system

Abstract

We provide the system. [Solution] A means of obtaining investment-related profile data from users, A means for performing analysis to propose the optimal asset allocation to the user based on the profile data, A means of collecting market data in real time and analyzing that data to support investment decisions, A means to automatically trade users' assets, A means to track portfolio performance and generate reports, A system that includes means of notifying users to encourage them to review their portfolios.

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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In conventional investment activities, users had to manually set asset allocation based on their own risk tolerance and investment goals, and further rebalance the portfolio according to market fluctuations. As a result, for beginners and users who do not have enough time, there was a problem that investment management was very complicated and it was difficult to make appropriate investment decisions. In addition, performance tracking and report creation also required manual work, and efficient management was required. 【Means for Solving the Problems】 【0005】 This invention provides a system that supports investment decisions by using AI to propose optimal asset allocation based on profile data acquired from users, and by collecting and analyzing market data in real time. It features an automatic trading function for the user's assets and performs periodic rebalancing to manage portfolio risk and maximize profits. Furthermore, it tracks portfolio performance and automatically generates reports, allowing users to constantly understand their investment status. It also supports users' investment decisions by providing a notification function that prompts portfolio review. 【0006】 A "user" refers to an individual or organization that uses the system to conduct investment activities. 【0007】 "Profile data" refers to information necessary to support investment activities, such as a user's risk tolerance, investment goals, and investment period. 【0008】 "Asset allocation" refers to the proportion of different types of assets in a portfolio. 【0009】 "Market data" refers to information such as prices, trading volume, and interest rates collected from stock, bond, and other financial markets. 【0010】 "Real-time" refers to the characteristic of data processing or information provision that is nearly instantaneous. 【0011】 A "portfolio" refers to the combination of all assets owned by a user. 【0012】 "Rebalancing" refers to the process of readjusting the allocation of assets in a portfolio to a set target ratio. 【0013】 "Performance" refers to the evaluation of investment results, particularly profits and losses, over a certain period. 【0014】 A "report" refers to a document or data set that summarizes the results and progress of investment activities. [Brief explanation of the drawing] 【0015】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. 【Embodiments for Carrying Out the Invention】 【0016】 An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0017】 First, the terms used in the following description will be explained. 【0018】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0019】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0020】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【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, 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】 In one embodiment of this invention, first, the user inputs information such as risk tolerance, investment goals, and investment period into a terminal in order to set up their investment profile. This information is then transmitted from the terminal to the server. 【0037】 The server uses an AI algorithm based on the received profile data to calculate the optimal asset allocation for each user. For example, for a user with a moderate risk tolerance, it suggests a standard asset allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device. 【0038】 The server also collects and analyzes market data in real time from market information providers. If the analysis reveals any information that could influence investment decisions, it takes that into account and adjusts the proposed portfolio accordingly. 【0039】 The system incorporates an automated trading function, where the server executes actual trades based on recommended asset allocations using the user's funds. Trades are executed via APIs with financial institutions and securities trading platforms. 【0040】 The server continuously tracks portfolio performance data and generates performance reports at regular intervals. These reports include key metrics such as current asset status, profits and losses, and return rates, and are provided to the user via their device. 【0041】 Furthermore, the system automatically rebalances the portfolio in response to its risk profile and market fluctuations. This rebalancing is performed when the asset allocation deviates from the target ratio, assisting the user in managing their risk. 【0042】 For example, if the stock market surges and the proportion of stocks reaches 70%, the server will automatically reallocate bonds and cash to adjust the portfolio back to the target ratio. 【0043】 Users can stay informed of their investment status at all times by checking reports that are regularly notified via their devices, and can update their profile information or reset their goals as needed. This notification feature encourages investment reviews and supports users in making optimal decisions. 【0044】 Through these embodiments, the invention is designed to enable users to manage their investments effectively and sustainably. 【0045】 The following describes the processing flow. 【0046】 Step 1: 【0047】 The user accesses the initial setup screen using their device and enters profile data such as risk tolerance, investment goals, and investment period. This entered data is then sent from the device to the server. 【0048】 Step 2: 【0049】 The server analyzes the received user profile data and uses AI to design the optimal asset allocation for the user. The designed asset allocation is then proposed to the user again via the terminal. 【0050】 Step 3: 【0051】 The server collects market data in real time from market information providers via APIs. The collected market data is analyzed by AI algorithms to generate insights that influence users' investment decisions. 【0052】 Step 4: 【0053】 The server automatically trades the user's assets based on the generated asset allocation plan. It executes orders via API from financial institutions and securities trading platforms, buying and selling based on the recommended asset allocation. 【0054】 Step 5: 【0055】 The server combines trading information and market data to periodically evaluate and track portfolio performance. The resulting data is generated as a performance report, which is then provided to the user via their terminal. 【0056】 Step 6: 【0057】 The server automatically rebalances the portfolio if the asset allocation deviates from the set target ratio. In this process, it buys and sells assets as needed to adjust the asset allocation back to the appropriate ratio. 【0058】 Step 7: 【0059】 Users receive notifications via their devices, allowing them to check the latest portfolio performance and recommended actions. They can update their profile information or reset their investment goals as needed. The server then re-analyzes these changes and provides new recommendations. 【0060】 (Example 1) 【0061】 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." 【0062】 It is difficult for investors to dynamically manage asset allocation according to their risk tolerance and goals in a diverse market environment. In particular, analyzing vast amounts of market information in real time and making efficient investment decisions based on that analysis requires advanced skills and knowledge. Therefore, there is a challenge in that it is difficult for individual investors to immediately respond to market fluctuations and make optimal adjustments to their asset portfolios. 【0063】 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. 【0064】 In this invention, the server includes means for acquiring investment profile information from a user, means for performing analysis based on the profile information using a generated AI algorithm to propose an optimal asset allocation to the user, and means for collecting market information in real time and analyzing the information using data processing technology to support investment decisions. As a result, the user can use the automated system to maintain an optimal asset allocation in response to market fluctuations in real time and perform effective investment management. 【0065】 A "user" refers to an individual or organization that uses the system to set up an investment profile and receive asset allocation suggestions. 【0066】 "Investment profile information" refers to information that users enter into the system, such as their risk tolerance, investment goals, and investment period, and is used to propose asset allocations. 【0067】 "Generated AI algorithms" refer to programs developed using artificial intelligence technology that optimize asset allocation based on the user's profile information. 【0068】 "Asset allocation" refers to the proportion of how a user distributes their assets across different investment targets (e.g., stocks, bonds, cash). 【0069】 "Market information" refers to data related to financial markets, including price trends, economic indicators, and financial news. 【0070】 "Data processing technology" refers to methods and techniques for analyzing collected information and generating useful data from it. 【0071】 "Investment decision" refers to the evaluation and consideration made by a user or system to determine a specific investment action. 【0072】 "Real-time" refers to a timeframe in which the latest information is immediately retrieved, processed, or made available, typically involving extremely short waiting times. 【0073】 An "asset portfolio" refers to the collection of all investment assets a user owns, and the overall asset allocation within this portfolio influences the investment strategy. 【0074】 This invention is a system aimed at enabling users to properly set up their investment profiles, optimize asset allocation using AI algorithms, and manage their assets sustainably. 【0075】 First, the user uses their device to enter profile information such as their risk tolerance, investment goals, and investment timeframe. This input process is performed via a regular web browser or a dedicated application. 【0076】 The device sends this entered profile information to the server. The server receives this information, and the generated AI algorithm utilizes machine learning libraries such as Python's Scikit-learn and TENSORFLOW®. This allows the server to calculate and propose the optimal asset allocation based on the user's profile information. 【0077】 As a concrete example, for a user with a moderate risk tolerance, the AI ​​suggests an allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device and displayed in a visually easy-to-understand format. 【0078】 Furthermore, the server collects real-time market data from market information providers using APIs. This collected data is stored in a database, analyzed using data processing technologies, and used to inform investment decisions. Specific examples include using the Yahoo Finance API or similar market data APIs. 【0079】 Based on this analysis, the server continuously tracks the performance of the user's portfolio. The results are periodically generated as performance reports and provided to the user via their terminal. This process utilizes data visualization libraries such as Matplotlib. 【0080】 Furthermore, the server automatically rebalances the portfolio in response to market fluctuations. For example, if the stock market rises and the proportion of stocks reaches 70%, it automatically adjusts the proportion of bonds and cash. This ensures that users always maintain an optimal asset balance. 【0081】 As an example of a prompt, you might use text such as, "Suggest a portfolio of 60% stocks, 30% bonds, and 10% cash for an investor with a moderate risk tolerance. Explain how to automatically rebalance the portfolio in case of market fluctuations." 【0082】 This invention integrates such diverse technologies to help users consistently and effectively manage their investments. 【0083】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0084】 Step 1: 【0085】 Users enter investment profile information using a terminal. Specifically, they enter and confirm information such as risk tolerance, investment goals, and investment period. The entered data is formatted by the terminal's input interface and sent to the server. 【0086】 Step 2: 【0087】 The server analyzes investment profile information received from the terminal. It records the input information in a database and feeds that data into an AI algorithm to calculate the optimal asset allocation for the user. This calculation uses a generative AI model to perform data calculations and output a specific asset allocation (e.g., 60% stocks, 30% bonds, 10% cash). 【0088】 Step 3: 【0089】 The server sends the generated asset allocation proposal to the terminal and presents it to the user. Visualization tools are used to visually represent the data, making it easy for the user to understand the proposal. The terminal receives this proposal and displays it to the user. 【0090】 Step 4: 【0091】 The server collects market data in real time from market information providers. The data obtained using the API is stored in a database and analyzed using data processing techniques as needed. During this process, analysis is performed based on the latest market conditions, and information that influences investment decisions is extracted. 【0092】 Step 5: 【0093】 Based on the analyzed market information, the server determines whether the user's asset allocation needs to be adjusted. This analysis may prompt a recalculation of the asset allocation, and in some cases, an automatic rebalancing may occur. If applicable, the user will be notified of the recalculated allocation. 【0094】 Step 6: 【0095】 After obtaining user consent, the server executes automated trades. Based on the investment portfolio, it buys and sells appropriate assets via API communication with the trading platform. This ensures that trades are executed according to the calculated asset allocation. 【0096】 Step 7: 【0097】 The server continuously monitors portfolio performance after trades and generates performance reports at specified intervals. These reports include an analysis of investment results and risk levels, and are provided to the user via their terminal. Users can use these reports to make further investment decisions. 【0098】 (Application Example 1) 【0099】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0100】 Modern consumers have diverse financial situations and needs, requiring efficient and optimal capital management. However, conventional capital management systems struggle to provide personalized capital plans based on detailed user purchase history and future spending forecasts, often resulting in inefficient capital allocation. Therefore, a system that enables capital management optimized for each user is necessary. 【0101】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0102】 In this invention, the server includes means for acquiring financial profile information from the user, means for performing analysis to propose an optimal capital allocation to the user based on the profile information, and means for collecting the user's purchase history and balance information and predicting future spending. This enables the user to enjoy a capital plan that is tailored to their individual financial situation and predicted economic activity. 【0103】 "Financial profile information" refers to data about the user's economic situation and capital goals, including information such as risk tolerance and planned spending. 【0104】 "Capital allocation" is a method of managing risk and return by optimally distributing a user's capital across different asset categories. 【0105】 "Performing analysis" is the process of using algorithms based on collected data to generate insights for optimal capital management. 【0106】 "Purchase history" refers to a record of products and services that a user has purchased in the past, and is information used to understand their consumption trends. 【0107】 "Future spending forecasting" is the process of predicting what kind of spending a user is likely to do in the future, based on past data and trends. 【0108】 A "capital plan" refers to a strategic plan that uses a user's assets to achieve future financial goals. 【0109】 To implement this invention, the server executes a program that generates an optimal capital allocation based on financial profile information obtained from the user. This program analyzes the user's profile information, including their risk tolerance and capital targets, along with acquired purchase history and balance information, to predict future spending. This allows the program to propose a specific capital plan for each individual user. 【0110】 This system executes AI algorithms using Python on the server to perform analysis and prediction. Pandas and NumPy are used for data processing, and scikit-learn and TensorFlow are used to build machine learning models. The server also uses MySQL® as its database management system to efficiently store and manipulate user information. On the user's device, a frontend built with React Native allows users to easily view information about their financial status and future spending. 【0111】 As a concrete example, when a user plans a trip, the server predicts other spending patterns that may affect the travel expenses and suggests appropriate capital allocation before and after the trip. This allows the user to maintain sound capital management even after the impact of the trip. 【0112】 Example of an input prompt for a generating AI model: "The user is planning to buy a new house. Please propose a financial plan for the next five years, taking this large expenditure into consideration." 【0113】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0114】 Step 1: 【0115】 The server receives user profile information. This information includes the user's risk tolerance, capital targets, purchase history, and balance information. This data is first stored in a database and prepared for subsequent processing. 【0116】 Step 2: 【0117】 The server retrieves user information from the database and preprocesses the data using the Python Pandas library. The output at this stage is user information converted into a parseable format. Preprocessing includes imputing missing data and preparing non-digital data. 【0118】 Step 3: 【0119】 The server passes pre-processed data as input to an AI algorithm to predict future spending. In this process, a model built using TensorFlow analyzes the user's spending habits and predicts future expenditures. The output is predictive data showing what the user is likely to spend in the future. 