system

Abstract

Provide a system. 【Solution means】 Means for inputting information provided by the user, Means for transmitting the input information to the server, Means for analyzing the input information on the server and constructing a user profile, Means for generating an optimal portfolio for the user based on professional investment policies, Means for presenting the generated portfolio to the user's terminal, Means for monitoring the operation status based on the portfolio presented to the terminal, Means for rebalancing the portfolio based on market data, Means for generating a report on the operation status and providing it to the user, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, the importance of asset management has been increasing. However, many users are troubled by its complexity and information overload, and it is difficult for them to find an operation method suitable for themselves. In addition, there are limited people to consult about asset management and reliable information sources, and there is also a possibility of making inappropriate investments without knowing. Furthermore, it is difficult to make a long-term asset management plan according to one's own life stage, and there is a current situation where assets are not effectively utilized. There is a need for a system that solves these problems and supports safe and effective asset management for users.

Means for Solving the Problems

[0005] This invention provides a system that offers an optimal investment plan based on the user's inputted household asset situation, life stage, and desired goals. The user inputs information using a dedicated terminal, which then transmits it to a server. The server analyzes the information to build a user profile and generates an optimal portfolio using an AI algorithm that references professional investment strategies. This portfolio is then displayed on the user's terminal for review. Furthermore, the terminal monitors the investment performance and automatically rebalances the portfolio based on market data. It also periodically generates and provides the user with investment performance reports, ensuring the establishment of an optimal investment strategy at all times. This allows users to easily obtain a highly reliable investment plan and achieve asset management tailored to their life stage.

[0006] "Information provided by the user" refers to data that users provide to the system, such as their asset status, life stage, and desired goals.

[0007] A "server" is a central computer system that receives information sent by users and performs analysis and portfolio generation.

[0008] A "terminal" is a device used by a user to input information and view the results received from a server.

[0009] A "user profile" is a data structure built based on information provided by the user, representing the user's characteristics and needs regarding asset management.

[0010] A "professional investment policy" refers to guidelines and strategies for asset management and investment set by experienced investment professionals.

[0011] A "portfolio" is a combination of investments designed to diversify a user's assets across multiple asset classes or products.

[0012] "Rebalancing" is the process of readjusting the proportions of each asset class within a portfolio to a target ratio.

[0013] A "report" is a document created to analyze operational status and market conditions and provide this information to users. [Brief explanation of the drawing]

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

Embodiments for Carrying out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention provides a system that allows users with limited knowledge and experience in asset management to easily and safely begin investing. This system provides an individually optimized portfolio that takes into account the user's life stage and financial situation, and enables continuous monitoring and adjustment.

[0036] Users first input basic information such as their household assets, life stage, and goals using a dedicated terminal. This information is crucial for initial setup and serves as the basis for the system to determine the optimal investment strategy for the user.

[0037] Information entered into the terminal is encrypted and securely transmitted to the server. The server analyzes the received information and automatically builds a user profile that takes into account the user's income, expenses, savings, investment experience, etc. Based on this profile, the server uses AI algorithms, referencing professional investment strategies, to generate a diversified portfolio across various asset classes.

[0038] The generated portfolio is automatically adjusted in real time based on market data. The terminal monitors the operational status and immediately notifies the server of any changes, maintaining the portfolio's balance. Portfolio rebalancing is performed automatically to quickly respond to market changes and user requests for changes.

[0039] As a concrete example, consider a young working couple who begin investing with the goal of purchasing a home and saving for their children's education. By inputting their current income, expenses, savings, and future aspirations into a terminal, the server presents a portfolio that aims for high returns while minimizing risk. The presented plan is primarily composed of stocks and ETFs, with bonds and other assets added as needed. Furthermore, the portfolio is automatically rebalanced every six months or during periods of significant market fluctuations to optimize risk and return.

[0040] The progress and achievements of the operations are provided to users in the form of detailed reports that are generated regularly. This allows users to stay informed about the current status of operations and revise their operational policies as needed.

[0041] Thus, the system of the present invention automates complex asset management on behalf of the user and performs appropriate risk management, thereby achieving optimal asset utilization.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters information about their household assets, life stage, and desired goals into a form on the device. The device temporarily stores the entered information locally and converts it into an encrypted format.

[0045] Step 2:

[0046] The terminal sends encrypted user information to the server using a secure communication protocol. The server decrypts the received information and prepares to begin analysis.

[0047] Step 3:

[0048] The server builds a user profile based on the received data. It creates a profile to determine the optimal investment strategy for the user, based on factors such as income, expenses, assets, life stage, and investment objectives.

[0049] Step 4:

[0050] The server generates an optimal portfolio using professional investment strategies and AI algorithms. This portfolio is adjusted based on the user's risk tolerance and investment timeframe, and is constructed to ensure diversification across various asset classes.

[0051] Step 5:

[0052] The server generates a portfolio and sends it to the terminal, which visualizes and presents it to the user. The user can review the presented portfolio and provide feedback as needed.

[0053] Step 6:

[0054] The terminal monitors the market and portfolio performance in real time and sends necessary updates to the server. This allows the server to automatically rebalance the portfolio based on market data.

[0055] Step 7:

[0056] The server periodically analyzes its operational status and generates a report that includes status updates and improvement suggestions. The report is sent to the terminal and displayed in a user-friendly format.

[0057] Step 8:

[0058] Users can review their investment strategy based on regularly provided reports and provide feedback to the server via their terminals to make necessary adjustments. This ensures that asset management is always optimized.

[0059] (Example 1)

[0060] 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."

[0061] Currently, there are challenges in enabling individuals to start asset management without requiring specialized knowledge or experience, and to adjust their asset allocation at the appropriate time in response to market changes. Furthermore, there is a need for a system that allows users to continuously monitor the progress and achievements of their asset management and easily revise their investment strategy as needed.

[0062] 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.

[0063] In this invention, the server includes a device for the user to input information, a technology for transmitting the input information to a processing unit, and a technology for analyzing the input information on the processing unit and constructing a user profile. This makes it possible for users to generate an optimal portfolio and make automatic adjustments based on market information in asset management, even without specialized knowledge.

[0064] A "user" refers to an individual who uses the system to manage their assets.

[0065] "Information" refers to data that users input into the system, including asset status, life stage, and goals.

[0066] "Device" refers to a device used by users to input information, and includes dedicated terminals and personal computers.

[0067] A "processing device" refers to a server or computer system that receives and analyzes information sent by a user.

[0068] A "user profile" refers to data that compiles personal attribute information generated based on a user's asset status, income, expenses, savings, and investment experience.

[0069] "Technology" refers to the methods and methodologies used by a system to achieve specific functions, and includes encryption, data analysis, and generative AI models.

[0070] "Asset allocation" refers to the combination of various asset classes recommended for users as investment targets.

[0071] "Market information" refers to the latest data obtained from financial markets, including stock prices, interest rates, and exchange rates.

[0072] "Re-adjustment" refers to the process of re-optimizing a user's portfolio based on market information.

[0073] A "report" is a document containing detailed information about the operational status, and is provided to the user.

[0074] This system automates the complex processes of asset management and provides users with an optimal portfolio. In this invention, users input basic information such as their asset status, life stage, and goals using a dedicated terminal. The terminal securely encrypts this information using AES encryption technology and transmits it to the server via the SSL / TLS protocol.

[0075] The server analyzes the received information using a programming language such as Python. This analysis generates a user profile that takes into account the user's income, expenses, savings, and investment experience. Based on the generated profile, the server uses an AI algorithm (e.g., TENSORFLOW®) to create a portfolio diversified across various asset classes.

[0076] This portfolio is adjusted in real time while referencing the latest financial market data. The server retrieves market information via API and optimizes the portfolio based on that data. For example, during a sharp decline in stock prices, it adjusts asset allocation to minimize risk. The server also continuously monitors the operational status and immediately notifies the user's device, allowing for a rapid response to changes in market conditions and user requests.

[0077] A concrete example is a scenario where a young, dual-income couple begins investing to save for future housing purchases and their children's education. The user inputs their current income, expenses, savings, and desired goals into their device, and the server presents a portfolio focused on stocks and ETFs, aiming for high returns while minimizing risk. The portfolio is automatically rebalanced every six months, or during significant market fluctuations, to optimize the balance between risk and return.

[0078] Examples of prompts for a generative AI model:

[0079] "Please describe in detail the processing flow of a system that automates asset management, and indicate what technologies and methods are used at each step."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users use a dedicated terminal to input basic information such as their financial situation, life stage, and goals. This input includes data such as income, expenses, savings, and investment experience. The terminal then compiles this information and formats it into a database.

[0083] Step 2:

[0084] The terminal encrypts the input information using AES encryption technology. The input data is passed to an encryption module and converted into a secure format. The terminal then sends the encrypted data to the server using the SSL / TLS protocol. The output is encrypted data that can be received by the server.

[0085] Step 3:

[0086] The server decrypts the received encrypted data and analyzes it using a Python script. The input consists of user asset information and life stage data, which are broken down by the data analysis module and organized into related information. The output is a user profile constructed according to the user's investment objectives.

[0087] Step 4:

[0088] The server generates an optimal portfolio based on the user profile using an AI algorithm. This step leverages machine learning techniques such as TensorFlow, and uses a model incorporating professional investment strategies to compute data. The input is the previously constructed user profile, and the output is an optimal portfolio composed of diverse asset classes.

[0089] Step 5:

[0090] The generated portfolio is continuously adjusted by the processing unit based on real-time market information. The server acquires market data and dynamically optimizes the allocation of each element in the portfolio. The input is market information collected via an external API, and the output is an adjusted portfolio that takes into account the latest market fluctuations.

[0091] Step 6:

[0092] The server monitors the portfolio's operational status and immediately notifies the terminal of any changes. The operational monitoring module tracks portfolio performance in real time and provides alerts to the user. The output is a report of the latest portfolio status, which is provided as feedback to the user.

[0093] (Application Example 1)

[0094] 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."

[0095] When individuals begin investing, a challenge exists in finding the optimal investment strategy due to a lack of specialized knowledge and experience. Traditional methods are also problematic because they require time-consuming asset monitoring and rebalancing, and they cannot respond quickly to market changes. To solve these problems, there is a need for a system that allows users to easily start investing and grow their assets efficiently and safely.

[0096] 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.

[0097] In this invention, the server includes means for building a user profile, means for generating asset selections based on expert investment guidelines, means for reconfiguring asset selections based on market information, and means for proposing asset management strategies using a generated AI model. This makes it possible for users to easily start asset management and efficiently manage the progress of their investments while maintaining an optimal portfolio in response to market changes.

[0098] A "user" is an individual who uses the system to manage their own assets, and is the entity that inputs the necessary information and receives suggestions for asset selection.

[0099] An "information processing device" is a computing device used to analyze information entered by a user and generate asset selection and investment strategies based on that information.

[0100] A "user profile" is an individualized information system built based on a user's life stage, asset information, investment experience, etc., and forms the basis for an optimal investment strategy.

[0101] "Asset selection" refers to the combination of various asset classes chosen to construct the optimal portfolio for the user, and investment guidelines are used to generate it.

[0102] A "generative AI model" is a model that uses AI technology to propose asset management strategies for users, analyzing user information and market data to provide an optimal portfolio.

[0103] "Market information" refers to real-time updated financial market data necessary for restructuring asset selections and adjusting investment strategies.

[0104] "Restructuring asset selection" is a process of reviewing a user's asset selection based on market information and optimizing the balance between risk and return.

[0105] In the system of this invention, the user first inputs their life stage and asset information using an information terminal. This information is securely transmitted to an information processing device (server) via a network. The server then constructs a user profile based on the received user information. The user profile is created in detail, taking into account the user's life stage, investment experience, asset information, and other factors.

[0106] Based on the generated user profile and market information, the server uses a generating AI model to create the optimal asset selection (portfolio) for the user. This AI model utilizes professional investment guidelines and the latest market data to propose a strategy that balances risk and return for the user.

[0107] The information terminal displays the generated asset selection, allowing users to check the investment status at any time. Furthermore, market information is constantly updated, and the server automatically reconfigures the asset selection based on this information. This allows the user's portfolio to flexibly respond to market fluctuations and maintain an optimal state.

