system

Abstract

To provide a system.SOLUTION: The system includes: means for collecting financial economy information by using a public API or a web scraping technology on the Internet; means for shaping the collected data and normalizing the data; means for storing the shaped and normalized data into a database; means for searching a database for data according to a request from a user; and means for sending the acquired data to a browser of the user.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] There is a need for a system that can efficiently analyze financial and economic information, provide the results to users, and deliver advertisements. However, existing systems lack the ability to perform heat map analysis of individual stock evaluations and the functionality to deliver advertisements to the user interface. [Means for solving the problem]

[0005] The present invention uses artificial intelligence to create a database of financial and economic information and perform heat map analysis of individual stock evaluations from that database. Furthermore, it provides a means for providing users with the results of this analysis by the artificial intelligence and a means for delivering advertisements to the user interface. This allows users to efficiently obtain the information they need and enables effective advertisement delivery. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15]FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] As one embodiment of the present invention, a means for creating a database of financial and economic information is provided. Specifically, a database is constructed that collects financial data of listed companies, information such as the Nikkei average and TOPIX, and manages this information in an integrated manner.

[0029] "Example 2"

[0030] In addition, AI is provided that performs heat map analysis of the ratings of individual stocks from the database. This AI analyzes the "good!" and "bad!" ratings of individual stocks based on the information in the database and visualizes them as a heat map. For example, if a particular stock has received many "good!" ratings, that stock will be displayed in red on the heat map.

[0031] "Example 3"

[0032] In addition, a means is provided for users to receive answers based on the results of AI analysis. When a user searches for an individual stock or sector, the AI ​​generates answers based on the search results and provides them to the user. For example, if a user searches for the "electronic components" sector, the AI ​​will provide an answer based on the evaluation of stocks in that sector.

[0033] "Example 4"

[0034] Additionally, a means is provided for delivering advertisements to the user interface. As a user uses the system, advertisements are displayed on the user interface. These advertisements may be targeted advertisements for securities, banking, cards, etc., and are selected based on the user's interests and behavior.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: Collect financial and economic information. Specifically, collect financial data of listed companies, Nikkei average, TOPIX, etc. from the Internet.

[0038] Step 2: The collected financial and economic information is stored in a database. This database allows for centralized management of the information.

[0039] "Example 2"

[0040] Step 1: Obtain individual stock information from the database.

[0041] Step 2: Input the information about individual stocks into an artificial intelligence system that performs heat map analysis.

[0042] Step 3: Based on the input information, the artificial intelligence analyzes the "good!" or "bad!" ratings of individual stocks and visualizes them as a heat map.

[0043] "Example 3"

[0044] Step 1: User searches for an individual stock or sector.

[0045] Step 2: Feed the search results into artificial intelligence.

[0046] Step 3: The AI ​​generates an answer based on the search results entered and provides it to the user.

[0047] "Example 4"

[0048] Step 1: Analyze user behavior and interests as they use the system.

[0049] Step 2: Based on the analysis results, target advertisements for securities, banking, cards, etc. are selected. Step 3: The selected advertisements are displayed on the user interface.

[0050] Example 1

[0051] Next, a description will be given of Example 1 of Form 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."

[0052] In conventional financial and economic information management systems, the series of processes, such as information collection, formatting, normalization, database storage, user request processing, data search and retrieval, and data display, are often not carried out efficiently. Another issue is that it is difficult to provide users with the information they need quickly and accurately. Furthermore, there is a lack of means to effectively utilize collected data and improve the quality and efficiency of marketing department operations.

[0053] 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.

[0054] In this invention, the server includes means for collecting financial and economic information using public APIs on the Internet or web scraping technology, means for formatting and normalizing the collected data, and means for storing the formatted and normalized data in a database. This enables efficient collection and management of financial and economic information. It also includes means for users to access the database through their terminals and obtain the necessary financial and economic information, means for the server to receive user requests, search for and obtain the relevant data from the database, and means for displaying the obtained data to the user. This enables users to quickly and accurately obtain the information they need. Furthermore, by including means for delivering targeted advertisements based on the results of analysis using artificial intelligence, it is possible to improve the quality and efficiency of marketing department operations.

[0055] "Financial and economic information" refers to information related to financial markets and economic activity, such as corporate financial data, stock price indexes, and economic indicators.

[0056] "Database creation" is the process of organizing collected data and storing it in a database in a format that is easy to search and manage.

[0057] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0058] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[0059] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[0060] "Ad serving" is the process of displaying advertisements to specific users.

[0061] A "public API" is an application programming interface that is made publicly available for access by external developers.

[0062] "Web scraping" is the technique of automatically extracting data from websites.

[0063] "Format" is the process of converting collected data into a certain format.

[0064] "Normalization" is the process of standardizing data to ensure consistency and accuracy.

[0065] "Request processing" is the process of receiving a request from a user and providing data in response to that request.

[0066] "Targeted advertising" refers to advertising delivered to a specific user demographic.

