System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an input of an asset situation, a risk tolerance, and an investment objective from a user; means for constructing a portfolio using a deep learning technique based on the input information; means for presenting the constructed portfolio to the user; means for receiving a trading order from the user and executing the order; and means for notifying the user of an execution result of the trading order.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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 Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Individual investors, especially beginners and small investors, need appropriate portfolio construction and investment advice to invest efficiently and effectively in the market. However, it is difficult for these investors to analyze market trends and create optimal investment plans on their own. Furthermore, as the market size expands, it is becoming more difficult to obtain accurate and useful information from the wide variety of investment information available. Another issue is the lack of a system that centrally manages the wide range of processes required for investment activities, such as executing trades, calculating profits, and managing subscription fees. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: A means for a user to input their asset status, risk tolerance, and investment goals, and an optimal portfolio is constructed using deep learning technology based on this. It also includes means for presenting the constructed portfolio to the user, allowing the user to input buy and sell orders and execute those orders. It also includes means for notifying the user of the results of buy and sell executions and generating and presenting additional investment advice based on the portfolio. In addition, it provides means for periodically calculating profits generated from the user's investment activities and calculating and collecting subscription fees based on those profits. This allows even beginners and small-scale investors to invest effectively and efficiently.

[0006] "Asset status" refers to the total amount of funds and investment assets currently owned and their breakdown.

[0007] "Risk tolerance" is a concept that represents the range or level of risk that a user can tolerate in an investment.

[0008] "Investment objectives" indicate the specific objectives or goals that a user wants to achieve through investment.

[0009] "Deep learning technology" is a field of artificial intelligence that uses multi-layered neural networks to learn data patterns and make predictions and classifications.

[0010] A "portfolio" is a collection of assets that combines multiple investment targets, and is a means of diversifying risk and maximizing profits.

[0011] "Buy / Sell Order" means an instruction to buy or sell a particular investment (e.g., stocks, bonds, etc.).

[0012] "Subscription Fee" means a periodic fee charged in exchange for the Services provided.

[0013] "User interface" refers to the screen and operating environment through which a user interacts with an application or system.

[0014] "Investment Advice" means professional advice or recommendations provided to assist in making an investment decision. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF 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, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0033] The storage 32 stores a data generation model 58 and an 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 process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] A system embodying the present invention provides support for individual investors to conduct their investment activities effectively and efficiently. The system accepts input from users about their asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes a means for accepting buy and sell orders from users, notifying them of the execution results, and calculating and collecting subscription fees from revenue.

[0037] Overview of program processing flow

[0038] 1. User Registration and Authentication

[0039] The user installs the app and launches it.

[0040] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[0041] The terminal transmits the input information to the server.

[0042] The server receives the input information and stores it in a database.

[0043] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0044] The device receives the authentication token and uses it for subsequent requests.

[0045] 2. Portfolio Construction

[0046] The user opens the "Portfolio Construction" screen.

[0047] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[0048] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[0049] The terminal transmits the input information to the server.

[0050] The server receives the user data and stores it in a database.

[0051] The server invokes the deep learning model, passing the user data as input.

[0052] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0053] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[0054] The terminal displays the recommended portfolio to the user.

[0055] 3. Investment advice and planning

[0056] The terminal presents the received recommended portfolio to the user.

[0057] The user inputs detailed investment planning consultations based on the portfolio (e.g., additional questions and revision requests).

[0058] The device sends additional questions or correction requests to the server.

[0059] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[0060] The server generates additional advice and modified portfolios and sends them to the terminal.

[0061] The terminal displays additional advice and modified portfolios to the user.

[0062] 4. Providing trading functions

[0063] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[0064] The terminal transmits the entered order to the server.

[0065] The server receives the order information and prepares to call the broker API.

[0066] The server executes the order through the broker API.

[0067] The server receives the order execution result (success or failure) and sends it to the terminal.

[0068] The terminal displays the order execution results to the user.

[0069] 5. Calculating Revenue and Collecting Subscription Fees

[0070] The server periodically acquires the user's investment activity data from the database.

[0071] The server calculates the user's revenue.

[0072] The server will calculate a commission of 1% to 3% of the revenue.

[0073] The server generates a payment notice for the subscription fee and sends it to the terminal.

[0074] The terminal displays a subscription fee payment notification to the user.

[0075] Specific examples

[0076] A user installs the app and registers

[0077] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[0078] The server receives the input information and stores it in a database.

[0079] The server generates a user ID and authentication token and sends them to the terminal.

[0080] The device stores the authentication token and uses it for subsequent requests.

[0081] Request a portfolio build

[0082] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[0083] The terminal sends the input data to the server.

[0084] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[0085] The server generates a recommended portfolio and transmits it to the terminal.

[0086] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[0087] Buying and selling investments

[0088] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[0089] The terminal sends the order information to the server.

[0090] The server executes the order through the broker API.

[0091] The server receives the order execution results and sends them to the terminal.

[0092] The terminal displays the order execution results to the user.

[0093] This system will enable even beginners and small investors to carry out investment activities in a simple and reliable manner.

[0094] The processing flow will be explained below.

[0095] Step 1: The user installs the app and launches it.

[0096] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[0097] Step 3: The terminal sends the entered information to the server.

[0098] Step 4: The server receives the input information and stores it in a database.

[0099] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0100] Step 6: The device receives the authentication token and stores it for use in future requests.

[0101] Step 7: User opens the "Portfolio Construction" screen.

[0102] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[0103] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[0104] Step 10: The terminal sends the input information to the server.

[0105] Step 11: The server receives the user data and stores it in the database.

[0106] Step 12: The server invokes the deep learning model, passing the user data as input.

[0107] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0108] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[0109] Step 15: The terminal displays the recommended portfolio to the user.

[0110] Step 16: The user enters detailed investment planning consultations based on the portfolio (e.g., additional questions or revision requests).

[0111] Step 17: The terminal sends any additional questions or correction requests to the server.

[0112] Step 18: The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[0113] Step 19: The server generates additional advice and / or a modified portfolio and sends it to the terminal.

[0114] Step 20: The terminal displays additional advice and / or modified portfolios to the user.

[0115] Step 21: The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[0116] Step 22: The terminal sends the entered order to the server.

[0117] Step 23: The server receives the order information and prepares to call the broker API.

[0118] Step 24: The server executes the order through the broker API.

[0119] Step 25: The server receives the order execution result (success or failure) and sends it to the terminal.

[0120] Step 26: The terminal displays the order execution results to the user.

[0121] Step 27: The server periodically obtains the user's investment activity data from the database.

[0122] Step 28: The server calculates the user's revenue.

[0123] Step 29: The server calculates a commission of 1% to 3% of the revenue.

[0124] Step 30: The server generates a payment notice for the subscription fee and sends it to the terminal.

[0125] Step 31: The terminal displays a subscription fee payment notice to the user.

[0126] These processing steps allow even beginners and small investors to carry out investment activities easily and reliably.

[0127] Example 1

[0128] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] There is a need to provide support to individual investors to easily and efficiently conduct their investment activities. However, conventional systems have the problem of requiring a lot of time and effort to develop investment plans, execute trades, and manage profits. In addition, receiving appropriate risk management and investment advice requires specialized knowledge, making it difficult for beginners and small investors. To solve these problems, a comprehensive system is needed that provides consistent support from data input to portfolio construction, investment trading, and profit management.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0131] In this invention, the server includes: means for accepting input of a user's asset status, risk tolerance, and investment goals; means for constructing a portfolio using machine learning technology based on the input information; means for presenting the constructed portfolio to the user; means for accepting buy / sell orders from the user and executing the orders via an external system; means for notifying the user of the execution results of the buy / sell orders; means for calculating the user's profit, calculating a fee based on the profit, and providing the user with a payment notification; means for authenticating users; and means including a database for securely managing information on multiple users. This enables individual investors to conduct investment activities reliably and efficiently.

[0132] "User" refers to an individual investor who uses the system to conduct investment activities.

[0133] "Asset status" refers to information regarding the total amount and type of assets currently held by the user.

[0134] "Risk tolerance" refers to the range or level of investment risk that a user can tolerate.

[0135] "Investment Objective" refers to the specific investment objectives and timeframe that a User seeks to achieve.

[0136] "Machine learning technology" refers to algorithms and models that allow computers to make predictions and classifications using large amounts of data.

[0137] "Portfolio" refers to a user's assets diversified across various investment vehicles (e.g., stocks, bonds, cash, etc.).

[0138] "External System" refers to the broker or exchange system that is connected to execute a user's buy or sell orders.

[0139] "Buy / Sell Order" refers to an instruction issued by a User to purchase or sell an investment.

[0140] "Revenue" refers to the profit or loss derived from a User's investment activities.

[0141] "Commission" refers to the fee calculated and collected by the system based on the user's revenue.

[0142] "Payment notice" refers to a notice to prompt the user to pay a fee.

[0143] "User authentication" refers to the process of verifying a user's identity when accessing a system.

[0144] "Database" refers to information storage within a system that securely stores and manages information for multiple users.

[0145] The system embodying the present invention provides support for individual investors to carry out their investment activities effectively and efficiently. Specific embodiments of the system will be described below.

[0146] This system accepts input from users about their asset status, risk tolerance, and investment goals, and uses machine learning technology to build an optimal portfolio based on that data. It also accepts and executes buy and sell orders from users and notifies the users of the results. It also periodically calculates users' profits and provides a means to calculate and collect fees based on the profits. Specific examples of the hardware and software used are as follows:

[0147] First, to perform user authentication, the user must launch the application through their device and register. The user enters their first and last name, email address, and password, and this information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL (registered trademark)) and generates a new user ID and authentication token. This token is sent to the device and used in subsequent API requests.

[0148] Next, the portfolio is constructed. The user opens the "Portfolio Construction" screen and enters their asset status (e.g., 500,000 yen), risk tolerance (e.g., medium), and investment goal (e.g., purchasing a new car in three years). The device sends this data to the server. The server stores the data in a database and calls a deep learning model (e.g., constructed with TENSORFLOW (registered trademark)) to calculate the optimal portfolio. The calculation results are sent to the device and displayed to the user.

[0149] Furthermore, if additional investment advice or adjustments are needed, the user can input questions or requests for adjustments into the terminal. The server then uses the deep learning model to generate a new portfolio and sends it to the terminal, allowing the user to create a more refined investment plan.

[0150] For the buying and selling function, the user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order. This information is sent from the terminal to the server, and the server executes the order by calling the broker's API. The order execution results are sent to the terminal and notified to the user.

[0151] Regarding the calculation of revenue and collection of subscription fees, the server periodically retrieves the user's investment activity data from the database and calculates the revenue. The server calculates a commission of 1% to 3% of the revenue and provides a payment notice to the user. The terminal displays this notice to the user and prompts them to pay the commission.

[0152] As a concrete example, the following prompt sentence can be used:

[0153] example:

[0154] Please enter your name, email address, and password to register.

[0155] "Enter your financial situation, risk tolerance, and investment goals to get the portfolio that's right for you."

[0156] "Please purchase 50,000 yen worth of stock A."

[0157] Based on this prompt, users can effectively carry out investment activities through the system.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1: User Registration and Authentication

[0160] Specific behavior:

[0161] The user installs and launches the app.

[0162] The device will display a "New Registration" screen and ask you to enter your first and last name, email address, and password.

[0163] The terminal transmits the input information to the server.

[0164] input:

[0165] The first name, last name, email address, and password entered by the user on the device.

[0166] output:

[0167] User information sent to the server.

[0168] Data processing / calculation:

[0169] The entered information is converted into JSON format and securely sent to the server.

[0170] Server behavior:

[0171] The server stores the received information in a database (e.g. MySQL).

[0172] The server generates a new user ID and authentication token and sends them to the device.

[0173] input:

[0174] User information sent from the device.

[0175] output:

[0176] The generated user ID and authentication token.

[0177] Data processing / calculation:

[0178] Save the user information in the database and generate a user ID and authentication token.

[0179] Terminal behavior:

[0180] The device receives the authentication token and stores it in secure storage for use in later requests.

[0181] input:

[0182] The authentication token sent by the server.

[0183] output:

[0184] A securely stored authentication token.

[0185] Data processing / calculation:

[0186] Securely store the received token.

[0187] Step 2: Build your portfolio

[0188] Specific behavior:

[0189] The user opens the "Portfolio Construction" screen.

[0190] The terminal displays a form for inputting your financial situation, risk tolerance, and investment goals.

[0191] The user enters the required information.

[0192] input:

[0193] Your financial situation, risk tolerance, and investment goals.

[0194] output:

[0195] Investment information sent from the terminal to the server.

[0196] Data processing / calculation:

[0197] The input information is converted to JSON format and sent to the server.

[0198] Server behavior:

[0199] The server receives the user data and stores it in a database.

[0200] The server invokes a deep learning model (e.g., TensorFlow) and passes the user data as input.

[0201] A deep learning model calculates the optimal portfolio.

[0202] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[0203] input:

[0204] User investment information.

[0205] output:

[0206] Calculated optimal portfolio.

[0207] Data processing / calculation:

[0208] The portfolio is calculated using a deep learning model using user data, and the results are converted into JSON format and sent to the terminal.

[0209] Terminal behavior:

[0210] The terminal displays the recommended portfolio to the user.

[0211] input:

[0212] The recommended portfolio sent from the server.

[0213] output:

[0214] The portfolio that is displayed to the user.

[0215] Data processing / calculation:

[0216] Converting received portfolios into a suitable format for display on screen.

[0217] Step 3: Investment advice and planning

[0218] Specific behavior:

[0219] The terminal presents the received recommended portfolio to the user.

[0220] The user enters detailed questions and correction requests.

[0221] The device sends questions and correction requests to the server.

[0222] input:

[0223] Additional questions or correction requests.

[0224] output:

[0225] Questions and correction requests sent from your device to the server.

[0226] Data processing / calculation:

[0227] The entered questions and correction requests are converted into JSON format and sent to the server.

[0228] Server behavior:

[0229] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[0230] The server generates additional advice and modified portfolios and sends them to the terminal.

[0231] input:

[0232] User questions and correction requests.

[0233] output:

[0234] Additional advice and revised portfolios.

[0235] Data processing / calculation:

[0236] It analyzes the user's question, generates a new portfolio, converts the results into JSON format, and sends it to the terminal.

[0237] Terminal behavior:

[0238] The terminal displays additional advice and modified portfolios to the user.

[0239] input:

[0240] Advice and correction portfolio sent from the server.

[0241] output:

[0242] The new portfolio as it appears to the user.

[0243] Data processing / calculation:

[0244] Converts received information into a suitable format for display on the screen.

[0245] Step 4: Providing trading functionality

[0246] Specific behavior:

[0247] The user opens the "Buy / Sell" screen and enters the name and order amount.

[0248] The terminal transmits the input order information to the server.

[0249] input:

[0250] Buy and sell orders.

[0251] output:

[0252] Order information sent from the terminal to the server.

[0253] Data processing / calculation:

[0254] The entered order information is converted into JSON format and sent to the server.

[0255] Server behavior:

[0256] The server receives the order information and executes the order by calling the broker API.

[0257] The server receives the order execution results and sends them to the terminal.

[0258] input:

[0259] Order information.

[0260] output:

[0261] Order execution results via broker API.

[0262] Data processing / calculation:

[0263] The order information is passed to the broker API for execution, the results are received, converted into JSON format, and sent to the terminal.

[0264] Terminal behavior:

[0265] The terminal displays the order execution results to the user.

[0266] input:

[0267] Order execution result sent from the server.

[0268] output:

[0269] Order execution results displayed to the user.

[0270] Data processing / calculation:

[0271] Converts received information into a suitable format for display on the screen.

[0272] Step 5: Calculate your revenue and collect subscription fees

[0273] Specific behavior:

[0274] The server periodically acquires the user's investment activity data from the database.

[0275] The server calculates the user's revenue.

[0276] The server will calculate a commission of 1% to 3% of the revenue.

[0277] The server generates a payment notice for the subscription fee and sends it to the terminal.

[0278] input:

[0279] User investment activity data.

[0280] output:

[0281] Fees and Payment Notices.

[0282] Data processing / calculation:

[0283] Based on the investment activity data, profits are calculated, fees are calculated, and a payment notice is generated and sent to the terminal.

[0284] Terminal behavior:

[0285] The terminal displays a subscription fee payment notification to the user.

[0286] input:

[0287] Payment advice sent by the server.

[0288] output:

[0289] The payment notice displayed to the user.

[0290] Data processing / calculation:

[0291] Converts received notifications into a suitable format for display on the screen.

[0292] (Application example 1)

[0293] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] In today's world, for individual investors to effectively and efficiently manage their assets, it is important to build an appropriate portfolio, execute trades, and manage profits. However, performing these steps manually is extremely complex and requires a lot of time and effort. Furthermore, beginners and small investors find it difficult to make optimal investment decisions due to a lack of specialized knowledge. For this reason, there is a need for the development of a simple, reliable investment support system that can solve these issues.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0296] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for presenting the constructed portfolio to the user, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, means for calculating profits and subscription fees, and means for collecting subscription fees via an electronic payment API. This enables individual investors to easily manage their own investment information, construct an optimal portfolio and execute buy / sell, and seamlessly manage profits and pay subscription fees.

[0297] A "user" is an individual who utilizes the system to input their asset status, risk tolerance, and investment goals and engage in investment activities.

[0298] "Asset status" refers to the status of the total assets owned by the user, such as cash, stocks, bonds, real estate, etc.

[0299] "Risk tolerance" refers to the level of risk a user is willing to accept in an investment.

[0300] "Investment goal" refers to the purpose or goal of investment activities set by the user, such as purchasing a new car or a house.

[0301] "Deep learning technology" is a technology that uses multi-layer neural networks based on large amounts of data to perform advanced pattern recognition and prediction.

[0302] "Portfolio" refers to an investment allocation that optimizes risk by diversifying a user's funds across multiple investment targets.

[0303] A "buy / sell order" is a trading instruction issued by a user to sell or buy a particular investment.

[0304] An "electronic payment API" is a programmatic interface for making payments electronically over the Internet.

[0305] "Subscription Fee" means the fee paid periodically by a User for use of the System.

[0306] This invention provides a support system for individual investors to effectively and efficiently conduct investment activities. The system uses a smartphone as its main platform and utilizes deep learning technology to provide optimal portfolios. Specific embodiments for realizing this system are described below.

[0307] Hardware and Software

[0308] Hardware:

[0309] Smartphone (iOS or ANDROID (registered trademark))

[0310] software:

[0311] Python3

[0312] TensorFlow (Keras)

[0313] Requests library

[0314] REST API Server

[0315] Electronic Payment API

[0316] Data processing and calculation

[0317] 1. User Registration and Authentication:

[0318] The user launches the app on their smartphone and enters the required information (first name, last name, email address, and password) on the "New Registration" screen.

[0319] The terminal transmits this information to the server.

[0320] The server stores the information in a database, generates a user ID and authentication token, and sends them to the terminal.

[0321] The device stores the authentication token and uses it for subsequent requests.

[0322] 2. Portfolio Construction:

[0323] Users enter their current asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen.

[0324] The terminal transmits these input data to the server.

[0325] The server uses a deep learning model to input this data and calculate the optimal portfolio.

[0326] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[0327] The terminal displays the recommended portfolio to the user.

[0328] 3. Providing trading functions:

[0329] The user inputs a buy / sell order by specifying the name and amount on the "Buy / Sell" screen.

[0330] The terminal transmits this order information to the server.

[0331] The server executes buy and sell orders through the broker API and sends the order execution results to the terminal.

[0332] The terminal notifies the user of the order execution result.

[0333] 4. Calculating Revenue and Collecting Subscription Fees:

[0334] The server periodically retrieves the user's investment activity data from the database and calculates the profit.

[0335] Based on the calculated revenue, the server will calculate a subscription fee of 2% of the revenue.

[0336] The server sends a payment notification to the terminal to collect the subscription fee via the electronic payment API.

[0337] The terminal displays the payment advice to the user and makes the electronic payment.

[0338] Specific examples

[0339] For example, a user may enter their investment information (net worth of ¥500,000, medium risk tolerance, and new car purchase in three years). Based on this information, the system generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash). Furthermore, if the user enters an order to purchase ¥50,000 worth of stocks, the system executes the order via the broker API and notifies the user of the results.

[0340] Prompt Sentence Examples

[0341] "Write a prompt that generates the optimal investment strategy for a user with a net worth of 500,000 yen, a medium risk tolerance, and who is looking to buy a new car."

[0342] In this way, the present invention enables individual investors to easily and efficiently manage complex investment activities. The use of concrete examples and prompts makes it even easier to understand.

[0343] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0344] Step 1:

[0345] User Registration and Authentication

[0346] Input: A user launches the app and enters their first name, last name, email address, and password on the sign-up screen.

[0347] Specific operations: The device sends the input information to the server. The server receives the input information and saves it in a database. The server generates a user ID and authentication token and sends them to the device.

[0348] Output: The device stores the authentication token and uses it for subsequent requests.

[0349] Step 2:

[0350] Portfolio Construction

[0351] Input: The user enters their financial situation, risk tolerance, and investment goals into the "Portfolio Construction" screen.

[0352] Specific operation: The device sends the input data to the server, which then inputs the received data into the deep learning model and calculates the optimal portfolio.

[0353] Output: The server generates the calculation result (recommended portfolio) and sends it to the terminal. The terminal displays the recommended portfolio to the user.

[0354] Step 3:

[0355] Additional Investment Advice

[0356] Input: User requests additional investment advice based on presented portfolio.

[0357] How it works: The device sends additional questions or correction requests to the server, which analyzes the questions and uses deep learning models to make predictions or corrections.

[0358] Output: The server generates additional advice and / or modified portfolios and sends them to the terminal, which displays them to the user.