[0108] As a concrete example, when a user starts investing, they input their monthly income and expenses, as well as their existing savings, and the server provides a recommended investment strategy based on this information. For instance, the server might receive a prompt from the user such as, "I'm a beginner at investing, so please use AI to suggest an appropriate investment strategy based on my expenses and savings," and then present a suitable asset allocation. In this way, users can easily manage their investments and obtain the optimal strategy to achieve their financial goals.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device receives basic information from the user, such as life stage, asset information, income, and expenses. The user then inputs details about their financial situation and goals through the interface on the device.

[0112] Step 2:

[0113] The device transmits the information entered by the user to the server using a security protocol. At this stage, the information is encrypted, protecting it from unauthorized access by third parties.

[0114] Step 3:

[0115] The server analyzes the received user information and builds a user profile based on the entered data. Data processing includes evaluating life stage and asset information, and performing calculations to determine investment experience and risk tolerance.

[0116] Step 4:

[0117] The server uses a generated AI model based on the constructed user profile to create the optimal asset selection. The AI ​​model analyzes prompt messages and market information as input and outputs a portfolio suitable for the user.

[0118] Step 5:

[0119] The server sends the generated asset selection to the user's terminal and presents it visually on the terminal. Based on this information, the user can review the proposed investment strategy and make adjustments as needed.

[0120] Step 6:

[0121] The server monitors real-time updated market information and reconfigures asset selections as needed. Using market information as input, it re-evaluates existing portfolios and outputs optimized new portfolios.

[0122] Step 7:

[0123] The device provides users with periodic operational reports. These reports record the status of asset management and the portfolio's response to market fluctuations, allowing users to revise their investment strategies based on this information.

[0124] 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.

[0125] This invention combines an emotion engine with a conventional asset management system to create a system that provides sophisticated investment plans that take into account the user's psychological state. This reduces the influence of user emotions on asset management decision-making and supports more rational investment decisions.

[0126] Users input basic information into the device, such as their household finances, life stage, and desired goals. In addition, the device incorporates an emotion engine that continuously monitors the user's emotional state. This emotion engine utilizes facial recognition and voice analysis technologies to convert the user's emotions into digital data.

[0127] The entered information and emotional data are sent from the terminal to the server. The server uses the received data to build a user profile. The profile reflects information about the user's financial situation, life stage, and emotional state. Based on this profile, the server uses an AI algorithm to generate an optimal portfolio for the user.

[0128] The generated portfolio is based on the output of the emotion engine and is used as a basis for selecting an investment plan that suits the user's current emotions. For example, if the user is feeling stressed, a stable investment with reduced risk may be recommended.

[0129] Furthermore, the device can continuously monitor the user's emotional state and send updates to the server, allowing for dynamic portfolio adjustments. This cycle enables users to manage their assets in a way that suits their emotions and provides them with confidence in their investment plan.

[0130] As a concrete example, if a user feels anxious about unstable market conditions, the emotion engine detects this emotion, and the server automatically reconfigures the portfolio to reduce risk. This proposed investment plan is presented to the user's device, allowing them to continue investing with peace of mind.

[0131] Thus, the system of the present invention provides flexible and personalized asset management that takes user emotions into account, thereby realizing a more appropriate and secure investment environment.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs their household financial situation, life stage, and desired goals into the interface on the device. The device saves this information to local storage and simultaneously activates an emotion engine to analyze the user's facial expressions and voice tone in real time, collecting emotional data.

[0135] Step 2:

[0136] The device encrypts the collected user data and sentiment data and sends it to the server using a secure communication protocol. The server receives the data, decrypts it, and prepares it for analysis.

[0137] Step 3:

[0138] The server builds a user profile based on the received user data. This profile reflects the user's financial situation, life stage, and goals, as well as collected emotional data. Emotional data is used as an indicator of the user's decision-making tendencies.

[0139] Step 4:

[0140] The server uses the constructed user profile and sentiment data to generate an optimal portfolio using an AI algorithm. This portfolio is based on professional investment strategies, but adjusts the risk approach according to the user's emotional state. For example, if anxiety is detected, stable investments with reduced risk are prioritized.

[0141] Step 5:

[0142] The server generates a portfolio and sends it to the user's device, presenting it to them along with recommendation reasons based on sentiment data. The user can review the displayed portfolio and provide additional feedback via the device if needed.

[0143] Step 6:

[0144] The device continuously monitors the user's emotional state and periodically sends emotional data to the server. Based on this data, the server dynamically rebalances the portfolio, constantly maintaining the optimal investment strategy for the user.

[0145] Step 7:

[0146] The server periodically generates reports containing the latest operational status and sends these reports to the terminal. The terminal displays these reports in a user-friendly format, supporting the user in continuing operations with confidence.

[0147] This system manages assets in a way that smoothly responds to users' emotional fluctuations, providing an optimal investment environment at all times.

[0148] (Example 2)

[0149] 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".

[0150] Traditional asset management systems failed to consider the user's psychological state and emotions, leading to the risk of irrational investment decisions based on feelings. Furthermore, investment plans were generally static and could not immediately adapt to changes in the user's life stage or emotional state. As a result, users lacked a sense of security, and optimal asset management was not possible.

[0151] 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.

[0152] In this invention, the server includes means for acquiring basic information provided by the user, means for monitoring the user's emotional state, and means for generating an optimal financial strategy for the user based on the user profile using a generative AI model. This enables flexible and personalized asset management that responds to the user's emotional state.

[0153] A "user" refers to an individual or organization that uses an asset management system to make investment decisions based on their own financial situation and goals.

[0154] "Basic information" refers to data related to asset management, such as financial status, life stage, and goals, provided by the user.

[0155] "Emotional state" refers to data that indicates the user's psychological and emotional state, and this is used to consider the rationality of investment decisions.

[0156] "Monitoring" refers to the process of continuously observing and recording the user's situation and status, and reflecting that information in the system.

[0157] A "server" refers to a computing device that receives data sent from users, analyzes it, performs calculations related to asset management, and generates and provides data based on those calculations.

[0158] "Generative AI models" refer to artificial intelligence technology that generates optimal asset management strategies using user profile data and external data.

[0159] A "user profile" refers to a dataset built based on information and sentiment data provided by the user, used to develop individual asset management strategies.

[0160] "Financial strategy" refers to the specific investment plans and policies developed to effectively manage a user's assets.

[0161] "Personalization" means providing services and products that are optimized according to the individual characteristics and needs of each user.

[0162] This invention takes the form of providing a system that integrates basic information and emotional data to support users' asset management.

[0163] The user inputs basic information such as their household finances, life stage, and goals into the device. The device has a built-in emotion engine that uses facial recognition and voice analysis technologies to convert the user's emotional state into digital data. This data is transmitted from the device to the server using a secure communication protocol.

[0164] The server uses the received basic information and sentiment data to build a user profile. This profile includes the user's asset information, life stage, and sentiment data. Based on this profile, the server uses a generative AI model to generate the optimal financial strategy for the user.

[0165] For example, a generative AI model can consider the user's emotional state and suggest an aggressive investment strategy if their risk tolerance is high, or a conservative strategy if they want to avoid risk. The generated financial strategy is sent to the device and presented as a personalized investment plan tailored to the user's needs.

[0166] As a concrete example, when a user encounters a market situation that causes anxiety, the emotion engine captures that emotion, and the server reconstructs a portfolio that minimizes risk. This allows the user to continue investing with peace of mind. An example of a prompt message would be, "Please generate an investment plan that takes into account the market situation that is causing the user anxiety and proposes a portfolio with reduced risk."

[0167] This system enables asset management that takes user emotions into account, mitigating the impact of irrational investment decisions based on emotions and achieving more efficient asset management.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user enters basic information such as their financial status, life stage, and goals. The terminal receives this information via a dedicated interface. The entered information is recorded as numerical and text data and prepared for later transmission to the server.

[0171] Step 2:

[0172] The device monitors the user's emotional state using an emotion engine. A facial recognition device captures changes in the user's face in real time, and voice analysis technology analyzes the tone of the user's voice. The obtained emotional data is converted into numerical or categorical formats.

[0173] Step 3:

[0174] The device sends collected basic information and sentiment data to the server. The data is transmitted using a secure communication protocol, and the server receives and stores it. Input includes basic information and sentiment data, while output is a dataset stored in the server's database.

[0175] Step 4:

[0176] The server builds a user profile based on the received data. It references the database and generates a detailed profile by combining it with existing user data. The output is a profile that reflects the user's financial situation, life stage, and emotional state.

[0177] Step 5:

[0178] The server uses a generative AI model to generate an optimal financial strategy based on the user profile. The AI ​​model analyzes the profile data, evaluates multiple investment scenarios, and selects the most rational strategy. The output is a specific investment portfolio recommended to the user.

[0179] Step 6:

[0180] The server sends the generated financial strategy to the terminal. The terminal displays this information in a visually easy-to-understand interface for the user. The terminal also presents adjustment suggestions that reflect the user's sentiment data and prompts changes to the strategy as needed.

[0181] Step 7:

[0182] The device continuously monitors the user's emotional state and sends data back to the server if there are changes in emotions or market conditions. The server receives this data and dynamically adjusts the financial strategy. Based on the feedback from the device, the user can always enjoy the optimal operational plan.

[0183] (Application Example 2)

[0184] 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".

[0185] In asset management, user emotions can influence decision-making, making rational investment decisions difficult. This problem is particularly pronounced during periods of rapid market fluctuations, where user anxiety makes it difficult to formulate appropriate investment plans. Furthermore, current asset management systems cannot take user emotions into account in real time, making it impossible to instantly provide personalized investment plans.

[0186] 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.

[0187] In this invention, the server includes means for inputting information provided by the user, means for recognizing emotions to analyze the user's emotional state, and means for generating an optimal portfolio for the user according to their emotional state. This provides a flexible and personalized asset management system that reflects the user's emotional state and supports rational investment decisions.

[0188] A "user" is an individual or group that utilizes an asset management system and provides the system with their emotional state and asset information.

[0189] "Means of inputting information" refers to interfaces that allow users to input their asset information and life stage, and are provided on various devices.

[0190] "Emotion recognition means" refers to technologies used to analyze a user's emotional state, converting emotions into digital data through voice analysis and facial recognition.

[0191] A "portfolio" refers to a combination of assets recommended to a user, and includes a specific investment plan tailored to their investment strategy.

[0192] A "user profile" is a dataset that integrates user-provided information and emotional data, and serves as the foundation for proposing optimal asset management strategies.

[0193] "Rebalancing" is the process of restructuring a portfolio based on market data and sentiment data, and making adjustments according to risk and objectives.

[0194] An "investment performance report" is a report that is created periodically to inform users about the current status and performance of their asset management.

[0195] To implement this invention, it is necessary to build a system that analyzes the user's emotional state in real time and provides information useful for asset management decision-making. Specifically, the user inputs asset information and life stage information via an input interface on the user's terminal. A terminal equipped with emotion recognition software analyzes the user's emotions using voice and image data. The resulting emotional data is transmitted to a server along with the information provided by the user.

[0196] The server builds a user profile based on the received data and uses artificial intelligence algorithms to generate an optimal investment portfolio in real time. During this process, machine learning frameworks such as TensorFlow are used to adjust the portfolio based on the user's emotional state.

[0197] This system allows users to manage their assets in a way that suits their emotions at any given time, enabling them to make investment decisions with peace of mind. For example, if a user feels anxious about market fluctuations, the emotion engine detects this situation and proposes a portfolio that reduces risk, allowing the user to make stable investments while managing risk.

[0198] As a concrete example, the system is designed to provide reassurance even when a user is feeling anxious after seeing news of rapid market fluctuations. In this case, an example of a prompt message fed into the generation AI model would be: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's anxiety."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal receives the user's asset information and life stage information through an input interface. The input data is structured as numerical and text data and sent to the server.

[0202] Step 2:

[0203] The device performs speech recognition and image analysis, analyzing the user's emotional state based on their voice and video data. This process utilizes an emotion recognition algorithm, outputting the emotional state as numerical data.

[0204] Step 3:

[0205] The server integrates received asset information, life stage information, and emotional data to build a user profile. This profile serves as foundational data to support future decision-making and is input into AI algorithms.

[0206] Step 4:

[0207] The server uses an AI algorithm to generate an optimal investment portfolio based on the user's profile. This process involves a generation AI model and uses an example prompt: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's concerns."