[0067] MODE FOR CARRYING OUT THE INVENTION

[0068] The present invention is a system for efficiently collecting, managing, and analyzing financial and economic information, and providing the information to users quickly and accurately. A specific embodiment of this system will be described below.

[0069] Data collection

[0070] The server collects financial and economic information using public APIs on the Internet and web scraping technology. Specifically, it uses Python's BeautifulSoup and Selenium to obtain information such as financial results data of listed companies, stock indexes, and economic indicators. For example, the server visits "https: / / example.com / financials / companyA" and extracts data from specific HTML tags.

[0071] Data Shaping and Normalization

[0072] The server formats and normalizes the collected data. Using the Python Pandas library, it converts the collected data into a DataFrame and converts data in different formats into a unified format. For example, it standardizes dates to YYYY-MM-DD format and aligns the number of decimal points for numeric data.

[0073] Storage in the database

[0074] The server stores the formatted and normalized data in a database. MySQL (registered trademark) or PostgreSQL is used as the database. The server generates an SQL query and inserts the data into the appropriate table. For example, it executes the SQL query "INSERT INTO financials (company, date, revenue, profit) VALUES ('CompanyA', '2023-03-31', 3000000000, 500000000)".

[0075] Processing user requests

[0076] A user accesses the web interface through a terminal and requests the necessary financial and economic information. For example, if a user wants to search for "Company A's financial results data for the first quarter of 2023," the user enters "Company A 2023 Q1 financial results data" in the search bar and clicks the search button.

[0077] Searching and retrieving data

[0078] The server receives the user's request and searches the database for the relevant data. The server generates an SQL query and executes it against the database. For example, it executes the SQL query "SELECT FROM financials WHERE company='CompanyA' AND date='2023-03-31'".

[0079] Viewing Data

[0080] The server converts the acquired data into HTML format and sends it to the user's browser. The user can then view the information displayed in the browser, such as "Company A's financial results for Q1 2023: Sales of 30 billion yen, Profit of 5 billion yen."

[0081] Specific examples

[0082] As a concrete example, consider a case where a user wants to retrieve "Company A's financial results data for the first quarter of 2023." The user logs into the web interface and types "Company A 2023 Q1 financial results data" into the search bar. The server receives this request, searches the database for the relevant data, and displays it to the user.

[0083] Examples of prompts to be input to a generative AI model include:

[0084] Prompt: "Please retrieve Company A's financial results for the first quarter of 2023."

[0085] By feeding this prompt into a generative AI model, the model can generate the appropriate API requests and database queries to automate the process of retrieving the required data.

[0086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0087] Step 1:

[0088] The server collects financial and economic information using public APIs on the Internet and web scraping technology. Specifically, the server uses Python's BeautifulSoup and Selenium to obtain information such as financial results data of listed companies, stock indexes, and economic indicators. For example, the server visits "https: / / example.com / financials / companyA" and extracts data from specific HTML tags.

[0089] Enter: Website URL

[0090] Output: Extracted raw data (HTML format)

[0091] Step 2:

[0092] The server formats and normalizes the collected data. Using the Python Pandas library, the collected data is converted into a DataFrame and data in different formats is converted into a unified format. For example, dates are unified into the YYYY-MM-DD format, and the number of decimal points for numeric data is adjusted.

[0093] Input: Extracted raw data (HTML format)

[0094] Output: Reformatted and normalized data (DataFrame format)

[0095] Step 3:

[0096] The server stores the formatted and normalized data in a database. MySQL or PostgreSQL is used as the database. The server generates an SQL query and inserts the data into the appropriate table. For example, it executes the SQL query "INSERT INTO financials (company, date, revenue, profit) VALUES ('CompanyA', '2023-03-31', 3000000000, 500000000)".

[0097] Input: Reformatted and normalized data (DataFrame format)

[0098] Output: Data stored in the database

[0099] Step 4:

[0100] A user accesses the web interface through a terminal and requests the necessary financial and economic information. For example, if a user wants to search for "Company A's financial results data for the first quarter of 2023," he or she enters "Company A 2023 Q1 financial results data" in the search bar and clicks the search button.

[0101] Input: User's search query ("Company A 2023 Q1 financial results data")

[0102] Output: Request to server

[0103] Step 5:

[0104] The server receives the user's request and searches the database for the relevant data. The server generates an SQL query and executes it against the database. For example, it executes the SQL query "SELECT FROM financials WHERE company='CompanyA' AND date='2023-03-31'".

[0105] Input: User request

[0106] Output: Search results (matching data)

[0107] Step 6:

[0108] The server converts the acquired data into HTML format and sends it to the user's browser. The user can then view the information displayed in the browser, such as "Company A's financial results for Q1 2023: Sales of 30 billion yen, Profit of 5 billion yen."

[0109] Input: Search results (relevant data)

[0110] Output: The data that is displayed in the user's browser

[0111] (Application example 1)

[0112] Next, a description will be given of Application Example 1 of Embodiment 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."