[0359] Step 4:

[0360] Investment buy and sell orders

[0361] Input: The user enters a buy or sell order by specifying the stock and amount on the "Buy / Sell" screen.

[0362] Specific operations: The terminal sends the entered order to the server. The server receives the order information and executes the order by calling the broker API. The server receives the order execution results and sends them to the terminal.

[0363] Output: The terminal notifies the user of the order execution result.

[0364] Step 5:

[0365] Calculating revenue and collecting subscription fees

[0366] Input: The server periodically retrieves the user's investment activity data from the database.

[0367] Specific operation: The server calculates the user's revenue and calculates the subscription fee based on that revenue. The server generates a payment notice for the subscription fee through the electronic payment API and sends it to the terminal.

[0368] Output: The terminal displays the payment advice to the user and makes the electronic payment.

[0369] The above are the specific processing steps of the system that realizes the application example.

[0370] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0371] A system embodying the present invention combines an emotion engine that recognizes user emotions to enable individual investors to conduct their investment activities effectively and efficiently. This system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes means for incorporating emotion recognition data from the emotion engine into investment advice, accepting and executing buy / sell orders from users, and notifying them of the results. Furthermore, the system provides means for periodically calculating profits generated from the user's investment activities, and calculating and collecting subscription fees based on those profits.

[0372] Overview of program processing flow

[0373] 1. User Registration and Authentication

[0374] The user installs the app and launches it.

[0375] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[0376] The terminal transmits the input information to the server.

[0377] The server receives the input information and stores it in a database.

[0378] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0379] The device receives the authentication token and stores it for use in future requests.

[0380] 2. Portfolio Construction

[0381] The user opens the "Portfolio Construction" screen.

[0382] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[0383] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[0384] The terminal transmits the input information to the server.

[0385] The server receives the user data and stores it in a database.

[0386] The server invokes the deep learning model, passing the user data as input.

[0387] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0388] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[0389] The terminal displays the recommended portfolio to the user.

[0390] 3. Emotion Recognition by Emotion Engine

[0391] The user uses the "emotion recognition" feature (e.g., detecting emotions using a camera or voice input).

[0392] The data acquired by the device is sent to the emotion engine.

[0393] The emotion engine performs analysis and recognizes the user's emotions (e.g., level of stress, level of satisfaction).

[0394] The device transmits the recognized emotion data to the server.

[0395] 4. Investment advice and planning

[0396] The server receives the emotion recognition data and inputs it into the deep learning model.

[0397] The server generates additional investment advice based on the sentiment data.

[0398] The server sends new investment advice to the terminal.

[0399] The terminal displays additional advice and modified portfolios to the user.

[0400] 5. Providing trading functions

[0401] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[0402] The terminal transmits the entered order to the server.

[0403] The server receives the order information and prepares to call the broker API.

[0404] The server executes the order through the broker API.

[0405] The server receives the order execution result (success or failure) and sends it to the terminal.

[0406] The terminal displays the order execution results to the user.

[0407] 6. Calculating Revenue and Collecting Subscription Fees

[0408] The server periodically acquires the user's investment activity data from the database.

[0409] The server calculates the user's revenue.

[0410] The server will calculate a commission of 1% to 3% of the revenue.

[0411] The server generates a payment notice for the subscription fee and sends it to the terminal.

[0412] The terminal displays a subscription fee payment notification to the user.

[0413] Specific examples

[0414] A user installs the app and registers

[0415] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[0416] The server receives the input information and stores it in a database.

[0417] The server generates a user ID and authentication token and sends them to the terminal.

[0418] The device stores the authentication token and uses it for subsequent requests.

[0419] Request a portfolio build

[0420] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[0421] The terminal sends the input data to the server.

[0422] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[0423] The server generates a recommended portfolio and transmits it to the terminal.

[0424] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[0425] Perform emotion recognition

[0426] The user uses the "emotion recognition" function to input emotions using the camera or voice.

[0427] The emotion data acquired by the device is sent to the emotion engine.

[0428] The emotion engine recognizes the user's emotions and returns the results to the terminal.

[0429] The device sends the emotion recognition results to the server.

[0430] Providing emotionally-based investment advice

[0431] The server receives the emotion recognition data and inputs it into the deep learning model.

[0432] The server modifies the portfolio based on the sentiment data and generates additional investment advice.

[0433] The server sends the generated advice to the terminal.

[0434] The terminal displays the revised portfolio and advice to the user.

[0435] Buying and selling investments

[0436] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[0437] The terminal sends the order information to the server.

[0438] The server sends the received order information to the broker API and executes the order.

[0439] The server sends the order execution results to the terminal.

[0440] The terminal displays the results to the user.

[0441] This system allows for detailed advice that takes into consideration the user's emotions, enabling even beginners and small investors to carry out investment activities in a simple and reliable manner.

[0442] The processing flow will be explained below.

[0443] Step 1: The user installs the app and launches it.

[0444] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[0445] Step 3: The terminal sends the entered information to the server.

[0446] Step 4: The server receives the input information and stores it in a database.

[0447] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0448] Step 6: The device receives the authentication token and stores it for use in future requests.

[0449] Step 7: User opens the "Portfolio Construction" screen.

[0450] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[0451] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[0452] Step 10: The terminal sends the input information to the server.

[0453] Step 11: The server receives the user data and stores it in the database.

[0454] Step 12: The server invokes the deep learning model, passing the user data as input.

[0455] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0456] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[0457] Step 15: The terminal displays the recommended portfolio to the user.

[0458] Step 16: The user uses the "Emotion Recognition" function to input emotions using face or voice.

[0459] Step 17: The terminal transmits the acquired emotion data to the emotion engine.

[0460] Step 18: The emotion engine recognizes the user's emotion and sends the result to the terminal.

[0461] Step 19: The terminal sends the emotion recognition result to the server.

[0462] Step 20: The server receives the emotion recognition data and inputs it into the deep learning model.

[0463] Step 21: The server modifies the portfolio and investment advice based on the sentiment data.

[0464] Step 22: The server generates the revised advice or portfolio and sends it to the terminal.

[0465] Step 23: The terminal displays the revised portfolio and advice to the user.

[0466] Step 24: The user opens the "Buy / Sell" screen and enters an order by specifying the stock and amount (e.g., purchase "Stock A" for 50,000 yen).

[0467] Step 25: The terminal sends the entered order to the server.

[0468] Step 26: The server receives the order information and prepares to call the broker API.

[0469] Step 27: The server executes the order through the broker API.

[0470] Step 28: The server receives the order execution result (success or failure) and sends it to the terminal.

[0471] Step 29: The terminal displays the order execution results to the user.

[0472] Step 30: The server periodically acquires the user's investment activity data from the database.

[0473] Step 31: The server calculates the user's revenue.

[0474] Step 32: The server calculates a commission of 1% to 3% of the revenue.

[0475] Step 33: The server generates a payment notice for the subscription fee and sends it to the terminal.

[0476] Step 34: The terminal displays a subscription fee payment notification to the user.

[0477] This system allows for detailed advice that takes into consideration the user's emotions, enabling even beginners and small investors to carry out investment activities in a simple and reliable manner.

[0478] Example 2

[0479] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0480] Conventional investment support systems have difficulty providing investment advice that takes users' emotions into account, resulting in users' investment decisions being easily influenced by their emotions. They also lack a means to calculate appropriate subscription fees based on the profits from a user's investment activities. This makes it difficult for beginners and small investors to invest effectively.

[0481] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0482] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology, means for acquiring the user's emotional data via the terminal and transmitting the data to an emotion recognition engine, means for the emotion recognition engine to analyze the user's emotions and transmit the results to the server, means for the server to input the emotional data into a deep learning model and generate additional investment advice based on the emotions, and means for presenting the additional investment advice to the user. This makes it possible to provide detailed investment advice that takes the user's emotions into consideration, thereby supporting effective investment decisions that are not influenced by emotions.

[0483] 1. "User" refers to an individual or legal entity that uses the System to conduct investment activities.

[0484] 2. "Asset Status" refers to information indicating the total amount and type of assets held by the User.

[0485] 3. "Risk tolerance" refers to an indicator that indicates how much risk a user can tolerate.

[0486] 4. "Investment Objectives" means the specific investment goals or objectives that a User wishes to achieve.

[0487] 5. "Deep learning technology" is a type of artificial intelligence that uses multi-layer neural networks to perform advanced data analysis.

[0488] 6. "Portfolio" refers to the combination of financial assets held by a User.

[0489] 7. "Terminal" refers to a device such as a computer, smartphone, or tablet that a User uses to access the System.

[0490] 8. "Emotional Data" refers to data that indicates the user's emotional state, and is obtained through camera or voice input, etc.

[0491] 9. "Emotion Recognition Engine" refers to software or hardware that analyzes acquired emotion data and recognizes the user's emotions.

[0492] 10. "Deep Learning Model" refers to a data analysis model trained using deep learning technology.

[0493] 11. "Investment Advice" means specific instructions or suggestions recommending investment activities to users.

[0494] 12. "Buy / Sell Order" means an instruction given by a User to buy or sell a specific financial instrument.

[0495] 13. "Broker API" refers to the application programming interface of a broker platform used to execute orders to buy and sell financial instruments.

[0496] 14. "Subscription Fee" means the fee paid periodically by a User for use of the System.

[0497] MODE FOR CARRYING OUT THE INVENTION

[0498] An embodiment of the present invention will be described. The system of this invention recognizes user emotions and provides investment advice based on them, enabling individual investors to invest effectively and efficiently. The system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on these inputs. The system also has a function to reflect emotion recognition data generated by an emotion engine in investment advice. Furthermore, the system also includes functions to accept buy and sell orders from users, execute the orders, notify the results, and calculate and collect subscription fees based on revenue.

[0499] Hardware and software used

[0500] Deep learning models: built using TensorFlow or PyTorch.

[0501] Database: Use MySQL.

[0502] Emotion recognition engine: A practical implementation would use a dedicated natural language processing library or computer vision algorithm.

[0503] Device: Computer, smartphone, tablet, etc.

[0504] Broker API: Use the API of Alpaca or Interactive Brokers.

[0505] System processing flow

[0506] 1. User Registration and Authentication

[0507] The user installs and launches the app. This causes the device to display the initial launch screen and the "New Registration" screen. The user enters the required information (first name, last name, email address, and password) and taps the submit button. The device sends the information to the server, which receives it and stores it in a database. The server then generates a user ID and authentication token and sends them to the device.

[0508] 2. Portfolio Construction

[0509] The user opens the "Portfolio Construction" screen and enters information such as asset status, risk tolerance, and investment goals. The device then sends the information to the server, which receives the data and stores it in a database. The deep learning model is then invoked to calculate the optimal portfolio, and the server generates the results and sends them to the device.

[0510] 3. Emotion recognition

[0511] The user uses the emotion recognition function to input their emotions using the camera or voice. The device acquires the emotion data and sends it to the emotion recognition engine. The emotion recognition engine analyzes the emotion, returns the results to the device, and then sends them back to the server.

[0512] 4. Investment advice and planning

[0513] The server receives the emotion recognition data, inputs it into a deep learning model, generates additional investment advice based on it, and then sends the new investment advice to the device, which displays it to the user.

[0514] 5. Buying and Selling Function

[0515] The user enters a purchase order on the "Buy / Sell" screen. The terminal sends the order information to the server, which then calls the broker API to execute the order. The order results are passed to the terminal via the server and displayed to the user.

[0516] 6. Calculating Revenue and Collecting Subscription Fees

[0517] The server periodically retrieves the user's investment activity data from the database, calculates the revenue, calculates the subscription fee based on the revenue, generates a payment notice, and sends it to the terminal. The terminal then displays the payment notice to the user.

[0518] Specific examples

[0519] A user installs the app and registers

[0520] The device displays a "New Registration" screen, and the user enters their name, email address, password, etc., and the information is sent to the server.

[0521] The server receives the input information, stores it in a database, generates a user ID and authentication token, and sends them to the terminal.

[0522] Request a portfolio build

[0523] The user accesses the "Portfolio Construction" screen, enters their assets, risk tolerance, and investment goals, and the information is sent to the server.

[0524] The server uses a deep learning model to calculate the optimal portfolio and displays the results on the device (e.g., 50% stocks, 30% bonds, 20% cash).

[0525] Perform emotion recognition

[0526] The user uses the emotion recognition function to input their emotional state using a camera or voice.

[0527] The device sends the emotional data to an emotion recognition engine, which then sends the analysis results to a server, which uses a deep learning model to generate additional investment advice.

[0528] Buying and selling investments

[0529] The user enters an order on the "Buy / Sell" screen, the information is sent to the server, and the order is executed via the broker's API.

[0530] The server sends the order results to the terminal and displays them to the user.

[0531] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0532] Program processing steps

[0533] User Registration and Authentication

[0534] Step 1:

[0535] The user installs and launches the app.

[0536] Input: User action (installing the app, launching it).

[0537] Output: The initial launch screen of the app.

[0538] Specific operation: The device displays the initial startup screen.

[0539] Step 2:

[0540] The device will display a "New Registration" screen and prompt you to enter your name, email address, password, etc.

[0541] Input: User's personal information (first name, last name, email address, password).

[0542] Output: User input information.

[0543] Specific operation: The user enters the required information and taps the send button.

[0544] Step 3:

[0545] The terminal sends the input information to the server.

[0546] Input: User input information sent from the device.

[0547] Output: User information data sent to the server.

[0548] Specific operation: The terminal sends the user's input information to the server.

[0549] Step 4:

[0550] The server receives the information and stores it in a database.

[0551] Input: User information data.

[0552] Output: User information stored in the database.

[0553] Specific operation: The server receives the input information and stores it in a MySQL database.

[0554] Step 5:

[0555] The server generates a new user ID and issues an authentication token.

[0556] Input: Saved user information.

[0557] Output: User ID, authentication token.

[0558] Specific operation: The server generates a user ID and authentication token (such as a JWT).

[0559] Step 6:

[0560] The server sends the user ID and authentication token to the terminal.

[0561] Input: User ID, authentication token.

[0562] Output: User ID and authentication token sent to the terminal.

[0563] Specific operation: The server sends the generated information to the terminal.

[0564] Step 7:

[0565] The device receives the authentication token and stores it in local storage.

[0566] Input: The authentication token sent by the server.

[0567] Output: The authentication token stored in local storage.

[0568] What happens: The device receives the authentication token and stores it in local storage for use in later requests.

[0569] Portfolio Construction

[0570] Step 8:

[0571] The user opens the "Portfolio Construction" screen.

[0572] Input: User action (selection on portfolio building screen).

[0573] Output: Portfolio building screen.

[0574] Specific operation: The terminal displays a form for entering financial status, risk tolerance, and investment goals.

[0575] Step 9:

[0576] The user enters the required information.

[0577] Inputs: Financial situation, risk tolerance, investment goals.

[0578] Output: The user's input data.

[0579] Specific behavior: The user enters their net worth, risk tolerance, and investment goals into a form.

[0580] Step 10:

[0581] The terminal transmits the input information to the server.

[0582] Input: User input data.

[0583] Output: The portfolio information sent to the server.

[0584] Specific operation: The terminal sends the user's input data to the server.

[0585] Step 11:

[0586] The server receives the user data and stores it in a database.

[0587] Input: Portfolio information data.

[0588] Output: Portfolio information stored in a database.

[0589] Specific operation: The server receives the information and stores it in a database.

[0590] Step 12:

[0591] The server invokes the deep learning model, passing the user data as input.

[0592] Input: User data stored in the database.

[0593] Output: The data input to the deep learning model.

[0594] Specific operation: The server calls a deep learning model in TensorFlow or PyTorch and passes the data.

[0595] Step 13:

[0596] A deep learning model calculates the optimal portfolio.

[0597] Input: User data.

[0598] Output: Calculation results of the optimal portfolio.

[0599] How it works: The deep learning model performs the calculations and generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0600] Step 14:

[0601] The server generates the calculation result and sends it to the terminal.

[0602] Input: Optimal portfolio calculation results.

[0603] Output: The calculation result sent to the terminal.

[0604] Specific operation: The server sends the calculation result to the terminal.

[0605] Step 15:

[0606] The terminal displays the recommended portfolio to the user.

[0607] Input: The calculation result sent from the server.

[0608] Output: The recommended portfolio displayed to the user.

[0609] Specific operation: The device displays the recommended portfolio on the screen.

[0610] Emotion recognition by emotion engine

[0611] Step 16:

[0612] The user uses the "emotion recognition" feature.

[0613] Input: User action (activation of emotion recognition function).

[0614] Output: Beginning emotion recognition.

[0615] Specific operation: The device activates the camera and microphone to collect emotional data.

[0616] Step 17:

[0617] The emotion data acquired by the device is sent to the emotion engine.

[0618] Input: Emotion data obtained from a camera or microphone.

[0619] Output: Emotion data sent to the emotion engine.

[0620] Specific operation: The device sends emotion data to the emotion recognition engine.

[0621] Step 18:

[0622] The emotion engine performs analysis and recognizes the user's emotions.

[0623] Input: Emotion data.

[0624] Output: Emotion recognition result.

[0625] Specific operation: The emotion engine analyzes the data and recognizes the user's emotional state (e.g., level of stress, level of satisfaction).

[0626] Step 19:

[0627] The device transmits the recognized emotion data to the server.

[0628] Input: Emotion recognition results.

[0629] Output: Emotion recognition results sent to the server.

[0630] Specific operation: The device sends the emotion recognition results to the server.

[0631] Investment Advice and Planning

[0632] Step 20:

[0633] The server receives the emotion recognition data and inputs it into the deep learning model.

[0634] Input: Emotion recognition data.

[0635] Output: Emotion data fed into the deep learning model.

[0636] Specific operation: The server receives emotion data and inputs it into the deep learning model.

[0637] Step 21:

[0638] The server generates additional investment advice based on the sentiment data.

[0639] Input: Emotion data fed into the deep learning model.

[0640] Output: Additional investment advice.

[0641] Specific operation: The server modifies the portfolio based on the sentiment data and generates new investment advice.

[0642] Step 22:

[0643] The server sends new investment advice to the terminal.

[0644] Input: Generated additional investment advice.

[0645] Output: Investment advice sent to the terminal.

[0646] Specific operation: The server sends the generated investment advice to the terminal.

[0647] Step 23:

[0648] The terminal displays additional advice and modified portfolios to the user.

[0649] Input: Additional advice sent by the server.

[0650] Output: The additional advice and revised portfolio displayed to the user.

[0651] Specific operation: The device displays additional advice and revised portfolios on the screen.

[0652] Providing buying and selling functions

[0653] Step 24:

[0654] The user opens the "Buy / Sell" screen and enters a purchase order.

[0655] Input: User action (selecting a buy or sell screen, entering a purchase order).

[0656] Output: Purchase orders entered.

[0657] Specific operation: The user enters an order by specifying the stock and amount on the trading screen (e.g., purchasing 50,000 yen worth of "Stock A").

[0658] Step 25:

[0659] The terminal sends the order information to the server.

[0660] Input: Purchase orders entered.

[0661] Output: The order information sent to the server.

[0662] Specific operation: The terminal sends the user's order information to the server.

[0663] Step 26:

[0664] The server receives the order information and prepares to call the broker API.

[0665] Input: The order information sent to the server.

[0666] Output: Prepare a request to the broker API.

[0667] Specific operation: The server prepares to execute the order by calling the broker API.

[0668] Step 27:

[0669] The server executes the order through the broker API.

[0670] Input: API request preparation data.

[0671] Output: The order data sent to the broker API.

[0672] Specific operation: The server sends the order data to the broker API and executes it.

[0673] Step 28:

[0674] The server receives the order execution results and sends them to the terminal.

[0675] Input: Order execution result from broker API.

[0676] Output: Order execution result sent to the terminal.

[0677] Specific operation: The server receives the order execution result and sends it to the terminal.

[0678] Step 29:

[0679] The terminal displays the order execution results to the user.

[0680] Input: Order execution result sent from the server.

[0681] Output: Order execution result displayed to the user.

[0682] Specific operation: The terminal displays the order execution results on the screen.

[0683] Calculating revenue and collecting subscription fees

[0684] Step 30:

[0685] The server periodically acquires the user's investment activity data from the database.

[0686] Input: Investment activity data retrieved from the database.

[0687] Output: Investment activity data for profit calculation.

[0688] Specific operation: The server periodically obtains the user's investment activity data from the database.

[0689] Step 31:

[0690] The server calculates the user's revenue.

[0691] Input: Obtained investment activity data.

[0692] Output: Calculated revenue data.

[0693] Specific operation: The server calculates profits based on investment activity data.

[0694] Step 32:

[0695] The server will calculate a commission of 1% to 3% of the revenue.

[0696] Input: Calculated revenue data.

[0697] Output: Calculated commission data.

[0698] Specific operation: The server calculates a certain percentage of the revenue as a commission.

[0699] Step 33:

[0700] The server generates a payment notice for the subscription fee and sends it to the terminal.

[0701] Input: Calculated commission data.

[0702] Output: Payment advice data sent to the terminal.

[0703] Specific operation: The server creates a payment notification and sends it to the terminal.

[0704] Step 34:

[0705] The terminal displays the payment notice to the user.

[0706] Input: Payment advice data sent from the server.

[0707] Output: Payment notice displayed to the user.

[0708] Specific behavior: The terminal displays a payment notification on the screen.

[0709] (Application example 2)

[0710] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0711] Conventional investment support systems build portfolios and provide investment advice based on the user's asset status and risk tolerance, but do not take the user's emotional state into consideration, which can lead to emotionally driven errors in judgment and inappropriate investment behavior.Furthermore, there are few systems that are involved in users' purchasing activities as well as investments, making total asset management difficult.

