System

Abstract

A system is provided.SOLUTION: This system includes an input means for inputting a theme and an investment amount by a user, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting a related company, a portfolio construction means for constructing a portfolio based on the selected company, a saving setting means for performing saving setting every month based on the portfolio, and a display means for displaying an investment result.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] Currently, investing in individual stocks requires a high level of expertise and time, making it difficult for many individual investors. Furthermore, financial products such as investment trusts incur management fees, creating a high cost hurdle. Furthermore, it is difficult for beginners to diversify their investments based on a theme. This causes many individual investors to miss out on opportunities to properly build their assets. This invention solves these issues by using AI technology to automatically select stocks and build portfolios based on a theme specified by the user. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes the following means: an input means for a user to input a theme and investment amount; a data collection means for collecting company data related to the theme; an analysis means for analyzing the collected data and selecting related companies; a portfolio construction means for constructing a portfolio based on the selected companies; a savings setting means for setting monthly savings based on the portfolio; and a display means for displaying investment results. Furthermore, the analysis means uses an AI algorithm to select companies, enabling accurate and efficient stock selection. Furthermore, by constructing a portfolio based on investment rules specified by the user, the system provides diversified individual stock investments that meet the user's needs.

[0006] "User" means an individual or corporation that uses this system to make investments based on a specific theme.

[0007] "Themes" refer to areas of interest or subjects that users consider when selecting investment targets, such as renewable energy and medical technology.

[0008] "Investment Amount" refers to the amount of money a User invests monthly or periodically, which is used to build and manage a portfolio.

[0009] "Input means" refers to the interface or device through which users input the theme and investment amount, and specifically includes smartphones and personal computers.

[0010] "Data collection tools" refers to software and hardware used to collect company data related to a user-specified topic from the Internet or databases.

[0011] "Analysis methods" refer to the algorithms and calculation methods used to analyze collected corporate data and evaluate and select related companies.

[0012] "Portfolio Construction Method" refers to a method or system for automatically constructing an investment portfolio based on selected companies.

[0013] "Funding instrument" refers to the mechanism or process for setting monthly fund allocations based on the portfolio and allocating the investment amount to each company.

[0014] "Display" means a display or software interface used to visually present investment results or portfolio performance to a user.

[0015] "AI algorithm" refers to a mathematical and statistical method for evaluating and selecting companies using artificial intelligence.

[0016] "Investment rules" refer to the rules or standards applied when constructing or rebalancing a portfolio, including, for example, market capitalization-weighted averages and equal-weighted averages. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[0039] Getting User Input

[0040] First, the user inputs the theme and investment amount. Through the user interface provided by the terminal, the user inputs the theme they are interested in (e.g., renewable energy or medical technology) and specifies the amount they wish to invest each month. This input data is sent to the server via the input means.

[0041] Data collection and analysis

[0042] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[0043] Stock selection and portfolio construction

[0044] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[0045] Implementing fund setting

[0046] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[0047] Viewing and updating results

[0048] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check the portfolio's performance through the terminal using graphs and figures. In addition, the server periodically checks market data to evaluate the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy.

[0049] Specific examples

[0050] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. As a result, users can easily make long-term diversified investments based on the themes of their interest.

[0051] In this way, the present invention allows users to invest efficiently and effectively without specialized knowledge, which is expected to make it easier for individual investors to build assets.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] The user uses the input fields on the device to enter an investment theme and monthly investment amount. Specifically, for example, the theme is "renewable energy" and "50,000 yen per month."

[0055] Step 2:

[0056] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[0057] Step 3:

[0058] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[0059] Step 4:

[0060] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[0061] Step 5:

[0062] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[0063] Step 6:

[0064] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[0065] Step 7:

[0066] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[0067] Step 8:

[0068] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[0069] Step 9:

[0070] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[0071] Step 10:

[0072] The server sends the latest investment results and portfolio performance to the terminal.

[0073] Step 11:

[0074] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[0075] Step 12:

[0076] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain an optimal portfolio composition.

[0077] Step 13:

[0078] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[0079] By following the above steps, users can easily achieve long-term diversified investment based on themes that interest them.

[0080] Example 1

[0081] 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."

[0082] Conventional investment systems often required users to have advanced financial knowledge and specialized data analysis skills when making investments based on specific themes, making them difficult for average individual investors to use. Furthermore, management tasks such as rebalancing investment portfolios were complex and time-consuming, making it difficult to maintain optimal investment conditions at all times. Furthermore, performing these operations manually also led to errors and timing issues. A solution to these issues was needed.

[0083] 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.

[0084] In this invention, the server includes input means for a user to input a theme and an investment amount, data collection means for collecting company data related to the theme, analysis means for analyzing the collected data and selecting related companies, portfolio construction means for constructing a portfolio based on the selected companies, savings setting means for setting monthly savings based on the portfolio, display means for displaying investment results, and rebalancing means for periodically rebalancing the portfolio in accordance with the user's settings. This enables individual investors to easily invest efficiently and effectively based on a specific theme.

[0085] "Input means" is an interface that allows a user to input a theme and investment amount into the system.

[0086] "Data collection means" refers to a mechanism for collecting company data related to the theme from stock market databases and financial APIs on the Internet.

[0087] "Analysis methods" refers to algorithms or programs used to analyze collected corporate data and select relevant companies. This may include AI algorithms.

[0088] The "portfolio construction tool" is a system for constructing a portfolio based on selected companies in accordance with investment rules specified by the user.

[0089] A "savings setting method" is a system for allocating monthly investment amounts to each stock based on the portfolio.

[0090] "Display means" refers to an interface that displays graphs and figures so that users can check their investment results.

[0091] A "rebalancing tool" is a mechanism for periodically reviewing a portfolio according to user settings and adjusting it to an optimal balance.

[0092] "Artificial intelligence algorithms" refers to machine learning and deep learning techniques used to analyze collected data and select relevant companies.

[0093] "Investment rules" are standards and guidelines for portfolio construction, such as market capitalization-weighted average or equal-weighted average, specified by the user.

[0094] This invention is a system that allows users to easily make investments based on specific themes. This system is realized by linking a server and a terminal, and incorporates various data analysis and AI technologies.

[0095] First, the user inputs the investment theme and investment amount using the terminal. The terminal then sends the data entered by the user to the server. A web application (e.g., React.js) is used as the user interface.

[0096] The server collects company data from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud) on the Internet. This data collection method gathers company financial information and market data. The collected data is stored on the server in JSON format.

[0097] The server then uses Python's Pandas and NumPy libraries to calculate metrics such as revenue, growth rate, and stock price volatility, allowing for detailed analysis of the collected data to assess a company's financial situation and market influence.

[0098] Furthermore, the server uses artificial intelligence algorithms (e.g., TENSORFLOW (registered trademark) or PyTorch) to select the most suitable stocks based on the analysis data. The selected companies are incorporated into a portfolio using a portfolio construction tool. At this time, allocation is determined according to the investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average).

[0099] Monthly savings settings are automatically made by the server. The server obtains the user's monthly investment amount and calculates the amount to invest in each stock. For example, if you set up a monthly investment of 50,000 yen in 10 companies, 5,000 yen will be invested in each company. This setting is executed periodically using scheduling techniques such as CRON jobs.

[0100] The server periodically collects market data and evaluates portfolio performance. The evaluation results are sent to the terminal in JSON format, and the terminal visualizes the investment results using a JavaScript (registered trademark) graph library (e.g., D3.js or Chart.js). Users can check the performance of their portfolio using graphs and numerical values ​​on the terminal.

[0101] The server also has the ability to periodically rebalance the portfolio based on the user's settings, ensuring that the portfolio is always optimally balanced. This rebalancing method ensures that an investment strategy that matches the user's investment policy is continuously implemented.

[0102] As a specific example, if a user sets up a monthly investment of 50,000 yen on the theme of "renewable energy," the server first collects data on renewable energy-related companies. The data is then analyzed using Python's analysis library, and related companies are selected using TensorFlow. A portfolio is then constructed in which 5,000 yen is invested equally in each of the top 10 selected companies. This setting is executed automatically every month using the server's scheduling function. Users can check the performance of their portfolio at any time via their device, and it is rebalanced as necessary.

[0103] Examples of prompts include:

[0104] "Can you please provide me some Python code to gather and parse the company data needed to create a list of companies to invest in in the renewable energy sector?"

[0105] The present invention allows users to invest efficiently and effectively without specialized knowledge, and also makes it easier for individual investors to build assets easily.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1: Getting User Input

[0108] Input: The user inputs the investment theme and investment amount using the terminal.

[0109] Specific operation: The user accesses the user interface on the device and enters a theme (e.g., renewable energy) and a monthly investment amount (e.g., 50,000 yen) into the input form. The device then sends the input data to the server in JSON format.

[0110] Output: User input data is sent from the device to the server

[0111] Step 2: Data collection

[0112] Input: The server receives a request based on the theme submitted by the user.

[0113] What it does: The server collects company data related to the theme from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud).

[0114] Output: The server retrieves financial and market data for companies related to the theme in JSON format.

[0115] Step 3: Data analysis

[0116] Input: Company data collected on the server

[0117] What it does: The server uses Python's Pandas and NumPy libraries to analyze the collected data, which includes calculating metrics such as revenue, growth rate, and stock price volatility.

[0118] Output: Analyzed company data and its indicator values

[0119] Step 4: Stock selection

[0120] Input: Parsed company data

[0121] Specific operation: The server uses artificial intelligence algorithms such as TensorFlow and PyTorch to select the best stocks.

[0122] Output: A list of top companies based on the user's theme (e.g., top 10 companies)

[0123] Step 5: Portfolio Construction

[0124] Input: Selected company list and user investment rules

[0125] Specific operation: The server builds a portfolio based on the selected companies based on investment rules (e.g., market capitalization weighted average or equal weighted average) and determines the investment allocation for each company.

[0126] Output: A portfolio based on the user's themes and investment rules.

[0127] Step 6: Set up your savings

[0128] Input: User's monthly investment amount and constructed portfolio

[0129] Specific operation: The server obtains the user's monthly investment amount and calculates the investment amount for each stock (e.g., 50,000 yen is distributed to 10 companies in the form of 5,000 yen each each month).

[0130] Output: Monthly investment amount for each stock

[0131] Step 7: View the results

[0132] Input: Monthly investment results data

[0133] Specific operation: The server sends the investment results in JSON format to the terminal, and the terminal uses a JavaScript graph library (e.g., D3.js or Chart.js) to visualize and display the investment results to the user.

[0134] Output: Visualized investment results (graphs and figures)

[0135] Step 8: Rebalance

[0136] Inputs: User preferences and latest market data

[0137] What it does: The server periodically collects market data, evaluates portfolio performance, and automatically rebalances as needed to achieve optimal balance.

[0138] Output: Rebalanced portfolio

[0139] This system allows users to invest efficiently and effectively without specialized knowledge, making it easier for individual investors to build assets.

[0140] (Application example 1)

[0141] 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."

[0142] Conventional investment systems require users to have advanced financial knowledge in order to properly select companies and make investments, and they lack a means to intuitively understand the results of their investments. In addition, many investment systems have complex user interfaces that make them difficult for beginners to use. As a result, it has been difficult for individual investors to easily build up their assets.

[0143] 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.

[0144] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data based on the input theme and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies and distributing the investment amount evenly among multiple stocks, a savings setting means for setting monthly savings based on the portfolio, and a display means for displaying investment results in graphs and figures. This allows users without advanced financial knowledge to invest efficiently and intuitively and easily build assets.

[0145] A "subject" is a particular area, topic, or cause of interest to a user.

[0146] "Investment Amount" means the total amount of money that a User allocates to an Investment on a monthly or periodic basis.

[0147] The "input means" is an interface that allows the user to input the theme and investment amount.

[0148] "Data collection methods" are methods and techniques for collecting company data related to a specified topic.

[0149] "Analysis means" refers to the technologies and algorithms used to analyze collected data and select relevant companies.

[0150] "Portfolio construction methods" are techniques and methods for constructing an optimal investment portfolio based on selected companies.

[0151] A "savings setting method" is a technique or method for allocating monthly investment amounts to each stock based on a portfolio.

[0152] "Display means" refers to an interface that visually displays investment results and portfolio performance to the user.

[0153] A "generative AI model" is an artificial intelligence algorithm that selects companies based on collected and analyzed data.

[0154] An "interactive user interface" is an interface that is intuitive and easy for users to use, allowing them to visually check investment themes and results.

[0155] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[0156] Getting User Input

[0157] First, users use the terminal to input the theme they are interested in and the monthly investment amount. The user interface is designed to be interactive and intuitive. For example, if you select a theme such as renewable energy or medical technology and set the investment amount to 50,000 yen, you can use the following simple prompt:

[0158] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[0159] Data collection and analysis

[0160] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data includes a wide range of information, such as company financials, performance, and market capitalization. The collected data is then analyzed using a generative AI model like Keras. Specifically, the company's past revenue, growth rate, and stock price volatility are used to evaluate the data.

[0161] Stock selection and portfolio construction

[0162] Next, the server selects the best stocks based on the analysis results. The generative AI model lists the top performing companies, and the top 10 companies are selected from that list. Based on the selected companies, a portfolio is created using the portfolio construction tool. At this time, investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average) are applied.

[0163] Implementing fund setting

[0164] After the portfolio is constructed, the server sets up a savings plan that distributes the amount of the user's monthly investment equally among each stock. For example, if 50,000 yen is to be distributed equally among 10 companies, the server will set up an investment of 5,000 yen in each company. This setting allows the savings investment to be carried out automatically and continuously.

[0165] Viewing and updating results

[0166] Finally, the server calculates the latest investment results and sends them to the user's device. Through an interactive user interface, users can visually view their portfolio's performance using graphs and figures. The server also periodically checks market data to evaluate portfolio performance. Automatic rebalancing is performed as needed to maintain an optimal portfolio in line with the user's investment strategy.

[0167] In this way, the system according to the present invention enables users, even those with little financial knowledge, to invest efficiently and intuitively, helping them to easily build up assets.

[0168] Specific prompt examples:

[0169] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[0170] The above is a detailed description of the embodiments of the present invention.

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The user inputs the theme and investment amount. The user uses a terminal to input the theme (e.g., renewable energy) and investment amount (e.g., 50,000 yen per month) through an interactive user interface. The input theme and investment amount are sent to the server.

[0174] Input: Theme, investment amount

[0175] Output: The theme and investment amount are sent to the server.

[0176] Step 2:

[0177] The server collects company data related to the theme. The server uses stock market databases and financial APIs on the Internet to collect data such as financial information, performance, and market capitalization of companies related to the specified theme.

[0178] Input: Theme

[0179] Output: Related company data

[0180] Step 3:

[0181] The server analyzes the collected data. The server uses generative AI models such as Keras to analyze the collected company data and evaluate companies. Specifically, it generates rankings based on company revenue, growth rate, stock price volatility, etc.

[0182] Input: Affiliated company data

[0183] Output: Company ratings and rankings

[0184] Step 4:

[0185] The server selects the top companies. Based on the analysis results, the server selects the top 10 companies. This selected list of companies forms the basis of the investment portfolio.

[0186] Input: Company ratings and rankings

[0187] Output: Top 10 companies list

[0188] Step 5:

[0189] The server builds the portfolio. The server builds an investment portfolio based on the selected companies according to the user's investment rules. For example, the server may allocate the investment amount to each company with equal weighting.

[0190] Input: Top 10 company list, investment amount, investment rules

[0191] Output: Constructed portfolio

[0192] Step 6:

[0193] The server sets up monthly investments. Based on the user's monthly investment amount (e.g., 50,000 yen), the server distributes it equally among the selected stocks. This setting allows for automatic monthly investments.

[0194] Input: Constructed portfolio, monthly investment amount

[0195] Output: Monthly savings settings

[0196] Step 7:

[0197] The server calculates and displays investment results. The server calculates the latest investment results and sends them to the device. Users can check the performance of their investment portfolio through the device using graphs and figures.

[0198] Inputs: Portfolio data, latest market data

[0199] Output: Display of calculated investment results

[0200] Step 8:

[0201] The server evaluates the performance of the portfolio and rebalances it, periodically checking market data and rebalancing as needed to ensure the portfolio is optimized.

[0202] Inputs: Portfolio data, latest market data

[0203] Output: Rebalanced portfolio

[0204] 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.

[0205] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes user emotions and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[0206] Getting User Input

[0207] First, the user inputs the theme and investment amount. Through the user interface provided by the device, the user inputs the theme of interest (such as renewable energy or medical technology) and specifies the amount they wish to invest each month. The system also incorporates an emotion engine that recognizes the user's emotions from their facial expressions, voice, or keystrokes while they are typing. This allows the emotion engine to analyze the user's emotional state in real time as they type and adjust the input data as necessary.

[0208] Data collection and analysis

[0209] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[0210] Stock selection and portfolio construction

[0211] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[0212] Implementing fund setting

[0213] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[0214] Incorporating an emotion engine

[0215] The sentiment engine analyzes user sentiment in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the sentiment engine will carefully allocate investment amounts and adjust to reduce risk. On the other hand, if the user's sentiment is stable, the normal investment settings will be maintained. The sentiment engine aims to achieve more stable investment results by adjusting the portfolio rebalancing frequency according to changes in the user's sentiment.

