patterns" is the process of analyzing a user's daily spending habits and deriving designs that reduce waste.

[0198] The system for implementing this invention consists of various devices and software modules. The hardware primarily used includes a server and user terminals such as smartphones or smart glasses. The server collects the user's financial data anonymized and obtains the latest market information and financial product data from external data sources. Based on this data, the server analyzes the user's asset allocation and generates an optimal asset management strategy.

[0199] Emotion analysis utilizes the device's camera and microphone to monitor the user's facial expressions and voice tone in real time. Based on this, the server evaluates the user's emotional state using emotion analysis technology and adjusts how it presents the optimal asset management strategy. Specifically, it is possible to utilize existing technologies such as "Google Cloud's Speech-to-Text" and "Microsoft's Face API."

[0200] Furthermore, it analyzes users' spending patterns and provides advice based on this to curb unnecessary spending. It manages users' schedules and life events, provides timely notifications related to asset management, and selects learning content tailored to the user's knowledge level. This allows users to manage their assets in a personalized way.

[0201] As a concrete example, this system may perform sentiment analysis while a user is shopping on a holiday and adjust their asset management strategy based on that information. If the sentiment analysis detects stress in the user, it will adjust future asset management policies along with the results of the spending pattern analysis and generate a recommendation that strongly advises investing in safe assets.

[0202] An example of a prompt for a generating AI model is as follows: "Explain how your personal finance app would intervene and provide appropriate savings advice if the user is feeling stressed at a cafe."

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server collects users' financial data in an anonymized form. It receives individual transaction history and account balance information as input and converts it into a format that does not identify individuals. The output is anonymized financial data.

[0206] Step 2:

[0207] The server retrieves the latest market information and financial product data from external data sources. It collects real-time market data via APIs and stores it in a database. The output consists of the collected market information and financial product data, which are used for subsequent analysis.

[0208] Step 3:

[0209] The server integrates the user's anonymized financial data with the latest market information to analyze asset allocation. Through data processing, it evaluates investment risk, profitability, and the user's asset portfolio, and generates an optimal asset management strategy. The output is a proposed investment strategy.

[0210] Step 4:

[0211] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Input is real-time video images and audio data, while output is sentiment data generated by sentiment analysis technology. This sentiment data forms the basis for adjusting operational strategies.

[0212] Step 5:

[0213] The server uses emotion analysis technology to evaluate the user's emotional state. It analyzes the input emotional data and quantifies the user's perceived stress and sense of security. The output is the analyzed emotional state.

[0214] Step 6:

[0215] The server combines the user's emotional state with the generated investment strategy to adjust how optimal investment advice is presented. For example, if emotional data indicates that the user is stressed, it prioritizes presenting low-risk assets. The output is investment advice tailored to the user's emotional state.

[0216] Step 7:

[0217] The device manages the user's schedule and life events and provides timely investment-related notifications. Inputs are calendar information and emotional states, while outputs are timely investment information.

[0218] Step 8:

[0219] Users manage their assets individually based on operational advice received from their devices. User behavioral feedback is incorporated into subsequent analyses, leading to more refined strategic proposals.

[0220] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0221] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0223] [Second Embodiment]

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

[0225] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0227] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0231] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0232] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0233] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0234] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0235] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0236] This invention relates to an asset management advice system based on a method for anonymizing users' financial data and securely transmitting it to a server. This system securely manages users' asset information and acquires and analyzes the latest market information to propose the optimal asset management strategy for the user.

[0237] The server receives asset information submitted by the user and obtains the latest market data from various financial institutions. Based on this data, the server analyzes the collected information and generates asset management strategies tailored to each user's unique risk tolerance and investment goals. The generated strategies are then sent to the user's terminal for notification. Upon receiving this notification, the user can review the proposed strategy and adjust their asset allocation as needed.

[0238] Furthermore, the terminal manages the user's schedule information and has a function to remind users of important timings for operational advice received from the server. By entering life events and other appointments into the terminal, users can manage important events that are useful for asset management without missing any.

[0239] Furthermore, the server has a function that provides learning content tailored to the user's knowledge level. This allows users to effectively improve the knowledge necessary for asset management.

[0240] The 3D simulation function provides a tool for users to visually experience future scenarios based on their investment goals. The server generates predictive scenarios for the user's goals and sends them to the terminal. The terminal interactively displays the 3D simulation received from the server, making it easier for users to concretely visualize their future success in investment management.

[0241] As a concrete example, suppose User A regularly invests in stocks as part of their asset management. The user enters their schedule into a terminal, and the server analyzes market information to provide the next most effective investment timing. If the user accepts the advice provided, the terminal sends a reminder at that time, allowing the user to make the investment. This system enables the user to achieve efficient asset management and aim for reliable wealth building.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The user enters asset information into the terminal. The terminal then transmits the entered information to the server using a secure communication protocol.

[0245] Step 2:

[0246] The server anonymizes the asset information it receives and securely stores it in a database. It also obtains the latest market information and financial product data from trusted external data sources.

[0247] Step 3:

[0248] The server uses collected market information and user asset information to perform risk and return analysis. Based on the analysis results, it generates an asset management strategy that is suitable for the user's investment goals and risk tolerance.

[0249] Step 4:

[0250] The operational strategy generated by the server is sent to the terminal, and the terminal notifies the user of the proposed operational strategy. The user reviews the proposal and revise their asset allocation as needed.

[0251] Step 5:

[0252] Users input life events and schedule information into the device. The device manages the entered information and is configured to remind users of important investment timings based on operational strategies received from the server.

[0253] Step 6:

[0254] The server assesses the user's knowledge level and selects appropriate learning content based on that assessment. The learning content is then sent to the user's device, and the user improves their knowledge of asset management through the provided content.

[0255] Step 7:

[0256] The server generates a 3D simulation of future investment results based on the user's asset management goals. This simulation is sent to the terminal, which interactively displays the 3D simulation to the user, visualizing a concrete vision of future success.

[0257] (Example 1)

[0258] 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."

[0259] Traditional asset management systems have suffered from problems such as reduced investment efficiency for users due to insufficient security of users' financial data, lack of access to the latest market information, and inadequate provision of individually optimized asset management strategies. Furthermore, the lack of content to support user knowledge enhancement and visualization of future forecasts made it difficult for users to make effective asset management decisions.

[0260] 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.

[0261] In this invention, the server includes means for anonymizing and collecting users' financial data, means for acquiring the latest market information, and means for generating and providing optimal asset management strategies that differ for each user. This makes it possible to provide individually optimized strategies based on the latest market information while ensuring the security of users' financial data. Furthermore, it deepens users' understanding of asset management and supports more effective investment decision-making by providing educational content tailored to the user's knowledge level and visualizing future predictions through three-dimensional simulations.

[0262] "Financial data" refers to information about a user's asset status and investment activities, specifically including deposit balances, stock and bond holdings, and investment goals.

[0263] "Anonymization" refers to a technology that processes data in a way that prevents individuals from being identified, thereby protecting privacy.

[0264] "Market information" refers to the latest data on stock prices, exchange rates, commodity prices, etc., in financial markets, and serves as fundamental information for formulating asset management strategies.

[0265] An "asset management strategy" refers to a plan regarding asset allocation and investment timing, which is formulated based on the user's risk tolerance and investment goals.

[0266] "Schedule" refers to the management information for appointments related to the user's daily life and asset management, and plays a role in ensuring that important events are not missed.

[0267] "Life events" refer to significant events in a user's life that may influence decisions regarding asset management.

[0268] "Notifications" refer to means of informing users of important information, and are provided in the form of push notifications, etc.

[0269] "Educational content" refers to learning materials provided to improve users' knowledge of asset management, and includes lessons, quizzes, and other similar materials.

[0270] "Three-dimensional simulation" is a technology that visually represents future scenarios based on the user's asset management goals, providing an interactive experience.

[0271] A "generative model" refers to an algorithm or machine learning technique used to generate optimal asset management strategies based on available data.

[0272] A "central processing unit" refers to a central device used for data analysis and asset management strategy generation, and typically possesses the capability to handle large-scale calculations.

[0273] "Device" refers to electronic devices used by users to access the system, and includes smartphones, personal computers, and other similar devices.

[0274] This invention is a system that provides individually customized asset management strategies while anonymizing and securely managing users' financial information. This system is built through the interaction of a server, a terminal, and the user.

[0275] The server utilizes SSL / TLS as a highly secure protocol to receive anonymized financial data sent by users. The server organizes and stores the data using various database systems (e.g., MySQL). Subsequently, the server leverages data analysis libraries such as Pandas and NumPy to generate asset management strategies based on the user's risk profile and market data. This involves the use of generative AI models and information obtained from diverse data sources. Furthermore, the generated investment strategy is notified to the user's terminal.

[0276] The terminal presents the operational strategy transmitted from the server to the user, providing a visually easy-to-understand interface. The terminal also manages the user's schedule and sends important notifications via reminders at specific times. This enables the user to make investment decisions efficiently and at the right time.

[0277] Users can access learning content about asset management using their devices. This content is tailored to the user's knowledge level and is delivered through an e-learning platform (e.g., a general education management system). Users can also experience server-generated 3D simulations on their devices. This allows users to visually understand future asset management scenarios and develop a clearer vision for their investment strategy.

[0278] As a specific example, User A sets their own investment plan on the terminal, and the server analyzes the market data and notifies the appropriate asset management strategy. When this strategy is notified, the terminal reminds the user of the investment timing. The user can utilize the information provided by the system to smoothly manage their assets.

[0279] As an example of a prompt text for the generative AI model, text such as "Based on the current market situation, please tell me the appropriate timing for stock investment for User A." can be considered. This prompt is used as part of the data collection and analysis for the system to provide a strategy suitable for the user.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user inputs their financial-related information into the terminal. The information to be input includes the status of financial assets and investment goals. This input data is anonymized for personal information protection. Specifically, personal information such as name and address is hashed and AES encryption is performed. The anonymized data is sent to the server.

[0283] Step 2:

[0284] The server securely receives the anonymized data sent from the terminal through the SSL / TLS protocol. The received data is stored in the database and used as the basis for subsequent analysis. This enables discrimination of which user has which asset data, but does not include information for identifying individuals.

[0285] Step 3:

[0286] The server uses the API of an external financial data provider to obtain the latest market information. The obtained market data is stored in the database and processed into an analyzable form by combining it with user data. This enables understanding of market trends and movements.

[0287] Step 4:

[0288] The server uses data analysis libraries such as Pandas and NumPy to integrate and analyze the user's asset information and market data. From this analysis, an optimal asset management strategy based on each user's risk tolerance and investment goals is generated, and the generated AI model is used. This strategy is customized to reflect the user's unique conditions.

[0289] Step 5:

[0290] The server encrypts the generated management strategy and sends it to the terminal. The terminal notifies the user of the received management strategy and displays it in a visually understandable form. The user can confirm the presented management strategy and adjust the investment plan as needed.