【0120】 Step 4: 【0121】 The server generates an optimal capital plan based on future expenditure forecasts. A model is applied using the Python scikit-learn library to suggest how to allocate capital. The output is a capital allocation proposal optimized for the user's objectives. 【0122】 Step 5: 【0123】 The terminal receives the proposed capital plan from the server and presents the information to the user. Through an application built with React Native, the user can view the plan and provide feedback or make modifications as needed. 【0124】 Step 6: 【0125】 When a user provides feedback through their device, the server saves that information back into the database. This process is continuous, and the feedback is taken into account when generating the next plan, enabling better capital management. 【0126】 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. 【0127】 In one embodiment of this invention, first, the user sets up an investment profile using a terminal. Here, the user inputs information such as risk tolerance, investment goals, and investment period into the terminal. This input data is transmitted from the terminal to the server. 【0128】 The server utilizes AI to analyze the received profile data and establish an optimal asset allocation for each user. This allocation is based on the user's risk tolerance and investment goals and is presented to the user via their device. 【0129】 Next, the server collects market data in real time. The market data obtained from market information providers is analyzed by an AI algorithm to generate insights that support investment decisions. This allows the user to be presented with an optimal asset allocation. 【0130】 Furthermore, this configuration incorporates an emotion analysis engine to understand the user's emotional state. The user's emotions are analyzed based on text messages entered through the device and data obtained from biosensors. The results of the emotion analysis are reflected in the user's investment strategy, and asset allocation and investment policies are adjusted in real time as needed. 【0131】 The emotion engine can assess the user's stress and anxiety levels, and based on this, risk tolerance is dynamically adjusted. For example, if high stress is detected through emotion analysis, the server will increase investment in lower-risk assets. 【0132】 The system can also automate trading, with the server using user funds to execute trades based on an evaluated asset allocation. It connects with financial institutions and securities trading platforms via APIs, ensuring secure and rapid buying and selling. 【0133】 The server periodically monitors performance, and the progress of the investment portfolio is sent to the terminal as a report. Through this report, users can always see how their investments are progressing. 【0134】 For example, if a user becomes emotionally unstable during a transaction, the server immediately analyzes the information and automatically adjusts the asset allocation to alleviate their anxiety. This allows users to continue making stable investments without being swayed by their emotions. 【0135】 Thus, by integrating AI and an emotion analysis engine, this invention provides investment support tailored to individual users, thereby supporting safer and more efficient asset management. 【0136】 The following describes the processing flow. 【0137】 Step 1: 【0138】 Users access the investment profile settings screen using their device and enter information such as risk tolerance, investment goals, and investment period. This profile data is then sent from the device to the server. 【0139】 Step 2: 【0140】 The server analyzes the received user profile data and uses an AI algorithm to calculate the optimal asset allocation for the user. The calculated allocation result is then presented to the user via their device. 【0141】 Step 3: 【0142】 The server collects market data in real time through market information providers' APIs and analyzes it using AI. Based on the analysis results, it generates insights to support investment decision-making. 【0143】 Step 4: 【0144】 The server automatically trades the user's assets based on the recommended asset allocation. It executes buy and sell orders through API connections with financial institutions and securities trading platforms. 【0145】 Step 5: 【0146】 The server uses an emotion analysis engine to determine the user's emotional state. It analyzes text data or sensor data transmitted from the terminal to assess stress and anxiety levels. 【0147】 Step 6: 【0148】 The server dynamically adjusts the user's risk tolerance as needed, based on their emotional state. Based on the adjusted risk profile, it recalculates the asset allocation and reflects it in the user's investment strategy. 【0149】 Step 7: 【0150】 The server periodically tracks portfolio performance and generates a report summarizing the evaluation results. This report is provided to the user via the terminal. 【0151】 Step 8: 【0152】 Users can receive notifications via their devices and check the current status of their portfolio and suggested actions. Profile information can be updated in response to changes in emotional state and market conditions. 【0153】 (Example 2) 【0154】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0155】 The problems that this invention aims to solve are to dynamically propose the optimal resource allocation in investment activities according to the user's risk tolerance and investment goals, thereby improving the accuracy of investment decisions, and to provide an environment in which users can continue investing with peace of mind while mitigating the influence of their emotional state on their investment strategy. Furthermore, it aims to streamline the user's asset management by enabling the execution of rapid and secure transactions using market data. 【0156】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0157】 In this invention, the server includes means for acquiring characteristic information about investments from the user, means for collecting and analyzing exchange information in real time to support investment decisions, and means for analyzing the user's emotional state using an emotion analysis engine and dynamically adjusting resource allocation and investment policies based on the results. This makes it possible to optimize investments to suit the individual needs of the user, execute transactions safely and quickly, and enhance user confidence. 【0158】 A "user" is an individual or legal entity that uses the system to conduct investment activities. 【0159】 "Investment-related characteristic information" refers to individual investment-related information such as the user's risk tolerance, investment goals, and investment period. 【0160】 "Analysis" involves collecting and analyzing data to derive resource allocation and investment strategies that are appropriate for the user. 【0161】 "Exchange information" refers to financial data collected from the market in real time, including information such as price fluctuations and trading volume. 【0162】 An "emotion analysis engine" is a technology that evaluates emotional states based on data provided by users and uses this information to adjust investment strategies. 【0163】 "Resource allocation" refers to distributing a user's investment assets across different investment targets, and is done while considering the balance between risk and return. 【0164】 "Supporting investment decisions" means providing users with information based on collected data and analysis results to support their investment decisions. 【0165】 "Executing a transaction" means using a user's assets to buy or sell in the market. 【0166】 "Safe and fast" means that transactions are conducted without delay while maintaining security. 【0167】 To implement this invention, the roles of the server, terminal, and user are clearly defined, and a configuration is adopted that ensures the entire system operates efficiently. 【0168】 The user first uses a terminal to input characteristic information about their investment. Here, they enter detailed information such as risk tolerance, investment goals, and investment period to form their characteristic profile. This data is transmitted to the server via a secure protocol such as SSL. 【0169】 The server utilizes a generative AI model to analyze the received characteristic information. This model calculates the optimal resource allocation tailored to the user's needs based on the profile data. The server also collects exchange information from the market in real time and analyzes it using AI algorithms to provide crucial insights for investment decisions. 【0170】 Simultaneously, the server uses an emotion analysis engine to assess the user's emotional state. This uses text messages entered by the user and data from biosensors to identify the user's stress and anxiety levels, and then dynamically adjusts resource allocation and investment strategies based on the results. 【0171】 When a financial transaction occurs, the server communicates with financial institutions and financial transaction platforms via APIs, creating a system that ensures secure and rapid transaction execution. 【0172】 For example, if a user feels anxious due to fluctuations in the stock market, that information is immediately analyzed by the server, and a conservative resource allocation is proposed to alleviate the user's anxiety, allowing the user to continue their investment activities with peace of mind. 【0173】 Examples of prompts to input into a generative AI model include the following: 【0174】 "As a company employee in my 30s, I have a moderate risk tolerance and want to increase my assets to buy a house within the next 10 years. I want to be able to cope with market fluctuations and have emotional security. What would be the optimal asset allocation?" 【0175】 This invention, with its configuration and functions as described above, enables flexible and secure asset management tailored to the individual needs of users. 【0176】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0177】 Step 1: 【0178】 The user uses a terminal to input characteristic information about their investment. This includes risk tolerance, investment goals, and investment period. This input data is sent to the server. Specifically, the user types the necessary information into a dedicated input form and presses the "Submit" button, which securely transfers the data to the server. 【0179】 Step 2: 【0180】 The server analyzes the received characteristic information. A generative AI model is used for this analysis. User profile data is received as input, and the model uses this to calculate the optimal resource allocation for the user. As output, investment options and their ratios are generated and stored on the server. Specifically, the server processes the profile data through the AI ​​model, and the algorithm returns the optimal result. 【0181】 Step 3: 【0182】 The server collects exchange information in real time and analyzes it using AI algorithms. It receives price data and trading volume information from various markets as input. This generates insights to support investment decisions, which are then provided to the user. In its specific operation, the server obtains market data via APIs, analyzes it, and extracts trends. 【0183】 Step 4: 【0184】 The server uses an emotion analysis engine to evaluate the user's emotional state. It receives text messages and biometric data as input, analyzes them, and outputs stress and anxiety levels. Based on this, resource allocation and investment strategies are adjusted. Specifically, the server uses the emotion analysis engine to analyze text and calculate emotional indicators extracted from the data. 【0185】 Step 5: 【0186】 The server executes transactions using user resources. It interacts with financial institutions and financial trading platforms via APIs to conduct safe and rapid buying and selling. During transactions, commands regarding the addition or reduction of resources are issued and executed accordingly. Specifically, the server sends orders via APIs and confirms their completion in real time. 【0187】 Step 6: 【0188】 The server monitors operational performance and generates periodic reports. The server takes operational data as input and analyzes the results based on this data. As output, a performance report is generated and sent to the user's terminal. Specifically, the server periodically aggregates data, formats it into a report, and then sends it. 【0189】 (Application Example 2) 【0190】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0191】 Conventional investment support systems lacked dynamic risk management that took into account the user's emotional state, resulting in the inability to appropriately allocate assets according to the user's psychological condition. Furthermore, it was difficult to integrate real-time market information with the user's emotional state to adjust investment strategies. Therefore, the challenge was to create a system that allowed users to maintain stable asset management without being influenced by their emotions. 【0192】 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. 【0193】 In this invention, the server includes means for obtaining investment profile information from the user, means including an emotion analysis engine that dynamically adjusts risk tolerance based on the user's emotional state, means for collecting and analyzing market information in real time to support investment decisions, means for tracking portfolio performance and generating reports, and means for notifying the user to prompt a re-evaluation of the portfolio. This enables stable asset management that is not dependent on emotions by integrating flexible risk management that responds to the user's emotional state with real-time market information analysis. 【0194】 "Profile information" refers to data that includes personal information about the user, such as investment goals, risk tolerance, and investment timeframe. 【0195】 "Asset allocation" is the process of determining the optimal combination of financial products and assets based on the user's profile information. 【0196】 "Market information" refers to fluctuation data in financial markets, such as stock prices, interest rates, and other economic indicators. 【0197】 A "sentiment analysis engine" is an algorithm that evaluates a user's emotional state and dynamically adjusts investment strategies based on that evaluation. 【0198】 "Risk tolerance" is an indicator that represents the level of risk a user is willing to accept in their investments. 【0199】 A "report" is a document that shows the performance of a portfolio and the results of an evaluation of its asset allocation. 【0200】 A "portfolio" is a combination of multiple financial products or assets held by a user. 【0201】 The system that implements this application consists of a user's terminal, such as a smartphone or computer, and a server that performs AI and data analysis in the backend. The server implements a generative AI model for investment analysis using Python and TensorFlow, and performs sentiment analysis using the natural language processing library NLTK. 【0202】 The terminal collects investment profile information from the user. This includes data such as investment goals, risk tolerance, and investment timeframe. This information is transmitted to the server via the internet. Upon receiving the user's profile information, the server uses an AI model to calculate the optimal asset allocation. Market information is obtained by the server in real time from external market data providers and used to make investment strategy decisions. 【0203】 The sentiment analysis engine analyzes text data sent from the user's device and evaluates their emotional state using NLTK. For example, if a user enters text indicating anxiety or stress, their risk tolerance is dynamically adjusted based on that emotional state, and a more conservative asset allocation is automatically suggested. 【0204】 The server has the capability to automatically trade users' assets and executes transactions securely through the APIs of appropriate financial institutions. It also continuously monitors asset performance and periodically generates reports to send to the user's terminal. 【0205】 For example, if a user enters a message into the system such as "I'm worried about recent economic news," the server will perform sentiment analysis and detect a state of "anxiety." As a result, the server will adjust the user's asset allocation to a lower-risk plan and present a new allocation proposal. 【0206】 An example of a prompt that utilizes a generative AI model is, "Consider the user's latest sentiment analysis results and suggest a low-risk asset allocation." Using this prompt, the system can provide investment strategies tailored to individual users. 【0207】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0208】 Step 1: 【0209】 The terminal retrieves investment profile information from the user. This profile information includes risk tolerance, investment goals, and investment timeframe. The entered data is then prepared for transmission to the server. 【0210】 Step 2: 【0211】 The device also acquires sentiment data from the user. This data may be entered as a text message. The acquired sentiment data is then put into a waiting state to be sent to the server. 【0212】 Step 3: 【0213】 The server receives profile information and sentiment data sent from the terminal. It analyzes the received data, processes it using a generative AI model based on TensorFlow, and calculates the optimal asset allocation for the user. As a result, an asset allocation proposal is output. 【0214】 Step 4: 【0215】 The server collects market information in real time from external data providers. This market information is used as input for AI models and serves as foundational data for up-to-date investment decisions. Subsequently, investment strategies are generated through the analysis of this market information. 【0216】 Step 5: 【0217】 The server uses an NLTK-based sentiment analysis engine to analyze user sentiment data. Based on the analysis results, it dynamically adjusts the user's risk tolerance. This process influences the reassessment of asset allocation. 【0218】 Step 6: 【0219】 The server sets up automated trading based on the new asset allocation plan. It issues trading orders for the held assets through the financial institution's API, ensuring secure buying and selling of assets. 【0220】 Step 7: 【0221】 The server regularly tracks the latest asset performance and generates an analysis report. The generated report is sent to the terminal, allowing the user to check the progress of their investments. 【0222】 Step 8: 【0223】 Based on the sentiment analysis results as a concrete example, the server generates and presents the next optimal investment proposal to the user using the prompt message, "Considering the user's latest sentiment analysis results, please propose a low-risk asset allocation." This process allows the user to continue making appropriate investments without relying on emotions. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 [Second Embodiment] 【0228】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0229】 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. 