[0216] Viewing and updating results

[0217] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check their portfolio performance through the terminal in graphs and figures. In addition, the server periodically checks market data and evaluates the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy and emotional state.

[0218] Specific examples

[0219] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be further diversified to reduce risk.

[0220] In this way, the present invention not only enables users to make efficient and effective investments without specialized knowledge, but also allows users to adjust their investment strategies based on their emotions, thereby achieving stable asset formation while managing risk.

[0221] The processing flow will be explained below.

[0222] Step 1:

[0223] The user uses the input fields on the device to enter an investment theme and a monthly investment amount, for example, "renewable energy" and "50,000 yen per month."

[0224] Step 2:

[0225] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[0226] Step 3:

[0227] The device analyzes the user's facial expressions, voice, or keystrokes and sends them to the emotion engine, which then recognizes the user's emotions and sends the data to the server.

[0228] Step 4:

[0229] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[0230] Step 5:

[0231] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[0232] Step 6:

[0233] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[0234] Step 7:

[0235] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[0236] Step 8:

[0237] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[0238] Step 9:

[0239] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[0240] Step 10:

[0241] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[0242] Step 11:

[0243] The server sends the latest investment results and portfolio performance to the terminal.

[0244] Step 12:

[0245] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[0246] Step 13:

[0247] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain the user's investment size.

[0248] Step 14:

[0249] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[0250] Step 15:

[0251] The emotional engine adjusts investment decisions in real time based on the user's emotional state. For example, if a user feels anxious, the engine will split their investments into smaller amounts to reduce risk and create a more diversified portfolio.

[0252] Step 16:

[0253] The server adjusts the portfolio rebalancing frequency based on the analysis results of the emotion engine. If the user's emotions are stable, the normal rebalancing frequency is maintained, but if the user is anxious or overexcited, the frequency is revised.

[0254] By following the above steps, users can easily achieve long-term diversified investment based on the themes they are interested in. In addition, by using the emotion engine to make investment decisions based on the user's emotions, it is possible to support stable asset formation while managing risk.

[0255] Example 2

[0256] 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."

[0257] In conventional investment systems, users' emotions are not reflected in investment decisions, resulting in inappropriate risk management. It is also difficult for beginner users to determine which companies to invest in, making it difficult to build effective and stable assets. Furthermore, monthly fund setting and portfolio rebalancing are typically done manually, which is time-consuming for users.

[0258] 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.

[0259] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's sentiment in real time and adjusting investment decisions based on the results, and a display means for displaying investment results. This allows the user to make appropriate investment decisions based on their own sentiment, enabling effective and stable asset formation while appropriately managing risk. Furthermore, the system automatically collects data, analyzes it, constructs a portfolio, sets savings, and rebalances it, significantly reducing the user's workload.

[0260] "Input means" refers to a means for providing an interface function for a user to input an investment theme and monthly investment amount.

[0261] "Data collection means" refers to a means of collecting corporate data related to a topic specified by the user from stock market databases, financial APIs, etc. on the Internet.

[0262] "Analysis methods" are means for analyzing collected corporate data and selecting related companies, and primarily involve using AI algorithms to evaluate companies.

[0263] The "portfolio construction means" is a means for determining investment allocations based on companies selected by the analysis means and constructing a portfolio in accordance with investment rules specified by the user.

[0264] The "accumulation setting means" is a means for allocating the monthly investment amount designated by the user to each stock and automatically executing the accumulation investment.

[0265] An "emotion analysis method" is a method for analyzing a user's emotions in real time based on their facial expressions, voice, keystrokes, etc., and adjusting investment decisions based on the results.

[0266] The "display means" is a means for visually displaying the latest investment results calculated by the server to the user.

[0267] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes users' emotions in real time and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[0268] Getting User Input

[0269] First, the user enters their investment theme and monthly investment amount through the device's user interface. For example, the user may select renewable energy as their theme and set up a monthly investment of 50,000 yen. The device also uses a webcam and microphone to capture the user's facial expressions and voice, which are then transmitted in real time to the emotion engine. The emotion engine then analyzes the user's emotional state and returns the analysis results to the device.

[0270] Data collection and analysis

[0271] The server uses online stock market databases and financial APIs (e.g., Yahoo Finance API, Alpha Vantage) to collect company data related to the user-specified topic. The collected data includes company financial information, performance, market capitalization, etc. The server then analyzes the collected data using AI algorithms such as TensorFlow and PyTorch to evaluate indicators such as each company's revenue, growth rate, and stock price volatility.

[0272] Stock selection and portfolio construction

[0273] The server uses an AI algorithm based on the analyzed data to select the most suitable stocks. For example, it may select the top 10 companies related to renewable energy. After the selection, the server builds a portfolio and determines the allocation of each stock based on the investment rules specified by the user (e.g., market capitalization weighted average, equal weighted average). For example, if 50,000 yen is to be allocated equally to 10 companies each month, the server will set it up to invest 5,000 yen in each company.

[0274] Implementing fund setting

[0275] The server calculates and automatically sets the investment amount for each stock based on the user's monthly investment amount. This accumulation setting is executed automatically based on a programmed schedule. For example, if you allocate 50,000 yen to 10 companies every month, 5,000 yen will be invested in each company.

[0276] Incorporating an emotion engine

[0277] The emotion engine analyzes user emotions in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the server allocates investment amounts to safer options and reduces risk. On the other hand, if the user's emotions are stable, the server continues with normal investment settings. The emotion engine also adjusts the portfolio rebalancing frequency to aim for stable investment results.

[0278] Viewing and updating results

[0279] The server calculates the latest investment results and sends them to the user's device. The device displays the portfolio's performance in graphs and figures, allowing the user to see it. The server periodically checks market data to evaluate the portfolio's performance, automatically rebalancing as needed to maintain an optimal portfolio based on the user's investment strategy and emotional state.

[0280] Specific examples

[0281] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes it using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be diversified more finely to reduce risk.

[0282] Prompt Sentence Examples

[0283] "Please explain a system that invests 50,000 yen per month based on renewable energy and uses an emotion engine to take emotions into account and reduce risk."

[0284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0285] Step 1:

[0286] The user inputs the investment theme and monthly investment amount through the terminal's user interface.

[0287] Specific operation: The user enters the theme "renewable energy" and "50,000 yen per month" in the input field and presses the send button.

[0288] Input: Investment theme "Renewable energy", monthly investment amount "50,000 yen".

[0289] Data processing: The entered investment theme and investment amount are registered in the system.

[0290] Output: Registered investment themes and investment amounts.

[0291] Step 2:

[0292] The device captures the user's facial expressions, voice, and keystrokes and sends them to the emotion engine.

[0293] Specific operation: The device records facial expressions with a webcam and audio with a microphone, and sends the data to the emotion engine.

[0294] Input: Real-time user facial expression, voice, and keystroke data.

[0295] Data calculation: The emotion engine analyzes the recorded data and calls an API to determine the user's emotional state.

[0296] Output: The analyzed emotional state of the user (e.g., calm, anxious, excited).

[0297] Step 3:

[0298] The server collects company data related to the user's investment themes.

[0299] Specific operation: The server calls the Yahoo Finance API or Alpha Vantage API to collect company information related to the specified theme.

[0300] Input: Investment theme "Renewable Energy".

[0301] Data processing: Obtain data such as company financial information, performance, and market capitalization from stock market databases on the Internet.

[0302] Output: A dataset of collected relevant companies.

[0303] Step 4:

[0304] The server analyzes the collected corporate data and evaluates and selects relevant companies.

[0305] How it works: The server runs analytical models using TensorFlow and PyTorch to evaluate company revenues, growth rates, and stock price volatility.

[0306] Input: Collected corporate dataset.

[0307] Data calculation: A comprehensive evaluation score is calculated based on each company's indicators, and the top 10 companies are selected.

[0308] Output: A list of the top 10 selected companies.

[0309] Step 5:

[0310] A portfolio is constructed based on companies selected by the server.

[0311] Specific operation: The server determines the investment allocation for each stock according to the user's investment rules (e.g., equal weighted average).

[0312] Input: List of selected top 10 companies, user investment amount: 50,000 yen.

[0313] Data calculation: Allocate the investment amount equally to each stock and calculate the specific investment amount (e.g., invest 5,000 yen in each company).

[0314] Output: A list of the constructed portfolio allocations.

[0315] Step 6:

[0316] The server allocates the monthly investment amount to each stock and executes the savings setting.

[0317] Specific operation: Automatically allocates monthly investment amounts to each company based on the calculated investment allocation.

[0318] Input: Portfolio Allocation List.

[0319] Data calculation: Set the monthly investment amount for each stock and create an automatic investment schedule.

[0320] Output: The configured automatic savings schedule.

[0321] Step 7:

[0322] An emotion engine adjusts investment decisions based on user emotions.

[0323] How it works: The emotion engine reanalyzes the user's emotional state and adjusts portfolio rebalancing and investment allocation as needed.

[0324] Input: parsed user emotional state, configured portfolio allocation.

[0325] Data Computing: Adjusting investment allocation to reduce risk based on emotional state.

[0326] Output: Adjusted portfolio allocation list.

[0327] Step 8:

[0328] The server calculates the latest investment results and sends them to the terminal.

[0329] Specific operation: The server calculates investment results based on the latest company stock price data and portfolio allocation, and formats them into graphs and numerical formats.

[0330] Inputs: Latest market data, user portfolio information.

[0331] Data Processing: Combining market data with investment allocations to analyze and visualize investment performance.

[0332] Output: Graphs and numerical data of investment results.

[0333] Step 9:

[0334] The terminal displays the investment results to the user.

[0335] Specific operation: The terminal displays the investment results received from the server to the user in graph and numerical format.

[0336] Input: Investment result data sent from the server.

[0337] Data processing: Formatting the data for display on the user interface.

[0338] Output: Investment results displayed to the user in graphical and numerical form.

[0339] (Application example 2)

[0340] 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."

[0341] Traditional investment systems make it difficult for users without specialized knowledge to invest, and lack effective ways to manage the impact of emotions on investment decisions. There is also a need to provide users with a deeper learning experience by linking investment education content with investment execution. Another issue is that investment allocation adjustments in response to emotional fluctuations are not automated, forcing users to manage risk themselves.

[0342] 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.

[0343] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's emotions in real time and reflecting them in investment decisions, an adjustment means for adjusting investment allocations based on the sentiment analysis results, and a display means for displaying investment results. This allows users to make investment decisions based on their emotions without specialized knowledge and automatically achieve appropriate risk management. Furthermore, by linking with educational content, a more effective investment learning experience can be provided.

[0344] "Input means" refers to an interface and device for a user to input a theme and an investment amount.

[0345] "Data collection means" refers to the means for collecting company data related to a user-specified topic from stock market databases and financial APIs on the Internet.

[0346] "Analysis means" refers to the function of analyzing data using technologies such as AI algorithms in order to select relevant companies based on the collected data.

[0347] "Portfolio construction means" refers to a device or program that has the function of constructing an investment portfolio based on companies selected by the analytical means.

[0348] "Savings setting means" refers to a function that automatically sets monthly savings investments based on a portfolio.

[0349] "Sentiment analysis means" refers to the function of detecting and analyzing user emotions in real time and adjusting investment decisions based on that.

[0350] "Adjustment measures" refer to the function of appropriately adjusting investment allocation and risk management based on the results of sentiment analysis measures.

[0351] "Display means" refers to an interface and device for visually displaying investment results, portfolio composition, etc. to the user.

[0352] "Educational content" refers to learning materials such as videos and documents that provide knowledge and know-how about investing.

[0353] To implement this invention, a system is required in which the user, the server, and the terminal function in cooperation with each other. Each function and the hardware and software used will be specifically described below.

[0354] First, the user uses a device such as a smartphone or smart glasses to input the theme of interest and the investment amount. The input method is a touch screen or a voice recognition interface. At this time, the user's facial expressions and voice are captured in real time, and an emotion analysis method is activated.

[0355] Emotion analysis is performed using an emotion recognition SDK such as EmotionRecognition. This analyzes data acquired from the device's camera and microphone to identify the user's current emotion. The results of this emotion analysis are passed on to the adjustment method described below.

[0356] Next, the server uses data collection means to collect company data related to the theme entered by the user. This collection is done using stock market databases on the Internet and Financial APIs. The collected company data is analyzed using an AI algorithm, which is the analytical means. The StockSelectionModel is applied as the analytical means, and appropriate related companies are selected.

[0357] Based on the analyzed data, the server uses a portfolio construction tool to generate an investment portfolio. Based on the selected list of companies, the portfolio is constructed according to investment rules specified by the user, such as "market capitalization weighted average" or "equal weighted average."

[0358] Next, the investment setup tool calculates the monthly investment amount and sets the investment plan appropriately based on the portfolio, using a method such as allocating 50,000 yen equally to each company as the monthly investment specified by the user.

[0359] The results of real-time user sentiment analysis are reflected in investment allocation through adjustments. For example, if a user feels anxious, the investment amount will be further diversified to reduce risk. In this way, emotional states directly affect investment decisions.

[0360] Finally, the server calculates the investment results and visually presents them to the user through the device's display means, which may include graphs and tables of figures, allowing the user to see the performance of their portfolio and the results of any adjustments.

[0361] Specific examples

[0362] For example, if a user uses a smartphone to set up a monthly investment of 50,000 yen in the "renewable energy" theme, the server will execute the following process.

[0363] 1. Collect company data related to renewable energy through data collection methods.

[0364] 2. Use AI algorithms as analytical tools to select relevant companies.

[0365] 3. Using the portfolio construction method, create a portfolio in which you invest 5,000 yen equally in each of the top 10 companies.

[0366] 4. If the emotion analysis means detects an anxious expression from the user, the adjustment means further diversifies the investment allocation to manage risk.

[0367] 5. The final investment results will be provided to the user through the terminal display means.

[0368] Prompt Sentence Examples

[0369] A user wants to invest in "renewable energy." The monthly investment amount is 50,000 yen. If the camera detects pressure or anxiety, the user should reduce the investment amount by 20% and offer an investment portfolio that diversifies the risk.

[0370] In this way, the system incorporates users' emotions into investment decisions, enabling better risk management and linkage with educational content.

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] The user uses the terminal to input the theme of interest and the investment amount.

[0374] Input is made via a touchscreen or voice recognition interface, and the input data is stored on the device for use in subsequent steps.

[0375] Step 2:

[0376] The device captures the user's facial expressions and voice in real time and analyzes their emotional state using the EmotionRecognition SDK, an emotion analysis tool.

[0377] The input is camera footage and audio data, which are used for emotion analysis, and the output is the user's current emotional state (e.g., joy, anxiety, anger).

[0378] Step 3:

[0379] The server collects business data related to the theme.

[0380] Using FinancialAPI, data such as financial information and market capitalization of companies related to a specified theme is collected from stock market databases on the Internet. The input is the theme specified by the user, and the output is a data list of related companies.

[0381] Step 4:

[0382] The server analyzes the collected data and identifies related companies.

[0383] As an analytical method, we use the StockSelectionModel, which uses an AI algorithm to evaluate and select companies based on the collected data. The input is a list of company data, and the output is a list of selected related companies.

[0384] Step 5:

[0385] The server builds a portfolio based on the selected companies.

[0386] The portfolio construction tool creates a portfolio based on the selected company list and specified investment rules such as "market capitalization weighted average" or "equal weighted average." The input is the selected company list and the investment rules specified by the user, and the output is the constructed investment portfolio.

[0387] Step 6:

[0388] The server sets up monthly savings.

[0389] Using the investment setting method, the monthly investment amount set by the user is appropriately allocated to each company. For example, if 50,000 yen is to be allocated equally to 10 companies each month, 5,000 yen will be allocated to each company. The input is the investment amount and portfolio specified by the user, and the output is the investment setting for each company.

[0390] Step 7:

[0391] The server adjusts investment allocation based on the sentiment analysis results.

[0392] The user's emotional state obtained from the emotion analysis means is used in the adjustment means to optimize investment allocation. For example, if the user feels anxious, the investment amount is reduced by 20% to better diversify risk. The input is the user's emotional state and existing investment settings, and the output is the adjusted investment plan.

[0393] Step 8:

[0394] The server calculates the investment results and sends them to the terminal.

[0395] The terminal uses a display means to visually present the investment results to the user. The input is the adjusted investment plan, and the output is a visual display of the investment results (e.g., graphs or numerical tables). This allows the user to check their investment performance.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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."

[0412] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[0413] Getting User Input

[0414] First, the user inputs the theme and investment amount. Through the user interface provided by the terminal, the user inputs the theme they are interested in (e.g., renewable energy or medical technology) and specifies the amount they wish to invest each month. This input data is sent to the server via the input means.

[0415] Data collection and analysis

[0416] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[0417] Stock selection and portfolio construction

[0418] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[0419] Implementing fund setting

[0420] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[0421] Viewing and updating results

[0422] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check the portfolio's performance through the terminal using graphs and figures. In addition, the server periodically checks market data to evaluate the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy.