[0712] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0713] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for generating emotion recognition data using an emotion engine that recognizes the user's emotion data, means for reflecting investment advice based on the emotion recognition data, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, and means for recommending optimal products based on the user's purchase history and emotion data. This enables investment advice and purchase recommendations that take into account the user's emotional state, enabling comprehensive asset management and reducing judgment errors.

[0714] "Asset status" is information that refers to the total amount and type of cash, stocks, real estate, and other assets held by the user.

[0715] "Risk tolerance" is an indicator that indicates the range of risk that a user can accept in an investment.

[0716] "Investment goal" is information that indicates the specific goal or objective that a user wants to achieve through investment.

[0717] "Deep learning technology" is a type of machine learning technology that enables artificial intelligence to automatically learn from large amounts of data and make advanced judgments.

[0718] A "portfolio" is a combination of multiple investment products held by a user, with the aim of diversifying risk and maximizing profits.

[0719] "Emotion engine" is a general term for software and hardware that analyzes a user's facial expressions, voice data, etc., and recognizes their emotional state at that time.

[0720] "Emotion recognition data" is data that represents the emotional state of the user analyzed by the emotion engine.

[0721] "Investment Advice" is specific advice or instruction provided to assist a user in making an investment decision.

[0722] A "buy / sell order" is an instruction by a user to buy or sell a particular investment.

[0723] "Purchase history" is a record of products and services purchased by a user in the past.

[0724] "Product recommendation" refers to recommending appropriate products to users based on their purchasing history and emotional data.

[0725] A "server" is a computer system that provides services and data to a large number of clients over a network.

[0726] MODE FOR CARRYING OUT THE INVENTION

[0727] The system for implementing the present invention is comprised of several different modules and hardware and software components, which enable users to effectively manage their assets and carry out their investment activities.

[0728] 1. Overall structure

[0729] The system mainly consists of the following elements:

[0730] User device: A device that is directly operated by the user, such as a smartphone, tablet, or PC.

[0731] Server: A back-end system that receives input data from users and performs various data processing and analysis.

[0732] Emotion engine: Software and hardware that recognizes a user's emotions and generates data about them.

[0733] Deep learning model: A machine learning model for building investment portfolios based on user input data and generating investment advice that reflects sentiment data.

[0734] 2. User Registration and Authentication

[0735] Users must install the application and enter their first name, last name, email address, and password on the "New Registration" screen when they first launch it. This information is sent from the device to the server, which stores it in a database and issues a user ID and authentication token.

[0736] 3. Portfolio Construction

[0737] Users input their asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen. This data is sent from the device to the server. The deep learning model uses this data to calculate the optimal portfolio, and the results are sent to the device via the server and displayed to the user.

[0738] 4. Emotion recognition using emotion engine

[0739] When a user uses the "emotion recognition function," emotional data is collected through the device's camera and microphone. This data is sent to the emotion engine, and the analysis results are sent to the server. The emotion recognition data is input into a deep learning model, which generates appropriate investment advice based on emotions.

[0740] 5.Buying and selling function

[0741] Users use the "Buy / Sell" screen to select specific investment products and enter orders to buy or sell. These orders are sent from the terminal to the server, which executes them via the broker's API. The execution results are sent back from the server to the terminal and notified to the user.

[0742] 6. Revenue calculation and subscription fee collection

[0743] The server periodically calculates the revenue generated from the user's investments and purchasing activities, retrieves the revenue information from the database, and calculates the subscription fee based on the revenue. A payment notification for the subscription fee is sent to the terminal and displayed to the user.

[0744] Examples of specific examples and prompts

[0745] Below are some specific examples.

[0746] The user enters information such as net worth of 500,000 yen, medium risk tolerance, and plans to travel abroad in one year: This information is sent from the device to the server, and the optimal portfolio is calculated using a deep learning model.

[0747] Emotion recognition is performed and product recommendations are displayed based on the user's emotional state (stress level "high" or satisfaction level "low"). Emotional data is sent to the server, and appropriate investment advice and product recommendations are generated by a deep learning model.

[0748] Prompt Sentence Examples

[0749] Take the user's sentiment data and generate purchase recommendations based on their current emotional state. The input data is:

[0750] Stress level: High

[0751] Satisfaction: Low

[0752] Purchase history: Electronic products, books

[0753] Output the recommendations in the following format:

[0754] Recommended categories: (e.g., Relaxation items)

[0755] Recommended items: (e.g. scented candles)

[0756] Reason: (e.g., item that reduces stress)

[0757] In this way, by combining the emotion engine with deep learning technology, it is possible to provide effective investment and purchasing advice that is tailored to the user's emotional state.

[0758] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0759] Program processing steps

[0760] Step 1: User Registration and Authentication

[0761] Input: First name, last name, email address, password

[0762] Specific behavior:

[0763] The user installs the app and launches it.

[0764] The terminal displays the "New Registration" screen and the user enters the required information.

[0765] The terminal transmits the input information to the server.

[0766] The server receives the input information and stores it in a database.

[0767] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0768] The device receives the authentication token and stores it for use in later requests.

[0769] Output: User ID, authentication token

[0770] Step 2: Build your portfolio

[0771] Input: Asset status, risk tolerance, investment goals

[0772] Specific behavior:

[0773] The user opens the "Portfolio Construction" screen.

[0774] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[0775] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," overseas travel in one year).

[0776] The terminal transmits the input information to the server.

[0777] The server receives the user data and stores it in a database.

[0778] The server invokes the deep learning model, passing the user data as input.

[0779] A deep learning model calculates the optimal portfolio.

[0780] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[0781] The terminal displays the recommended portfolio to the user.

[0782] Output: Recommended portfolio

[0783] Step 3: Emotion recognition by the emotion engine

[0784] Input: Emotion data (camera / voice input)

[0785] Specific behavior:

[0786] The user uses the "emotion recognition" feature.

[0787] The device captures emotion data using a camera and microphone.

[0788] The data acquired by the device is sent to the emotion engine.

[0789] The emotion engine performs analysis and recognizes the user's emotions.

[0790] The device transmits the recognized emotion data to the server.

[0791] Output: Recognized emotion data

[0792] Step 4: Investment advice and planning

[0793] Input: Emotion recognition data, existing portfolio data

[0794] Specific behavior:

[0795] The server receives the emotion recognition data and inputs it into the deep learning model.

[0796] The server generates additional investment advice based on the sentiment data.

[0797] The server sends new investment advice to the terminal.

[0798] The terminal displays additional advice and modified portfolios to the user.

[0799] Output: Additional investment advice, revised portfolio

[0800] Step 5: Providing trading functionality

[0801] Input: Buy / sell order information (stock, amount, etc.)

[0802] Specific behavior:

[0803] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order.

[0804] The terminal transmits the entered order to the server.

[0805] The server receives the order information and prepares to call the broker API.

[0806] The server executes the order through the broker API.

[0807] The server receives the order execution result (success or failure) and sends it to the terminal.

[0808] The terminal displays the order execution results to the user.

[0809] Output: Order execution result

[0810] Step 6: Calculate your revenue and collect subscription fees

[0811] Input: User investment and purchasing activity data

[0812] Specific behavior:

[0813] The server periodically retrieves the user's investment and purchasing activity data from the database.

[0814] The server calculates the user's revenue.

[0815] The server will calculate a commission of 1% to 3% of the revenue.

[0816] The server generates a payment notice for the subscription fee and sends it to the terminal.

[0817] The terminal displays a subscription fee payment notification to the user.

[0818] Output: Calculated revenue, subscription fee payment notification

[0819] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0820] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0822] [Second embodiment]

[0823] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0824] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0825] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0826] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0827] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0828] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0830] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0831] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0832] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0833] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0834] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0835] A system embodying the present invention provides support for individual investors to conduct their investment activities effectively and efficiently. The system accepts input from users about their asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes a means for accepting buy and sell orders from users, notifying them of the execution results, and calculating and collecting subscription fees from revenue.

[0836] Overview of program processing flow

[0837] 1. User Registration and Authentication

[0838] The user installs the app and launches it.

[0839] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[0840] The terminal transmits the input information to the server.

[0841] The server receives the input information and stores it in a database.

[0842] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0843] The device receives the authentication token and uses it for subsequent requests.

[0844] 2. Portfolio Construction

[0845] The user opens the "Portfolio Construction" screen.

[0846] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[0847] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[0848] The terminal transmits the input information to the server.

[0849] The server receives the user data and stores it in a database.

[0850] The server invokes the deep learning model, passing the user data as input.

[0851] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0852] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[0853] The terminal displays the recommended portfolio to the user.

[0854] 3. Investment advice and planning

[0855] The terminal presents the received recommended portfolio to the user.

[0856] The user inputs detailed investment planning consultations based on the portfolio (e.g., additional questions and revision requests).

[0857] The device sends additional questions or correction requests to the server.

[0858] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[0859] The server generates additional advice and modified portfolios and sends them to the terminal.

[0860] The terminal displays additional advice and modified portfolios to the user.

[0861] 4. Providing trading functions

[0862] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[0863] The terminal transmits the entered order to the server.

[0864] The server receives the order information and prepares to call the broker API.

[0865] The server executes the order through the broker API.

[0866] The server receives the order execution result (success or failure) and sends it to the terminal.

[0867] The terminal displays the order execution results to the user.

[0868] 5. Calculating Revenue and Collecting Subscription Fees

[0869] The server periodically acquires the user's investment activity data from the database.

[0870] The server calculates the user's revenue.

[0871] The server will calculate a commission of 1% to 3% of the revenue.

[0872] The server generates a payment notice for the subscription fee and sends it to the terminal.

[0873] The terminal displays a subscription fee payment notification to the user.

[0874] Specific examples

[0875] A user installs the app and registers

[0876] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[0877] The server receives the input information and stores it in a database.

[0878] The server generates a user ID and authentication token and sends them to the terminal.

[0879] The device stores the authentication token and uses it for subsequent requests.

[0880] Request a portfolio build

[0881] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[0882] The terminal sends the input data to the server.

[0883] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[0884] The server generates a recommended portfolio and transmits it to the terminal.

[0885] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[0886] Buying and selling investments

[0887] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[0888] The terminal sends the order information to the server.

[0889] The server executes the order through the broker API.

[0890] The server receives the order execution results and sends them to the terminal.

[0891] The terminal displays the order execution results to the user.

[0892] This system will enable even beginners and small investors to carry out investment activities in a simple and reliable manner.

[0893] The processing flow will be explained below.

[0894] Step 1: The user installs the app and launches it.

[0895] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[0896] Step 3: The terminal sends the entered information to the server.

[0897] Step 4: The server receives the input information and stores it in a database.

[0898] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[0899] Step 6: The device receives the authentication token and stores it for use in future requests.

[0900] Step 7: User opens the "Portfolio Construction" screen.

[0901] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[0902] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[0903] Step 10: The terminal sends the input information to the server.

[0904] Step 11: The server receives the user data and stores it in the database.

[0905] Step 12: The server invokes the deep learning model, passing the user data as input.

[0906] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[0907] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[0908] Step 15: The terminal displays the recommended portfolio to the user.

[0909] Step 16: The user enters detailed investment planning consultations based on the portfolio (e.g., additional questions or revision requests).

[0910] Step 17: The terminal sends any additional questions or correction requests to the server.

[0911] Step 18: The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[0912] Step 19: The server generates additional advice and / or a modified portfolio and sends it to the terminal.

[0913] Step 20: The terminal displays additional advice and / or modified portfolios to the user.

[0914] Step 21: The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[0915] Step 22: The terminal sends the entered order to the server.

[0916] Step 23: The server receives the order information and prepares to call the broker API.

[0917] Step 24: The server executes the order through the broker API.

[0918] Step 25: The server receives the order execution result (success or failure) and sends it to the terminal.

[0919] Step 26: The terminal displays the order execution results to the user.

[0920] Step 27: The server periodically obtains the user's investment activity data from the database.

[0921] Step 28: The server calculates the user's revenue.

[0922] Step 29: The server calculates a commission of 1% to 3% of the revenue.

[0923] Step 30: The server generates a payment notice for the subscription fee and sends it to the terminal.

[0924] Step 31: The terminal displays a subscription fee payment notice to the user.

[0925] These processing steps allow even beginners and small investors to carry out investment activities easily and reliably.

[0926] Example 1

[0927] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0928] There is a need to provide support to individual investors to easily and efficiently conduct their investment activities. However, conventional systems have the problem of requiring a lot of time and effort to develop investment plans, execute trades, and manage profits. In addition, receiving appropriate risk management and investment advice requires specialized knowledge, making it difficult for beginners and small investors. To solve these problems, a comprehensive system is needed that provides consistent support from data input to portfolio construction, investment trading, and profit management.

[0929] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0930] In this invention, the server includes: means for accepting input of a user's asset status, risk tolerance, and investment goals; means for constructing a portfolio using machine learning technology based on the input information; means for presenting the constructed portfolio to the user; means for accepting buy / sell orders from the user and executing the orders via an external system; means for notifying the user of the execution results of the buy / sell orders; means for calculating the user's profit, calculating a fee based on the profit, and providing the user with a payment notification; means for authenticating users; and means including a database for securely managing information on multiple users. This enables individual investors to conduct investment activities reliably and efficiently.

[0931] "User" refers to an individual investor who uses the system to conduct investment activities.

[0932] "Asset status" refers to information regarding the total amount and type of assets currently held by the user.

[0933] "Risk tolerance" refers to the range or level of investment risk that a user can tolerate.

[0934] "Investment Objective" refers to the specific investment objectives and timeframe that a User seeks to achieve.

[0935] "Machine learning technology" refers to algorithms and models that allow computers to make predictions and classifications using large amounts of data.

[0936] "Portfolio" refers to a user's assets diversified across various investment vehicles (e.g., stocks, bonds, cash, etc.).

[0937] "External System" refers to the broker or exchange system that is connected to execute a user's buy or sell orders.

[0938] "Buy / Sell Order" refers to an instruction issued by a User to purchase or sell an investment.

[0939] "Revenue" refers to the profit or loss derived from a User's investment activities.

[0940] "Commission" refers to the fee calculated and collected by the system based on the user's revenue.

[0941] "Payment notice" refers to a notice to prompt the user to pay a fee.

[0942] "User authentication" refers to the process of verifying a user's identity when accessing a system.

[0943] "Database" refers to information storage within a system that securely stores and manages information for multiple users.

[0944] The system embodying the present invention provides support for individual investors to carry out their investment activities effectively and efficiently. Specific embodiments of the system will be described below.

[0945] This system accepts input from users about their asset status, risk tolerance, and investment goals, and uses machine learning technology to build an optimal portfolio based on that data. It also accepts and executes buy and sell orders from users and notifies the users of the results. It also periodically calculates users' profits and provides a means to calculate and collect fees based on the profits. Specific examples of the hardware and software used are as follows:

[0946] First, to authenticate a user, they must launch the application through their device and register. The user enters their first name, last name, email address, and password, and this information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL) and generates a new user ID and authentication token. This token is sent to the device and used in subsequent API requests.

[0947] Next, the portfolio is constructed. The user opens the "Portfolio Construction" screen and enters their asset status (e.g., 500,000 yen), risk tolerance (e.g., medium), and investment goal (e.g., purchasing a new car in three years). The device sends this data to the server. The server stores the data in a database and calls a deep learning model (e.g., built with TensorFlow) to calculate the optimal portfolio. The calculation results are sent to the device and displayed to the user.

[0948] Furthermore, if additional investment advice or adjustments are needed, the user can input questions or requests for adjustments into the terminal. The server then uses the deep learning model to generate a new portfolio and sends it to the terminal, allowing the user to create a more refined investment plan.

[0949] For the buying and selling function, the user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order. This information is sent from the terminal to the server, and the server executes the order by calling the broker's API. The order execution results are sent to the terminal and notified to the user.

[0950] Regarding the calculation of revenue and collection of subscription fees, the server periodically retrieves the user's investment activity data from the database and calculates the revenue. The server calculates a commission of 1% to 3% of the revenue and provides a payment notice to the user. The terminal displays this notice to the user and prompts them to pay the commission.

[0951] As a concrete example, the following prompt sentence can be used:

[0952] example:

[0953] Please enter your name, email address, and password to register.

[0954] "Enter your financial situation, risk tolerance, and investment goals to get the portfolio that's right for you."

[0955] "Please purchase 50,000 yen worth of stock A."

[0956] Based on this prompt, users can effectively carry out investment activities through the system.

[0957] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0958] Step 1: User Registration and Authentication

[0959] Specific behavior:

[0960] The user installs and launches the app.

[0961] The device will display a "New Registration" screen and ask you to enter your first and last name, email address, and password.

[0962] The terminal transmits the input information to the server.

[0963] input:

[0964] The first name, last name, email address, and password entered by the user on the device.

[0965] output:

[0966] User information sent to the server.

[0967] Data processing / calculation:

[0968] The entered information is converted into JSON format and securely sent to the server.

[0969] Server behavior:

[0970] The server stores the received information in a database (e.g. MySQL).

[0971] The server generates a new user ID and authentication token and sends them to the device.

[0972] input:

[0973] User information sent from the device.

[0974] output:

[0975] The generated user ID and authentication token.

[0976] Data processing / calculation:

[0977] Save the user information in the database and generate a user ID and authentication token.

[0978] Terminal behavior:

[0979] The device receives the authentication token and stores it in secure storage for use in later requests.

[0980] input:

[0981] The authentication token sent by the server.

[0982] output:

[0983] A securely stored authentication token.

[0984] Data processing / calculation:

[0985] Securely store the received token.

[0986] Step 2: Build your portfolio

[0987] Specific behavior:

[0988] The user opens the "Portfolio Construction" screen.

[0989] The terminal displays a form for inputting your financial situation, risk tolerance, and investment goals.

[0990] The user enters the required information.

[0991] input:

[0992] Your financial situation, risk tolerance, and investment goals.

[0993] output:

[0994] Investment information sent from the terminal to the server.

[0995] Data processing / calculation:

[0996] The input information is converted to JSON format and sent to the server.

[0997] Server behavior:

[0998] The server receives the user data and stores it in a database.

[0999] The server invokes a deep learning model (e.g., TensorFlow) and passes the user data as input.

[1000] A deep learning model calculates the optimal portfolio.

[1001] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1002] input:

[1003] User investment information.

[1004] output:

[1005] Calculated optimal portfolio.

[1006] Data processing / calculation:

[1007] The portfolio is calculated using a deep learning model using user data, and the results are converted into JSON format and sent to the terminal.

[1008] Terminal behavior:

[1009] The terminal displays the recommended portfolio to the user.

[1010] input:

[1011] The recommended portfolio sent from the server.

[1012] output:

[1013] The portfolio that is displayed to the user.

[1014] Data processing / calculation:

[1015] Converting received portfolios into a suitable format for display on screen.

[1016] Step 3: Investment advice and planning

[1017] Specific behavior:

[1018] The terminal presents the received recommended portfolio to the user.

[1019] The user enters detailed questions and correction requests.

[1020] The device sends questions and correction requests to the server.

[1021] input:

[1022] Additional questions or correction requests.

[1023] output:

[1024] Questions and correction requests sent from your device to the server.

[1025] Data processing / calculation:

[1026] The entered questions and correction requests are converted into JSON format and sent to the server.

[1027] Server behavior:

[1028] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[1029] The server generates additional advice and modified portfolios and sends them to the terminal.

[1030] input:

[1031] User questions and correction requests.

[1032] output:

[1033] Additional advice and revised portfolios.

[1034] Data processing / calculation:

[1035] It analyzes the user's question, generates a new portfolio, converts the results into JSON format, and sends it to the terminal.

[1036] Terminal behavior:

[1037] The terminal displays additional advice and modified portfolios to the user.

[1038] input:

[1039] Advice and correction portfolio sent from the server.

[1040] output:

[1041] The new portfolio as it appears to the user.

[1042] Data processing / calculation:

[1043] Converts received information into a suitable format for display on the screen.

[1044] Step 4: Providing trading functionality

[1045] Specific behavior:

[1046] The user opens the "Buy / Sell" screen and enters the name and order amount.

[1047] The terminal transmits the input order information to the server.

[1048] input:

[1049] Buy and sell orders.

[1050] output:

[1051] Order information sent from the terminal to the server.

[1052] Data processing / calculation:

[1053] The entered order information is converted into JSON format and sent to the server.

[1054] Server behavior:

[1055] The server receives the order information and executes the order by calling the broker API.

[1056] The server receives the order execution results and sends them to the terminal.

[1057] input:

[1058] Order information.

[1059] output:

[1060] Order execution results via broker API.

[1061] Data processing / calculation:

[1062] The order information is passed to the broker API for execution, the results are received, converted into JSON format, and sent to the terminal.

[1063] Terminal behavior:

[1064] The terminal displays the order execution results to the user.

[1065] input:

[1066] Order execution result sent from the server.

[1067] output:

[1068] Order execution results displayed to the user.

[1069] Data processing / calculation:

[1070] Converts received information into a suitable format for display on the screen.

[1071] Step 5: Calculate your revenue and collect subscription fees

[1072] Specific behavior:

[1073] The server periodically acquires the user's investment activity data from the database.

[1074] The server calculates the user's revenue.

[1075] The server will calculate a commission of 1% to 3% of the revenue.

[1076] The server generates a payment notice for the subscription fee and sends it to the terminal.

[1077] input:

[1078] User investment activity data.

[1079] output:

[1080] Fees and Payment Notices.

[1081] Data processing / calculation:

[1082] Based on the investment activity data, profits are calculated, fees are calculated, and a payment notice is generated and sent to the terminal.

[1083] Terminal behavior:

[1084] The terminal displays a subscription fee payment notification to the user.

[1085] input:

[1086] Payment advice sent by the server.

[1087] output:

[1088] The payment notice displayed to the user.