【0230】 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). 【0231】 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. 【0232】 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. 【0233】 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). 【0234】 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. 【0235】 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. 【0236】 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. 【0237】 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. 【0238】 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. 【0239】 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". 【0240】 In one embodiment of this invention, first, the user inputs information such as risk tolerance, investment goals, and investment period into a terminal in order to set up their investment profile. This information is then transmitted from the terminal to the server. 【0241】 The server uses an AI algorithm based on the received profile data to calculate the optimal asset allocation for each user. For example, for a user with a moderate risk tolerance, it suggests a standard asset allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device. 【0242】 The server also collects and analyzes market data in real time from market information providers. If the analysis reveals any information that could influence investment decisions, it takes that into account and adjusts the proposed portfolio accordingly. 【0243】 The system incorporates an automated trading function, where the server executes actual trades based on recommended asset allocations using the user's funds. Trades are executed via APIs with financial institutions and securities trading platforms. 【0244】 The server continuously tracks portfolio performance data and generates performance reports at regular intervals. These reports include key metrics such as current asset status, profits and losses, and return rates, and are provided to the user via their device. 【0245】 Furthermore, the system automatically rebalances the portfolio in response to its risk profile and market fluctuations. This rebalancing is performed when the asset allocation deviates from the target ratio, assisting the user in managing their risk. 【0246】 For example, if the stock market surges and the proportion of stocks reaches 70%, the server will automatically reallocate bonds and cash to adjust the portfolio back to the target ratio. 【0247】 Users can stay informed of their investment status at all times by checking reports that are regularly notified via their devices, and can update their profile information or reset their goals as needed. This notification feature encourages investment reviews and supports users in making optimal decisions. 【0248】 Through these embodiments, the invention is designed to enable users to manage their investments effectively and sustainably. 【0249】 The following describes the processing flow. 【0250】 Step 1: 【0251】 The user accesses the initial setup screen using their device and enters profile data such as risk tolerance, investment goals, and investment period. This entered data is then sent from the device to the server. 【0252】 Step 2: 【0253】 The server analyzes the received user profile data and uses AI to design the optimal asset allocation for the user. The designed asset allocation is then proposed to the user again via the terminal. 【0254】 Step 3: 【0255】 The server collects market data in real time from market information providers via APIs. The collected market data is analyzed by AI algorithms to generate insights that influence users' investment decisions. 【0256】 Step 4: 【0257】 The server automatically trades the user's assets based on the generated asset allocation plan. It executes orders via API from financial institutions and securities trading platforms, buying and selling based on the recommended asset allocation. 【0258】 Step 5: 【0259】 The server combines trading information and market data to periodically evaluate and track portfolio performance. The resulting data is generated as a performance report, which is then provided to the user via their terminal. 【0260】 Step 6: 【0261】 The server automatically rebalances the portfolio if the asset allocation deviates from the set target ratio. In this process, it buys and sells assets as needed to adjust the asset allocation back to the appropriate ratio. 【0262】 Step 7: 【0263】 Users receive notifications via their devices, allowing them to check the latest portfolio performance and recommended actions. They can update their profile information or reset their investment goals as needed. The server then re-analyzes these changes and provides new recommendations. 【0264】 (Example 1) 【0265】 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". 【0266】 It is difficult for investors to dynamically manage asset allocation according to their risk tolerance and goals in a diverse market environment. In particular, analyzing vast amounts of market information in real time and making efficient investment decisions based on that analysis requires advanced skills and knowledge. Therefore, there is a challenge in that it is difficult for individual investors to immediately respond to market fluctuations and make optimal adjustments to their asset portfolios. 【0267】 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. 【0268】 In this invention, the server includes means for acquiring investment profile information from a user, means for performing analysis based on the profile information using a generated AI algorithm to propose an optimal asset allocation to the user, and means for collecting market information in real time and analyzing the information using data processing technology to support investment decisions. As a result, the user can use the automated system to maintain an optimal asset allocation in response to market fluctuations in real time and perform effective investment management. 【0269】 A "user" refers to an individual or organization that uses the system to set up an investment profile and receive asset allocation suggestions. 【0270】 "Investment profile information" refers to information that users enter into the system, such as their risk tolerance, investment goals, and investment period, and is used to propose asset allocations. 【0271】 "Generated AI algorithms" refer to programs developed using artificial intelligence technology that optimize asset allocation based on the user's profile information. 【0272】 "Asset allocation" refers to the proportion of how a user distributes their assets across different investment targets (e.g., stocks, bonds, cash). 【0273】 "Market information" refers to data related to financial markets, including price trends, economic indicators, and financial news. 【0274】 "Data processing technology" refers to methods and techniques for analyzing collected information and generating useful data from it. 【0275】 "Investment decision" refers to the evaluation and consideration made by a user or system to determine a specific investment action. 【0276】 "Real-time" refers to a timeframe in which the latest information is immediately retrieved, processed, or made available, typically involving extremely short waiting times. 【0277】 An "asset portfolio" refers to the collection of all investment assets a user owns, and the overall asset allocation within this portfolio influences the investment strategy. 【0278】 This invention is a system aimed at enabling users to properly set up their investment profiles, optimize asset allocation using AI algorithms, and manage their assets sustainably. 【0279】 First, the user uses the terminal to input profile information such as their risk tolerance, investment goals, investment period, etc. This input operation is performed through a normal web browser or a dedicated application. 【0280】 The terminal sends this input profile information to the server. The server receives this and, in the generated AI algorithm, utilizes machine learning libraries such as Python's Scikit-learn and TensorFlow. Based on this, it calculates and proposes an optimal asset allocation based on the user's profile information. 【0281】 As a specific example, for a user with a medium risk tolerance, the AI proposes an allocation of 60% stocks, 30% bonds, and 10% cash. This proposal is presented to the user through the terminal and displayed in a visually understandable format. 【0282】 Furthermore, the server collects real-time market data from market information providers using an API. This collected data is stored in a database, analyzed using data processing techniques, and utilized for investment decisions. As a specific example, the Yahoo Finance API or similar market data APIs may be used. 【0283】 Based on this analysis, the server tracks the continuous performance of the user's portfolio. The results are periodically generated as a performance report and provided to the user through the terminal. Data visualization libraries such as Matplotlib are utilized in this process. 【0284】 Also, the server automatically rebalances the portfolio according to market fluctuations. For example, when the stock market rises and the proportion of stocks reaches 70%, the proportions of bonds and cash are automatically adjusted. This enables the user to always maintain an optimal asset balance. 【0285】 As an example of a prompt text, it is conceivable to use text such as "Propose a portfolio of 60% stocks, 30% bonds, and 10% cash for investors with a medium risk tolerance. Explain how to automatically rebalance in case of market fluctuations." 【0286】 The present invention integrates such various technologies and supports the user to perform consistent and effective investment management. 【0287】 The flow of the specific process in Example 1 will be described using FIG. 11. 【0288】 Step 1: 【0289】 The user inputs investment profile information using a terminal. Specifically, information such as risk tolerance, investment goal, and investment period is input and confirmed. The input data is formatted by the input interface of the terminal and transmitted to the server. 【0290】 Step 2: 【0291】 The server analyzes the investment profile information received from the terminal. The input information is recorded in the database, and by inputting the data into an AI algorithm, the optimal asset allocation for the user is calculated. For this calculation, data operations are performed using a generated AI model, and specific asset allocation (e.g., 60% stocks, 30% bonds, 10% cash) is output. 【0292】 Step 3: 【0293】 The server sends the generated asset allocation proposal to the terminal and presents it to the user. At this time, visualization tools are used to visually represent the data so that the user can easily understand the proposed content. The terminal receives this proposal and displays it to the user. 【0294】 Step 4: 【0295】 The server collects market data in real time from market information providers. The data obtained using the API is stored in a database and analyzed using data processing techniques as needed. During this process, analysis is performed based on the latest market conditions, and information that influences investment decisions is extracted. 【0296】 Step 5: 【0297】 Based on the analyzed market information, the server determines whether the user's asset allocation needs to be adjusted. This analysis may prompt a recalculation of the asset allocation, and in some cases, an automatic rebalancing may occur. If applicable, the user will be notified of the recalculated allocation. 【0298】 Step 6: 【0299】 After obtaining user consent, the server executes automated trades. Based on the investment portfolio, it buys and sells appropriate assets via API communication with the trading platform. This ensures that trades are executed according to the calculated asset allocation. 【0300】 Step 7: 【0301】 The server continuously monitors portfolio performance after trades and generates performance reports at specified intervals. These reports include an analysis of investment results and risk levels, and are provided to the user via their terminal. Users can use these reports to make further investment decisions. 【0302】 (Application Example 1) 【0303】 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." 【0304】 Modern consumers have diverse financial situations and needs, and are required to conduct efficient and optimal capital management. However, in conventional capital management systems, it is difficult to individually propose a capital plan based on a user's detailed purchase history and future expenditure prediction, and as a result, capital allocation tends to be inefficient. Therefore, a system that realizes optimized capital management for each user is necessary. 【0305】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means. 【0306】 In this invention, the server includes means for acquiring financial profile information from a user, means for performing an analysis for proposing an optimal capital allocation for the user based on the profile information, and means for collecting the user's purchase history and balance information and predicting future expenditures. Thereby, the user can enjoy a capital plan corresponding to their individual financial situation and predicted economic activities. 【0307】 "Financial profile information" is data regarding a user's economic situation and capital goals, and includes information such as risk tolerance and expenditure plans. 【0308】 "Capital allocation" is a method for managing risk and return by optimally distributing the capital a user has among different asset categories. 【0309】 "Performing an analysis" is a process of generating insights for optimal capital management using an algorithm based on the collected data. 【0310】 "Purchase history" is a record of a user's past purchases of goods and services, and is information for grasping consumption trends. 【0311】 "Prediction of future expenditures" is to predict what expenditures a user may make in the future based on past data and trends. 【0312】 A "capital plan" refers to a strategic plan that uses a user's assets to achieve future financial goals. 【0313】 To implement this invention, the server executes a program that generates an optimal capital allocation based on financial profile information obtained from the user. This program analyzes the user's profile information, including their risk tolerance and capital targets, along with acquired purchase history and balance information, to predict future spending. This allows the program to propose a specific capital plan for each individual user. 【0314】 This system executes AI algorithms using Python on the server to perform analysis and prediction. Pandas and NumPy are used for data processing, and scikit-learn and TensorFlow are used to build machine learning models. The server also uses MySQL or similar database management systems to efficiently store and manipulate user information. On the user's end, a frontend built with React Native allows users to easily view information about their financial status and future spending. 【0315】 As a concrete example, when a user plans a trip, the server predicts other spending patterns that may affect the travel expenses and suggests appropriate capital allocation before and after the trip. This allows the user to maintain sound capital management even after the impact of the trip. 【0316】 Example of an input prompt for a generating AI model: "The user is planning to buy a new house. Please propose a financial plan for the next five years, taking this large expenditure into consideration." 【0317】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0318】 Step 1: 【0319】 The server receives user profile information. This information includes the user's risk tolerance, capital targets, purchase history, and balance information. This data is first stored in a database and prepared for subsequent processing. 【0320】 Step 2: 【0321】 The server retrieves user information from the database and preprocesses the data using the Python Pandas library. The output at this stage is user information converted into a parseable format. Preprocessing includes imputing missing data and preparing non-digital data. 【0322】 Step 3: 【0323】 The server passes pre-processed data as input to an AI algorithm to predict future spending. In this process, a model built using TensorFlow analyzes the user's spending habits and predicts future expenditures. The output is predictive data showing what the user is likely to spend in the future. 【0324】 Step 4: 【0325】 The server generates an optimal capital plan based on future expenditure forecasts. A model is applied using the Python scikit-learn library to suggest how to allocate capital. The output is a capital allocation proposal optimized for the user's objectives. 【0326】 Step 5: 【0327】 The terminal receives the proposed capital plan from the server and presents the information to the user. Through an application built with React Native, the user can view the plan and provide feedback or make modifications as needed. 【0328】 Step 6: 【0329】 When a user provides feedback through their device, the server saves that information back into the database. This process is continuous, and the feedback is taken into account when generating the next plan, enabling better capital management. 【0330】 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. 【0331】 In one embodiment of this invention, first, the user sets up an investment profile using a terminal. Here, the user inputs information such as risk tolerance, investment goals, and investment period into the terminal. This input data is transmitted from the terminal to the server. 【0332】 The server utilizes AI to analyze the received profile data and establish an optimal asset allocation for each user. This allocation is based on the user's risk tolerance and investment goals and is presented to the user via their device. 【0333】 Next, the server collects market data in real time. The market data obtained from market information providers is analyzed by an AI algorithm to generate insights that support investment decisions. This allows the user to be presented with an optimal asset allocation. 【0334】 Furthermore, this configuration incorporates an emotion analysis engine to understand the user's emotional state. The user's emotions are analyzed based on text messages entered through the device and data obtained from biosensors. The results of the emotion analysis are reflected in the user's investment strategy, and asset allocation and investment policies are adjusted in real time as needed. 【0335】 The emotion engine can assess the user's stress and anxiety levels, and based on this, risk tolerance is dynamically adjusted. For example, if high stress is detected through emotion analysis, the server will increase investment in lower-risk assets. 【0336】 The system can also automate trading, with the server using user funds to execute trades based on an evaluated asset allocation. It connects with financial institutions and securities trading platforms via APIs, ensuring secure and rapid buying and selling. 