[0423] Specific examples

[0424] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. As a result, users can easily make long-term diversified investments based on the themes of their interest.

[0425] In this way, the present invention allows users to invest efficiently and effectively without specialized knowledge, which is expected to make it easier for individual investors to build assets.

[0426] The processing flow will be explained below.

[0427] Step 1:

[0428] The user uses the input fields on the device to enter an investment theme and monthly investment amount. Specifically, for example, the theme is "renewable energy" and "50,000 yen per month."

[0429] Step 2:

[0430] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[0431] Step 3:

[0432] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[0433] Step 4:

[0434] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[0435] Step 5:

[0436] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[0437] Step 6:

[0438] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[0439] Step 7:

[0440] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[0441] Step 8:

[0442] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[0443] Step 9:

[0444] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[0445] Step 10:

[0446] The server sends the latest investment results and portfolio performance to the terminal.

[0447] Step 11:

[0448] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[0449] Step 12:

[0450] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain an optimal portfolio composition.

[0451] Step 13:

[0452] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[0453] By following the above steps, users can easily achieve long-term diversified investment based on themes that interest them.

[0454] Example 1

[0455] 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."

[0456] Conventional investment systems often required users to have advanced financial knowledge and specialized data analysis skills when making investments based on specific themes, making them difficult for average individual investors to use. Furthermore, management tasks such as rebalancing investment portfolios were complex and time-consuming, making it difficult to maintain optimal investment conditions at all times. Furthermore, performing these operations manually also led to errors and timing issues. A solution to these issues was needed.

[0457] 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.

[0458] In this invention, the server includes input means for a user to input a theme and an investment amount, data collection means for collecting company data related to the theme, analysis means for analyzing the collected data and selecting related companies, portfolio construction means for constructing a portfolio based on the selected companies, savings setting means for setting monthly savings based on the portfolio, display means for displaying investment results, and rebalancing means for periodically rebalancing the portfolio in accordance with the user's settings. This enables individual investors to easily invest efficiently and effectively based on a specific theme.

[0459] "Input means" is an interface that allows a user to input a theme and investment amount into the system.

[0460] "Data collection means" refers to a mechanism for collecting company data related to the theme from stock market databases and financial APIs on the Internet.

[0461] "Analysis methods" refers to algorithms or programs used to analyze collected corporate data and select relevant companies. This may include AI algorithms.

[0462] The "portfolio construction tool" is a system for constructing a portfolio based on selected companies in accordance with investment rules specified by the user.

[0463] A "savings setting method" is a system for allocating monthly investment amounts to each stock based on the portfolio.

[0464] "Display means" refers to an interface that displays graphs and figures so that users can check their investment results.

[0465] A "rebalancing tool" is a mechanism for periodically reviewing a portfolio according to user settings and adjusting it to an optimal balance.

[0466] "Artificial intelligence algorithms" refers to machine learning and deep learning techniques used to analyze collected data and select relevant companies.

[0467] "Investment rules" are standards and guidelines for portfolio construction, such as market capitalization-weighted average or equal-weighted average, specified by the user.

[0468] This invention is a system that allows users to easily make investments based on specific themes. This system is realized by linking a server and a terminal, and incorporates various data analysis and AI technologies.

[0469] First, the user inputs the investment theme and investment amount using the terminal. The terminal then sends the data entered by the user to the server. A web application (e.g., React.js) is used as the user interface.

[0470] The server collects company data from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud) on the Internet. This data collection method gathers company financial information and market data. The collected data is stored on the server in JSON format.

[0471] The server then uses Python's Pandas and NumPy libraries to calculate metrics such as revenue, growth rate, and stock price volatility, allowing for detailed analysis of the collected data to assess a company's financial situation and market influence.

[0472] Furthermore, the server uses artificial intelligence algorithms (e.g., TensorFlow or PyTorch) to select the most suitable stocks based on the analysis data. The selected companies are incorporated into a portfolio using a portfolio construction tool. At this time, allocation is determined according to the investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average).

[0473] Monthly savings settings are automatically made by the server. The server obtains the user's monthly investment amount and calculates the amount to invest in each stock. For example, if you set up a monthly investment of 50,000 yen in 10 companies, 5,000 yen will be invested in each company. This setting is executed periodically using scheduling techniques such as CRON jobs.

[0474] The server periodically collects market data and evaluates the performance of the portfolio. The evaluation results are sent to the terminal in JSON format, and the terminal visualizes the investment results using a JavaScript graph library (e.g., D3.js or Chart.js). The user can check the performance of their portfolio using graphs and figures on the terminal.

[0475] The server also has the ability to periodically rebalance the portfolio based on the user's settings, ensuring that the portfolio is always optimally balanced. This rebalancing method ensures that an investment strategy that matches the user's investment policy is continuously implemented.

[0476] As a specific example, if a user sets up a monthly investment of 50,000 yen on the theme of "renewable energy," the server first collects data on renewable energy-related companies. The data is then analyzed using Python's analysis library, and related companies are selected using TensorFlow. A portfolio is then constructed in which 5,000 yen is invested equally in each of the top 10 selected companies. This setting is executed automatically every month using the server's scheduling function. Users can check the performance of their portfolio at any time via their device, and it is rebalanced as necessary.

[0477] Examples of prompts include:

[0478] "Can you please provide me some Python code to gather and parse the company data needed to create a list of companies to invest in in the renewable energy sector?"

[0479] The present invention allows users to invest efficiently and effectively without specialized knowledge, and also makes it easier for individual investors to build assets easily.

[0480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0481] Step 1: Getting User Input

[0482] Input: The user inputs the investment theme and investment amount using the terminal.

[0483] Specific operation: The user accesses the user interface on the device and enters a theme (e.g., renewable energy) and a monthly investment amount (e.g., 50,000 yen) into the input form. The device then sends the input data to the server in JSON format.

[0484] Output: User input data is sent from the device to the server

[0485] Step 2: Data collection

[0486] Input: The server receives a request based on the theme submitted by the user.

[0487] What it does: The server collects company data related to the theme from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud).

[0488] Output: The server retrieves financial and market data for companies related to the theme in JSON format.

[0489] Step 3: Data analysis

[0490] Input: Company data collected on the server

[0491] What it does: The server uses Python's Pandas and NumPy libraries to analyze the collected data, which includes calculating metrics such as revenue, growth rate, and stock price volatility.

[0492] Output: Analyzed company data and its indicator values

[0493] Step 4: Stock selection

[0494] Input: Parsed company data

[0495] Specific operation: The server uses artificial intelligence algorithms such as TensorFlow and PyTorch to select the best stocks.

[0496] Output: A list of top companies based on the user's theme (e.g., top 10 companies)

[0497] Step 5: Portfolio Construction

[0498] Input: Selected company list and user investment rules

[0499] Specific operation: The server builds a portfolio based on the selected companies based on investment rules (e.g., market capitalization weighted average or equal weighted average) and determines the investment allocation for each company.

[0500] Output: A portfolio based on the user's themes and investment rules.

[0501] Step 6: Set up your savings

[0502] Input: User's monthly investment amount and constructed portfolio

[0503] Specific operation: The server obtains the user's monthly investment amount and calculates the investment amount for each stock (e.g., 50,000 yen is distributed to 10 companies in the form of 5,000 yen each each month).

[0504] Output: Monthly investment amount for each stock

[0505] Step 7: View the results

[0506] Input: Monthly investment results data

[0507] Specific operation: The server sends the investment results in JSON format to the terminal, and the terminal uses a JavaScript graph library (e.g., D3.js or Chart.js) to visualize and display the investment results to the user.

[0508] Output: Visualized investment results (graphs and figures)

[0509] Step 8: Rebalance

[0510] Inputs: User preferences and latest market data

[0511] What it does: The server periodically collects market data, evaluates portfolio performance, and automatically rebalances as needed to achieve optimal balance.

[0512] Output: Rebalanced portfolio

[0513] This system allows users to invest efficiently and effectively without specialized knowledge, making it easier for individual investors to build assets.

[0514] (Application example 1)

[0515] 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."

[0516] Conventional investment systems require users to have advanced financial knowledge in order to properly select companies and make investments, and they lack a means to intuitively understand the results of their investments. In addition, many investment systems have complex user interfaces that make them difficult for beginners to use. As a result, it has been difficult for individual investors to easily build up their assets.

[0517] 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.

[0518] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data based on the input theme and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies and distributing the investment amount evenly among multiple stocks, a savings setting means for setting monthly savings based on the portfolio, and a display means for displaying investment results in graphs and figures. This allows users without advanced financial knowledge to invest efficiently and intuitively and easily build assets.

[0519] A "subject" is a particular area, topic, or cause of interest to a user.

[0520] "Investment Amount" means the total amount of money that a User allocates to an Investment on a monthly or periodic basis.

[0521] The "input means" is an interface that allows the user to input the theme and investment amount.

[0522] "Data collection methods" are methods and techniques for collecting company data related to a specified topic.

[0523] "Analysis means" refers to the technologies and algorithms used to analyze collected data and select relevant companies.

[0524] "Portfolio construction methods" are techniques and methods for constructing an optimal investment portfolio based on selected companies.

[0525] A "savings setting method" is a technique or method for allocating monthly investment amounts to each stock based on a portfolio.

[0526] "Display means" refers to an interface that visually displays investment results and portfolio performance to the user.

[0527] A "generative AI model" is an artificial intelligence algorithm that selects companies based on collected and analyzed data.

[0528] An "interactive user interface" is an interface that is intuitive and easy for users to use, allowing them to visually check investment themes and results.

[0529] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[0530] Getting User Input

[0531] First, users use the terminal to input the theme they are interested in and the monthly investment amount. The user interface is designed to be interactive and intuitive. For example, if you select a theme such as renewable energy or medical technology and set the investment amount to 50,000 yen, you can use the following simple prompt:

[0532] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[0533] Data collection and analysis

[0534] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data includes a wide range of information, such as company financials, performance, and market capitalization. The collected data is then analyzed using a generative AI model like Keras. Specifically, the company's past revenue, growth rate, and stock price volatility are used to evaluate the data.

[0535] Stock selection and portfolio construction

[0536] Next, the server selects the best stocks based on the analysis results. The generative AI model lists the top performing companies, and the top 10 companies are selected from that list. Based on the selected companies, a portfolio is created using the portfolio construction tool. At this time, investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average) are applied.

[0537] Implementing fund setting

[0538] After the portfolio is constructed, the server sets up a savings plan that distributes the amount of the user's monthly investment equally among each stock. For example, if 50,000 yen is to be distributed equally among 10 companies, the server will set up an investment of 5,000 yen in each company. This setting allows the savings investment to be carried out automatically and continuously.

[0539] Viewing and updating results

[0540] Finally, the server calculates the latest investment results and sends them to the user's device. Through an interactive user interface, users can visually view their portfolio's performance using graphs and figures. The server also periodically checks market data to evaluate portfolio performance. Automatic rebalancing is performed as needed to maintain an optimal portfolio in line with the user's investment strategy.

[0541] In this way, the system according to the present invention enables users, even those with little financial knowledge, to invest efficiently and intuitively, helping them to easily build up assets.

[0542] Specific prompt examples:

[0543] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[0544] The above is a detailed description of the embodiments of the present invention.

[0545] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0546] Step 1:

[0547] The user inputs the theme and investment amount. The user uses a terminal to input the theme (e.g., renewable energy) and investment amount (e.g., 50,000 yen per month) through an interactive user interface. The input theme and investment amount are sent to the server.

[0548] Input: Theme, investment amount

[0549] Output: The theme and investment amount are sent to the server.

[0550] Step 2:

[0551] The server collects company data related to the theme. The server uses stock market databases and financial APIs on the Internet to collect data such as financial information, performance, and market capitalization of companies related to the specified theme.

[0552] Input: Theme

[0553] Output: Related company data

[0554] Step 3:

[0555] The server analyzes the collected data. The server uses generative AI models such as Keras to analyze the collected company data and evaluate companies. Specifically, it generates rankings based on company revenue, growth rate, stock price volatility, etc.

[0556] Input: Affiliated company data

[0557] Output: Company ratings and rankings

[0558] Step 4:

[0559] The server selects the top companies. Based on the analysis results, the server selects the top 10 companies. This selected list of companies forms the basis of the investment portfolio.

[0560] Input: Company ratings and rankings

[0561] Output: Top 10 companies list

[0562] Step 5:

[0563] The server builds the portfolio. The server builds an investment portfolio based on the selected companies according to the user's investment rules. For example, the server may allocate the investment amount to each company with equal weighting.

[0564] Input: Top 10 company list, investment amount, investment rules

[0565] Output: Constructed portfolio

[0566] Step 6:

[0567] The server sets up monthly investments. Based on the user's monthly investment amount (e.g., 50,000 yen), the server distributes it equally among the selected stocks. This setting allows for automatic monthly investments.

[0568] Input: Constructed portfolio, monthly investment amount

[0569] Output: Monthly savings settings

[0570] Step 7:

[0571] The server calculates and displays investment results. The server calculates the latest investment results and sends them to the device. Users can check the performance of their investment portfolio through the device using graphs and figures.

[0572] Inputs: Portfolio data, latest market data

[0573] Output: Display of calculated investment results

[0574] Step 8:

[0575] The server evaluates the performance of the portfolio and rebalances it, periodically checking market data and rebalancing as needed to ensure the portfolio is optimized.

[0576] Inputs: Portfolio data, latest market data

[0577] Output: Rebalanced portfolio

[0578] 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.

[0579] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes user emotions and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[0580] Getting User Input

[0581] First, the user inputs the theme and investment amount. Through the user interface provided by the device, the user inputs the theme of interest (such as renewable energy or medical technology) and specifies the amount they wish to invest each month. The system also incorporates an emotion engine that recognizes the user's emotions from their facial expressions, voice, or keystrokes while they are typing. This allows the emotion engine to analyze the user's emotional state in real time as they type and adjust the input data as necessary.

[0582] Data collection and analysis

[0583] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[0584] Stock selection and portfolio construction

[0585] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[0586] Implementing fund setting

[0587] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[0588] Incorporating an emotion engine

[0589] The sentiment engine analyzes user sentiment in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the sentiment engine will carefully allocate investment amounts and adjust to reduce risk. On the other hand, if the user's sentiment is stable, the normal investment settings will be maintained. The sentiment engine aims to achieve more stable investment results by adjusting the portfolio rebalancing frequency according to changes in the user's sentiment.

[0590] Viewing and updating results

[0591] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check their portfolio performance through the terminal in graphs and figures. In addition, the server periodically checks market data and evaluates the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy and emotional state.

[0592] Specific examples

[0593] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be further diversified to reduce risk.

[0594] In this way, the present invention not only enables users to make efficient and effective investments without specialized knowledge, but also allows users to adjust their investment strategies based on their emotions, thereby achieving stable asset formation while managing risk.

[0595] The processing flow will be explained below.

[0596] Step 1:

[0597] The user uses the input fields on the device to enter an investment theme and a monthly investment amount, for example, "renewable energy" and "50,000 yen per month."

[0598] Step 2:

[0599] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[0600] Step 3:

[0601] The device analyzes the user's facial expressions, voice, or keystrokes and sends them to the emotion engine, which then recognizes the user's emotions and sends the data to the server.

[0602] Step 4:

[0603] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[0604] Step 5:

[0605] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[0606] Step 6:

[0607] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[0608] Step 7:

[0609] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[0610] Step 8:

[0611] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[0612] Step 9:

[0613] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[0614] Step 10:

[0615] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[0616] Step 11:

[0617] The server sends the latest investment results and portfolio performance to the terminal.

[0618] Step 12:

[0619] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[0620] Step 13:

[0621] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain the user's investment size.

[0622] Step 14:

[0623] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[0624] Step 15:

[0625] The emotional engine adjusts investment decisions in real time based on the user's emotional state. For example, if a user feels anxious, the engine will split their investments into smaller amounts to reduce risk and create a more diversified portfolio.

[0626] Step 16:

[0627] The server adjusts the portfolio rebalancing frequency based on the analysis results of the emotion engine. If the user's emotions are stable, the normal rebalancing frequency is maintained, but if the user is anxious or overexcited, the frequency is revised.

[0628] By following the above steps, users can easily achieve long-term diversified investment based on the themes they are interested in. In addition, by using the emotion engine to make investment decisions based on the user's emotions, it is possible to support stable asset formation while managing risk.

[0629] Example 2

[0630] 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."

[0631] In conventional investment systems, users' emotions are not reflected in investment decisions, resulting in inappropriate risk management. It is also difficult for beginner users to determine which companies to invest in, making it difficult to build effective and stable assets. Furthermore, monthly fund setting and portfolio rebalancing are typically done manually, which is time-consuming for users.

[0632] 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.

[0633] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's sentiment in real time and adjusting investment decisions based on the results, and a display means for displaying investment results. This allows the user to make appropriate investment decisions based on their own sentiment, enabling effective and stable asset formation while appropriately managing risk. Furthermore, the system automatically collects data, analyzes it, constructs a portfolio, sets savings, and rebalances it, significantly reducing the user's workload.

[0634] "Input means" refers to a means for providing an interface function for a user to input an investment theme and monthly investment amount.