[1089] Data processing / calculation:

[1090] Converts received notifications into a suitable format for display on the screen.

[1091] (Application example 1)

[1092] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1093] In today's world, for individual investors to effectively and efficiently manage their assets, it is important to build an appropriate portfolio, execute trades, and manage profits. However, performing these steps manually is extremely complex and requires a lot of time and effort. Furthermore, beginners and small investors find it difficult to make optimal investment decisions due to a lack of specialized knowledge. For this reason, there is a need for the development of a simple, reliable investment support system that can solve these issues.

[1094] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1095] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for presenting the constructed portfolio to the user, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, means for calculating profits and subscription fees, and means for collecting subscription fees via an electronic payment API. This enables individual investors to easily manage their own investment information, construct an optimal portfolio and execute buy / sell, and seamlessly manage profits and pay subscription fees.

[1096] A "user" is an individual who utilizes the system to input their asset status, risk tolerance, and investment goals and engage in investment activities.

[1097] "Asset status" refers to the status of the total assets owned by the user, such as cash, stocks, bonds, real estate, etc.

[1098] "Risk tolerance" refers to the level of risk a user is willing to accept in an investment.

[1099] "Investment goal" refers to the purpose or goal of investment activities set by the user, such as purchasing a new car or a house.

[1100] "Deep learning technology" is a technology that uses multi-layer neural networks based on large amounts of data to perform advanced pattern recognition and prediction.

[1101] "Portfolio" refers to an investment allocation that optimizes risk by diversifying a user's funds across multiple investment targets.

[1102] A "buy / sell order" is a trading instruction issued by a user to sell or buy a particular investment.

[1103] An "electronic payment API" is a programmatic interface for making payments electronically over the Internet.

[1104] "Subscription Fee" means the fee paid periodically by a User for use of the System.

[1105] This invention provides a support system for individual investors to effectively and efficiently conduct investment activities. The system uses a smartphone as its main platform and utilizes deep learning technology to provide optimal portfolios. Specific embodiments for realizing this system are described below.

[1106] Hardware and Software

[1107] Hardware:

[1108] Smartphone (iOS or Android)

[1109] software:

[1110] Python3

[1111] TensorFlow (Keras)

[1112] Requests library

[1113] REST API Server

[1114] Electronic Payment API

[1115] Data processing and calculation

[1116] 1. User Registration and Authentication:

[1117] The user launches the app on their smartphone and enters the required information (first name, last name, email address, and password) on the "New Registration" screen.

[1118] The terminal transmits this information to the server.

[1119] The server stores the information in a database, generates a user ID and authentication token, and sends them to the terminal.

[1120] The device stores the authentication token and uses it for subsequent requests.

[1121] 2. Portfolio Construction:

[1122] Users enter their current asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen.

[1123] The terminal transmits these input data to the server.

[1124] The server uses a deep learning model to input this data and calculate the optimal portfolio.

[1125] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1126] The terminal displays the recommended portfolio to the user.

[1127] 3. Providing trading functions:

[1128] The user inputs a buy / sell order by specifying the name and amount on the "Buy / Sell" screen.

[1129] The terminal transmits this order information to the server.

[1130] The server executes buy and sell orders through the broker API and sends the order execution results to the terminal.

[1131] The terminal notifies the user of the order execution result.

[1132] 4. Calculating Revenue and Collecting Subscription Fees:

[1133] The server periodically retrieves the user's investment activity data from the database and calculates the profit.

[1134] Based on the calculated revenue, the server will calculate a subscription fee of 2% of the revenue.

[1135] The server sends a payment notification to the terminal to collect the subscription fee via the electronic payment API.

[1136] The terminal displays the payment advice to the user and makes the electronic payment.

[1137] Specific examples

[1138] For example, a user may enter their investment information (net worth of ¥500,000, medium risk tolerance, and new car purchase in three years). Based on this information, the system generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash). Furthermore, if the user enters an order to purchase ¥50,000 worth of stocks, the system executes the order via the broker API and notifies the user of the results.

[1139] Prompt Sentence Examples

[1140] "Write a prompt that generates the optimal investment strategy for a user with a net worth of 500,000 yen, a medium risk tolerance, and who is looking to buy a new car."

[1141] In this way, the present invention enables individual investors to easily and efficiently manage complex investment activities. The use of concrete examples and prompts makes it even easier to understand.

[1142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1143] Step 1:

[1144] User Registration and Authentication

[1145] Input: A user launches the app and enters their first name, last name, email address, and password on the sign-up screen.

[1146] Specific operations: The device sends the input information to the server. The server receives the input information and saves it in a database. The server generates a user ID and authentication token and sends them to the device.

[1147] Output: The device stores the authentication token and uses it for subsequent requests.

[1148] Step 2:

[1149] Portfolio Construction

[1150] Input: The user enters their financial situation, risk tolerance, and investment goals into the "Portfolio Construction" screen.

[1151] Specific operation: The device sends the input data to the server, which then inputs the received data into the deep learning model and calculates the optimal portfolio.

[1152] Output: The server generates the calculation result (recommended portfolio) and sends it to the terminal. The terminal displays the recommended portfolio to the user.

[1153] Step 3:

[1154] Additional Investment Advice

[1155] Input: User requests additional investment advice based on presented portfolio.

[1156] How it works: The device sends additional questions or correction requests to the server, which analyzes the questions and uses deep learning models to make predictions or corrections.

[1157] Output: The server generates additional advice and / or modified portfolios and sends them to the terminal, which displays them to the user.

[1158] Step 4:

[1159] Investment buy and sell orders

[1160] Input: The user enters a buy or sell order by specifying the stock and amount on the "Buy / Sell" screen.

[1161] Specific operations: The terminal sends the entered order to the server. The server receives the order information and executes the order by calling the broker API. The server receives the order execution results and sends them to the terminal.

[1162] Output: The terminal notifies the user of the order execution result.

[1163] Step 5:

[1164] Calculating revenue and collecting subscription fees

[1165] Input: The server periodically retrieves the user's investment activity data from the database.

[1166] Specific operation: The server calculates the user's revenue and calculates the subscription fee based on that revenue. The server generates a payment notice for the subscription fee through the electronic payment API and sends it to the terminal.

[1167] Output: The terminal displays the payment advice to the user and makes the electronic payment.

[1168] The above are the specific processing steps of the system that realizes the application example.

[1169] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1170] A system embodying the present invention combines an emotion engine that recognizes user emotions to enable individual investors to conduct their investment activities effectively and efficiently. This system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes means for incorporating emotion recognition data from the emotion engine into investment advice, accepting and executing buy / sell orders from users, and notifying them of the results. Furthermore, the system provides means for periodically calculating profits generated from the user's investment activities, and calculating and collecting subscription fees based on those profits.

[1171] Overview of program processing flow

[1172] 1. User Registration and Authentication

[1173] The user installs the app and launches it.

[1174] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[1175] The terminal transmits the input information to the server.

[1176] The server receives the input information and stores it in a database.

[1177] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[1178] The device receives the authentication token and stores it for use in future requests.

[1179] 2. Portfolio Construction

[1180] The user opens the "Portfolio Construction" screen.

[1181] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[1182] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[1183] The terminal transmits the input information to the server.

[1184] The server receives the user data and stores it in a database.

[1185] The server invokes the deep learning model, passing the user data as input.

[1186] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[1187] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1188] The terminal displays the recommended portfolio to the user.

[1189] 3. Emotion Recognition by Emotion Engine

[1190] The user uses the "emotion recognition" feature (e.g., detecting emotions using a camera or voice input).

[1191] The data acquired by the device is sent to the emotion engine.

[1192] The emotion engine performs analysis and recognizes the user's emotions (e.g., level of stress, level of satisfaction).

[1193] The device transmits the recognized emotion data to the server.

[1194] 4. Investment advice and planning

[1195] The server receives the emotion recognition data and inputs it into the deep learning model.

[1196] The server generates additional investment advice based on the sentiment data.

[1197] The server sends new investment advice to the terminal.

[1198] The terminal displays additional advice and modified portfolios to the user.

[1199] 5. Providing trading functions

[1200] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[1201] The terminal transmits the entered order to the server.

[1202] The server receives the order information and prepares to call the broker API.

[1203] The server executes the order through the broker API.

[1204] The server receives the order execution result (success or failure) and sends it to the terminal.

[1205] The terminal displays the order execution results to the user.

[1206] 6. Calculating Revenue and Collecting Subscription Fees

[1207] The server periodically acquires the user's investment activity data from the database.

[1208] The server calculates the user's revenue.

[1209] The server will calculate a commission of 1% to 3% of the revenue.

[1210] The server generates a payment notice for the subscription fee and sends it to the terminal.

[1211] The terminal displays a subscription fee payment notification to the user.

[1212] Specific examples

[1213] A user installs the app and registers

[1214] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[1215] The server receives the input information and stores it in a database.

[1216] The server generates a user ID and authentication token and sends them to the terminal.

[1217] The device stores the authentication token and uses it for subsequent requests.

[1218] Request a portfolio build

[1219] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[1220] The terminal sends the input data to the server.

[1221] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[1222] The server generates a recommended portfolio and transmits it to the terminal.

[1223] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[1224] Perform emotion recognition

[1225] The user uses the "emotion recognition" function to input emotions using the camera or voice.

[1226] The emotion data acquired by the device is sent to the emotion engine.

[1227] The emotion engine recognizes the user's emotions and returns the results to the terminal.

[1228] The device sends the emotion recognition results to the server.

[1229] Providing emotionally-based investment advice

[1230] The server receives the emotion recognition data and inputs it into the deep learning model.

[1231] The server modifies the portfolio based on the sentiment data and generates additional investment advice.

[1232] The server sends the generated advice to the terminal.

[1233] The terminal displays the revised portfolio and advice to the user.

[1234] Buying and selling investments

[1235] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[1236] The terminal sends the order information to the server.

[1237] The server sends the received order information to the broker API and executes the order.

[1238] The server sends the order execution results to the terminal.

[1239] The terminal displays the results to the user.

[1240] This system allows for detailed advice that takes into consideration the user's emotions, enabling even beginners and small investors to carry out investment activities in a simple and reliable manner.

[1241] The processing flow will be explained below.

[1242] Step 1: The user installs the app and launches it.

[1243] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[1244] Step 3: The terminal sends the entered information to the server.

[1245] Step 4: The server receives the input information and stores it in a database.

[1246] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[1247] Step 6: The device receives the authentication token and stores it for use in future requests.

[1248] Step 7: User opens the "Portfolio Construction" screen.

[1249] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[1250] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[1251] Step 10: The terminal sends the input information to the server.

[1252] Step 11: The server receives the user data and stores it in the database.

[1253] Step 12: The server invokes the deep learning model, passing the user data as input.

[1254] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[1255] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[1256] Step 15: The terminal displays the recommended portfolio to the user.

[1257] Step 16: The user uses the "Emotion Recognition" function to input emotions using face or voice.

[1258] Step 17: The terminal transmits the acquired emotion data to the emotion engine.

[1259] Step 18: The emotion engine recognizes the user's emotion and sends the result to the terminal.

[1260] Step 19: The terminal sends the emotion recognition result to the server.

[1261] Step 20: The server receives the emotion recognition data and inputs it into the deep learning model.

[1262] Step 21: The server modifies the portfolio and investment advice based on the sentiment data.

[1263] Step 22: The server generates the revised advice or portfolio and sends it to the terminal.

[1264] Step 23: The terminal displays the revised portfolio and advice to the user.

[1265] Step 24: The user opens the "Buy / Sell" screen and enters an order by specifying the stock and amount (e.g., purchase "Stock A" for 50,000 yen).

[1266] Step 25: The terminal sends the entered order to the server.

[1267] Step 26: The server receives the order information and prepares to call the broker API.

[1268] Step 27: The server executes the order through the broker API.

[1269] Step 28: The server receives the order execution result (success or failure) and sends it to the terminal.

[1270] Step 29: The terminal displays the order execution results to the user.

[1271] Step 30: The server periodically acquires the user's investment activity data from the database.

[1272] Step 31: The server calculates the user's revenue.

[1273] Step 32: The server calculates a commission of 1% to 3% of the revenue.

[1274] Step 33: The server generates a payment notice for the subscription fee and sends it to the terminal.

[1275] Step 34: The terminal displays a subscription fee payment notification to the user.

[1276] This system allows for detailed advice that takes into consideration the user's emotions, enabling even beginners and small investors to carry out investment activities in a simple and reliable manner.

[1277] Example 2

[1278] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1279] Conventional investment support systems have difficulty providing investment advice that takes users' emotions into account, resulting in users' investment decisions being easily influenced by their emotions. They also lack a means to calculate appropriate subscription fees based on the profits from a user's investment activities. This makes it difficult for beginners and small investors to invest effectively.

[1280] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1281] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology, means for acquiring the user's emotional data via the terminal and transmitting the data to an emotion recognition engine, means for the emotion recognition engine to analyze the user's emotions and transmit the results to the server, means for the server to input the emotional data into a deep learning model and generate additional investment advice based on the emotions, and means for presenting the additional investment advice to the user. This makes it possible to provide detailed investment advice that takes the user's emotions into consideration, thereby supporting effective investment decisions that are not influenced by emotions.

[1282] 1. "User" refers to an individual or legal entity that uses the System to conduct investment activities.

[1283] 2. "Asset Status" refers to information indicating the total amount and type of assets held by the User.

[1284] 3. "Risk tolerance" refers to an indicator that indicates how much risk a user can tolerate.

[1285] 4. "Investment Objectives" means the specific investment goals or objectives that a User wishes to achieve.

[1286] 5. "Deep learning technology" is a type of artificial intelligence that uses multi-layer neural networks to perform advanced data analysis.

[1287] 6. "Portfolio" refers to the combination of financial assets held by a User.

[1288] 7. "Terminal" refers to a device such as a computer, smartphone, or tablet that a User uses to access the System.

[1289] 8. "Emotional Data" refers to data that indicates the user's emotional state, and is obtained through camera or voice input, etc.

[1290] 9. "Emotion Recognition Engine" refers to software or hardware that analyzes acquired emotion data and recognizes the user's emotions.

[1291] 10. "Deep Learning Model" refers to a data analysis model trained using deep learning technology.

[1292] 11. "Investment Advice" means specific instructions or suggestions recommending investment activities to users.

[1293] 12. "Buy / Sell Order" means an instruction given by a User to buy or sell a specific financial instrument.

[1294] 13. "Broker API" refers to the application programming interface of a broker platform used to execute orders to buy and sell financial instruments.

[1295] 14. "Subscription Fee" means the fee paid periodically by a User for use of the System.

[1296] MODE FOR CARRYING OUT THE INVENTION

[1297] An embodiment of the present invention will be described. The system of this invention recognizes user emotions and provides investment advice based on them, enabling individual investors to invest effectively and efficiently. The system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on these inputs. The system also has a function to reflect emotion recognition data generated by an emotion engine in investment advice. Furthermore, the system also includes functions to accept buy and sell orders from users, execute the orders, notify the results, and calculate and collect subscription fees based on revenue.

[1298] Hardware and software used

[1299] Deep learning models: built using TensorFlow or PyTorch.

[1300] Database: Use MySQL.

[1301] Emotion recognition engine: A practical implementation would use a dedicated natural language processing library or computer vision algorithm.

[1302] Device: Computer, smartphone, tablet, etc.

[1303] Broker API: Use the API of Alpaca or Interactive Brokers.

[1304] System processing flow

[1305] 1. User Registration and Authentication

[1306] The user installs and launches the app. This causes the device to display the initial launch screen and the "New Registration" screen. The user enters the required information (first name, last name, email address, and password) and taps the submit button. The device sends the information to the server, which receives it and stores it in a database. The server then generates a user ID and authentication token and sends them to the device.

[1307] 2. Portfolio Construction

[1308] The user opens the "Portfolio Construction" screen and enters information such as asset status, risk tolerance, and investment goals. The device then sends the information to the server, which receives the data and stores it in a database. The deep learning model is then invoked to calculate the optimal portfolio, and the server generates the results and sends them to the device.

[1309] 3. Emotion recognition

[1310] The user uses the emotion recognition function to input their emotions using the camera or voice. The device acquires the emotion data and sends it to the emotion recognition engine. The emotion recognition engine analyzes the emotion, returns the results to the device, and then sends them back to the server.

[1311] 4. Investment advice and planning

[1312] The server receives the emotion recognition data, inputs it into a deep learning model, generates additional investment advice based on it, and then sends the new investment advice to the device, which displays it to the user.

[1313] 5. Buying and Selling Function

[1314] The user enters a purchase order on the "Buy / Sell" screen. The terminal sends the order information to the server, which then calls the broker API to execute the order. The order results are passed to the terminal via the server and displayed to the user.

[1315] 6. Calculating Revenue and Collecting Subscription Fees

[1316] The server periodically retrieves the user's investment activity data from the database, calculates the revenue, calculates the subscription fee based on the revenue, generates a payment notice, and sends it to the terminal. The terminal then displays the payment notice to the user.

[1317] Specific examples

[1318] A user installs the app and registers

[1319] The device displays a "New Registration" screen, and the user enters their name, email address, password, etc., and the information is sent to the server.

[1320] The server receives the input information, stores it in a database, generates a user ID and authentication token, and sends them to the terminal.

[1321] Request a portfolio build

[1322] The user accesses the "Portfolio Construction" screen, enters their assets, risk tolerance, and investment goals, and the information is sent to the server.

[1323] The server uses a deep learning model to calculate the optimal portfolio and displays the results on the device (e.g., 50% stocks, 30% bonds, 20% cash).

[1324] Perform emotion recognition

[1325] The user uses the emotion recognition function to input their emotional state using a camera or voice.

[1326] The device sends the emotional data to an emotion recognition engine, which then sends the analysis results to a server, which uses a deep learning model to generate additional investment advice.

[1327] Buying and selling investments

[1328] The user enters an order on the "Buy / Sell" screen, the information is sent to the server, and the order is executed via the broker's API.

[1329] The server sends the order results to the terminal and displays them to the user.

[1330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1331] Program processing steps

[1332] User Registration and Authentication

[1333] Step 1:

[1334] The user installs and launches the app.

[1335] Input: User action (installing the app, launching it).

[1336] Output: The initial launch screen of the app.

[1337] Specific operation: The device displays the initial startup screen.

[1338] Step 2:

[1339] The device will display a "New Registration" screen and prompt you to enter your name, email address, password, etc.

[1340] Input: User's personal information (first name, last name, email address, password).

[1341] Output: User input information.

[1342] Specific operation: The user enters the required information and taps the send button.

[1343] Step 3:

[1344] The terminal sends the input information to the server.

[1345] Input: User input information sent from the device.

[1346] Output: User information data sent to the server.

[1347] Specific operation: The terminal sends the user's input information to the server.

[1348] Step 4:

[1349] The server receives the information and stores it in a database.

[1350] Input: User information data.

[1351] Output: User information stored in the database.

[1352] Specific operation: The server receives the input information and stores it in a MySQL database.

[1353] Step 5:

[1354] The server generates a new user ID and issues an authentication token.

[1355] Input: Saved user information.

[1356] Output: User ID, authentication token.

[1357] Specific operation: The server generates a user ID and authentication token (such as a JWT).

[1358] Step 6:

[1359] The server sends the user ID and authentication token to the terminal.

[1360] Input: User ID, authentication token.

[1361] Output: User ID and authentication token sent to the terminal.

[1362] Specific operation: The server sends the generated information to the terminal.

[1363] Step 7:

[1364] The device receives the authentication token and stores it in local storage.

[1365] Input: The authentication token sent by the server.

[1366] Output: The authentication token stored in local storage.

[1367] What happens: The device receives the authentication token and stores it in local storage for use in later requests.

[1368] Portfolio Construction

[1369] Step 8:

[1370] The user opens the "Portfolio Construction" screen.

[1371] Input: User action (selection on portfolio building screen).

[1372] Output: Portfolio building screen.

[1373] Specific operation: The terminal displays a form for entering financial status, risk tolerance, and investment goals.

[1374] Step 9:

[1375] The user enters the required information.

[1376] Inputs: Financial situation, risk tolerance, investment goals.

[1377] Output: The user's input data.

[1378] Specific behavior: The user enters their net worth, risk tolerance, and investment goals into a form.

[1379] Step 10:

[1380] The terminal transmits the input information to the server.

[1381] Input: User input data.

[1382] Output: The portfolio information sent to the server.

[1383] Specific operation: The terminal sends the user's input data to the server.

[1384] Step 11:

[1385] The server receives the user data and stores it in a database.

[1386] Input: Portfolio information data.

[1387] Output: Portfolio information stored in a database.

[1388] Specific operation: The server receives the information and stores it in a database.

[1389] Step 12:

[1390] The server invokes the deep learning model, passing the user data as input.

[1391] Input: User data stored in the database.

[1392] Output: The data input to the deep learning model.

[1393] Specific operation: The server calls a deep learning model in TensorFlow or PyTorch and passes the data.

[1394] Step 13:

[1395] A deep learning model calculates the optimal portfolio.

[1396] Input: User data.

[1397] Output: Calculation results of the optimal portfolio.

[1398] How it works: The deep learning model performs the calculations and generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[1399] Step 14:

[1400] The server generates the calculation result and sends it to the terminal.

[1401] Input: Optimal portfolio calculation results.

[1402] Output: The calculation result sent to the terminal.

[1403] Specific operation: The server sends the calculation result to the terminal.

[1404] Step 15:

[1405] The terminal displays the recommended portfolio to the user.

[1406] Input: The calculation result sent from the server.

[1407] Output: The recommended portfolio displayed to the user.

[1408] Specific operation: The device displays the recommended portfolio on the screen.

[1409] Emotion recognition by emotion engine

[1410] Step 16:

[1411] The user uses the "emotion recognition" feature.