[0291] Step 6:

[0292] The terminal sets reminders for important events related to asset management based on the user's life schedule. Based on the date and time specified by the user, reminders are sent at appropriate times to assist in not missing important investment opportunities.

[0293] Step 7:

[0294] The server generates educational content according to the user's knowledge level and provides it through the terminal. The user can access this content at any time and can deepen their understanding of asset management.

[0295] Step 8:

[0296] The server generates a three-dimensional simulation of future scenarios based on the user's asset management goals. This simulation is created using a 3D engine such as Unity, and the terminal displays it in an interactive form. The user can use this to visually confirm future asset management results.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] Traditional asset management systems struggle to provide personalized investment strategies while ensuring the anonymity of users' financial data. Furthermore, they lack the means to achieve efficient asset management by considering users' daily schedules and life events. As a result, users may miss investment opportunities or receive proposals that don't match their risk profile. Additionally, there is insufficient provision of educational content to deepen users' understanding of asset management.

[0300] 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.

[0301] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a 3D simulation based on the user's asset management goals; means for estimating important life events based on payment history and suggesting appropriate investment timing; and means for creating inputs for adjusting individual asset management strategies using a generative AI model. This enables flexible and accurate asset management tailored to the user's lifestyle and goals, promoting improved investment knowledge and successful wealth building.

[0302] "Methods for collecting users' financial data in an anonymized form" refers to a function that converts individual user information into a format that does not allow others to identify the user, and then securely aggregates that information.

[0303] The means for "obtaining the latest market information and financial product data from external data sources" refers to the function of importing the latest data reflecting market trends and the characteristics of financial products from external information providers.

[0304] The means for "analyzing the user's asset allocation and generating an optimal asset management strategy" refers to the analysis function for formulating an operation plan most suitable for that person based on the collected user asset information.

[0305] The means for "managing the user's schedule and life events and providing notifications regarding operations" refers to the function of recording the user's schedule and important events and making proposals and reminders for asset management accordingly.

[0306] The means for "providing learning content according to the user's knowledge level" refers to the function of appropriately providing educational materials for deepening understanding according to the degree of the user's financial knowledge.

[0307] The means for "generating a 3D simulation based on the user's asset management goals" refers to the function of generating a three-dimensional video for visually reproducing the economic results the user aims for as a scenario.

[0308] The means for "estimating important life events based on the payment history and proposing appropriate investment timing" refers to the function of predicting future events from past payment patterns and advising optimal investment actions.

[0309] The means for "creating an input for adjusting an individual asset management strategy using a generative AI model" refers to the function of using artificial intelligence technology to generate data for strategy adjustment according to the user's operation policy.

[0310] This invention centers on sensing technology for anonymizing and collecting users' financial data and securely storing it in a database, and data analysis technology for incorporating the latest market information and generating asset management strategies based on it. The server uses data analysis languages ​​such as Python and R to generate an optimal asset management strategy based on market data and the user's risk tolerance.

[0311] The device manages the user's schedule and life events, and sends reminders at appropriate times based on those events. Reminder notifications are implemented using the smartphone's notification function. The device also provides learning content and visually displays 3D simulations. These 3D simulations utilize 3D engines such as Unity and Unreal Engine to visualize future asset management scenarios.

[0312] The server analyzes payment history data and estimates life events. In this process, machine learning models are used to predict important future events from past history, and system adjustments are made using generative AI models as needed. The generative AI models utilize prompts to provide better asset management advice tailored to the user's unique operational needs, offering individual strategic suggestions. For example, a user can register travel plans on their device and receive suggestions to liquidate their assets before spending. An example of a prompt using the generative AI model is, "Develop an investment strategy tailored to the user's travel plan and suggest the optimal timing for liquidating assets."

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

[0314] Step 1:

[0315] The server receives the user's financial data and anonymizes it. It takes raw financial data provided by the user as input and transforms it using an anonymization algorithm. The output is anonymized data, which is stored in a secure database. Specifically, encryption technologies such as RSA and AES are used to make the data impossible to identify individuals.

[0316] Step 2:

[0317] The server retrieves the latest market information and financial product data from external market information providers. It takes external datasets as input via HTTP requests through APIs. The output is the latest market dataset, which is used in the data analysis layer. Specifically, it performs scheduled periodic data retrieval and on-demand retrieval as needed.

[0318] Step 3:

[0319] The server analyzes acquired market information and anonymized user asset data to generate asset management strategies. It accepts pre-processed market data and user asset data as input. As output, it generates individually tailored asset management strategies. Specifically, it uses Python or R to perform data analysis using machine learning algorithms, and then adjusts simulations and recommendations using a generated AI model.

[0320] Step 4:

[0321] The device manages the user's schedule information and life event data. It takes schedule data registered by the user as input. The output is a timeline of scheduled events, which serves as the basis for reminder logic. Specific operations include scheduling using the calendar function and setting event priorities.

[0322] Step 5:

[0323] The terminal displays asset management strategy notifications provided by the server to the user and sends reminders. It receives asset management advice data sent from the server as input. The output is a notification message directed to the user. Specifically, it uses the smartphone's push notification function to inform the user in a timely manner.

[0324] Step 6:

[0325] The server estimates life events based on the user's operation and transaction history and proposes new investment timings. It analyzes the user's transaction logs as input. Based on this input, it uses a machine learning model to predict future life events and generates new investment suggestions as output. Specifically, it performs filtering and feature extraction of historical data.

[0326] Step 7:

[0327] The server uses a generative AI model to create prompts to refine the user's individual asset management strategy. It receives the user's investment status and preferences as input, and the generative AI model generates prompts accordingly. The output is the refined prompt. Specifically, natural language processing techniques are applied to generate input sentences that improve the quality of the AI ​​model's response.

[0328] 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.

[0329] This invention combines an emotion engine with a system that supports effective asset management by utilizing users' financial data. This system recognizes users' emotions in real time and provides investment advice and learning support tailored to their psychological state, thereby realizing a more personalized experience.

[0330] The server receives anonymized financial data from users and retrieves the latest market information and financial product data from external data sources. Based on this, it analyzes the user's asset allocation and generates an optimal investment strategy. It also uses an emotion engine to analyze the user's emotional state and adjust the way the generated investment strategy is presented and its content. Specifically, if the user is feeling stressed, it can emphasize low-risk, safe options.

[0331] The device manages the user's schedule and life events and provides operational notifications that take emotional states into account. The emotion engine analyzes the user's facial expressions and tone of voice through sensor devices such as cameras and microphones to infer their emotions.

[0332] Furthermore, the device assesses the user's knowledge level and provides appropriate learning content based on the results. The emotion engine responds to the user's emotional state and presents content at the optimal timing according to the user's learning progress, thereby enhancing the user's learning effectiveness.

[0333] In the 3D simulation function, the server generates simulations based on the user's asset management goals, and the emotion engine reflects the user's psychological state. For example, if the user is feeling anxious, the simulation can prioritize presenting stable asset growth scenarios.

[0334] As a concrete example, consider a scenario where User B manages their assets on a holiday. User B checks market trends while viewing their investment information entered into the terminal. The terminal then analyzes User B's voice and detects their stress levels. The server adjusts future investment strategies accordingly and generates suggestions strongly recommending investment in safe assets. In this way, the system of the present invention provides more user-centric asset management advice in real time, increasing confidence and peace of mind in asset management.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The user enters asset information into the terminal. The terminal transmits the user's input data to the server using a secure communication method. Simultaneously, the terminal's sensors begin to monitor the user's emotions in real time.

[0338] Step 2:

[0339] The server anonymizes the transmitted asset information and stores it in a database. Next, it retrieves the latest market information and financial product data from external data sources.

[0340] Step 3:

[0341] The server analyzes acquired market information and user asset information to perform risk and return analysis. Based on this, it generates an asset management strategy that matches the user's risk tolerance and investment goals.

[0342] Step 4:

[0343] The server uses an emotion engine to analyze the user's emotional state. Based on the analysis results, it adjusts its operational policies and presentation methods to formulate asset management advice that takes the user's emotions into consideration.

[0344] Step 5:

[0345] The terminal notifies the user of operational strategies and advice from the server. If the user's emotional state falls below a certain level, the advice is presented in a gentler manner, or lower-risk options are emphasized.

[0346] Step 6:

[0347] The user inputs life events and daily schedules into the device. The device reflects these in the schedule and, based on advice generated by the server, sets up notifications for appropriate investment timing, taking into account the user's emotional state.

[0348] Step 7:

[0349] The server selects appropriate learning content based on the user's knowledge level and emotional state. It then delivers the content to the device according to a presentation schedule that takes the user's emotions into consideration.

[0350] Step 8:

[0351] The server generates a 3D simulation that reflects the user's investment goals and emotional state. The terminal receives this and displays it interactively to the user. Depending on the user's current emotional state, the aim is to enhance their sense of security by highlighting specific scenarios.

[0352] (Example 2)

[0353] 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".

[0354] Traditional asset management systems have a problem in that they only propose standardized investment policies based on the user's financial information, and do not adequately provide flexible investment support that takes into account the emotional state and learning needs of individual users. As a result, users are unable to make appropriate investment decisions when they feel stressed or anxious, and are unable to achieve sufficient asset growth.

[0355] 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.

[0356] In this invention, the server includes means for analyzing the user's emotional state and adjusting the operational policy according to that emotion, means for providing learning materials according to the user's knowledge level, and means for generating a 3D simulation based on the user's asset management goals. This enables optimal asset management support tailored to the user's psychological state and promotes effective learning.

[0357] "User financial information" refers to information about an individual's financial resources and how they are being managed, including the contents of bank accounts and investment portfolios.

[0358] "Anonymization" refers to a technical process that makes it impossible to identify a specific individual in order to protect their privacy.

[0359] "External data sources" refer to the sources of data provided by information providers outside the system, and include information on financial markets and details of financial products.

[0360] "Asset allocation" refers to the proportion of a user's assets that are distributed across different investment destinations or asset classes.

[0361] An "asset management policy" refers to an investment strategy designed to efficiently increase assets, and includes risk management and an approach tailored to investment objectives.

[0362] "Emotional state" refers to the user's psychological feelings and mental reactions, encompassing states such as stress, anxiety, and a sense of security.

[0363] "Life events" refer to important events or scheduled activities in a user's lifestyle, including changes in work, marriage, and raising children.

[0364] "Learning materials" refer to informational content provided to deepen users' understanding and knowledge, including educational materials and training materials.

[0365] "3D simulation" refers to a technology that visually and three-dimensionally represents the results and scenarios of asset management in a digital environment.

[0366] This invention relates to a system that utilizes users' financial information to provide asset management support tailored to their individual emotional states. The system works in cooperation with a server, terminals, and users to collect, analyze, and notify data.