[0208] Step 5:

[0209] The generated portfolio information is sent to the device and presented to the user. Based on this information, the user can make appropriate investment decisions with confidence.

[0210] Step 6:

[0211] The device continuously monitors the user's investment behavior and market data, tracking any changes. If a change is detected in sentiment data, the portfolio is rebalanced accordingly.

[0212] Step 7:

[0213] The server generates and provides operational status reports to users. These reports are detailed, taking into account historical data analysis and fluctuations in sentiment data, to support users' daily investment activities.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] This invention provides a system that allows users with limited knowledge and experience in asset management to easily and safely begin investing. This system provides an individually optimized portfolio that takes into account the user's life stage and financial situation, and enables continuous monitoring and adjustment.

[0231] Users first input basic information such as their household assets, life stage, and goals using a dedicated terminal. This information is crucial for initial setup and serves as the basis for the system to determine the optimal investment strategy for the user.

[0232] Information entered into the terminal is encrypted and securely transmitted to the server. The server analyzes the received information and automatically builds a user profile that takes into account the user's income, expenses, savings, investment experience, etc. Based on this profile, the server uses AI algorithms, referencing professional investment strategies, to generate a diversified portfolio across various asset classes.

[0233] The generated portfolio is automatically adjusted in real time based on market data. The terminal monitors the operational status and immediately notifies the server of any changes, maintaining the portfolio's balance. Portfolio rebalancing is performed automatically to quickly respond to market changes and user requests for changes.

[0234] As a concrete example, consider a young working couple who begin investing with the goal of purchasing a home and saving for their children's education. By inputting their current income, expenses, savings, and future aspirations into a terminal, the server presents a portfolio that aims for high returns while minimizing risk. The presented plan is primarily composed of stocks and ETFs, with bonds and other assets added as needed. Furthermore, the portfolio is automatically rebalanced every six months or during periods of significant market fluctuations to optimize risk and return.

[0235] The progress and achievements of the operations are provided to users in the form of detailed reports that are generated regularly. This allows users to stay informed about the current status of operations and revise their operational policies as needed.

[0236] Thus, the system of the present invention automates complex asset management on behalf of the user and performs appropriate risk management, thereby achieving optimal asset utilization.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user enters information about their household assets, life stage, and desired goals into a form on the device. The device temporarily stores the entered information locally and converts it into an encrypted format.

[0240] Step 2:

[0241] The terminal sends encrypted user information to the server using a secure communication protocol. The server decrypts the received information and prepares to begin analysis.

[0242] Step 3:

[0243] The server builds a user profile based on the received data. It creates a profile to determine the optimal investment strategy for the user, based on factors such as income, expenses, assets, life stage, and investment objectives.

[0244] Step 4:

[0245] The server generates an optimal portfolio using professional investment strategies and AI algorithms. This portfolio is adjusted based on the user's risk tolerance and investment timeframe, and is constructed to ensure diversification across various asset classes.

[0246] Step 5:

[0247] The server generates a portfolio and sends it to the terminal, which visualizes and presents it to the user. The user can review the presented portfolio and provide feedback as needed.

[0248] Step 6:

[0249] The terminal monitors the market and portfolio performance in real time and sends necessary updates to the server. This allows the server to automatically rebalance the portfolio based on market data.

[0250] Step 7:

[0251] The server periodically analyzes its operational status and generates a report that includes status updates and improvement suggestions. The report is sent to the terminal and displayed in a user-friendly format.

[0252] Step 8:

[0253] Users can review their investment strategy based on regularly provided reports and provide feedback to the server via their terminals to make necessary adjustments. This ensures that asset management is always optimized.

[0254] (Example 1)

[0255] 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."

[0256] Currently, there are challenges in enabling individuals to start asset management without requiring specialized knowledge or experience, and to adjust their asset allocation at the appropriate time in response to market changes. Furthermore, there is a need for a system that allows users to continuously monitor the progress and achievements of their asset management and easily revise their investment strategy as needed.

[0257] 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.

[0258] In this invention, the server includes a device for the user to input information, a technology for transmitting the input information to a processing unit, and a technology for analyzing the input information on the processing unit and constructing a user profile. This makes it possible for users to generate an optimal portfolio and make automatic adjustments based on market information in asset management, even without specialized knowledge.

[0259] A "user" refers to an individual who uses the system to manage their assets.

[0260] "Information" refers to data that users input into the system, including asset status, life stage, and goals.

[0261] "Device" refers to a device used by users to input information, and includes dedicated terminals and personal computers.

[0262] A "processing device" refers to a server or computer system that receives and analyzes information sent by a user.

[0263] A "user profile" refers to data that compiles personal attribute information generated based on a user's asset status, income, expenses, savings, and investment experience.

[0264] "Technology" refers to the methods and methodologies used by a system to achieve specific functions, and includes encryption, data analysis, and generative AI models.

[0265] "Asset allocation" refers to the combination of various asset classes recommended for users as investment targets.

[0266] "Market information" refers to the latest data obtained from financial markets, including stock prices, interest rates, and exchange rates.

[0267] "Re-adjustment" refers to the process of re-optimizing a user's portfolio based on market information.

[0268] A "report" is a document containing detailed information about the operational status, and is provided to the user.

[0269] This system automates the complex processes of asset management and provides users with an optimal portfolio. In this invention, users input basic information such as their asset status, life stage, and goals using a dedicated terminal. The terminal securely encrypts this information using AES encryption technology and transmits it to the server via the SSL / TLS protocol.

[0270] The server analyzes the received information using a programming language such as Python. This analysis generates a user profile that takes into account the user's income, expenses, savings, and investment experience. Based on the generated profile, the server uses an AI algorithm (e.g., TensorFlow) to create a portfolio diversified across various asset classes.

[0271] This portfolio is adjusted in real time while referencing the latest financial market data. The server retrieves market information via API and optimizes the portfolio based on that data. For example, during a sharp decline in stock prices, it adjusts asset allocation to minimize risk. The server also continuously monitors the operational status and immediately notifies the user's device, allowing for a rapid response to changes in market conditions and user requests.

[0272] A concrete example is a scenario where a young, dual-income couple begins investing to save for future housing purchases and their children's education. The user inputs their current income, expenses, savings, and desired goals into their device, and the server presents a portfolio focused on stocks and ETFs, aiming for high returns while minimizing risk. The portfolio is automatically rebalanced every six months, or during significant market fluctuations, to optimize the balance between risk and return.

[0273] Examples of prompts for a generative AI model:

[0274] "Please describe in detail the processing flow of a system that automates asset management, and indicate what technologies and methods are used at each step."

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user uses a dedicated terminal to input basic information such as their asset status, life stage, goals, etc. This input includes data such as income, expenses, savings amount, investment experience, etc. The terminal summarizes this information and formats it into a database form.

[0278] Step 2:

[0279] The terminal encrypts the input information using AES encryption technology. The input data is passed to an encryption module and converted into a secure format. The terminal then uses the SSL / TLS protocol to send the encrypted data to the server. The output is encrypted data that can be received on the server side.

[0280] Step 3:

[0281] The server decrypts the received encrypted data and analyzes the data using a Python script. The input is the user's asset information and life stage data, which is decomposed by a data analysis module and organized into relevant information. The output is a user profile constructed according to the user's investment objectives.

[0282] Step 4:

[0283] The server generates an optimal portfolio using an AI algorithm based on the user profile. In this step, machine learning technologies such as TensorFlow are utilized, and data is calculated using a model incorporating professional investment strategies. The input is the previously constructed user profile, and the output is an optimal portfolio composed of various asset classes.

[0284] Step 5:

[0285] The generated portfolio is continuously adjusted by the processing device based on real-time market information. The server acquires market data and dynamically optimizes the allocation of each element of the portfolio. The input is market information collected via an external API, and the output is an adjusted portfolio taking into account the latest market fluctuations.

[0286] Step 6:

[0287] The server monitors the operation status of the portfolio and immediately notifies the terminal if there are any changes. The operation monitoring module has a mechanism to track the performance of the portfolio in real time and provide alerts to users. The output is the latest status report of the portfolio, which is provided as feedback to the user.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] When an individual starts asset management, there is a problem that it is difficult to find an optimal investment strategy due to lack of professional knowledge and experience. In the conventional method, it takes time to monitor and rebalance assets, and it is also a problem that it cannot respond quickly to market changes. In order to solve such problems, there is a demand for a system that allows users to easily start asset management and increase assets efficiently and safely.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes means for building a user profile, means for generating asset selections based on expert investment guidelines, means for reconfiguring asset selections based on market information, and means for proposing asset management strategies using a generated AI model. This makes it possible for users to easily start asset management and efficiently manage the progress of their investments while maintaining an optimal portfolio in response to market changes.

[0293] A "user" is an individual who uses the system to manage their own assets, and is the entity that inputs the necessary information and receives suggestions for asset selection.

[0294] An "information processing device" is a computing device used to analyze information entered by a user and generate asset selection and investment strategies based on that information.

[0295] A "user profile" is an individualized information system built based on a user's life stage, asset information, investment experience, etc., and forms the basis for an optimal investment strategy.

[0296] "Asset selection" refers to the combination of various asset classes chosen to construct the optimal portfolio for the user, and investment guidelines are used to generate it.

[0297] A "generative AI model" is a model that uses AI technology to propose asset management strategies for users, analyzing user information and market data to provide an optimal portfolio.

[0298] "Market information" refers to real-time updated financial market data necessary for restructuring asset selections and adjusting investment strategies.

[0299] "Restructuring asset selection" is a process of reviewing a user's asset selection based on market information and optimizing the balance between risk and return.

[0300] In the system of this invention, the user first inputs their life stage and asset information using an information terminal. This information is securely transmitted to an information processing device (server) via a network. The server then constructs a user profile based on the received user information. The user profile is created in detail, taking into account the user's life stage, investment experience, asset information, and other factors.

[0301] Based on the generated user profile and market information, the server uses a generating AI model to create the optimal asset selection (portfolio) for the user. This AI model utilizes professional investment guidelines and the latest market data to propose a strategy that balances risk and return for the user.

[0302] The information terminal displays the generated asset selection, allowing users to check the investment status at any time. Furthermore, market information is constantly updated, and the server automatically reconfigures the asset selection based on this information. This allows the user's portfolio to flexibly respond to market fluctuations and maintain an optimal state.

[0303] As a concrete example, when a user starts investing, they input their monthly income and expenses, as well as their existing savings, and the server provides a recommended investment strategy based on this information. For instance, the server might receive a prompt from the user such as, "I'm a beginner at investing, so please use AI to suggest an appropriate investment strategy based on my expenses and savings," and then present a suitable asset allocation. In this way, users can easily manage their investments and obtain the optimal strategy to achieve their financial goals.

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The terminal receives basic information such as the user's life stage, asset information, income, and expenses as input from the user. The user enters details about their financial situation and goals through the interface on the terminal.

[0307] Step 2:

[0308] The terminal sends the information entered by the user to the server using a security protocol. At this stage, since the information is encrypted, it is protected from unauthorized access by third parties.

[0309] Step 3:

[0310] The server analyzes the received user information and constructs a user profile based on the input data. As data processing, it evaluates the life stage and asset information and performs calculations to determine the investment experience and risk tolerance.

[0311] Step 4:

[0312] The server generates an optimal asset selection using a generative AI model based on the constructed user profile. The AI model analyzes the prompt text and market information as input and outputs a portfolio suitable for the user.

[0313] Step 5:

[0314] The server sends the generated asset selection to the user's terminal and visually presents it to the user on the terminal. Based on this information, the user can confirm the proposed investment strategy and make adjustments if necessary.

[0315] Step 6:

[0316] The server monitors the market information updated in real time and reconfigures the asset selection as needed. Using the market information as input, it re-evaluates the existing portfolio and outputs an optimized new portfolio.

[0317] Step 7:

[0318] The device provides users with periodic operational reports. These reports record the status of asset management and the portfolio's response to market fluctuations, allowing users to revise their investment strategies based on this information.

[0319] 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.

[0320] This invention combines an emotion engine with a conventional asset management system to create a system that provides sophisticated investment plans that take into account the user's psychological state. This reduces the influence of user emotions on asset management decision-making and supports more rational investment decisions.