[1412] Input: User action (activation of emotion recognition function).

[1413] Output: Beginning emotion recognition.

[1414] Specific operation: The device activates the camera and microphone to collect emotional data.

[1415] Step 17:

[1416] The emotion data acquired by the device is sent to the emotion engine.

[1417] Input: Emotion data obtained from a camera or microphone.

[1418] Output: Emotion data sent to the emotion engine.

[1419] Specific operation: The device sends emotion data to the emotion recognition engine.

[1420] Step 18:

[1421] The emotion engine performs analysis and recognizes the user's emotions.

[1422] Input: Emotion data.

[1423] Output: Emotion recognition result.

[1424] Specific operation: The emotion engine analyzes the data and recognizes the user's emotional state (e.g., level of stress, level of satisfaction).

[1425] Step 19:

[1426] The device transmits the recognized emotion data to the server.

[1427] Input: Emotion recognition results.

[1428] Output: Emotion recognition results sent to the server.

[1429] Specific operation: The device sends the emotion recognition results to the server.

[1430] Investment Advice and Planning

[1431] Step 20:

[1432] The server receives the emotion recognition data and inputs it into the deep learning model.

[1433] Input: Emotion recognition data.

[1434] Output: Emotion data fed into the deep learning model.

[1435] Specific operation: The server receives emotion data and inputs it into the deep learning model.

[1436] Step 21:

[1437] The server generates additional investment advice based on the sentiment data.

[1438] Input: Emotion data fed into the deep learning model.

[1439] Output: Additional investment advice.

[1440] Specific operation: The server modifies the portfolio based on the sentiment data and generates new investment advice.

[1441] Step 22:

[1442] The server sends new investment advice to the terminal.

[1443] Input: Generated additional investment advice.

[1444] Output: Investment advice sent to the terminal.

[1445] Specific operation: The server sends the generated investment advice to the terminal.

[1446] Step 23:

[1447] The terminal displays additional advice and modified portfolios to the user.

[1448] Input: Additional advice sent by the server.

[1449] Output: The additional advice and revised portfolio displayed to the user.

[1450] Specific operation: The device displays additional advice and revised portfolios on the screen.

[1451] Providing buying and selling functions

[1452] Step 24:

[1453] The user opens the "Buy / Sell" screen and enters a purchase order.

[1454] Input: User action (selecting a buy or sell screen, entering a purchase order).

[1455] Output: Purchase orders entered.

[1456] Specific operation: The user enters an order by specifying the stock and amount on the trading screen (e.g., purchasing 50,000 yen worth of "Stock A").

[1457] Step 25:

[1458] The terminal sends the order information to the server.

[1459] Input: Purchase orders entered.

[1460] Output: The order information sent to the server.

[1461] Specific operation: The terminal sends the user's order information to the server.

[1462] Step 26:

[1463] The server receives the order information and prepares to call the broker API.

[1464] Input: The order information sent to the server.

[1465] Output: Prepare a request to the broker API.

[1466] Specific operation: The server prepares to execute the order by calling the broker API.

[1467] Step 27:

[1468] The server executes the order through the broker API.

[1469] Input: API request preparation data.

[1470] Output: The order data sent to the broker API.

[1471] Specific operation: The server sends the order data to the broker API and executes it.

[1472] Step 28:

[1473] The server receives the order execution results and sends them to the terminal.

[1474] Input: Order execution result from broker API.

[1475] Output: Order execution result sent to the terminal.

[1476] Specific operation: The server receives the order execution result and sends it to the terminal.

[1477] Step 29:

[1478] The terminal displays the order execution results to the user.

[1479] Input: Order execution result sent from the server.

[1480] Output: Order execution result displayed to the user.

[1481] Specific operation: The terminal displays the order execution results on the screen.

[1482] Calculating revenue and collecting subscription fees

[1483] Step 30:

[1484] The server periodically acquires the user's investment activity data from the database.

[1485] Input: Investment activity data retrieved from the database.

[1486] Output: Investment activity data for profit calculation.

[1487] Specific operation: The server periodically obtains the user's investment activity data from the database.

[1488] Step 31:

[1489] The server calculates the user's revenue.

[1490] Input: Obtained investment activity data.

[1491] Output: Calculated revenue data.

[1492] Specific operation: The server calculates profits based on investment activity data.

[1493] Step 32:

[1494] The server will calculate a commission of 1% to 3% of the revenue.

[1495] Input: Calculated revenue data.

[1496] Output: Calculated commission data.

[1497] Specific operation: The server calculates a certain percentage of the revenue as a commission.

[1498] Step 33:

[1499] The server generates a payment notice for the subscription fee and sends it to the terminal.

[1500] Input: Calculated commission data.

[1501] Output: Payment advice data sent to the terminal.

[1502] Specific operation: The server creates a payment notification and sends it to the terminal.

[1503] Step 34:

[1504] The terminal displays the payment notice to the user.

[1505] Input: Payment advice data sent from the server.

[1506] Output: Payment notice displayed to the user.

[1507] Specific behavior: The terminal displays a payment notification on the screen.

[1508] (Application example 2)

[1509] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1510] Conventional investment support systems build portfolios and provide investment advice based on the user's asset status and risk tolerance, but do not take the user's emotional state into consideration, which can lead to emotionally driven errors in judgment and inappropriate investment behavior.Furthermore, there are few systems that are involved in users' purchasing activities as well as investments, making total asset management difficult.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1512] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for generating emotion recognition data using an emotion engine that recognizes the user's emotion data, means for reflecting investment advice based on the emotion recognition data, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, and means for recommending optimal products based on the user's purchase history and emotion data. This enables investment advice and purchase recommendations that take into account the user's emotional state, enabling comprehensive asset management and reducing judgment errors.

[1513] "Asset status" is information that refers to the total amount and type of cash, stocks, real estate, and other assets held by the user.

[1514] "Risk tolerance" is an indicator that indicates the range of risk that a user can accept in an investment.

[1515] "Investment goal" is information that indicates the specific goal or objective that a user wants to achieve through investment.

[1516] "Deep learning technology" is a type of machine learning technology that enables artificial intelligence to automatically learn from large amounts of data and make advanced judgments.

[1517] A "portfolio" is a combination of multiple investment products held by a user, with the aim of diversifying risk and maximizing profits.

[1518] "Emotion engine" is a general term for software and hardware that analyzes a user's facial expressions, voice data, etc., and recognizes their emotional state at that time.

[1519] "Emotion recognition data" is data that represents the emotional state of the user analyzed by the emotion engine.

[1520] "Investment Advice" is specific advice or instruction provided to assist a user in making an investment decision.

[1521] A "buy / sell order" is an instruction by a user to buy or sell a particular investment.

[1522] "Purchase history" is a record of products and services purchased by a user in the past.

[1523] "Product recommendation" refers to recommending appropriate products to users based on their purchasing history and emotional data.

[1524] A "server" is a computer system that provides services and data to a large number of clients over a network.

[1525] MODE FOR CARRYING OUT THE INVENTION

[1526] The system for implementing the present invention is comprised of several different modules and hardware and software components, which enable users to effectively manage their assets and carry out their investment activities.

[1527] 1. Overall structure

[1528] The system mainly consists of the following elements:

[1529] User device: A device that is directly operated by the user, such as a smartphone, tablet, or PC.

[1530] Server: A back-end system that receives input data from users and performs various data processing and analysis.

[1531] Emotion engine: Software and hardware that recognizes a user's emotions and generates data about them.

[1532] Deep learning model: A machine learning model for building investment portfolios based on user input data and generating investment advice that reflects sentiment data.

[1533] 2. User Registration and Authentication

[1534] Users must install the application and enter their first name, last name, email address, and password on the "New Registration" screen when they first launch it. This information is sent from the device to the server, which stores it in a database and issues a user ID and authentication token.

[1535] 3. Portfolio Construction

[1536] Users input their asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen. This data is sent from the device to the server. The deep learning model uses this data to calculate the optimal portfolio, and the results are sent to the device via the server and displayed to the user.

[1537] 4. Emotion recognition using emotion engine

[1538] When a user uses the "emotion recognition function," emotional data is collected through the device's camera and microphone. This data is sent to the emotion engine, and the analysis results are sent to the server. The emotion recognition data is input into a deep learning model, which generates appropriate investment advice based on emotions.

[1539] 5.Buying and selling function

[1540] Users use the "Buy / Sell" screen to select specific investment products and enter orders to buy or sell. These orders are sent from the terminal to the server, which executes them via the broker's API. The execution results are sent back from the server to the terminal and notified to the user.

[1541] 6. Revenue calculation and subscription fee collection

[1542] The server periodically calculates the revenue generated from the user's investments and purchasing activities, retrieves the revenue information from the database, and calculates the subscription fee based on the revenue. A payment notification for the subscription fee is sent to the terminal and displayed to the user.

[1543] Examples of specific examples and prompts

[1544] Below are some specific examples.

[1545] The user enters information such as net worth of 500,000 yen, medium risk tolerance, and plans to travel abroad in one year: This information is sent from the device to the server, and the optimal portfolio is calculated using a deep learning model.

[1546] Emotion recognition is performed and product recommendations are displayed based on the user's emotional state (stress level "high" or satisfaction level "low"). Emotional data is sent to the server, and appropriate investment advice and product recommendations are generated by a deep learning model.

[1547] Prompt Sentence Examples

[1548] Take the user's sentiment data and generate purchase recommendations based on their current emotional state. The input data is:

[1549] Stress level: High

[1550] Satisfaction: Low

[1551] Purchase history: Electronic products, books

[1552] Output the recommendations in the following format:

[1553] Recommended categories: (e.g., Relaxation items)

[1554] Recommended items: (e.g. scented candles)

[1555] Reason: (e.g., item that reduces stress)

[1556] In this way, by combining the emotion engine with deep learning technology, it is possible to provide effective investment and purchasing advice that is tailored to the user's emotional state.

[1557] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1558] Program processing steps

[1559] Step 1: User Registration and Authentication

[1560] Input: First name, last name, email address, password

[1561] Specific behavior:

[1562] The user installs the app and launches it.

[1563] The terminal displays the "New Registration" screen and the user enters the required information.

[1564] The terminal transmits the input information to the server.

[1565] The server receives the input information and stores it in a database.

[1566] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[1567] The device receives the authentication token and stores it for use in later requests.

[1568] Output: User ID, authentication token

[1569] Step 2: Build your portfolio

[1570] Input: Asset status, risk tolerance, investment goals

[1571] Specific behavior:

[1572] The user opens the "Portfolio Construction" screen.

[1573] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[1574] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," overseas travel in one year).

[1575] The terminal transmits the input information to the server.

[1576] The server receives the user data and stores it in a database.

[1577] The server invokes the deep learning model, passing the user data as input.

[1578] A deep learning model calculates the optimal portfolio.

[1579] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1580] The terminal displays the recommended portfolio to the user.

[1581] Output: Recommended portfolio

[1582] Step 3: Emotion recognition by the emotion engine

[1583] Input: Emotion data (camera / voice input)

[1584] Specific behavior:

[1585] The user uses the "emotion recognition" feature.

[1586] The device captures emotion data using a camera and microphone.

[1587] The data acquired by the device is sent to the emotion engine.

[1588] The emotion engine performs analysis and recognizes the user's emotions.

[1589] The device transmits the recognized emotion data to the server.

[1590] Output: Recognized emotion data

[1591] Step 4: Investment advice and planning

[1592] Input: Emotion recognition data, existing portfolio data

[1593] Specific behavior:

[1594] The server receives the emotion recognition data and inputs it into the deep learning model.

[1595] The server generates additional investment advice based on the sentiment data.

[1596] The server sends new investment advice to the terminal.

[1597] The terminal displays additional advice and modified portfolios to the user.

[1598] Output: Additional investment advice, revised portfolio

[1599] Step 5: Providing trading functionality

[1600] Input: Buy / sell order information (stock, amount, etc.)

[1601] Specific behavior:

[1602] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order.

[1603] The terminal transmits the entered order to the server.

[1604] The server receives the order information and prepares to call the broker API.

[1605] The server executes the order through the broker API.

[1606] The server receives the order execution result (success or failure) and sends it to the terminal.

[1607] The terminal displays the order execution results to the user.

[1608] Output: Order execution result

[1609] Step 6: Calculate your revenue and collect subscription fees

[1610] Input: User investment and purchasing activity data

[1611] Specific behavior:

[1612] The server periodically retrieves the user's investment and purchasing activity data from the database.

[1613] The server calculates the user's revenue.

[1614] The server will calculate a commission of 1% to 3% of the revenue.

[1615] The server generates a payment notice for the subscription fee and sends it to the terminal.

[1616] The terminal displays a subscription fee payment notification to the user.

[1617] Output: Calculated revenue, subscription fee payment notification

[1618] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1619] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1620] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1621] [Third embodiment]

[1622] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1623] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1624] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1625] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1626] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1627] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1628] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1629] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1630] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1631] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1632] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1633] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1634] A system embodying the present invention provides support for individual investors to conduct their investment activities effectively and efficiently. The system accepts input from users about their asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes a means for accepting buy and sell orders from users, notifying them of the execution results, and calculating and collecting subscription fees from revenue.

[1635] Overview of program processing flow

[1636] 1. User Registration and Authentication

[1637] The user installs the app and launches it.

[1638] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[1639] The terminal transmits the input information to the server.

[1640] The server receives the input information and stores it in a database.

[1641] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[1642] The device receives the authentication token and uses it for subsequent requests.

[1643] 2. Portfolio Construction

[1644] The user opens the "Portfolio Construction" screen.

[1645] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[1646] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[1647] The terminal transmits the input information to the server.

[1648] The server receives the user data and stores it in a database.

[1649] The server invokes the deep learning model, passing the user data as input.

[1650] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[1651] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1652] The terminal displays the recommended portfolio to the user.

[1653] 3. Investment advice and planning

[1654] The terminal presents the received recommended portfolio to the user.

[1655] The user inputs detailed investment planning consultations based on the portfolio (e.g., additional questions and revision requests).

[1656] The device sends additional questions or correction requests to the server.

[1657] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[1658] The server generates additional advice and modified portfolios and sends them to the terminal.

[1659] The terminal displays additional advice and modified portfolios to the user.

[1660] 4. Providing trading functions

[1661] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[1662] The terminal transmits the entered order to the server.

[1663] The server receives the order information and prepares to call the broker API.

[1664] The server executes the order through the broker API.

[1665] The server receives the order execution result (success or failure) and sends it to the terminal.

[1666] The terminal displays the order execution results to the user.

[1667] 5. Calculating Revenue and Collecting Subscription Fees

[1668] The server periodically acquires the user's investment activity data from the database.

[1669] The server calculates the user's revenue.

[1670] The server will calculate a commission of 1% to 3% of the revenue.

[1671] The server generates a payment notice for the subscription fee and sends it to the terminal.

[1672] The terminal displays a subscription fee payment notification to the user.

[1673] Specific examples

[1674] A user installs the app and registers

[1675] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[1676] The server receives the input information and stores it in a database.

[1677] The server generates a user ID and authentication token and sends them to the terminal.

[1678] The device stores the authentication token and uses it for subsequent requests.

[1679] Request a portfolio build

[1680] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[1681] The terminal sends the input data to the server.

[1682] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[1683] The server generates a recommended portfolio and transmits it to the terminal.

[1684] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[1685] Buying and selling investments

[1686] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[1687] The terminal sends the order information to the server.

[1688] The server executes the order through the broker API.

[1689] The server receives the order execution results and sends them to the terminal.

[1690] The terminal displays the order execution results to the user.

[1691] This system will enable even beginners and small investors to carry out investment activities in a simple and reliable manner.

[1692] The processing flow will be explained below.

[1693] Step 1: The user installs the app and launches it.

[1694] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[1695] Step 3: The terminal sends the entered information to the server.

[1696] Step 4: The server receives the input information and stores it in a database.

[1697] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[1698] Step 6: The device receives the authentication token and stores it for use in future requests.

[1699] Step 7: User opens the "Portfolio Construction" screen.

[1700] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[1701] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[1702] Step 10: The terminal sends the input information to the server.

[1703] Step 11: The server receives the user data and stores it in the database.

[1704] Step 12: The server invokes the deep learning model, passing the user data as input.

[1705] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[1706] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[1707] Step 15: The terminal displays the recommended portfolio to the user.

[1708] Step 16: The user enters detailed investment planning consultations based on the portfolio (e.g., additional questions or revision requests).

[1709] Step 17: The terminal sends any additional questions or correction requests to the server.

[1710] Step 18: The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[1711] Step 19: The server generates additional advice and / or a modified portfolio and sends it to the terminal.

[1712] Step 20: The terminal displays additional advice and / or modified portfolios to the user.

[1713] Step 21: The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[1714] Step 22: The terminal sends the entered order to the server.

[1715] Step 23: The server receives the order information and prepares to call the broker API.

[1716] Step 24: The server executes the order through the broker API.

[1717] Step 25: The server receives the order execution result (success or failure) and sends it to the terminal.

[1718] Step 26: The terminal displays the order execution results to the user.

[1719] Step 27: The server periodically obtains the user's investment activity data from the database.

[1720] Step 28: The server calculates the user's revenue.

[1721] Step 29: The server calculates a commission of 1% to 3% of the revenue.

[1722] Step 30: The server generates a payment notice for the subscription fee and sends it to the terminal.

[1723] Step 31: The terminal displays a subscription fee payment notice to the user.

[1724] These processing steps allow even beginners and small investors to carry out investment activities easily and reliably.

[1725] Example 1

[1726] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1727] There is a need to provide support to individual investors to easily and efficiently conduct their investment activities. However, conventional systems have the problem of requiring a lot of time and effort to develop investment plans, execute trades, and manage profits. In addition, receiving appropriate risk management and investment advice requires specialized knowledge, making it difficult for beginners and small investors. To solve these problems, a comprehensive system is needed that provides consistent support from data input to portfolio construction, investment trading, and profit management.

[1728] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1729] In this invention, the server includes: means for accepting input of a user's asset status, risk tolerance, and investment goals; means for constructing a portfolio using machine learning technology based on the input information; means for presenting the constructed portfolio to the user; means for accepting buy / sell orders from the user and executing the orders via an external system; means for notifying the user of the execution results of the buy / sell orders; means for calculating the user's profit, calculating a fee based on the profit, and providing the user with a payment notification; means for authenticating users; and means including a database for securely managing information on multiple users. This enables individual investors to conduct investment activities reliably and efficiently.

[1730] "User" refers to an individual investor who uses the system to conduct investment activities.

[1731] "Asset status" refers to information regarding the total amount and type of assets currently held by the user.

[1732] "Risk tolerance" refers to the range or level of investment risk that a user can tolerate.

[1733] "Investment Objective" refers to the specific investment objectives and timeframe that a User seeks to achieve.

[1734] "Machine learning technology" refers to algorithms and models that allow computers to make predictions and classifications using large amounts of data.

[1735] "Portfolio" refers to a user's assets diversified across various investment vehicles (e.g., stocks, bonds, cash, etc.).

[1736] "External System" refers to the broker or exchange system that is connected to execute a user's buy or sell orders.

[1737] "Buy / Sell Order" refers to an instruction issued by a User to purchase or sell an investment.

[1738] "Revenue" refers to the profit or loss derived from a User's investment activities.

[1739] "Commission" refers to the fee calculated and collected by the system based on the user's revenue.

[1740] "Payment notice" refers to a notice to prompt the user to pay a fee.

[1741] "User authentication" refers to the process of verifying a user's identity when accessing a system.

[1742] "Database" refers to information storage within a system that securely stores and manages information for multiple users.

[1743] The system embodying the present invention provides support for individual investors to carry out their investment activities effectively and efficiently. Specific embodiments of the system will be described below.

[1744] This system accepts input from users about their asset status, risk tolerance, and investment goals, and uses machine learning technology to build an optimal portfolio based on that data. It also accepts and executes buy and sell orders from users and notifies the users of the results. It also periodically calculates users' profits and provides a means to calculate and collect fees based on the profits. Specific examples of the hardware and software used are as follows:

[1745] First, to authenticate a user, they must launch the application through their device and register. The user enters their first name, last name, email address, and password, and this information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL) and generates a new user ID and authentication token. This token is sent to the device and used in subsequent API requests.

[1746] Next, the portfolio is constructed. The user opens the "Portfolio Construction" screen and enters their asset status (e.g., 500,000 yen), risk tolerance (e.g., medium), and investment goal (e.g., purchasing a new car in three years). The device sends this data to the server. The server stores the data in a database and calls a deep learning model (e.g., built with TensorFlow) to calculate the optimal portfolio. The calculation results are sent to the device and displayed to the user.

[1747] Furthermore, if additional investment advice or adjustments are needed, the user can input questions or requests for adjustments into the terminal. The server then uses the deep learning model to generate a new portfolio and sends it to the terminal, allowing the user to create a more refined investment plan.

[1748] For the buying and selling function, the user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order. This information is sent from the terminal to the server, and the server executes the order by calling the broker's API. The order execution results are sent to the terminal and notified to the user.

[1749] Regarding the calculation of revenue and collection of subscription fees, the server periodically retrieves the user's investment activity data from the database and calculates the revenue. The server calculates a commission of 1% to 3% of the revenue and provides a payment notice to the user. The terminal displays this notice to the user and prompts them to pay the commission.

[1750] As a concrete example, the following prompt sentence can be used:

[1751] example:

[1752] Please enter your name, email address, and password to register.

[1753] "Enter your financial situation, risk tolerance, and investment goals to get the portfolio that's right for you."

[1754] "Please purchase 50,000 yen worth of stock A."

[1755] Based on this prompt, users can effectively carry out investment activities through the system.