[0635] "Data collection means" refers to a means of collecting corporate data related to a topic specified by the user from stock market databases, financial APIs, etc. on the Internet.

[0636] "Analysis methods" are means for analyzing collected corporate data and selecting related companies, and primarily involve using AI algorithms to evaluate companies.

[0637] The "portfolio construction means" is a means for determining investment allocations based on companies selected by the analysis means and constructing a portfolio in accordance with investment rules specified by the user.

[0638] The "accumulation setting means" is a means for allocating the monthly investment amount designated by the user to each stock and automatically executing the accumulation investment.

[0639] An "emotion analysis method" is a method for analyzing a user's emotions in real time based on their facial expressions, voice, keystrokes, etc., and adjusting investment decisions based on the results.

[0640] The "display means" is a means for visually displaying the latest investment results calculated by the server to the user.

[0641] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes users' emotions in real time and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[0642] Getting User Input

[0643] First, the user enters their investment theme and monthly investment amount through the device's user interface. For example, the user may select renewable energy as their theme and set up a monthly investment of 50,000 yen. The device also uses a webcam and microphone to capture the user's facial expressions and voice, which are then transmitted in real time to the emotion engine. The emotion engine then analyzes the user's emotional state and returns the analysis results to the device.

[0644] Data collection and analysis

[0645] The server uses online stock market databases and financial APIs (e.g., Yahoo Finance API, Alpha Vantage) to collect company data related to the user-specified topic. The collected data includes company financial information, performance, market capitalization, etc. The server then analyzes the collected data using AI algorithms such as TensorFlow and PyTorch to evaluate indicators such as each company's revenue, growth rate, and stock price volatility.

[0646] Stock selection and portfolio construction

[0647] The server uses an AI algorithm based on the analyzed data to select the most suitable stocks. For example, it may select the top 10 companies related to renewable energy. After the selection, the server builds a portfolio and determines the allocation of each stock based on the investment rules specified by the user (e.g., market capitalization weighted average, equal weighted average). For example, if 50,000 yen is to be allocated equally to 10 companies each month, the server will set it up to invest 5,000 yen in each company.

[0648] Implementing fund setting

[0649] The server calculates and automatically sets the investment amount for each stock based on the user's monthly investment amount. This accumulation setting is executed automatically based on a programmed schedule. For example, if you allocate 50,000 yen to 10 companies every month, 5,000 yen will be invested in each company.

[0650] Incorporating an emotion engine

[0651] The emotion engine analyzes user emotions in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the server allocates investment amounts to safer options and reduces risk. On the other hand, if the user's emotions are stable, the server continues with normal investment settings. The emotion engine also adjusts the portfolio rebalancing frequency to aim for stable investment results.

[0652] Viewing and updating results

[0653] The server calculates the latest investment results and sends them to the user's device. The device displays the portfolio's performance in graphs and figures, allowing the user to see it. The server periodically checks market data to evaluate the portfolio's performance, automatically rebalancing as needed to maintain an optimal portfolio based on the user's investment strategy and emotional state.

[0654] Specific examples

[0655] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes it using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be diversified more finely to reduce risk.

[0656] Prompt Sentence Examples

[0657] "Please explain a system that invests 50,000 yen per month based on renewable energy and uses an emotion engine to take emotions into account and reduce risk."

[0658] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0659] Step 1:

[0660] The user inputs the investment theme and monthly investment amount through the terminal's user interface.

[0661] Specific operation: The user enters the theme "renewable energy" and "50,000 yen per month" in the input field and presses the send button.

[0662] Input: Investment theme "Renewable energy", monthly investment amount "50,000 yen".

[0663] Data processing: The entered investment theme and investment amount are registered in the system.

[0664] Output: Registered investment themes and investment amounts.

[0665] Step 2:

[0666] The device captures the user's facial expressions, voice, and keystrokes and sends them to the emotion engine.

[0667] Specific operation: The device records facial expressions with a webcam and audio with a microphone, and sends the data to the emotion engine.

[0668] Input: Real-time user facial expression, voice, and keystroke data.

[0669] Data calculation: The emotion engine analyzes the recorded data and calls an API to determine the user's emotional state.

[0670] Output: The analyzed emotional state of the user (e.g., calm, anxious, excited).

[0671] Step 3:

[0672] The server collects company data related to the user's investment themes.

[0673] Specific operation: The server calls the Yahoo Finance API or Alpha Vantage API to collect company information related to the specified theme.

[0674] Input: Investment theme "Renewable Energy".

[0675] Data processing: Obtain data such as company financial information, performance, and market capitalization from stock market databases on the Internet.

[0676] Output: A dataset of collected relevant companies.

[0677] Step 4:

[0678] The server analyzes the collected corporate data and evaluates and selects relevant companies.

[0679] How it works: The server runs analytical models using TensorFlow and PyTorch to evaluate company revenues, growth rates, and stock price volatility.

[0680] Input: Collected corporate dataset.

[0681] Data calculation: A comprehensive evaluation score is calculated based on each company's indicators, and the top 10 companies are selected.

[0682] Output: A list of the top 10 selected companies.

[0683] Step 5:

[0684] A portfolio is constructed based on companies selected by the server.

[0685] Specific operation: The server determines the investment allocation for each stock according to the user's investment rules (e.g., equal weighted average).

[0686] Input: List of selected top 10 companies, user investment amount: 50,000 yen.

[0687] Data calculation: Allocate the investment amount equally to each stock and calculate the specific investment amount (e.g., invest 5,000 yen in each company).

[0688] Output: A list of the constructed portfolio allocations.

[0689] Step 6:

[0690] The server allocates the monthly investment amount to each stock and executes the savings setting.

[0691] Specific operation: Automatically allocates monthly investment amounts to each company based on the calculated investment allocation.

[0692] Input: Portfolio Allocation List.

[0693] Data calculation: Set the monthly investment amount for each stock and create an automatic investment schedule.

[0694] Output: The configured automatic savings schedule.

[0695] Step 7:

[0696] An emotion engine adjusts investment decisions based on user emotions.

[0697] How it works: The emotion engine reanalyzes the user's emotional state and adjusts portfolio rebalancing and investment allocation as needed.

[0698] Input: parsed user emotional state, configured portfolio allocation.

[0699] Data Computing: Adjusting investment allocation to reduce risk based on emotional state.

[0700] Output: Adjusted portfolio allocation list.

[0701] Step 8:

[0702] The server calculates the latest investment results and sends them to the terminal.

[0703] Specific operation: The server calculates investment results based on the latest company stock price data and portfolio allocation, and formats them into graphs and numerical formats.

[0704] Inputs: Latest market data, user portfolio information.

[0705] Data Processing: Combining market data with investment allocations to analyze and visualize investment performance.

[0706] Output: Graphs and numerical data of investment results.

[0707] Step 9:

[0708] The terminal displays the investment results to the user.

[0709] Specific operation: The terminal displays the investment results received from the server to the user in graph and numerical format.

[0710] Input: Investment result data sent from the server.

[0711] Data processing: Formatting the data for display on the user interface.

[0712] Output: Investment results displayed to the user in graphical and numerical form.

[0713] (Application example 2)

[0714] 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."

[0715] Traditional investment systems make it difficult for users without specialized knowledge to invest, and lack effective ways to manage the impact of emotions on investment decisions. There is also a need to provide users with a deeper learning experience by linking investment education content with investment execution. Another issue is that investment allocation adjustments in response to emotional fluctuations are not automated, forcing users to manage risk themselves.

[0716] 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.

[0717] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's emotions in real time and reflecting them in investment decisions, an adjustment means for adjusting investment allocations based on the sentiment analysis results, and a display means for displaying investment results. This allows users to make investment decisions based on their emotions without specialized knowledge and automatically achieve appropriate risk management. Furthermore, by linking with educational content, a more effective investment learning experience can be provided.

[0718] "Input means" refers to an interface and device for a user to input a theme and an investment amount.

[0719] "Data collection means" refers to the means for collecting company data related to a user-specified topic from stock market databases and financial APIs on the Internet.

[0720] "Analysis means" refers to the function of analyzing data using technologies such as AI algorithms in order to select relevant companies based on the collected data.

[0721] "Portfolio construction means" refers to a device or program that has the function of constructing an investment portfolio based on companies selected by the analytical means.

[0722] "Savings setting means" refers to a function that automatically sets monthly savings investments based on a portfolio.

[0723] "Sentiment analysis means" refers to the function of detecting and analyzing user emotions in real time and adjusting investment decisions based on that.

[0724] "Adjustment measures" refer to the function of appropriately adjusting investment allocation and risk management based on the results of sentiment analysis measures.

[0725] "Display means" refers to an interface and device for visually displaying investment results, portfolio composition, etc. to the user.

[0726] "Educational content" refers to learning materials such as videos and documents that provide knowledge and know-how about investing.

[0727] To implement this invention, a system is required in which the user, the server, and the terminal function in cooperation with each other. Each function and the hardware and software used will be specifically described below.

[0728] First, the user uses a device such as a smartphone or smart glasses to input the theme of interest and the investment amount. The input method is a touch screen or a voice recognition interface. At this time, the user's facial expressions and voice are captured in real time, and an emotion analysis method is activated.

[0729] Emotion analysis is performed using an emotion recognition SDK such as EmotionRecognition. This analyzes data acquired from the device's camera and microphone to identify the user's current emotion. The results of this emotion analysis are passed on to the adjustment method described below.

[0730] Next, the server uses data collection means to collect company data related to the theme entered by the user. This collection is done using stock market databases on the Internet and Financial APIs. The collected company data is analyzed using an AI algorithm, which is the analytical means. The StockSelectionModel is applied as the analytical means, and appropriate related companies are selected.

[0731] Based on the analyzed data, the server uses a portfolio construction tool to generate an investment portfolio. Based on the selected list of companies, the portfolio is constructed according to investment rules specified by the user, such as "market capitalization weighted average" or "equal weighted average."

[0732] Next, the investment setup tool calculates the monthly investment amount and sets the investment plan appropriately based on the portfolio, using a method such as allocating 50,000 yen equally to each company as the monthly investment specified by the user.

[0733] The results of real-time user sentiment analysis are reflected in investment allocation through adjustments. For example, if a user feels anxious, the investment amount will be further diversified to reduce risk. In this way, emotional states directly affect investment decisions.

[0734] Finally, the server calculates the investment results and visually presents them to the user through the device's display means, which may include graphs and tables of figures, allowing the user to see the performance of their portfolio and the results of any adjustments.

[0735] Specific examples

[0736] For example, if a user uses a smartphone to set up a monthly investment of 50,000 yen in the "renewable energy" theme, the server will execute the following process.

[0737] 1. Collect company data related to renewable energy through data collection methods.

[0738] 2. Use AI algorithms as analytical tools to select relevant companies.

[0739] 3. Using the portfolio construction method, create a portfolio in which you invest 5,000 yen equally in each of the top 10 companies.

[0740] 4. If the emotion analysis means detects an anxious expression from the user, the adjustment means further diversifies the investment allocation to manage risk.

[0741] 5. The final investment results will be provided to the user through the terminal display means.

[0742] Prompt Sentence Examples

[0743] A user wants to invest in "renewable energy." The monthly investment amount is 50,000 yen. If the camera detects pressure or anxiety, the user should reduce the investment amount by 20% and offer an investment portfolio that diversifies the risk.

[0744] In this way, the system incorporates users' emotions into investment decisions, enabling better risk management and linkage with educational content.

[0745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0746] Step 1:

[0747] The user uses the terminal to input the theme of interest and the investment amount.

[0748] Input is made via a touchscreen or voice recognition interface, and the input data is stored on the device for use in subsequent steps.

[0749] Step 2:

[0750] The device captures the user's facial expressions and voice in real time and analyzes their emotional state using the EmotionRecognition SDK, an emotion analysis tool.

[0751] The input is camera footage and audio data, which are used for emotion analysis, and the output is the user's current emotional state (e.g., joy, anxiety, anger).

[0752] Step 3:

[0753] The server collects business data related to the theme.

[0754] Using FinancialAPI, data such as financial information and market capitalization of companies related to a specified theme is collected from stock market databases on the Internet. The input is the theme specified by the user, and the output is a data list of related companies.

[0755] Step 4:

[0756] The server analyzes the collected data and identifies related companies.

[0757] As an analytical method, we use the StockSelectionModel, which uses an AI algorithm to evaluate and select companies based on the collected data. The input is a list of company data, and the output is a list of selected related companies.

[0758] Step 5:

[0759] The server builds a portfolio based on the selected companies.

[0760] The portfolio construction tool creates a portfolio based on the selected company list and specified investment rules such as "market capitalization weighted average" or "equal weighted average." The input is the selected company list and the investment rules specified by the user, and the output is the constructed investment portfolio.

[0761] Step 6:

[0762] The server sets up monthly savings.

[0763] Using the investment setting method, the monthly investment amount set by the user is appropriately allocated to each company. For example, if 50,000 yen is to be allocated equally to 10 companies each month, 5,000 yen will be allocated to each company. The input is the investment amount and portfolio specified by the user, and the output is the investment setting for each company.

[0764] Step 7:

[0765] The server adjusts investment allocation based on the sentiment analysis results.

[0766] The user's emotional state obtained from the emotion analysis means is used in the adjustment means to optimize investment allocation. For example, if the user feels anxious, the investment amount is reduced by 20% to better diversify risk. The input is the user's emotional state and existing investment settings, and the output is the adjusted investment plan.

[0767] Step 8:

[0768] The server calculates the investment results and sends them to the terminal.

[0769] The terminal uses a display means to visually present the investment results to the user. The input is the adjusted investment plan, and the output is a visual display of the investment results (e.g., graphs or numerical tables). This allows the user to check their investment performance.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] [Third embodiment]

[0774] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0775] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0776] 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).

[0777] 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.

[0778] 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.

[0779] 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).

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[0787] Getting User Input

[0788] First, the user inputs the theme and investment amount. Through the user interface provided by the terminal, the user inputs the theme they are interested in (e.g., renewable energy or medical technology) and specifies the amount they wish to invest each month. This input data is sent to the server via the input means.

[0789] Data collection and analysis

[0790] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[0791] Stock selection and portfolio construction

[0792] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[0793] Implementing fund setting

[0794] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[0795] Viewing and updating results

[0796] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check the portfolio's performance through the terminal using graphs and figures. In addition, the server periodically checks market data to evaluate the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy.

[0797] Specific examples

[0798] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. As a result, users can easily make long-term diversified investments based on the themes of their interest.

[0799] In this way, the present invention allows users to invest efficiently and effectively without specialized knowledge, which is expected to make it easier for individual investors to build assets.

[0800] The processing flow will be explained below.

[0801] Step 1:

[0802] The user uses the input fields on the device to enter an investment theme and monthly investment amount. Specifically, for example, the theme is "renewable energy" and "50,000 yen per month."

[0803] Step 2:

[0804] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[0805] Step 3:

[0806] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[0807] Step 4:

[0808] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[0809] Step 5:

[0810] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[0811] Step 6:

[0812] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[0813] Step 7:

[0814] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[0815] Step 8:

[0816] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[0817] Step 9:

[0818] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[0819] Step 10:

[0820] The server sends the latest investment results and portfolio performance to the terminal.

[0821] Step 11:

[0822] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[0823] Step 12:

[0824] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain an optimal portfolio composition.

[0825] Step 13:

[0826] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[0827] By following the above steps, users can easily achieve long-term diversified investment based on themes that interest them.

[0828] Example 1

[0829] 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."

[0830] Conventional investment systems often required users to have advanced financial knowledge and specialized data analysis skills when making investments based on specific themes, making them difficult for average individual investors to use. Furthermore, management tasks such as rebalancing investment portfolios were complex and time-consuming, making it difficult to maintain optimal investment conditions at all times. Furthermore, performing these operations manually also led to errors and timing issues. A solution to these issues was needed.

[0831] 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.

[0832] In this invention, the server includes input means for a user to input a theme and an investment amount, data collection means for collecting company data related to the theme, analysis means for analyzing the collected data and selecting related companies, portfolio construction means for constructing a portfolio based on the selected companies, savings setting means for setting monthly savings based on the portfolio, display means for displaying investment results, and rebalancing means for periodically rebalancing the portfolio in accordance with the user's settings. This enables individual investors to easily invest efficiently and effectively based on a specific theme.

[0833] "Input means" is an interface that allows a user to input a theme and investment amount into the system.

[0834] "Data collection means" refers to a mechanism for collecting company data related to the theme from stock market databases and financial APIs on the Internet.

[0835] "Analysis methods" refers to algorithms or programs used to analyze collected corporate data and select relevant companies. This may include AI algorithms.

[0836] The "portfolio construction tool" is a system for constructing a portfolio based on selected companies in accordance with investment rules specified by the user.

[0837] A "savings setting method" is a system for allocating monthly investment amounts to each stock based on the portfolio.

[0838] "Display means" refers to an interface that displays graphs and figures so that users can check their investment results.

[0839] A "rebalancing tool" is a mechanism for periodically reviewing a portfolio according to user settings and adjusting it to an optimal balance.