【0337】 The server periodically monitors performance, and the progress of the investment portfolio is sent to the terminal as a report. Through this report, users can always see how their investments are progressing. 【0338】 For example, if a user becomes emotionally unstable during a transaction, the server immediately analyzes the information and automatically adjusts the asset allocation to alleviate their anxiety. This allows users to continue making stable investments without being swayed by their emotions. 【0339】 Thus, by integrating AI and an emotion analysis engine, this invention provides investment support tailored to individual users, thereby supporting safer and more efficient asset management. 【0340】 The following describes the processing flow. 【0341】 Step 1: 【0342】 Users access the investment profile settings screen using their device and enter information such as risk tolerance, investment goals, and investment period. This profile data is then sent from the device to the server. 【0343】 Step 2: 【0344】 The server analyzes the received user profile data and uses an AI algorithm to calculate the optimal asset allocation for the user. The calculated allocation result is then presented to the user via their device. 【0345】 Step 3: 【0346】 The server collects market data in real time through market information providers' APIs and analyzes it using AI. Based on the analysis results, it generates insights to support investment decision-making. 【0347】 Step 4: 【0348】 The server automatically trades the user's assets based on the recommended asset allocation. It executes buy and sell orders through API connections with financial institutions and securities trading platforms. 【0349】 Step 5: 【0350】 The server uses an emotion analysis engine to determine the user's emotional state. It analyzes text data or sensor data transmitted from the terminal to assess stress and anxiety levels. 【0351】 Step 6: 【0352】 The server dynamically adjusts the user's risk tolerance as needed, based on their emotional state. Based on the adjusted risk profile, it recalculates the asset allocation and reflects it in the user's investment strategy. 【0353】 Step 7: 【0354】 The server periodically tracks portfolio performance and generates a report summarizing the evaluation results. This report is provided to the user via the terminal. 【0355】 Step 8: 【0356】 Users can receive notifications via their devices and check the current status of their portfolio and suggested actions. Profile information can be updated in response to changes in emotional state and market conditions. 【0357】 (Example 2) 【0358】 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". 【0359】 The problems that this invention aims to solve are to dynamically propose the optimal resource allocation in investment activities according to the user's risk tolerance and investment goals, thereby improving the accuracy of investment decisions, and to provide an environment in which users can continue investing with peace of mind while mitigating the influence of their emotional state on their investment strategy. Furthermore, it aims to streamline the user's asset management by enabling the execution of rapid and secure transactions using market data. 【0360】 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. 【0361】 In this invention, the server includes means for acquiring characteristic information about investments from the user, means for collecting and analyzing exchange information in real time to support investment decisions, and means for analyzing the user's emotional state using an emotion analysis engine and dynamically adjusting resource allocation and investment policies based on the results. This makes it possible to optimize investments to suit the individual needs of the user, execute transactions safely and quickly, and enhance user confidence. 【0362】 A "user" is an individual or legal entity that uses the system to conduct investment activities. 【0363】 "Investment-related characteristic information" refers to individual investment-related information such as the user's risk tolerance, investment goals, and investment period. 【0364】 "Analysis" involves collecting and analyzing data to derive resource allocation and investment strategies that are appropriate for the user. 【0365】 "Exchange information" refers to financial data collected from the market in real time, including information such as price fluctuations and trading volume. 【0366】 An "emotion analysis engine" is a technology that evaluates emotional states based on data provided by users and uses this information to adjust investment strategies. 【0367】 "Resource allocation" refers to distributing a user's investment assets across different investment targets, and is done while considering the balance between risk and return. 【0368】 "Supporting investment decisions" means providing users with information based on collected data and analysis results to support their investment decisions. 【0369】 "Executing a transaction" means using a user's assets to buy or sell in the market. 【0370】 "Safe and fast" means that transactions are conducted without delay while maintaining security. 【0371】 To implement this invention, the roles of the server, terminal, and user are clearly defined, and a configuration is adopted that ensures the entire system operates efficiently. 【0372】 The user first uses a terminal to input characteristic information about their investment. Here, they enter detailed information such as risk tolerance, investment goals, and investment period to form their characteristic profile. This data is transmitted to the server via a secure protocol such as SSL. 【0373】 The server utilizes a generative AI model to analyze the received characteristic information. This model calculates the optimal resource allocation tailored to the user's needs based on the profile data. The server also collects exchange information from the market in real time and analyzes it using AI algorithms to provide crucial insights for investment decisions. 【0374】 Simultaneously, the server uses an emotion analysis engine to assess the user's emotional state. This uses text messages entered by the user and data from biosensors to identify the user's stress and anxiety levels, and then dynamically adjusts resource allocation and investment strategies based on the results. 【0375】 When a financial transaction occurs, the server communicates with financial institutions and financial transaction platforms via APIs, creating a system that ensures secure and rapid transaction execution. 【0376】 For example, if a user feels anxious due to fluctuations in the stock market, that information is immediately analyzed by the server, and a conservative resource allocation is proposed to alleviate the user's anxiety, allowing the user to continue their investment activities with peace of mind. 【0377】 Examples of prompts to input into a generative AI model include the following: 【0378】 "As a company employee in my 30s, I have a moderate risk tolerance and want to increase my assets to buy a house within the next 10 years. I want to be able to cope with market fluctuations and have emotional security. What would be the optimal asset allocation?" 【0379】 This invention, with its configuration and functions as described above, enables flexible and secure asset management tailored to the individual needs of users. 【0380】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0381】 Step 1: 【0382】 The user uses a terminal to input characteristic information about their investment. This includes risk tolerance, investment goals, and investment period. This input data is sent to the server. Specifically, the user types the necessary information into a dedicated input form and presses the "Submit" button, which securely transfers the data to the server. 【0383】 Step 2: 【0384】 The server analyzes the received characteristic information. A generative AI model is used for this analysis. User profile data is received as input, and the model uses this to calculate the optimal resource allocation for the user. As output, investment options and their ratios are generated and stored on the server. Specifically, the server processes the profile data through the AI ​​model, and the algorithm returns the optimal result. 【0385】 Step 3: 【0386】 The server collects exchange information in real time and analyzes it using AI algorithms. It receives price data and trading volume information from various markets as input. This generates insights to support investment decisions, which are then provided to the user. In its specific operation, the server obtains market data via APIs, analyzes it, and extracts trends. 【0387】 Step 4: 【0388】 The server uses an emotion analysis engine to evaluate the user's emotional state. It receives text messages and biometric data as input, analyzes them, and outputs stress and anxiety levels. Based on this, resource allocation and investment strategies are adjusted. Specifically, the server uses the emotion analysis engine to analyze text and calculate emotional indicators extracted from the data. 【0389】 Step 5: 【0390】 The server executes transactions using user resources. It interacts with financial institutions and financial trading platforms via APIs to conduct safe and rapid buying and selling. During transactions, commands regarding the addition or reduction of resources are issued and executed accordingly. Specifically, the server sends orders via APIs and confirms their completion in real time. 【0391】 Step 6: 【0392】 The server monitors operational performance and generates periodic reports. The server takes operational data as input and analyzes the results based on this data. As output, a performance report is generated and sent to the user's terminal. Specifically, the server periodically aggregates data, formats it into a report, and then sends it. 【0393】 (Application Example 2) 【0394】 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 as the "terminal". 【0395】 Conventional investment support systems lacked dynamic risk management that took into account the user's emotional state, resulting in the inability to appropriately allocate assets according to the user's psychological condition. Furthermore, it was difficult to integrate real-time market information with the user's emotional state to adjust investment strategies. Therefore, the challenge was to create a system that allowed users to maintain stable asset management without being influenced by their emotions. 【0396】 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. 【0397】 In this invention, the server includes means for obtaining investment profile information from the user, means including an emotion analysis engine that dynamically adjusts risk tolerance based on the user's emotional state, means for collecting and analyzing market information in real time to support investment decisions, means for tracking portfolio performance and generating reports, and means for notifying the user to prompt a re-evaluation of the portfolio. This enables stable asset management that is not dependent on emotions by integrating flexible risk management that responds to the user's emotional state with real-time market information analysis. 【0398】 "Profile information" refers to data that includes personal information about the user, such as investment goals, risk tolerance, and investment timeframe. 【0399】 "Asset allocation" is the process of determining the optimal combination of financial products and assets based on the user's profile information. 【0400】 "Market information" refers to fluctuation data in financial markets, such as stock prices, interest rates, and other economic indicators. 【0401】 A "sentiment analysis engine" is an algorithm that evaluates a user's emotional state and dynamically adjusts investment strategies based on that evaluation. 【0402】 "Risk tolerance" is an indicator that represents the level of risk a user is willing to accept in their investments. 【0403】 A "report" is a document that shows the performance of a portfolio and the results of an evaluation of its asset allocation. 【0404】 A "portfolio" is a combination of multiple financial products or assets held by a user. 【0405】 The system that implements this application consists of a user's terminal, such as a smartphone or computer, and a server that performs AI and data analysis in the backend. The server implements a generative AI model for investment analysis using Python and TensorFlow, and performs sentiment analysis using the natural language processing library NLTK. 【0406】 The terminal collects investment profile information from the user. This includes data such as investment goals, risk tolerance, and investment timeframe. This information is transmitted to the server via the internet. Upon receiving the user's profile information, the server uses an AI model to calculate the optimal asset allocation. Market information is obtained by the server in real time from external market data providers and used to make investment strategy decisions. 【0407】 The sentiment analysis engine analyzes text data sent from the user's device and evaluates their emotional state using NLTK. For example, if a user enters text indicating anxiety or stress, their risk tolerance is dynamically adjusted based on that emotional state, and a more conservative asset allocation is automatically suggested. 【0408】 The server has the capability to automatically trade users' assets and executes transactions securely through the APIs of appropriate financial institutions. It also continuously monitors asset performance and periodically generates reports to send to the user's terminal. 【0409】 For example, if a user enters a message into the system such as "I'm worried about recent economic news," the server will perform sentiment analysis and detect a state of "anxiety." As a result, the server will adjust the user's asset allocation to a lower-risk plan and present a new allocation proposal. 【0410】 An example of a prompt that utilizes a generative AI model is, "Consider the user's latest sentiment analysis results and suggest a low-risk asset allocation." Using this prompt, the system can provide investment strategies tailored to individual users. 【0411】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0412】 Step 1: 【0413】 The terminal retrieves investment profile information from the user. This profile information includes risk tolerance, investment goals, and investment timeframe. The entered data is then prepared for transmission to the server. 【0414】 Step 2: 【0415】 The device also acquires sentiment data from the user. This data may be entered as a text message. The acquired sentiment data is then put into a waiting state to be sent to the server. 【0416】 Step 3: 【0417】 The server receives profile information and sentiment data sent from the terminal. It analyzes the received data, processes it using a generative AI model based on TensorFlow, and calculates the optimal asset allocation for the user. As a result, an asset allocation proposal is output. 【0418】 Step 4: 【0419】 The server collects market information in real time from external data providers. This market information is used as input for AI models and serves as foundational data for up-to-date investment decisions. Subsequently, investment strategies are generated through the analysis of this market information. 【0420】 Step 5: 【0421】 The server uses an NLTK-based sentiment analysis engine to analyze user sentiment data. Based on the analysis results, it dynamically adjusts the user's risk tolerance. This process influences the reassessment of asset allocation. 【0422】 Step 6: 【0423】 The server sets up automated trading based on the new asset allocation plan. It issues trading orders for the held assets through the financial institution's API, ensuring secure buying and selling of assets. 【0424】 Step 7: 【0425】 The server regularly tracks the latest asset performance and generates an analysis report. The generated report is sent to the terminal, allowing the user to check the progress of their investments. 【0426】 Step 8: 【0427】 Based on the sentiment analysis results as a concrete example, the server generates and presents the next optimal investment proposal to the user using the prompt message, "Considering the user's latest sentiment analysis results, please propose a low-risk asset allocation." This process allows the user to continue making appropriate investments without relying on emotions. 【0428】 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. 【0429】 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. 【0430】 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. 【0431】 [Third Embodiment] 【0432】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0433】 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. 【0434】 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). 【0435】 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. 【0436】 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. 【0437】 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). 【0438】 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. 【0439】 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. 【0440】 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. 【0441】 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. 【0442】 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. 【0443】 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". 【0444】 In one embodiment of this invention, first, the user inputs information such as risk tolerance, investment goals, and investment period into a terminal in order to set up their investment profile. This information is then transmitted from the terminal to the server. 【0445】 The server uses an AI algorithm based on the received profile data to calculate the optimal asset allocation for each user. For example, for a user with a moderate risk tolerance, it suggests a standard asset allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device. 【0446】 The server also collects and analyzes market data in real time from market information providers. If the analysis reveals any information that could influence investment decisions, it takes that into account and adjusts the proposed portfolio accordingly. 【0447】 The system incorporates an automated trading function, where the server executes actual trades based on recommended asset allocations using the user's funds. Trades are executed via APIs with financial institutions and securities trading platforms. 【0448】 The server continuously tracks portfolio performance data and generates performance reports at regular intervals. These reports include key metrics such as current asset status, profits and losses, and return rates, and are provided to the user via their device. 【0449】 Furthermore, the system automatically rebalances the portfolio in response to its risk profile and market fluctuations. This rebalancing is performed when the asset allocation deviates from the target ratio, assisting the user in managing their risk. 【0450】 For example, if the stock market surges and the proportion of stocks reaches 70%, the server will automatically reallocate bonds and cash to adjust the portfolio back to the target ratio. 