[0367] The server is responsible for processing financial information, analyzing the user's asset allocation, and generating the optimal investment strategy. In this process, it uses a generative AI model to construct effective strategies from a large amount of market and financial product information. It also utilizes an emotion engine to adjust the investment strategy according to the user's emotional state. Furthermore, the server collects the latest financial information in real time from external data sources and uses this information to propose the most effective investment method for the user.

[0368] Meanwhile, the device functions as the user's interface, transmitting financial information and emotional states to the server. The device uses sensors such as cameras and microphones to analyze the user's facial expressions and tone of voice, collecting emotional data in real time. The collected data is encrypted and sent to the server in a privacy-preserving manner. The device also provides learning materials to the user, supporting them in deepening their knowledge of asset management.

[0369] Through this system, users can input their financial information into a terminal and create a plan that reflects their investment stance and life events. The system features a 3D simulation function that visually presents the user's long-term asset growth scenario. This allows for discussion and review, and consideration of the next steps.

[0370] As a concrete example, consider a scenario where a user checks market trends using a device. If the device detects the user's voice and determines that their stress levels are high, it retrieves and displays advice from the server recommending investment in safe assets. The generating AI model constructs an investment strategy using the prompt message, "Consider the user's current investment portfolio and emotional state, and propose an investment strategy that minimizes risk." Through this mechanism, users can achieve asset management that reflects their own emotional state and gain a sense of security.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user enters their financial information into the terminal. This data includes bank account information and investment portfolios. The terminal encrypts this information and prepares it for processing in a privacy-enhanced manner. The entered information is anonymized and formatted into a dataset for processing.

[0374] Step 2:

[0375] The device uses a camera and microphone to monitor the user's facial expressions and voice tone in real time and collect emotional data. The collected data is analyzed to identify the user's current emotional state. The results of the emotional analysis are output as an index representing the user's psychological state and sent to the server.

[0376] Step 3:

[0377] The server receives financial information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to analyze asset allocation and construct an optimal investment strategy. Specifically, the model uses the prompt message, "Consider the user's current investment portfolio and sentiment state, and propose an investment strategy that minimizes risk." The resulting investment strategy is then adjusted to reflect characteristics such as the degree of risk.

[0378] Step 4:

[0379] The server retrieves the latest market information from external data sources to verify the validity of operational policies. Data processing here includes information collection via external APIs and periodic data updates. Market information is added to operational policies, enabling strategies that take real-time market changes into account.

[0380] Step 5:

[0381] The server generates operational policies tailored to the user's emotional state, along with a 3D simulation that includes market information. The simulation visualizes multiple asset growth scenarios and presents them in an easy-to-understand format. The generated simulation results are sent to the terminal as material to support the user's decision-making.

[0382] Step 6:

[0383] The terminal presents the user with operational policies and simulation results received from the server. Specifically, it visually represents the situation through a graphical UI. It assists the user in reviewing the information and deciding on the next steps.

[0384] (Application Example 2)

[0385] 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."

[0386] Current asset management systems fail to consider the user's emotional state, making it difficult to provide optimal advice. Furthermore, they cannot reflect individual lifestyles or emotions in user spending management, potentially leading to inefficient asset management. There is a need to solve these problems and realize more personalized asset management support.

[0387] 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.

[0388] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a three-dimensional simulation based on the user's asset management goals; means for analyzing the user's emotional state in real time using emotion analysis technology and adjusting the method of presenting the investment strategy accordingly; and means for analyzing the user's spending patterns and providing advice to reduce unnecessary spending. This enables more personalized asset management support and spending management based on the user's emotions and lifestyle.

[0389] "User financial data" refers to financial information such as the user's assets, liabilities, income, and expenses, which is anonymized and analyzed by the system.

[0390] "Market information" refers to external information that affects the value of financial assets, such as the latest economic trends, financial market data, stock indices, and interest rates.

[0391] "Financial product data" refers to detailed information about products such as investment trusts, stocks, and bond options offered by various financial institutions.

[0392] "Asset allocation" refers to a strategy for how a user's assets will be distributed across different investment targets.

[0393] "3D simulation" is a function that visually simulates the asset management process in three dimensions based on the user's asset management goals.

[0394] "Emotion analysis technology" is a technology that analyzes a user's facial expressions, voice tone, etc., and evaluates their emotional state in real time.

[0395] "Adjusting the presentation method of investment strategies" refers to customizing the way and content of asset management strategies are presented to users based on sentiment analysis results.

[0396] "Analyzing spending patterns" is the process of analyzing a user's daily spending habits and deriving designs that reduce waste.

[0397] The system for implementing this invention consists of various devices and software modules. The hardware primarily used includes a server and user terminals such as smartphones or smart glasses. The server collects the user's financial data anonymized and obtains the latest market information and financial product data from external data sources. Based on this data, the server analyzes the user's asset allocation and generates an optimal asset management strategy.

[0398] Emotion analysis utilizes the device's camera and microphone to monitor the user's facial expressions and voice tone in real time. Based on this, the server evaluates the user's emotional state using emotion analysis technology and adjusts how the optimal asset management strategy is presented. Specifically, it is possible to utilize existing technologies such as "Google Cloud's Speech-to-Text" and "Microsoft's Face API".

[0399] Furthermore, it analyzes users' spending patterns and provides advice based on this to curb unnecessary spending. It manages users' schedules and life events, provides timely notifications related to asset management, and selects learning content tailored to the user's knowledge level. This allows users to manage their assets in a personalized way.

[0400] As a concrete example, this system may perform sentiment analysis while a user is shopping on a holiday and adjust their asset management strategy based on that information. If the sentiment analysis detects stress in the user, it will adjust future asset management policies along with the results of the spending pattern analysis and generate a recommendation that strongly advises investing in safe assets.

[0401] An example of a prompt for a generating AI model is as follows: "Explain how your personal finance app would intervene and provide appropriate savings advice if the user is feeling stressed at a cafe."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server collects users' financial data in an anonymized form. It receives individual transaction history and account balance information as input and converts it into a format that does not identify individuals. The output is anonymized financial data.

[0405] Step 2:

[0406] The server retrieves the latest market information and financial product data from external data sources. It collects real-time market data via APIs and stores it in a database. The output consists of the collected market information and financial product data, which are used for subsequent analysis.

[0407] Step 3:

[0408] The server integrates the user's anonymized financial data with the latest market information to analyze asset allocation. Through data processing, it evaluates investment risk, profitability, and the user's asset portfolio, and generates an optimal asset management strategy. The output is a proposed investment strategy.

[0409] Step 4:

[0410] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Input is real-time video images and audio data, while output is sentiment data generated by sentiment analysis technology. This sentiment data forms the basis for adjusting operational strategies.

[0411] Step 5:

[0412] The server uses emotion analysis technology to evaluate the user's emotional state. It analyzes the input emotional data and quantifies the user's perceived stress and sense of security. The output is the analyzed emotional state.

[0413] Step 6:

[0414] The server combines the user's emotional state with the generated investment strategy to adjust how optimal investment advice is presented. For example, if emotional data indicates that the user is stressed, it prioritizes presenting low-risk assets. The output is investment advice tailored to the user's emotional state.

[0415] Step 7:

[0416] The device manages the user's schedule and life events and provides timely investment-related notifications. Inputs are calendar information and emotional states, while outputs are timely investment information.

[0417] Step 8:

[0418] Users manage their assets individually based on operational advice received from their devices. User behavioral feedback is incorporated into subsequent analyses, leading to more refined strategic proposals.

[0419] 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.

[0420] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention relates to an asset management advice system based on a method for anonymizing users' financial data and securely transmitting it to a server. This system securely manages users' asset information and acquires and analyzes the latest market information to propose the optimal asset management strategy for the user.

[0436] The server receives asset information submitted by the user and obtains the latest market data from various financial institutions. Based on this data, the server analyzes the collected information and generates asset management strategies tailored to each user's unique risk tolerance and investment goals. The generated strategies are then sent to the user's terminal for notification. Upon receiving this notification, the user can review the proposed strategy and adjust their asset allocation as needed.

[0437] Furthermore, the terminal manages the user's schedule information and has a function to remind users of important timings for operational advice received from the server. By entering life events and other appointments into the terminal, users can manage important events that are useful for asset management without missing any.

[0438] Furthermore, the server has a function that provides learning content tailored to the user's knowledge level. This allows users to effectively improve the knowledge necessary for asset management.

[0439] The 3D simulation function provides a tool for users to visually experience future scenarios based on their investment goals. The server generates predictive scenarios for the user's goals and sends them to the terminal. The terminal interactively displays the 3D simulation received from the server, making it easier for users to concretely visualize their future success in investment management.

[0440] As a concrete example, suppose User A regularly invests in stocks as part of their asset management. The user enters their schedule into a terminal, and the server analyzes market information to provide the next most effective investment timing. If the user accepts the advice provided, the terminal sends a reminder at that time, allowing the user to make the investment. This system enables the user to achieve efficient asset management and aim for reliable wealth building.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The user enters asset information into the terminal. The terminal then transmits the entered information to the server using a secure communication protocol.

[0444] Step 2:

[0445] The server anonymizes the asset information it receives and securely stores it in a database. It also obtains the latest market information and financial product data from trusted external data sources.

[0446] Step 3:

[0447] The server uses collected market information and user asset information to perform risk and return analysis. Based on the analysis results, it generates an asset management strategy that is suitable for the user's investment goals and risk tolerance.

[0448] Step 4:

[0449] The operational strategy generated by the server is sent to the terminal, and the terminal notifies the user of the proposed operational strategy. The user reviews the proposal and revise their asset allocation as needed.

[0450] Step 5:

[0451] Users input life events and schedule information into the device. The device manages the entered information and is configured to remind users of important investment timings based on operational strategies received from the server.

[0452] Step 6:

[0453] The server assesses the user's knowledge level and selects appropriate learning content based on that assessment. The learning content is then sent to the user's device, and the user improves their knowledge of asset management through the provided content.

[0454] Step 7:

[0455] The server generates a 3D simulation of future investment results based on the user's asset management goals. This simulation is sent to the terminal, which interactively displays the 3D simulation to the user, visualizing a concrete vision of future success.

[0456] (Example 1)

[0457] 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."

[0458] Traditional asset management systems have suffered from problems such as reduced investment efficiency for users due to insufficient security of users' financial data, lack of access to the latest market information, and inadequate provision of individually optimized asset management strategies. Furthermore, the lack of content to support user knowledge enhancement and visualization of future forecasts made it difficult for users to make effective asset management decisions.

[0459] 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.

[0460] In this invention, the server includes means for anonymizing and collecting users' financial data, means for acquiring the latest market information, and means for generating and providing optimal asset management strategies that differ for each user. This makes it possible to provide individually optimized strategies based on the latest market information while ensuring the security of users' financial data. Furthermore, it deepens users' understanding of asset management and supports more effective investment decision-making by providing educational content tailored to the user's knowledge level and visualizing future predictions through three-dimensional simulations.