[0840] "Artificial intelligence algorithms" refers to machine learning and deep learning techniques used to analyze collected data and select relevant companies.

[0841] "Investment rules" are standards and guidelines for portfolio construction, such as market capitalization-weighted average or equal-weighted average, specified by the user.

[0842] This invention is a system that allows users to easily make investments based on specific themes. This system is realized by linking a server and a terminal, and incorporates various data analysis and AI technologies.

[0843] First, the user inputs the investment theme and investment amount using the terminal. The terminal then sends the data entered by the user to the server. A web application (e.g., React.js) is used as the user interface.

[0844] The server collects company data from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud) on the Internet. This data collection method gathers company financial information and market data. The collected data is stored on the server in JSON format.

[0845] The server then uses Python's Pandas and NumPy libraries to calculate metrics such as revenue, growth rate, and stock price volatility, allowing for detailed analysis of the collected data to assess a company's financial situation and market influence.

[0846] Furthermore, the server uses artificial intelligence algorithms (e.g., TensorFlow or PyTorch) to select the most suitable stocks based on the analysis data. The selected companies are incorporated into a portfolio using a portfolio construction tool. At this time, allocation is determined according to the investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average).

[0847] Monthly savings settings are automatically made by the server. The server obtains the user's monthly investment amount and calculates the amount to invest in each stock. For example, if you set up a monthly investment of 50,000 yen in 10 companies, 5,000 yen will be invested in each company. This setting is executed periodically using scheduling techniques such as CRON jobs.

[0848] The server periodically collects market data and evaluates the performance of the portfolio. The evaluation results are sent to the terminal in JSON format, and the terminal visualizes the investment results using a JavaScript graph library (e.g., D3.js or Chart.js). The user can check the performance of their portfolio using graphs and figures on the terminal.

[0849] The server also has the ability to periodically rebalance the portfolio based on the user's settings, ensuring that the portfolio is always optimally balanced. This rebalancing method ensures that an investment strategy that matches the user's investment policy is continuously implemented.

[0850] As a specific example, if a user sets up a monthly investment of 50,000 yen on the theme of "renewable energy," the server first collects data on renewable energy-related companies. The data is then analyzed using Python's analysis library, and related companies are selected using TensorFlow. A portfolio is then constructed in which 5,000 yen is invested equally in each of the top 10 selected companies. This setting is executed automatically every month using the server's scheduling function. Users can check the performance of their portfolio at any time via their device, and it is rebalanced as necessary.

[0851] Examples of prompts include:

[0852] "Can you please provide me some Python code to gather and parse the company data needed to create a list of companies to invest in in the renewable energy sector?"

[0853] The present invention allows users to invest efficiently and effectively without specialized knowledge, and also makes it easier for individual investors to build assets easily.

[0854] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0855] Step 1: Getting User Input

[0856] Input: The user inputs the investment theme and investment amount using the terminal.

[0857] Specific operation: The user accesses the user interface on the device and enters a theme (e.g., renewable energy) and a monthly investment amount (e.g., 50,000 yen) into the input form. The device then sends the input data to the server in JSON format.

[0858] Output: User input data is sent from the device to the server

[0859] Step 2: Data collection

[0860] Input: The server receives a request based on the theme submitted by the user.

[0861] What it does: The server collects company data related to the theme from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud).

[0862] Output: The server retrieves financial and market data for companies related to the theme in JSON format.

[0863] Step 3: Data analysis

[0864] Input: Company data collected on the server

[0865] What it does: The server uses Python's Pandas and NumPy libraries to analyze the collected data, which includes calculating metrics such as revenue, growth rate, and stock price volatility.

[0866] Output: Analyzed company data and its indicator values

[0867] Step 4: Stock selection

[0868] Input: Parsed company data

[0869] Specific operation: The server uses artificial intelligence algorithms such as TensorFlow and PyTorch to select the best stocks.

[0870] Output: A list of top companies based on the user's theme (e.g., top 10 companies)

[0871] Step 5: Portfolio Construction

[0872] Input: Selected company list and user investment rules

[0873] Specific operation: The server builds a portfolio based on the selected companies based on investment rules (e.g., market capitalization weighted average or equal weighted average) and determines the investment allocation for each company.

[0874] Output: A portfolio based on the user's themes and investment rules.

[0875] Step 6: Set up your savings

[0876] Input: User's monthly investment amount and constructed portfolio

[0877] Specific operation: The server obtains the user's monthly investment amount and calculates the investment amount for each stock (e.g., 50,000 yen is distributed to 10 companies in the form of 5,000 yen each each month).

[0878] Output: Monthly investment amount for each stock

[0879] Step 7: View the results

[0880] Input: Monthly investment results data

[0881] Specific operation: The server sends the investment results in JSON format to the terminal, and the terminal uses a JavaScript graph library (e.g., D3.js or Chart.js) to visualize and display the investment results to the user.

[0882] Output: Visualized investment results (graphs and figures)

[0883] Step 8: Rebalance

[0884] Inputs: User preferences and latest market data

[0885] What it does: The server periodically collects market data, evaluates portfolio performance, and automatically rebalances as needed to achieve optimal balance.

[0886] Output: Rebalanced portfolio

[0887] This system allows users to invest efficiently and effectively without specialized knowledge, making it easier for individual investors to build assets.

[0888] (Application example 1)

[0889] 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."

[0890] Conventional investment systems require users to have advanced financial knowledge in order to properly select companies and make investments, and they lack a means to intuitively understand the results of their investments. In addition, many investment systems have complex user interfaces that make them difficult for beginners to use. As a result, it has been difficult for individual investors to easily build up their assets.

[0891] 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.

[0892] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data based on the input theme and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies and distributing the investment amount evenly among multiple stocks, a savings setting means for setting monthly savings based on the portfolio, and a display means for displaying investment results in graphs and figures. This allows users without advanced financial knowledge to invest efficiently and intuitively and easily build assets.

[0893] A "subject" is a particular area, topic, or cause of interest to a user.

[0894] "Investment Amount" means the total amount of money that a User allocates to an Investment on a monthly or periodic basis.

[0895] The "input means" is an interface that allows the user to input the theme and investment amount.

[0896] "Data collection methods" are methods and techniques for collecting company data related to a specified topic.

[0897] "Analysis means" refers to the technologies and algorithms used to analyze collected data and select relevant companies.

[0898] "Portfolio construction methods" are techniques and methods for constructing an optimal investment portfolio based on selected companies.

[0899] A "savings setting method" is a technique or method for allocating monthly investment amounts to each stock based on a portfolio.

[0900] "Display means" refers to an interface that visually displays investment results and portfolio performance to the user.

[0901] A "generative AI model" is an artificial intelligence algorithm that selects companies based on collected and analyzed data.

[0902] An "interactive user interface" is an interface that is intuitive and easy for users to use, allowing them to visually check investment themes and results.

[0903] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[0904] Getting User Input

[0905] First, users use the terminal to input the theme they are interested in and the monthly investment amount. The user interface is designed to be interactive and intuitive. For example, if you select a theme such as renewable energy or medical technology and set the investment amount to 50,000 yen, you can use the following simple prompt:

[0906] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[0907] Data collection and analysis

[0908] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data includes a wide range of information, such as company financials, performance, and market capitalization. The collected data is then analyzed using a generative AI model like Keras. Specifically, the company's past revenue, growth rate, and stock price volatility are used to evaluate the data.

[0909] Stock selection and portfolio construction

[0910] Next, the server selects the best stocks based on the analysis results. The generative AI model lists the top performing companies, and the top 10 companies are selected from that list. Based on the selected companies, a portfolio is created using the portfolio construction tool. At this time, investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average) are applied.

[0911] Implementing fund setting

[0912] After the portfolio is constructed, the server sets up a savings plan that distributes the amount of the user's monthly investment equally among each stock. For example, if 50,000 yen is to be distributed equally among 10 companies, the server will set up an investment of 5,000 yen in each company. This setting allows the savings investment to be carried out automatically and continuously.

[0913] Viewing and updating results

[0914] Finally, the server calculates the latest investment results and sends them to the user's device. Through an interactive user interface, users can visually view their portfolio's performance using graphs and figures. The server also periodically checks market data to evaluate portfolio performance. Automatic rebalancing is performed as needed to maintain an optimal portfolio in line with the user's investment strategy.

[0915] In this way, the system according to the present invention enables users, even those with little financial knowledge, to invest efficiently and intuitively, helping them to easily build up assets.

[0916] Specific prompt examples:

[0917] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[0918] The above is a detailed description of the embodiments of the present invention.

[0919] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0920] Step 1:

[0921] The user inputs the theme and investment amount. The user uses a terminal to input the theme (e.g., renewable energy) and investment amount (e.g., 50,000 yen per month) through an interactive user interface. The input theme and investment amount are sent to the server.

[0922] Input: Theme, investment amount

[0923] Output: The theme and investment amount are sent to the server.

[0924] Step 2:

[0925] The server collects company data related to the theme. The server uses stock market databases and financial APIs on the Internet to collect data such as financial information, performance, and market capitalization of companies related to the specified theme.

[0926] Input: Theme

[0927] Output: Related company data

[0928] Step 3:

[0929] The server analyzes the collected data. The server uses generative AI models such as Keras to analyze the collected company data and evaluate companies. Specifically, it generates rankings based on company revenue, growth rate, stock price volatility, etc.

[0930] Input: Affiliated company data

[0931] Output: Company ratings and rankings

[0932] Step 4:

[0933] The server selects the top companies. Based on the analysis results, the server selects the top 10 companies. This selected list of companies forms the basis of the investment portfolio.

[0934] Input: Company ratings and rankings

[0935] Output: Top 10 companies list

[0936] Step 5:

[0937] The server builds the portfolio. The server builds an investment portfolio based on the selected companies according to the user's investment rules. For example, the server may allocate the investment amount to each company with equal weighting.

[0938] Input: Top 10 company list, investment amount, investment rules

[0939] Output: Constructed portfolio

[0940] Step 6:

[0941] The server sets up monthly investments. Based on the user's monthly investment amount (e.g., 50,000 yen), the server distributes it equally among the selected stocks. This setting allows for automatic monthly investments.

[0942] Input: Constructed portfolio, monthly investment amount

[0943] Output: Monthly savings settings

[0944] Step 7:

[0945] The server calculates and displays investment results. The server calculates the latest investment results and sends them to the device. Users can check the performance of their investment portfolio through the device using graphs and figures.

[0946] Inputs: Portfolio data, latest market data

[0947] Output: Display of calculated investment results

[0948] Step 8:

[0949] The server evaluates the performance of the portfolio and rebalances it, periodically checking market data and rebalancing as needed to ensure the portfolio is optimized.

[0950] Inputs: Portfolio data, latest market data

[0951] Output: Rebalanced portfolio

[0952] 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.

[0953] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes user emotions and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[0954] Getting User Input

[0955] First, the user inputs the theme and investment amount. Through the user interface provided by the device, the user inputs the theme of interest (such as renewable energy or medical technology) and specifies the amount they wish to invest each month. The system also incorporates an emotion engine that recognizes the user's emotions from their facial expressions, voice, or keystrokes while they are typing. This allows the emotion engine to analyze the user's emotional state in real time as they type and adjust the input data as necessary.

[0956] Data collection and analysis

[0957] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[0958] Stock selection and portfolio construction

[0959] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[0960] Implementing fund setting

[0961] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[0962] Incorporating an emotion engine

[0963] The sentiment engine analyzes user sentiment in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the sentiment engine will carefully allocate investment amounts and adjust to reduce risk. On the other hand, if the user's sentiment is stable, the normal investment settings will be maintained. The sentiment engine aims to achieve more stable investment results by adjusting the portfolio rebalancing frequency according to changes in the user's sentiment.

[0964] Viewing and updating results

[0965] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check their portfolio performance through the terminal in graphs and figures. In addition, the server periodically checks market data and evaluates the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy and emotional state.

[0966] Specific examples

[0967] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be further diversified to reduce risk.

[0968] In this way, the present invention not only enables users to make efficient and effective investments without specialized knowledge, but also allows users to adjust their investment strategies based on their emotions, thereby achieving stable asset formation while managing risk.

[0969] The processing flow will be explained below.

[0970] Step 1:

[0971] The user uses the input fields on the device to enter an investment theme and a monthly investment amount, for example, "renewable energy" and "50,000 yen per month."

[0972] Step 2:

[0973] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[0974] Step 3:

[0975] The device analyzes the user's facial expressions, voice, or keystrokes and sends them to the emotion engine, which then recognizes the user's emotions and sends the data to the server.

[0976] Step 4:

[0977] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[0978] Step 5:

[0979] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[0980] Step 6:

[0981] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[0982] Step 7:

[0983] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[0984] Step 8:

[0985] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[0986] Step 9:

[0987] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[0988] Step 10:

[0989] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[0990] Step 11:

[0991] The server sends the latest investment results and portfolio performance to the terminal.

[0992] Step 12:

[0993] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[0994] Step 13:

[0995] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain the user's investment size.

[0996] Step 14:

[0997] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[0998] Step 15:

[0999] The emotional engine adjusts investment decisions in real time based on the user's emotional state. For example, if a user feels anxious, the engine will split their investments into smaller amounts to reduce risk and create a more diversified portfolio.

[1000] Step 16:

[1001] The server adjusts the portfolio rebalancing frequency based on the analysis results of the emotion engine. If the user's emotions are stable, the normal rebalancing frequency is maintained, but if the user is anxious or overexcited, the frequency is revised.

[1002] By following the above steps, users can easily achieve long-term diversified investment based on the themes they are interested in. In addition, by using the emotion engine to make investment decisions based on the user's emotions, it is possible to support stable asset formation while managing risk.

[1003] Example 2

[1004] 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."

[1005] In conventional investment systems, users' emotions are not reflected in investment decisions, resulting in inappropriate risk management. It is also difficult for beginner users to determine which companies to invest in, making it difficult to build effective and stable assets. Furthermore, monthly fund setting and portfolio rebalancing are typically done manually, which is time-consuming for users.

[1006] 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.

[1007] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's sentiment in real time and adjusting investment decisions based on the results, and a display means for displaying investment results. This allows the user to make appropriate investment decisions based on their own sentiment, enabling effective and stable asset formation while appropriately managing risk. Furthermore, the system automatically collects data, analyzes it, constructs a portfolio, sets savings, and rebalances it, significantly reducing the user's workload.

[1008] "Input means" refers to a means for providing an interface function for a user to input an investment theme and monthly investment amount.

[1009] "Data collection means" refers to a means of collecting corporate data related to a topic specified by the user from stock market databases, financial APIs, etc. on the Internet.

[1010] "Analysis methods" are means for analyzing collected corporate data and selecting related companies, and primarily involve using AI algorithms to evaluate companies.

[1011] The "portfolio construction means" is a means for determining investment allocations based on companies selected by the analysis means and constructing a portfolio in accordance with investment rules specified by the user.

[1012] The "accumulation setting means" is a means for allocating the monthly investment amount designated by the user to each stock and automatically executing the accumulation investment.

[1013] An "emotion analysis method" is a method for analyzing a user's emotions in real time based on their facial expressions, voice, keystrokes, etc., and adjusting investment decisions based on the results.

[1014] The "display means" is a means for visually displaying the latest investment results calculated by the server to the user.

[1015] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes users' emotions in real time and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[1016] Getting User Input

[1017] First, the user enters their investment theme and monthly investment amount through the device's user interface. For example, the user may select renewable energy as their theme and set up a monthly investment of 50,000 yen. The device also uses a webcam and microphone to capture the user's facial expressions and voice, which are then transmitted in real time to the emotion engine. The emotion engine then analyzes the user's emotional state and returns the analysis results to the device.

[1018] Data collection and analysis

[1019] The server uses online stock market databases and financial APIs (e.g., Yahoo Finance API, Alpha Vantage) to collect company data related to the user-specified topic. The collected data includes company financial information, performance, market capitalization, etc. The server then analyzes the collected data using AI algorithms such as TensorFlow and PyTorch to evaluate indicators such as each company's revenue, growth rate, and stock price volatility.

[1020] Stock selection and portfolio construction

[1021] The server uses an AI algorithm based on the analyzed data to select the most suitable stocks. For example, it may select the top 10 companies related to renewable energy. After the selection, the server builds a portfolio and determines the allocation of each stock based on the investment rules specified by the user (e.g., market capitalization weighted average, equal weighted average). For example, if 50,000 yen is to be allocated equally to 10 companies each month, the server will set it up to invest 5,000 yen in each company.

[1022] Implementing fund setting

[1023] The server calculates and automatically sets the investment amount for each stock based on the user's monthly investment amount. This accumulation setting is executed automatically based on a programmed schedule. For example, if you allocate 50,000 yen to 10 companies every month, 5,000 yen will be invested in each company.

[1024] Incorporating an emotion engine

[1025] The emotion engine analyzes user emotions in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the server allocates investment amounts to safer options and reduces risk. On the other hand, if the user's emotions are stable, the server continues with normal investment settings. The emotion engine also adjusts the portfolio rebalancing frequency to aim for stable investment results.

