system

Abstract

We provide the system. [Solution] A means for receiving user attribute information and calculating an optimized asset allocation based on it, A means for acquiring market data in real time from an external data source and analyzing said market data, A means for automatically adjusting the user's asset group based on the analysis results, A means of learning from past investment history and providing investment strategies suitable for the user, A means of providing learning content to support users in improving their financial knowledge, A means of visualizing market simulations using augmented reality technology, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction 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] Novice investors and people with limited investment knowledge have difficulty making efficient asset allocations and investment decisions suitable for market conditions, and it is also difficult to quickly respond to market fluctuations. Furthermore, finding an appropriate investment strategy based on past investment history is a complex task that requires a lot of time and effort to do on one's own. Therefore, there is a need for a system that provides effective and efficient investment support while reducing the psychological burden associated with investment.

Means for Solving the Problems

[0005] This invention provides a system that proposes asset groups that match individual risk tolerance and investment objectives by incorporating means for calculating optimized asset allocation based on user attribute information. It also provides means for acquiring and analyzing market data in real time to support investment decisions in accordance with the latest market conditions. Furthermore, it supports user investment behavior by learning from past investment history and providing optimized investment strategies. This enables efficient investment activities that allow even novice investors to respond quickly to market fluctuations.

[0006] A "user" refers to an individual or group that uses the system to conduct investment activities.

[0007] "Attribute information" refers to personal data related to investment, such as a user's risk tolerance, investment goals, and initial asset amount.

[0008] "Asset allocation" refers to the process of adjusting the proportions of different asset types in an investment portfolio.

[0009] "Market data" refers to market-related information that influences investment decisions, such as stock prices, economic indicators, and news.

[0010] "Analysis" refers to the process of identifying and interpreting patterns and trends based on collected data.

[0011] An "asset group" refers to the entire portfolio, which shows how a user's assets are categorized and arranged.

[0012] "Investment history" refers to a record of a user's past investment actions and results.

[0013] An "investment strategy" refers to a plan or method for optimizing asset allocation according to market trends and the user's circumstances.

[0014] "System" refers to a combination of hardware and software for executing a series of processes provided by the present invention.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. 4] [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system according to the present invention automatically manages and optimizes asset groups using AI technology, targeting novice investors and those with limited knowledge. This system is realized through the interaction of a server, a terminal, and a user.

[0037] First, the user enters their personal information through their device, such as their risk tolerance, investment goals, and current investment amount. This information is collected and recorded by the server.

[0038] The server receives attribute information provided by the user and uses AI algorithms to calculate the optimal asset allocation. Based on this calculation, it sends a portfolio suggestion to the user's terminal.

[0039] Furthermore, the server collaborates with external market data sources to collect market-related information in real time. The acquired market data is processed by an AI analysis engine to detect market trends and unusual price fluctuations, and to generate information necessary for investment decisions.

[0040] Based on the analysis results, the server automatically performs rebalancing according to market conditions. In this rebalancing process, the user's asset portfolio is optimized to match market conditions, and the configuration is adjusted as needed. This is to maintain overall balance when the value of a particular asset class increases or decreases.

[0041] Furthermore, the server learns the user's past investment history and analyzes patterns and trends using AI technology. Based on this, it proposes personalized investment strategies to the user. The proposals are notified to the user's device, supporting the user's daily investment decisions.

[0042] For example, if a user is willing to accept moderate risk and seeks long-term profits, the server will create a portfolio that takes this into account. A proposed portfolio with a balanced mix of stocks, bonds, and commodities will be presented to the user's terminal. Similarly, the server system will operate to immediately adjust the user's asset allocation in the event of a clear economic fluctuation.

[0043] In this way, the system of the present invention provides technical support to enable novice investors to engage in investment activities with confidence. This support improves investment efficiency, and users can strengthen their risk management without wasting time.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user inputs attribute information such as their risk tolerance, investment goals, and initial asset amount through their device. The device then transmits this information to the server.

[0047] Step 2:

[0048] The server records attribute information received from the user in a database. Based on the recorded information, an AI algorithm calculates the optimal asset allocation and generates a portfolio suggestion suitable for the user.

[0049] Step 3:

[0050] The server sends the proposed portfolio to the user's terminal. The user reviews the received portfolio and makes adjustments as needed, or accepts the proposal.

[0051] Step 4:

[0052] The server continuously acquires market data in real time through external market data APIs. This data includes stock prices, economic indicators, news, and more.

[0053] Step 5:

[0054] The server processes acquired market data using an AI analysis engine to detect price fluctuations and trends. Based on the analysis results, the risk of rapid market fluctuations is assessed.

[0055] Step 6:

[0056] The server automatically rebalances the user's portfolio based on market analysis results. Specifically, it performs a process of changing the proportion of certain assets to optimize the overall balance.

[0057] Step 7:

[0058] The server analyzes the user's past investment history and learns patterns and trends. Using AI technology, it generates an investment strategy tailored to the user and notifies the user of the results on their device.

[0059] Step 8:

[0060] The server periodically evaluates the performance of the user's asset portfolio and generates a detailed report. This report is sent to the user's terminal as graphs and numerical data, serving as material for the user to review their investment performance.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] A challenge exists in that it is difficult for novice investors and users with limited knowledge to effectively obtain the information necessary to make appropriate investment decisions. Furthermore, responding quickly to market fluctuations and optimizing asset allocation requires effort and expertise, posing a barrier for many users. As a result, asset management efficiency may decrease, and risk management may become inadequate.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for receiving user characteristic information and calculating a rationalized asset allocation based on said characteristic information, means for instantly acquiring market information from an external information source and interpreting said market information, and means for automatically changing the user's asset composition based on the interpretation results. This enables diverse users to effectively manage their assets according to their risk tolerance and investment objectives, and to make investment decisions in real time in response to market changes.

[0066] "Characteristic information" refers to information about the user's attributes and characteristics, such as their risk tolerance, investment goals, and investment amount.

[0067] Asset allocation is the process of determining the proportion of assets to be divided among different investment targets, and it is an important method for managing investment risk and return.

[0068] "External information sources" refer to various data providers and platforms that offer market information, economic data, and other similar information.

[0069] "Market information" is a general term for information that influences investment decisions in financial markets, such as price trends, trading volume, and economic indicators.

[0070] "Asset allocation" refers to the overall distribution of the types and quantities of assets held by a user, and represents the contents of a portfolio that reflects their investment strategy.

[0071] "Rationalization" refers to eliminating waste to increase efficiency and making adjustments and improvements to achieve optimal results.

[0072] "Artificial intelligence technology" is a technology that enables machines to imitate human intelligent behavior, supporting processes such as data analysis, prediction, and decision-making.

[0073] This invention constructs a system that improves investment efficiency and enhances risk management through the interaction between a server, a terminal, and a user.

[0074] First, the user inputs characteristic information such as their risk tolerance and investment goals using the terminal's interface. This terminal is typically implemented as a web application or mobile application. This information is then transmitted to the server via network communication.

[0075] Next, the server calculates asset allocation based on the received characteristic information. Specifically, machine learning libraries such as TENSORFLOW® and PyTorch are used for this calculation, and backpropagation and deep learning models are applied as AI algorithms. This method builds an investment strategy optimized for each user.

[0076] Furthermore, the server collects market information from external market sources. This is done through APIs of Yahoo Finance and other economic data providers. The acquired market information is analyzed using analytical engines such as Scikit-learn to detect trends and anomalies.

[0077] Based on this analysis, the server automatically rebalances the user's asset allocation. Specific asset adjustments, such as shifting from stocks to bonds, are made as needed. The results of this rebalancing are notified to the user's device, allowing them to easily track their latest investment status.

[0078] Finally, the server learns from past investment history and provides users with personalized investment strategies. This process utilizes Keras or similar deep learning frameworks to analyze behavioral patterns from investment history and generate optimal investment recommendations for the user.

[0079] For example, if a user states, "I want to pursue long-term profits while accepting moderate risk," the server interprets this as a prompt and generates a portfolio that matches that need. In more specific terms, this might be expressed as, "Please suggest an investment strategy that accepts moderate risk while pursuing long-term profits."

[0080] Thus, the system of the present invention provides users with a strategic yet user-friendly investment environment, supporting their daily investment decisions.

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

[0082] Step 1:

[0083] Users input characteristic information such as their risk tolerance, investment goals, and current investment amount. This input is done through the terminal interface and recorded on the terminal as text or numerical data. The entered data is sent to the server in JSON or XML format.

[0084] Step 2:

[0085] The server analyzes the characteristic information received from the user and stores it in a database. Based on this stored information, a generative AI model is applied to calculate an optimized asset allocation. Specifically, the AI ​​algorithm uses TensorFlow and employs backpropagation techniques for the calculation. The output is the recommended portfolio composition.

[0086] Step 3:

[0087] The server accesses external sources to obtain market information. Market data collected via API calls is immediately processed by an AI analysis engine. Scikit-learn is used to analyze the data and detect market trends and anomalies. The output is the analyzed market information and its evaluation results.

[0088] Step 4:

[0089] The server automatically rebalances the asset allocation based on analyzed market information and user characteristics. The ratios of stocks and bonds are adjusted according to market conditions. The rebalanced asset allocation is sent to the user's terminal as a re-evaluated portfolio.

[0090] Step 5:

[0091] The server extracts past investment history from a database and analyzes patterns using machine learning techniques. It learns from past data using tools like Keras and generates an optimal investment strategy for the user. The generated strategy is output as a customized investment suggestion and notified to the user's device.

[0092] Step 6:

[0093] The terminal visually displays the latest portfolio information, rebalancing results, and personalized investment strategies transmitted from the server. Users receive support in making daily investment decisions based on this information. Specifically, the terminal visualizes data through a GUI, making it easy for users to understand and operate.

[0094] (Application Example 1)

[0095] 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."

[0096] For novice investors and those with limited knowledge, efficiently learning about asset management and investment strategies in financial markets is a challenging task. Furthermore, there is a need to provide users with appropriate educational content and support them in deepening their understanding of investing while responding to real-time market fluctuations. It is essential to bridge this complexity of investing and the knowledge gap, and to provide an environment where investors can confidently engage in investment activities.

[0097] 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.

[0098] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for learning past investment history and providing an investment strategy suitable for the user; means for providing learning content to support the improvement of the user's financial knowledge; and means for visualizing market simulations using augmented reality technology. This makes it possible for even novice investors to safely deepen their investment knowledge and engage in strategic investment activities while adapting to real-time market conditions.

[0099] "User attribute information" refers to personal profile data provided by investors, such as their risk tolerance, investment goals, and current investment amount.

[0100] "Optimized asset allocation" refers to the composition of an investment portfolio that balances risk and return, calculated by an AI algorithm based on the user's attribute information.

[0101] "Market data" refers to the latest information on the prices of assets traded in financial markets, such as stock prices, bond prices, and exchange rates.

[0102] "Means of analysis" refers to a function that uses an AI analysis engine to analyze collected market data and identify trends and anomalies in price fluctuations.

[0103] "Means for automatically adjusting asset groups" refers to a function that, based on analysis results, buys or sells assets to adapt the portfolio to the latest market conditions.

[0104] "Means of providing investment strategies" refers to a function that learns from past investment history and presents investment policies and action guidelines customized for each individual user.

[0105] "Means of providing learning content" refers to the function of supplying educational resources and simulation tools to improve users' financial knowledge.

[0106] "Methods for visualizing market simulations using augmented reality technology" refers to a function that uses AR technology to realistically reproduce market movements and investment results in a virtual environment, providing users with a visual experience.

[0107] This invention is a system designed to enable novice investors and users with limited knowledge to efficiently manage their assets and deepen their financial understanding. The system operates through the interconnectedness of the user's terminal, server, and external data sources.

[0108] On the server, attribute information is received through the user's terminal. This attribute information includes risk tolerance, investment goals, and current investment amount. Based on the received information, the server uses an AI algorithm and the Python library TensorFlow to calculate an optimized asset allocation.

[0109] The server retrieves market data in real time from sources such as the Yahoo Finance API and processes this data with an AI analysis engine. This allows the system to analyze market risks and opportunities and automatically adjust the user's assets based on the analysis results. By learning from the user's past investment history, a personalized investment strategy is provided and notified to the user's device.

[0110] Furthermore, the server delivers learning content to improve users' financial literacy. This content is customized using a generative AI model. In addition, AR technology using Unity is used to perform market simulations through augmented reality, providing users with an intuitive visual experience.

[0111] As a concrete example, when market fluctuations are anticipated, a scenario can be provided in which users learn risk management through an augmented reality system. For instance, a prompt such as "Consider the user's investment skill level and propose a simulation that teaches the basics of risk management" can be used to generate appropriate content based on the user's understanding.