【0451】 Users can stay informed of their investment status at all times by checking reports that are regularly notified via their devices, and can update their profile information or reset their goals as needed. This notification feature encourages investment reviews and supports users in making optimal decisions. 【0452】 Through these embodiments, the invention is designed to enable users to manage their investments effectively and sustainably. 【0453】 The following describes the processing flow. 【0454】 Step 1: 【0455】 The user accesses the initial setup screen using their device and enters profile data such as risk tolerance, investment goals, and investment period. This entered data is then sent from the device to the server. 【0456】 Step 2: 【0457】 The server analyzes the received user profile data and uses AI to design the optimal asset allocation for the user. The designed asset allocation is then proposed to the user again via the terminal. 【0458】 Step 3: 【0459】 The server collects market data in real time from market information providers via APIs. The collected market data is analyzed by AI algorithms to generate insights that influence users' investment decisions. 【0460】 Step 4: 【0461】 The server automatically trades the user's assets based on the generated asset allocation plan. It executes orders via API from financial institutions and securities trading platforms, buying and selling based on the recommended asset allocation. 【0462】 Step 5: 【0463】 The server combines trading information and market data to periodically evaluate and track portfolio performance. The resulting data is generated as a performance report, which is then provided to the user via their terminal. 【0464】 Step 6: 【0465】 The server automatically rebalances the portfolio if the asset allocation deviates from the set target ratio. In this process, it buys and sells assets as needed to adjust the asset allocation back to the appropriate ratio. 【0466】 Step 7: 【0467】 Users receive notifications via their devices, allowing them to check the latest portfolio performance and recommended actions. They can update their profile information or reset their investment goals as needed. The server then re-analyzes these changes and provides new recommendations. 【0468】 (Example 1) 【0469】 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." 【0470】 It is difficult for investors to dynamically manage asset allocation according to their risk tolerance and goals in a diverse market environment. In particular, analyzing vast amounts of market information in real time and making efficient investment decisions based on that analysis requires advanced skills and knowledge. Therefore, there is a challenge in that it is difficult for individual investors to immediately respond to market fluctuations and make optimal adjustments to their asset portfolios. 【0471】 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. 【0472】 In this invention, the server includes means for acquiring investment profile information from a user, means for performing analysis based on the profile information using a generated AI algorithm to propose an optimal asset allocation to the user, and means for collecting market information in real time and analyzing the information using data processing technology to support investment decisions. As a result, the user can use the automated system to maintain an optimal asset allocation in response to market fluctuations in real time and perform effective investment management. 【0473】 A "user" refers to an individual or organization that uses the system to set up an investment profile and receive asset allocation suggestions. 【0474】 "Investment profile information" refers to information that users enter into the system, such as their risk tolerance, investment goals, and investment period, and is used to propose asset allocations. 【0475】 "Generated AI algorithms" refer to programs developed using artificial intelligence technology that optimize asset allocation based on the user's profile information. 【0476】 "Asset allocation" refers to the proportion of how a user distributes their assets across different investment targets (e.g., stocks, bonds, cash). 【0477】 "Market information" refers to data related to financial markets, including price trends, economic indicators, and financial news. 【0478】 "Data processing technology" refers to methods and techniques for analyzing collected information and generating useful data from it. 【0479】 "Investment decision" refers to the evaluation and consideration made by a user or system to determine a specific investment action. 【0480】 "Real-time" refers to a timeframe in which the latest information is immediately retrieved, processed, or made available, typically involving extremely short waiting times. 【0481】 An "asset portfolio" refers to the collection of all investment assets a user owns, and the overall asset allocation within this portfolio influences the investment strategy. 【0482】 This invention is a system aimed at enabling users to properly set up their investment profiles, optimize asset allocation using AI algorithms, and manage their assets sustainably. 【0483】 First, the user uses their device to enter profile information such as their risk tolerance, investment goals, and investment timeframe. This input process is performed via a regular web browser or a dedicated application. 【0484】 The device sends this entered profile information to the server. The server receives this information, and the generated AI algorithm utilizes machine learning libraries such as Python's Scikit-learn and TensorFlow. This allows the server to calculate and propose the optimal asset allocation based on the user's profile information. 【0485】 As a concrete example, for a user with a moderate risk tolerance, the AI ​​suggests an allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device and displayed in a visually easy-to-understand format. 【0486】 Furthermore, the server collects real-time market data from market information providers using APIs. This collected data is stored in a database, analyzed using data processing technologies, and used to inform investment decisions. Specific examples include using the Yahoo Finance API or similar market data APIs. 【0487】 Based on this analysis, the server continuously tracks the performance of the user's portfolio. The results are periodically generated as performance reports and provided to the user via their terminal. This process utilizes data visualization libraries such as Matplotlib. 【0488】 Furthermore, the server automatically rebalances the portfolio in response to market fluctuations. For example, if the stock market rises and the proportion of stocks reaches 70%, it automatically adjusts the proportion of bonds and cash. This ensures that users always maintain an optimal asset balance. 【0489】 As an example of a prompt, you might use text such as, "Suggest a portfolio of 60% stocks, 30% bonds, and 10% cash for an investor with a moderate risk tolerance. Explain how to automatically rebalance the portfolio in case of market fluctuations." 【0490】 This invention integrates such diverse technologies to help users consistently and effectively manage their investments. 【0491】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0492】 Step 1: 【0493】 Users enter investment profile information using a terminal. Specifically, they enter and confirm information such as risk tolerance, investment goals, and investment period. The entered data is formatted by the terminal's input interface and sent to the server. 【0494】 Step 2: 【0495】 The server analyzes investment profile information received from the terminal. It records the input information in a database and feeds that data into an AI algorithm to calculate the optimal asset allocation for the user. This calculation uses a generative AI model to perform data calculations and output a specific asset allocation (e.g., 60% stocks, 30% bonds, 10% cash). 【0496】 Step 3: 【0497】 The server sends the generated asset allocation proposal to the terminal and presents it to the user. Visualization tools are used to visually represent the data, making it easy for the user to understand the proposal. The terminal receives this proposal and displays it to the user. 【0498】 Step 4: 【0499】 The server collects market data in real time from market information providers. The data obtained using the API is stored in a database and analyzed using data processing techniques as needed. During this process, analysis is performed based on the latest market conditions, and information that influences investment decisions is extracted. 【0500】 Step 5: 【0501】 Based on the analyzed market information, the server determines whether the user's asset allocation needs to be adjusted. This analysis may prompt a recalculation of the asset allocation, and in some cases, an automatic rebalancing may occur. If applicable, the user will be notified of the recalculated allocation. 【0502】 Step 6: 【0503】 After obtaining user consent, the server executes automated trades. Based on the investment portfolio, it buys and sells appropriate assets via API communication with the trading platform. This ensures that trades are executed according to the calculated asset allocation. 【0504】 Step 7: 【0505】 The server continuously monitors portfolio performance after trades and generates performance reports at specified intervals. These reports include an analysis of investment results and risk levels, and are provided to the user via their terminal. Users can use these reports to make further investment decisions. 【0506】 (Application Example 1) 【0507】 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." 【0508】 Modern consumers have diverse financial situations and needs, requiring efficient and optimal capital management. However, conventional capital management systems struggle to provide personalized capital plans based on detailed user purchase history and future spending forecasts, often resulting in inefficient capital allocation. Therefore, a system that enables capital management optimized for each user is necessary. 【0509】 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. 【0510】 In this invention, the server includes means for acquiring financial profile information from the user, means for performing analysis to propose an optimal capital allocation to the user based on the profile information, and means for collecting the user's purchase history and balance information and predicting future spending. This enables the user to enjoy a capital plan that is tailored to their individual financial situation and predicted economic activity. 【0511】 "Financial profile information" refers to data about the user's economic situation and capital goals, including information such as risk tolerance and planned spending. 【0512】 "Capital allocation" is a method of managing risk and return by optimally distributing a user's capital across different asset categories. 【0513】 "Performing analysis" is the process of using algorithms based on collected data to generate insights for optimal capital management. 【0514】 "Purchase history" refers to a record of products and services that a user has purchased in the past, and is information used to understand their consumption trends. 【0515】 "Future spending forecasting" is the process of predicting what kind of spending a user is likely to do in the future, based on past data and trends. 【0516】 A "capital plan" refers to a strategic plan that uses a user's assets to achieve future financial goals. 【0517】 To implement this invention, the server executes a program that generates an optimal capital allocation based on financial profile information obtained from the user. This program analyzes the user's profile information, including their risk tolerance and capital targets, along with acquired purchase history and balance information, to predict future spending. This allows the program to propose a specific capital plan for each individual user. 【0518】 This system executes AI algorithms using Python on the server to perform analysis and prediction. Pandas and NumPy are used for data processing, and scikit-learn and TensorFlow are used to build machine learning models. The server also uses MySQL or similar database management systems to efficiently store and manipulate user information. On the user's end, a frontend built with React Native allows users to easily view information about their financial status and future spending. 【0519】 As a concrete example, when a user plans a trip, the server predicts other spending patterns that may affect the travel expenses and suggests appropriate capital allocation before and after the trip. This allows the user to maintain sound capital management even after the impact of the trip. 【0520】 Example of an input prompt for a generating AI model: "The user is planning to buy a new house. Please propose a financial plan for the next five years, taking this large expenditure into consideration." 【0521】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0522】 Step 1: 【0523】 The server receives user profile information. This information includes the user's risk tolerance, capital targets, purchase history, and balance information. This data is first stored in a database and prepared for subsequent processing. 【0524】 Step 2: 【0525】 The server retrieves user information from the database and preprocesses the data using the Python Pandas library. The output at this stage is user information converted into a parseable format. Preprocessing includes imputing missing data and preparing non-digital data. 【0526】 Step 3: 【0527】 The server passes pre-processed data as input to an AI algorithm to predict future spending. In this process, a model built using TensorFlow analyzes the user's spending habits and predicts future expenditures. The output is predictive data showing what the user is likely to spend in the future. 【0528】 Step 4: 【0529】 The server generates an optimal capital plan based on future expenditure forecasts. A model is applied using the Python scikit-learn library to suggest how to allocate capital. The output is a capital allocation proposal optimized for the user's objectives. 【0530】 Step 5: 【0531】 The terminal receives the proposed capital plan from the server and presents the information to the user. Through an application built with React Native, the user can view the plan and provide feedback or make modifications as needed. 【0532】 Step 6: 【0533】 When a user provides feedback through their device, the server saves that information back into the database. This process is continuous, and the feedback is taken into account when generating the next plan, enabling better capital management. 【0534】 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. 【0535】 In one embodiment of this invention, first, the user sets up an investment profile using a terminal. Here, the user inputs information such as risk tolerance, investment goals, and investment period into the terminal. This input data is transmitted from the terminal to the server. 【0536】 The server utilizes AI to analyze the received profile data and establish an optimal asset allocation for each user. This allocation is based on the user's risk tolerance and investment goals and is presented to the user via their device. 【0537】 Next, the server collects market data in real time. The market data obtained from market information providers is analyzed by an AI algorithm to generate insights that support investment decisions. This allows the user to be presented with an optimal asset allocation. 【0538】 Furthermore, this configuration incorporates an emotion analysis engine to understand the user's emotional state. The user's emotions are analyzed based on text messages entered through the device and data obtained from biosensors. The results of the emotion analysis are reflected in the user's investment strategy, and asset allocation and investment policies are adjusted in real time as needed. 【0539】 The emotion engine can assess the user's stress and anxiety levels, and based on this, risk tolerance is dynamically adjusted. For example, if high stress is detected through emotion analysis, the server will increase investment in lower-risk assets. 【0540】 The system can also automate trading, with the server using user funds to execute trades based on an evaluated asset allocation. It connects with financial institutions and securities trading platforms via APIs, ensuring secure and rapid buying and selling. 【0541】 The server periodically monitors performance, and the progress of the investment portfolio is sent to the terminal as a report. Through this report, users can always see how their investments are progressing. 【0542】 For example, if a user becomes emotionally unstable during a transaction, the server immediately analyzes the information and automatically adjusts the asset allocation to alleviate their anxiety. This allows users to continue making stable investments without being swayed by their emotions. 【0543】 Thus, by integrating AI and an emotion analysis engine, this invention provides investment support tailored to individual users, thereby supporting safer and more efficient asset management. 【0544】 The following describes the processing flow. 【0545】 Step 1: 【0546】 Users access the investment profile settings screen using their device and enter information such as risk tolerance, investment goals, and investment period. This profile data is then sent from the device to the server. 【0547】 Step 2: 【0548】 The server analyzes the received user profile data and uses an AI algorithm to calculate the optimal asset allocation for the user. The calculated allocation result is then presented to the user via their device. 【0549】 Step 3: 【0550】 The server collects market data in real time through market information providers' APIs and analyzes it using AI. Based on the analysis results, it generates insights to support investment decision-making. 【0551】 Step 4: 【0552】 The server automatically trades the user's assets based on the recommended asset allocation. It executes buy and sell orders through API connections with financial institutions and securities trading platforms. 【0553】 Step 5: 【0554】 The server uses an emotion analysis engine to determine the user's emotional state. It analyzes text data or sensor data transmitted from the terminal to assess stress and anxiety levels. 【0555】 Step 6: 【0556】 The server dynamically adjusts the user's risk tolerance as needed, based on their emotional state. Based on the adjusted risk profile, it recalculates the asset allocation and reflects it in the user's investment strategy. 【0557】 Step 7: 【0558】 The server periodically tracks portfolio performance and generates a report summarizing the evaluation results. This report is provided to the user via the terminal. 【0559】 Step 8: 【0560】 Users can receive notifications via their devices and check the current status of their portfolio and suggested actions. Profile information can be updated in response to changes in emotional state and market conditions. 【0561】 (Example 2) 【0562】 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." 