[0321] Users input basic information into the device, such as their household finances, life stage, and desired goals. In addition, the device incorporates an emotion engine that continuously monitors the user's emotional state. This emotion engine utilizes facial recognition and voice analysis technologies to convert the user's emotions into digital data.

[0322] The entered information and emotional data are sent from the terminal to the server. The server uses the received data to build a user profile. The profile reflects information about the user's financial situation, life stage, and emotional state. Based on this profile, the server uses an AI algorithm to generate an optimal portfolio for the user.

[0323] The generated portfolio is based on the output of the emotion engine and is used as a basis for selecting an investment plan that suits the user's current emotions. For example, if the user is feeling stressed, a stable investment with reduced risk may be recommended.

[0324] Furthermore, the device can continuously monitor the user's emotional state and send updates to the server, allowing for dynamic portfolio adjustments. This cycle enables users to manage their assets in a way that suits their emotions and provides them with confidence in their investment plan.

[0325] As a concrete example, if a user feels anxious about unstable market conditions, the emotion engine detects this emotion, and the server automatically reconfigures the portfolio to reduce risk. This proposed investment plan is presented to the user's device, allowing them to continue investing with peace of mind.

[0326] Thus, the system of the present invention provides flexible and personalized asset management that takes user emotions into account, thereby realizing a more appropriate and secure investment environment.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user inputs their household financial situation, life stage, and desired goals into the interface on the device. The device saves this information to local storage and simultaneously activates an emotion engine to analyze the user's facial expressions and voice tone in real time, collecting emotional data.

[0330] Step 2:

[0331] The device encrypts the collected user data and sentiment data and sends it to the server using a secure communication protocol. The server receives the data, decrypts it, and prepares it for analysis.

[0332] Step 3:

[0333] The server builds a user profile based on the received user data. This profile reflects the user's financial situation, life stage, and goals, as well as collected emotional data. Emotional data is used as an indicator of the user's decision-making tendencies.

[0334] Step 4:

[0335] The server uses the constructed user profile and sentiment data to generate an optimal portfolio using an AI algorithm. This portfolio is based on professional investment strategies, but adjusts the risk approach according to the user's emotional state. For example, if anxiety is detected, stable investments with reduced risk are prioritized.

[0336] Step 5:

[0337] The server generates a portfolio and sends it to the user's device, presenting it to them along with recommendation reasons based on sentiment data. The user can review the displayed portfolio and provide additional feedback via the device if needed.

[0338] Step 6:

[0339] The device continuously monitors the user's emotional state and periodically sends emotional data to the server. Based on this data, the server dynamically rebalances the portfolio, constantly maintaining the optimal investment strategy for the user.

[0340] Step 7:

[0341] The server periodically generates reports containing the latest operational status and sends these reports to the terminal. The terminal displays these reports in a user-friendly format, supporting the user in continuing operations with confidence.

[0342] This system manages assets in a way that smoothly responds to users' emotional fluctuations, providing an optimal investment environment at all times.

[0343] (Example 2)

[0344] 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".

[0345] Traditional asset management systems failed to consider the user's psychological state and emotions, leading to the risk of irrational investment decisions based on feelings. Furthermore, investment plans were generally static and could not immediately adapt to changes in the user's life stage or emotional state. As a result, users lacked a sense of security, and optimal asset management was not possible.

[0346] 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.

[0347] In this invention, the server includes means for acquiring basic information provided by the user, means for monitoring the user's emotional state, and means for generating an optimal financial strategy for the user based on the user profile using a generative AI model. This enables flexible and personalized asset management that responds to the user's emotional state.

[0348] A "user" refers to an individual or organization that uses an asset management system to make investment decisions based on their own financial situation and goals.

[0349] "Basic information" refers to data related to asset management, such as financial status, life stage, and goals, provided by the user.

[0350] "Emotional state" refers to data that indicates the user's psychological and emotional state, and this is used to consider the rationality of investment decisions.

[0351] "Monitoring" refers to the process of continuously observing and recording the user's situation and status, and reflecting that information in the system.

[0352] A "server" refers to a computing device that receives data sent from users, analyzes it, performs calculations related to asset management, and generates and provides data based on those calculations.

[0353] "Generative AI models" refer to artificial intelligence technology that generates optimal asset management strategies using user profile data and external data.

[0354] A "user profile" refers to a dataset built based on information and sentiment data provided by the user, used to develop individual asset management strategies.

[0355] "Financial strategy" refers to the specific investment plans and policies developed to effectively manage a user's assets.

[0356] "Personalization" means providing services and products that are optimized according to the individual characteristics and needs of each user.

[0357] This invention takes the form of providing a system that integrates basic information and emotional data to support users' asset management.

[0358] The user inputs basic information such as their household finances, life stage, and goals into the device. The device has a built-in emotion engine that uses facial recognition and voice analysis technologies to convert the user's emotional state into digital data. This data is transmitted from the device to the server using a secure communication protocol.

[0359] The server uses the received basic information and sentiment data to build a user profile. This profile includes the user's asset information, life stage, and sentiment data. Based on this profile, the server uses a generative AI model to generate the optimal financial strategy for the user.

[0360] For example, a generative AI model can consider the user's emotional state and suggest an aggressive investment strategy if their risk tolerance is high, or a conservative strategy if they want to avoid risk. The generated financial strategy is sent to the device and presented as a personalized investment plan tailored to the user's needs.

[0361] As a concrete example, when a user encounters a market situation that causes anxiety, the emotion engine captures that emotion, and the server reconstructs a portfolio that minimizes risk. This allows the user to continue investing with peace of mind. An example of a prompt message would be, "Please generate an investment plan that takes into account the market situation that is causing the user anxiety and proposes a portfolio with reduced risk."

[0362] This system enables asset management that takes user emotions into account, mitigating the impact of irrational investment decisions based on emotions and achieving more efficient asset management.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user enters basic information such as their financial status, life stage, and goals. The terminal receives this information via a dedicated interface. The entered information is recorded as numerical and text data and prepared for later transmission to the server.

[0366] Step 2:

[0367] The device monitors the user's emotional state using an emotion engine. A facial recognition device captures changes in the user's face in real time, and voice analysis technology analyzes the tone of the user's voice. The obtained emotional data is converted into numerical or categorical formats.

[0368] Step 3:

[0369] The device sends collected basic information and sentiment data to the server. The data is transmitted using a secure communication protocol, and the server receives and stores it. Input includes basic information and sentiment data, while output is a dataset stored in the server's database.

[0370] Step 4:

[0371] The server builds a user profile based on the received data. It references the database and generates a detailed profile by combining it with existing user data. The output is a profile that reflects the user's financial situation, life stage, and emotional state.

[0372] Step 5:

[0373] The server uses a generative AI model to generate an optimal financial strategy based on the user profile. The AI ​​model analyzes the profile data, evaluates multiple investment scenarios, and selects the most rational strategy. The output is a specific investment portfolio recommended to the user.

[0374] Step 6:

[0375] The server sends the generated financial strategy to the terminal. The terminal displays this information in a visually easy-to-understand interface for the user. The terminal also presents adjustment suggestions that reflect the user's sentiment data and prompts changes to the strategy as needed.

[0376] Step 7:

[0377] The device continuously monitors the user's emotional state and sends data back to the server if there are changes in emotions or market conditions. The server receives this data and dynamically adjusts the financial strategy. Based on the feedback from the device, the user can always enjoy the optimal operational plan.

[0378] (Application Example 2)

[0379] 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."

[0380] In asset management, user emotions can influence decision-making, making rational investment decisions difficult. This problem is particularly pronounced during periods of rapid market fluctuations, where user anxiety makes it difficult to formulate appropriate investment plans. Furthermore, current asset management systems cannot take user emotions into account in real time, making it impossible to instantly provide personalized investment plans.

[0381] 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.

[0382] In this invention, the server includes means for inputting information provided by the user, means for recognizing emotions to analyze the user's emotional state, and means for generating an optimal portfolio for the user according to their emotional state. This provides a flexible and personalized asset management system that reflects the user's emotional state and supports rational investment decisions.

[0383] A "user" is an individual or group that utilizes an asset management system and provides the system with their emotional state and asset information.

[0384] "Means of inputting information" refers to interfaces that allow users to input their asset information and life stage, and are provided on various devices.

[0385] "Emotion recognition means" refers to technologies used to analyze a user's emotional state, converting emotions into digital data through voice analysis and facial recognition.

[0386] A "portfolio" refers to a combination of assets recommended to a user, and includes a specific investment plan tailored to their investment strategy.

[0387] A "user profile" is a dataset that integrates user-provided information and emotional data, and serves as the foundation for proposing optimal asset management strategies.

[0388] "Rebalancing" is the process of restructuring a portfolio based on market data and sentiment data, and making adjustments according to risk and objectives.

[0389] An "investment performance report" is a report that is created periodically to inform users about the current status and performance of their asset management.

[0390] To implement this invention, it is necessary to build a system that analyzes the user's emotional state in real time and provides information useful for asset management decision-making. Specifically, the user inputs asset information and life stage information via an input interface on the user's terminal. A terminal equipped with emotion recognition software analyzes the user's emotions using voice and image data. The resulting emotional data is transmitted to a server along with the information provided by the user.

[0391] The server builds a user profile based on the received data and uses artificial intelligence algorithms to generate an optimal investment portfolio in real time. During this process, machine learning frameworks such as TensorFlow are used to adjust the portfolio based on the user's emotional state.

[0392] This system allows users to manage their assets in a way that suits their emotions at any given time, enabling them to make investment decisions with peace of mind. For example, if a user feels anxious about market fluctuations, the emotion engine detects this situation and proposes a portfolio that reduces risk, allowing the user to make stable investments while managing risk.

[0393] As a concrete example, the system is designed to provide reassurance even when a user is feeling anxious after seeing news of rapid market fluctuations. In this case, an example of a prompt message fed into the generation AI model would be: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's anxiety."

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The terminal receives the user's asset information and life stage information through an input interface. The input data is structured as numerical and text data and sent to the server.

[0397] Step 2:

[0398] The device performs speech recognition and image analysis, analyzing the user's emotional state based on their voice and video data. This process utilizes an emotion recognition algorithm, outputting the emotional state as numerical data.

[0399] Step 3:

[0400] The server integrates received asset information, life stage information, and emotional data to build a user profile. This profile serves as foundational data to support future decision-making and is input into AI algorithms.

[0401] Step 4:

[0402] The server uses an AI algorithm to generate an optimal investment portfolio based on the user's profile. This process involves a generation AI model and uses an example prompt: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's concerns."

[0403] Step 5:

[0404] The generated portfolio information is sent to the device and presented to the user. Based on this information, the user can make appropriate investment decisions with confidence.

[0405] Step 6:

[0406] The device continuously monitors the user's investment behavior and market data, tracking any changes. If a change is detected in sentiment data, the portfolio is rebalanced accordingly.

[0407] Step 7:

[0408] The server generates and provides operational status reports to users. These reports are detailed, taking into account historical data analysis and fluctuations in sentiment data, to support users' daily investment activities.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] 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.

[0415] 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).

[0416] 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.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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".

[0425] This invention provides a system that allows users with limited knowledge and experience in asset management to easily and safely begin investing. This system provides an individually optimized portfolio that takes into account the user's life stage and financial situation, and enables continuous monitoring and adjustment.

[0426] Users first input basic information such as their household assets, life stage, and goals using a dedicated terminal. This information is crucial for initial setup and serves as the basis for the system to determine the optimal investment strategy for the user.

[0427] Information entered into the terminal is encrypted and securely transmitted to the server. The server analyzes the received information and automatically builds a user profile that takes into account the user's income, expenses, savings, investment experience, etc. Based on this profile, the server uses AI algorithms, referencing professional investment strategies, to generate a diversified portfolio across various asset classes.

[0428] The generated portfolio is automatically adjusted in real time based on market data. The terminal monitors the operational status and immediately notifies the server of any changes, maintaining the portfolio's balance. Portfolio rebalancing is performed automatically to quickly respond to market changes and user requests for changes.

[0429] As a concrete example, consider a young working couple who begin investing with the goal of purchasing a home and saving for their children's education. By inputting their current income, expenses, savings, and future aspirations into a terminal, the server presents a portfolio that aims for high returns while minimizing risk. The presented plan is primarily composed of stocks and ETFs, with bonds and other assets added as needed. Furthermore, the portfolio is automatically rebalanced every six months or during periods of significant market fluctuations to optimize risk and return.