[0112] This system allows users to access a wealth of information and visual insights when making real-time investment decisions, enabling them to invest with confidence.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user's device receives attribute information such as risk tolerance, investment goals, and current investment amount. This input information is sent to the server via a secure protocol. The output at this stage is a dataset of attribute information.

[0116] Step 2:

[0117] The server inputs the received attribute information into an AI algorithm to calculate an optimized asset allocation for the user. This calculation uses the AI ​​framework TensorFlow and outputs a portfolio with an optimized risk-return balance.

[0118] Step 3:

[0119] The server acquires market data in real time from external data sources. This input market data includes stock prices, exchange rates, and commodity prices. This data is processed by an analysis engine, which then generates market trends and risk assessments as output.

[0120] Step 4:

[0121] The server automatically adjusts the user's assets based on analyzed market data and user attribute information. Specifically, it uses an AI algorithm to identify assets that should be bought or sold and outputs an optimized portfolio configuration.

[0122] Step 5:

[0123] The server learns from the user's past investment history and generates personalized investment strategies. It analyzes the user's past behavioral patterns and outputs advice that will be useful for future investment actions. This information is notified to the user's device.

[0124] Step 6:

[0125] The server provides users with customized learning content. Using a generative AI model, it generates prompts based on analysis results and outputs appropriate learning materials.

[0126] Step 7:

[0127] The user's device displays a market simulation using AR technology powered by Unity. Through the AR device, the user visually experiences market trends and investment results, gaining a practical learning experience as output.

[0128] 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.

[0129] The system according to the present invention provides investment optimization functions and investment advice that takes user emotions into consideration. The system exchanges data between the server, terminal, and user, and utilizes AI technology and an emotion engine.

[0130] In running the system, the user first inputs their personal information using a terminal. This includes risk tolerance, investment goals, and past investment history. This information is then transmitted to the server via the terminal.

[0131] The server uses AI algorithms to calculate the optimal asset allocation based on the received user attribute information and market data. Market data is acquired in real time using an external API and processed by the analysis engine. Furthermore, the server automatically rebalances the user's asset portfolio based on the analysis results.

[0132] This system incorporates an emotion engine that can recognize the user's emotional state. It learns the user's past emotional data and behavioral patterns, and the AI ​​predicts the user's emotional tendencies in the current investment situation. The device understands the user's current emotional state based on user input and data from biosensors. For example, if the user is stressed, the emotion engine will detect this and generate an alert to help adjust the investment strategy.

[0133] As a concrete example, suppose a user faces rapid market fluctuations and begins to feel anxious. In this situation, the emotion engine analyzes the user's unstable emotions, and the server recommends an investment strategy based on that analysis. For example, by suggesting a shift of funds to more stable assets, the user's mental burden can be reduced.

[0134] This system aims to improve investment performance by providing more personalized support for users' investment behavior and reducing the risks of emotional decisions. It is expected to function as a reliable investment support tool for both novice and experienced investors.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user inputs attribute information such as their risk tolerance, investment goals, and past investment history through their device. The device then sends this information to the server.

[0138] Step 2:

[0139] The server records attribute information received from the user in a database and uses an AI algorithm to calculate the optimal asset allocation. At this stage, real-time market data is obtained using an external API and data analysis is performed.

[0140] Step 3:

[0141] Based on the market data analyzed by the server, the user's asset portfolio is automatically adjusted. The system verifies that the asset allocation is optimized and performs rebalancing as needed.

[0142] Step 4:

[0143] The system collects data about the user's current emotional state from their device. This may include user input and data from biosensors.

[0144] Step 5:

[0145] The server uses an emotion engine to analyze the user's past emotional data and current emotional state. It learns emotional patterns to evaluate the impact of emotional changes on investment decisions.

[0146] Step 6:

[0147] The emotion engine detects the user's emotional state, and the server adjusts its investment strategy based on that information. For example, if stress or anxiety levels rise, it will recommend more stable assets.

[0148] Step 7:

[0149] The server periodically evaluates the performance of the user's asset portfolio and generates reports that reflect the latest market conditions and sentiment analysis results. By sending these reports to the user's terminal, the user can check their investment status and make investment decisions with confidence.

[0150] (Example 2)

[0151] 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".

[0152] In investing, it is necessary to provide investment strategies that take into account not only user attribute information and market data, but also the user's emotional state. However, conventional systems have not adequately provided mechanisms for delivering personalized investment advice that takes emotions into account. As a result, users were at risk of making irrational investment decisions due to emotions such as stress and anxiety.

[0153] 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.

[0154] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing the market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for collecting biometric data including the user's emotional state and analyzing the user's emotional tendencies using the data; and means for providing an investment strategy suitable for the user using a generative AI model based on the emotional tendencies. This makes it possible to provide investment advice that also takes the user's emotions into consideration.

[0155] "User attribute information" refers to individual information such as each user's risk tolerance, investment goals, and past investment history.

[0156] "Optimized asset allocation" refers to a portfolio composition that distributes investment assets in a way that maximizes returns, based on user attribute information and market data.

[0157] "External data sources" refer to internet-based data providers that provide market data in real time.

[0158] "Market data" refers to data related to financial markets, such as stock prices, exchange rates, and economic indicators.

[0159] "Asset set" refers to the totality of all assets owned by a user.

[0160] "Rebalancing" refers to readjusting the composition of an asset portfolio to restore the optimal allocation.

[0161] "Biometric data" refers to physical or physiological information such as a user's heart rate and facial expressions.

[0162] "Emotional tendencies" refer to a user's emotional response patterns in specific situations.

[0163] A "generative AI model" refers to a computational model created using artificial intelligence to perform learning and inference from data.

[0164] An "investment strategy" refers to a plan or method for how to allocate and manage assets.

[0165] This invention is a system that combines user attribute information, emotional state, and market data to provide an investment strategy appropriate to the situation. Details regarding its implementation are provided below.

[0166] First, the user inputs attribute information such as risk tolerance, investment goals, and past investment history through a terminal. The terminal uses a standard input interface, such as a touchscreen or keyboard. This transmits the user's attribute information to the system.

[0167] Next, the server receives attribute information sent from the terminal, combines it with market data obtained from external data sources, and calculates an optimized asset allocation. For this purpose, an AI algorithm is used. Specifically, libraries such as NumPy and TensorFlow are utilized to perform calculations efficiently. Market data is obtained in real time from reliable data providers and processed by the analysis engine.

[0168] Furthermore, the device uses biosensors to collect the user's emotional state in real time. These biosensors include a heart rate monitor and a camera, which help to understand the user's stress level and emotional tendencies. This clarifies the user's current psychological state and makes it possible to detect anxiety and stress that may affect investment decisions.

[0169] The server uses collected emotional data to analyze it through an AI model and generate investment strategies based on emotional tendencies. This generation process involves inputting prompts into the generative AI model, such as "What asset classes are recommended when I feel stressed?", to derive accurate advice.

[0170] This system provides users with continuously personalized investment advice, supporting more rational and emotion-free investment decisions. Therefore, it aims to reduce the risks of emotional decisions and improve investment performance, making it a system that can be used with confidence by everyone from novice investors to experienced investors.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users enter investment attribute information using a terminal. This information includes risk tolerance, investment goals, and past investment history. The entered data is temporarily stored on the terminal for use in subsequent processing.

[0174] Step 2:

[0175] The terminal encrypts the attribute information entered by the user and securely transmits it to the server. SSL / TLS protocol is used to ensure the confidentiality of the information. The server stores the received data in a database and prepares to begin the analysis process.

[0176] Step 3:

[0177] The server accesses external data sources to acquire market data in real time. This data includes the latest stock prices, exchange rates, and economic indicators. This data is collected via APIs and processed by an analysis engine. The server then uses this information to understand market trends and prepares it for later use in AI calculations.

[0178] Step 4:

[0179] The server inputs user attribute information and acquired market data into an AI algorithm. The AI ​​algorithm utilizes machine learning, for example, and leverages libraries such as NumPy and TensorFlow. These tools are used to calculate an optimized asset allocation. At this stage, the calculation results are temporarily stored in preparation for later output.

[0180] Step 5:

[0181] The device uses biosensors to collect the user's emotional state. Data such as heart rate and facial expressions are collected in real time and temporarily stored on the device. This biometric data indicates stress levels and emotional tendencies and may influence investment strategies.

[0182] Step 6:

[0183] The server receives biometric data and analyzes it using an emotion engine. This analysis reveals the user's emotional tendencies, and the AI ​​model suggests investment strategies that are more suitable for them. Using the generative AI model, the prompt "Suggest the optimal investment strategy based on the user's emotional state" is entered, and specific advice is generated.

[0184] Step 7:

[0185] The server calculates the final investment strategy and notifies the user of the results. This notification is delivered, for example, via email or in-app messages, providing the user with the latest investment advice. This allows the user to make optimal investment decisions while considering market fluctuations and their own emotions.

[0186] (Application Example 2)

[0187] 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 device 14 will be referred to as the "terminal."

[0188] Modern consumers are often influenced by emotions when optimizing investments and spending, making it difficult to execute long-term plans. Furthermore, they struggle to make appropriate judgments in the face of rapid market fluctuations, resulting in a failure to properly revise asset allocations and spending. Additionally, there is a lack of personalized support based on each user's investment behavior and spending patterns, creating a demand for reliable assistance.

[0189] 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.

[0190] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; and means for recognizing the user's emotional state and optimizing the spending plan based on that emotional state. This enables the proposal of investment strategies and spending plans that take the user's emotional state into consideration, as well as rapid responses to sudden market changes.

[0191] "User attribute information" refers to information including an individual user's risk tolerance, investment goals, and past investment history, and is data that forms the basis for individual investment decisions.

[0192] "Optimized asset allocation" refers to an allocation calculated to ensure that assets are distributed most efficiently for the user, taking into account investment risk and return.

[0193] "External data sources" refer to information sources such as institutions and APIs that provide information on market trends and economic indicators, and include data obtained in real time.

[0194] "Market data analysis" refers to data processing that uses received market data to evaluate the current market situation and predict future trends.

[0195] "User asset portfolio" refers to the collective term for the diverse asset classes owned by a user, and to an investment portfolio constructed to achieve a specific objective.

[0196] "Learning from past investment history" is a process of analyzing a user's past investment activities and learning from the patterns and trends that can be derived from them.

[0197] "Recognition of the user's emotional state" refers to a system function that detects and understands the user's psychological state, and is evaluated using biosensors or self-reporting.

[0198] "Optimizing spending plans" means systematically managing payments to achieve short-term and long-term financial goals while maintaining a balance between income and expenses.

[0199] "Proposing a savings plan" involves suggesting specific actions to reduce unnecessary spending, based on the user's financial situation and spending habits.

[0200] The system for realizing this invention mainly consists of a server, a terminal, and a user. The server receives the user's attribute information and calculates an optimized asset allocation based on it. It acquires market data in real time from external data sources and analyzes it to adjust the user's asset portfolio as needed. In addition, it learns from past investment history and provides the most suitable investment strategy for the user. Furthermore, the server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on the results.

[0201] The device transmits user input information and data acquired from biosensors to the server. This information is processed by AI analysis and an emotion engine to gain a more accurate understanding of the user's emotional state and optimize investment strategies. Furthermore, savings plans and spending review suggestions, taking emotional states into account, are provided to the user through the device. As a result, users can manage their assets and daily expenses with greater peace of mind.

[0202] For example, if a user experiences stress due to a sudden increase in spending, the system will sense this emotional state and suggest revisions to their spending plan and savings strategies to alleviate their financial burden. This allows the user to manage their assets in a more planned manner. An example of a prompt from the generating AI model might be: "When the user's stress level increases, generate a plan to optimize spending. Specifically, after purchasing an expensive item, suggest savings that take into account future necessary expenses."

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

[0204] Step 1:

[0205] The server receives user attribute information from the terminal. Input includes user attribute information such as risk tolerance, investment goals, and past investment history. Output is data that is stored on the server side and ready for subsequent processing. Specifically, each time the user completes information input, the data is sent to the server, which receives it and stores it in the database.

[0206] Step 2:

[0207] The server acquires market data in real time from external data sources. The input is the latest market data provided via an API. The output is market information used by an analysis engine to perform predetermined analyses. Specifically, the server calls the API at regular intervals to update the market information.

[0208] Step 3:

[0209] The server calculates the optimal asset allocation using the received user attribute information and acquired market data. The input is a combination of user attribute information and market data. The output is data on the optimized asset allocation. Specifically, it utilizes an AI algorithm to calculate the ideal allocation to each asset class.