【0563】 The problems that this invention aims to solve are to dynamically propose the optimal resource allocation in investment activities according to the user's risk tolerance and investment goals, thereby improving the accuracy of investment decisions, and to provide an environment in which users can continue investing with peace of mind while mitigating the influence of their emotional state on their investment strategy. Furthermore, it aims to streamline the user's asset management by enabling the execution of rapid and secure transactions using market data. 【0564】 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. 【0565】 In this invention, the server includes means for acquiring characteristic information about investments from the user, means for collecting and analyzing exchange information in real time to support investment decisions, and means for analyzing the user's emotional state using an emotion analysis engine and dynamically adjusting resource allocation and investment policies based on the results. This makes it possible to optimize investments to suit the individual needs of the user, execute transactions safely and quickly, and enhance user confidence. 【0566】 A "user" is an individual or legal entity that uses the system to conduct investment activities. 【0567】 "Investment-related characteristic information" refers to individual investment-related information such as the user's risk tolerance, investment goals, and investment period. 【0568】 "Analysis" involves collecting and analyzing data to derive resource allocation and investment strategies that are appropriate for the user. 【0569】 "Exchange information" refers to financial data collected from the market in real time, including information such as price fluctuations and trading volume. 【0570】 An "emotion analysis engine" is a technology that evaluates emotional states based on data provided by users and uses this information to adjust investment strategies. 【0571】 "Resource allocation" refers to distributing a user's investment assets across different investment targets, and is done while considering the balance between risk and return. 【0572】 "Supporting investment decisions" means providing users with information based on collected data and analysis results to support their investment decisions. 【0573】 "Executing a transaction" means using a user's assets to buy or sell in the market. 【0574】 "Safe and fast" means that transactions are conducted without delay while maintaining security. 【0575】 To implement this invention, the roles of the server, terminal, and user are clearly defined, and a configuration is adopted that ensures the entire system operates efficiently. 【0576】 The user first uses a terminal to input characteristic information about their investment. Here, they enter detailed information such as risk tolerance, investment goals, and investment period to form their characteristic profile. This data is transmitted to the server via a secure protocol such as SSL. 【0577】 The server utilizes a generative AI model to analyze the received characteristic information. This model calculates the optimal resource allocation tailored to the user's needs based on the profile data. The server also collects exchange information from the market in real time and analyzes it using AI algorithms to provide crucial insights for investment decisions. 【0578】 Simultaneously, the server uses an emotion analysis engine to assess the user's emotional state. This uses text messages entered by the user and data from biosensors to identify the user's stress and anxiety levels, and then dynamically adjusts resource allocation and investment strategies based on the results. 【0579】 When a financial transaction occurs, the server communicates with financial institutions and financial transaction platforms via APIs, creating a system that ensures secure and rapid transaction execution. 【0580】 For example, if a user feels anxious due to fluctuations in the stock market, that information is immediately analyzed by the server, and a conservative resource allocation is proposed to alleviate the user's anxiety, allowing the user to continue their investment activities with peace of mind. 【0581】 Examples of prompts to input into a generative AI model include the following: 【0582】 "As a company employee in my 30s, I have a moderate risk tolerance and want to increase my assets to buy a house within the next 10 years. I want to be able to cope with market fluctuations and have emotional security. What would be the optimal asset allocation?" 【0583】 This invention, with its configuration and functions as described above, enables flexible and secure asset management tailored to the individual needs of users. 【0584】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0585】 Step 1: 【0586】 The user uses a terminal to input characteristic information about their investment. This includes risk tolerance, investment goals, and investment period. This input data is sent to the server. Specifically, the user types the necessary information into a dedicated input form and presses the "Submit" button, which securely transfers the data to the server. 【0587】 Step 2: 【0588】 The server analyzes the received characteristic information. A generative AI model is used for this analysis. User profile data is received as input, and the model uses this to calculate the optimal resource allocation for the user. As output, investment options and their ratios are generated and stored on the server. Specifically, the server processes the profile data through the AI ​​model, and the algorithm returns the optimal result. 【0589】 Step 3: 【0590】 The server collects exchange information in real time and analyzes it using AI algorithms. It receives price data and trading volume information from various markets as input. This generates insights to support investment decisions, which are then provided to the user. In its specific operation, the server obtains market data via APIs, analyzes it, and extracts trends. 【0591】 Step 4: 【0592】 The server uses an emotion analysis engine to evaluate the user's emotional state. It receives text messages and biometric data as input, analyzes them, and outputs stress and anxiety levels. Based on this, resource allocation and investment strategies are adjusted. Specifically, the server uses the emotion analysis engine to analyze text and calculate emotional indicators extracted from the data. 【0593】 Step 5: 【0594】 The server executes transactions using user resources. It interacts with financial institutions and financial trading platforms via APIs to conduct safe and rapid buying and selling. During transactions, commands regarding the addition or reduction of resources are issued and executed accordingly. Specifically, the server sends orders via APIs and confirms their completion in real time. 【0595】 Step 6: 【0596】 The server monitors operational performance and generates periodic reports. The server takes operational data as input and analyzes the results based on this data. As output, a performance report is generated and sent to the user's terminal. Specifically, the server periodically aggregates data, formats it into a report, and then sends it. 【0597】 (Application Example 2) 【0598】 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." 【0599】 Conventional investment support systems lacked dynamic risk management that took into account the user's emotional state, resulting in the inability to appropriately allocate assets according to the user's psychological condition. Furthermore, it was difficult to integrate real-time market information with the user's emotional state to adjust investment strategies. Therefore, the challenge was to create a system that allowed users to maintain stable asset management without being influenced by their emotions. 【0600】 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. 【0601】 In this invention, the server includes means for obtaining investment profile information from the user, means including an emotion analysis engine that dynamically adjusts risk tolerance based on the user's emotional state, means for collecting and analyzing market information in real time to support investment decisions, means for tracking portfolio performance and generating reports, and means for notifying the user to prompt a re-evaluation of the portfolio. This enables stable asset management that is not dependent on emotions by integrating flexible risk management that responds to the user's emotional state with real-time market information analysis. 【0602】 "Profile information" refers to data that includes personal information about the user, such as investment goals, risk tolerance, and investment timeframe. 【0603】 "Asset allocation" is the process of determining the optimal combination of financial products and assets based on the user's profile information. 【0604】 "Market information" refers to fluctuation data in financial markets, such as stock prices, interest rates, and other economic indicators. 【0605】 A "sentiment analysis engine" is an algorithm that evaluates a user's emotional state and dynamically adjusts investment strategies based on that evaluation. 【0606】 "Risk tolerance" is an indicator that represents the level of risk a user is willing to accept in their investments. 【0607】 A "report" is a document that shows the performance of a portfolio and the results of an evaluation of its asset allocation. 【0608】 A "portfolio" is a combination of multiple financial products or assets held by a user. 【0609】 The system that implements this application consists of a user's terminal, such as a smartphone or computer, and a server that performs AI and data analysis in the backend. The server implements a generative AI model for investment analysis using Python and TensorFlow, and performs sentiment analysis using the natural language processing library NLTK. 【0610】 The terminal collects investment profile information from the user. This includes data such as investment goals, risk tolerance, and investment timeframe. This information is transmitted to the server via the internet. Upon receiving the user's profile information, the server uses an AI model to calculate the optimal asset allocation. Market information is obtained by the server in real time from external market data providers and used to make investment strategy decisions. 【0611】 The sentiment analysis engine analyzes text data sent from the user's device and evaluates their emotional state using NLTK. For example, if a user enters text indicating anxiety or stress, their risk tolerance is dynamically adjusted based on that emotional state, and a more conservative asset allocation is automatically suggested. 【0612】 The server has the capability to automatically trade users' assets and executes transactions securely through the APIs of appropriate financial institutions. It also continuously monitors asset performance and periodically generates reports to send to the user's terminal. 【0613】 For example, if a user enters a message into the system such as "I'm worried about recent economic news," the server will perform sentiment analysis and detect a state of "anxiety." As a result, the server will adjust the user's asset allocation to a lower-risk plan and present a new allocation proposal. 【0614】 An example of a prompt that utilizes a generative AI model is, "Consider the user's latest sentiment analysis results and suggest a low-risk asset allocation." Using this prompt, the system can provide investment strategies tailored to individual users. 【0615】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0616】 Step 1: 【0617】 The terminal retrieves investment profile information from the user. This profile information includes risk tolerance, investment goals, and investment timeframe. The entered data is then prepared for transmission to the server. 【0618】 Step 2: 【0619】 The device also acquires sentiment data from the user. This data may be entered as a text message. The acquired sentiment data is then put into a waiting state to be sent to the server. 【0620】 Step 3: 【0621】 The server receives profile information and sentiment data sent from the terminal. It analyzes the received data, processes it using a generative AI model based on TensorFlow, and calculates the optimal asset allocation for the user. As a result, an asset allocation proposal is output. 【0622】 Step 4: 【0623】 The server collects market information in real time from external data providers. This market information is used as input for AI models and serves as foundational data for up-to-date investment decisions. Subsequently, investment strategies are generated through the analysis of this market information. 【0624】 Step 5: 【0625】 The server uses an NLTK-based sentiment analysis engine to analyze user sentiment data. Based on the analysis results, it dynamically adjusts the user's risk tolerance. This process influences the reassessment of asset allocation. 【0626】 Step 6: 【0627】 The server sets up automated trading based on the new asset allocation plan. It issues trading orders for the held assets through the financial institution's API, ensuring secure buying and selling of assets. 【0628】 Step 7: 【0629】 The server regularly tracks the latest asset performance and generates an analysis report. The generated report is sent to the terminal, allowing the user to check the progress of their investments. 【0630】 Step 8: 【0631】 Based on the sentiment analysis results as a concrete example, the server generates and presents the next optimal investment proposal to the user using the prompt message, "Considering the user's latest sentiment analysis results, please propose a low-risk asset allocation." This process allows the user to continue making appropriate investments without relying on emotions. 【0632】 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. 【0633】 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. 【0634】 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. 【0635】 [Fourth Embodiment] 【0636】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0637】 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. 【0638】 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). 【0639】 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. 【0640】 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. 【0641】 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). 【0642】 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. 【0643】 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. 【0644】 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. 【0645】 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. 【0646】 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. 【0647】 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. 【0648】 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". 【0649】 In one embodiment of this invention, first, the user inputs information such as risk tolerance, investment goals, and investment period into a terminal in order to set up their investment profile. This information is then transmitted from the terminal to the server. 【0650】 The server uses an AI algorithm based on the received profile data to calculate the optimal asset allocation for each user. For example, for a user with a moderate risk tolerance, it suggests a standard asset allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device. 【0651】 The server also collects and analyzes market data in real time from market information providers. If the analysis reveals any information that could influence investment decisions, it takes that into account and adjusts the proposed portfolio accordingly. 【0652】 The system incorporates an automated trading function, where the server executes actual trades based on recommended asset allocations using the user's funds. Trades are executed via APIs with financial institutions and securities trading platforms. 【0653】 The server continuously tracks portfolio performance data and generates performance reports at regular intervals. These reports include key metrics such as current asset status, profits and losses, and return rates, and are provided to the user via their device. 【0654】 Furthermore, the system automatically rebalances the portfolio in response to its risk profile and market fluctuations. This rebalancing is performed when the asset allocation deviates from the target ratio, assisting the user in managing their risk. 【0655】 For example, if the stock market surges and the proportion of stocks reaches 70%, the server will automatically reallocate bonds and cash to adjust the portfolio back to the target ratio. 【0656】 Users can stay informed of their investment status at all times by checking reports that are regularly notified via their devices, and can update their profile information or reset their goals as needed. This notification feature encourages investment reviews and supports users in making optimal decisions. 【0657】 Through these embodiments, the invention is designed to enable users to manage their investments effectively and sustainably. 【0658】 The following describes the processing flow. 【0659】 Step 1: 【0660】 The user accesses the initial setup screen using their device and enters profile data such as risk tolerance, investment goals, and investment period. This entered data is then sent from the device to the server. 【0661】 Step 2: 【0662】 The server analyzes the received user profile data and uses AI to design the optimal asset allocation for the user. The designed asset allocation is then proposed to the user again via the terminal. 【0663】 Step 3: 【0664】 The server collects market data in real time from market information providers via APIs. The collected market data is analyzed by AI algorithms to generate insights that influence users' investment decisions. 【0665】 Step 4: 【0666】 The server automatically trades the user's assets based on the generated asset allocation plan. It executes orders via API from financial institutions and securities trading platforms, buying and selling based on the recommended asset allocation. 【0667】 Step 5: 【0668】 The server combines trading information and market data to periodically evaluate and track portfolio performance. The resulting data is generated as a performance report, which is then provided to the user via their terminal. 【0669】 Step 6: 【0670】 The server automatically rebalances the portfolio if the asset allocation deviates from the set target ratio. In this process, it buys and sells assets as needed to adjust the asset allocation back to the appropriate ratio. 【0671】 Step 7: 【0672】 Users receive notifications via their devices, allowing them to check the latest portfolio performance and recommended actions. They can update their profile information or reset their investment goals as needed. The server then re-analyzes these changes and provides new recommendations. 【0673】 (Example 1) 【0674】 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". 