[1756] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1757] Step 1: User Registration and Authentication

[1758] Specific behavior:

[1759] The user installs and launches the app.

[1760] The device will display a "New Registration" screen and ask you to enter your first and last name, email address, and password.

[1761] The terminal transmits the input information to the server.

[1762] input:

[1763] The first name, last name, email address, and password entered by the user on the device.

[1764] output:

[1765] User information sent to the server.

[1766] Data processing / calculation:

[1767] The entered information is converted into JSON format and securely sent to the server.

[1768] Server behavior:

[1769] The server stores the received information in a database (e.g. MySQL).

[1770] The server generates a new user ID and authentication token and sends them to the device.

[1771] input:

[1772] User information sent from the device.

[1773] output:

[1774] The generated user ID and authentication token.

[1775] Data processing / calculation:

[1776] Save the user information in the database and generate a user ID and authentication token.

[1777] Terminal behavior:

[1778] The device receives the authentication token and stores it in secure storage for use in later requests.

[1779] input:

[1780] The authentication token sent by the server.

[1781] output:

[1782] A securely stored authentication token.

[1783] Data processing / calculation:

[1784] Securely store the received token.

[1785] Step 2: Build your portfolio

[1786] Specific behavior:

[1787] The user opens the "Portfolio Construction" screen.

[1788] The terminal displays a form for inputting your financial situation, risk tolerance, and investment goals.

[1789] The user enters the required information.

[1790] input:

[1791] Your financial situation, risk tolerance, and investment goals.

[1792] output:

[1793] Investment information sent from the terminal to the server.

[1794] Data processing / calculation:

[1795] The input information is converted to JSON format and sent to the server.

[1796] Server behavior:

[1797] The server receives the user data and stores it in a database.

[1798] The server invokes a deep learning model (e.g., TensorFlow) and passes the user data as input.

[1799] A deep learning model calculates the optimal portfolio.

[1800] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1801] input:

[1802] User investment information.

[1803] output:

[1804] Calculated optimal portfolio.

[1805] Data processing / calculation:

[1806] The portfolio is calculated using a deep learning model using user data, and the results are converted into JSON format and sent to the terminal.

[1807] Terminal behavior:

[1808] The terminal displays the recommended portfolio to the user.

[1809] input:

[1810] The recommended portfolio sent from the server.

[1811] output:

[1812] The portfolio that is displayed to the user.

[1813] Data processing / calculation:

[1814] Converting received portfolios into a suitable format for display on screen.

[1815] Step 3: Investment advice and planning

[1816] Specific behavior:

[1817] The terminal presents the received recommended portfolio to the user.

[1818] The user enters detailed questions and correction requests.

[1819] The device sends questions and correction requests to the server.

[1820] input:

[1821] Additional questions or correction requests.

[1822] output:

[1823] Questions and correction requests sent from your device to the server.

[1824] Data processing / calculation:

[1825] The entered questions and correction requests are converted into JSON format and sent to the server.

[1826] Server behavior:

[1827] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[1828] The server generates additional advice and modified portfolios and sends them to the terminal.

[1829] input:

[1830] User questions and correction requests.

[1831] output:

[1832] Additional advice and revised portfolios.

[1833] Data processing / calculation:

[1834] It analyzes the user's question, generates a new portfolio, converts the results into JSON format, and sends it to the terminal.

[1835] Terminal behavior:

[1836] The terminal displays additional advice and modified portfolios to the user.

[1837] input:

[1838] Advice and correction portfolio sent from the server.

[1839] output:

[1840] The new portfolio as it appears to the user.

[1841] Data processing / calculation:

[1842] Converts received information into a suitable format for display on the screen.

[1843] Step 4: Providing trading functionality

[1844] Specific behavior:

[1845] The user opens the "Buy / Sell" screen and enters the name and order amount.

[1846] The terminal transmits the input order information to the server.

[1847] input:

[1848] Buy and sell orders.

[1849] output:

[1850] Order information sent from the terminal to the server.

[1851] Data processing / calculation:

[1852] The entered order information is converted into JSON format and sent to the server.

[1853] Server behavior:

[1854] The server receives the order information and executes the order by calling the broker API.

[1855] The server receives the order execution results and sends them to the terminal.

[1856] input:

[1857] Order information.

[1858] output:

[1859] Order execution results via broker API.

[1860] Data processing / calculation:

[1861] The order information is passed to the broker API for execution, the results are received, converted into JSON format, and sent to the terminal.

[1862] Terminal behavior:

[1863] The terminal displays the order execution results to the user.

[1864] input:

[1865] Order execution result sent from the server.

[1866] output:

[1867] Order execution results displayed to the user.

[1868] Data processing / calculation:

[1869] Converts received information into a suitable format for display on the screen.

[1870] Step 5: Calculate your revenue and collect subscription fees

[1871] Specific behavior:

[1872] The server periodically acquires the user's investment activity data from the database.

[1873] The server calculates the user's revenue.

[1874] The server will calculate a commission of 1% to 3% of the revenue.

[1875] The server generates a payment notice for the subscription fee and sends it to the terminal.

[1876] input:

[1877] User investment activity data.

[1878] output:

[1879] Fees and Payment Notices.

[1880] Data processing / calculation:

[1881] Based on the investment activity data, profits are calculated, fees are calculated, and a payment notice is generated and sent to the terminal.

[1882] Terminal behavior:

[1883] The terminal displays a subscription fee payment notification to the user.

[1884] input:

[1885] Payment advice sent by the server.

[1886] output:

[1887] The payment notice displayed to the user.

[1888] Data processing / calculation:

[1889] Converts received notifications into a suitable format for display on the screen.

[1890] (Application example 1)

[1891] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1892] In today's world, for individual investors to effectively and efficiently manage their assets, it is important to build an appropriate portfolio, execute trades, and manage profits. However, performing these steps manually is extremely complex and requires a lot of time and effort. Furthermore, beginners and small investors find it difficult to make optimal investment decisions due to a lack of specialized knowledge. For this reason, there is a need for the development of a simple, reliable investment support system that can solve these issues.

[1893] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1894] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for presenting the constructed portfolio to the user, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, means for calculating profits and subscription fees, and means for collecting subscription fees via an electronic payment API. This enables individual investors to easily manage their own investment information, construct an optimal portfolio and execute buy / sell, and seamlessly manage profits and pay subscription fees.

[1895] A "user" is an individual who utilizes the system to input their asset status, risk tolerance, and investment goals and engage in investment activities.

[1896] "Asset status" refers to the status of the total assets owned by the user, such as cash, stocks, bonds, real estate, etc.

[1897] "Risk tolerance" refers to the level of risk a user is willing to accept in an investment.

[1898] "Investment goal" refers to the purpose or goal of investment activities set by the user, such as purchasing a new car or a house.

[1899] "Deep learning technology" is a technology that uses multi-layer neural networks based on large amounts of data to perform advanced pattern recognition and prediction.

[1900] "Portfolio" refers to an investment allocation that optimizes risk by diversifying a user's funds across multiple investment targets.

[1901] A "buy / sell order" is a trading instruction issued by a user to sell or buy a particular investment.

[1902] An "electronic payment API" is a programmatic interface for making payments electronically over the Internet.

[1903] "Subscription Fee" means the fee paid periodically by a User for use of the System.

[1904] This invention provides a support system for individual investors to effectively and efficiently conduct investment activities. The system uses a smartphone as its main platform and utilizes deep learning technology to provide optimal portfolios. Specific embodiments for realizing this system are described below.

[1905] Hardware and Software

[1906] Hardware:

[1907] Smartphone (iOS or Android)

[1908] software:

[1909] Python3

[1910] TensorFlow (Keras)

[1911] Requests library

[1912] REST API Server

[1913] Electronic Payment API

[1914] Data processing and calculation

[1915] 1. User Registration and Authentication:

[1916] The user launches the app on their smartphone and enters the required information (first name, last name, email address, and password) on the "New Registration" screen.

[1917] The terminal transmits this information to the server.

[1918] The server stores the information in a database, generates a user ID and authentication token, and sends them to the terminal.

[1919] The device stores the authentication token and uses it for subsequent requests.

[1920] 2. Portfolio Construction:

[1921] Users enter their current asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen.

[1922] The terminal transmits these input data to the server.

[1923] The server uses a deep learning model to input this data and calculate the optimal portfolio.

[1924] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1925] The terminal displays the recommended portfolio to the user.

[1926] 3. Providing trading functions:

[1927] The user inputs a buy / sell order by specifying the name and amount on the "Buy / Sell" screen.

[1928] The terminal transmits this order information to the server.

[1929] The server executes buy and sell orders through the broker API and sends the order execution results to the terminal.

[1930] The terminal notifies the user of the order execution result.

[1931] 4. Calculating Revenue and Collecting Subscription Fees:

[1932] The server periodically retrieves the user's investment activity data from the database and calculates the profit.

[1933] Based on the calculated revenue, the server will calculate a subscription fee of 2% of the revenue.

[1934] The server sends a payment notification to the terminal to collect the subscription fee via the electronic payment API.

[1935] The terminal displays the payment advice to the user and makes the electronic payment.

[1936] Specific examples

[1937] For example, a user may enter their investment information (net worth of ¥500,000, medium risk tolerance, and new car purchase in three years). Based on this information, the system generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash). Furthermore, if the user enters an order to purchase ¥50,000 worth of stocks, the system executes the order via the broker API and notifies the user of the results.

[1938] Prompt Sentence Examples

[1939] "Write a prompt that generates the optimal investment strategy for a user with a net worth of 500,000 yen, a medium risk tolerance, and who is looking to buy a new car."

[1940] In this way, the present invention enables individual investors to easily and efficiently manage complex investment activities. The use of concrete examples and prompts makes it even easier to understand.

[1941] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1942] Step 1:

[1943] User Registration and Authentication

[1944] Input: A user launches the app and enters their first name, last name, email address, and password on the sign-up screen.

[1945] Specific operations: The device sends the input information to the server. The server receives the input information and saves it in a database. The server generates a user ID and authentication token and sends them to the device.

[1946] Output: The device stores the authentication token and uses it for subsequent requests.

[1947] Step 2:

[1948] Portfolio Construction

[1949] Input: The user enters their financial situation, risk tolerance, and investment goals into the "Portfolio Construction" screen.

[1950] Specific operation: The device sends the input data to the server, which then inputs the received data into the deep learning model and calculates the optimal portfolio.

[1951] Output: The server generates the calculation result (recommended portfolio) and sends it to the terminal. The terminal displays the recommended portfolio to the user.

[1952] Step 3:

[1953] Additional Investment Advice

[1954] Input: User requests additional investment advice based on presented portfolio.

[1955] How it works: The device sends additional questions or correction requests to the server, which analyzes the questions and uses deep learning models to make predictions or corrections.

[1956] Output: The server generates additional advice and / or modified portfolios and sends them to the terminal, which displays them to the user.

[1957] Step 4:

[1958] Investment buy and sell orders

[1959] Input: The user enters a buy or sell order by specifying the stock and amount on the "Buy / Sell" screen.

[1960] Specific operations: The terminal sends the entered order to the server. The server receives the order information and executes the order by calling the broker API. The server receives the order execution results and sends them to the terminal.

[1961] Output: The terminal notifies the user of the order execution result.

[1962] Step 5:

[1963] Calculating revenue and collecting subscription fees

[1964] Input: The server periodically retrieves the user's investment activity data from the database.

[1965] Specific operation: The server calculates the user's revenue and calculates the subscription fee based on that revenue. The server generates a payment notice for the subscription fee through the electronic payment API and sends it to the terminal.

[1966] Output: The terminal displays the payment advice to the user and makes the electronic payment.

[1967] The above are the specific processing steps of the system that realizes the application example.

[1968] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1969] A system embodying the present invention combines an emotion engine that recognizes user emotions to enable individual investors to conduct their investment activities effectively and efficiently. This system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes means for incorporating emotion recognition data from the emotion engine into investment advice, accepting and executing buy / sell orders from users, and notifying them of the results. Furthermore, the system provides means for periodically calculating profits generated from the user's investment activities, and calculating and collecting subscription fees based on those profits.

[1970] Overview of program processing flow

[1971] 1. User Registration and Authentication

[1972] The user installs the app and launches it.

[1973] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[1974] The terminal transmits the input information to the server.

[1975] The server receives the input information and stores it in a database.

[1976] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[1977] The device receives the authentication token and stores it for use in future requests.

[1978] 2. Portfolio Construction

[1979] The user opens the "Portfolio Construction" screen.

[1980] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[1981] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[1982] The terminal transmits the input information to the server.

[1983] The server receives the user data and stores it in a database.

[1984] The server invokes the deep learning model, passing the user data as input.

[1985] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[1986] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[1987] The terminal displays the recommended portfolio to the user.

[1988] 3. Emotion Recognition by Emotion Engine

[1989] The user uses the "emotion recognition" feature (e.g., detecting emotions using a camera or voice input).

[1990] The data acquired by the device is sent to the emotion engine.

[1991] The emotion engine performs analysis and recognizes the user's emotions (e.g., level of stress, level of satisfaction).

[1992] The device transmits the recognized emotion data to the server.

[1993] 4. Investment advice and planning

[1994] The server receives the emotion recognition data and inputs it into the deep learning model.

[1995] The server generates additional investment advice based on the sentiment data.

[1996] The server sends new investment advice to the terminal.

[1997] The terminal displays additional advice and modified portfolios to the user.

[1998] 5. Providing trading functions

[1999] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[2000] The terminal transmits the entered order to the server.

[2001] The server receives the order information and prepares to call the broker API.

[2002] The server executes the order through the broker API.

[2003] The server receives the order execution result (success or failure) and sends it to the terminal.

[2004] The terminal displays the order execution results to the user.

[2005] 6. Calculating Revenue and Collecting Subscription Fees

[2006] The server periodically acquires the user's investment activity data from the database.

[2007] The server calculates the user's revenue.

[2008] The server will calculate a commission of 1% to 3% of the revenue.

[2009] The server generates a payment notice for the subscription fee and sends it to the terminal.

[2010] The terminal displays a subscription fee payment notification to the user.

[2011] Specific examples

[2012] A user installs the app and registers

[2013] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[2014] The server receives the input information and stores it in a database.

[2015] The server generates a user ID and authentication token and sends them to the terminal.

[2016] The device stores the authentication token and uses it for subsequent requests.

[2017] Request a portfolio build

[2018] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[2019] The terminal sends the input data to the server.

[2020] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[2021] The server generates a recommended portfolio and transmits it to the terminal.

[2022] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[2023] Perform emotion recognition

[2024] The user uses the "emotion recognition" function to input emotions using the camera or voice.

[2025] The emotion data acquired by the device is sent to the emotion engine.

[2026] The emotion engine recognizes the user's emotions and returns the results to the terminal.

[2027] The device sends the emotion recognition results to the server.

[2028] Providing emotionally-based investment advice

[2029] The server receives the emotion recognition data and inputs it into the deep learning model.

[2030] The server modifies the portfolio based on the sentiment data and generates additional investment advice.

[2031] The server sends the generated advice to the terminal.

[2032] The terminal displays the revised portfolio and advice to the user.

[2033] Buying and selling investments

[2034] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[2035] The terminal sends the order information to the server.

[2036] The server sends the received order information to the broker API and executes the order.

[2037] The server sends the order execution results to the terminal.

[2038] The terminal displays the results to the user.

[2039] This system allows for detailed advice that takes into consideration the user's emotions, enabling even beginners and small investors to carry out investment activities in a simple and reliable manner.

[2040] The processing flow will be explained below.

[2041] Step 1: The user installs the app and launches it.

[2042] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[2043] Step 3: The terminal sends the entered information to the server.

[2044] Step 4: The server receives the input information and stores it in a database.

[2045] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[2046] Step 6: The device receives the authentication token and stores it for use in future requests.

[2047] Step 7: User opens the "Portfolio Construction" screen.

[2048] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[2049] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[2050] Step 10: The terminal sends the input information to the server.

[2051] Step 11: The server receives the user data and stores it in the database.

[2052] Step 12: The server invokes the deep learning model, passing the user data as input.

[2053] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[2054] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[2055] Step 15: The terminal displays the recommended portfolio to the user.

[2056] Step 16: The user uses the "Emotion Recognition" function to input emotions using face or voice.

[2057] Step 17: The terminal transmits the acquired emotion data to the emotion engine.

[2058] Step 18: The emotion engine recognizes the user's emotion and sends the result to the terminal.

[2059] Step 19: The terminal sends the emotion recognition result to the server.

[2060] Step 20: The server receives the emotion recognition data and inputs it into the deep learning model.

[2061] Step 21: The server modifies the portfolio and investment advice based on the sentiment data.

[2062] Step 22: The server generates the revised advice or portfolio and sends it to the terminal.

[2063] Step 23: The terminal displays the revised portfolio and advice to the user.

[2064] Step 24: The user opens the "Buy / Sell" screen and enters an order by specifying the stock and amount (e.g., purchase "Stock A" for 50,000 yen).

[2065] Step 25: The terminal sends the entered order to the server.

[2066] Step 26: The server receives the order information and prepares to call the broker API.

[2067] Step 27: The server executes the order through the broker API.

[2068] Step 28: The server receives the order execution result (success or failure) and sends it to the terminal.

[2069] Step 29: The terminal displays the order execution results to the user.

[2070] Step 30: The server periodically acquires the user's investment activity data from the database.

[2071] Step 31: The server calculates the user's revenue.

[2072] Step 32: The server calculates a commission of 1% to 3% of the revenue.

[2073] Step 33: The server generates a payment notice for the subscription fee and sends it to the terminal.

[2074] Step 34: The terminal displays a subscription fee payment notification to the user.

[2075] This system allows for detailed advice that takes into consideration the user's emotions, enabling even beginners and small investors to carry out investment activities in a simple and reliable manner.

[2076] Example 2

[2077] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2078] Conventional investment support systems have difficulty providing investment advice that takes users' emotions into account, resulting in users' investment decisions being easily influenced by their emotions. They also lack a means to calculate appropriate subscription fees based on the profits from a user's investment activities. This makes it difficult for beginners and small investors to invest effectively.

[2079] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2080] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology, means for acquiring the user's emotional data via the terminal and transmitting the data to an emotion recognition engine, means for the emotion recognition engine to analyze the user's emotions and transmit the results to the server, means for the server to input the emotional data into a deep learning model and generate additional investment advice based on the emotions, and means for presenting the additional investment advice to the user. This makes it possible to provide detailed investment advice that takes the user's emotions into consideration, thereby supporting effective investment decisions that are not influenced by emotions.

[2081] 1. "User" refers to an individual or legal entity that uses the System to conduct investment activities.

[2082] 2. "Asset Status" refers to information indicating the total amount and type of assets held by the User.

[2083] 3. "Risk tolerance" refers to an indicator that indicates how much risk a user can tolerate.

[2084] 4. "Investment Objectives" means the specific investment goals or objectives that a User wishes to achieve.

[2085] 5. "Deep learning technology" is a type of artificial intelligence that uses multi-layer neural networks to perform advanced data analysis.

[2086] 6. "Portfolio" refers to the combination of financial assets held by a User.

[2087] 7. "Terminal" refers to a device such as a computer, smartphone, or tablet that a User uses to access the System.

[2088] 8. "Emotional Data" refers to data that indicates the user's emotional state, and is obtained through camera or voice input, etc.

[2089] 9. "Emotion Recognition Engine" refers to software or hardware that analyzes acquired emotion data and recognizes the user's emotions.

[2090] 10. "Deep Learning Model" refers to a data analysis model trained using deep learning technology.

[2091] 11. "Investment Advice" means specific instructions or suggestions recommending investment activities to users.

[2092] 12. "Buy / Sell Order" means an instruction given by a User to buy or sell a specific financial instrument.

[2093] 13. "Broker API" refers to the application programming interface of a broker platform used to execute orders to buy and sell financial instruments.

[2094] 14. "Subscription Fee" means the fee paid periodically by a User for use of the System.

[2095] MODE FOR CARRYING OUT THE INVENTION

[2096] An embodiment of the present invention will be described. The system of this invention recognizes user emotions and provides investment advice based on them, enabling individual investors to invest effectively and efficiently. The system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on these inputs. The system also has a function to reflect emotion recognition data generated by an emotion engine in investment advice. Furthermore, the system also includes functions to accept buy and sell orders from users, execute the orders, notify the results, and calculate and collect subscription fees based on revenue.

[2097] Hardware and software used

[2098] Deep learning models: built using TensorFlow or PyTorch.

[2099] Database: Use MySQL.

[2100] Emotion recognition engine: A practical implementation would use a dedicated natural language processing library or computer vision algorithm.

[2101] Device: Computer, smartphone, tablet, etc.

[2102] Broker API: Use the API of Alpaca or Interactive Brokers.

[2103] System processing flow

[2104] 1. User Registration and Authentication

[2105] The user installs and launches the app. This causes the device to display the initial launch screen and the "New Registration" screen. The user enters the required information (first name, last name, email address, and password) and taps the submit button. The device sends the information to the server, which receives it and stores it in a database. The server then generates a user ID and authentication token and sends them to the device.

[2106] 2. Portfolio Construction

[2107] The user opens the "Portfolio Construction" screen and enters information such as asset status, risk tolerance, and investment goals. The device then sends the information to the server, which receives the data and stores it in a database. The deep learning model is then invoked to calculate the optimal portfolio, and the server generates the results and sends them to the device.

[2108] 3. Emotion recognition

[2109] The user uses the emotion recognition function to input their emotions using the camera or voice. The device acquires the emotion data and sends it to the emotion recognition engine. The emotion recognition engine analyzes the emotion, returns the results to the device, and then sends them back to the server.

[2110] 4. Investment advice and planning

[2111] The server receives the emotion recognition data, inputs it into a deep learning model, generates additional investment advice based on it, and then sends the new investment advice to the device, which displays it to the user.