[0210] Step 4:

[0211] The device collects emotional state data from biosensors and user input. Inputs include physiological data from sensors and user self-reports. Output is emotional information used for evaluation by an emotion engine. Specifically, it periodically acquires data from sensors and transmits that information to a server.

[0212] Step 5:

[0213] The server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on this. The inputs are emotional information and the existing spending plan. The output is a proposed improved spending plan. Specifically, it applies an algorithm that revises the user's spending plan while considering their current emotional state.

[0214] Step 6:

[0215] The server generates prompt messages using a generative AI model and provides suggestions to the user via alerts. Inputs include emotional states and a calculated optimal spending plan. The output is advice provided to the user through prompt messages. Specifically, it automatically generates appropriate savings suggestions based on the user's situation and notifies the user via their device.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] The system according to the present invention automatically manages and optimizes asset groups using AI technology, targeting novice investors and those with limited knowledge. This system is realized through the interaction of a server, a terminal, and a user.

[0233] First, the user enters their personal information through their device, such as their risk tolerance, investment goals, and current investment amount. This information is collected and recorded by the server.

[0234] The server receives attribute information provided by the user and uses AI algorithms to calculate the optimal asset allocation. Based on this calculation, it sends a portfolio suggestion to the user's terminal.

[0235] Furthermore, the server collaborates with external market data sources to collect market-related information in real time. The acquired market data is processed by an AI analysis engine to detect market trends and unusual price fluctuations, and to generate information necessary for investment decisions.

[0236] Based on the analysis results, the server automatically performs rebalancing according to market conditions. In this rebalancing process, the user's asset portfolio is optimized to match market conditions, and the configuration is adjusted as needed. This is to maintain overall balance when the value of a particular asset class increases or decreases.

[0237] Furthermore, the server learns the user's past investment history and analyzes patterns and trends using AI technology. Based on this, it proposes personalized investment strategies to the user. The proposals are notified to the user's device, supporting the user's daily investment decisions.

[0238] For example, if a user is willing to accept moderate risk and seeks long-term profits, the server will create a portfolio that takes this into account. A proposed portfolio with a balanced mix of stocks, bonds, and commodities will be presented to the user's terminal. Similarly, the server system will operate to immediately adjust the user's asset allocation in the event of a clear economic fluctuation.

[0239] In this way, the system of the present invention provides technical support to enable novice investors to engage in investment activities with confidence. This support improves investment efficiency, and users can strengthen their risk management without wasting time.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user inputs attribute information such as their risk tolerance, investment goals, and initial asset amount through their device. The device then transmits this information to the server.

[0243] Step 2:

[0244] The server records attribute information received from the user in a database. Based on the recorded information, an AI algorithm calculates the optimal asset allocation and generates a portfolio suggestion suitable for the user.

[0245] Step 3:

[0246] The server sends the proposed portfolio to the user's terminal. The user reviews the received portfolio and makes adjustments as needed, or accepts the proposal.

[0247] Step 4:

[0248] The server continuously acquires market data in real time through external market data APIs. This data includes stock prices, economic indicators, news, and more.

[0249] Step 5:

[0250] The server processes acquired market data using an AI analysis engine to detect price fluctuations and trends. Based on the analysis results, the risk of rapid market fluctuations is assessed.

[0251] Step 6:

[0252] The server automatically rebalances the user's portfolio based on market analysis results. Specifically, it performs a process of changing the proportion of certain assets to optimize the overall balance.

[0253] Step 7:

[0254] The server analyzes the user's past investment history and learns patterns and trends. Using AI technology, it generates an investment strategy tailored to the user and notifies the user of the results on their device.

[0255] Step 8:

[0256] The server periodically evaluates the performance of the user's asset portfolio and generates a detailed report. This report is sent to the user's terminal as graphs and numerical data, serving as material for the user to review their investment performance.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] A challenge exists in that it is difficult for novice investors and users with limited knowledge to effectively obtain the information necessary to make appropriate investment decisions. Furthermore, responding quickly to market fluctuations and optimizing asset allocation requires effort and expertise, posing a barrier for many users. As a result, asset management efficiency may decrease, and risk management may become inadequate.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0261] In this invention, the server includes means for receiving user characteristic information and calculating a rationalized asset allocation based on said characteristic information, means for instantly acquiring market information from an external information source and interpreting said market information, and means for automatically changing the user's asset composition based on the interpretation results. This enables diverse users to effectively manage their assets according to their risk tolerance and investment objectives, and to make investment decisions in real time in response to market changes.

[0262] "Characteristic information" refers to information about the user's attributes and characteristics, such as their risk tolerance, investment goals, and investment amount.

[0263] Asset allocation is the process of determining the proportion of assets to be divided among different investment targets, and it is an important method for managing investment risk and return.

[0264] "External information sources" refer to various data providers and platforms that offer market information, economic data, and other similar information.

[0265] "Market information" is a general term for information that influences investment decisions in financial markets, such as price trends, trading volume, and economic indicators.

[0266] "Asset allocation" refers to the overall distribution of the types and quantities of assets held by a user, and represents the contents of a portfolio that reflects their investment strategy.

[0267] "Rationalization" refers to eliminating waste to increase efficiency and making adjustments and improvements to achieve optimal results.

[0268] "Artificial intelligence technology" is a technology that enables machines to imitate human intelligent behavior, supporting processes such as data analysis, prediction, and decision-making.

[0269] This invention constructs a system that improves investment efficiency and enhances risk management through the interaction between a server, a terminal, and a user.

[0270] First, the user inputs characteristic information such as their risk tolerance and investment goals using the terminal's interface. This terminal is typically implemented as a web application or mobile application. This information is then transmitted to the server via network communication.

[0271] Next, the server calculates asset allocation based on the received characteristic information. Specifically, machine learning libraries such as TensorFlow and PyTorch are used for this calculation, and backpropagation and deep learning models are applied as AI algorithms. This method builds an investment strategy optimized for each user.

[0272] Furthermore, the server collects market information from external market sources. This is done through APIs of Yahoo Finance and other economic data providers. The acquired market information is analyzed using analytical engines such as Scikit-learn to detect trends and anomalies.

[0273] Based on this analysis, the server automatically rebalances the user's asset allocation. Specific asset adjustments, such as shifting from stocks to bonds, are made as needed. The results of this rebalancing are notified to the user's device, allowing them to easily track their latest investment status.

[0274] Finally, the server learns from past investment history and provides users with personalized investment strategies. This process utilizes Keras or similar deep learning frameworks to analyze behavioral patterns from investment history and generate optimal investment recommendations for the user.

[0275] For example, if a user states, "I want to pursue long-term profits while accepting moderate risk," the server interprets this as a prompt and generates a portfolio that matches that need. In more specific terms, this might be expressed as, "Please suggest an investment strategy that accepts moderate risk while pursuing long-term profits."

[0276] Thus, the system of the present invention provides users with a strategic yet user-friendly investment environment, supporting their daily investment decisions.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user inputs characteristic information such as their risk tolerance, investment goals, and current investment amount. The input is made through the interface of the terminal and recorded on the terminal as text or numerical data. The input data is sent to the server in JSON or XML format.

[0280] Step 2:

[0281] The server analyzes the characteristic information received from the user and saves it in the database. Based on this saved information, a generated AI model is applied to calculate an optimized asset allocation. Specifically, an AI algorithm uses TensorFlow to perform calculations using the backpropagation method. The output is the composition of the recommended portfolio.

[0282] Step 3:

[0283] The server accesses an external information source to obtain market information. The market data collected through API calls is immediately processed by the AI analysis engine. The data is analyzed using Scikit-learn to detect market trends and outliers. The output is the analyzed market information and its evaluation result.

[0284] Step 4:

[0285] The server automatically rebalances the asset composition based on the analyzed market information and the user's characteristic information. The ratio of stocks and bonds is adjusted according to the market situation. The rebalanced asset composition is sent to the user's terminal as a re-evaluated portfolio.

[0286] Step 5:

[0287] The server extracts past investment history from a database and analyzes patterns using machine learning techniques. It learns from past data using tools like Keras and generates an optimal investment strategy for the user. The generated strategy is output as a customized investment suggestion and notified to the user's device.

[0288] Step 6:

[0289] The terminal visually displays the latest portfolio information, rebalancing results, and personalized investment strategies transmitted from the server. Users receive support in making daily investment decisions based on this information. Specifically, the terminal visualizes data through a GUI, making it easy for users to understand and operate.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] For novice investors and those with limited knowledge, efficiently learning about asset management and investment strategies in financial markets is a challenging task. Furthermore, there is a need to provide users with appropriate educational content and support them in deepening their understanding of investing while responding to real-time market fluctuations. It is essential to bridge this complexity of investing and the knowledge gap, and to provide an environment where investors can confidently engage in investment activities.

[0293] 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.

[0294] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for learning past investment history and providing an investment strategy suitable for the user; means for providing learning content to support the improvement of the user's financial knowledge; and means for visualizing market simulations using augmented reality technology. This makes it possible for even novice investors to safely deepen their investment knowledge and engage in strategic investment activities while adapting to real-time market conditions.

[0295] "User attribute information" refers to personal profile data provided by investors, such as their risk tolerance, investment goals, and current investment amount.

[0296] "Optimized asset allocation" refers to the composition of an investment portfolio that balances risk and return, calculated by an AI algorithm based on the user's attribute information.

[0297] "Market data" refers to the latest information on the prices of assets traded in financial markets, such as stock prices, bond prices, and exchange rates.

[0298] "Means of analysis" refers to a function that uses an AI analysis engine to analyze collected market data and identify trends and anomalies in price fluctuations.

[0299] "Means for automatically adjusting asset groups" refers to a function that, based on analysis results, buys or sells assets to adapt the portfolio to the latest market conditions.

[0300] "Means of providing investment strategies" refers to a function that learns from past investment history and presents investment policies and action guidelines customized for each individual user.

[0301] "Means of providing learning content" refers to the function of supplying educational resources and simulation tools to improve users' financial knowledge.

[0302] "Methods for visualizing market simulations using augmented reality technology" refers to a function that uses AR technology to realistically reproduce market movements and investment results in a virtual environment, providing users with a visual experience.

[0303] This invention is a system designed to enable novice investors and users with limited knowledge to efficiently manage their assets and deepen their financial understanding. The system operates through the interconnectedness of the user's terminal, server, and external data sources.

[0304] On the server, attribute information is received through the user's terminal. This attribute information includes risk tolerance, investment goals, and current investment amount. Based on the received information, the server uses an AI algorithm and the Python library TensorFlow to calculate an optimized asset allocation.

[0305] The server retrieves market data in real time from sources such as the Yahoo Finance API and processes this data with an AI analysis engine. This allows the system to analyze market risks and opportunities and automatically adjust the user's assets based on the analysis results. By learning from the user's past investment history, a personalized investment strategy is provided and notified to the user's device.

[0306] Furthermore, the server delivers learning content to improve users' financial literacy. This content is customized using a generative AI model. In addition, AR technology using Unity is used to perform market simulations through augmented reality, providing users with an intuitive visual experience.

[0307] As a specific example, when market fluctuations are expected, a scenario can be provided where users learn risk management through an extended reality system. For example, by leveraging a prompt such as "Please propose a simulation that teaches the basics of risk management considering the user's investment skill level," appropriate content based on the user's level of understanding is generated.

[0308] With this system, users can obtain rich information and visual experiences when making real-time investment decisions, enabling them to proceed with investments with confidence.

[0309] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user's terminal receives attribute information such as risk tolerance, investment goals, and the current investment amount. These input information are sent to the server through a secure protocol. The output at this stage is a dataset of attribute information.

[0312] Step 2:

[0313] The server inputs the received attribute information into an AI algorithm to calculate an asset allocation optimized for the user. For this calculation, TensorFlow, an AI framework, is used to output a portfolio with an optimized risk-return balance.

[0314] Step 3:

[0315] The server retrieves market data in real-time from an external data source. The input market data includes stock prices, exchange rates, commodity prices, etc. This data is processed by an analysis engine to generate market trends and risk assessments as output.

[0316] Step 4:

[0317] The server automatically adjusts the user's assets based on analyzed market data and user attribute information. Specifically, it uses an AI algorithm to identify assets that should be bought or sold and outputs an optimized portfolio configuration.

[0318] Step 5:

[0319] The server learns from the user's past investment history and generates personalized investment strategies. It analyzes the user's past behavioral patterns and outputs advice that will be useful for future investment actions. This information is notified to the user's device.

[0320] Step 6:

[0321] The server provides users with customized learning content. Using a generative AI model, it generates prompts based on analysis results and outputs appropriate learning materials.

[0322] Step 7:

[0323] The user's device displays a market simulation using AR technology powered by Unity. Through the AR device, the user visually experiences market trends and investment results, gaining a practical learning experience as output.

[0324] 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.