[0430] The progress and achievements of the operations are provided to users in the form of detailed reports that are generated regularly. This allows users to stay informed about the current status of operations and revise their operational policies as needed.

[0431] Thus, the system of the present invention automates complex asset management on behalf of the user and performs appropriate risk management, thereby achieving optimal asset utilization.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user enters information about their household assets, life stage, and desired goals into a form on the device. The device temporarily stores the entered information locally and converts it into an encrypted format.

[0435] Step 2:

[0436] The terminal sends encrypted user information to the server using a secure communication protocol. The server decrypts the received information and prepares to begin analysis.

[0437] Step 3:

[0438] The server builds a user profile based on the received data. It creates a profile to determine the optimal investment strategy for the user, based on factors such as income, expenses, assets, life stage, and investment objectives.

[0439] Step 4:

[0440] The server generates an optimal portfolio using professional investment strategies and AI algorithms. This portfolio is adjusted based on the user's risk tolerance and investment timeframe, and is constructed to ensure diversification across various asset classes.

[0441] Step 5:

[0442] The server generates a portfolio and sends it to the terminal, which visualizes and presents it to the user. The user can review the presented portfolio and provide feedback as needed.

[0443] Step 6:

[0444] The terminal monitors the market and portfolio performance in real time and sends necessary updates to the server. This allows the server to automatically rebalance the portfolio based on market data.

[0445] Step 7:

[0446] The server periodically analyzes its operational status and generates a report that includes status updates and improvement suggestions. The report is sent to the terminal and displayed in a user-friendly format.

[0447] Step 8:

[0448] Users can review their investment strategy based on regularly provided reports and provide feedback to the server via their terminals to make necessary adjustments. This ensures that asset management is always optimized.

[0449] (Example 1)

[0450] 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."

[0451] Currently, there are challenges in enabling individuals to start asset management without requiring specialized knowledge or experience, and to adjust their asset allocation at the appropriate time in response to market changes. Furthermore, there is a need for a system that allows users to continuously monitor the progress and achievements of their asset management and easily revise their investment strategy as needed.

[0452] 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.

[0453] In this invention, the server includes a device for the user to input information, a technology for transmitting the input information to a processing unit, and a technology for analyzing the input information on the processing unit and constructing a user profile. This makes it possible for users to generate an optimal portfolio and make automatic adjustments based on market information in asset management, even without specialized knowledge.

[0454] A "user" refers to an individual who uses the system to manage their assets.

[0455] "Information" refers to data that users input into the system, including asset status, life stage, and goals.

[0456] "Device" refers to a device used by users to input information, and includes dedicated terminals and personal computers.

[0457] A "processing device" refers to a server or computer system that receives and analyzes information sent by a user.

[0458] A "user profile" refers to data that compiles personal attribute information generated based on a user's asset status, income, expenses, savings, and investment experience.

[0459] "Technology" refers to the methods and methodologies used by a system to achieve specific functions, and includes encryption, data analysis, and generative AI models.

[0460] "Asset allocation" refers to the combination of various asset classes recommended for users as investment targets.

[0461] "Market information" refers to the latest data obtained from financial markets, including stock prices, interest rates, and exchange rates.

[0462] "Re-adjustment" refers to the process of re-optimizing a user's portfolio based on market information.

[0463] A "report" is a document containing detailed information about the operational status, and is provided to the user.

[0464] This system automates the complex processes of asset management and provides users with an optimal portfolio. In this invention, users input basic information such as their asset status, life stage, and goals using a dedicated terminal. The terminal securely encrypts this information using AES encryption technology and transmits it to the server via the SSL / TLS protocol.

[0465] The server analyzes the received information using a programming language such as Python. This analysis generates a user profile that takes into account the user's income, expenses, savings, and investment experience. Based on the generated profile, the server uses an AI algorithm (e.g., TensorFlow) to create a portfolio diversified across various asset classes.

[0466] This portfolio is adjusted in real time while referencing the latest financial market data. The server retrieves market information via API and optimizes the portfolio based on that data. For example, during a sharp decline in stock prices, it adjusts asset allocation to minimize risk. The server also continuously monitors the operational status and immediately notifies the user's device, allowing for a rapid response to changes in market conditions and user requests.

[0467] A concrete example is a scenario where a young, dual-income couple begins investing to save for future housing purchases and their children's education. The user inputs their current income, expenses, savings, and desired goals into their device, and the server presents a portfolio focused on stocks and ETFs, aiming for high returns while minimizing risk. The portfolio is automatically rebalanced every six months, or during significant market fluctuations, to optimize the balance between risk and return.

[0468] Examples of prompts for a generative AI model:

[0469] "Please describe in detail the processing flow of a system that automates asset management, and indicate what technologies and methods are used at each step."

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] Users use a dedicated terminal to input basic information such as their financial situation, life stage, and goals. This input includes data such as income, expenses, savings, and investment experience. The terminal then compiles this information and formats it into a database.

[0473] Step 2:

[0474] The terminal encrypts the input information using AES encryption technology. The input data is passed to an encryption module and converted into a secure format. The terminal then sends the encrypted data to the server using the SSL / TLS protocol. The output is encrypted data that can be received by the server.

[0475] Step 3:

[0476] The server decrypts the received encrypted data and analyzes it using a Python script. The input consists of user asset information and life stage data, which are broken down by the data analysis module and organized into related information. The output is a user profile constructed according to the user's investment objectives.

[0477] Step 4:

[0478] The server generates an optimal portfolio based on the user profile using an AI algorithm. This step leverages machine learning techniques such as TensorFlow, and uses a model incorporating professional investment strategies to compute data. The input is the previously constructed user profile, and the output is an optimal portfolio composed of diverse asset classes.

[0479] Step 5:

[0480] The generated portfolio is continuously adjusted by the processing unit based on real-time market information. The server acquires market data and dynamically optimizes the allocation of each element in the portfolio. The input is market information collected via an external API, and the output is an adjusted portfolio that takes into account the latest market fluctuations.

[0481] Step 6:

[0482] The server monitors the portfolio's operational status and immediately notifies the terminal of any changes. The operational monitoring module tracks portfolio performance in real time and provides alerts to the user. The output is a report of the latest portfolio status, which is provided as feedback to the user.

[0483] (Application Example 1)

[0484] 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."

[0485] When individuals begin investing, a challenge exists in finding the optimal investment strategy due to a lack of specialized knowledge and experience. Traditional methods are also problematic because they require time-consuming asset monitoring and rebalancing, and they cannot respond quickly to market changes. To solve these problems, there is a need for a system that allows users to easily start investing and grow their assets efficiently and safely.

[0486] 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.

[0487] In this invention, the server includes means for building a user profile, means for generating asset selections based on expert investment guidelines, means for reconfiguring asset selections based on market information, and means for proposing asset management strategies using a generated AI model. This makes it possible for users to easily start asset management and efficiently manage the progress of their investments while maintaining an optimal portfolio in response to market changes.

[0488] A "user" is an individual who uses the system to manage their own assets, and is the entity that inputs the necessary information and receives suggestions for asset selection.

[0489] An "information processing device" is a computing device used to analyze information entered by a user and generate asset selection and investment strategies based on that information.

[0490] A "user profile" is an individualized information system built based on a user's life stage, asset information, investment experience, etc., and forms the basis for an optimal investment strategy.

[0491] "Asset selection" refers to the combination of various asset classes chosen to construct the optimal portfolio for the user, and investment guidelines are used to generate it.

[0492] A "generative AI model" is a model that uses AI technology to propose asset management strategies for users, analyzing user information and market data to provide an optimal portfolio.

[0493] "Market information" refers to real-time updated financial market data necessary for restructuring asset selections and adjusting investment strategies.

[0494] "Restructuring asset selection" is a process of reviewing a user's asset selection based on market information and optimizing the balance between risk and return.

[0495] In the system of this invention, the user first inputs their life stage and asset information using an information terminal. This information is securely transmitted to an information processing device (server) via a network. The server then constructs a user profile based on the received user information. The user profile is created in detail, taking into account the user's life stage, investment experience, asset information, and other factors.

[0496] Based on the generated user profile and market information, the server uses a generating AI model to create the optimal asset selection (portfolio) for the user. This AI model utilizes professional investment guidelines and the latest market data to propose a strategy that balances risk and return for the user.

[0497] The information terminal displays the generated asset selection, allowing users to check the investment status at any time. Furthermore, market information is constantly updated, and the server automatically reconfigures the asset selection based on this information. This allows the user's portfolio to flexibly respond to market fluctuations and maintain an optimal state.

[0498] As a concrete example, when a user starts investing, they input their monthly income and expenses, as well as their existing savings, and the server provides a recommended investment strategy based on this information. For instance, the server might receive a prompt from the user such as, "I'm a beginner at investing, so please use AI to suggest an appropriate investment strategy based on my expenses and savings," and then present a suitable asset allocation. In this way, users can easily manage their investments and obtain the optimal strategy to achieve their financial goals.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The device receives basic information from the user, such as life stage, asset information, income, and expenses. The user then inputs details about their financial situation and goals through the interface on the device.

[0502] Step 2:

[0503] The device transmits the information entered by the user to the server using a security protocol. At this stage, the information is encrypted, protecting it from unauthorized access by third parties.

[0504] Step 3:

[0505] The server analyzes the received user information and builds a user profile based on the entered data. Data processing includes evaluating life stage and asset information, and performing calculations to determine investment experience and risk tolerance.

[0506] Step 4:

[0507] The server uses a generated AI model based on the constructed user profile to create the optimal asset selection. The AI ​​model analyzes prompt messages and market information as input and outputs a portfolio suitable for the user.

[0508] Step 5:

[0509] The server sends the generated asset selection to the user's terminal and presents it visually on the terminal. Based on this information, the user can review the proposed investment strategy and make adjustments as needed.

[0510] Step 6:

[0511] The server monitors real-time updated market information and reconfigures asset selections as needed. Using market information as input, it re-evaluates existing portfolios and outputs optimized new portfolios.

[0512] Step 7:

[0513] The device provides users with periodic operational reports. These reports record the status of asset management and the portfolio's response to market fluctuations, allowing users to revise their investment strategies based on this information.

[0514] 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.

[0515] This invention combines an emotion engine with a conventional asset management system to create a system that provides sophisticated investment plans that take into account the user's psychological state. This reduces the influence of user emotions on asset management decision-making and supports more rational investment decisions.

[0516] Users input basic information into the device, such as their household finances, life stage, and desired goals. In addition, the device incorporates an emotion engine that continuously monitors the user's emotional state. This emotion engine utilizes facial recognition and voice analysis technologies to convert the user's emotions into digital data.

[0517] The entered information and emotional data are sent from the terminal to the server. The server uses the received data to build a user profile. The profile reflects information about the user's financial situation, life stage, and emotional state. Based on this profile, the server uses an AI algorithm to generate an optimal portfolio for the user.

[0518] The generated portfolio is based on the output of the emotion engine and is used as a basis for selecting an investment plan that suits the user's current emotions. For example, if the user is feeling stressed, a stable investment with reduced risk may be recommended.

[0519] Furthermore, the device can continuously monitor the user's emotional state and send updates to the server, allowing for dynamic portfolio adjustments. This cycle enables users to manage their assets in a way that suits their emotions and provides them with confidence in their investment plan.

[0520] As a concrete example, if a user feels anxious about unstable market conditions, the emotion engine detects this emotion, and the server automatically reconfigures the portfolio to reduce risk. This proposed investment plan is presented to the user's device, allowing them to continue investing with peace of mind.

[0521] Thus, the system of the present invention provides flexible and personalized asset management that takes user emotions into account, thereby realizing a more appropriate and secure investment environment.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user inputs their household financial situation, life stage, and desired goals into the interface on the device. The device saves this information to local storage and simultaneously activates an emotion engine to analyze the user's facial expressions and voice tone in real time, collecting emotional data.

[0525] Step 2:

[0526] The device encrypts the collected user data and sentiment data and sends it to the server using a secure communication protocol. The server receives the data, decrypts it, and prepares it for analysis.