[0461] "Financial data" refers to information about a user's asset status and investment activities, specifically including deposit balances, stock and bond holdings, and investment goals.

[0462] "Anonymization" refers to a technology that processes data in a way that prevents individuals from being identified, thereby protecting privacy.

[0463] "Market information" refers to the latest data on stock prices, exchange rates, commodity prices, etc., in financial markets, and serves as fundamental information for formulating asset management strategies.

[0464] An "asset management strategy" refers to a plan regarding asset allocation and investment timing, which is formulated based on the user's risk tolerance and investment goals.

[0465] "Schedule" refers to the management information for appointments related to the user's daily life and asset management, and plays a role in ensuring that important events are not missed.

[0466] "Life events" refer to significant events in a user's life that may influence decisions regarding asset management.

[0467] "Notifications" refer to means of informing users of important information, and are provided in the form of push notifications, etc.

[0468] "Educational content" refers to learning materials provided to improve users' knowledge of asset management, and includes lessons, quizzes, and other similar materials.

[0469] "Three-dimensional simulation" is a technology that visually represents future scenarios based on the user's asset management goals, providing an interactive experience.

[0470] A "generative model" refers to an algorithm or machine learning technique used to generate optimal asset management strategies based on available data.

[0471] A "central processing unit" refers to a central device used for data analysis and asset management strategy generation, and typically possesses the capability to handle large-scale calculations.

[0472] "Device" refers to electronic devices used by users to access the system, and includes smartphones, personal computers, and other similar devices.

[0473] This invention is a system that provides individually customized asset management strategies while anonymizing and securely managing users' financial information. This system is built through the interaction of a server, a terminal, and the user.

[0474] The server utilizes SSL / TLS as a highly secure protocol to receive anonymized financial data sent by users. The server organizes and stores the data using various database systems (e.g., MySQL). Subsequently, the server leverages data analysis libraries such as Pandas and NumPy to generate asset management strategies based on the user's risk profile and market data. This involves the use of generative AI models and information obtained from diverse data sources. Furthermore, the generated investment strategy is notified to the user's terminal.

[0475] The terminal presents the operational strategy transmitted from the server to the user, providing a visually easy-to-understand interface. The terminal also manages the user's schedule and sends important notifications via reminders at specific times. This enables the user to make investment decisions efficiently and at the right time.

[0476] Users can access learning content about asset management using their devices. This content is tailored to the user's knowledge level and is delivered through an e-learning platform (e.g., a general education management system). Users can also experience server-generated 3D simulations on their devices. This allows users to visually understand future asset management scenarios and develop a clearer vision for their investment strategy.

[0477] As a concrete example, user A sets up their investment plan on a terminal, and the server analyzes market data to inform them of an appropriate asset management strategy. Once this strategy is announced, the terminal reminds the user of the timing for investment. The user can then smoothly manage their assets by utilizing the information provided by the system.

[0478] An example of a prompt for a generative AI model might be text such as, "Based on the current market conditions, please tell me the appropriate timing for stock investment for user A." This prompt is used as part of the data collection and analysis process to provide the system with a suitable strategy for the user.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The user enters their financial information into the terminal. This information includes the status of their financial assets and investment goals. This input data is anonymized to protect personal information; specifically, personal information such as name and address is hashed and AES encryption is performed. The anonymized data is then sent to the server.

[0482] Step 2:

[0483] The server securely receives anonymized data sent from the terminal via the SSL / TLS protocol. The received data is stored in a database, serving as the basis for subsequent analysis. This allows the server to determine which users possess which asset data, but it does not include any personally identifiable information.

[0484] Step 3:

[0485] The server uses APIs from external financial data providers to obtain the latest market information. The retrieved market data is stored in a database and combined with user data to create an analyzable format. This allows for a clear understanding of market trends and developments.

[0486] Step 4:

[0487] The server uses data analysis libraries such as Pandas and NumPy to integrate and analyze user asset information and market data. From this analysis, an optimal asset management strategy is generated based on each user's risk tolerance and investment goals, and a generative AI model is used. This strategy is customized to reflect the user's unique conditions.

[0488] Step 5:

[0489] The server encrypts the generated operational strategy and sends it to the terminal. The terminal notifies the user of the received operational strategy and displays it in a visually easy-to-understand format. The user can review the presented operational strategy and adjust the investment plan as needed.

[0490] Step 6:

[0491] The device sets reminders for important events related to asset management based on the user's daily schedule. It provides timely reminders based on the user's specified date and time, helping users avoid missing important investment opportunities.

[0492] Step 7:

[0493] The server generates educational content tailored to the user's knowledge level and provides it through the terminal. Users can access this content at any time to deepen their understanding of asset management.

[0494] Step 8:

[0495] The server generates a three-dimensional simulation of future scenarios based on the user's asset management goals. This simulation is created using a 3D engine such as Unity, and the terminal displays it in an interactive format. The user can use this to visually confirm future asset management results.

[0496] (Application Example 1)

[0497] 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."

[0498] Traditional asset management systems struggle to provide personalized investment strategies while ensuring the anonymity of users' financial data. Furthermore, they lack the means to achieve efficient asset management by considering users' daily schedules and life events. As a result, users may miss investment opportunities or receive proposals that don't match their risk profile. Additionally, there is insufficient provision of educational content to deepen users' understanding of asset management.

[0499] 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.

[0500] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a 3D simulation based on the user's asset management goals; means for estimating important life events based on payment history and suggesting appropriate investment timing; and means for creating inputs for adjusting individual asset management strategies using a generative AI model. This enables flexible and accurate asset management tailored to the user's lifestyle and goals, promoting improved investment knowledge and successful wealth building.

[0501] "Methods for collecting users' financial data in an anonymized form" refers to a function that converts individual user information into a format that does not allow others to identify the user, and then securely aggregates that information.

[0502] "Means of obtaining the latest market information and financial product data from external data sources" refers to the function of importing the latest data reflecting market trends and the characteristics of financial products from external information providers.

[0503] "A means of analyzing a user's asset allocation and generating the optimal asset management strategy" refers to an analytical function that uses collected user asset information to formulate the most suitable investment plan for that individual.

[0504] "Means for managing user schedules and life events and providing investment-related notifications" refers to functions that record users' appointments and important events, and provide investment suggestions and reminders accordingly.

[0505] "Means of providing learning content tailored to the user's knowledge level" refers to a function that appropriately provides educational materials to deepen the user's understanding, according to their level of financial knowledge.

[0506] "Means for generating 3D simulations based on the user's asset management goals" refers to a function that generates three-dimensional images to visually reproduce the user's desired economic outcomes as a scenario.

[0507] "A means of estimating important life events based on payment history and suggesting appropriate investment timing" refers to a function that predicts future events from past payment patterns and advises on optimal investment actions.

[0508] "Means of creating inputs for adjusting individual asset management strategies using generative AI models" refers to a function that utilizes artificial intelligence technology to generate data for strategic adjustments tailored to the user's investment policy.

[0509] This invention centers on sensing technology for anonymizing and collecting users' financial data and securely storing it in a database, and data analysis technology for incorporating the latest market information and generating asset management strategies based on it. The server uses data analysis languages ​​such as Python and R to generate an optimal asset management strategy based on market data and the user's risk tolerance.

[0510] The device manages the user's schedule and life events, and sends reminders at appropriate times based on those events. Reminder notifications are implemented using the smartphone's notification function. The device also provides learning content and visually displays 3D simulations. These 3D simulations utilize 3D engines such as Unity and Unreal Engine to visualize future asset management scenarios.

[0511] The server analyzes payment history data and estimates life events. In this process, machine learning models are used to predict important future events from past history, and system adjustments are made using generative AI models as needed. The generative AI models utilize prompts to provide better asset management advice tailored to the user's unique operational needs, offering individual strategic suggestions. For example, a user can register travel plans on their device and receive suggestions to liquidate their assets before spending. An example of a prompt using the generative AI model is, "Develop an investment strategy tailored to the user's travel plan and suggest the optimal timing for liquidating assets."

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The server receives the user's financial data and anonymizes it. It takes raw financial data provided by the user as input and transforms it using an anonymization algorithm. The output is anonymized data, which is stored in a secure database. Specifically, encryption technologies such as RSA and AES are used to make the data impossible to identify individuals.

[0515] Step 2:

[0516] The server retrieves the latest market information and financial product data from external market information providers. It takes external datasets as input via HTTP requests through APIs. The output is the latest market dataset, which is used in the data analysis layer. Specifically, it performs scheduled periodic data retrieval and on-demand retrieval as needed.

[0517] Step 3:

[0518] The server analyzes acquired market information and anonymized user asset data to generate asset management strategies. It accepts pre-processed market data and user asset data as input. As output, it generates individually tailored asset management strategies. Specifically, it uses Python or R to perform data analysis using machine learning algorithms, and then adjusts simulations and recommendations using a generated AI model.

[0519] Step 4:

[0520] The device manages the user's schedule information and life event data. It takes schedule data registered by the user as input. The output is a timeline of scheduled events, which serves as the basis for reminder logic. Specific operations include scheduling using the calendar function and setting event priorities.

[0521] Step 5:

[0522] The terminal displays asset management strategy notifications provided by the server to the user and sends reminders. It receives asset management advice data sent from the server as input. The output is a notification message directed to the user. Specifically, it uses the smartphone's push notification function to inform the user in a timely manner.

[0523] Step 6:

[0524] The server estimates life events based on the user's operation and transaction history and proposes new investment timings. It analyzes the user's transaction logs as input. Based on this input, it uses a machine learning model to predict future life events and generates new investment suggestions as output. Specifically, it performs filtering and feature extraction of historical data.

[0525] Step 7:

[0526] The server uses a generative AI model to create prompts to refine the user's individual asset management strategy. It receives the user's investment status and preferences as input, and the generative AI model generates prompts accordingly. The output is the refined prompt. Specifically, natural language processing techniques are applied to generate input sentences that improve the quality of the AI ​​model's response.

[0527] 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.

[0528] This invention combines an emotion engine with a system that supports effective asset management by utilizing users' financial data. This system recognizes users' emotions in real time and provides investment advice and learning support tailored to their psychological state, thereby realizing a more personalized experience.

[0529] The server receives anonymized financial data from users and retrieves the latest market information and financial product data from external data sources. Based on this, it analyzes the user's asset allocation and generates an optimal investment strategy. It also uses an emotion engine to analyze the user's emotional state and adjust the way the generated investment strategy is presented and its content. Specifically, if the user is feeling stressed, it can emphasize low-risk, safe options.