[0325] The system according to the present invention provides investment optimization functions and investment advice that takes user emotions into consideration. The system exchanges data between the server, terminal, and user, and utilizes AI technology and an emotion engine.

[0326] In running the system, the user first inputs their personal information using a terminal. This includes risk tolerance, investment goals, and past investment history. This information is then transmitted to the server via the terminal.

[0327] The server uses AI algorithms to calculate the optimal asset allocation based on the received user attribute information and market data. Market data is acquired in real time using an external API and processed by the analysis engine. Furthermore, the server automatically rebalances the user's asset portfolio based on the analysis results.

[0328] This system incorporates an emotion engine that can recognize the user's emotional state. It learns the user's past emotional data and behavioral patterns, and the AI ​​predicts the user's emotional tendencies in the current investment situation. The device understands the user's current emotional state based on user input and data from biosensors. For example, if the user is stressed, the emotion engine will detect this and generate an alert to help adjust the investment strategy.

[0329] As a concrete example, suppose a user faces rapid market fluctuations and begins to feel anxious. In this situation, the emotion engine analyzes the user's unstable emotions, and the server recommends an investment strategy based on that analysis. For example, by suggesting a shift of funds to more stable assets, the user's mental burden can be reduced.

[0330] This system aims to improve investment performance by providing more personalized support for users' investment behavior and reducing the risks of emotional decisions. It is expected to function as a reliable investment support tool for both novice and experienced investors.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The user inputs attribute information such as their risk tolerance, investment goals, and past investment history through their device. The device then sends this information to the server.

[0334] Step 2:

[0335] The server records attribute information received from the user in a database and uses an AI algorithm to calculate the optimal asset allocation. At this stage, real-time market data is obtained using an external API and data analysis is performed.

[0336] Step 3:

[0337] Based on the market data analyzed by the server, the user's asset portfolio is automatically adjusted. The system verifies that the asset allocation is optimized and performs rebalancing as needed.

[0338] Step 4:

[0339] The system collects data about the user's current emotional state from their device. This may include user input and data from biosensors.

[0340] Step 5:

[0341] The server uses an emotion engine to analyze the user's past emotional data and current emotional state. It learns emotional patterns to evaluate the impact of emotional changes on investment decisions.

[0342] Step 6:

[0343] The emotion engine detects the user's emotional state, and the server adjusts its investment strategy based on that information. For example, if stress or anxiety levels rise, it will recommend more stable assets.

[0344] Step 7:

[0345] The server periodically evaluates the performance of the user's asset portfolio and generates reports that reflect the latest market conditions and sentiment analysis results. By sending these reports to the user's terminal, the user can check their investment status and make investment decisions with confidence.

[0346] (Example 2)

[0347] 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".

[0348] In investing, it is necessary to provide investment strategies that take into account not only user attribute information and market data, but also the user's emotional state. However, conventional systems have not adequately provided mechanisms for delivering personalized investment advice that takes emotions into account. As a result, users were at risk of making irrational investment decisions due to emotions such as stress and anxiety.

[0349] 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.

[0350] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing the market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for collecting biometric data including the user's emotional state and analyzing the user's emotional tendencies using the data; and means for providing an investment strategy suitable for the user using a generative AI model based on the emotional tendencies. This makes it possible to provide investment advice that also takes the user's emotions into consideration.

[0351] "User attribute information" refers to individual information such as each user's risk tolerance, investment goals, and past investment history.

[0352] "Optimized asset allocation" refers to a portfolio composition that distributes investment assets in a way that maximizes returns, based on user attribute information and market data.

[0353] "External data sources" refer to internet-based data providers that provide market data in real time.

[0354] "Market data" refers to data related to financial markets, such as stock prices, exchange rates, and economic indicators.

[0355] "Asset set" refers to the totality of all assets owned by a user.

[0356] "Rebalancing" refers to readjusting the composition of an asset portfolio to restore the optimal allocation.

[0357] "Biometric data" refers to physical or physiological information such as a user's heart rate and facial expressions.

[0358] "Emotional tendencies" refer to a user's emotional response patterns in specific situations.

[0359] A "generative AI model" refers to a computational model created using artificial intelligence to perform learning and inference from data.

[0360] An "investment strategy" refers to a plan or method for how to allocate and manage assets.

[0361] This invention is a system that combines user attribute information, emotional state, and market data to provide an investment strategy appropriate to the situation. Details regarding its implementation are provided below.

[0362] First, the user inputs attribute information such as risk tolerance, investment goals, and past investment history through a terminal. The terminal uses a standard input interface, such as a touchscreen or keyboard. This transmits the user's attribute information to the system.

[0363] Next, the server receives attribute information sent from the terminal, combines it with market data obtained from external data sources, and calculates an optimized asset allocation. For this purpose, an AI algorithm is used. Specifically, libraries such as NumPy and TensorFlow are utilized to perform calculations efficiently. Market data is obtained in real time from reliable data providers and processed by the analysis engine.

[0364] Furthermore, the device uses biosensors to collect the user's emotional state in real time. These biosensors include a heart rate monitor and a camera, which help to understand the user's stress level and emotional tendencies. This clarifies the user's current psychological state and makes it possible to detect anxiety and stress that may affect investment decisions.

[0365] The server uses collected emotional data to analyze it through an AI model and generate investment strategies based on emotional tendencies. This generation process involves inputting prompts into the generative AI model, such as "What asset classes are recommended when I feel stressed?", to derive accurate advice.

[0366] This system provides users with continuously personalized investment advice, supporting more rational and emotion-free investment decisions. Therefore, it aims to reduce the risks of emotional decisions and improve investment performance, making it a system that can be used with confidence by everyone from novice investors to experienced investors.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users enter investment attribute information using a terminal. This information includes risk tolerance, investment goals, and past investment history. The entered data is temporarily stored on the terminal for use in subsequent processing.

[0370] Step 2:

[0371] The terminal encrypts the attribute information entered by the user and securely transmits it to the server. SSL / TLS protocol is used to ensure the confidentiality of the information. The server stores the received data in a database and prepares to begin the analysis process.

[0372] Step 3:

[0373] The server accesses external data sources to acquire market data in real time. This data includes the latest stock prices, exchange rates, and economic indicators. This data is collected via APIs and processed by an analysis engine. The server then uses this information to understand market trends and prepares it for later use in AI calculations.

[0374] Step 4:

[0375] The server inputs user attribute information and acquired market data into an AI algorithm. The AI ​​algorithm utilizes machine learning, for example, and leverages libraries such as NumPy and TensorFlow. These tools are used to calculate an optimized asset allocation. At this stage, the calculation results are temporarily stored in preparation for later output.

[0376] Step 5:

[0377] The device uses biosensors to collect the user's emotional state. Data such as heart rate and facial expressions are collected in real time and temporarily stored on the device. This biometric data indicates stress levels and emotional tendencies and may influence investment strategies.

[0378] Step 6:

[0379] The server receives biometric data and analyzes it using an emotion engine. This analysis reveals the user's emotional tendencies, and the AI ​​model suggests investment strategies that are more suitable for them. Using the generative AI model, the prompt "Suggest the optimal investment strategy based on the user's emotional state" is entered, and specific advice is generated.

[0380] Step 7:

[0381] The server calculates the final investment strategy and notifies the user of the results. This notification is delivered, for example, via email or in-app messages, providing the user with the latest investment advice. This allows the user to make optimal investment decisions while considering market fluctuations and their own emotions.

[0382] (Application Example 2)

[0383] 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."

[0384] Modern consumers are often influenced by emotions when optimizing investments and spending, making it difficult to execute long-term plans. Furthermore, they struggle to make appropriate judgments in the face of rapid market fluctuations, resulting in a failure to properly revise asset allocations and spending. Additionally, there is a lack of personalized support based on each user's investment behavior and spending patterns, creating a demand for reliable assistance.

[0385] 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.

[0386] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; and means for recognizing the user's emotional state and optimizing the spending plan based on that emotional state. This enables the proposal of investment strategies and spending plans that take the user's emotional state into consideration, as well as rapid responses to sudden market changes.

[0387] "User attribute information" refers to information including an individual user's risk tolerance, investment goals, and past investment history, and is data that forms the basis for individual investment decisions.

[0388] "Optimized asset allocation" refers to an allocation calculated to ensure that assets are distributed most efficiently for the user, taking into account investment risk and return.

[0389] "External data sources" refer to information sources such as institutions and APIs that provide information on market trends and economic indicators, and include data obtained in real time.

[0390] "Market data analysis" refers to data processing that uses received market data to evaluate the current market situation and predict future trends.

[0391] "User asset portfolio" refers to the collective term for the diverse asset classes owned by a user, and to an investment portfolio constructed to achieve a specific objective.

[0392] "Learning from past investment history" is a process of analyzing a user's past investment activities and learning from the patterns and trends that can be derived from them.

[0393] "Recognition of the user's emotional state" refers to a system function that detects and understands the user's psychological state, and is evaluated using biosensors or self-reporting.

[0394] "Optimizing spending plans" means systematically managing payments to achieve short-term and long-term financial goals while maintaining a balance between income and expenses.

[0395] "Proposing a savings plan" involves suggesting specific actions to reduce unnecessary spending, based on the user's financial situation and spending habits.

[0396] The system for realizing this invention mainly consists of a server, a terminal, and a user. The server receives the user's attribute information and calculates an optimized asset allocation based on it. It acquires market data in real time from external data sources and analyzes it to adjust the user's asset portfolio as needed. In addition, it learns from past investment history and provides the most suitable investment strategy for the user. Furthermore, the server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on the results.

[0397] The device transmits user input information and data acquired from biosensors to the server. This information is processed by AI analysis and an emotion engine to gain a more accurate understanding of the user's emotional state and optimize investment strategies. Furthermore, savings plans and spending review suggestions, taking emotional states into account, are provided to the user through the device. As a result, users can manage their assets and daily expenses with greater peace of mind.

[0398] For example, if a user experiences stress due to a sudden increase in spending, the system will sense this emotional state and suggest revisions to their spending plan and savings strategies to alleviate their financial burden. This allows the user to manage their assets in a more planned manner. An example of a prompt from the generating AI model might be: "When the user's stress level increases, generate a plan to optimize spending. Specifically, after purchasing an expensive item, suggest savings that take into account future necessary expenses."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The server receives user attribute information from the terminal. Input includes user attribute information such as risk tolerance, investment goals, and past investment history. Output is data that is stored on the server side and ready for subsequent processing. Specifically, each time the user completes information input, the data is sent to the server, which receives it and stores it in the database.

[0402] Step 2:

[0403] The server acquires market data in real time from external data sources. The input is the latest market data provided via an API. The output is market information used by an analysis engine to perform predetermined analyses. Specifically, the server calls the API at regular intervals to update the market information.

[0404] Step 3:

[0405] The server calculates the optimal asset allocation using the received user attribute information and acquired market data. The input is a combination of user attribute information and market data. The output is data on the optimized asset allocation. Specifically, it utilizes an AI algorithm to calculate the ideal allocation to each asset class.

[0406] Step 4:

[0407] The device collects emotional state data from biosensors and user input. Inputs include physiological data from sensors and user self-reports. Output is emotional information used for evaluation by an emotion engine. Specifically, it periodically acquires data from sensors and transmits that information to a server.

[0408] Step 5:

[0409] The server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on this. The inputs are emotional information and the existing spending plan. The output is a proposed improved spending plan. Specifically, it applies an algorithm that revises the user's spending plan while considering their current emotional state.

[0410] Step 6:

[0411] The server generates prompt messages using a generative AI model and provides suggestions to the user via alerts. Inputs include emotional states and a calculated optimal spending plan. The output is advice provided to the user through prompt messages. Specifically, it automatically generates appropriate savings suggestions based on the user's situation and notifies the user via their device.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] The system according to the present invention automatically manages and optimizes asset groups using AI technology, targeting novice investors and those with limited knowledge. This system is realized through the interaction of a server, a terminal, and a user.

[0429] First, the user enters their personal information through their device, such as their risk tolerance, investment goals, and current investment amount. This information is collected and recorded by the server.

[0430] The server receives attribute information provided by the user and uses AI algorithms to calculate the optimal asset allocation. Based on this calculation, it sends a portfolio suggestion to the user's terminal.

[0431] Furthermore, the server collaborates with external market data sources to collect market-related information in real time. The acquired market data is processed by an AI analysis engine to detect market trends and unusual price fluctuations, and to generate information necessary for investment decisions.

[0432] Based on the analysis results, the server automatically performs rebalancing according to market conditions. In this rebalancing process, the user's asset portfolio is optimized to match market conditions, and the configuration is adjusted as needed. This is to maintain overall balance when the value of a particular asset class increases or decreases.