[1026] Viewing and updating results

[1027] The server calculates the latest investment results and sends them to the user's device. The device displays the portfolio's performance in graphs and figures, allowing the user to see it. The server periodically checks market data to evaluate the portfolio's performance, automatically rebalancing as needed to maintain an optimal portfolio based on the user's investment strategy and emotional state.

[1028] Specific examples

[1029] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes it using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be diversified more finely to reduce risk.

[1030] Prompt Sentence Examples

[1031] "Please explain a system that invests 50,000 yen per month based on renewable energy and uses an emotion engine to take emotions into account and reduce risk."

[1032] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1033] Step 1:

[1034] The user inputs the investment theme and monthly investment amount through the terminal's user interface.

[1035] Specific operation: The user enters the theme "renewable energy" and "50,000 yen per month" in the input field and presses the send button.

[1036] Input: Investment theme "Renewable energy", monthly investment amount "50,000 yen".

[1037] Data processing: The entered investment theme and investment amount are registered in the system.

[1038] Output: Registered investment themes and investment amounts.

[1039] Step 2:

[1040] The device captures the user's facial expressions, voice, and keystrokes and sends them to the emotion engine.

[1041] Specific operation: The device records facial expressions with a webcam and audio with a microphone, and sends the data to the emotion engine.

[1042] Input: Real-time user facial expression, voice, and keystroke data.

[1043] Data calculation: The emotion engine analyzes the recorded data and calls an API to determine the user's emotional state.

[1044] Output: The analyzed emotional state of the user (e.g., calm, anxious, excited).

[1045] Step 3:

[1046] The server collects company data related to the user's investment themes.

[1047] Specific operation: The server calls the Yahoo Finance API or Alpha Vantage API to collect company information related to the specified theme.

[1048] Input: Investment theme "Renewable Energy".

[1049] Data processing: Obtain data such as company financial information, performance, and market capitalization from stock market databases on the Internet.

[1050] Output: A dataset of collected relevant companies.

[1051] Step 4:

[1052] The server analyzes the collected corporate data and evaluates and selects relevant companies.

[1053] How it works: The server runs analytical models using TensorFlow and PyTorch to evaluate company revenues, growth rates, and stock price volatility.

[1054] Input: Collected corporate dataset.

[1055] Data calculation: A comprehensive evaluation score is calculated based on each company's indicators, and the top 10 companies are selected.

[1056] Output: A list of the top 10 selected companies.

[1057] Step 5:

[1058] A portfolio is constructed based on companies selected by the server.

[1059] Specific operation: The server determines the investment allocation for each stock according to the user's investment rules (e.g., equal weighted average).

[1060] Input: List of selected top 10 companies, user investment amount: 50,000 yen.

[1061] Data calculation: Allocate the investment amount equally to each stock and calculate the specific investment amount (e.g., invest 5,000 yen in each company).

[1062] Output: A list of the constructed portfolio allocations.

[1063] Step 6:

[1064] The server allocates the monthly investment amount to each stock and executes the savings setting.

[1065] Specific operation: Automatically allocates monthly investment amounts to each company based on the calculated investment allocation.

[1066] Input: Portfolio Allocation List.

[1067] Data calculation: Set the monthly investment amount for each stock and create an automatic investment schedule.

[1068] Output: The configured automatic savings schedule.

[1069] Step 7:

[1070] An emotion engine adjusts investment decisions based on user emotions.

[1071] How it works: The emotion engine reanalyzes the user's emotional state and adjusts portfolio rebalancing and investment allocation as needed.

[1072] Input: parsed user emotional state, configured portfolio allocation.

[1073] Data Computing: Adjusting investment allocation to reduce risk based on emotional state.

[1074] Output: Adjusted portfolio allocation list.

[1075] Step 8:

[1076] The server calculates the latest investment results and sends them to the terminal.

[1077] Specific operation: The server calculates investment results based on the latest company stock price data and portfolio allocation, and formats them into graphs and numerical formats.

[1078] Inputs: Latest market data, user portfolio information.

[1079] Data Processing: Combining market data with investment allocations to analyze and visualize investment performance.

[1080] Output: Graphs and numerical data of investment results.

[1081] Step 9:

[1082] The terminal displays the investment results to the user.

[1083] Specific operation: The terminal displays the investment results received from the server to the user in graph and numerical format.

[1084] Input: Investment result data sent from the server.

[1085] Data processing: Formatting the data for display on the user interface.

[1086] Output: Investment results displayed to the user in graphical and numerical form.

[1087] (Application example 2)

[1088] 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."

[1089] Traditional investment systems make it difficult for users without specialized knowledge to invest, and lack effective ways to manage the impact of emotions on investment decisions. There is also a need to provide users with a deeper learning experience by linking investment education content with investment execution. Another issue is that investment allocation adjustments in response to emotional fluctuations are not automated, forcing users to manage risk themselves.

[1090] 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.

[1091] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's emotions in real time and reflecting them in investment decisions, an adjustment means for adjusting investment allocations based on the sentiment analysis results, and a display means for displaying investment results. This allows users to make investment decisions based on their emotions without specialized knowledge and automatically achieve appropriate risk management. Furthermore, by linking with educational content, a more effective investment learning experience can be provided.

[1092] "Input means" refers to an interface and device for a user to input a theme and an investment amount.

[1093] "Data collection means" refers to the means for collecting company data related to a user-specified topic from stock market databases and financial APIs on the Internet.

[1094] "Analysis means" refers to the function of analyzing data using technologies such as AI algorithms in order to select relevant companies based on the collected data.

[1095] "Portfolio construction means" refers to a device or program that has the function of constructing an investment portfolio based on companies selected by the analytical means.

[1096] "Savings setting means" refers to a function that automatically sets monthly savings investments based on a portfolio.

[1097] "Sentiment analysis means" refers to the function of detecting and analyzing user emotions in real time and adjusting investment decisions based on that.

[1098] "Adjustment measures" refer to the function of appropriately adjusting investment allocation and risk management based on the results of sentiment analysis measures.

[1099] "Display means" refers to an interface and device for visually displaying investment results, portfolio composition, etc. to the user.

[1100] "Educational content" refers to learning materials such as videos and documents that provide knowledge and know-how about investing.

[1101] To implement this invention, a system is required in which the user, the server, and the terminal function in cooperation with each other. Each function and the hardware and software used will be specifically described below.

[1102] First, the user uses a device such as a smartphone or smart glasses to input the theme of interest and the investment amount. The input method is a touch screen or a voice recognition interface. At this time, the user's facial expressions and voice are captured in real time, and an emotion analysis method is activated.

[1103] Emotion analysis is performed using an emotion recognition SDK such as EmotionRecognition. This analyzes data acquired from the device's camera and microphone to identify the user's current emotion. The results of this emotion analysis are passed on to the adjustment method described below.

[1104] Next, the server uses data collection means to collect company data related to the theme entered by the user. This collection is done using stock market databases on the Internet and Financial APIs. The collected company data is analyzed using an AI algorithm, which is the analytical means. The StockSelectionModel is applied as the analytical means, and appropriate related companies are selected.

[1105] Based on the analyzed data, the server uses a portfolio construction tool to generate an investment portfolio. Based on the selected list of companies, the portfolio is constructed according to investment rules specified by the user, such as "market capitalization weighted average" or "equal weighted average."

[1106] Next, the investment setup tool calculates the monthly investment amount and sets the investment plan appropriately based on the portfolio, using a method such as allocating 50,000 yen equally to each company as the monthly investment specified by the user.

[1107] The results of real-time user sentiment analysis are reflected in investment allocation through adjustments. For example, if a user feels anxious, the investment amount will be further diversified to reduce risk. In this way, emotional states directly affect investment decisions.

[1108] Finally, the server calculates the investment results and visually presents them to the user through the device's display means, which may include graphs and tables of figures, allowing the user to see the performance of their portfolio and the results of any adjustments.

[1109] Specific examples

[1110] For example, if a user uses a smartphone to set up a monthly investment of 50,000 yen in the "renewable energy" theme, the server will execute the following process.

[1111] 1. Collect company data related to renewable energy through data collection methods.

[1112] 2. Use AI algorithms as analytical tools to select relevant companies.

[1113] 3. Using the portfolio construction method, create a portfolio in which you invest 5,000 yen equally in each of the top 10 companies.

[1114] 4. If the emotion analysis means detects an anxious expression from the user, the adjustment means further diversifies the investment allocation to manage risk.

[1115] 5. The final investment results will be provided to the user through the terminal display means.

[1116] Prompt Sentence Examples

[1117] A user wants to invest in "renewable energy." The monthly investment amount is 50,000 yen. If the camera detects pressure or anxiety, the user should reduce the investment amount by 20% and offer an investment portfolio that diversifies the risk.

[1118] In this way, the system incorporates users' emotions into investment decisions, enabling better risk management and linkage with educational content.

[1119] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1120] Step 1:

[1121] The user uses the terminal to input the theme of interest and the investment amount.

[1122] Input is made via a touchscreen or voice recognition interface, and the input data is stored on the device for use in subsequent steps.

[1123] Step 2:

[1124] The device captures the user's facial expressions and voice in real time and analyzes their emotional state using the EmotionRecognition SDK, an emotion analysis tool.

[1125] The input is camera footage and audio data, which are used for emotion analysis, and the output is the user's current emotional state (e.g., joy, anxiety, anger).

[1126] Step 3:

[1127] The server collects business data related to the theme.

[1128] Using FinancialAPI, data such as financial information and market capitalization of companies related to a specified theme is collected from stock market databases on the Internet. The input is the theme specified by the user, and the output is a data list of related companies.

[1129] Step 4:

[1130] The server analyzes the collected data and identifies related companies.

[1131] As an analytical method, we use the StockSelectionModel, which uses an AI algorithm to evaluate and select companies based on the collected data. The input is a list of company data, and the output is a list of selected related companies.

[1132] Step 5:

[1133] The server builds a portfolio based on the selected companies.

[1134] The portfolio construction tool creates a portfolio based on the selected company list and specified investment rules such as "market capitalization weighted average" or "equal weighted average." The input is the selected company list and the investment rules specified by the user, and the output is the constructed investment portfolio.

[1135] Step 6:

[1136] The server sets up monthly savings.

[1137] Using the investment setting method, the monthly investment amount set by the user is appropriately allocated to each company. For example, if 50,000 yen is to be allocated equally to 10 companies each month, 5,000 yen will be allocated to each company. The input is the investment amount and portfolio specified by the user, and the output is the investment setting for each company.

[1138] Step 7:

[1139] The server adjusts investment allocation based on the sentiment analysis results.

[1140] The user's emotional state obtained from the emotion analysis means is used in the adjustment means to optimize investment allocation. For example, if the user feels anxious, the investment amount is reduced by 20% to better diversify risk. The input is the user's emotional state and existing investment settings, and the output is the adjusted investment plan.

[1141] Step 8:

[1142] The server calculates the investment results and sends them to the terminal.

[1143] The terminal uses a display means to visually present the investment results to the user. The input is the adjusted investment plan, and the output is a visual display of the investment results (e.g., graphs or numerical tables). This allows the user to check their investment performance.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] [Fourth embodiment]

[1148] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1149] 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.

[1150] 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).

[1151] 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.

[1152] 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.

[1153] 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).

[1154] 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.

[1155] 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.

[1156] 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.

[1157] 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.

[1158] 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.

[1159] 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.

[1160] 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."

[1161] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[1162] Getting User Input

[1163] First, the user inputs the theme and investment amount. Through the user interface provided by the terminal, the user inputs the theme they are interested in (e.g., renewable energy or medical technology) and specifies the amount they wish to invest each month. This input data is sent to the server via the input means.

[1164] Data collection and analysis

[1165] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[1166] Stock selection and portfolio construction

[1167] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[1168] Implementing fund setting

[1169] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[1170] Viewing and updating results

[1171] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check the portfolio's performance through the terminal using graphs and figures. In addition, the server periodically checks market data to evaluate the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy.

[1172] Specific examples

[1173] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. As a result, users can easily make long-term diversified investments based on the themes of their interest.

[1174] In this way, the present invention allows users to invest efficiently and effectively without specialized knowledge, which is expected to make it easier for individual investors to build assets.

[1175] The processing flow will be explained below.

[1176] Step 1:

[1177] The user uses the input fields on the device to enter an investment theme and monthly investment amount. Specifically, for example, the theme is "renewable energy" and "50,000 yen per month."

[1178] Step 2:

[1179] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[1180] Step 3:

[1181] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[1182] Step 4:

[1183] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[1184] Step 5:

[1185] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[1186] Step 6:

[1187] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[1188] Step 7:

[1189] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[1190] Step 8:

[1191] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[1192] Step 9:

[1193] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[1194] Step 10:

[1195] The server sends the latest investment results and portfolio performance to the terminal.

[1196] Step 11:

[1197] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[1198] Step 12:

[1199] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain an optimal portfolio composition.

[1200] Step 13:

[1201] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[1202] By following the above steps, users can easily achieve long-term diversified investment based on themes that interest them.

[1203] Example 1

[1204] 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."

[1205] Conventional investment systems often required users to have advanced financial knowledge and specialized data analysis skills when making investments based on specific themes, making them difficult for average individual investors to use. Furthermore, management tasks such as rebalancing investment portfolios were complex and time-consuming, making it difficult to maintain optimal investment conditions at all times. Furthermore, performing these operations manually also led to errors and timing issues. A solution to these issues was needed.

[1206] 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.

[1207] In this invention, the server includes input means for a user to input a theme and an investment amount, data collection means for collecting company data related to the theme, analysis means for analyzing the collected data and selecting related companies, portfolio construction means for constructing a portfolio based on the selected companies, savings setting means for setting monthly savings based on the portfolio, display means for displaying investment results, and rebalancing means for periodically rebalancing the portfolio in accordance with the user's settings. This enables individual investors to easily invest efficiently and effectively based on a specific theme.

[1208] "Input means" is an interface that allows a user to input a theme and investment amount into the system.

[1209] "Data collection means" refers to a mechanism for collecting company data related to the theme from stock market databases and financial APIs on the Internet.

[1210] "Analysis methods" refers to algorithms or programs used to analyze collected corporate data and select relevant companies. This may include AI algorithms.

[1211] The "portfolio construction tool" is a system for constructing a portfolio based on selected companies in accordance with investment rules specified by the user.

[1212] A "savings setting method" is a system for allocating monthly investment amounts to each stock based on the portfolio.

[1213] "Display means" refers to an interface that displays graphs and figures so that users can check their investment results.

[1214] A "rebalancing tool" is a mechanism for periodically reviewing a portfolio according to user settings and adjusting it to an optimal balance.

[1215] "Artificial intelligence algorithms" refers to machine learning and deep learning techniques used to analyze collected data and select relevant companies.

[1216] "Investment rules" are standards and guidelines for portfolio construction, such as market capitalization-weighted average or equal-weighted average, specified by the user.

[1217] This invention is a system that allows users to easily make investments based on specific themes. This system is realized by linking a server and a terminal, and incorporates various data analysis and AI technologies.

[1218] First, the user inputs the investment theme and investment amount using the terminal. The terminal then sends the data entered by the user to the server. A web application (e.g., React.js) is used as the user interface.

[1219] The server collects company data from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud) on the Internet. This data collection method gathers company financial information and market data. The collected data is stored on the server in JSON format.

[1220] The server then uses Python's Pandas and NumPy libraries to calculate metrics such as revenue, growth rate, and stock price volatility, allowing for detailed analysis of the collected data to assess a company's financial situation and market influence.

[1221] Furthermore, the server uses artificial intelligence algorithms (e.g., TensorFlow or PyTorch) to select the most suitable stocks based on the analysis data. The selected companies are incorporated into a portfolio using a portfolio construction tool. At this time, allocation is determined according to the investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average).

[1222] Monthly savings settings are automatically made by the server. The server obtains the user's monthly investment amount and calculates the amount to invest in each stock. For example, if you set up a monthly investment of 50,000 yen in 10 companies, 5,000 yen will be invested in each company. This setting is executed periodically using scheduling techniques such as CRON jobs.

[1223] The server periodically collects market data and evaluates the performance of the portfolio. The evaluation results are sent to the terminal in JSON format, and the terminal visualizes the investment results using a JavaScript graph library (e.g., D3.js or Chart.js). The user can check the performance of their portfolio using graphs and figures on the terminal.

[1224] The server also has the ability to periodically rebalance the portfolio based on the user's settings, ensuring that the portfolio is always optimally balanced. This rebalancing method ensures that an investment strategy that matches the user's investment policy is continuously implemented.

[1225] As a specific example, if a user sets up a monthly investment of 50,000 yen on the theme of "renewable energy," the server first collects data on renewable energy-related companies. The data is then analyzed using Python's analysis library, and related companies are selected using TensorFlow. A portfolio is then constructed in which 5,000 yen is invested equally in each of the top 10 selected companies. This setting is executed automatically every month using the server's scheduling function. Users can check the performance of their portfolio at any time via their device, and it is rebalanced as necessary.

[1226] Examples of prompts include:

[1227] "Can you please provide me some Python code to gather and parse the company data needed to create a list of companies to invest in in the renewable energy sector?"