[0527] Step 3:

[0528] The server builds a user profile based on the received user data. This profile reflects the user's financial situation, life stage, and goals, as well as collected emotional data. Emotional data is used as an indicator of the user's decision-making tendencies.

[0529] Step 4:

[0530] The server uses the constructed user profile and sentiment data to generate an optimal portfolio using an AI algorithm. This portfolio is based on professional investment strategies, but adjusts the risk approach according to the user's emotional state. For example, if anxiety is detected, stable investments with reduced risk are prioritized.

[0531] Step 5:

[0532] The server generates a portfolio and sends it to the user's device, presenting it to them along with recommendation reasons based on sentiment data. The user can review the displayed portfolio and provide additional feedback via the device if needed.

[0533] Step 6:

[0534] The device continuously monitors the user's emotional state and periodically sends emotional data to the server. Based on this data, the server dynamically rebalances the portfolio, constantly maintaining the optimal investment strategy for the user.

[0535] Step 7:

[0536] The server periodically generates reports containing the latest operational status and sends these reports to the terminal. The terminal displays these reports in a user-friendly format, supporting the user in continuing operations with confidence.

[0537] This system manages assets in a way that smoothly responds to users' emotional fluctuations, providing an optimal investment environment at all times.

[0538] (Example 2)

[0539] 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."

[0540] Traditional asset management systems failed to consider the user's psychological state and emotions, leading to the risk of irrational investment decisions based on feelings. Furthermore, investment plans were generally static and could not immediately adapt to changes in the user's life stage or emotional state. As a result, users lacked a sense of security, and optimal asset management was not possible.

[0541] 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.

[0542] In this invention, the server includes means for acquiring basic information provided by the user, means for monitoring the user's emotional state, and means for generating an optimal financial strategy for the user based on the user profile using a generative AI model. This enables flexible and personalized asset management that responds to the user's emotional state.

[0543] A "user" refers to an individual or organization that uses an asset management system to make investment decisions based on their own financial situation and goals.

[0544] "Basic information" refers to data related to asset management, such as financial status, life stage, and goals, provided by the user.

[0545] "Emotional state" refers to data that indicates the user's psychological and emotional state, and this is used to consider the rationality of investment decisions.

[0546] "Monitoring" refers to the process of continuously observing and recording the user's situation and status, and reflecting that information in the system.

[0547] A "server" refers to a computing device that receives data sent from users, analyzes it, performs calculations related to asset management, and generates and provides data based on those calculations.

[0548] "Generative AI models" refer to artificial intelligence technology that generates optimal asset management strategies using user profile data and external data.

[0549] A "user profile" refers to a dataset built based on information and sentiment data provided by the user, used to develop individual asset management strategies.

[0550] "Financial strategy" refers to the specific investment plans and policies developed to effectively manage a user's assets.

[0551] "Personalization" means providing services and products that are optimized according to the individual characteristics and needs of each user.

[0552] This invention takes the form of providing a system that integrates basic information and emotional data to support users' asset management.

[0553] The user inputs basic information such as their household finances, life stage, and goals into the device. The device has a built-in emotion engine that uses facial recognition and voice analysis technologies to convert the user's emotional state into digital data. This data is transmitted from the device to the server using a secure communication protocol.

[0554] The server uses the received basic information and sentiment data to build a user profile. This profile includes the user's asset information, life stage, and sentiment data. Based on this profile, the server uses a generative AI model to generate the optimal financial strategy for the user.

[0555] For example, a generative AI model can consider the user's emotional state and suggest an aggressive investment strategy if their risk tolerance is high, or a conservative strategy if they want to avoid risk. The generated financial strategy is sent to the device and presented as a personalized investment plan tailored to the user's needs.

[0556] As a concrete example, when a user encounters a market situation that causes anxiety, the emotion engine captures that emotion, and the server reconstructs a portfolio that minimizes risk. This allows the user to continue investing with peace of mind. An example of a prompt message would be, "Please generate an investment plan that takes into account the market situation that is causing the user anxiety and proposes a portfolio with reduced risk."

[0557] This system enables asset management that takes user emotions into account, mitigating the impact of irrational investment decisions based on emotions and achieving more efficient asset management.

[0558] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0559] Step 1:

[0560] The user enters basic information such as their financial status, life stage, and goals. The terminal receives this information via a dedicated interface. The entered information is recorded as numerical and text data and prepared for later transmission to the server.

[0561] Step 2:

[0562] The device monitors the user's emotional state using an emotion engine. A facial recognition device captures changes in the user's face in real time, and voice analysis technology analyzes the tone of the user's voice. The obtained emotional data is converted into numerical or categorical formats.

[0563] Step 3:

[0564] The device sends collected basic information and sentiment data to the server. The data is transmitted using a secure communication protocol, and the server receives and stores it. Input includes basic information and sentiment data, while output is a dataset stored in the server's database.

[0565] Step 4:

[0566] The server builds a user profile based on the received data. It references the database and generates a detailed profile by combining it with existing user data. The output is a profile that reflects the user's financial situation, life stage, and emotional state.

[0567] Step 5:

[0568] The server uses a generative AI model to generate an optimal financial strategy based on the user profile. The AI ​​model analyzes the profile data, evaluates multiple investment scenarios, and selects the most rational strategy. The output is a specific investment portfolio recommended to the user.

[0569] Step 6:

[0570] The server sends the generated financial strategy to the terminal. The terminal displays this information in a visually easy-to-understand interface for the user. The terminal also presents adjustment suggestions that reflect the user's sentiment data and prompts changes to the strategy as needed.

[0571] Step 7:

[0572] The device continuously monitors the user's emotional state and sends data back to the server if there are changes in emotions or market conditions. The server receives this data and dynamically adjusts the financial strategy. Based on the feedback from the device, the user can always enjoy the optimal operational plan.

[0573] (Application Example 2)

[0574] 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."

[0575] In asset management, user emotions can influence decision-making, making rational investment decisions difficult. This problem is particularly pronounced during periods of rapid market fluctuations, where user anxiety makes it difficult to formulate appropriate investment plans. Furthermore, current asset management systems cannot take user emotions into account in real time, making it impossible to instantly provide personalized investment plans.

[0576] 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.

[0577] In this invention, the server includes means for inputting information provided by the user, means for recognizing emotions to analyze the user's emotional state, and means for generating an optimal portfolio for the user according to their emotional state. This provides a flexible and personalized asset management system that reflects the user's emotional state and supports rational investment decisions.

[0578] A "user" is an individual or group that utilizes an asset management system and provides the system with their emotional state and asset information.

[0579] "Means of inputting information" refers to interfaces that allow users to input their asset information and life stage, and are provided on various devices.

[0580] "Emotion recognition means" refers to technologies used to analyze a user's emotional state, converting emotions into digital data through voice analysis and facial recognition.

[0581] A "portfolio" refers to a combination of assets recommended to a user, and includes a specific investment plan tailored to their investment strategy.

[0582] A "user profile" is a dataset that integrates user-provided information and emotional data, and serves as the foundation for proposing optimal asset management strategies.

[0583] "Rebalancing" is the process of restructuring a portfolio based on market data and sentiment data, and making adjustments according to risk and objectives.

[0584] An "investment performance report" is a report that is created periodically to inform users about the current status and performance of their asset management.

[0585] To implement this invention, it is necessary to build a system that analyzes the user's emotional state in real time and provides information useful for asset management decision-making. Specifically, the user inputs asset information and life stage information via an input interface on the user's terminal. A terminal equipped with emotion recognition software analyzes the user's emotions using voice and image data. The resulting emotional data is transmitted to a server along with the information provided by the user.

[0586] The server builds a user profile based on the received data and uses artificial intelligence algorithms to generate an optimal investment portfolio in real time. During this process, machine learning frameworks such as TensorFlow are used to adjust the portfolio based on the user's emotional state.

[0587] This system allows users to manage their assets in a way that suits their emotions at any given time, enabling them to make investment decisions with peace of mind. For example, if a user feels anxious about market fluctuations, the emotion engine detects this situation and proposes a portfolio that reduces risk, allowing the user to make stable investments while managing risk.

[0588] As a concrete example, the system is designed to provide reassurance even when a user is feeling anxious after seeing news of rapid market fluctuations. In this case, an example of a prompt message fed into the generation AI model would be: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's anxiety."

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The terminal receives the user's asset information and life stage information through an input interface. The input data is structured as numerical and text data and sent to the server.

[0592] Step 2:

[0593] The device performs speech recognition and image analysis, analyzing the user's emotional state based on their voice and video data. This process utilizes an emotion recognition algorithm, outputting the emotional state as numerical data.

[0594] Step 3:

[0595] The server integrates received asset information, life stage information, and emotional data to build a user profile. This profile serves as foundational data to support future decision-making and is input into AI algorithms.

[0596] Step 4:

[0597] The server uses an AI algorithm to generate an optimal investment portfolio based on the user's profile. This process involves a generation AI model and uses an example prompt: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's concerns."

[0598] Step 5:

[0599] The generated portfolio information is sent to the device and presented to the user. Based on this information, the user can make appropriate investment decisions with confidence.

[0600] Step 6:

[0601] The device continuously monitors the user's investment behavior and market data, tracking any changes. If a change is detected in sentiment data, the portfolio is rebalanced accordingly.

[0602] Step 7:

[0603] The server generates and provides operational status reports to users. These reports are detailed, taking into account historical data analysis and fluctuations in sentiment data, to support users' daily investment activities.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] [Fourth Embodiment]

[0608] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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".

[0621] This invention provides a system that allows users with limited knowledge and experience in asset management to easily and safely begin investing. This system provides an individually optimized portfolio that takes into account the user's life stage and financial situation, and enables continuous monitoring and adjustment.

[0622] Users first input basic information such as their household assets, life stage, and goals using a dedicated terminal. This information is crucial for initial setup and serves as the basis for the system to determine the optimal investment strategy for the user.

[0623] Information entered into the terminal is encrypted and securely transmitted to the server. The server analyzes the received information and automatically builds a user profile that takes into account the user's income, expenses, savings, investment experience, etc. Based on this profile, the server uses AI algorithms, referencing professional investment strategies, to generate a diversified portfolio across various asset classes.

[0624] The generated portfolio is automatically adjusted in real time based on market data. The terminal monitors the operational status and immediately notifies the server of any changes, maintaining the portfolio's balance. Portfolio rebalancing is performed automatically to quickly respond to market changes and user requests for changes.

[0625] As a concrete example, consider a young working couple who begin investing with the goal of purchasing a home and saving for their children's education. By inputting their current income, expenses, savings, and future aspirations into a terminal, the server presents a portfolio that aims for high returns while minimizing risk. The presented plan is primarily composed of stocks and ETFs, with bonds and other assets added as needed. Furthermore, the portfolio is automatically rebalanced every six months or during periods of significant market fluctuations to optimize risk and return.

[0626] The progress and achievements of the operations are provided to users in the form of detailed reports that are generated regularly. This allows users to stay informed about the current status of operations and revise their operational policies as needed.

[0627] Thus, the system of the present invention automates complex asset management on behalf of the user and performs appropriate risk management, thereby achieving optimal asset utilization.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] The user enters information about their household assets, life stage, and desired goals into a form on the device. The device temporarily stores the entered information locally and converts it into an encrypted format.

[0631] Step 2:

[0632] The terminal sends encrypted user information to the server using a secure communication protocol. The server decrypts the received information and prepares to begin analysis.

[0633] Step 3:

[0634] The server builds a user profile based on the received data. It creates a profile to determine the optimal investment strategy for the user, based on factors such as income, expenses, assets, life stage, and investment objectives.

[0635] Step 4:

[0636] The server generates an optimal portfolio using professional investment strategies and AI algorithms. This portfolio is adjusted based on the user's risk tolerance and investment timeframe, and is constructed to ensure diversification across various asset classes.

[0637] Step 5:

[0638] The server generates a portfolio and sends it to the terminal, which visualizes and presents it to the user. The user can review the presented portfolio and provide feedback as needed.

[0639] Step 6:

[0640] The terminal monitors the market and portfolio performance in real time and sends necessary updates to the server. This allows the server to automatically rebalance the portfolio based on market data.

[0641] Step 7:

[0642] The server periodically analyzes its operational status and generates a report that includes status updates and improvement suggestions. The report is sent to the terminal and displayed in a user-friendly format.