[0433] Furthermore, the server learns the user's past investment history and analyzes patterns and trends using AI technology. Based on this, it proposes personalized investment strategies to the user. The proposals are notified to the user's device, supporting the user's daily investment decisions.

[0434] For example, if a user is willing to accept moderate risk and seeks long-term profits, the server will create a portfolio that takes this into account. A proposed portfolio with a balanced mix of stocks, bonds, and commodities will be presented to the user's terminal. Similarly, the server system will operate to immediately adjust the user's asset allocation in the event of a clear economic fluctuation.

[0435] In this way, the system of the present invention provides technical support to enable novice investors to engage in investment activities with confidence. This support improves investment efficiency, and users can strengthen their risk management without wasting time.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user inputs attribute information such as their risk tolerance, investment goals, and initial asset amount through their device. The device then transmits this information to the server.

[0439] Step 2:

[0440] The server records attribute information received from the user in a database. Based on the recorded information, an AI algorithm calculates the optimal asset allocation and generates a portfolio suggestion suitable for the user.

[0441] Step 3:

[0442] The server sends the proposed portfolio to the user's terminal. The user reviews the received portfolio and makes adjustments as needed, or accepts the proposal.

[0443] Step 4:

[0444] The server continuously acquires market data in real time through external market data APIs. This data includes stock prices, economic indicators, news, and more.

[0445] Step 5:

[0446] The server processes acquired market data using an AI analysis engine to detect price fluctuations and trends. Based on the analysis results, the risk of rapid market fluctuations is assessed.

[0447] Step 6:

[0448] The server automatically rebalances the user's portfolio based on market analysis results. Specifically, it performs a process of changing the proportion of certain assets to optimize the overall balance.

[0449] Step 7:

[0450] The server analyzes the user's past investment history and learns patterns and trends. Using AI technology, it generates an investment strategy tailored to the user and notifies the user of the results on their device.

[0451] Step 8:

[0452] The server periodically evaluates the performance of the user's asset portfolio and generates a detailed report. This report is sent to the user's terminal as graphs and numerical data, serving as material for the user to review their investment performance.

[0453] (Example 1)

[0454] 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."

[0455] A challenge exists in that it is difficult for novice investors and users with limited knowledge to effectively obtain the information necessary to make appropriate investment decisions. Furthermore, responding quickly to market fluctuations and optimizing asset allocation requires effort and expertise, posing a barrier for many users. As a result, asset management efficiency may decrease, and risk management may become inadequate.

[0456] 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.

[0457] In this invention, the server includes means for receiving user characteristic information and calculating a rationalized asset allocation based on said characteristic information, means for instantly acquiring market information from an external information source and interpreting said market information, and means for automatically changing the user's asset composition based on the interpretation results. This enables diverse users to effectively manage their assets according to their risk tolerance and investment objectives, and to make investment decisions in real time in response to market changes.

[0458] "Characteristic information" refers to information about the user's attributes and characteristics, such as their risk tolerance, investment goals, and investment amount.

[0459] Asset allocation is the process of determining the proportion of assets to be divided among different investment targets, and it is an important method for managing investment risk and return.

[0460] "External information sources" refer to various data providers and platforms that offer market information, economic data, and other similar information.

[0461] "Market information" is a general term for information that influences investment decisions in financial markets, such as price trends, trading volume, and economic indicators.

[0462] "Asset allocation" refers to the overall distribution of the types and quantities of assets held by a user, and represents the contents of a portfolio that reflects their investment strategy.

[0463] "Rationalization" refers to eliminating waste to increase efficiency and making adjustments and improvements to achieve optimal results.

[0464] "Artificial intelligence technology" is a technology that enables machines to imitate human intelligent behavior, supporting processes such as data analysis, prediction, and decision-making.

[0465] This invention constructs a system that improves investment efficiency and enhances risk management through the interaction between a server, a terminal, and a user.

[0466] First, the user inputs characteristic information such as their risk tolerance and investment goals using the terminal's interface. This terminal is typically implemented as a web application or mobile application. This information is then transmitted to the server via network communication.

[0467] Next, the server calculates asset allocation based on the received characteristic information. Specifically, machine learning libraries such as TensorFlow and PyTorch are used for this calculation, and backpropagation and deep learning models are applied as AI algorithms. This method builds an investment strategy optimized for each user.

[0468] Furthermore, the server collects market information from external market sources. This is done through APIs of Yahoo Finance and other economic data providers. The acquired market information is analyzed using analytical engines such as Scikit-learn to detect trends and anomalies.

[0469] Based on this analysis, the server automatically rebalances the user's asset allocation. Specific asset adjustments, such as shifting from stocks to bonds, are made as needed. The results of this rebalancing are notified to the user's device, allowing them to easily track their latest investment status.

[0470] Finally, the server learns from past investment history and provides users with personalized investment strategies. This process utilizes Keras or similar deep learning frameworks to analyze behavioral patterns from investment history and generate optimal investment recommendations for the user.

[0471] For example, if a user states, "I want to pursue long-term profits while accepting moderate risk," the server interprets this as a prompt and generates a portfolio that matches that need. In more specific terms, this might be expressed as, "Please suggest an investment strategy that accepts moderate risk while pursuing long-term profits."

[0472] Thus, the system of the present invention provides users with a strategic yet user-friendly investment environment, supporting their daily investment decisions.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] Users input characteristic information such as their risk tolerance, investment goals, and current investment amount. This input is done through the terminal interface and recorded on the terminal as text or numerical data. The entered data is sent to the server in JSON or XML format.

[0476] Step 2:

[0477] The server analyzes the characteristic information received from the user and stores it in a database. Based on this stored information, a generative AI model is applied to calculate an optimized asset allocation. Specifically, the AI ​​algorithm uses TensorFlow and employs backpropagation techniques for the calculation. The output is the recommended portfolio composition.

[0478] Step 3:

[0479] The server accesses external sources to obtain market information. Market data collected via API calls is immediately processed by an AI analysis engine. Scikit-learn is used to analyze the data and detect market trends and anomalies. The output is the analyzed market information and its evaluation results.

[0480] Step 4:

[0481] The server automatically rebalances the asset allocation based on analyzed market information and user characteristics. The ratios of stocks and bonds are adjusted according to market conditions. The rebalanced asset allocation is sent to the user's terminal as a re-evaluated portfolio.

[0482] Step 5:

[0483] The server extracts past investment history from a database and analyzes patterns using machine learning techniques. It learns from past data using tools like Keras and generates an optimal investment strategy for the user. The generated strategy is output as a customized investment suggestion and notified to the user's device.

[0484] Step 6:

[0485] The terminal visually displays the latest portfolio information, rebalancing results, and personalized investment strategies transmitted from the server. Users receive support in making daily investment decisions based on this information. Specifically, the terminal visualizes data through a GUI, making it easy for users to understand and operate.

[0486] (Application Example 1)

[0487] 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."

[0488] For novice investors and those with limited knowledge, efficiently learning about asset management and investment strategies in financial markets is a challenging task. Furthermore, there is a need to provide users with appropriate educational content and support them in deepening their understanding of investing while responding to real-time market fluctuations. It is essential to bridge this complexity of investing and the knowledge gap, and to provide an environment where investors can confidently engage in investment activities.

[0489] 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.

[0490] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for learning past investment history and providing an investment strategy suitable for the user; means for providing learning content to support the improvement of the user's financial knowledge; and means for visualizing market simulations using augmented reality technology. This makes it possible for even novice investors to safely deepen their investment knowledge and engage in strategic investment activities while adapting to real-time market conditions.

[0491] "User attribute information" refers to personal profile data provided by investors, such as their risk tolerance, investment goals, and current investment amount.

[0492] "Optimized asset allocation" refers to the composition of an investment portfolio that balances risk and return, calculated by an AI algorithm based on the user's attribute information.

[0493] "Market data" refers to the latest information on the prices of assets traded in financial markets, such as stock prices, bond prices, and exchange rates.

[0494] "Means of analysis" refers to a function that uses an AI analysis engine to analyze collected market data and identify trends and anomalies in price fluctuations.

[0495] "Means for automatically adjusting asset groups" refers to a function that, based on analysis results, buys or sells assets to adapt the portfolio to the latest market conditions.

[0496] "Means of providing investment strategies" refers to a function that learns from past investment history and presents investment policies and action guidelines customized for each individual user.

[0497] "Means of providing learning content" refers to the function of supplying educational resources and simulation tools to improve users' financial knowledge.

[0498] "Methods for visualizing market simulations using augmented reality technology" refers to a function that uses AR technology to realistically reproduce market movements and investment results in a virtual environment, providing users with a visual experience.

[0499] This invention is a system designed to enable novice investors and users with limited knowledge to efficiently manage their assets and deepen their financial understanding. The system operates through the interconnectedness of the user's terminal, server, and external data sources.

[0500] On the server, attribute information is received through the user's terminal. This attribute information includes risk tolerance, investment goals, and current investment amount. Based on the received information, the server uses an AI algorithm and the Python library TensorFlow to calculate an optimized asset allocation.

[0501] The server retrieves market data in real time from sources such as the Yahoo Finance API and processes this data with an AI analysis engine. This allows the system to analyze market risks and opportunities and automatically adjust the user's assets based on the analysis results. By learning from the user's past investment history, a personalized investment strategy is provided and notified to the user's device.

[0502] Furthermore, the server delivers learning content to improve users' financial literacy. This content is customized using a generative AI model. In addition, AR technology using Unity is used to perform market simulations through augmented reality, providing users with an intuitive visual experience.

[0503] As a concrete example, when market fluctuations are anticipated, a scenario can be provided in which users learn risk management through an augmented reality system. For instance, a prompt such as "Consider the user's investment skill level and propose a simulation that teaches the basics of risk management" can be used to generate appropriate content based on the user's understanding.

[0504] This system allows users to access a wealth of information and visual insights when making real-time investment decisions, enabling them to invest with confidence.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The user's device receives attribute information such as risk tolerance, investment goals, and current investment amount. This input information is sent to the server via a secure protocol. The output at this stage is a dataset of attribute information.

[0508] Step 2:

[0509] The server inputs the received attribute information into an AI algorithm to calculate an optimized asset allocation for the user. This calculation uses the AI ​​framework TensorFlow and outputs a portfolio with an optimized risk-return balance.

[0510] Step 3:

[0511] The server acquires market data in real time from external data sources. This input market data includes stock prices, exchange rates, and commodity prices. This data is processed by an analysis engine, which then generates market trends and risk assessments as output.

[0512] Step 4:

[0513] The server automatically adjusts the user's assets based on analyzed market data and user attribute information. Specifically, it uses an AI algorithm to identify assets that should be bought or sold and outputs an optimized portfolio configuration.

[0514] Step 5:

[0515] The server learns from the user's past investment history and generates personalized investment strategies. It analyzes the user's past behavioral patterns and outputs advice that will be useful for future investment actions. This information is notified to the user's device.

[0516] Step 6:

[0517] The server provides users with customized learning content. Using a generative AI model, it generates prompts based on analysis results and outputs appropriate learning materials.

[0518] Step 7:

[0519] The user's device displays a market simulation using AR technology powered by Unity. Through the AR device, the user visually experiences market trends and investment results, gaining a practical learning experience as output.

[0520] 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.

[0521] The system according to the present invention provides investment optimization functions and investment advice that takes user emotions into consideration. The system exchanges data between the server, terminal, and user, and utilizes AI technology and an emotion engine.

[0522] In running the system, the user first inputs their personal information using a terminal. This includes risk tolerance, investment goals, and past investment history. This information is then transmitted to the server via the terminal.

[0523] The server uses AI algorithms to calculate the optimal asset allocation based on the received user attribute information and market data. Market data is acquired in real time using an external API and processed by the analysis engine. Furthermore, the server automatically rebalances the user's asset portfolio based on the analysis results.

[0524] This system incorporates an emotion engine that can recognize the user's emotional state. It learns the user's past emotional data and behavioral patterns, and the AI ​​predicts the user's emotional tendencies in the current investment situation. The device understands the user's current emotional state based on user input and data from biosensors. For example, if the user is stressed, the emotion engine will detect this and generate an alert to help adjust the investment strategy.

[0525] As a concrete example, suppose a user faces rapid market fluctuations and begins to feel anxious. In this situation, the emotion engine analyzes the user's unstable emotions, and the server recommends an investment strategy based on that analysis. For example, by suggesting a shift of funds to more stable assets, the user's mental burden can be reduced.

[0526] This system aims to improve investment performance by providing more personalized support for users' investment behavior and reducing the risks of emotional decisions. It is expected to function as a reliable investment support tool for both novice and experienced investors.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user inputs attribute information such as their risk tolerance, investment goals, and past investment history through their device. The device then sends this information to the server.