【0675】 It is difficult for investors to dynamically manage asset allocation according to their risk tolerance and goals in a diverse market environment. In particular, analyzing vast amounts of market information in real time and making efficient investment decisions based on that analysis requires advanced skills and knowledge. Therefore, there is a challenge in that it is difficult for individual investors to immediately respond to market fluctuations and make optimal adjustments to their asset portfolios. 【0676】 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. 【0677】 In this invention, the server includes means for acquiring investment profile information from a user, means for performing analysis based on the profile information using a generated AI algorithm to propose an optimal asset allocation to the user, and means for collecting market information in real time and analyzing the information using data processing technology to support investment decisions. As a result, the user can use the automated system to maintain an optimal asset allocation in response to market fluctuations in real time and perform effective investment management. 【0678】 A "user" refers to an individual or organization that uses the system to set up an investment profile and receive asset allocation suggestions. 【0679】 "Investment profile information" refers to information that users enter into the system, such as their risk tolerance, investment goals, and investment period, and is used to propose asset allocations. 【0680】 "Generated AI algorithms" refer to programs developed using artificial intelligence technology that optimize asset allocation based on the user's profile information. 【0681】 "Asset allocation" refers to the proportion of how a user distributes their assets across different investment targets (e.g., stocks, bonds, cash). 【0682】 "Market information" refers to data related to financial markets, including price trends, economic indicators, and financial news. 【0683】 "Data processing technology" refers to methods and techniques for analyzing collected information and generating useful data from it. 【0684】 "Investment decision" refers to the evaluation and consideration made by a user or system to determine a specific investment action. 【0685】 "Real-time" refers to a timeframe in which the latest information is immediately retrieved, processed, or made available, typically involving extremely short waiting times. 【0686】 An "asset portfolio" refers to the collection of all investment assets a user owns, and the overall asset allocation within this portfolio influences the investment strategy. 【0687】 This invention is a system aimed at enabling users to properly set up their investment profiles, optimize asset allocation using AI algorithms, and manage their assets sustainably. 【0688】 First, the user uses their device to enter profile information such as their risk tolerance, investment goals, and investment timeframe. This input process is performed via a regular web browser or a dedicated application. 【0689】 The device sends this entered profile information to the server. The server receives this information, and the generated AI algorithm utilizes machine learning libraries such as Python's Scikit-learn and TensorFlow. This allows the server to calculate and propose the optimal asset allocation based on the user's profile information. 【0690】 As a concrete example, for a user with a moderate risk tolerance, the AI ​​suggests an allocation of 60% stocks, 30% bonds, and 10% cash. This suggestion is presented to the user via their device and displayed in a visually easy-to-understand format. 【0691】 Furthermore, the server collects real-time market data from market information providers using APIs. This collected data is stored in a database, analyzed using data processing technologies, and used to inform investment decisions. Specific examples include using the Yahoo Finance API or similar market data APIs. 【0692】 Based on this analysis, the server continuously tracks the performance of the user's portfolio. The results are periodically generated as performance reports and provided to the user via their terminal. This process utilizes data visualization libraries such as Matplotlib. 【0693】 Furthermore, the server automatically rebalances the portfolio in response to market fluctuations. For example, if the stock market rises and the proportion of stocks reaches 70%, it automatically adjusts the proportion of bonds and cash. This ensures that users always maintain an optimal asset balance. 【0694】 As an example of a prompt, you might use text such as, "Suggest a portfolio of 60% stocks, 30% bonds, and 10% cash for an investor with a moderate risk tolerance. Explain how to automatically rebalance the portfolio in case of market fluctuations." 【0695】 This invention integrates such diverse technologies to help users consistently and effectively manage their investments. 【0696】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0697】 Step 1: 【0698】 Users enter investment profile information using a terminal. Specifically, they enter and confirm information such as risk tolerance, investment goals, and investment period. The entered data is formatted by the terminal's input interface and sent to the server. 【0699】 Step 2: 【0700】 The server analyzes investment profile information received from the terminal. It records the input information in a database and feeds that data into an AI algorithm to calculate the optimal asset allocation for the user. This calculation uses a generative AI model to perform data calculations and output a specific asset allocation (e.g., 60% stocks, 30% bonds, 10% cash). 【0701】 Step 3: 【0702】 The server sends the generated asset allocation proposal to the terminal and presents it to the user. Visualization tools are used to visually represent the data, making it easy for the user to understand the proposal. The terminal receives this proposal and displays it to the user. 【0703】 Step 4: 【0704】 The server collects market data in real time from market information providers. The data obtained using the API is stored in a database and analyzed using data processing techniques as needed. During this process, analysis is performed based on the latest market conditions, and information that influences investment decisions is extracted. 【0705】 Step 5: 【0706】 Based on the analyzed market information, the server determines whether the user's asset allocation needs to be adjusted. This analysis may prompt a recalculation of the asset allocation, and in some cases, an automatic rebalancing may occur. If applicable, the user will be notified of the recalculated allocation. 【0707】 Step 6: 【0708】 After obtaining user consent, the server executes automated trades. Based on the investment portfolio, it buys and sells appropriate assets via API communication with the trading platform. This ensures that trades are executed according to the calculated asset allocation. 【0709】 Step 7: 【0710】 The server continuously monitors portfolio performance after trades and generates performance reports at specified intervals. These reports include an analysis of investment results and risk levels, and are provided to the user via their terminal. Users can use these reports to make further investment decisions. 【0711】 (Application Example 1) 【0712】 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". 【0713】 Modern consumers have diverse financial situations and needs, requiring efficient and optimal capital management. However, conventional capital management systems struggle to provide personalized capital plans based on detailed user purchase history and future spending forecasts, often resulting in inefficient capital allocation. Therefore, a system that enables capital management optimized for each user is necessary. 【0714】 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. 【0715】 In this invention, the server includes means for acquiring financial profile information from the user, means for performing analysis to propose an optimal capital allocation to the user based on the profile information, and means for collecting the user's purchase history and balance information and predicting future spending. This enables the user to enjoy a capital plan that is tailored to their individual financial situation and predicted economic activity. 【0716】 "Financial profile information" refers to data about the user's economic situation and capital goals, including information such as risk tolerance and planned spending. 【0717】 "Capital allocation" is a method of managing risk and return by optimally distributing a user's capital across different asset categories. 【0718】 "Performing analysis" is the process of using algorithms based on collected data to generate insights for optimal capital management. 【0719】 "Purchase history" refers to a record of products and services that a user has purchased in the past, and is information used to understand their consumption trends. 【0720】 "Future spending forecasting" is the process of predicting what kind of spending a user is likely to do in the future, based on past data and trends. 【0721】 A "capital plan" refers to a strategic plan that uses a user's assets to achieve future financial goals. 【0722】 To implement this invention, the server executes a program that generates an optimal capital allocation based on financial profile information obtained from the user. This program analyzes the user's profile information, including their risk tolerance and capital targets, along with acquired purchase history and balance information, to predict future spending. This allows the program to propose a specific capital plan for each individual user. 【0723】 This system executes AI algorithms using Python on the server to perform analysis and prediction. Pandas and NumPy are used for data processing, and scikit-learn and TensorFlow are used to build machine learning models. The server also uses MySQL or similar database management systems to efficiently store and manipulate user information. On the user's end, a frontend built with React Native allows users to easily view information about their financial status and future spending. 【0724】 As a concrete example, when a user plans a trip, the server predicts other spending patterns that may affect the travel expenses and suggests appropriate capital allocation before and after the trip. This allows the user to maintain sound capital management even after the impact of the trip. 【0725】 Example of an input prompt for a generating AI model: "The user is planning to buy a new house. Please propose a financial plan for the next five years, taking this large expenditure into consideration." 【0726】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0727】 Step 1: 【0728】 The server receives user profile information. This information includes the user's risk tolerance, capital targets, purchase history, and balance information. This data is first stored in a database and prepared for subsequent processing. 【0729】 Step 2: 【0730】 The server retrieves user information from the database and preprocesses the data using the Python Pandas library. The output at this stage is user information converted into a parseable format. Preprocessing includes imputing missing data and preparing non-digital data. 【0731】 Step 3: 【0732】 The server passes pre-processed data as input to an AI algorithm to predict future spending. In this process, a model built using TensorFlow analyzes the user's spending habits and predicts future expenditures. The output is predictive data showing what the user is likely to spend in the future. 【0733】 Step 4: 【0734】 The server generates an optimal capital plan based on future expenditure forecasts. A model is applied using the Python scikit-learn library to suggest how to allocate capital. The output is a capital allocation proposal optimized for the user's objectives. 【0735】 Step 5: 【0736】 The terminal receives the proposed capital plan from the server and presents the information to the user. Through an application built with React Native, the user can view the plan and provide feedback or make modifications as needed. 【0737】 Step 6: 【0738】 When a user provides feedback through their device, the server saves that information back into the database. This process is continuous, and the feedback is taken into account when generating the next plan, enabling better capital management. 【0739】 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. 【0740】 In one embodiment of this invention, first, the user sets up an investment profile using a terminal. Here, the user inputs information such as risk tolerance, investment goals, and investment period into the terminal. This input data is transmitted from the terminal to the server. 【0741】 The server utilizes AI to analyze the received profile data and establish an optimal asset allocation for each user. This allocation is based on the user's risk tolerance and investment goals and is presented to the user via their device. 【0742】 Next, the server collects market data in real time. The market data obtained from market information providers is analyzed by an AI algorithm to generate insights that support investment decisions. This allows the user to be presented with an optimal asset allocation. 【0743】 Furthermore, this configuration incorporates an emotion analysis engine to understand the user's emotional state. The user's emotions are analyzed based on text messages entered through the device and data obtained from biosensors. The results of the emotion analysis are reflected in the user's investment strategy, and asset allocation and investment policies are adjusted in real time as needed. 【0744】 The emotion engine can assess the user's stress and anxiety levels, and based on this, risk tolerance is dynamically adjusted. For example, if high stress is detected through emotion analysis, the server will increase investment in lower-risk assets. 【0745】 The system can also automate trading, with the server using user funds to execute trades based on an evaluated asset allocation. It connects with financial institutions and securities trading platforms via APIs, ensuring secure and rapid buying and selling. 【0746】 The server periodically monitors performance, and the progress of the investment portfolio is sent to the terminal as a report. Through this report, users can always see how their investments are progressing. 【0747】 For example, if a user becomes emotionally unstable during a transaction, the server immediately analyzes the information and automatically adjusts the asset allocation to alleviate their anxiety. This allows users to continue making stable investments without being swayed by their emotions. 【0748】 Thus, by integrating AI and an emotion analysis engine, this invention provides investment support tailored to individual users, thereby supporting safer and more efficient asset management. 【0749】 The following describes the processing flow. 【0750】 Step 1: 【0751】 Users access the investment profile settings screen using their device and enter information such as risk tolerance, investment goals, and investment period. This profile data is then sent from the device to the server. 【0752】 Step 2: 【0753】 The server analyzes the received user profile data and uses an AI algorithm to calculate the optimal asset allocation for the user. The calculated allocation result is then presented to the user via their device. 【0754】 Step 3: 【0755】 The server collects market data in real time through market information providers' APIs and analyzes it using AI. Based on the analysis results, it generates insights to support investment decision-making. 【0756】 Step 4: 【0757】 The server automatically trades the user's assets based on the recommended asset allocation. It executes buy and sell orders through API connections with financial institutions and securities trading platforms. 【0758】 Step 5: 【0759】 The server uses an emotion analysis engine to determine the user's emotional state. It analyzes text data or sensor data transmitted from the terminal to assess stress and anxiety levels. 【0760】 Step 6: 【0761】 The server dynamically adjusts the user's risk tolerance as needed, based on their emotional state. Based on the adjusted risk profile, it recalculates the asset allocation and reflects it in the user's investment strategy. 【0762】 Step 7: 【0763】 The server periodically tracks portfolio performance and generates a report summarizing the evaluation results. This report is provided to the user via the terminal. 【0764】 Step 8: 【0765】 Users can receive notifications via their devices and check the current status of their portfolio and suggested actions. Profile information can be updated in response to changes in emotional state and market conditions. 【0766】 (Example 2) 【0767】 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". 【0768】 The problems that this invention aims to solve are to dynamically propose the optimal resource allocation in investment activities according to the user's risk tolerance and investment goals, thereby improving the accuracy of investment decisions, and to provide an environment in which users can continue investing with peace of mind while mitigating the influence of their emotional state on their investment strategy. Furthermore, it aims to streamline the user's asset management by enabling the execution of rapid and secure transactions using market data. 【0769】 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. 【0770】 In this invention, the server includes means for acquiring characteristic information about investments from the user, means for collecting and analyzing exchange information in real time to support investment decisions, and means for analyzing the user's emotional state using an emotion analysis engine and dynamically adjusting resource allocation and investment policies based on the results. This makes it possible to optimize investments to suit the individual needs of the user, execute transactions safely and quickly, and enhance user confidence. 【0771】 A "user" is an individual or legal entity that uses the system to conduct investment activities. 【0772】 "Investment-related characteristic information" refers to individual investment-related information such as the user's risk tolerance, investment goals, and investment period. 【0773】 "Analysis" involves collecting and analyzing data to derive resource allocation and investment strategies that are appropriate for the user. 【0774】 "Exchange information" refers to financial data collected from the market in real time, including information such as price fluctuations and trading volume. 【0775】 An "emotion analysis engine" is a technology that evaluates emotional states based on data provided by users and uses this information to adjust investment strategies. 【0776】 "Resource allocation" refers to distributing a user's investment assets across different investment targets, and is done while considering the balance between risk and return. 【0777】 "Supporting investment decisions" means providing users with information based on collected data and analysis results to support their investment decisions. 【0778】 "Executing a transaction" means using a user's assets to buy or sell in the market. 【0779】 "Safe and fast" means that transactions are conducted without delay while maintaining security. 