[0530] The device manages the user's schedule and life events and provides operational notifications that take emotional states into account. The emotion engine analyzes the user's facial expressions and tone of voice through sensor devices such as cameras and microphones to infer their emotions.

[0531] Furthermore, the device assesses the user's knowledge level and provides appropriate learning content based on the results. The emotion engine responds to the user's emotional state and presents content at the optimal timing according to the user's learning progress, thereby enhancing the user's learning effectiveness.

[0532] In the 3D simulation function, the server generates simulations based on the user's asset management goals, and the emotion engine reflects the user's psychological state. For example, if the user is feeling anxious, the simulation can prioritize presenting stable asset growth scenarios.

[0533] As a concrete example, consider a scenario where User B manages their assets on a holiday. User B checks market trends while viewing their investment information entered into the terminal. The terminal then analyzes User B's voice and detects their stress levels. The server adjusts future investment strategies accordingly and generates suggestions strongly recommending investment in safe assets. In this way, the system of the present invention provides more user-centric asset management advice in real time, increasing confidence and peace of mind in asset management.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user enters asset information into the terminal. The terminal transmits the user's input data to the server using a secure communication method. Simultaneously, the terminal's sensors begin to monitor the user's emotions in real time.

[0537] Step 2:

[0538] The server anonymizes the transmitted asset information and stores it in a database. Next, it retrieves the latest market information and financial product data from external data sources.

[0539] Step 3:

[0540] The server analyzes acquired market information and user asset information to perform risk and return analysis. Based on this, it generates an asset management strategy that matches the user's risk tolerance and investment goals.

[0541] Step 4:

[0542] The server uses an emotion engine to analyze the user's emotional state. Based on the analysis results, it adjusts its operational policies and presentation methods to formulate asset management advice that takes the user's emotions into consideration.

[0543] Step 5:

[0544] The terminal notifies the user of operational strategies and advice from the server. If the user's emotional state falls below a certain level, the advice is presented in a gentler manner, or lower-risk options are emphasized.

[0545] Step 6:

[0546] The user inputs life events and daily schedules into the device. The device reflects these in the schedule and, based on advice generated by the server, sets up notifications for appropriate investment timing, taking into account the user's emotional state.

[0547] Step 7:

[0548] The server selects appropriate learning content based on the user's knowledge level and emotional state. It then delivers the content to the device according to a presentation schedule that takes the user's emotions into consideration.

[0549] Step 8:

[0550] The server generates a 3D simulation that reflects the user's investment goals and emotional state. The terminal receives this and displays it interactively to the user. Depending on the user's current emotional state, the aim is to enhance their sense of security by highlighting specific scenarios.

[0551] (Example 2)

[0552] 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."

[0553] Traditional asset management systems have a problem in that they only propose standardized investment policies based on the user's financial information, and do not adequately provide flexible investment support that takes into account the emotional state and learning needs of individual users. As a result, users are unable to make appropriate investment decisions when they feel stressed or anxious, and are unable to achieve sufficient asset growth.

[0554] 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.

[0555] In this invention, the server includes means for analyzing the user's emotional state and adjusting the operational policy according to that emotion, means for providing learning materials according to the user's knowledge level, and means for generating a 3D simulation based on the user's asset management goals. This enables optimal asset management support tailored to the user's psychological state and promotes effective learning.

[0556] "User financial information" refers to information about an individual's financial resources and how they are being managed, including the contents of bank accounts and investment portfolios.

[0557] "Anonymization" refers to a technical process that makes it impossible to identify a specific individual in order to protect their privacy.

[0558] "External data sources" refer to the sources of data provided by information providers outside the system, and include information on financial markets and details of financial products.

[0559] "Asset allocation" refers to the proportion of a user's assets that are distributed across different investment destinations or asset classes.

[0560] An "asset management policy" refers to an investment strategy designed to efficiently increase assets, and includes risk management and an approach tailored to investment objectives.

[0561] "Emotional state" refers to the user's psychological feelings and mental reactions, encompassing states such as stress, anxiety, and a sense of security.

[0562] "Life events" refer to important events or scheduled activities in a user's lifestyle, including changes in work, marriage, and raising children.

[0563] "Learning materials" refer to informational content provided to deepen users' understanding and knowledge, including educational materials and training materials.

[0564] "3D simulation" refers to a technology that visually and three-dimensionally represents the results and scenarios of asset management in a digital environment.

[0565] This invention relates to a system that utilizes users' financial information to provide asset management support tailored to their individual emotional states. The system works in cooperation with a server, terminals, and users to collect, analyze, and notify data.

[0566] The server is responsible for processing financial information, analyzing the user's asset allocation, and generating the optimal investment strategy. In this process, it uses a generative AI model to construct effective strategies from a large amount of market and financial product information. It also utilizes an emotion engine to adjust the investment strategy according to the user's emotional state. Furthermore, the server collects the latest financial information in real time from external data sources and uses this information to propose the most effective investment method for the user.

[0567] Meanwhile, the device functions as the user's interface, transmitting financial information and emotional states to the server. The device uses sensors such as cameras and microphones to analyze the user's facial expressions and tone of voice, collecting emotional data in real time. The collected data is encrypted and sent to the server in a privacy-preserving manner. The device also provides learning materials to the user, supporting them in deepening their knowledge of asset management.

[0568] Through this system, users can input their financial information into a terminal and create a plan that reflects their investment stance and life events. The system features a 3D simulation function that visually presents the user's long-term asset growth scenario. This allows for discussion and review, and consideration of the next steps.

[0569] As a concrete example, consider a scenario where a user checks market trends using a device. If the device detects the user's voice and determines that their stress levels are high, it retrieves and displays advice from the server recommending investment in safe assets. The generating AI model constructs an investment strategy using the prompt message, "Consider the user's current investment portfolio and emotional state, and propose an investment strategy that minimizes risk." Through this mechanism, users can achieve asset management that reflects their own emotional state and gain a sense of security.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user enters their financial information into the terminal. This data includes bank account information and investment portfolios. The terminal encrypts this information and prepares it for processing in a privacy-enhanced manner. The entered information is anonymized and formatted into a dataset for processing.

[0573] Step 2:

[0574] The device uses a camera and microphone to monitor the user's facial expressions and voice tone in real time and collect emotional data. The collected data is analyzed to identify the user's current emotional state. The results of the emotional analysis are output as an index representing the user's psychological state and sent to the server.

[0575] Step 3:

[0576] The server receives financial information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to analyze asset allocation and construct an optimal investment strategy. Specifically, the model uses the prompt message, "Consider the user's current investment portfolio and sentiment state, and propose an investment strategy that minimizes risk." The resulting investment strategy is then adjusted to reflect characteristics such as the degree of risk.

[0577] Step 4:

[0578] The server retrieves the latest market information from external data sources to verify the validity of operational policies. Data processing here includes information collection via external APIs and periodic data updates. Market information is added to operational policies, enabling strategies that take real-time market changes into account.

[0579] Step 5:

[0580] The server generates operational policies tailored to the user's emotional state, along with a 3D simulation that includes market information. The simulation visualizes multiple asset growth scenarios and presents them in an easy-to-understand format. The generated simulation results are sent to the terminal as material to support the user's decision-making.

[0581] Step 6:

[0582] The terminal presents the user with operational policies and simulation results received from the server. Specifically, it visually represents the situation through a graphical UI. It assists the user in reviewing the information and deciding on the next steps.

[0583] (Application Example 2)

[0584] 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."

[0585] Current asset management systems fail to consider the user's emotional state, making it difficult to provide optimal advice. Furthermore, they cannot reflect individual lifestyles or emotions in user spending management, potentially leading to inefficient asset management. There is a need to solve these problems and realize more personalized asset management support.

[0586] 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.

[0587] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a three-dimensional simulation based on the user's asset management goals; means for analyzing the user's emotional state in real time using emotion analysis technology and adjusting the method of presenting the investment strategy accordingly; and means for analyzing the user's spending patterns and providing advice to reduce unnecessary spending. This enables more personalized asset management support and spending management based on the user's emotions and lifestyle.

[0588] "User financial data" refers to financial information such as the user's assets, liabilities, income, and expenses, which is anonymized and analyzed by the system.

[0589] "Market information" refers to external information that affects the value of financial assets, such as the latest economic trends, financial market data, stock indices, and interest rates.

[0590] "Financial product data" refers to detailed information about products such as investment trusts, stocks, and bond options offered by various financial institutions.

[0591] "Asset allocation" refers to a strategy for how a user's assets will be distributed across different investment targets.

[0592] "3D simulation" is a function that visually simulates the asset management process in three dimensions based on the user's asset management goals.

[0593] "Emotion analysis technology" is a technology that analyzes a user's facial expressions, voice tone, etc., and evaluates their emotional state in real time.

[0594] "Adjusting the presentation method of investment strategies" refers to customizing the way and content of asset management strategies are presented to users based on sentiment analysis results.

[0595] "Analyzing spending patterns" is the process of analyzing a user's daily spending habits and deriving designs that reduce waste.

[0596] The system for implementing this invention consists of various devices and software modules. The hardware primarily used includes a server and user terminals such as smartphones or smart glasses. The server collects the user's financial data anonymized and obtains the latest market information and financial product data from external data sources. Based on this data, the server analyzes the user's asset allocation and generates an optimal asset management strategy.

[0597] Emotion analysis utilizes the device's camera and microphone to monitor the user's facial expressions and voice tone in real time. Based on this, the server evaluates the user's emotional state using emotion analysis technology and adjusts how the optimal asset management strategy is presented. Specifically, it is possible to utilize existing technologies such as "Google Cloud's Speech-to-Text" and "Microsoft's Face API".

[0598] Furthermore, it analyzes users' spending patterns and provides advice based on this to curb unnecessary spending. It manages users' schedules and life events, provides timely notifications related to asset management, and selects learning content tailored to the user's knowledge level. This allows users to manage their assets in a personalized way.

[0599] As a concrete example, this system may perform sentiment analysis while a user is shopping on a holiday and adjust their asset management strategy based on that information. If the sentiment analysis detects stress in the user, it will adjust future asset management policies along with the results of the spending pattern analysis and generate a recommendation that strongly advises investing in safe assets.

[0600] An example of a prompt for a generating AI model is as follows: "Explain how your personal finance app would intervene and provide appropriate savings advice if the user is feeling stressed at a cafe."

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The server collects users' financial data in an anonymized form. It receives individual transaction history and account balance information as input and converts it into a format that does not identify individuals. The output is anonymized financial data.

[0604] Step 2:

[0605] The server retrieves the latest market information and financial product data from external data sources. It collects real-time market data via APIs and stores it in a database. The output consists of the collected market information and financial product data, which are used for subsequent analysis.

[0606] Step 3:

[0607] The server integrates the user's anonymized financial data with the latest market information to analyze asset allocation. Through data processing, it evaluates investment risk, profitability, and the user's asset portfolio, and generates an optimal asset management strategy. The output is a proposed investment strategy.