[0530] Step 2:

[0531] The server records attribute information received from the user in a database and uses an AI algorithm to calculate the optimal asset allocation. At this stage, real-time market data is obtained using an external API and data analysis is performed.

[0532] Step 3:

[0533] Based on the market data analyzed by the server, the user's asset portfolio is automatically adjusted. The system verifies that the asset allocation is optimized and performs rebalancing as needed.

[0534] Step 4:

[0535] The system collects data about the user's current emotional state from their device. This may include user input and data from biosensors.

[0536] Step 5:

[0537] The server uses an emotion engine to analyze the user's past emotional data and current emotional state. It learns emotional patterns to evaluate the impact of emotional changes on investment decisions.

[0538] Step 6:

[0539] The emotion engine detects the user's emotional state, and the server adjusts its investment strategy based on that information. For example, if stress or anxiety levels rise, it will recommend more stable assets.

[0540] Step 7:

[0541] The server periodically evaluates the performance of the user's asset portfolio and generates reports that reflect the latest market conditions and sentiment analysis results. By sending these reports to the user's terminal, the user can check their investment status and make investment decisions with confidence.

[0542] (Example 2)

[0543] 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."

[0544] In investing, it is necessary to provide investment strategies that take into account not only user attribute information and market data, but also the user's emotional state. However, conventional systems have not adequately provided mechanisms for delivering personalized investment advice that takes emotions into account. As a result, users were at risk of making irrational investment decisions due to emotions such as stress and anxiety.

[0545] 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.

[0546] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing the market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for collecting biometric data including the user's emotional state and analyzing the user's emotional tendencies using the data; and means for providing an investment strategy suitable for the user using a generative AI model based on the emotional tendencies. This makes it possible to provide investment advice that also takes the user's emotions into consideration.

[0547] "User attribute information" refers to individual information such as each user's risk tolerance, investment goals, and past investment history.

[0548] "Optimized asset allocation" refers to a portfolio composition that distributes investment assets in a way that maximizes returns, based on user attribute information and market data.

[0549] "External data sources" refer to internet-based data providers that provide market data in real time.

[0550] "Market data" refers to data related to financial markets, such as stock prices, exchange rates, and economic indicators.

[0551] "Asset set" refers to the totality of all assets owned by a user.

[0552] "Rebalancing" refers to readjusting the composition of an asset portfolio to restore the optimal allocation.

[0553] "Biometric data" refers to physical or physiological information such as a user's heart rate and facial expressions.

[0554] "Emotional tendencies" refer to a user's emotional response patterns in specific situations.

[0555] A "generative AI model" refers to a computational model created using artificial intelligence to perform learning and inference from data.

[0556] An "investment strategy" refers to a plan or method for how to allocate and manage assets.

[0557] This invention is a system that combines user attribute information, emotional state, and market data to provide an investment strategy appropriate to the situation. Details regarding its implementation are provided below.

[0558] First, the user inputs attribute information such as risk tolerance, investment goals, and past investment history through a terminal. The terminal uses a standard input interface, such as a touchscreen or keyboard. This transmits the user's attribute information to the system.

[0559] Next, the server receives attribute information sent from the terminal, combines it with market data obtained from external data sources, and calculates an optimized asset allocation. For this purpose, an AI algorithm is used. Specifically, libraries such as NumPy and TensorFlow are utilized to perform calculations efficiently. Market data is obtained in real time from reliable data providers and processed by the analysis engine.

[0560] Furthermore, the device uses biosensors to collect the user's emotional state in real time. These biosensors include a heart rate monitor and a camera, which help to understand the user's stress level and emotional tendencies. This clarifies the user's current psychological state and makes it possible to detect anxiety and stress that may affect investment decisions.

[0561] The server uses collected emotional data to analyze it through an AI model and generate investment strategies based on emotional tendencies. This generation process involves inputting prompts into the generative AI model, such as "What asset classes are recommended when I feel stressed?", to derive accurate advice.

[0562] This system provides users with continuously personalized investment advice, supporting more rational and emotion-free investment decisions. Therefore, it aims to reduce the risks of emotional decisions and improve investment performance, making it a system that can be used with confidence by everyone from novice investors to experienced investors.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] Users enter investment attribute information using a terminal. This information includes risk tolerance, investment goals, and past investment history. The entered data is temporarily stored on the terminal for use in subsequent processing.

[0566] Step 2:

[0567] The terminal encrypts the attribute information entered by the user and securely transmits it to the server. SSL / TLS protocol is used to ensure the confidentiality of the information. The server stores the received data in a database and prepares to begin the analysis process.

[0568] Step 3:

[0569] The server accesses external data sources to acquire market data in real time. This data includes the latest stock prices, exchange rates, and economic indicators. This data is collected via APIs and processed by an analysis engine. The server then uses this information to understand market trends and prepares it for later use in AI calculations.

[0570] Step 4:

[0571] The server inputs user attribute information and acquired market data into an AI algorithm. The AI ​​algorithm utilizes machine learning, for example, and leverages libraries such as NumPy and TensorFlow. These tools are used to calculate an optimized asset allocation. At this stage, the calculation results are temporarily stored in preparation for later output.

[0572] Step 5:

[0573] The device uses biosensors to collect the user's emotional state. Data such as heart rate and facial expressions are collected in real time and temporarily stored on the device. This biometric data indicates stress levels and emotional tendencies and may influence investment strategies.

[0574] Step 6:

[0575] The server receives biometric data and analyzes it using an emotion engine. This analysis reveals the user's emotional tendencies, and the AI ​​model suggests investment strategies that are more suitable for them. Using the generative AI model, the prompt "Suggest the optimal investment strategy based on the user's emotional state" is entered, and specific advice is generated.

[0576] Step 7:

[0577] The server calculates the final investment strategy and notifies the user of the results. This notification is delivered, for example, via email or in-app messages, providing the user with the latest investment advice. This allows the user to make optimal investment decisions while considering market fluctuations and their own emotions.

[0578] (Application Example 2)

[0579] 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."

[0580] Modern consumers are often influenced by emotions when optimizing investments and spending, making it difficult to execute long-term plans. Furthermore, they struggle to make appropriate judgments in the face of rapid market fluctuations, resulting in a failure to properly revise asset allocations and spending. Additionally, there is a lack of personalized support based on each user's investment behavior and spending patterns, creating a demand for reliable assistance.

[0581] 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.

[0582] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; and means for recognizing the user's emotional state and optimizing the spending plan based on that emotional state. This enables the proposal of investment strategies and spending plans that take the user's emotional state into consideration, as well as rapid responses to sudden market changes.

[0583] "User attribute information" refers to information including an individual user's risk tolerance, investment goals, and past investment history, and is data that forms the basis for individual investment decisions.

[0584] "Optimized asset allocation" refers to an allocation calculated to ensure that assets are distributed most efficiently for the user, taking into account investment risk and return.

[0585] "External data sources" refer to information sources such as institutions and APIs that provide information on market trends and economic indicators, and include data obtained in real time.

[0586] "Market data analysis" refers to data processing that uses received market data to evaluate the current market situation and predict future trends.

[0587] "User asset portfolio" refers to the collective term for the diverse asset classes owned by a user, and to an investment portfolio constructed to achieve a specific objective.

[0588] "Learning from past investment history" is a process of analyzing a user's past investment activities and learning from the patterns and trends that can be derived from them.

[0589] "Recognition of the user's emotional state" refers to a system function that detects and understands the user's psychological state, and is evaluated using biosensors or self-reporting.

[0590] "Optimizing spending plans" means systematically managing payments to achieve short-term and long-term financial goals while maintaining a balance between income and expenses.

[0591] "Proposing a savings plan" involves suggesting specific actions to reduce unnecessary spending, based on the user's financial situation and spending habits.

[0592] The system for realizing this invention mainly consists of a server, a terminal, and a user. The server receives the user's attribute information and calculates an optimized asset allocation based on it. It acquires market data in real time from external data sources and analyzes it to adjust the user's asset portfolio as needed. In addition, it learns from past investment history and provides the most suitable investment strategy for the user. Furthermore, the server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on the results.

[0593] The device transmits user input information and data acquired from biosensors to the server. This information is processed by AI analysis and an emotion engine to gain a more accurate understanding of the user's emotional state and optimize investment strategies. Furthermore, savings plans and spending review suggestions, taking emotional states into account, are provided to the user through the device. As a result, users can manage their assets and daily expenses with greater peace of mind.

[0594] For example, if a user experiences stress due to a sudden increase in spending, the system will sense this emotional state and suggest revisions to their spending plan and savings strategies to alleviate their financial burden. This allows the user to manage their assets in a more planned manner. An example of a prompt from the generating AI model might be: "When the user's stress level increases, generate a plan to optimize spending. Specifically, after purchasing an expensive item, suggest savings that take into account future necessary expenses."

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The server receives user attribute information from the terminal. Input includes user attribute information such as risk tolerance, investment goals, and past investment history. Output is data that is stored on the server side and ready for subsequent processing. Specifically, each time the user completes information input, the data is sent to the server, which receives it and stores it in the database.

[0598] Step 2:

[0599] The server acquires market data in real time from external data sources. The input is the latest market data provided via an API. The output is market information used by an analysis engine to perform predetermined analyses. Specifically, the server calls the API at regular intervals to update the market information.

[0600] Step 3:

[0601] The server calculates the optimal asset allocation using the received user attribute information and acquired market data. The input is a combination of user attribute information and market data. The output is data on the optimized asset allocation. Specifically, it utilizes an AI algorithm to calculate the ideal allocation to each asset class.

[0602] Step 4:

[0603] The device collects emotional state data from biosensors and user input. Inputs include physiological data from sensors and user self-reports. Output is emotional information used for evaluation by an emotion engine. Specifically, it periodically acquires data from sensors and transmits that information to a server.

[0604] Step 5:

[0605] The server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on this. The inputs are emotional information and the existing spending plan. The output is a proposed improved spending plan. Specifically, it applies an algorithm that revises the user's spending plan while considering their current emotional state.

[0606] Step 6:

[0607] The server generates prompt messages using a generative AI model and provides suggestions to the user via alerts. Inputs include emotional states and a calculated optimal spending plan. The output is advice provided to the user through prompt messages. Specifically, it automatically generates appropriate savings suggestions based on the user's situation and notifies the user via their device.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] The system according to the present invention automatically manages and optimizes asset groups using AI technology, targeting novice investors and those with limited knowledge. This system is realized through the interaction of a server, a terminal, and a user.

[0626] First, the user enters their personal information through their device, such as their risk tolerance, investment goals, and current investment amount. This information is collected and recorded by the server.

[0627] The server receives attribute information provided by the user and uses AI algorithms to calculate the optimal asset allocation. Based on this calculation, it sends a portfolio suggestion to the user's terminal.

[0628] Furthermore, the server collaborates with external market data sources to collect market-related information in real time. The acquired market data is processed by an AI analysis engine to detect market trends and unusual price fluctuations, and to generate information necessary for investment decisions.

[0629] Based on the analysis results, the server automatically performs rebalancing according to market conditions. In this rebalancing process, the user's asset portfolio is optimized to match market conditions, and the configuration is adjusted as needed. This is to maintain overall balance when the value of a particular asset class increases or decreases.

[0630] Furthermore, the server learns the user's past investment history and analyzes patterns and trends using AI technology. Based on this, it proposes personalized investment strategies to the user. The proposals are notified to the user's device, supporting the user's daily investment decisions.

[0631] For example, if a user is willing to accept moderate risk and seeks long-term profits, the server will create a portfolio that takes this into account. A proposed portfolio with a balanced mix of stocks, bonds, and commodities will be presented to the user's terminal. Similarly, the server system will operate to immediately adjust the user's asset allocation in the event of a clear economic fluctuation.

[0632] In this way, the system of the present invention provides technical support to enable novice investors to engage in investment activities with confidence. This support improves investment efficiency, and users can strengthen their risk management without wasting time.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The user inputs attribute information such as their risk tolerance, investment goals, and initial asset amount through their device. The device then transmits this information to the server.

[0636] Step 2:

[0637] The server records attribute information received from the user in a database. Based on the recorded information, an AI algorithm calculates the optimal asset allocation and generates a portfolio suggestion suitable for the user.

[0638] Step 3:

[0639] The server sends the proposed portfolio to the user's terminal. The user reviews the received portfolio and makes adjustments as needed, or accepts the proposal.

[0640] Step 4:

[0641] The server continuously acquires market data in real time through external market data APIs. This data includes stock prices, economic indicators, news, and more.

[0642] Step 5:

[0643] The server processes acquired market data using an AI analysis engine to detect price fluctuations and trends. Based on the analysis results, the risk of rapid market fluctuations is assessed.