[2112] 5. Buying and Selling Function

[2113] The user enters a purchase order on the "Buy / Sell" screen. The terminal sends the order information to the server, which then calls the broker API to execute the order. The order results are passed to the terminal via the server and displayed to the user.

[2114] 6. Calculating Revenue and Collecting Subscription Fees

[2115] The server periodically retrieves the user's investment activity data from the database, calculates the revenue, calculates the subscription fee based on the revenue, generates a payment notice, and sends it to the terminal. The terminal then displays the payment notice to the user.

[2116] Specific examples

[2117] A user installs the app and registers

[2118] The device displays a "New Registration" screen, and the user enters their name, email address, password, etc., and the information is sent to the server.

[2119] The server receives the input information, stores it in a database, generates a user ID and authentication token, and sends them to the terminal.

[2120] Request a portfolio build

[2121] The user accesses the "Portfolio Construction" screen, enters their assets, risk tolerance, and investment goals, and the information is sent to the server.

[2122] The server uses a deep learning model to calculate the optimal portfolio and displays the results on the device (e.g., 50% stocks, 30% bonds, 20% cash).

[2123] Perform emotion recognition

[2124] The user uses the emotion recognition function to input their emotional state using a camera or voice.

[2125] The device sends the emotional data to an emotion recognition engine, which then sends the analysis results to a server, which uses a deep learning model to generate additional investment advice.

[2126] Buying and selling investments

[2127] The user enters an order on the "Buy / Sell" screen, the information is sent to the server, and the order is executed via the broker's API.

[2128] The server sends the order results to the terminal and displays them to the user.

[2129] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2130] Program processing steps

[2131] User Registration and Authentication

[2132] Step 1:

[2133] The user installs and launches the app.

[2134] Input: User action (installing the app, launching it).

[2135] Output: The initial launch screen of the app.

[2136] Specific operation: The device displays the initial startup screen.

[2137] Step 2:

[2138] The device will display a "New Registration" screen and prompt you to enter your name, email address, password, etc.

[2139] Input: User's personal information (first name, last name, email address, password).

[2140] Output: User input information.

[2141] Specific operation: The user enters the required information and taps the send button.

[2142] Step 3:

[2143] The terminal sends the input information to the server.

[2144] Input: User input information sent from the device.

[2145] Output: User information data sent to the server.

[2146] Specific operation: The terminal sends the user's input information to the server.

[2147] Step 4:

[2148] The server receives the information and stores it in a database.

[2149] Input: User information data.

[2150] Output: User information stored in the database.

[2151] Specific operation: The server receives the input information and stores it in a MySQL database.

[2152] Step 5:

[2153] The server generates a new user ID and issues an authentication token.

[2154] Input: Saved user information.

[2155] Output: User ID, authentication token.

[2156] Specific operation: The server generates a user ID and authentication token (such as a JWT).

[2157] Step 6:

[2158] The server sends the user ID and authentication token to the terminal.

[2159] Input: User ID, authentication token.

[2160] Output: User ID and authentication token sent to the terminal.

[2161] Specific operation: The server sends the generated information to the terminal.

[2162] Step 7:

[2163] The device receives the authentication token and stores it in local storage.

[2164] Input: The authentication token sent by the server.

[2165] Output: The authentication token stored in local storage.

[2166] What happens: The device receives the authentication token and stores it in local storage for use in later requests.

[2167] Portfolio Construction

[2168] Step 8:

[2169] The user opens the "Portfolio Construction" screen.

[2170] Input: User action (selection on portfolio building screen).

[2171] Output: Portfolio building screen.

[2172] Specific operation: The terminal displays a form for entering financial status, risk tolerance, and investment goals.

[2173] Step 9:

[2174] The user enters the required information.

[2175] Inputs: Financial situation, risk tolerance, investment goals.

[2176] Output: The user's input data.

[2177] Specific behavior: The user enters their net worth, risk tolerance, and investment goals into a form.

[2178] Step 10:

[2179] The terminal transmits the input information to the server.

[2180] Input: User input data.

[2181] Output: The portfolio information sent to the server.

[2182] Specific operation: The terminal sends the user's input data to the server.

[2183] Step 11:

[2184] The server receives the user data and stores it in a database.

[2185] Input: Portfolio information data.

[2186] Output: Portfolio information stored in a database.

[2187] Specific operation: The server receives the information and stores it in a database.

[2188] Step 12:

[2189] The server invokes the deep learning model, passing the user data as input.

[2190] Input: User data stored in the database.

[2191] Output: The data input to the deep learning model.

[2192] Specific operation: The server calls a deep learning model in TensorFlow or PyTorch and passes the data.

[2193] Step 13:

[2194] A deep learning model calculates the optimal portfolio.

[2195] Input: User data.

[2196] Output: Calculation results of the optimal portfolio.

[2197] How it works: The deep learning model performs the calculations and generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[2198] Step 14:

[2199] The server generates the calculation result and sends it to the terminal.

[2200] Input: Optimal portfolio calculation results.

[2201] Output: The calculation result sent to the terminal.

[2202] Specific operation: The server sends the calculation result to the terminal.

[2203] Step 15:

[2204] The terminal displays the recommended portfolio to the user.

[2205] Input: The calculation result sent from the server.

[2206] Output: The recommended portfolio displayed to the user.

[2207] Specific operation: The device displays the recommended portfolio on the screen.

[2208] Emotion recognition by emotion engine

[2209] Step 16:

[2210] The user uses the "emotion recognition" feature.

[2211] Input: User action (activation of emotion recognition function).

[2212] Output: Beginning emotion recognition.

[2213] Specific operation: The device activates the camera and microphone to collect emotional data.

[2214] Step 17:

[2215] The emotion data acquired by the device is sent to the emotion engine.

[2216] Input: Emotion data obtained from a camera or microphone.

[2217] Output: Emotion data sent to the emotion engine.

[2218] Specific operation: The device sends emotion data to the emotion recognition engine.

[2219] Step 18:

[2220] The emotion engine performs analysis and recognizes the user's emotions.

[2221] Input: Emotion data.

[2222] Output: Emotion recognition result.

[2223] Specific operation: The emotion engine analyzes the data and recognizes the user's emotional state (e.g., level of stress, level of satisfaction).

[2224] Step 19:

[2225] The device transmits the recognized emotion data to the server.

[2226] Input: Emotion recognition results.

[2227] Output: Emotion recognition results sent to the server.

[2228] Specific operation: The device sends the emotion recognition results to the server.

[2229] Investment Advice and Planning

[2230] Step 20:

[2231] The server receives the emotion recognition data and inputs it into the deep learning model.

[2232] Input: Emotion recognition data.

[2233] Output: Emotion data fed into the deep learning model.

[2234] Specific operation: The server receives emotion data and inputs it into the deep learning model.

[2235] Step 21:

[2236] The server generates additional investment advice based on the sentiment data.

[2237] Input: Emotion data fed into the deep learning model.

[2238] Output: Additional investment advice.

[2239] Specific operation: The server modifies the portfolio based on the sentiment data and generates new investment advice.

[2240] Step 22:

[2241] The server sends new investment advice to the terminal.

[2242] Input: Generated additional investment advice.

[2243] Output: Investment advice sent to the terminal.

[2244] Specific operation: The server sends the generated investment advice to the terminal.

[2245] Step 23:

[2246] The terminal displays additional advice and modified portfolios to the user.

[2247] Input: Additional advice sent by the server.

[2248] Output: The additional advice and revised portfolio displayed to the user.

[2249] Specific operation: The device displays additional advice and revised portfolios on the screen.

[2250] Providing buying and selling functions

[2251] Step 24:

[2252] The user opens the "Buy / Sell" screen and enters a purchase order.

[2253] Input: User action (selecting a buy or sell screen, entering a purchase order).

[2254] Output: Purchase orders entered.

[2255] Specific operation: The user enters an order by specifying the stock and amount on the trading screen (e.g., purchasing 50,000 yen worth of "Stock A").

[2256] Step 25:

[2257] The terminal sends the order information to the server.

[2258] Input: Purchase orders entered.

[2259] Output: The order information sent to the server.

[2260] Specific operation: The terminal sends the user's order information to the server.

[2261] Step 26:

[2262] The server receives the order information and prepares to call the broker API.

[2263] Input: The order information sent to the server.

[2264] Output: Prepare a request to the broker API.

[2265] Specific operation: The server prepares to execute the order by calling the broker API.

[2266] Step 27:

[2267] The server executes the order through the broker API.

[2268] Input: API request preparation data.

[2269] Output: The order data sent to the broker API.

[2270] Specific operation: The server sends the order data to the broker API and executes it.

[2271] Step 28:

[2272] The server receives the order execution results and sends them to the terminal.

[2273] Input: Order execution result from broker API.

[2274] Output: Order execution result sent to the terminal.

[2275] Specific operation: The server receives the order execution result and sends it to the terminal.

[2276] Step 29:

[2277] The terminal displays the order execution results to the user.

[2278] Input: Order execution result sent from the server.

[2279] Output: Order execution result displayed to the user.

[2280] Specific operation: The terminal displays the order execution results on the screen.

[2281] Calculating revenue and collecting subscription fees

[2282] Step 30:

[2283] The server periodically acquires the user's investment activity data from the database.

[2284] Input: Investment activity data retrieved from the database.

[2285] Output: Investment activity data for profit calculation.

[2286] Specific operation: The server periodically obtains the user's investment activity data from the database.

[2287] Step 31:

[2288] The server calculates the user's revenue.

[2289] Input: Obtained investment activity data.

[2290] Output: Calculated revenue data.

[2291] Specific operation: The server calculates profits based on investment activity data.

[2292] Step 32:

[2293] The server will calculate a commission of 1% to 3% of the revenue.

[2294] Input: Calculated revenue data.

[2295] Output: Calculated commission data.

[2296] Specific operation: The server calculates a certain percentage of the revenue as a commission.

[2297] Step 33:

[2298] The server generates a payment notice for the subscription fee and sends it to the terminal.

[2299] Input: Calculated commission data.

[2300] Output: Payment advice data sent to the terminal.

[2301] Specific operation: The server creates a payment notification and sends it to the terminal.

[2302] Step 34:

[2303] The terminal displays the payment notice to the user.

[2304] Input: Payment advice data sent from the server.

[2305] Output: Payment notice displayed to the user.

[2306] Specific behavior: The terminal displays a payment notification on the screen.

[2307] (Application example 2)

[2308] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2309] Conventional investment support systems build portfolios and provide investment advice based on the user's asset status and risk tolerance, but do not take the user's emotional state into consideration, which can lead to emotionally driven errors in judgment and inappropriate investment behavior.Furthermore, there are few systems that are involved in users' purchasing activities as well as investments, making total asset management difficult.

[2310] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2311] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for generating emotion recognition data using an emotion engine that recognizes the user's emotion data, means for reflecting investment advice based on the emotion recognition data, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, and means for recommending optimal products based on the user's purchase history and emotion data. This enables investment advice and purchase recommendations that take into account the user's emotional state, enabling comprehensive asset management and reducing judgment errors.

[2312] "Asset status" is information that refers to the total amount and type of cash, stocks, real estate, and other assets held by the user.

[2313] "Risk tolerance" is an indicator that indicates the range of risk that a user can accept in an investment.

[2314] "Investment goal" is information that indicates the specific goal or objective that a user wants to achieve through investment.

[2315] "Deep learning technology" is a type of machine learning technology that enables artificial intelligence to automatically learn from large amounts of data and make advanced judgments.

[2316] A "portfolio" is a combination of multiple investment products held by a user, with the aim of diversifying risk and maximizing profits.

[2317] "Emotion engine" is a general term for software and hardware that analyzes a user's facial expressions, voice data, etc., and recognizes their emotional state at that time.

[2318] "Emotion recognition data" is data that represents the emotional state of the user analyzed by the emotion engine.

[2319] "Investment Advice" is specific advice or instruction provided to assist a user in making an investment decision.

[2320] A "buy / sell order" is an instruction by a user to buy or sell a particular investment.

[2321] "Purchase history" is a record of products and services purchased by a user in the past.

[2322] "Product recommendation" refers to recommending appropriate products to users based on their purchasing history and emotional data.

[2323] A "server" is a computer system that provides services and data to a large number of clients over a network.

[2324] MODE FOR CARRYING OUT THE INVENTION

[2325] The system for implementing the present invention is comprised of several different modules and hardware and software components, which enable users to effectively manage their assets and carry out their investment activities.

[2326] 1. Overall structure

[2327] The system mainly consists of the following elements:

[2328] User device: A device that is directly operated by the user, such as a smartphone, tablet, or PC.

[2329] Server: A back-end system that receives input data from users and performs various data processing and analysis.

[2330] Emotion engine: Software and hardware that recognizes a user's emotions and generates data about them.

[2331] Deep learning model: A machine learning model for building investment portfolios based on user input data and generating investment advice that reflects sentiment data.

[2332] 2. User Registration and Authentication

[2333] Users must install the application and enter their first name, last name, email address, and password on the "New Registration" screen when they first launch it. This information is sent from the device to the server, which stores it in a database and issues a user ID and authentication token.

[2334] 3. Portfolio Construction

[2335] Users input their asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen. This data is sent from the device to the server. The deep learning model uses this data to calculate the optimal portfolio, and the results are sent to the device via the server and displayed to the user.

[2336] 4. Emotion recognition using emotion engine

[2337] When a user uses the "emotion recognition function," emotional data is collected through the device's camera and microphone. This data is sent to the emotion engine, and the analysis results are sent to the server. The emotion recognition data is input into a deep learning model, which generates appropriate investment advice based on emotions.

[2338] 5.Buying and selling function

[2339] Users use the "Buy / Sell" screen to select specific investment products and enter orders to buy or sell. These orders are sent from the terminal to the server, which executes them via the broker's API. The execution results are sent back from the server to the terminal and notified to the user.

[2340] 6. Revenue calculation and subscription fee collection

[2341] The server periodically calculates the revenue generated from the user's investments and purchasing activities, retrieves the revenue information from the database, and calculates the subscription fee based on the revenue. A payment notification for the subscription fee is sent to the terminal and displayed to the user.

[2342] Examples of specific examples and prompts

[2343] Below are some specific examples.

[2344] The user enters information such as net worth of 500,000 yen, medium risk tolerance, and plans to travel abroad in one year: This information is sent from the device to the server, and the optimal portfolio is calculated using a deep learning model.

[2345] Emotion recognition is performed and product recommendations are displayed based on the user's emotional state (stress level "high" or satisfaction level "low"). Emotional data is sent to the server, and appropriate investment advice and product recommendations are generated by a deep learning model.

[2346] Prompt Sentence Examples

[2347] Take the user's sentiment data and generate purchase recommendations based on their current emotional state. The input data is:

[2348] Stress level: High

[2349] Satisfaction: Low

[2350] Purchase history: Electronic products, books

[2351] Output the recommendations in the following format:

[2352] Recommended categories: (e.g., Relaxation items)

[2353] Recommended items: (e.g. scented candles)

[2354] Reason: (e.g., item that reduces stress)

[2355] In this way, by combining the emotion engine with deep learning technology, it is possible to provide effective investment and purchasing advice that is tailored to the user's emotional state.

[2356] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2357] Program processing steps

[2358] Step 1: User Registration and Authentication

[2359] Input: First name, last name, email address, password

[2360] Specific behavior:

[2361] The user installs the app and launches it.

[2362] The terminal displays the "New Registration" screen and the user enters the required information.

[2363] The terminal transmits the input information to the server.

[2364] The server receives the input information and stores it in a database.

[2365] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[2366] The device receives the authentication token and stores it for use in later requests.

[2367] Output: User ID, authentication token

[2368] Step 2: Build your portfolio

[2369] Input: Asset status, risk tolerance, investment goals

[2370] Specific behavior:

[2371] The user opens the "Portfolio Construction" screen.

[2372] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[2373] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," overseas travel in one year).

[2374] The terminal transmits the input information to the server.

[2375] The server receives the user data and stores it in a database.

[2376] The server invokes the deep learning model, passing the user data as input.

[2377] A deep learning model calculates the optimal portfolio.

[2378] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[2379] The terminal displays the recommended portfolio to the user.

[2380] Output: Recommended portfolio

[2381] Step 3: Emotion recognition by the emotion engine

[2382] Input: Emotion data (camera / voice input)

[2383] Specific behavior:

[2384] The user uses the "emotion recognition" feature.

[2385] The device captures emotion data using a camera and microphone.

[2386] The data acquired by the device is sent to the emotion engine.

[2387] The emotion engine performs analysis and recognizes the user's emotions.

[2388] The device transmits the recognized emotion data to the server.

[2389] Output: Recognized emotion data

[2390] Step 4: Investment advice and planning

[2391] Input: Emotion recognition data, existing portfolio data

[2392] Specific behavior:

[2393] The server receives the emotion recognition data and inputs it into the deep learning model.

[2394] The server generates additional investment advice based on the sentiment data.

[2395] The server sends new investment advice to the terminal.

[2396] The terminal displays additional advice and modified portfolios to the user.

[2397] Output: Additional investment advice, revised portfolio

[2398] Step 5: Providing trading functionality

[2399] Input: Buy / sell order information (stock, amount, etc.)

[2400] Specific behavior:

[2401] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order.

[2402] The terminal transmits the entered order to the server.

[2403] The server receives the order information and prepares to call the broker API.

[2404] The server executes the order through the broker API.

[2405] The server receives the order execution result (success or failure) and sends it to the terminal.

[2406] The terminal displays the order execution results to the user.

[2407] Output: Order execution result

[2408] Step 6: Calculate your revenue and collect subscription fees

[2409] Input: User investment and purchasing activity data

[2410] Specific behavior:

[2411] The server periodically retrieves the user's investment and purchasing activity data from the database.

[2412] The server calculates the user's revenue.

[2413] The server will calculate a commission of 1% to 3% of the revenue.

[2414] The server generates a payment notice for the subscription fee and sends it to the terminal.

[2415] The terminal displays a subscription fee payment notification to the user.

[2416] Output: Calculated revenue, subscription fee payment notification

[2417] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[2418] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2419] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2420] [Fourth embodiment]

[2421] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2422] 7, a 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.

[2423] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2424] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[2425] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2427] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2428] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[2429] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[2430] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[2431] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2432] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2433] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2434] A system embodying the present invention provides support for individual investors to conduct their investment activities effectively and efficiently. The system accepts input from users about their asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes a means for accepting buy and sell orders from users, notifying them of the execution results, and calculating and collecting subscription fees from revenue.

[2435] Overview of program processing flow

[2436] 1. User Registration and Authentication

[2437] The user installs the app and launches it.

[2438] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[2439] The terminal transmits the input information to the server.

[2440] The server receives the input information and stores it in a database.

[2441] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[2442] The device receives the authentication token and uses it for subsequent requests.

[2443] 2. Portfolio Construction

[2444] The user opens the "Portfolio Construction" screen.

[2445] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[2446] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[2447] The terminal transmits the input information to the server.

[2448] The server receives the user data and stores it in a database.

[2449] The server invokes the deep learning model, passing the user data as input.

[2450] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[2451] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[2452] The terminal displays the recommended portfolio to the user.

[2453] 3. Investment advice and planning

[2454] The terminal presents the received recommended portfolio to the user.

[2455] The user inputs detailed investment planning consultations based on the portfolio (e.g., additional questions and revision requests).

[2456] The device sends additional questions or correction requests to the server.

[2457] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[2458] The server generates additional advice and modified portfolios and sends them to the terminal.

[2459] The terminal displays additional advice and modified portfolios to the user.

[2460] 4. Providing trading functions

[2461] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[2462] The terminal transmits the entered order to the server.

[2463] The server receives the order information and prepares to call the broker API.

[2464] The server executes the order through the broker API.

[2465] The server receives the order execution result (success or failure) and sends it to the terminal.

[2466] The terminal displays the order execution results to the user.

[2467] 5. Calculating Revenue and Collecting Subscription Fees

[2468] The server periodically acquires the user's investment activity data from the database.

[2469] The server calculates the user's revenue.

[2470] The server will calculate a commission of 1% to 3% of the revenue.

[2471] The server generates a payment notice for the subscription fee and sends it to the terminal.

[2472] The terminal displays a subscription fee payment notification to the user.

[2473] Specific examples

[2474] A user installs the app and registers

[2475] The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[2476] The server receives the input information and stores it in a database.

[2477] The server generates a user ID and authentication token and sends them to the terminal.

[2478] The device stores the authentication token and uses it for subsequent requests.

[2479] Request a portfolio build

[2480] On the "Portfolio Construction" screen, the user enters information such as net worth of 500,000 yen, risk tolerance of "medium," and the purchase of a new car in three years.

[2481] The terminal sends the input data to the server.

[2482] The server inputs the received data into a deep learning model to calculate the optimal portfolio.

[2483] The server generates a recommended portfolio and transmits it to the terminal.

[2484] The terminal displays a recommended portfolio to the user (e.g., 50% stocks, 30% bonds, 20% cash).

[2485] Buying and selling investments

[2486] A user enters an order to purchase 50,000 yen worth of "Stock A" on the "Buy / Sell" screen.

[2487] The terminal sends the order information to the server.

[2488] The server executes the order through the broker API.

[2489] The server receives the order execution results and sends them to the terminal.

[2490] The terminal displays the order execution results to the user.

[2491] This system will enable even beginners and small investors to carry out investment activities in a simple and reliable manner.

[2492] The processing flow will be explained below.

[2493] Step 1: The user installs the app and launches it.

[2494] Step 2: The device displays the "New Registration" screen, and the user enters their first and last name, email address, and password.

[2495] Step 3: The terminal sends the entered information to the server.

[2496] Step 4: The server receives the input information and stores it in a database.