[0608] Step 4:

[0609] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Input is real-time video images and audio data, while output is sentiment data generated by sentiment analysis technology. This sentiment data forms the basis for adjusting operational strategies.

[0610] Step 5:

[0611] The server uses emotion analysis technology to evaluate the user's emotional state. It analyzes the input emotional data and quantifies the user's perceived stress and sense of security. The output is the analyzed emotional state.

[0612] Step 6:

[0613] The server combines the user's emotional state with the generated investment strategy to adjust how optimal investment advice is presented. For example, if emotional data indicates that the user is stressed, it prioritizes presenting low-risk assets. The output is investment advice tailored to the user's emotional state.

[0614] Step 7:

[0615] The device manages the user's schedule and life events and provides timely investment-related notifications. Inputs are calendar information and emotional states, while outputs are timely investment information.

[0616] Step 8:

[0617] Users manage their assets individually based on operational advice received from their devices. User behavioral feedback is incorporated into subsequent analyses, leading to more refined strategic proposals.

[0618] 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.

[0619] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention relates to an asset management advice system based on a method for anonymizing users' financial data and securely transmitting it to a server. This system securely manages users' asset information and acquires and analyzes the latest market information to propose the optimal asset management strategy for the user.

[0636] The server receives asset information submitted by the user and obtains the latest market data from various financial institutions. Based on this data, the server analyzes the collected information and generates asset management strategies tailored to each user's unique risk tolerance and investment goals. The generated strategies are then sent to the user's terminal for notification. Upon receiving this notification, the user can review the proposed strategy and adjust their asset allocation as needed.

[0637] Furthermore, the terminal manages the user's schedule information and has a function to remind users of important timings for operational advice received from the server. By entering life events and other appointments into the terminal, users can manage important events that are useful for asset management without missing any.

[0638] Furthermore, the server has a function that provides learning content tailored to the user's knowledge level. This allows users to effectively improve the knowledge necessary for asset management.

[0639] The 3D simulation function provides a tool for users to visually experience future scenarios based on their investment goals. The server generates predictive scenarios for the user's goals and sends them to the terminal. The terminal interactively displays the 3D simulation received from the server, making it easier for users to concretely visualize their future success in investment management.

[0640] As a concrete example, suppose User A regularly invests in stocks as part of their asset management. The user enters their schedule into a terminal, and the server analyzes market information to provide the next most effective investment timing. If the user accepts the advice provided, the terminal sends a reminder at that time, allowing the user to make the investment. This system enables the user to achieve efficient asset management and aim for reliable wealth building.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The user enters asset information into the terminal. The terminal then transmits the entered information to the server using a secure communication protocol.

[0644] Step 2:

[0645] The server anonymizes the asset information it receives and securely stores it in a database. It also obtains the latest market information and financial product data from trusted external data sources.

[0646] Step 3:

[0647] The server uses collected market information and user asset information to perform risk and return analysis. Based on the analysis results, it generates an asset management strategy that is suitable for the user's investment goals and risk tolerance.

[0648] Step 4:

[0649] The operational strategy generated by the server is sent to the terminal, and the terminal notifies the user of the proposed operational strategy. The user reviews the proposal and revise their asset allocation as needed.

[0650] Step 5:

[0651] Users input life events and schedule information into the device. The device manages the entered information and is configured to remind users of important investment timings based on operational strategies received from the server.

[0652] Step 6:

[0653] The server assesses the user's knowledge level and selects appropriate learning content based on that assessment. The learning content is then sent to the user's device, and the user improves their knowledge of asset management through the provided content.

[0654] Step 7:

[0655] The server generates a 3D simulation of future investment results based on the user's asset management goals. This simulation is sent to the terminal, which interactively displays the 3D simulation to the user, visualizing a concrete vision of future success.

[0656] (Example 1)

[0657] 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".

[0658] Traditional asset management systems have suffered from problems such as reduced investment efficiency for users due to insufficient security of users' financial data, lack of access to the latest market information, and inadequate provision of individually optimized asset management strategies. Furthermore, the lack of content to support user knowledge enhancement and visualization of future forecasts made it difficult for users to make effective asset management decisions.

[0659] 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.

[0660] In this invention, the server includes means for anonymizing and collecting users' financial data, means for acquiring the latest market information, and means for generating and providing optimal asset management strategies that differ for each user. This makes it possible to provide individually optimized strategies based on the latest market information while ensuring the security of users' financial data. Furthermore, it deepens users' understanding of asset management and supports more effective investment decision-making by providing educational content tailored to the user's knowledge level and visualizing future predictions through three-dimensional simulations.

[0661] "Financial data" refers to information about a user's asset status and investment activities, specifically including deposit balances, stock and bond holdings, and investment goals.

[0662] "Anonymization" refers to a technology that processes data in a way that prevents individuals from being identified, thereby protecting privacy.

[0663] "Market information" refers to the latest data on stock prices, exchange rates, commodity prices, etc., in financial markets, and serves as fundamental information for formulating asset management strategies.

[0664] An "asset management strategy" refers to a plan regarding asset allocation and investment timing, which is formulated based on the user's risk tolerance and investment goals.

[0665] "Schedule" refers to the management information for appointments related to the user's daily life and asset management, and plays a role in ensuring that important events are not missed.

[0666] "Life events" refer to significant events in a user's life that may influence decisions regarding asset management.

[0667] "Notifications" refer to means of informing users of important information, and are provided in the form of push notifications, etc.

[0668] "Educational content" refers to learning materials provided to improve users' knowledge of asset management, and includes lessons, quizzes, and other similar materials.

[0669] "Three-dimensional simulation" is a technology that visually represents future scenarios based on the user's asset management goals, providing an interactive experience.

[0670] A "generative model" refers to an algorithm or machine learning technique used to generate optimal asset management strategies based on available data.

[0671] A "central processing unit" refers to a central device used for data analysis and asset management strategy generation, and typically possesses the capability to handle large-scale calculations.

[0672] "Device" refers to electronic devices used by users to access the system, and includes smartphones, personal computers, and other similar devices.

[0673] This invention is a system that provides individually customized asset management strategies while anonymizing and securely managing users' financial information. This system is built through the interaction of a server, a terminal, and the user.

[0674] The server utilizes SSL / TLS as a highly secure protocol to receive anonymized financial data sent by users. The server organizes and stores the data using various database systems (e.g., MySQL). Subsequently, the server leverages data analysis libraries such as Pandas and NumPy to generate asset management strategies based on the user's risk profile and market data. This involves the use of generative AI models and information obtained from diverse data sources. Furthermore, the generated investment strategy is notified to the user's terminal.

[0675] The terminal presents the operational strategy transmitted from the server to the user, providing a visually easy-to-understand interface. The terminal also manages the user's schedule and sends important notifications via reminders at specific times. This enables the user to make investment decisions efficiently and at the right time.

[0676] Users can access learning content about asset management using their devices. This content is tailored to the user's knowledge level and is delivered through an e-learning platform (e.g., a general education management system). Users can also experience server-generated 3D simulations on their devices. This allows users to visually understand future asset management scenarios and develop a clearer vision for their investment strategy.

[0677] As a concrete example, user A sets up their investment plan on a terminal, and the server analyzes market data to inform them of an appropriate asset management strategy. Once this strategy is announced, the terminal reminds the user of the timing for investment. The user can then smoothly manage their assets by utilizing the information provided by the system.

[0678] An example of a prompt for a generative AI model might be text such as, "Based on the current market conditions, please tell me the appropriate timing for stock investment for user A." This prompt is used as part of the data collection and analysis process to provide the system with a suitable strategy for the user.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The user enters their financial information into the terminal. This information includes the status of their financial assets and investment goals. This input data is anonymized to protect personal information; specifically, personal information such as name and address is hashed and AES encryption is performed. The anonymized data is then sent to the server.

[0682] Step 2:

[0683] The server securely receives anonymized data sent from the terminal via the SSL / TLS protocol. The received data is stored in a database, serving as the basis for subsequent analysis. This allows the server to determine which users possess which asset data, but it does not include any personally identifiable information.

[0684] Step 3:

[0685] The server uses APIs from external financial data providers to obtain the latest market information. The retrieved market data is stored in a database and combined with user data to create an analyzable format. This allows for a clear understanding of market trends and developments.

[0686] Step 4:

[0687] The server uses data analysis libraries such as Pandas and NumPy to integrate and analyze user asset information and market data. From this analysis, an optimal asset management strategy is generated based on each user's risk tolerance and investment goals, and a generative AI model is used. This strategy is customized to reflect the user's unique conditions.

[0688] Step 5:

[0689] The server encrypts the generated operational strategy and sends it to the terminal. The terminal notifies the user of the received operational strategy and displays it in a visually easy-to-understand format. The user can review the presented operational strategy and adjust the investment plan as needed.

[0690] Step 6:

[0691] The device sets reminders for important events related to asset management based on the user's daily schedule. It provides timely reminders based on the user's specified date and time, helping users avoid missing important investment opportunities.

[0692] Step 7:

[0693] The server generates educational content tailored to the user's knowledge level and provides it through the terminal. Users can access this content at any time to deepen their understanding of asset management.

[0694] Step 8:

[0695] The server generates a three-dimensional simulation of future scenarios based on the user's asset management goals. This simulation is created using a 3D engine such as Unity, and the terminal displays it in an interactive format. The user can use this to visually confirm future asset management results.

[0696] (Application Example 1)

[0697] 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".

[0698] Traditional asset management systems struggle to provide personalized investment strategies while ensuring the anonymity of users' financial data. Furthermore, they lack the means to achieve efficient asset management by considering users' daily schedules and life events. As a result, users may miss investment opportunities or receive proposals that don't match their risk profile. Additionally, there is insufficient provision of educational content to deepen users' understanding of asset management.

[0699] 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.

[0700] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a 3D simulation based on the user's asset management goals; means for estimating important life events based on payment history and suggesting appropriate investment timing; and means for creating inputs for adjusting individual asset management strategies using a generative AI model. This enables flexible and accurate asset management tailored to the user's lifestyle and goals, promoting improved investment knowledge and successful wealth building.

[0701] "Methods for collecting users' financial data in an anonymized form" refers to a function that converts individual user information into a format that does not allow others to identify the user, and then securely aggregates that information.

[0702] "Means of obtaining the latest market information and financial product data from external data sources" refers to the function of importing the latest data reflecting market trends and the characteristics of financial products from external information providers.

[0703] "A means of analyzing a user's asset allocation and generating the optimal asset management strategy" refers to an analytical function that uses collected user asset information to formulate the most suitable investment plan for that individual.

[0704] "Means for managing user schedules and life events and providing investment-related notifications" refers to functions that record users' appointments and important events, and provide investment suggestions and reminders accordingly.