[0643] Step 8:

[0644] Users can review their investment strategy based on regularly provided reports and provide feedback to the server via their terminals to make necessary adjustments. This ensures that asset management is always optimized.

[0645] (Example 1)

[0646] 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".

[0647] Currently, there are challenges in enabling individuals to start asset management without requiring specialized knowledge or experience, and to adjust their asset allocation at the appropriate time in response to market changes. Furthermore, there is a need for a system that allows users to continuously monitor the progress and achievements of their asset management and easily revise their investment strategy as needed.

[0648] 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.

[0649] In this invention, the server includes a device for the user to input information, a technology for transmitting the input information to a processing unit, and a technology for analyzing the input information on the processing unit and constructing a user profile. This makes it possible for users to generate an optimal portfolio and make automatic adjustments based on market information in asset management, even without specialized knowledge.

[0650] A "user" refers to an individual who uses the system to manage their assets.

[0651] "Information" refers to data that users input into the system, including asset status, life stage, and goals.

[0652] "Device" refers to a device used by users to input information, and includes dedicated terminals and personal computers.

[0653] A "processing device" refers to a server or computer system that receives and analyzes information sent by a user.

[0654] A "user profile" refers to data that compiles personal attribute information generated based on a user's asset status, income, expenses, savings, and investment experience.

[0655] "Technology" refers to the methods and methodologies used by a system to achieve specific functions, and includes encryption, data analysis, and generative AI models.

[0656] "Asset allocation" refers to the combination of various asset classes recommended for users as investment targets.

[0657] "Market information" refers to the latest data obtained from financial markets, including stock prices, interest rates, and exchange rates.

[0658] "Re-adjustment" refers to the process of re-optimizing a user's portfolio based on market information.

[0659] A "report" is a document containing detailed information about the operational status, and is provided to the user.

[0660] This system automates the complex processes of asset management and provides users with an optimal portfolio. In this invention, users input basic information such as their asset status, life stage, and goals using a dedicated terminal. The terminal securely encrypts this information using AES encryption technology and transmits it to the server via the SSL / TLS protocol.

[0661] The server analyzes the received information using a programming language such as Python. This analysis generates a user profile that takes into account the user's income, expenses, savings, and investment experience. Based on the generated profile, the server uses an AI algorithm (e.g., TensorFlow) to create a portfolio diversified across various asset classes.

[0662] This portfolio is adjusted in real time while referencing the latest financial market data. The server retrieves market information via API and optimizes the portfolio based on that data. For example, during a sharp decline in stock prices, it adjusts asset allocation to minimize risk. The server also continuously monitors the operational status and immediately notifies the user's device, allowing for a rapid response to changes in market conditions and user requests.

[0663] A concrete example is a scenario where a young, dual-income couple begins investing to save for future housing purchases and their children's education. The user inputs their current income, expenses, savings, and desired goals into their device, and the server presents a portfolio focused on stocks and ETFs, aiming for high returns while minimizing risk. The portfolio is automatically rebalanced every six months, or during significant market fluctuations, to optimize the balance between risk and return.

[0664] Examples of prompts for a generative AI model:

[0665] "Please describe in detail the processing flow of a system that automates asset management, and indicate what technologies and methods are used at each step."

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users use a dedicated terminal to input basic information such as their financial situation, life stage, and goals. This input includes data such as income, expenses, savings, and investment experience. The terminal then compiles this information and formats it into a database.

[0669] Step 2:

[0670] The terminal encrypts the input information using AES encryption technology. The input data is passed to an encryption module and converted into a secure format. The terminal then sends the encrypted data to the server using the SSL / TLS protocol. The output is encrypted data that can be received by the server.

[0671] Step 3:

[0672] The server decrypts the received encrypted data and analyzes it using a Python script. The input consists of user asset information and life stage data, which are broken down by the data analysis module and organized into related information. The output is a user profile constructed according to the user's investment objectives.

[0673] Step 4:

[0674] The server generates an optimal portfolio based on the user profile using an AI algorithm. This step leverages machine learning techniques such as TensorFlow, and uses a model incorporating professional investment strategies to compute data. The input is the previously constructed user profile, and the output is an optimal portfolio composed of diverse asset classes.

[0675] Step 5:

[0676] The generated portfolio is continuously adjusted by the processing unit based on real-time market information. The server acquires market data and dynamically optimizes the allocation of each element in the portfolio. The input is market information collected via an external API, and the output is an adjusted portfolio that takes into account the latest market fluctuations.

[0677] Step 6:

[0678] The server monitors the portfolio's operational status and immediately notifies the terminal of any changes. The operational monitoring module tracks portfolio performance in real time and provides alerts to the user. The output is a report of the latest portfolio status, which is provided as feedback to the user.

[0679] (Application Example 1)

[0680] 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".

[0681] When individuals begin investing, a challenge exists in finding the optimal investment strategy due to a lack of specialized knowledge and experience. Traditional methods are also problematic because they require time-consuming asset monitoring and rebalancing, and they cannot respond quickly to market changes. To solve these problems, there is a need for a system that allows users to easily start investing and grow their assets efficiently and safely.

[0682] 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.

[0683] In this invention, the server includes means for building a user profile, means for generating asset selections based on expert investment guidelines, means for reconfiguring asset selections based on market information, and means for proposing asset management strategies using a generated AI model. This makes it possible for users to easily start asset management and efficiently manage the progress of their investments while maintaining an optimal portfolio in response to market changes.

[0684] A "user" is an individual who uses the system to manage their own assets, and is the entity that inputs the necessary information and receives suggestions for asset selection.

[0685] An "information processing device" is a computing device used to analyze information entered by a user and generate asset selection and investment strategies based on that information.

[0686] A "user profile" is an individualized information system built based on a user's life stage, asset information, investment experience, etc., and forms the basis for an optimal investment strategy.

[0687] "Asset selection" refers to the combination of various asset classes chosen to construct the optimal portfolio for the user, and investment guidelines are used to generate it.

[0688] A "generative AI model" is a model that uses AI technology to propose asset management strategies for users, analyzing user information and market data to provide an optimal portfolio.

[0689] "Market information" refers to real-time updated financial market data necessary for restructuring asset selections and adjusting investment strategies.

[0690] "Restructuring asset selection" is a process of reviewing a user's asset selection based on market information and optimizing the balance between risk and return.

[0691] In the system of this invention, the user first inputs their life stage and asset information using an information terminal. This information is securely transmitted to an information processing device (server) via a network. The server then constructs a user profile based on the received user information. The user profile is created in detail, taking into account the user's life stage, investment experience, asset information, and other factors.

[0692] Based on the generated user profile and market information, the server uses a generating AI model to create the optimal asset selection (portfolio) for the user. This AI model utilizes professional investment guidelines and the latest market data to propose a strategy that balances risk and return for the user.

[0693] The information terminal displays the generated asset selection, allowing users to check the investment status at any time. Furthermore, market information is constantly updated, and the server automatically reconfigures the asset selection based on this information. This allows the user's portfolio to flexibly respond to market fluctuations and maintain an optimal state.

[0694] As a concrete example, when a user starts investing, they input their monthly income and expenses, as well as their existing savings, and the server provides a recommended investment strategy based on this information. For instance, the server might receive a prompt from the user such as, "I'm a beginner at investing, so please use AI to suggest an appropriate investment strategy based on my expenses and savings," and then present a suitable asset allocation. In this way, users can easily manage their investments and obtain the optimal strategy to achieve their financial goals.

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The device receives basic information from the user, such as life stage, asset information, income, and expenses. The user then inputs details about their financial situation and goals through the interface on the device.

[0698] Step 2:

[0699] The device transmits the information entered by the user to the server using a security protocol. At this stage, the information is encrypted, protecting it from unauthorized access by third parties.

[0700] Step 3:

[0701] The server analyzes the received user information and builds a user profile based on the entered data. Data processing includes evaluating life stage and asset information, and performing calculations to determine investment experience and risk tolerance.

[0702] Step 4:

[0703] The server uses a generated AI model based on the constructed user profile to create the optimal asset selection. The AI ​​model analyzes prompt messages and market information as input and outputs a portfolio suitable for the user.

[0704] Step 5:

[0705] The server sends the generated asset selection to the user's terminal and presents it visually on the terminal. Based on this information, the user can review the proposed investment strategy and make adjustments as needed.

[0706] Step 6:

[0707] The server monitors real-time updated market information and reconfigures asset selections as needed. Using market information as input, it re-evaluates existing portfolios and outputs optimized new portfolios.

[0708] Step 7:

[0709] The device provides users with periodic operational reports. These reports record the status of asset management and the portfolio's response to market fluctuations, allowing users to revise their investment strategies based on this information.

[0710] 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.

[0711] This invention combines an emotion engine with a conventional asset management system to create a system that provides sophisticated investment plans that take into account the user's psychological state. This reduces the influence of user emotions on asset management decision-making and supports more rational investment decisions.

[0712] Users input basic information into the device, such as their household finances, life stage, and desired goals. In addition, the device incorporates an emotion engine that continuously monitors the user's emotional state. This emotion engine utilizes facial recognition and voice analysis technologies to convert the user's emotions into digital data.

[0713] The entered information and emotional data are sent from the terminal to the server. The server uses the received data to build a user profile. The profile reflects information about the user's financial situation, life stage, and emotional state. Based on this profile, the server uses an AI algorithm to generate an optimal portfolio for the user.

[0714] The generated portfolio is based on the output of the emotion engine and is used as a basis for selecting an investment plan that suits the user's current emotions. For example, if the user is feeling stressed, a stable investment with reduced risk may be recommended.

[0715] Furthermore, the device can continuously monitor the user's emotional state and send updates to the server, allowing for dynamic portfolio adjustments. This cycle enables users to manage their assets in a way that suits their emotions and provides them with confidence in their investment plan.

[0716] As a concrete example, if a user feels anxious about unstable market conditions, the emotion engine detects this emotion, and the server automatically reconfigures the portfolio to reduce risk. This proposed investment plan is presented to the user's device, allowing them to continue investing with peace of mind.

[0717] Thus, the system of the present invention provides flexible and personalized asset management that takes user emotions into account, thereby realizing a more appropriate and secure investment environment.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The user inputs their household financial situation, life stage, and desired goals into the interface on the device. The device saves this information to local storage and simultaneously activates an emotion engine to analyze the user's facial expressions and voice tone in real time, collecting emotional data.

[0721] Step 2:

[0722] The device encrypts the collected user data and sentiment data and sends it to the server using a secure communication protocol. The server receives the data, decrypts it, and prepares it for analysis.

[0723] Step 3:

[0724] The server builds a user profile based on the received user data. This profile reflects the user's financial situation, life stage, and goals, as well as collected emotional data. Emotional data is used as an indicator of the user's decision-making tendencies.

[0725] Step 4:

[0726] The server uses the constructed user profile and sentiment data to generate an optimal portfolio using an AI algorithm. This portfolio is based on professional investment strategies, but adjusts the risk approach according to the user's emotional state. For example, if anxiety is detected, stable investments with reduced risk are prioritized.

[0727] Step 5:

[0728] The server generates a portfolio and sends it to the user's device, presenting it to them along with recommendation reasons based on sentiment data. The user can review the displayed portfolio and provide additional feedback via the device if needed.

[0729] Step 6:

[0730] The device continuously monitors the user's emotional state and periodically sends emotional data to the server. Based on this data, the server dynamically rebalances the portfolio, constantly maintaining the optimal investment strategy for the user.

[0731] Step 7:

[0732] The server periodically generates reports containing the latest operational status and sends these reports to the terminal. The terminal displays these reports in a user-friendly format, supporting the user in continuing operations with confidence.

[0733] This system manages assets in a way that smoothly responds to users' emotional fluctuations, providing an optimal investment environment at all times.

[0734] (Example 2)

[0735] 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".

[0736] Traditional asset management systems failed to consider the user's psychological state and emotions, leading to the risk of irrational investment decisions based on feelings. Furthermore, investment plans were generally static and could not immediately adapt to changes in the user's life stage or emotional state. As a result, users lacked a sense of security, and optimal asset management was not possible.

[0737] 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.

[0738] In this invention, the server includes means for acquiring basic information provided by the user, means for monitoring the user's emotional state, and means for generating an optimal financial strategy for the user based on the user profile using a generative AI model. This enables flexible and personalized asset management that responds to the user's emotional state.