[2497] Step 5: The server generates a user ID, issues an authentication token, and sends it to the terminal.

[2498] Step 6: The device receives the authentication token and stores it for use in future requests.

[2499] Step 7: User opens the "Portfolio Construction" screen.

[2500] Step 8: The terminal will display a form where you can enter your current financial situation, risk tolerance, and investment goals.

[2501] Step 9: The user enters the required information (e.g., net worth of 500,000 yen, medium risk tolerance, new car purchase in 3 years).

[2502] Step 10: The terminal sends the input information to the server.

[2503] Step 11: The server receives the user data and stores it in the database.

[2504] Step 12: The server invokes the deep learning model, passing the user data as input.

[2505] Step 13: The deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[2506] Step 14: The server generates the calculation result (recommended portfolio) and sends it to the terminal.

[2507] Step 15: The terminal displays the recommended portfolio to the user.

[2508] Step 16: The user enters detailed investment planning consultations based on the portfolio (e.g., additional questions or revision requests).

[2509] Step 17: The terminal sends any additional questions or correction requests to the server.

[2510] Step 18: The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[2511] Step 19: The server generates additional advice and / or a modified portfolio and sends it to the terminal.

[2512] Step 20: The terminal displays additional advice and / or modified portfolios to the user.

[2513] Step 21: The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[2514] Step 22: The terminal sends the entered order to the server.

[2515] Step 23: The server receives the order information and prepares to call the broker API.

[2516] Step 24: The server executes the order through the broker API.

[2517] Step 25: The server receives the order execution result (success or failure) and sends it to the terminal.

[2518] Step 26: The terminal displays the order execution results to the user.

[2519] Step 27: The server periodically obtains the user's investment activity data from the database.

[2520] Step 28: The server calculates the user's revenue.

[2521] Step 29: The server calculates a commission of 1% to 3% of the revenue.

[2522] Step 30: The server generates a payment notice for the subscription fee and sends it to the terminal.

[2523] Step 31: The terminal displays a subscription fee payment notice to the user.

[2524] These processing steps allow even beginners and small investors to carry out investment activities easily and reliably.

[2525] Example 1

[2526] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2527] There is a need to provide support to individual investors to easily and efficiently conduct their investment activities. However, conventional systems have the problem of requiring a lot of time and effort to develop investment plans, execute trades, and manage profits. In addition, receiving appropriate risk management and investment advice requires specialized knowledge, making it difficult for beginners and small investors. To solve these problems, a comprehensive system is needed that provides consistent support from data input to portfolio construction, investment trading, and profit management.

[2528] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2529] In this invention, the server includes: means for accepting input of a user's asset status, risk tolerance, and investment goals; means for constructing a portfolio using machine learning technology based on the input information; means for presenting the constructed portfolio to the user; means for accepting buy / sell orders from the user and executing the orders via an external system; means for notifying the user of the execution results of the buy / sell orders; means for calculating the user's profit, calculating a fee based on the profit, and providing the user with a payment notification; means for authenticating users; and means including a database for securely managing information on multiple users. This enables individual investors to conduct investment activities reliably and efficiently.

[2530] "User" refers to an individual investor who uses the system to conduct investment activities.

[2531] "Asset status" refers to information regarding the total amount and type of assets currently held by the user.

[2532] "Risk tolerance" refers to the range or level of investment risk that a user can tolerate.

[2533] "Investment Objective" refers to the specific investment objectives and timeframe that a User seeks to achieve.

[2534] "Machine learning technology" refers to algorithms and models that allow computers to make predictions and classifications using large amounts of data.

[2535] "Portfolio" refers to a user's assets diversified across various investment vehicles (e.g., stocks, bonds, cash, etc.).

[2536] "External System" refers to the broker or exchange system that is connected to execute a user's buy or sell orders.

[2537] "Buy / Sell Order" refers to an instruction issued by a User to purchase or sell an investment.

[2538] "Revenue" refers to the profit or loss derived from a User's investment activities.

[2539] "Commission" refers to the fee calculated and collected by the system based on the user's revenue.

[2540] "Payment notice" refers to a notice to prompt the user to pay a fee.

[2541] "User authentication" refers to the process of verifying a user's identity when accessing a system.

[2542] "Database" refers to information storage within a system that securely stores and manages information for multiple users.

[2543] The system embodying the present invention provides support for individual investors to carry out their investment activities effectively and efficiently. Specific embodiments of the system will be described below.

[2544] This system accepts input from users about their asset status, risk tolerance, and investment goals, and uses machine learning technology to build an optimal portfolio based on that data. It also accepts and executes buy and sell orders from users and notifies the users of the results. It also periodically calculates users' profits and provides a means to calculate and collect fees based on the profits. Specific examples of the hardware and software used are as follows:

[2545] First, to authenticate a user, they must launch the application through their device and register. The user enters their first name, last name, email address, and password, and this information is sent from the device to the server. The server stores the received information in a database (e.g., MySQL) and generates a new user ID and authentication token. This token is sent to the device and used in subsequent API requests.

[2546] Next, the portfolio is constructed. The user opens the "Portfolio Construction" screen and enters their asset status (e.g., 500,000 yen), risk tolerance (e.g., medium), and investment goal (e.g., purchasing a new car in three years). The device sends this data to the server. The server stores the data in a database and calls a deep learning model (e.g., built with TensorFlow) to calculate the optimal portfolio. The calculation results are sent to the device and displayed to the user.

[2547] Furthermore, if additional investment advice or adjustments are needed, the user can input questions or requests for adjustments into the terminal. The server then uses the deep learning model to generate a new portfolio and sends it to the terminal, allowing the user to create a more refined investment plan.

[2548] For the buying and selling function, the user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order. This information is sent from the terminal to the server, and the server executes the order by calling the broker's API. The order execution results are sent to the terminal and notified to the user.

[2549] Regarding the calculation of revenue and collection of subscription fees, the server periodically retrieves the user's investment activity data from the database and calculates the revenue. The server calculates a commission of 1% to 3% of the revenue and provides a payment notice to the user. The terminal displays this notice to the user and prompts them to pay the commission.

[2550] As a concrete example, the following prompt sentence can be used:

[2551] example:

[2552] Please enter your name, email address, and password to register.

[2553] "Enter your financial situation, risk tolerance, and investment goals to get the portfolio that's right for you."

[2554] "Please purchase 50,000 yen worth of stock A."

[2555] Based on this prompt, users can effectively carry out investment activities through the system.

[2556] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2557] Step 1: User Registration and Authentication

[2558] Specific behavior:

[2559] The user installs and launches the app.

[2560] The device will display a "New Registration" screen and ask you to enter your first and last name, email address, and password.

[2561] The terminal transmits the input information to the server.

[2562] input:

[2563] The first name, last name, email address, and password entered by the user on the device.

[2564] output:

[2565] User information sent to the server.

[2566] Data processing / calculation:

[2567] The entered information is converted into JSON format and securely sent to the server.

[2568] Server behavior:

[2569] The server stores the received information in a database (e.g. MySQL).

[2570] The server generates a new user ID and authentication token and sends them to the device.

[2571] input:

[2572] User information sent from the device.

[2573] output:

[2574] The generated user ID and authentication token.

[2575] Data processing / calculation:

[2576] Save the user information in the database and generate a user ID and authentication token.

[2577] Terminal behavior:

[2578] The device receives the authentication token and stores it in secure storage for use in later requests.

[2579] input:

[2580] The authentication token sent by the server.

[2581] output:

[2582] A securely stored authentication token.

[2583] Data processing / calculation:

[2584] Securely store the received token.

[2585] Step 2: Build your portfolio

[2586] Specific behavior:

[2587] The user opens the "Portfolio Construction" screen.

[2588] The terminal displays a form for inputting your financial situation, risk tolerance, and investment goals.

[2589] The user enters the required information.

[2590] input:

[2591] Your financial situation, risk tolerance, and investment goals.

[2592] output:

[2593] Investment information sent from the terminal to the server.

[2594] Data processing / calculation:

[2595] The input information is converted to JSON format and sent to the server.

[2596] Server behavior:

[2597] The server receives the user data and stores it in a database.

[2598] The server invokes a deep learning model (e.g., TensorFlow) and passes the user data as input.

[2599] A deep learning model calculates the optimal portfolio.

[2600] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[2601] input:

[2602] User investment information.

[2603] output:

[2604] Calculated optimal portfolio.

[2605] Data processing / calculation:

[2606] The portfolio is calculated using a deep learning model using user data, and the results are converted into JSON format and sent to the terminal.

[2607] Terminal behavior:

[2608] The terminal displays the recommended portfolio to the user.

[2609] input:

[2610] The recommended portfolio sent from the server.

[2611] output:

[2612] The portfolio that is displayed to the user.

[2613] Data processing / calculation:

[2614] Converting received portfolios into a suitable format for display on screen.

[2615] Step 3: Investment advice and planning

[2616] Specific behavior:

[2617] The terminal presents the received recommended portfolio to the user.

[2618] The user enters detailed questions and correction requests.

[2619] The device sends questions and correction requests to the server.

[2620] input:

[2621] Additional questions or correction requests.

[2622] output:

[2623] Questions and correction requests sent from your device to the server.

[2624] Data processing / calculation:

[2625] The entered questions and correction requests are converted into JSON format and sent to the server.

[2626] Server behavior:

[2627] The server analyzes the question and uses deep learning models or makes predictions or corrections based on specific conditions.

[2628] The server generates additional advice and modified portfolios and sends them to the terminal.

[2629] input:

[2630] User questions and correction requests.

[2631] output:

[2632] Additional advice and revised portfolios.

[2633] Data processing / calculation:

[2634] It analyzes the user's question, generates a new portfolio, converts the results into JSON format, and sends it to the terminal.

[2635] Terminal behavior:

[2636] The terminal displays additional advice and modified portfolios to the user.

[2637] input:

[2638] Advice and correction portfolio sent from the server.

[2639] output:

[2640] The new portfolio as it appears to the user.

[2641] Data processing / calculation:

[2642] Converts received information into a suitable format for display on the screen.

[2643] Step 4: Providing trading functionality

[2644] Specific behavior:

[2645] The user opens the "Buy / Sell" screen and enters the name and order amount.

[2646] The terminal transmits the input order information to the server.

[2647] input:

[2648] Buy and sell orders.

[2649] output:

[2650] Order information sent from the terminal to the server.

[2651] Data processing / calculation:

[2652] The entered order information is converted into JSON format and sent to the server.

[2653] Server behavior:

[2654] The server receives the order information and executes the order by calling the broker API.

[2655] The server receives the order execution results and sends them to the terminal.

[2656] input:

[2657] Order information.

[2658] output:

[2659] Order execution results via broker API.

[2660] Data processing / calculation:

[2661] The order information is passed to the broker API for execution, the results are received, converted into JSON format, and sent to the terminal.

[2662] Terminal behavior:

[2663] The terminal displays the order execution results to the user.

[2664] input:

[2665] Order execution result sent from the server.

[2666] output:

[2667] Order execution results displayed to the user.

[2668] Data processing / calculation:

[2669] Converts received information into a suitable format for display on the screen.

[2670] Step 5: Calculate your revenue and collect subscription fees

[2671] Specific behavior:

[2672] The server periodically acquires the user's investment activity data from the database.

[2673] The server calculates the user's revenue.

[2674] The server will calculate a commission of 1% to 3% of the revenue.

[2675] The server generates a payment notice for the subscription fee and sends it to the terminal.

[2676] input:

[2677] User investment activity data.

[2678] output:

[2679] Fees and Payment Notices.

[2680] Data processing / calculation:

[2681] Based on the investment activity data, profits are calculated, fees are calculated, and a payment notice is generated and sent to the terminal.

[2682] Terminal behavior:

[2683] The terminal displays a subscription fee payment notification to the user.

[2684] input:

[2685] Payment advice sent by the server.

[2686] output:

[2687] The payment notice displayed to the user.

[2688] Data processing / calculation:

[2689] Converts received notifications into a suitable format for display on the screen.

[2690] (Application example 1)

[2691] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2692] In today's world, for individual investors to effectively and efficiently manage their assets, it is important to build an appropriate portfolio, execute trades, and manage profits. However, performing these steps manually is extremely complex and requires a lot of time and effort. Furthermore, beginners and small investors find it difficult to make optimal investment decisions due to a lack of specialized knowledge. For this reason, there is a need for the development of a simple, reliable investment support system that can solve these issues.

[2693] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2694] In this invention, the server includes means for accepting input of a user's asset status, risk tolerance, and investment goals, means for constructing a portfolio using deep learning technology based on the input information, means for presenting the constructed portfolio to the user, means for accepting buy / sell orders from the user and executing those orders, means for notifying the user of the execution results of the buy / sell orders, means for calculating profits and subscription fees, and means for collecting subscription fees via an electronic payment API. This enables individual investors to easily manage their own investment information, construct an optimal portfolio and execute buy / sell, and seamlessly manage profits and pay subscription fees.

[2695] A "user" is an individual who utilizes the system to input their asset status, risk tolerance, and investment goals and engage in investment activities.

[2696] "Asset status" refers to the status of the total assets owned by the user, such as cash, stocks, bonds, real estate, etc.

[2697] "Risk tolerance" refers to the level of risk a user is willing to accept in an investment.

[2698] "Investment goal" refers to the purpose or goal of investment activities set by the user, such as purchasing a new car or a house.

[2699] "Deep learning technology" is a technology that uses multi-layer neural networks based on large amounts of data to perform advanced pattern recognition and prediction.

[2700] "Portfolio" refers to an investment allocation that optimizes risk by diversifying a user's funds across multiple investment targets.

[2701] A "buy / sell order" is a trading instruction issued by a user to sell or buy a particular investment.

[2702] An "electronic payment API" is a programmatic interface for making payments electronically over the Internet.

[2703] "Subscription Fee" means the fee paid periodically by a User for use of the System.

[2704] This invention provides a support system for individual investors to effectively and efficiently conduct investment activities. The system uses a smartphone as its main platform and utilizes deep learning technology to provide optimal portfolios. Specific embodiments for realizing this system are described below.

[2705] Hardware and Software

[2706] Hardware:

[2707] Smartphone (iOS or Android)

[2708] software:

[2709] Python3

[2710] TensorFlow (Keras)

[2711] Requests library

[2712] REST API Server

[2713] Electronic Payment API

[2714] Data processing and calculation

[2715] 1. User Registration and Authentication:

[2716] The user launches the app on their smartphone and enters the required information (first name, last name, email address, and password) on the "New Registration" screen.

[2717] The terminal transmits this information to the server.

[2718] The server stores the information in a database, generates a user ID and authentication token, and sends them to the terminal.

[2719] The device stores the authentication token and uses it for subsequent requests.

[2720] 2. Portfolio Construction:

[2721] Users enter their current asset status, risk tolerance, and investment goals on the "Portfolio Construction" screen.

[2722] The terminal transmits these input data to the server.

[2723] The server uses a deep learning model to input this data and calculate the optimal portfolio.

[2724] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[2725] The terminal displays the recommended portfolio to the user.

[2726] 3. Providing trading functions:

[2727] The user inputs a buy / sell order by specifying the name and amount on the "Buy / Sell" screen.

[2728] The terminal transmits this order information to the server.

[2729] The server executes buy and sell orders through the broker API and sends the order execution results to the terminal.

[2730] The terminal notifies the user of the order execution result.

[2731] 4. Calculating Revenue and Collecting Subscription Fees:

[2732] The server periodically retrieves the user's investment activity data from the database and calculates the profit.

[2733] Based on the calculated revenue, the server will calculate a subscription fee of 2% of the revenue.

[2734] The server sends a payment notification to the terminal to collect the subscription fee via the electronic payment API.

[2735] The terminal displays the payment advice to the user and makes the electronic payment.

[2736] Specific examples

[2737] For example, a user may enter their investment information (net worth of ¥500,000, medium risk tolerance, and new car purchase in three years). Based on this information, the system generates an optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash). Furthermore, if the user enters an order to purchase ¥50,000 worth of stocks, the system executes the order via the broker API and notifies the user of the results.

[2738] Prompt Sentence Examples

[2739] "Write a prompt that generates the optimal investment strategy for a user with a net worth of 500,000 yen, a medium risk tolerance, and who is looking to buy a new car."

[2740] In this way, the present invention enables individual investors to easily and efficiently manage complex investment activities. The use of concrete examples and prompts makes it even easier to understand.

[2741] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2742] Step 1:

[2743] User Registration and Authentication

[2744] Input: A user launches the app and enters their first name, last name, email address, and password on the sign-up screen.

[2745] Specific operations: The device sends the input information to the server. The server receives the input information and saves it in a database. The server generates a user ID and authentication token and sends them to the device.

[2746] Output: The device stores the authentication token and uses it for subsequent requests.

[2747] Step 2:

[2748] Portfolio Construction

[2749] Input: The user enters their financial situation, risk tolerance, and investment goals into the "Portfolio Construction" screen.

[2750] Specific operation: The device sends the input data to the server, which then inputs the received data into the deep learning model and calculates the optimal portfolio.

[2751] Output: The server generates the calculation result (recommended portfolio) and sends it to the terminal. The terminal displays the recommended portfolio to the user.

[2752] Step 3:

[2753] Additional Investment Advice

[2754] Input: User requests additional investment advice based on presented portfolio.

[2755] How it works: The device sends additional questions or correction requests to the server, which analyzes the questions and uses deep learning models to make predictions or corrections.

[2756] Output: The server generates additional advice and / or modified portfolios and sends them to the terminal, which displays them to the user.

[2757] Step 4:

[2758] Investment buy and sell orders

[2759] Input: The user enters a buy or sell order by specifying the stock and amount on the "Buy / Sell" screen.

[2760] Specific operations: The terminal sends the entered order to the server. The server receives the order information and executes the order by calling the broker API. The server receives the order execution results and sends them to the terminal.

[2761] Output: The terminal notifies the user of the order execution result.

[2762] Step 5:

[2763] Calculating revenue and collecting subscription fees

[2764] Input: The server periodically retrieves the user's investment activity data from the database.

[2765] Specific operation: The server calculates the user's revenue and calculates the subscription fee based on that revenue. The server generates a payment notice for the subscription fee through the electronic payment API and sends it to the terminal.

[2766] Output: The terminal displays the payment advice to the user and makes the electronic payment.

[2767] The above are the specific processing steps of the system that realizes the application example.

[2768] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2769] A system embodying the present invention combines an emotion engine that recognizes user emotions to enable individual investors to conduct their investment activities effectively and efficiently. This system accepts input from the user's asset status, risk tolerance, and investment goals, and uses deep learning technology to build an optimal portfolio based on that information. The system also includes means for incorporating emotion recognition data from the emotion engine into investment advice, accepting and executing buy / sell orders from users, and notifying them of the results. Furthermore, the system provides means for periodically calculating profits generated from the user's investment activities, and calculating and collecting subscription fees based on those profits.

[2770] Overview of program processing flow

[2771] 1. User Registration and Authentication

[2772] The user installs the app and launches it.

[2773] The device will display the "New Registration" screen and ask you to enter the necessary information, such as your name, email address, and password.

[2774] The terminal transmits the input information to the server.

[2775] The server receives the input information and stores it in a database.

[2776] The server generates a user ID, issues an authentication token, and sends it to the terminal.

[2777] The device receives the authentication token and stores it for use in future requests.

[2778] 2. Portfolio Construction

[2779] The user opens the "Portfolio Construction" screen.

[2780] The device will display a form for you to enter your current financial situation, risk tolerance, and investment goals.

[2781] The user enters the necessary information (e.g., net worth of 500,000 yen, risk tolerance "medium," new car purchase in 3 years).

[2782] The terminal transmits the input information to the server.

[2783] The server receives the user data and stores it in a database.

[2784] The server invokes the deep learning model, passing the user data as input.

[2785] A deep learning model calculates the optimal portfolio (e.g., 50% stocks, 30% bonds, 20% cash).

[2786] The server generates the calculation results (recommended portfolio) and sends them to the terminal.

[2787] The terminal displays the recommended portfolio to the user.

[2788] 3. Emotion Recognition by Emotion Engine

[2789] The user uses the "emotion recognition" feature (e.g., detecting emotions using a camera or voice input).

[2790] The data acquired by the device is sent to the emotion engine.

[2791] The emotion engine performs analysis and recognizes the user's emotions (e.g., level of stress, level of satisfaction).

[2792] The device transmits the recognized emotion data to the server.

[2793] 4. Investment advice and planning

[2794] The server receives the emotion recognition data and inputs it into the deep learning model.

[2795] The server generates additional investment advice based on the sentiment data.

[2796] The server sends new investment advice to the terminal.

[2797] The terminal displays additional advice and modified portfolios to the user.

[2798] 5. Providing trading functions

[2799] The user opens the "Buy / Sell" screen, specifies the stock and amount, and enters an order (e.g., purchase "Stock A" for 50,000 yen).

[2800] The terminal transmits the entered order to the server.

[2801] The server receives the order information and prepares to call the broker API.

[2802] The server executes the order through the broker API.

[2803] The server receives the order execution result (success or failure) and sends it to the terminal.

[2804] The terminal displays the order execution results to the user.

[2805] 6. Calculating Revenue...

Claims

1. means for accepting input of a user's asset status, risk tolerance, and investment goals; A means for constructing a portfolio using deep learning technology based on the input information; means for presenting the constructed portfolio to a user; means for accepting buy and sell orders from users and executing those orders; means for notifying a user of the execution result of the buy and sell orders; A system including:

2. The system of claim 1 , further comprising means for generating and presenting to the user additional investment advice based on the constructed portfolio.

3. The system of claim 1 , further comprising means for periodically calculating the profits generated from the user's investment activities and calculating and collecting subscription fees based on the profits.

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