[1228] The present invention allows users to invest efficiently and effectively without specialized knowledge, and also makes it easier for individual investors to build assets easily.

[1229] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1230] Step 1: Getting User Input

[1231] Input: The user inputs the investment theme and investment amount using the terminal.

[1232] Specific operation: The user accesses the user interface on the device and enters a theme (e.g., renewable energy) and a monthly investment amount (e.g., 50,000 yen) into the input form. The device then sends the input data to the server in JSON format.

[1233] Output: User input data is sent from the device to the server

[1234] Step 2: Data collection

[1235] Input: The server receives a request based on the theme submitted by the user.

[1236] What it does: The server collects company data related to the theme from stock market databases and financial APIs (e.g., Alpha Vantage and IEX Cloud).

[1237] Output: The server retrieves financial and market data for companies related to the theme in JSON format.

[1238] Step 3: Data analysis

[1239] Input: Company data collected on the server

[1240] What it does: The server uses Python's Pandas and NumPy libraries to analyze the collected data, which includes calculating metrics such as revenue, growth rate, and stock price volatility.

[1241] Output: Analyzed company data and its indicator values

[1242] Step 4: Stock selection

[1243] Input: Parsed company data

[1244] Specific operation: The server uses artificial intelligence algorithms such as TensorFlow and PyTorch to select the best stocks.

[1245] Output: A list of top companies based on the user's theme (e.g., top 10 companies)

[1246] Step 5: Portfolio Construction

[1247] Input: Selected company list and user investment rules

[1248] Specific operation: The server builds a portfolio based on the selected companies based on investment rules (e.g., market capitalization weighted average or equal weighted average) and determines the investment allocation for each company.

[1249] Output: A portfolio based on the user's themes and investment rules.

[1250] Step 6: Set up your savings

[1251] Input: User's monthly investment amount and constructed portfolio

[1252] Specific operation: The server obtains the user's monthly investment amount and calculates the investment amount for each stock (e.g., 50,000 yen is distributed to 10 companies in the form of 5,000 yen each each month).

[1253] Output: Monthly investment amount for each stock

[1254] Step 7: View the results

[1255] Input: Monthly investment results data

[1256] Specific operation: The server sends the investment results in JSON format to the terminal, and the terminal uses a JavaScript graph library (e.g., D3.js or Chart.js) to visualize and display the investment results to the user.

[1257] Output: Visualized investment results (graphs and figures)

[1258] Step 8: Rebalance

[1259] Inputs: User preferences and latest market data

[1260] What it does: The server periodically collects market data, evaluates portfolio performance, and automatically rebalances as needed to achieve optimal balance.

[1261] Output: Rebalanced portfolio

[1262] This system allows users to invest efficiently and effectively without specialized knowledge, making it easier for individual investors to build assets.

[1263] (Application example 1)

[1264] 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."

[1265] Conventional investment systems require users to have advanced financial knowledge in order to properly select companies and make investments, and they lack a means to intuitively understand the results of their investments. In addition, many investment systems have complex user interfaces that make them difficult for beginners to use. As a result, it has been difficult for individual investors to easily build up their assets.

[1266] 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.

[1267] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data based on the input theme and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies and distributing the investment amount evenly among multiple stocks, a savings setting means for setting monthly savings based on the portfolio, and a display means for displaying investment results in graphs and figures. This allows users without advanced financial knowledge to invest efficiently and intuitively and easily build assets.

[1268] A "subject" is a particular area, topic, or cause of interest to a user.

[1269] "Investment Amount" means the total amount of money that a User allocates to an Investment on a monthly or periodic basis.

[1270] The "input means" is an interface that allows the user to input the theme and investment amount.

[1271] "Data collection methods" are methods and techniques for collecting company data related to a specified topic.

[1272] "Analysis means" refers to the technologies and algorithms used to analyze collected data and select relevant companies.

[1273] "Portfolio construction methods" are techniques and methods for constructing an optimal investment portfolio based on selected companies.

[1274] A "savings setting method" is a technique or method for allocating monthly investment amounts to each stock based on a portfolio.

[1275] "Display means" refers to an interface that visually displays investment results and portfolio performance to the user.

[1276] A "generative AI model" is an artificial intelligence algorithm that selects companies based on collected and analyzed data.

[1277] An "interactive user interface" is an interface that is intuitive and easy for users to use, allowing them to visually check investment themes and results.

[1278] The present invention is a system that allows a user to easily make investments based on a specific theme. The following describes in detail the embodiments of the present invention.

[1279] Getting User Input

[1280] First, users use the terminal to input the theme they are interested in and the monthly investment amount. The user interface is designed to be interactive and intuitive. For example, if you select a theme such as renewable energy or medical technology and set the investment amount to 50,000 yen, you can use the following simple prompt:

[1281] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[1282] Data collection and analysis

[1283] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data includes a wide range of information, such as company financials, performance, and market capitalization. The collected data is then analyzed using a generative AI model like Keras. Specifically, the company's past revenue, growth rate, and stock price volatility are used to evaluate the data.

[1284] Stock selection and portfolio construction

[1285] Next, the server selects the best stocks based on the analysis results. The generative AI model lists the top performing companies, and the top 10 companies are selected from that list. Based on the selected companies, a portfolio is created using the portfolio construction tool. At this time, investment rules specified by the user (e.g., market capitalization weighted average or equal weighted average) are applied.

[1286] Implementing fund setting

[1287] After the portfolio is constructed, the server sets up a savings plan that distributes the amount of the user's monthly investment equally among each stock. For example, if 50,000 yen is to be distributed equally among 10 companies, the server will set up an investment of 5,000 yen in each company. This setting allows the savings investment to be carried out automatically and continuously.

[1288] Viewing and updating results

[1289] Finally, the server calculates the latest investment results and sends them to the user's device. Through an interactive user interface, users can visually view their portfolio's performance using graphs and figures. The server also periodically checks market data to evaluate portfolio performance. Automatic rebalancing is performed as needed to maintain an optimal portfolio in line with the user's investment strategy.

[1290] In this way, the system according to the present invention enables users, even those with little financial knowledge, to invest efficiently and intuitively, helping them to easily build up assets.

[1291] Specific prompt examples:

[1292] "I want to invest in renewable energy. I'll set a monthly investment of 50,000 yen."

[1293] The above is a detailed description of the embodiments of the present invention.

[1294] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1295] Step 1:

[1296] The user inputs the theme and investment amount. The user uses a terminal to input the theme (e.g., renewable energy) and investment amount (e.g., 50,000 yen per month) through an interactive user interface. The input theme and investment amount are sent to the server.

[1297] Input: Theme, investment amount

[1298] Output: The theme and investment amount are sent to the server.

[1299] Step 2:

[1300] The server collects company data related to the theme. The server uses stock market databases and financial APIs on the Internet to collect data such as financial information, performance, and market capitalization of companies related to the specified theme.

[1301] Input: Theme

[1302] Output: Related company data

[1303] Step 3:

[1304] The server analyzes the collected data. The server uses generative AI models such as Keras to analyze the collected company data and evaluate companies. Specifically, it generates rankings based on company revenue, growth rate, stock price volatility, etc.

[1305] Input: Affiliated company data

[1306] Output: Company ratings and rankings

[1307] Step 4:

[1308] The server selects the top companies. Based on the analysis results, the server selects the top 10 companies. This selected list of companies forms the basis of the investment portfolio.

[1309] Input: Company ratings and rankings

[1310] Output: Top 10 companies list

[1311] Step 5:

[1312] The server builds the portfolio. The server builds an investment portfolio based on the selected companies according to the user's investment rules. For example, the server may allocate the investment amount to each company with equal weighting.

[1313] Input: Top 10 company list, investment amount, investment rules

[1314] Output: Constructed portfolio

[1315] Step 6:

[1316] The server sets up monthly investments. Based on the user's monthly investment amount (e.g., 50,000 yen), the server distributes it equally among the selected stocks. This setting allows for automatic monthly investments.

[1317] Input: Constructed portfolio, monthly investment amount

[1318] Output: Monthly savings settings

[1319] Step 7:

[1320] The server calculates and displays investment results. The server calculates the latest investment results and sends them to the device. Users can check the performance of their investment portfolio through the device using graphs and figures.

[1321] Inputs: Portfolio data, latest market data

[1322] Output: Display of calculated investment results

[1323] Step 8:

[1324] The server evaluates the performance of the portfolio and rebalances it, periodically checking market data and rebalancing as needed to ensure the portfolio is optimized.

[1325] Inputs: Portfolio data, latest market data

[1326] Output: Rebalanced portfolio

[1327] 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.

[1328] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes user emotions and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[1329] Getting User Input

[1330] First, the user inputs the theme and investment amount. Through the user interface provided by the device, the user inputs the theme of interest (such as renewable energy or medical technology) and specifies the amount they wish to invest each month. The system also incorporates an emotion engine that recognizes the user's emotions from their facial expressions, voice, or keystrokes while they are typing. This allows the emotion engine to analyze the user's emotional state in real time as they type and adjust the input data as necessary.

[1331] Data collection and analysis

[1332] The server collects company data related to the user's chosen topic from online stock market databases and financial APIs. This data collection method is used to gather information such as company financial information, performance, and market capitalization. The server then analyzes the collected company data to assess each company's influence in the renewable energy market and its financial status. This analysis method includes indicators such as revenue, growth rate, and stock price volatility.

[1333] Stock selection and portfolio construction

[1334] The server uses an AI algorithm to select the best stocks from the analyzed data. For example, it selects the top 10 companies from a list of companies related to renewable energy. The server then builds a portfolio based on the selected companies. This portfolio construction method determines the allocation of each stock according to the investment rules specified by the user (for example, market capitalization weighted average or equal weighted average).

[1335] Implementing fund setting

[1336] The server calculates the investment amount for each stock based on the user's monthly budget. For this calculation, a method of equally allocating the investment amount to each stock (equal weighting) is used, for example. Specifically, if 50,000 yen is to be allocated to 10 companies each month, the server sets up an investment of 5,000 yen to each company. This investment setting method automatically allocates the investment amount to each company, and investments are made periodically.

[1337] Incorporating an emotion engine

[1338] The sentiment engine analyzes user sentiment in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the sentiment engine will carefully allocate investment amounts and adjust to reduce risk. On the other hand, if the user's sentiment is stable, the normal investment settings will be maintained. The sentiment engine aims to achieve more stable investment results by adjusting the portfolio rebalancing frequency according to changes in the user's sentiment.

[1339] Viewing and updating results

[1340] Finally, the server calculates the latest investment results and sends them to the terminal. Users can check their portfolio performance through the terminal in graphs and figures. In addition, the server periodically checks market data and evaluates the portfolio's performance. If necessary, it automatically rebalances the portfolio to maintain an optimal portfolio in line with the user's investment policy and emotional state.

[1341] Specific examples

[1342] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes them using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be further diversified to reduce risk.

[1343] In this way, the present invention not only enables users to make efficient and effective investments without specialized knowledge, but also allows users to adjust their investment strategies based on their emotions, thereby achieving stable asset formation while managing risk.

[1344] The processing flow will be explained below.

[1345] Step 1:

[1346] The user uses the input fields on the device to enter an investment theme and a monthly investment amount, for example, "renewable energy" and "50,000 yen per month."

[1347] Step 2:

[1348] The terminal sends the user's input data (theme and investment amount) to the server using secure communication means.

[1349] Step 3:

[1350] The device analyzes the user's facial expressions, voice, or keystrokes and sends them to the emotion engine, which then recognizes the user's emotions and sends the data to the server.

[1351] Step 4:

[1352] Based on the theme received by the server, relevant company data is collected from the internet and financial APIs, specifically, financial data and stock price information of renewable energy-related companies are obtained from stock market databases.

[1353] Step 5:

[1354] The server applies AI algorithms to analyze the company data collected, scoring each company based on indicators such as revenue, growth rate, and stock price volatility.

[1355] Step 6:

[1356] Based on the analysis results, the server selects the top related companies (for example, the top 10 companies) that are best suited to the user's theme.

[1357] Step 7:

[1358] The server builds an investment portfolio based on the companies selected by the server, and determines the allocation of each stock according to the investment rules specified by the user (e.g., equal weighting or market capitalization weighting).

[1359] Step 8:

[1360] The server calculates the amount to invest in each stock based on the user's monthly investment budget (for example, 50,000 yen). Specifically, in the case of equal weighting, 5,000 yen is invested in each stock.

[1361] Step 9:

[1362] The server then invests the calculated amount in the stock of each selected company through the user's brokerage account, an automated process.

[1363] Step 10:

[1364] The server calculates the investment results and evaluates the performance of the portfolio after the investment.

[1365] Step 11:

[1366] The server sends the latest investment results and portfolio performance to the terminal.

[1367] Step 12:

[1368] The device displays investment results to the user, including graphs and numerical data, allowing the user to track the progress and performance of their investments.

[1369] Step 13:

[1370] The server periodically checks market data and monitors portfolio performance, automatically rebalancing as needed to maintain the user's investment size.

[1371] Step 14:

[1372] The server sends information about portfolio changes and important market fluctuations to the terminal and notifies the user.

[1373] Step 15:

[1374] The emotional engine adjusts investment decisions in real time based on the user's emotional state. For example, if a user feels anxious, the engine will split their investments into smaller amounts to reduce risk and create a more diversified portfolio.

[1375] Step 16:

[1376] The server adjusts the portfolio rebalancing frequency based on the analysis results of the emotion engine. If the user's emotions are stable, the normal rebalancing frequency is maintained, but if the user is anxious or overexcited, the frequency is revised.

[1377] By following the above steps, users can easily achieve long-term diversified investment based on the themes they are interested in. In addition, by using the emotion engine to make investment decisions based on the user's emotions, it is possible to support stable asset formation while managing risk.

[1378] Example 2

[1379] 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 robot 414 will be referred to as a "terminal."

[1380] In conventional investment systems, users' emotions are not reflected in investment decisions, resulting in inappropriate risk management. It is also difficult for beginner users to determine which companies to invest in, making it difficult to build effective and stable assets. Furthermore, monthly fund setting and portfolio rebalancing are typically done manually, which is time-consuming for users.

[1381] 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.

[1382] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's sentiment in real time and adjusting investment decisions based on the results, and a display means for displaying investment results. This allows the user to make appropriate investment decisions based on their own sentiment, enabling effective and stable asset formation while appropriately managing risk. Furthermore, the system automatically collects data, analyzes it, constructs a portfolio, sets savings, and rebalances it, significantly reducing the user's workload.

[1383] "Input means" refers to a means for providing an interface function for a user to input an investment theme and monthly investment amount.

[1384] "Data collection means" refers to a means of collecting corporate data related to a topic specified by the user from stock market databases, financial APIs, etc. on the Internet.

[1385] "Analysis methods" are means for analyzing collected corporate data and selecting related companies, and primarily involve using AI algorithms to evaluate companies.

[1386] The "portfolio construction means" is a means for determining investment allocations based on companies selected by the analysis means and constructing a portfolio in accordance with investment rules specified by the user.

[1387] The "accumulation setting means" is a means for allocating the monthly investment amount designated by the user to each stock and automatically executing the accumulation investment.

[1388] An "emotion analysis method" is a method for analyzing a user's emotions in real time based on their facial expressions, voice, keystrokes, etc., and adjusting investment decisions based on the results.

[1389] The "display means" is a means for visually displaying the latest investment results calculated by the server to the user.

[1390] The present invention combines a system that allows users to easily make investments based on specific themes with an emotion engine that recognizes users' emotions in real time and reflects them in investment decisions. The following describes in more detail the modes for carrying out the present invention.

[1391] Getting User Input

[1392] First, the user enters their investment theme and monthly investment amount through the device's user interface. For example, the user may select renewable energy as their theme and set up a monthly investment of 50,000 yen. The device also uses a webcam and microphone to capture the user's facial expressions and voice, which are then transmitted in real time to the emotion engine. The emotion engine then analyzes the user's emotional state and returns the analysis results to the device.

[1393] Data collection and analysis

[1394] The server uses online stock market databases and financial APIs (e.g., Yahoo Finance API, Alpha Vantage) to collect company data related to the user-specified topic. The collected data includes company financial information, performance, market capitalization, etc. The server then analyzes the collected data using AI algorithms such as TensorFlow and PyTorch to evaluate indicators such as each company's revenue, growth rate, and stock price volatility.

[1395] Stock selection and portfolio construction

[1396] The server uses an AI algorithm based on the analyzed data to select the most suitable stocks. For example, it may select the top 10 companies related to renewable energy. After the selection, the server builds a portfolio and determines the allocation of each stock based on the investment rules specified by the user (e.g., market capitalization weighted average, equal weighted average). For example, if 50,000 yen is to be allocated equally to 10 companies each month, the server will set it up to invest 5,000 yen in each company.

[1397] Implementing fund setting

[1398] The server calculates and automatically sets the investment amount for each stock based on the user's monthly investment amount. This accumulation setting is executed automatically based on a programmed schedule. For example, if you allocate 50,000 yen to 10 companies every month, 5,000 yen will be invested in each company.