【0780】 To implement this invention, the roles of the server, terminal, and user are clearly defined, and a configuration is adopted that ensures the entire system operates efficiently. 【0781】 The user first uses a terminal to input characteristic information about their investment. Here, they enter detailed information such as risk tolerance, investment goals, and investment period to form their characteristic profile. This data is transmitted to the server via a secure protocol such as SSL. 【0782】 The server utilizes a generative AI model to analyze the received characteristic information. This model calculates the optimal resource allocation tailored to the user's needs based on the profile data. The server also collects exchange information from the market in real time and analyzes it using AI algorithms to provide crucial insights for investment decisions. 【0783】 Simultaneously, the server uses an emotion analysis engine to assess the user's emotional state. This uses text messages entered by the user and data from biosensors to identify the user's stress and anxiety levels, and then dynamically adjusts resource allocation and investment strategies based on the results. 【0784】 When a financial transaction occurs, the server communicates with financial institutions and financial transaction platforms via APIs, creating a system that ensures secure and rapid transaction execution. 【0785】 For example, if a user feels anxious due to fluctuations in the stock market, that information is immediately analyzed by the server, and a conservative resource allocation is proposed to alleviate the user's anxiety, allowing the user to continue their investment activities with peace of mind. 【0786】 Examples of prompts to input into a generative AI model include the following: 【0787】 "As a company employee in my 30s, I have a moderate risk tolerance and want to increase my assets to buy a house within the next 10 years. I want to be able to cope with market fluctuations and have emotional security. What would be the optimal asset allocation?" 【0788】 This invention, with its configuration and functions as described above, enables flexible and secure asset management tailored to the individual needs of users. 【0789】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0790】 Step 1: 【0791】 The user uses a terminal to input characteristic information about their investment. This includes risk tolerance, investment goals, and investment period. This input data is sent to the server. Specifically, the user types the necessary information into a dedicated input form and presses the "Submit" button, which securely transfers the data to the server. 【0792】 Step 2: 【0793】 The server analyzes the received characteristic information. A generative AI model is used for this analysis. User profile data is received as input, and the model uses this to calculate the optimal resource allocation for the user. As output, investment options and their ratios are generated and stored on the server. Specifically, the server processes the profile data through the AI ​​model, and the algorithm returns the optimal result. 【0794】 Step 3: 【0795】 The server collects exchange information in real time and analyzes it using AI algorithms. It receives price data and trading volume information from various markets as input. This generates insights to support investment decisions, which are then provided to the user. In its specific operation, the server obtains market data via APIs, analyzes it, and extracts trends. 【0796】 Step 4: 【0797】 The server uses an emotion analysis engine to evaluate the user's emotional state. It receives text messages and biometric data as input, analyzes them, and outputs stress and anxiety levels. Based on this, resource allocation and investment strategies are adjusted. Specifically, the server uses the emotion analysis engine to analyze text and calculate emotional indicators extracted from the data. 【0798】 Step 5: 【0799】 The server executes transactions using user resources. It interacts with financial institutions and financial trading platforms via APIs to conduct safe and rapid buying and selling. During transactions, commands regarding the addition or reduction of resources are issued and executed accordingly. Specifically, the server sends orders via APIs and confirms their completion in real time. 【0800】 Step 6: 【0801】 The server monitors operational performance and generates periodic reports. The server takes operational data as input and analyzes the results based on this data. As output, a performance report is generated and sent to the user's terminal. Specifically, the server periodically aggregates data, formats it into a report, and then sends it. 【0802】 (Application Example 2) 【0803】 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". 【0804】 Conventional investment support systems lacked dynamic risk management that took into account the user's emotional state, resulting in the inability to appropriately allocate assets according to the user's psychological condition. Furthermore, it was difficult to integrate real-time market information with the user's emotional state to adjust investment strategies. Therefore, the challenge was to create a system that allowed users to maintain stable asset management without being influenced by their emotions. 【0805】 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. 【0806】 In this invention, the server includes means for obtaining investment profile information from the user, means including an emotion analysis engine that dynamically adjusts risk tolerance based on the user's emotional state, means for collecting and analyzing market information in real time to support investment decisions, means for tracking portfolio performance and generating reports, and means for notifying the user to prompt a re-evaluation of the portfolio. This enables stable asset management that is not dependent on emotions by integrating flexible risk management that responds to the user's emotional state with real-time market information analysis. 【0807】 "Profile information" refers to data that includes personal information about the user, such as investment goals, risk tolerance, and investment timeframe. 【0808】 "Asset allocation" is the process of determining the optimal combination of financial products and assets based on the user's profile information. 【0809】 "Market information" refers to fluctuation data in financial markets, such as stock prices, interest rates, and other economic indicators. 【0810】 A "sentiment analysis engine" is an algorithm that evaluates a user's emotional state and dynamically adjusts investment strategies based on that evaluation. 【0811】 "Risk tolerance" is an indicator that represents the level of risk a user is willing to accept in their investments. 【0812】 A "report" is a document that shows the performance of a portfolio and the results of an evaluation of its asset allocation. 【0813】 A "portfolio" is a combination of multiple financial products or assets held by a user. 【0814】 The system that implements this application consists of a user's terminal, such as a smartphone or computer, and a server that performs AI and data analysis in the backend. The server implements a generative AI model for investment analysis using Python and TensorFlow, and performs sentiment analysis using the natural language processing library NLTK. 【0815】 The terminal collects investment profile information from the user. This includes data such as investment goals, risk tolerance, and investment timeframe. This information is transmitted to the server via the internet. Upon receiving the user's profile information, the server uses an AI model to calculate the optimal asset allocation. Market information is obtained by the server in real time from external market data providers and used to make investment strategy decisions. 【0816】 The sentiment analysis engine analyzes text data sent from the user's device and evaluates their emotional state using NLTK. For example, if a user enters text indicating anxiety or stress, their risk tolerance is dynamically adjusted based on that emotional state, and a more conservative asset allocation is automatically suggested. 【0817】 The server has the capability to automatically trade users' assets and executes transactions securely through the APIs of appropriate financial institutions. It also continuously monitors asset performance and periodically generates reports to send to the user's terminal. 【0818】 For example, if a user enters a message into the system such as "I'm worried about recent economic news," the server will perform sentiment analysis and detect a state of "anxiety." As a result, the server will adjust the user's asset allocation to a lower-risk plan and present a new allocation proposal. 【0819】 An example of a prompt that utilizes a generative AI model is, "Consider the user's latest sentiment analysis results and suggest a low-risk asset allocation." Using this prompt, the system can provide investment strategies tailored to individual users. 【0820】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0821】 Step 1: 【0822】 The terminal retrieves investment profile information from the user. This profile information includes risk tolerance, investment goals, and investment timeframe. The entered data is then prepared for transmission to the server. 【0823】 Step 2: 【0824】 The device also acquires sentiment data from the user. This data may be entered as a text message. The acquired sentiment data is then put into a waiting state to be sent to the server. 【0825】 Step 3: 【0826】 The server receives profile information and sentiment data sent from the terminal. It analyzes the received data, processes it using a generative AI model based on TensorFlow, and calculates the optimal asset allocation for the user. As a result, an asset allocation proposal is output. 【0827】 Step 4: 【0828】 The server collects market information in real time from external data providers. This market information is used as input for AI models and serves as foundational data for up-to-date investment decisions. Subsequently, investment strategies are generated through the analysis of this market information. 【0829】 Step 5: 【0830】 The server uses an NLTK-based sentiment analysis engine to analyze user sentiment data. Based on the analysis results, it dynamically adjusts the user's risk tolerance. This process influences the reassessment of asset allocation. 【0831】 Step 6: 【0832】 The server sets up automated trading based on the new asset allocation plan. It issues trading orders for the held assets through the financial institution's API, ensuring secure buying and selling of assets. 【0833】 Step 7: 【0834】 The server regularly tracks the latest asset performance and generates an analysis report. The generated report is sent to the terminal, allowing the user to check the progress of their investments. 【0835】 Step 8: 【0836】 Based on the sentiment analysis results as a concrete example, the server generates and presents the next optimal investment proposal to the user using the prompt message, "Considering the user's latest sentiment analysis results, please propose a low-risk asset allocation." This process allows the user to continue making appropriate investments without relying on emotions. 【0837】 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. 【0838】 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. 【0839】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0840】 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. 【0841】 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. 【0842】 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. 【0843】 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. 【0844】 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. 【0845】 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." 【0846】 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. 【0847】 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. 【0848】 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. 【0849】 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. 【0850】 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. 【0851】 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. 【0852】 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 this memory. 【0853】 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. 【0854】 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. 【0855】 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. 【0856】 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. 【0857】 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. 【0858】 The following is further disclosed regarding the embodiments described above. 【0859】 (Claim 1) 【0860】 A means of obtaining investment-related profile data from users, 【0861】 A means for performing analysis to propose the optimal asset allocation to the user based on the profile data, 【0862】 A means of collecting market data in real time and analyzing that data to support investment decisions, 【0863】 A means to automatically trade users' assets, 【0864】 A means to track portfolio performance and generate reports, 【0865】 A system that includes means of notifying users to encourage them to review their portfolios. 【0866】 (Claim 2) 【0867】 The system according to claim 1, which evaluates the user's risk tolerance and investment goals and automatically adjusts asset allocation based on them. 【0868】 (Claim 3) 【0869】 The system according to claim 1, which automatically rebalances a portfolio based on market data. 【0870】 "Example 1" 【0871】 (Claim 1) 【0872】 A means of obtaining investment profile information from users, 【0873】 A means for performing analysis to propose the optimal asset allocation to the user based on the profile information using the generated AI algorithm, 【0874】 A means of collecting market information in real time and analyzing that information using data processing technology to support investment decisions, 【0875】 A means of automatically trading assets based on the user's funds, 【0876】 A means of tracking portfolio performance and generating reports using information processing technology, 【0877】 A means of notifying users to review their portfolio based on the generated report, 【0878】 A system that includes means to automatically readjust asset allocation in response to portfolio risk conditions and market fluctuations. 【0879】 (Claim 2) 【0880】 The system according to claim 1, which evaluates a user's risk tolerance and investment goals, dynamically adjusts asset allocation based on these, and further executes electronic transactions. 【0881】 (Claim 3) 【0882】 The system according to claim 1, which readjusts the asset ratios of a portfolio in real time based on market information and optimizes profitability. 【0883】 "Application Example 1" 【0884】 (Claim 1) 【0885】 A means of obtaining financial profile information from users, 【0886】 A means for performing analysis to propose the optimal capital allocation to the user based on the profile information, 【0887】 A means of collecting market information in real time, analyzing that information, and supporting decision-making. 【0888】 A means for automatically processing user capital, 【0889】 A means of tracking capital performance and generating reports, 【0890】 A means of notifying users to encourage them to review their capital, 【0891】 A means of collecting users' purchase history and balance information to predict future spending, 【0892】 A means of proposing an optimal capital plan based on future expenditure forecasts, 【0893】 A system that includes this. 【0894】 (Claim 2) 【0895】 The system according to claim 1, which evaluates the user's risk tolerance and capital objectives and automatically adjusts capital allocation based on them. 【0896】 (Claim 3) 【0897】 The system according to claim 1, which automatically reallocates capital based on market information. 【0898】 "Example 2 of combining an emotion engine" 【0899】 (Claim 1) 【0900】 A means of obtaining characteristic information about investments from users, 【0901】 A means for performing analysis to present the optimal resource allocation to the user based on the characteristic information, 【0902】 A means of collecting exchange information in real time and analyzing that information to support investment decisions, 【0903】 Means for automatically managing user resources, 【0904】 A means of monitoring operational results and generating reports, 【0905】 A means of notifying users to check the details of the operation, 【0906】 A means of analyzing a user's emotional state using an emotion analysis engine and dynamically adjusting resource allocation and investment policies based on the results, 【0907】 A system that includes means of executing buy and sell transactions safely and quickly in cooperation with financial institutions and financial trading platforms. 【0908】 (Claim 2) 【0909】 The system according to claim 1, which evaluates the user's risk tolerance and investment goals and automatically adjusts resource allocation based on them. 【0910】 (Claim 3) 【0911】 The system according to claim 1, which automatically readjusts investment results based on exchange information. 【0912】 "Application example 2 when combining with an emotional engine" 【0913】 (Claim 1) 【0914】 A means of obtaining investment profile information from users, 【0915】 A means for performing analysis to propose the optimal asset allocation for the user based on the profile information, 【0916】 A means of collecting market information in real time, analyzing that information, and supporting investment decisions. 【0917】 A means to automatically trade users' assets, 【0918】 A means of tracking portfolio performance and generating reports, 【0919】 It includes an emotion analysis engine that dynamically adjusts risk tolerance based on the user's emotional state, thereby providing a means to automatically adjust investment strategies. 【0920】 A system that includes means of notifying users to prompt them to re-evaluate their portfolios. 【0921】 (Claim 2) 【0922】 The system according to claim 1, which evaluates the user's risk tolerance and investment goals and dynamically adjusts asset allocation based on emotional data. 【0923】 (Claim 3) 【0924】 The system according to claim 1, which automatically adjusts the portfolio composition based on market information and the user's emotional state. [Explanation of Symbols] 【0925】 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

[Claim 1] A means of obtaining investment-related profile data from users, A means for performing analysis to propose the optimal asset allocation to the user based on the profile data, A means of collecting market data in real time and analyzing that data to support investment decisions, A means to automatically trade users' assets, A means to track portfolio performance and generate reports, A system that includes means of notifying users to encourage them to review their portfolios. [Claim 2] The system according to claim 1, which evaluates the user's risk tolerance and investment goals and automatically adjusts asset allocation based on them. [Claim 3] The system according to claim 1, which automatically rebalances a portfolio based on market data.

Images