[0644] Step 6:

[0645] The server automatically rebalances the user's portfolio based on market analysis results. Specifically, it performs a process of changing the proportion of certain assets to optimize the overall balance.

[0646] Step 7:

[0647] The server analyzes the user's past investment history and learns patterns and trends. Using AI technology, it generates an investment strategy tailored to the user and notifies the user of the results on their device.

[0648] Step 8:

[0649] The server periodically evaluates the performance of the user's asset portfolio and generates a detailed report. This report is sent to the user's terminal as graphs and numerical data, serving as material for the user to review their investment performance.

[0650] (Example 1)

[0651] 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".

[0652] A challenge exists in that it is difficult for novice investors and users with limited knowledge to effectively obtain the information necessary to make appropriate investment decisions. Furthermore, responding quickly to market fluctuations and optimizing asset allocation requires effort and expertise, posing a barrier for many users. As a result, asset management efficiency may decrease, and risk management may become inadequate.

[0653] 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.

[0654] In this invention, the server includes means for receiving user characteristic information and calculating a rationalized asset allocation based on said characteristic information, means for instantly acquiring market information from an external information source and interpreting said market information, and means for automatically changing the user's asset composition based on the interpretation results. This enables diverse users to effectively manage their assets according to their risk tolerance and investment objectives, and to make investment decisions in real time in response to market changes.

[0655] "Characteristic information" refers to information about the user's attributes and characteristics, such as their risk tolerance, investment goals, and investment amount.

[0656] Asset allocation is the process of determining the proportion of assets to be divided among different investment targets, and it is an important method for managing investment risk and return.

[0657] "External information sources" refer to various data providers and platforms that offer market information, economic data, and other similar information.

[0658] "Market information" is a general term for information that influences investment decisions in financial markets, such as price trends, trading volume, and economic indicators.

[0659] "Asset allocation" refers to the overall distribution of the types and quantities of assets held by a user, and represents the contents of a portfolio that reflects their investment strategy.

[0660] "Rationalization" refers to eliminating waste to increase efficiency and making adjustments and improvements to achieve optimal results.

[0661] "Artificial intelligence technology" is a technology that enables machines to imitate human intelligent behavior, supporting processes such as data analysis, prediction, and decision-making.

[0662] This invention constructs a system that improves investment efficiency and enhances risk management through the interaction between a server, a terminal, and a user.

[0663] First, the user inputs characteristic information such as their risk tolerance and investment goals using the terminal's interface. This terminal is typically implemented as a web application or mobile application. This information is then transmitted to the server via network communication.

[0664] Next, the server calculates asset allocation based on the received characteristic information. Specifically, machine learning libraries such as TensorFlow and PyTorch are used for this calculation, and backpropagation and deep learning models are applied as AI algorithms. This method builds an investment strategy optimized for each user.

[0665] Furthermore, the server collects market information from external market sources. This is done through APIs of Yahoo Finance and other economic data providers. The acquired market information is analyzed using analytical engines such as Scikit-learn to detect trends and anomalies.

[0666] Based on this analysis, the server automatically rebalances the user's asset allocation. Specific asset adjustments, such as shifting from stocks to bonds, are made as needed. The results of this rebalancing are notified to the user's device, allowing them to easily track their latest investment status.

[0667] Finally, the server learns from past investment history and provides users with personalized investment strategies. This process utilizes Keras or similar deep learning frameworks to analyze behavioral patterns from investment history and generate optimal investment recommendations for the user.

[0668] For example, if a user states, "I want to pursue long-term profits while accepting moderate risk," the server interprets this as a prompt and generates a portfolio that matches that need. In more specific terms, this might be expressed as, "Please suggest an investment strategy that accepts moderate risk while pursuing long-term profits."

[0669] Thus, the system of the present invention provides users with a strategic yet user-friendly investment environment, supporting their daily investment decisions.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] Users input characteristic information such as their risk tolerance, investment goals, and current investment amount. This input is done through the terminal interface and recorded on the terminal as text or numerical data. The entered data is sent to the server in JSON or XML format.

[0673] Step 2:

[0674] The server analyzes the characteristic information received from the user and stores it in a database. Based on this stored information, a generative AI model is applied to calculate an optimized asset allocation. Specifically, the AI ​​algorithm uses TensorFlow and employs backpropagation techniques for the calculation. The output is the recommended portfolio composition.

[0675] Step 3:

[0676] The server accesses external sources to obtain market information. Market data collected via API calls is immediately processed by an AI analysis engine. Scikit-learn is used to analyze the data and detect market trends and anomalies. The output is the analyzed market information and its evaluation results.

[0677] Step 4:

[0678] The server automatically rebalances the asset allocation based on analyzed market information and user characteristics. The ratios of stocks and bonds are adjusted according to market conditions. The rebalanced asset allocation is sent to the user's terminal as a re-evaluated portfolio.

[0679] Step 5:

[0680] The server extracts past investment history from a database and analyzes patterns using machine learning techniques. It learns from past data using tools like Keras and generates an optimal investment strategy for the user. The generated strategy is output as a customized investment suggestion and notified to the user's device.

[0681] Step 6:

[0682] The terminal visually displays the latest portfolio information, rebalancing results, and personalized investment strategies transmitted from the server. Users receive support in making daily investment decisions based on this information. Specifically, the terminal visualizes data through a GUI, making it easy for users to understand and operate.

[0683] (Application Example 1)

[0684] 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".

[0685] For novice investors and those with limited knowledge, efficiently learning about asset management and investment strategies in financial markets is a challenging task. Furthermore, there is a need to provide users with appropriate educational content and support them in deepening their understanding of investing while responding to real-time market fluctuations. It is essential to bridge this complexity of investing and the knowledge gap, and to provide an environment where investors can confidently engage in investment activities.

[0686] 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.

[0687] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for learning past investment history and providing an investment strategy suitable for the user; means for providing learning content to support the improvement of the user's financial knowledge; and means for visualizing market simulations using augmented reality technology. This makes it possible for even novice investors to safely deepen their investment knowledge and engage in strategic investment activities while adapting to real-time market conditions.

[0688] "User attribute information" refers to personal profile data provided by investors, such as their risk tolerance, investment goals, and current investment amount.

[0689] "Optimized asset allocation" refers to the composition of an investment portfolio that balances risk and return, calculated by an AI algorithm based on the user's attribute information.

[0690] "Market data" refers to the latest information on the prices of assets traded in financial markets, such as stock prices, bond prices, and exchange rates.

[0691] "Means of analysis" refers to a function that uses an AI analysis engine to analyze collected market data and identify trends and anomalies in price fluctuations.

[0692] "Means for automatically adjusting asset groups" refers to a function that, based on analysis results, buys or sells assets to adapt the portfolio to the latest market conditions.

[0693] "Means of providing investment strategies" refers to a function that learns from past investment history and presents investment policies and action guidelines customized for each individual user.

[0694] "Means of providing learning content" refers to the function of supplying educational resources and simulation tools to improve users' financial knowledge.

[0695] "Methods for visualizing market simulations using augmented reality technology" refers to a function that uses AR technology to realistically reproduce market movements and investment results in a virtual environment, providing users with a visual experience.

[0696] This invention is a system designed to enable novice investors and users with limited knowledge to efficiently manage their assets and deepen their financial understanding. The system operates through the interconnectedness of the user's terminal, server, and external data sources.

[0697] On the server, attribute information is received through the user's terminal. This attribute information includes risk tolerance, investment goals, and current investment amount. Based on the received information, the server uses an AI algorithm and the Python library TensorFlow to calculate an optimized asset allocation.

[0698] The server retrieves market data in real time from sources such as the Yahoo Finance API and processes this data with an AI analysis engine. This allows the system to analyze market risks and opportunities and automatically adjust the user's assets based on the analysis results. By learning from the user's past investment history, a personalized investment strategy is provided and notified to the user's device.

[0699] Furthermore, the server delivers learning content to improve users' financial literacy. This content is customized using a generative AI model. In addition, AR technology using Unity is used to perform market simulations through augmented reality, providing users with an intuitive visual experience.

[0700] As a concrete example, when market fluctuations are anticipated, a scenario can be provided in which users learn risk management through an augmented reality system. For instance, a prompt such as "Consider the user's investment skill level and propose a simulation that teaches the basics of risk management" can be used to generate appropriate content based on the user's understanding.

[0701] This system allows users to access a wealth of information and visual insights when making real-time investment decisions, enabling them to invest with confidence.

[0702] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0703] Step 1:

[0704] The user's device receives attribute information such as risk tolerance, investment goals, and current investment amount. This input information is sent to the server via a secure protocol. The output at this stage is a dataset of attribute information.

[0705] Step 2:

[0706] The server inputs the received attribute information into an AI algorithm to calculate an optimized asset allocation for the user. This calculation uses the AI ​​framework TensorFlow and outputs a portfolio with an optimized risk-return balance.

[0707] Step 3:

[0708] The server acquires market data in real time from external data sources. This input market data includes stock prices, exchange rates, and commodity prices. This data is processed by an analysis engine, which then generates market trends and risk assessments as output.

[0709] Step 4:

[0710] The server automatically adjusts the user's assets based on analyzed market data and user attribute information. Specifically, it uses an AI algorithm to identify assets that should be bought or sold and outputs an optimized portfolio configuration.

[0711] Step 5:

[0712] The server learns from the user's past investment history and generates personalized investment strategies. It analyzes the user's past behavioral patterns and outputs advice that will be useful for future investment actions. This information is notified to the user's device.

[0713] Step 6:

[0714] The server provides users with customized learning content. Using a generative AI model, it generates prompts based on analysis results and outputs appropriate learning materials.

[0715] Step 7:

[0716] The user's device displays a market simulation using AR technology powered by Unity. Through the AR device, the user visually experiences market trends and investment results, gaining a practical learning experience as output.

[0717] 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.

[0718] The system according to the present invention provides investment optimization functions and investment advice that takes user emotions into consideration. The system exchanges data between the server, terminal, and user, and utilizes AI technology and an emotion engine.

[0719] In running the system, the user first inputs their personal information using a terminal. This includes risk tolerance, investment goals, and past investment history. This information is then transmitted to the server via the terminal.

[0720] The server uses AI algorithms to calculate the optimal asset allocation based on the received user attribute information and market data. Market data is acquired in real time using an external API and processed by the analysis engine. Furthermore, the server automatically rebalances the user's asset portfolio based on the analysis results.

[0721] This system incorporates an emotion engine that can recognize the user's emotional state. It learns the user's past emotional data and behavioral patterns, and the AI ​​predicts the user's emotional tendencies in the current investment situation. The device understands the user's current emotional state based on user input and data from biosensors. For example, if the user is stressed, the emotion engine will detect this and generate an alert to help adjust the investment strategy.

[0722] As a concrete example, suppose a user faces rapid market fluctuations and begins to feel anxious. In this situation, the emotion engine analyzes the user's unstable emotions, and the server recommends an investment strategy based on that analysis. For example, by suggesting a shift of funds to more stable assets, the user's mental burden can be reduced.

[0723] This system aims to improve investment performance by providing more personalized support for users' investment behavior and reducing the risks of emotional decisions. It is expected to function as a reliable investment support tool for both novice and experienced investors.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The user inputs attribute information such as their risk tolerance, investment goals, and past investment history through their device. The device then sends this information to the server.

[0727] Step 2:

[0728] The server records attribute information received from the user in a database and uses an AI algorithm to calculate the optimal asset allocation. At this stage, real-time market data is obtained using an external API and data analysis is performed.

[0729] Step 3:

[0730] Based on the market data analyzed by the server, the user's asset portfolio is automatically adjusted. The system verifies that the asset allocation is optimized and performs rebalancing as needed.

[0731] Step 4:

[0732] The system collects data about the user's current emotional state from their device. This may include user input and data from biosensors.

[0733] Step 5:

[0734] The server uses an emotion engine to analyze the user's past emotional data and current emotional state. It learns emotional patterns to evaluate the impact of emotional changes on investment decisions.

[0735] Step 6:

[0736] The emotion engine detects the user's emotional state, and the server adjusts its investment strategy based on that information. For example, if stress or anxiety levels rise, it will recommend more stable assets.

[0737] Step 7:

[0738] The server periodically evaluates the performance of the user's asset portfolio and generates reports that reflect the latest market conditions and sentiment analysis results. By sending these reports to the user's terminal, the user can check their investment status and make investment decisions with confidence.

[0739] (Example 2)

[0740] 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".

[0741] In investing, it is necessary to provide investment strategies that take into account not only user attribute information and market data, but also the user's emotional state. However, conventional systems have not adequately provided mechanisms for delivering personalized investment advice that takes emotions into account. As a result, users were at risk of making irrational investment decisions due to emotions such as stress and anxiety.