[0705] "Means of providing learning content tailored to the user's knowledge level" refers to a function that appropriately provides educational materials to deepen the user's understanding, according to their level of financial knowledge.

[0706] "Means for generating 3D simulations based on the user's asset management goals" refers to a function that generates three-dimensional images to visually reproduce the user's desired economic outcomes as a scenario.

[0707] "A means of estimating important life events based on payment history and suggesting appropriate investment timing" refers to a function that predicts future events from past payment patterns and advises on optimal investment actions.

[0708] "Means of creating inputs for adjusting individual asset management strategies using generative AI models" refers to a function that utilizes artificial intelligence technology to generate data for strategic adjustments tailored to the user's investment policy.

[0709] This invention centers on sensing technology for anonymizing and collecting users' financial data and securely storing it in a database, and data analysis technology for incorporating the latest market information and generating asset management strategies based on it. The server uses data analysis languages ​​such as Python and R to generate an optimal asset management strategy based on market data and the user's risk tolerance.

[0710] The device manages the user's schedule and life events, and sends reminders at appropriate times based on those events. Reminder notifications are implemented using the smartphone's notification function. The device also provides learning content and visually displays 3D simulations. These 3D simulations utilize 3D engines such as Unity and Unreal Engine to visualize future asset management scenarios.

[0711] The server analyzes payment history data and estimates life events. In this process, machine learning models are used to predict important future events from past history, and system adjustments are made using generative AI models as needed. The generative AI models utilize prompts to provide better asset management advice tailored to the user's unique operational needs, offering individual strategic suggestions. For example, a user can register travel plans on their device and receive suggestions to liquidate their assets before spending. An example of a prompt using the generative AI model is, "Develop an investment strategy tailored to the user's travel plan and suggest the optimal timing for liquidating assets."

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] The server receives the user's financial data and anonymizes it. It takes raw financial data provided by the user as input and transforms it using an anonymization algorithm. The output is anonymized data, which is stored in a secure database. Specifically, encryption technologies such as RSA and AES are used to make the data impossible to identify individuals.

[0715] Step 2:

[0716] The server retrieves the latest market information and financial product data from external market information providers. It takes external datasets as input via HTTP requests through APIs. The output is the latest market dataset, which is used in the data analysis layer. Specifically, it performs scheduled periodic data retrieval and on-demand retrieval as needed.

[0717] Step 3:

[0718] The server analyzes acquired market information and anonymized user asset data to generate asset management strategies. It accepts pre-processed market data and user asset data as input. As output, it generates individually tailored asset management strategies. Specifically, it uses Python or R to perform data analysis using machine learning algorithms, and then adjusts simulations and recommendations using a generated AI model.

[0719] Step 4:

[0720] The device manages the user's schedule information and life event data. It takes schedule data registered by the user as input. The output is a timeline of scheduled events, which serves as the basis for reminder logic. Specific operations include scheduling using the calendar function and setting event priorities.

[0721] Step 5:

[0722] The terminal displays asset management strategy notifications provided by the server to the user and sends reminders. It receives asset management advice data sent from the server as input. The output is a notification message directed to the user. Specifically, it uses the smartphone's push notification function to inform the user in a timely manner.

[0723] Step 6:

[0724] The server estimates life events based on the user's operation and transaction history and proposes new investment timings. It analyzes the user's transaction logs as input. Based on this input, it uses a machine learning model to predict future life events and generates new investment suggestions as output. Specifically, it performs filtering and feature extraction of historical data.

[0725] Step 7:

[0726] The server uses a generative AI model to create prompts to refine the user's individual asset management strategy. It receives the user's investment status and preferences as input, and the generative AI model generates prompts accordingly. The output is the refined prompt. Specifically, natural language processing techniques are applied to generate input sentences that improve the quality of the AI ​​model's response.

[0727] 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.

[0728] This invention combines an emotion engine with a system that supports effective asset management by utilizing users' financial data. This system recognizes users' emotions in real time and provides investment advice and learning support tailored to their psychological state, thereby realizing a more personalized experience.

[0729] The server receives anonymized financial data from users and retrieves the latest market information and financial product data from external data sources. Based on this, it analyzes the user's asset allocation and generates an optimal investment strategy. It also uses an emotion engine to analyze the user's emotional state and adjust the way the generated investment strategy is presented and its content. Specifically, if the user is feeling stressed, it can emphasize low-risk, safe options.

[0730] The device manages the user's schedule and life events and provides operational notifications that take emotional states into account. The emotion engine analyzes the user's facial expressions and tone of voice through sensor devices such as cameras and microphones to infer their emotions.

[0731] Furthermore, the device assesses the user's knowledge level and provides appropriate learning content based on the results. The emotion engine responds to the user's emotional state and presents content at the optimal timing according to the user's learning progress, thereby enhancing the user's learning effectiveness.

[0732] In the 3D simulation function, the server generates simulations based on the user's asset management goals, and the emotion engine reflects the user's psychological state. For example, if the user is feeling anxious, the simulation can prioritize presenting stable asset growth scenarios.

[0733] As a concrete example, consider a scenario where User B manages their assets on a holiday. User B checks market trends while viewing their investment information entered into the terminal. The terminal then analyzes User B's voice and detects their stress levels. The server adjusts future investment strategies accordingly and generates suggestions strongly recommending investment in safe assets. In this way, the system of the present invention provides more user-centric asset management advice in real time, increasing confidence and peace of mind in asset management.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] The user enters asset information into the terminal. The terminal transmits the user's input data to the server using a secure communication method. Simultaneously, the terminal's sensors begin to monitor the user's emotions in real time.

[0737] Step 2:

[0738] The server anonymizes the transmitted asset information and stores it in a database. Next, it retrieves the latest market information and financial product data from external data sources.

[0739] Step 3:

[0740] The server analyzes acquired market information and user asset information to perform risk and return analysis. Based on this, it generates an asset management strategy that matches the user's risk tolerance and investment goals.

[0741] Step 4:

[0742] The server uses an emotion engine to analyze the user's emotional state. Based on the analysis results, it adjusts its operational policies and presentation methods to formulate asset management advice that takes the user's emotions into consideration.

[0743] Step 5:

[0744] The terminal notifies the user of operational strategies and advice from the server. If the user's emotional state falls below a certain level, the advice is presented in a gentler manner, or lower-risk options are emphasized.

[0745] Step 6:

[0746] The user inputs life events and daily schedules into the device. The device reflects these in the schedule and, based on advice generated by the server, sets up notifications for appropriate investment timing, taking into account the user's emotional state.

[0747] Step 7:

[0748] The server selects appropriate learning content based on the user's knowledge level and emotional state. It then delivers the content to the device according to a presentation schedule that takes the user's emotions into consideration.

[0749] Step 8:

[0750] The server generates a 3D simulation that reflects the user's investment goals and emotional state. The terminal receives this and displays it interactively to the user. Depending on the user's current emotional state, the aim is to enhance their sense of security by highlighting specific scenarios.

[0751] (Example 2)

[0752] 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".

[0753] Traditional asset management systems have a problem in that they only propose standardized investment policies based on the user's financial information, and do not adequately provide flexible investment support that takes into account the emotional state and learning needs of individual users. As a result, users are unable to make appropriate investment decisions when they feel stressed or anxious, and are unable to achieve sufficient asset growth.

[0754] 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.

[0755] In this invention, the server includes means for analyzing the user's emotional state and adjusting the operational policy according to that emotion, means for providing learning materials according to the user's knowledge level, and means for generating a 3D simulation based on the user's asset management goals. This enables optimal asset management support tailored to the user's psychological state and promotes effective learning.

[0756] "User financial information" refers to information about an individual's financial resources and how they are being managed, including the contents of bank accounts and investment portfolios.

[0757] "Anonymization" refers to a technical process that makes it impossible to identify a specific individual in order to protect their privacy.

[0758] "External data sources" refer to the sources of data provided by information providers outside the system, and include information on financial markets and details of financial products.

[0759] "Asset allocation" refers to the proportion of a user's assets that are distributed across different investment destinations or asset classes.

[0760] An "asset management policy" refers to an investment strategy designed to efficiently increase assets, and includes risk management and an approach tailored to investment objectives.

[0761] "Emotional state" refers to the user's psychological feelings and mental reactions, encompassing states such as stress, anxiety, and a sense of security.

[0762] "Life events" refer to important events or scheduled activities in a user's lifestyle, including changes in work, marriage, and raising children.

[0763] "Learning materials" refer to informational content provided to deepen users' understanding and knowledge, including educational materials and training materials.

[0764] "3D simulation" refers to a technology that visually and three-dimensionally represents the results and scenarios of asset management in a digital environment.

[0765] This invention relates to a system that utilizes users' financial information to provide asset management support tailored to their individual emotional states. The system works in cooperation with a server, terminals, and users to collect, analyze, and notify data.

[0766] The server is responsible for processing financial information, analyzing the user's asset allocation, and generating the optimal investment strategy. In this process, it uses a generative AI model to construct effective strategies from a large amount of market and financial product information. It also utilizes an emotion engine to adjust the investment strategy according to the user's emotional state. Furthermore, the server collects the latest financial information in real time from external data sources and uses this information to propose the most effective investment method for the user.

[0767] Meanwhile, the device functions as the user's interface, transmitting financial information and emotional states to the server. The device uses sensors such as cameras and microphones to analyze the user's facial expressions and tone of voice, collecting emotional data in real time. The collected data is encrypted and sent to the server in a privacy-preserving manner. The device also provides learning materials to the user, supporting them in deepening their knowledge of asset management.

[0768] Through this system, users can input their financial information into a terminal and create a plan that reflects their investment stance and life events. The system features a 3D simulation function that visually presents the user's long-term asset growth scenario. This allows for discussion and review, and consideration of the next steps.

[0769] As a concrete example, consider a scenario where a user checks market trends using a device. If the device detects the user's voice and determines that their stress levels are high, it retrieves and displays advice from the server recommending investment in safe assets. The generating AI model constructs an investment strategy using the prompt message, "Consider the user's current investment portfolio and emotional state, and propose an investment strategy that minimizes risk." Through this mechanism, users can achieve asset management that reflects their own emotional state and gain a sense of security.

[0770] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0771] Step 1:

[0772] The user enters their financial information into the terminal. This data includes bank account information and investment portfolios. The terminal encrypts this information and prepares it for processing in a privacy-enhanced manner. The entered information is anonymized and formatted into a dataset for processing.

[0773] Step 2:

[0774] The device uses a camera and microphone to monitor the user's facial expressions and voice tone in real time and collect emotional data. The collected data is analyzed to identify the user's current emotional state. The results of the emotional analysis are output as an index representing the user's psychological state and sent to the server.