[1399] Incorporating an emotion engine

[1400] The emotion engine analyzes user emotions in real time and adjusts investment decisions based on the results. For example, if a user feels anxious, the server allocates investment amounts to safer options and reduces risk. On the other hand, if the user's emotions are stable, the server continues with normal investment settings. The emotion engine also adjusts the portfolio rebalancing frequency to aim for stable investment results.

[1401] Viewing and updating results

[1402] The server calculates the latest investment results and sends them to the user's device. The device displays the portfolio's performance in graphs and figures, allowing the user to see it. The server periodically checks market data to evaluate the portfolio's performance, automatically rebalancing as needed to maintain an optimal portfolio based on the user's investment strategy and emotional state.

[1403] Specific examples

[1404] For example, if a user sets a monthly investment of 50,000 yen on the theme of "renewable energy," the server collects data on companies related to renewable energy and analyzes it using an AI algorithm. It then selects the top 10 companies and builds a portfolio in which 5,000 yen is invested in each company with an equal weighting. At the same time, the emotion engine monitors the user's emotions and adjusts the investment allocation as necessary. For example, if the user is feeling anxious, the investment amount will be diversified more finely to reduce risk.

[1405] Prompt Sentence Examples

[1406] "Please explain a system that invests 50,000 yen per month based on renewable energy and uses an emotion engine to take emotions into account and reduce risk."

[1407] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1408] Step 1:

[1409] The user inputs the investment theme and monthly investment amount through the terminal's user interface.

[1410] Specific operation: The user enters the theme "renewable energy" and "50,000 yen per month" in the input field and presses the send button.

[1411] Input: Investment theme "Renewable energy", monthly investment amount "50,000 yen".

[1412] Data processing: The entered investment theme and investment amount are registered in the system.

[1413] Output: Registered investment themes and investment amounts.

[1414] Step 2:

[1415] The device captures the user's facial expressions, voice, and keystrokes and sends them to the emotion engine.

[1416] Specific operation: The device records facial expressions with a webcam and audio with a microphone, and sends the data to the emotion engine.

[1417] Input: Real-time user facial expression, voice, and keystroke data.

[1418] Data calculation: The emotion engine analyzes the recorded data and calls an API to determine the user's emotional state.

[1419] Output: The analyzed emotional state of the user (e.g., calm, anxious, excited).

[1420] Step 3:

[1421] The server collects company data related to the user's investment themes.

[1422] Specific operation: The server calls the Yahoo Finance API or Alpha Vantage API to collect company information related to the specified theme.

[1423] Input: Investment theme "Renewable Energy".

[1424] Data processing: Obtain data such as company financial information, performance, and market capitalization from stock market databases on the Internet.

[1425] Output: A dataset of collected relevant companies.

[1426] Step 4:

[1427] The server analyzes the collected corporate data and evaluates and selects relevant companies.

[1428] How it works: The server runs analytical models using TensorFlow and PyTorch to evaluate company revenues, growth rates, and stock price volatility.

[1429] Input: Collected corporate dataset.

[1430] Data calculation: A comprehensive evaluation score is calculated based on each company's indicators, and the top 10 companies are selected.

[1431] Output: A list of the top 10 selected companies.

[1432] Step 5:

[1433] A portfolio is constructed based on companies selected by the server.

[1434] Specific operation: The server determines the investment allocation for each stock according to the user's investment rules (e.g., equal weighted average).

[1435] Input: List of selected top 10 companies, user investment amount: 50,000 yen.

[1436] Data calculation: Allocate the investment amount equally to each stock and calculate the specific investment amount (e.g., invest 5,000 yen in each company).

[1437] Output: A list of the constructed portfolio allocations.

[1438] Step 6:

[1439] The server allocates the monthly investment amount to each stock and executes the savings setting.

[1440] Specific operation: Automatically allocates monthly investment amounts to each company based on the calculated investment allocation.

[1441] Input: Portfolio Allocation List.

[1442] Data calculation: Set the monthly investment amount for each stock and create an automatic investment schedule.

[1443] Output: The configured automatic savings schedule.

[1444] Step 7:

[1445] An emotion engine adjusts investment decisions based on user emotions.

[1446] How it works: The emotion engine reanalyzes the user's emotional state and adjusts portfolio rebalancing and investment allocation as needed.

[1447] Input: parsed user emotional state, configured portfolio allocation.

[1448] Data Computing: Adjusting investment allocation to reduce risk based on emotional state.

[1449] Output: Adjusted portfolio allocation list.

[1450] Step 8:

[1451] The server calculates the latest investment results and sends them to the terminal.

[1452] Specific operation: The server calculates investment results based on the latest company stock price data and portfolio allocation, and formats them into graphs and numerical formats.

[1453] Inputs: Latest market data, user portfolio information.

[1454] Data Processing: Combining market data with investment allocations to analyze and visualize investment performance.

[1455] Output: Graphs and numerical data of investment results.

[1456] Step 9:

[1457] The terminal displays the investment results to the user.

[1458] Specific operation: The terminal displays the investment results received from the server to the user in graph and numerical format.

[1459] Input: Investment result data sent from the server.

[1460] Data processing: Formatting the data for display on the user interface.

[1461] Output: Investment results displayed to the user in graphical and numerical form.

[1462] (Application example 2)

[1463] 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 robot 414 will be referred to as a "terminal."

[1464] Traditional investment systems make it difficult for users without specialized knowledge to invest, and lack effective ways to manage the impact of emotions on investment decisions. There is also a need to provide users with a deeper learning experience by linking investment education content with investment execution. Another issue is that investment allocation adjustments in response to emotional fluctuations are not automated, forcing users to manage risk themselves.

[1465] 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.

[1466] In this invention, the server includes an input means for the user to input a theme and investment amount, a data collection means for collecting company data related to the theme, an analysis means for analyzing the collected data and selecting related companies, a portfolio construction means for constructing a portfolio based on the selected companies, a savings setting means for setting monthly savings based on the portfolio, a sentiment analysis means for analyzing the user's emotions in real time and reflecting them in investment decisions, an adjustment means for adjusting investment allocations based on the sentiment analysis results, and a display means for displaying investment results. This allows users to make investment decisions based on their emotions without specialized knowledge and automatically achieve appropriate risk management. Furthermore, by linking with educational content, a more effective investment learning experience can be provided.

[1467] "Input means" refers to an interface and device for a user to input a theme and an investment amount.

[1468] "Data collection means" refers to the means for collecting company data related to a user-specified topic from stock market databases and financial APIs on the Internet.

[1469] "Analysis means" refers to the function of analyzing data using technologies such as AI algorithms in order to select relevant companies based on the collected data.

[1470] "Portfolio construction means" refers to a device or program that has the function of constructing an investment portfolio based on companies selected by the analytical means.

[1471] "Savings setting means" refers to a function that automatically sets monthly savings investments based on a portfolio.

[1472] "Sentiment analysis means" refers to the function of detecting and analyzing user emotions in real time and adjusting investment decisions based on that.

[1473] "Adjustment measures" refer to the function of appropriately adjusting investment allocation and risk management based on the results of sentiment analysis measures.

[1474] "Display means" refers to an interface and device for visually displaying investment results, portfolio composition, etc. to the user.

[1475] "Educational content" refers to learning materials such as videos and documents that provide knowledge and know-how about investing.

[1476] To implement this invention, a system is required in which the user, the server, and the terminal function in cooperation with each other. Each function and the hardware and software used will be specifically described below.

[1477] First, the user uses a device such as a smartphone or smart glasses to input the theme of interest and the investment amount. The input method is a touch screen or a voice recognition interface. At this time, the user's facial expressions and voice are captured in real time, and an emotion analysis method is activated.

[1478] Emotion analysis is performed using an emotion recognition SDK such as EmotionRecognition. This analyzes data acquired from the device's camera and microphone to identify the user's current emotion. The results of this emotion analysis are passed on to the adjustment method described below.

[1479] Next, the server uses data collection means to collect company data related to the theme entered by the user. This collection is done using stock market databases on the Internet and Financial APIs. The collected company data is analyzed using an AI algorithm, which is the analytical means. The StockSelectionModel is applied as the analytical means, and appropriate related companies are selected.

[1480] Based on the analyzed data, the server uses a portfolio construction tool to generate an investment portfolio. Based on the selected list of companies, the portfolio is constructed according to investment rules specified by the user, such as "market capitalization weighted average" or "equal weighted average."

[1481] Next, the investment setup tool calculates the monthly investment amount and sets the investment plan appropriately based on the portfolio, using a method such as allocating 50,000 yen equally to each company as the monthly investment specified by the user.

[1482] The results of real-time user sentiment analysis are reflected in investment allocation through adjustments. For example, if a user feels anxious, the investment amount will be further diversified to reduce risk. In this way, emotional states directly affect investment decisions.

[1483] Finally, the server calculates the investment results and visually presents them to the user through the device's display means, which may include graphs and tables of figures, allowing the user to see the performance of their portfolio and the results of any adjustments.

[1484] Specific examples

[1485] For example, if a user uses a smartphone to set up a monthly investment of 50,000 yen in the "renewable energy" theme, the server will execute the following process.

[1486] 1. Collect company data related to renewable energy through data collection methods.

[1487] 2. Use AI algorithms as analytical tools to select relevant companies.

[1488] 3. Using the portfolio construction method, create a portfolio in which you invest 5,000 yen equally in each of the top 10 companies.

[1489] 4. If the emotion analysis means detects an anxious expression from the user, the adjustment means further diversifies the investment allocation to manage risk.

[1490] 5. The final investment results will be provided to the user through the terminal display means.

[1491] Prompt Sentence Examples

[1492] A user wants to invest in "renewable energy." The monthly investment amount is 50,000 yen. If the camera detects pressure or anxiety, the user should reduce the investment amount by 20% and offer an investment portfolio that diversifies the risk.

[1493] In this way, the system incorporates users' emotions into investment decisions, enabling better risk management and linkage with educational content.

[1494] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1495] Step 1:

[1496] The user uses the terminal to input the theme of interest and the investment amount.

[1497] Input is made via a touchscreen or voice recognition interface, and the input data is stored on the device for use in subsequent steps.

[1498] Step 2:

[1499] The device captures the user's facial expressions and voice in real time and analyzes their emotional state using the EmotionRecognition SDK, an emotion analysis tool.

[1500] The input is camera footage and audio data, which are used for emotion analysis, and the output is the user's current emotional state (e.g., joy, anxiety, anger).

[1501] Step 3:

[1502] The server collects business data related to the theme.

[1503] Using FinancialAPI, data such as financial information and market capitalization of companies related to a specified theme is collected from stock market databases on the Internet. The input is the theme specified by the user, and the output is a data list of related companies.

[1504] Step 4:

[1505] The server analyzes the collected data and identifies related companies.

[1506] As an analytical method, we use the StockSelectionModel, which uses an AI algorithm to evaluate and select companies based on the collected data. The input is a list of company data, and the output is a list of selected related companies.

[1507] Step 5:

[1508] The server builds a portfolio based on the selected companies.

[1509] The portfolio construction tool creates a portfolio based on the selected company list and specified investment rules such as "market capitalization weighted average" or "equal weighted average." The input is the selected company list and the investment rules specified by the user, and the output is the constructed investment portfolio.

[1510] Step 6:

[1511] The server sets up monthly savings.

[1512] Using the investment setting method, the monthly investment amount set by the user is appropriately allocated to each company. For example, if 50,000 yen is to be allocated equally to 10 companies each month, 5,000 yen will be allocated to each company. The input is the investment amount and portfolio specified by the user, and the output is the investment setting for each company.

[1513] Step 7:

[1514] The server adjusts investment allocation based on the sentiment analysis results.

[1515] The user's emotional state obtained from the emotion analysis means is used in the adjustment means to optimize investment allocation. For example, if the user feels anxious, the investment amount is reduced by 20% to better diversify risk. The input is the user's emotional state and existing investment settings, and the output is the adjusted investment plan.

[1516] Step 8:

[1517] The server calculates the investment results and sends them to the terminal.

[1518] The terminal uses a display means to visually present the investment results to the user. The input is the adjusted investment plan, and the output is a visual display of the investment results (e.g., graphs or numerical tables). This allows the user to check their investment performance.

[1519] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice 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 voice data.

[1520] 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.

[1521] 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 robot 414.

[1522] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1523] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1524] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1525] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1526] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1527] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1528] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1529] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1530] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1531] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1532] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1533] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1534] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1535] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1536] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1537] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1538] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1539] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1540] The following is further disclosed regarding the above embodiment.

[1541] (Claim 1)

[1542] an input means for a user to input a theme and an investment amount;

[1543] a data collection means for collecting company data related to the theme;

[1544] An analytical method for analyzing the collected data and selecting relevant companies;

[1545] A portfolio construction method that builds a portfolio based on selected companies;

[1546] A savings setting means for setting monthly savings based on the portfolio;

[1547] The system includes a display means for displaying investment results.

[1548] (Claim 2)

[1549] 2. The system of claim 1, wherein the analysis means selects companies using an AI algorithm.

[1550] (Claim 3)

[1551] The system according to claim 1, which constructs a portfolio based on investment rules (market capitalization weighted average, equal weighted average) specified by the user.

[1552] "Example 1"

[1553] (Claim 1)

[1554] an input means for a user to input a theme and an investment amount;

[1555] a data collection means for collecting company data related to the theme;

[1556] An analytical method for analyzing the collected data and selecting relevant companies;

[1557] A portfolio construction method that builds a portfolio based on selected companies;

[1558] A savings setting means for setting monthly savings based on the portfolio;

[1559] a display means for displaying investment results;

[1560] A rebalancing tool that periodically rebalances the portfolio according to user preferences;

[1561] A system including:

[1562] (Claim 2)

[1563] 10. The system of claim 1, wherein the analysis means uses an artificial intelligence algorithm to select companies.

[1564] (Claim 3)

[1565] The system according to claim 1, which constructs a portfolio based on investment rules (market capitalization weighted average, equal weighted average) specified by the user.

[1566] "Application Example 1"

[1567] (Claim 1)

[1568] an input means for a user to input a theme and an investment amount;

[1569] a data collection means for collecting company data related to the theme;

[1570] An analytical means for analyzing the collected data based on the input theme and selecting related companies;

[1571] A portfolio construction method that builds a portfolio based on selected companies and distributes investment amounts equally among multiple stocks.

[1572] A savings setting means for setting monthly savings based on the portfolio;

[1573] A system that includes a display means for displaying investment results in graphs and figures.

[1574] (Claim 2)

[1575] 2. The system of claim 1, wherein the analysis means selects companies using a generative AI model.

[1576] (Claim 3)

[1577] 2. The system according to claim 1, which is provided with an interactive user interface that visually displays investment themes based on investment rules (market capitalization weighted average, equal weighted average) specified by the user and encourages investment.

[1578] "Example 2: Combining Emotion Engines"

[1579] (Claim 1)

[1580] an input means for a user to input a theme and an investment amount;

[1581] a data collection means for collecting company data related to the theme;

[1582] An analytical method for analyzing the collected data and selecting relevant companies;

[1583] A portfolio construction method that builds a portfolio based on selected companies;

[1584] A savings setting means for setting monthly savings based on the portfolio;

[1585] A sentiment analysis means for analyzing user sentiment in real time and adjusting investment decisions based on the results;

[1586] The system includes a display means for displaying investment results.

[1587] (Claim 2)

[1588] The system of claim 1, wherein the analysis means selects companies using an AI algorithm, and the sentiment analysis means adjusts the portfolio rebalancing frequency based on user sentiment.

[1589] (Claim 3)

[1590] The system described in claim 1, characterized in that it constructs a portfolio based on investment rules specified by the user (e.g., market capitalization weighted average, equal weighted average), and adjusts investment allocation based on the results of user sentiment analysis.

[1591] "Application example 2 when combining emotion engines"

[1592] (Claim 1)

[1593] an input means for a user to input a theme and an investment amount;

[1594] a data collection means for collecting company data related to the theme;

[1595] An analytical method for analyzing the collected data and selecting relevant companies;

[1596] A portfolio construction method that builds a portfolio based on selected companies;

[1597] A savings setting means for setting monthly savings based on the portfolio;

[1598] A sentiment analysis tool that analyzes user sentiment in real time and reflects it in investment decisions.

[1599] an adjustment means for adjusting investment allocation based on the sentiment analysis result;

[1600] The system includes a display means for displaying investment results.

[1601] (Claim 2)

[1602] 10. The system of claim 1, which operates in conjunction with educational content viewed by a user.

[1603] (Claim 3)

[1604] 2. The system of claim 1, wherein the analysis means selects companies using an AI algorithm.

[1605] (Claim 4)

[1606] The system according to claim 1, which constructs a portfolio based on investment rules (market capitalization weighted average, equal weighted average) specified by the user. [Explanation of symbols]

[1607] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a user to input a theme and an investment amount; a data collection means for collecting company data related to the theme; An analytical method for analyzing the collected data and selecting relevant companies; A portfolio construction method that builds a portfolio based on selected companies; A savings setting means for setting monthly savings based on the portfolio; The system includes a display means for displaying investment results.

2. The system of claim 1, wherein the analysis means selects companies using an AI algorithm.

3. The system according to claim 1, wherein the system constructs a portfolio based on investment rules specified by the user.

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