[0742] 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.

[0743] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing the market data; means for automatically adjusting the user's asset portfolio based on the analysis results; means for collecting biometric data including the user's emotional state and analyzing the user's emotional tendencies using the data; and means for providing an investment strategy suitable for the user using a generative AI model based on the emotional tendencies. This makes it possible to provide investment advice that also takes the user's emotions into consideration.

[0744] "User attribute information" refers to individual information such as each user's risk tolerance, investment goals, and past investment history.

[0745] "Optimized asset allocation" refers to a portfolio composition that distributes investment assets in a way that maximizes returns, based on user attribute information and market data.

[0746] "External data sources" refer to internet-based data providers that provide market data in real time.

[0747] "Market data" refers to data related to financial markets, such as stock prices, exchange rates, and economic indicators.

[0748] "Asset set" refers to the totality of all assets owned by a user.

[0749] "Rebalancing" refers to readjusting the composition of an asset portfolio to restore the optimal allocation.

[0750] "Biometric data" refers to physical or physiological information such as a user's heart rate and facial expressions.

[0751] "Emotional tendencies" refer to a user's emotional response patterns in specific situations.

[0752] A "generative AI model" refers to a computational model created using artificial intelligence to perform learning and inference from data.

[0753] An "investment strategy" refers to a plan or method for how to allocate and manage assets.

[0754] This invention is a system that combines user attribute information, emotional state, and market data to provide an investment strategy appropriate to the situation. Details regarding its implementation are provided below.

[0755] First, the user inputs attribute information such as risk tolerance, investment goals, and past investment history through a terminal. The terminal uses a standard input interface, such as a touchscreen or keyboard. This transmits the user's attribute information to the system.

[0756] Next, the server receives attribute information sent from the terminal, combines it with market data obtained from external data sources, and calculates an optimized asset allocation. For this purpose, an AI algorithm is used. Specifically, libraries such as NumPy and TensorFlow are utilized to perform calculations efficiently. Market data is obtained in real time from reliable data providers and processed by the analysis engine.

[0757] Furthermore, the device uses biosensors to collect the user's emotional state in real time. These biosensors include a heart rate monitor and a camera, which help to understand the user's stress level and emotional tendencies. This clarifies the user's current psychological state and makes it possible to detect anxiety and stress that may affect investment decisions.

[0758] The server uses collected emotional data to analyze it through an AI model and generate investment strategies based on emotional tendencies. This generation process involves inputting prompts into the generative AI model, such as "What asset classes are recommended when I feel stressed?", to derive accurate advice.

[0759] This system provides users with continuously personalized investment advice, supporting more rational and emotion-free investment decisions. Therefore, it aims to reduce the risks of emotional decisions and improve investment performance, making it a system that can be used with confidence by everyone from novice investors to experienced investors.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] Users enter investment attribute information using a terminal. This information includes risk tolerance, investment goals, and past investment history. The entered data is temporarily stored on the terminal for use in subsequent processing.

[0763] Step 2:

[0764] The terminal encrypts the attribute information entered by the user and securely transmits it to the server. SSL / TLS protocol is used to ensure the confidentiality of the information. The server stores the received data in a database and prepares to begin the analysis process.

[0765] Step 3:

[0766] The server accesses external data sources to acquire market data in real time. This data includes the latest stock prices, exchange rates, and economic indicators. This data is collected via APIs and processed by an analysis engine. The server then uses this information to understand market trends and prepares it for later use in AI calculations.

[0767] Step 4:

[0768] The server inputs user attribute information and acquired market data into an AI algorithm. The AI ​​algorithm utilizes machine learning, for example, and leverages libraries such as NumPy and TensorFlow. These tools are used to calculate an optimized asset allocation. At this stage, the calculation results are temporarily stored in preparation for later output.

[0769] Step 5:

[0770] The device uses biosensors to collect the user's emotional state. Data such as heart rate and facial expressions are collected in real time and temporarily stored on the device. This biometric data indicates stress levels and emotional tendencies and may influence investment strategies.

[0771] Step 6:

[0772] The server receives biometric data and analyzes it using an emotion engine. This analysis reveals the user's emotional tendencies, and the AI ​​model suggests investment strategies that are more suitable for them. Using the generative AI model, the prompt "Suggest the optimal investment strategy based on the user's emotional state" is entered, and specific advice is generated.

[0773] Step 7:

[0774] The server calculates the final investment strategy and notifies the user of the results. This notification is delivered, for example, via email or in-app messages, providing the user with the latest investment advice. This allows the user to make optimal investment decisions while considering market fluctuations and their own emotions.

[0775] (Application Example 2)

[0776] 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".

[0777] Modern consumers are often influenced by emotions when optimizing investments and spending, making it difficult to execute long-term plans. Furthermore, they struggle to make appropriate judgments in the face of rapid market fluctuations, resulting in a failure to properly revise asset allocations and spending. Additionally, there is a lack of personalized support based on each user's investment behavior and spending patterns, creating a demand for reliable assistance.

[0778] 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.

[0779] In this invention, the server includes means for receiving user attribute information and calculating an optimized asset allocation based thereon; means for acquiring market data from an external data source in real time and analyzing said market data; and means for recognizing the user's emotional state and optimizing the spending plan based on that emotional state. This enables the proposal of investment strategies and spending plans that take the user's emotional state into consideration, as well as rapid responses to sudden market changes.

[0780] "User attribute information" refers to information including an individual user's risk tolerance, investment goals, and past investment history, and is data that forms the basis for individual investment decisions.

[0781] "Optimized asset allocation" refers to an allocation calculated to ensure that assets are distributed most efficiently for the user, taking into account investment risk and return.

[0782] "External data sources" refer to information sources such as institutions and APIs that provide information on market trends and economic indicators, and include data obtained in real time.

[0783] "Market data analysis" refers to data processing that uses received market data to evaluate the current market situation and predict future trends.

[0784] "User asset portfolio" refers to the collective term for the diverse asset classes owned by a user, and to an investment portfolio constructed to achieve a specific objective.

[0785] "Learning from past investment history" is a process of analyzing a user's past investment activities and learning from the patterns and trends that can be derived from them.

[0786] "Recognition of the user's emotional state" refers to a system function that detects and understands the user's psychological state, and is evaluated using biosensors or self-reporting.

[0787] "Optimizing spending plans" means systematically managing payments to achieve short-term and long-term financial goals while maintaining a balance between income and expenses.

[0788] "Proposing a savings plan" involves suggesting specific actions to reduce unnecessary spending, based on the user's financial situation and spending habits.

[0789] The system for realizing this invention mainly consists of a server, a terminal, and a user. The server receives the user's attribute information and calculates an optimized asset allocation based on it. It acquires market data in real time from external data sources and analyzes it to adjust the user's asset portfolio as needed. In addition, it learns from past investment history and provides the most suitable investment strategy for the user. Furthermore, the server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on the results.

[0790] The device transmits user input information and data acquired from biosensors to the server. This information is processed by AI analysis and an emotion engine to gain a more accurate understanding of the user's emotional state and optimize investment strategies. Furthermore, savings plans and spending review suggestions, taking emotional states into account, are provided to the user through the device. As a result, users can manage their assets and daily expenses with greater peace of mind.

[0791] For example, if a user experiences stress due to a sudden increase in spending, the system will sense this emotional state and suggest revisions to their spending plan and savings strategies to alleviate their financial burden. This allows the user to manage their assets in a more planned manner. An example of a prompt from the generating AI model might be: "When the user's stress level increases, generate a plan to optimize spending. Specifically, after purchasing an expensive item, suggest savings that take into account future necessary expenses."

[0792] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0793] Step 1:

[0794] The server receives user attribute information from the terminal. Input includes user attribute information such as risk tolerance, investment goals, and past investment history. Output is data that is stored on the server side and ready for subsequent processing. Specifically, each time the user completes information input, the data is sent to the server, which receives it and stores it in the database.

[0795] Step 2:

[0796] The server acquires market data in real time from external data sources. The input is the latest market data provided via an API. The output is market information used by an analysis engine to perform predetermined analyses. Specifically, the server calls the API at regular intervals to update the market information.

[0797] Step 3:

[0798] The server calculates the optimal asset allocation using the received user attribute information and acquired market data. The input is a combination of user attribute information and market data. The output is data on the optimized asset allocation. Specifically, it utilizes an AI algorithm to calculate the ideal allocation to each asset class.

[0799] Step 4:

[0800] The device collects emotional state data from biosensors and user input. Inputs include physiological data from sensors and user self-reports. Output is emotional information used for evaluation by an emotion engine. Specifically, it periodically acquires data from sensors and transmits that information to a server.

[0801] Step 5:

[0802] The server uses an emotion engine to recognize the user's emotional state and optimizes the spending plan based on this. The inputs are emotional information and the existing spending plan. The output is a proposed improved spending plan. Specifically, it applies an algorithm that revises the user's spending plan while considering their current emotional state.

[0803] Step 6:

[0804] The server generates prompt messages using a generative AI model and provides suggestions to the user via alerts. Inputs include emotional states and a calculated optimal spending plan. The output is advice provided to the user through prompt messages. Specifically, it automatically generates appropriate savings suggestions based on the user's situation and notifies the user via their device.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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."

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] A means for receiving user attribute information and calculating an optimized asset allocation based on it,

[0829] A means for acquiring market data in real time from an external data source and analyzing said market data,

[0830] A means for automatically adjusting the user's asset group based on the analysis results,

[0831] A system that includes means for learning past investment history and providing investment strategies suitable for the user.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for notifying a user of changes to an asset group.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for periodically evaluating the performance of a user's asset portfolio and generating reports.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A means for receiving user characteristic information and calculating a rationalized asset allocation based on said characteristic information,

[0839] A means for instantly obtaining market information from external sources and interpreting said market information,

[0840] A means to automatically change the user's asset composition based on the interpretation results,

[0841] A means of learning from past investment history and providing an investment plan tailored to the user,

[0842] A system that includes means of enhancing user asset management using artificial intelligence technology.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for notifying a user of changes in asset composition.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for periodically analyzing the management status of a user's assets and generating a report document.

[0847] "Application Example 1"

[0848] (Claim 1)

[0849] A means for receiving user attribute information and calculating an optimized asset allocation based on it,

[0850] A means for acquiring market data in real time from an external data source and analyzing said market data,

[0851] A means for automatically adjusting the user's asset group based on the analysis results,

[0852] A means of learning from past investment history and providing investment strategies suitable for the user,

[0853] A means of providing learning content to support users in improving their financial knowledge,

[0854] A means of visualizing market simulations using augmented reality technology,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising means for notifying a user of changes to an asset group.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising means for periodically evaluating the performance of a user's asset portfolio and generating reports.

[0860] "Example 2 of combining an emotion engine"

[0861] (Claim 1)

[0862] A means for receiving user attribute information and calculating an optimized asset allocation based on it,

[0863] A means for acquiring market data in real time from an external data source and analyzing said market data,

[0864] A means to automatically adjust the user's asset set based on the analysis results,

[0865] A means for collecting biometric data including the user's emotional state and analyzing the user's emotional tendencies using said data,

[0866] A means of providing investment strategies tailored to users using a generative AI model based on emotional tendencies,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising means for notifying a user of changes to an asset set.

[0870] (Claim 3)

[0871] The system according to claim 1, further comprising means for periodically evaluating the performance of a user's asset collection and generating a report.

[0872] "Application example 2 when combining with an emotional engine"

[0873] (Claim 1)

[0874] A means for receiving user attribute information and calculating an optimized asset allocation based on it,

[0875] A means for acquiring market data in real time from an external data source and analyzing said market data,

[0876] A means for automatically adjusting the user's asset group based on the analysis results,

[0877] A means of learning from past investment history and providing investment strategies suitable for the user,

[0878] A means for recognizing the user's emotional state and optimizing the spending plan based on that emotional state,

[0879] A system that includes means to suggest spending reviews and savings plans when it detects an emotional state.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising means for notifying a user of changes to an asset group.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising means for periodically evaluating the performance of a user's asset portfolio and generating reports. [Explanation of Symbols]

[0884] 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 for receiving user attribute information and calculating an optimized asset allocation based on it, A means for acquiring market data in real time from an external data source and analyzing said market data, A means for automatically adjusting the user's asset group based on the analysis results, A means of learning from past investment history and providing investment strategies suitable for the user, A means of providing learning content to support users in improving their financial knowledge, A means of visualizing market simulations using augmented reality technology, A system that includes this.

2. The system according to claim 1, further comprising means for notifying a user of a change in an asset group.

3. The system according to claim 1, further comprising means for periodically evaluating the performance of a user's asset pool and generating a report.

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