[0775] Step 3:

[0776] The server receives financial information and sentiment data transmitted from the terminal. Based on this data, it uses a generative AI model to analyze asset allocation and construct an optimal investment strategy. Specifically, the model uses the prompt message, "Consider the user's current investment portfolio and sentiment state, and propose an investment strategy that minimizes risk." The resulting investment strategy is then adjusted to reflect characteristics such as the degree of risk.

[0777] Step 4:

[0778] The server retrieves the latest market information from external data sources to verify the validity of operational policies. Data processing here includes information collection via external APIs and periodic data updates. Market information is added to operational policies, enabling strategies that take real-time market changes into account.

[0779] Step 5:

[0780] The server generates operational policies tailored to the user's emotional state, along with a 3D simulation that includes market information. The simulation visualizes multiple asset growth scenarios and presents them in an easy-to-understand format. The generated simulation results are sent to the terminal as material to support the user's decision-making.

[0781] Step 6:

[0782] The terminal presents the user with operational policies and simulation results received from the server. Specifically, it visually represents the situation through a graphical UI. It assists the user in reviewing the information and deciding on the next steps.

[0783] (Application Example 2)

[0784] 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".

[0785] Current asset management systems fail to consider the user's emotional state, making it difficult to provide optimal advice. Furthermore, they cannot reflect individual lifestyles or emotions in user spending management, potentially leading to inefficient asset management. There is a need to solve these problems and realize more personalized asset management support.

[0786] 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.

[0787] In this invention, the server includes means for anonymizing and collecting the user's financial data; means for obtaining the latest market information and financial product data from external data sources; means for analyzing the user's asset allocation and generating an optimal asset management strategy; means for managing the user's schedule and life events and providing notifications regarding investment; means for providing learning content tailored to the user's knowledge level; means for generating a three-dimensional simulation based on the user's asset management goals; means for analyzing the user's emotional state in real time using emotion analysis technology and adjusting the method of presenting the investment strategy accordingly; and means for analyzing the user's spending patterns and providing advice to reduce unnecessary spending. This enables more personalized asset management support and spending management based on the user's emotions and lifestyle.

[0788] "User financial data" refers to financial information such as the user's assets, liabilities, income, and expenses, which is anonymized and analyzed by the system.

[0789] "Market information" refers to external information that affects the value of financial assets, such as the latest economic trends, financial market data, stock indices, and interest rates.

[0790] "Financial product data" refers to detailed information about products such as investment trusts, stocks, and bond options offered by various financial institutions.

[0791] "Asset allocation" refers to a strategy for how a user's assets will be distributed across different investment targets.

[0792] "3D simulation" is a function that visually simulates the asset management process in three dimensions based on the user's asset management goals.

[0793] "Emotion analysis technology" is a technology that analyzes a user's facial expressions, voice tone, etc., and evaluates their emotional state in real time.

[0794] "Adjusting the presentation method of investment strategies" refers to customizing the way and content of asset management strategies are presented to users based on sentiment analysis results.

[0795] "Analyzing spending patterns" is the process of analyzing a user's daily spending habits and deriving designs that reduce waste.

[0796] The system for implementing this invention consists of various devices and software modules. The hardware primarily used includes a server and user terminals such as smartphones or smart glasses. The server collects the user's financial data anonymized and obtains the latest market information and financial product data from external data sources. Based on this data, the server analyzes the user's asset allocation and generates an optimal asset management strategy.

[0797] Emotion analysis utilizes the device's camera and microphone to monitor the user's facial expressions and voice tone in real time. Based on this, the server evaluates the user's emotional state using emotion analysis technology and adjusts how the optimal asset management strategy is presented. Specifically, it is possible to utilize existing technologies such as "Google Cloud's Speech-to-Text" and "Microsoft's Face API".

[0798] Furthermore, it analyzes users' spending patterns and provides advice based on this to curb unnecessary spending. It manages users' schedules and life events, provides timely notifications related to asset management, and selects learning content tailored to the user's knowledge level. This allows users to manage their assets in a personalized way.

[0799] As a concrete example, this system may perform sentiment analysis while a user is shopping on a holiday and adjust their asset management strategy based on that information. If the sentiment analysis detects stress in the user, it will adjust future asset management policies along with the results of the spending pattern analysis and generate a recommendation that strongly advises investing in safe assets.

[0800] An example of a prompt for a generating AI model is as follows: "Explain how your personal finance app would intervene and provide appropriate savings advice if the user is feeling stressed at a cafe."

[0801] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0802] Step 1:

[0803] The server collects users' financial data in an anonymized form. It receives individual transaction history and account balance information as input and converts it into a format that does not identify individuals. The output is anonymized financial data.

[0804] Step 2:

[0805] The server retrieves the latest market information and financial product data from external data sources. It collects real-time market data via APIs and stores it in a database. The output consists of the collected market information and financial product data, which are used for subsequent analysis.

[0806] Step 3:

[0807] The server integrates the user's anonymized financial data with the latest market information to analyze asset allocation. Through data processing, it evaluates investment risk, profitability, and the user's asset portfolio, and generates an optimal asset management strategy. The output is a proposed investment strategy.

[0808] Step 4:

[0809] The device uses a camera and microphone to collect the user's facial expressions and voice tone in real time. Input is real-time video images and audio data, while output is sentiment data generated by sentiment analysis technology. This sentiment data forms the basis for adjusting operational strategies.

[0810] Step 5:

[0811] The server uses emotion analysis technology to evaluate the user's emotional state. It analyzes the input emotional data and quantifies the user's perceived stress and sense of security. The output is the analyzed emotional state.

[0812] Step 6:

[0813] The server combines the user's emotional state with the generated investment strategy to adjust how optimal investment advice is presented. For example, if emotional data indicates that the user is stressed, it prioritizes presenting low-risk assets. The output is investment advice tailored to the user's emotional state.

[0814] Step 7:

[0815] The device manages the user's schedule and life events and provides timely investment-related notifications. Inputs are calendar information and emotional states, while outputs are timely investment information.

[0816] Step 8:

[0817] Users manage their assets individually based on operational advice received from their devices. User behavioral feedback is incorporated into subsequent analyses, leading to more refined strategic proposals.

[0818] 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.

[0819] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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."

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] Methods for collecting users' financial data in an anonymized form,

[0842] Means for obtaining the latest market information and financial product data from external data sources,

[0843] A means to analyze a user's asset allocation and generate an optimal asset management strategy,

[0844] A means of managing user schedules and life events and providing operational notifications,

[0845] A means of providing learning content tailored to the user's knowledge level,

[0846] A system including means for generating 3D simulations based on the user's asset management goals.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the aforementioned asset management strategy is generated on a central server and notified to the user's device.

[0849] (Claim 3)

[0850] The system according to claim 1, which, based on the analysis of the aforementioned market information and financial product data, compares products offered by multiple financial institutions and presents unbiased information to the user.

[0851] "Example 1"

[0852] (Claim 1)

[0853] Methods for collecting users' financial data in an anonymized form,

[0854] Means for obtaining the latest market information and financial product data from external data sources,

[0855] A means for analyzing a user's asset allocation and generating an optimal asset management strategy using a generative model,

[0856] A means of managing the user's schedule and life events, and providing operational notifications,

[0857] A means of supplying educational content tailored to the user's knowledge level,

[0858] A means for generating a three-dimensional simulation based on the user's asset management goals,

[0859] A means of analyzing market data and providing optimal investment timing tailored to each user,

[0860] A system that includes means for interactively displaying user goal achievement scenarios.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the asset management strategy is generated in a central processing unit and notified to the user's device.

[0863] (Claim 3)

[0864] The system according to claim 1, which, based on the analysis of the aforementioned market information and financial product data, compares the products handled by multiple financial institutions and presents unbiased information to the user.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] Methods for collecting users' financial data in an anonymized form,

[0868] Means for obtaining the latest market information and financial product data from external data sources,

[0869] A means to analyze a user's asset allocation and generate an optimal asset management strategy,

[0870] A means of managing user schedules and life events and providing operational notifications,

[0871] A means of providing learning content tailored to the user's knowledge level,

[0872] A means for generating a 3D simulation based on the user's asset management goals,

[0873] A method to estimate important life events based on payment history and propose appropriate investment timing,

[0874] A system that includes means for creating inputs to tailor individual asset management strategies using generative AI models.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the asset management strategy is generated in a central data server and notified to the user's information processing device.

[0877] (Claim 3)

[0878] The system according to claim 1, which, based on the analysis of the aforementioned market information and financial product data, compares products offered by multiple financial institutions and presents unbiased information to the user.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] Methods for collecting users' financial information in an anonymized form,

[0882] Means for obtaining the latest market information and financial product information from external data sources,

[0883] A means to analyze a user's asset allocation and generate an optimal asset management policy,

[0884] A means of analyzing the user's emotional state and adjusting operational policies according to that emotion,

[0885] A means of managing the user's schedule and life events, and providing operational notifications,

[0886] A means of providing learning materials tailored to the user's knowledge level,

[0887] A system including means for generating 3D simulations based on the user's asset management goals.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the asset management policy is generated in a central processor and notified to the user's device.

[0890] (Claim 3)

[0891] The system according to claim 1, which, based on the analysis of the aforementioned market information and financial product information, compares products offered by multiple financial institutions and presents unbiased information to the user.

[0892] "Application example 2 of combining emotional engines"

[0893] (Claim 1)

[0894] Methods for collecting users' financial data in an anonymized form,

[0895] Means for obtaining the latest market information and financial product data from external data sources,

[0896] A means to analyze a user's asset allocation and generate an optimal asset management strategy,

[0897] A means of managing user schedules and life events and providing operational notifications,

[0898] A means of providing learning content tailored to the user's knowledge level,

[0899] A means for generating a three-dimensional simulation based on the user's asset management goals,

[0900] A means of analyzing a user's emotional state in real time using emotion analysis technology and adjusting the method of presenting operational strategies accordingly,

[0901] A system that includes means to analyze a user's spending patterns and provide advice to reduce unnecessary spending.

[0902] (Claim 2)

[0903] The system according to claim 1, wherein the aforementioned asset management strategy is generated on a central server and notified to the user's terminal.

[0904] (Claim 3)

[0905] The system according to claim 1, which, based on the analysis of the aforementioned market information and financial product data, compares products offered by multiple financial service providers and presents unbiased information to the user. [Explanation of Symbols]

[0906] 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. Methods for collecting users' financial data in an anonymized form, Means for obtaining the latest market information and financial product data from external data sources, A means to analyze a user's asset allocation and generate an optimal asset management strategy, A means of managing user schedules and life events and providing operational notifications, A means of providing learning content tailored to the user's knowledge level, A system including means for generating 3D simulations based on the user's asset management goals.

2. The system according to claim 1, wherein the aforementioned asset management strategy is generated on a central server and notified to the user's device.

3. The system according to claim 1, which, based on the analysis of the aforementioned market information and financial product data, compares products offered by multiple financial institutions and presents unbiased information to the user.

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