[0739] A "user" refers to an individual or organization that uses an asset management system to make investment decisions based on their own financial situation and goals.

[0740] "Basic information" refers to data related to asset management, such as financial status, life stage, and goals, provided by the user.

[0741] "Emotional state" refers to data that indicates the user's psychological and emotional state, and this is used to consider the rationality of investment decisions.

[0742] "Monitoring" refers to the process of continuously observing and recording the user's situation and status, and reflecting that information in the system.

[0743] A "server" refers to a computing device that receives data sent from users, analyzes it, performs calculations related to asset management, and generates and provides data based on those calculations.

[0744] "Generative AI models" refer to artificial intelligence technology that generates optimal asset management strategies using user profile data and external data.

[0745] A "user profile" refers to a dataset built based on information and sentiment data provided by the user, used to develop individual asset management strategies.

[0746] "Financial strategy" refers to the specific investment plans and policies developed to effectively manage a user's assets.

[0747] "Personalization" means providing services and products that are optimized according to the individual characteristics and needs of each user.

[0748] This invention takes the form of providing a system that integrates basic information and emotional data to support users' asset management.

[0749] The user inputs basic information such as their household finances, life stage, and goals into the device. The device has a built-in emotion engine that uses facial recognition and voice analysis technologies to convert the user's emotional state into digital data. This data is transmitted from the device to the server using a secure communication protocol.

[0750] The server uses the received basic information and sentiment data to build a user profile. This profile includes the user's asset information, life stage, and sentiment data. Based on this profile, the server uses a generative AI model to generate the optimal financial strategy for the user.

[0751] For example, a generative AI model can consider the user's emotional state and suggest an aggressive investment strategy if their risk tolerance is high, or a conservative strategy if they want to avoid risk. The generated financial strategy is sent to the device and presented as a personalized investment plan tailored to the user's needs.

[0752] As a concrete example, when a user encounters a market situation that causes anxiety, the emotion engine captures that emotion, and the server reconstructs a portfolio that minimizes risk. This allows the user to continue investing with peace of mind. An example of a prompt message would be, "Please generate an investment plan that takes into account the market situation that is causing the user anxiety and proposes a portfolio with reduced risk."

[0753] This system enables asset management that takes user emotions into account, mitigating the impact of irrational investment decisions based on emotions and achieving more efficient asset management.

[0754] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0755] Step 1:

[0756] The user enters basic information such as their financial status, life stage, and goals. The terminal receives this information via a dedicated interface. The entered information is recorded as numerical and text data and prepared for later transmission to the server.

[0757] Step 2:

[0758] The device monitors the user's emotional state using an emotion engine. A facial recognition device captures changes in the user's face in real time, and voice analysis technology analyzes the tone of the user's voice. The obtained emotional data is converted into numerical or categorical formats.

[0759] Step 3:

[0760] The device sends collected basic information and sentiment data to the server. The data is transmitted using a secure communication protocol, and the server receives and stores it. Input includes basic information and sentiment data, while output is a dataset stored in the server's database.

[0761] Step 4:

[0762] The server builds a user profile based on the received data. It references the database and generates a detailed profile by combining it with existing user data. The output is a profile that reflects the user's financial situation, life stage, and emotional state.

[0763] Step 5:

[0764] The server uses a generative AI model to generate an optimal financial strategy based on the user profile. The AI ​​model analyzes the profile data, evaluates multiple investment scenarios, and selects the most rational strategy. The output is a specific investment portfolio recommended to the user.

[0765] Step 6:

[0766] The server sends the generated financial strategy to the terminal. The terminal displays this information in a visually easy-to-understand interface for the user. The terminal also presents adjustment suggestions that reflect the user's sentiment data and prompts changes to the strategy as needed.

[0767] Step 7:

[0768] The device continuously monitors the user's emotional state and sends data back to the server if there are changes in emotions or market conditions. The server receives this data and dynamically adjusts the financial strategy. Based on the feedback from the device, the user can always enjoy the optimal operational plan.

[0769] (Application Example 2)

[0770] 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".

[0771] In asset management, user emotions can influence decision-making, making rational investment decisions difficult. This problem is particularly pronounced during periods of rapid market fluctuations, where user anxiety makes it difficult to formulate appropriate investment plans. Furthermore, current asset management systems cannot take user emotions into account in real time, making it impossible to instantly provide personalized investment plans.

[0772] 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.

[0773] In this invention, the server includes means for inputting information provided by the user, means for recognizing emotions to analyze the user's emotional state, and means for generating an optimal portfolio for the user according to their emotional state. This provides a flexible and personalized asset management system that reflects the user's emotional state and supports rational investment decisions.

[0774] A "user" is an individual or group that utilizes an asset management system and provides the system with their emotional state and asset information.

[0775] "Means of inputting information" refers to interfaces that allow users to input their asset information and life stage, and are provided on various devices.

[0776] "Emotion recognition means" refers to technologies used to analyze a user's emotional state, converting emotions into digital data through voice analysis and facial recognition.

[0777] A "portfolio" refers to a combination of assets recommended to a user, and includes a specific investment plan tailored to their investment strategy.

[0778] A "user profile" is a dataset that integrates user-provided information and emotional data, and serves as the foundation for proposing optimal asset management strategies.

[0779] "Rebalancing" is the process of restructuring a portfolio based on market data and sentiment data, and making adjustments according to risk and objectives.

[0780] An "investment performance report" is a report that is created periodically to inform users about the current status and performance of their asset management.

[0781] To implement this invention, it is necessary to build a system that analyzes the user's emotional state in real time and provides information useful for asset management decision-making. Specifically, the user inputs asset information and life stage information via an input interface on the user's terminal. A terminal equipped with emotion recognition software analyzes the user's emotions using voice and image data. The resulting emotional data is transmitted to a server along with the information provided by the user.

[0782] The server builds a user profile based on the received data and uses artificial intelligence algorithms to generate an optimal investment portfolio in real time. During this process, machine learning frameworks such as TensorFlow are used to adjust the portfolio based on the user's emotional state.

[0783] This system allows users to manage their assets in a way that suits their emotions at any given time, enabling them to make investment decisions with peace of mind. For example, if a user feels anxious about market fluctuations, the emotion engine detects this situation and proposes a portfolio that reduces risk, allowing the user to make stable investments while managing risk.

[0784] As a concrete example, the system is designed to provide reassurance even when a user is feeling anxious after seeing news of rapid market fluctuations. In this case, an example of a prompt message fed into the generation AI model would be: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's anxiety."

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The terminal receives the user's asset information and life stage information through an input interface. The input data is structured as numerical and text data and sent to the server.

[0788] Step 2:

[0789] The device performs speech recognition and image analysis, analyzing the user's emotional state based on their voice and video data. This process utilizes an emotion recognition algorithm, outputting the emotional state as numerical data.

[0790] Step 3:

[0791] The server integrates received asset information, life stage information, and emotional data to build a user profile. This profile serves as foundational data to support future decision-making and is input into AI algorithms.

[0792] Step 4:

[0793] The server uses an AI algorithm to generate an optimal investment portfolio based on the user's profile. This process involves a generation AI model and uses an example prompt: "Given the current market conditions, please generate investment proposals that minimize risk, taking into account the user's concerns."

[0794] Step 5:

[0795] The generated portfolio information is sent to the device and presented to the user. Based on this information, the user can make appropriate investment decisions with confidence.

[0796] Step 6:

[0797] The device continuously monitors the user's investment behavior and market data, tracking any changes. If a change is detected in sentiment data, the portfolio is rebalanced accordingly.

[0798] Step 7:

[0799] The server generates and provides operational status reports to users. These reports are detailed, taking into account historical data analysis and fluctuations in sentiment data, to support users' daily investment activities.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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."

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] The following is further disclosed regarding the embodiments described above.

[0822] (Claim 1)

[0823] A means of inputting information provided by the user,

[0824] A means of sending the input information to the server,

[0825] A means of analyzing input information on a server and building a user profile,

[0826] A means of generating an optimal portfolio for the user based on professional investment strategies,

[0827] A means of presenting the generated portfolio to the user's device,

[0828] A means of monitoring the operational status based on the portfolio presented on the terminal,

[0829] A means of rebalancing a portfolio based on market data,

[0830] A means of generating and providing operational status reports to users,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which creates a user profile by taking into account the user's life stage and asset information.

[0834] (Claim 3)

[0835] The system according to claim 1, which automatically adjusts the portfolio based on periodic updates of market information.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A device for users to input information,

[0839] A technology for transmitting input information to a processing unit,

[0840] A technology that analyzes input information on a processing unit and builds a user profile,

[0841] Technology that generates the optimal asset allocation for users based on expert investment strategies,

[0842] A technology that presents the generated asset configuration to the user's device,

[0843] A technology that monitors the operational status based on the asset configuration presented to the equipment,

[0844] A technology that readjusts asset allocation based on market information,

[0845] Technology to generate and provide operational status reports to users,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, which creates a user profile by taking into account the user's stage of life and asset information.

[0849] (Claim 3)

[0850] The system according to claim 1, which automatically adjusts the asset allocation based on periodic updates of market information.

[0851] "Application Example 1"

[0852] (Claim 1)

[0853] A device for inputting information provided by the user,

[0854] A device that transmits input information to an information processing device,

[0855] A device that analyzes input information on an information processing device and constructs a user profile,

[0856] A device that generates asset selections suitable for the user based on expert investment guidelines,

[0857] A device that presents the generated asset selection to the user's information terminal,

[0858] A device that monitors the operational status based on asset selections presented on an information terminal,

[0859] A device that reconfigures asset selection based on market information,

[0860] A device that generates and provides operational status reports to users,

[0861] A device that uses a generative AI model to propose asset management strategies based on user metrics,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, which creates a user profile taking into account the user's life stage and asset information, and proposes an investment strategy to the user using a generated AI model.

[0865] (Claim 3)

[0866] The system according to claim 1, which automatically adjusts asset selection based on periodic updates of market information and prompt statements provided by a generated AI model.

[0867] "Example 2 of combining an emotion engine"

[0868] (Claim 1)

[0869] Means of obtaining basic information provided by the user,

[0870] A means of monitoring the emotional state of users,

[0871] A means of sending the entered basic information and emotional data to the server,

[0872] A means of constructing a user profile by analyzing basic information and emotional data entered on the server,

[0873] A means of generating an optimal financial strategy for a user based on their user profile using a generative AI model,

[0874] A means of presenting the generated financial strategy to the user's visual device,

[0875] A means of adjusting financial strategies generated based on user sentiment data,

[0876] Means to increase user psychological satisfaction,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, which creates a user profile by taking into account emotional data in addition to life stage and asset information.

[0880] (Claim 3)

[0881] The system according to claim 1, which automatically adjusts financial strategies based on periodic updates of sentiment data.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means of inputting information provided by the user,

[0885] A means of sending the input information to the server,

[0886] A means of analyzing input information on a server and building a user profile,

[0887] A means of recognizing emotions to analyze the emotional state of a user,

[0888] A means of integrating input emotional data into a user profile,

[0889] A means of generating the optimal portfolio for the user according to their emotional state,

[0890] A means of presenting the generated portfolio to the user's device,

[0891] A means of monitoring the operational status based on the portfolio presented on the terminal,

[0892] A means of rebalancing a portfolio based on market data and sentiment data,

[0893] A means of generating and providing operational status reports to users,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which, in building a user profile, creates a profile by taking into account the user's emotional data in addition to life stage and asset information.

[0897] (Claim 3)

[0898] The system according to claim 1, which automatically adjusts a portfolio based on regular updates of market information and changes in emotional state. [Explanation of symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting information provided by the user, A means of sending the input information to the server, A means of analyzing input information on a server and building a user profile, A means of generating an optimal portfolio for the user based on professional investment strategies, A means of presenting the generated portfolio to the user's device, A means of monitoring the operational status based on the portfolio presented on the terminal, A means of rebalancing a portfolio based on market data, A means of generating and providing operational status reports to users, A system that includes this.

2. The system according to claim 1, which creates a user profile by taking into account the user's life stage and asset information.

3. The system according to claim 1, which automatically adjusts the portfolio based on periodic updates of market information.

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