[0113] With conventional financial and economic information systems, it was difficult to centrally manage information such as financial results data for listed companies, the Nikkei average, and TOPIX, making it difficult for users to obtain the latest financial information in real time. Furthermore, the system lacked investment advice and portfolio management functions, providing insufficient support for users to make appropriate investment decisions. Furthermore, the targeting of ad delivery was inaccurate, reducing the operational efficiency of the marketing department.

[0114] 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.

[0115] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) that performs heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the analysis results of the AI, a means for delivering advertisements to a user interface, a means for providing financial and economic information in real time, a means for generating investment advice, and a means for managing an investment portfolio and performing risk assessment and performance analysis. This allows users to obtain the latest financial information in real time and receive support for making appropriate investment decisions. Furthermore, the targeting of advertisement delivery is more accurate, improving the operational efficiency of the marketing department.

[0116] "Financial and economic information" refers to various data related to financial markets, including financial results data of listed companies and stock price indices (Nikkei Average, TOPIX, etc.).

[0117] "Database creation" is the process of storing collected information in a database that allows it to be managed centrally and efficiently searched and used.

[0118] "Heat map analysis" is a method of visually representing data, using shades of color to show the distribution and trends of data.

[0119] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0120] A "user interface" is the means or screen through which a user interacts with a system.

[0121] "Ad serving" is the process of displaying advertisements to specific targets.

[0122] "Real-time provision" means obtaining information instantly and providing it to users without delay.

[0123] "Investment Advice" means providing advice or recommendations to assist users in making investment decisions.

[0124] An "investment portfolio" is a combination of multiple investment assets held by a user.

[0125] "Risk assessment" is the process of analyzing and evaluating the risks associated with an investment.

[0126] "Performance analysis" is an analysis used to evaluate the results and effectiveness of an investment.

[0127] To implement the present invention, the following system configuration and program are required.

[0128] System Configuration

[0129] 1. Server: Collects financial and economic information and stores it in a database.

[0130] 2. Device: The smartphone or computer used by the user.

[0131] 3. Artificial Intelligence (AI): Retrieves information from the database and generates heat map analysis and investment advice.

[0132] 4. User Interface (UI): The screen through which users view and interact with information.

[0133] Program processing

[0134] Data collection and database creation

[0135] The server uses APIs to collect financial and economic information such as financial results data for listed companies, the Nikkei average, and TOPIX, etc. The collected data is stored in a database such as SQLite.

[0136] Heatmap Analysis

[0137] The AI ​​retrieves individual stock evaluation data from the database and performs heat map analysis, which visually shows the distribution and trends of the data.

[0138] Real-time information provision

[0139] The server provides the collected financial and economic information to users in real time, allowing them to instantly access the latest information via their smartphones or computers.

[0140] Investment advice generation

[0141] AI generates investment advice based on the collected data, which users can use to make appropriate investment decisions.

[0142] Portfolio Management

[0143] The server manages users' investment portfolios, performs risk assessments and performance analysis, and helps users understand their investment status.

[0144] Ad serving

[0145] The user interface will be delivered with targeted advertising, which will be precisely targeted based on the results of analysis using artificial intelligence.

[0146] Specific examples

[0147] Let's say a user opens the app and checks the latest Nikkei 225. They speak the following prompt:

[0148] Please tell me the latest value of the Nikkei average.

[0149] The server receives this prompt and retrieves the latest information from the database and provides it to the user, allowing the user to check the latest financial information in real time and make appropriate investment decisions.

[0150] In this way, the present invention realizes a system that provides users with financial and economic information in real time and supports investment advice and portfolio management.

[0151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0152] Step 1:

[0153] The server uses an API to collect financial and economic information such as financial results data for listed companies, the Nikkei average, and TOPIX.

[0154] Input: Financial and economic information obtained from the API.

[0155] Data processing: Convert the acquired data from JSON format to an appropriate data format.

[0156] Output: Transformed financial and economic information data.

[0157] Step 2:

[0158] The server stores the collected financial and economic information in a database such as SQLite.

[0159] Input: Transformed financial and economic information data.

[0160] Data processing: Generates SQL queries to store in the database.

[0161] Output: Financial and economic information stored in a database.

[0162] Step 3:

[0163] Users launch the application via their smartphone or computer and enter a prompt to check their latest financial information.

[0164] Input: User prompt (e.g. "What is the latest Nikkei average?").

[0165] Data processing: Analyze the prompt and extract the necessary information.

[0166] Output: Parsed information request.

[0167] Step 4:

[0168] The server retrieves the latest financial information from a database based on the parsed information request.

[0169] Input: Parsed information request.

[0170] Data processing: Generate SQL queries to retrieve the required information from the database.

[0171] Output: The latest financial information obtained.

[0172] Step 5:

[0173] The artificial intelligence performs heat map analysis based on the acquired financial information.

[0174] Input: The latest financial information obtained.

[0175] Data processing: Apply heat map analysis algorithms to visualize data distributions and trends.

[0176] Output: Heatmap analysis results.

[0177] Step 6:

[0178] The server provides the heat map analysis results to the user.

[0179] Input: Heatmap analysis results.

[0180] Data processing: Converting data into a format suitable for display on the user interface.

[0181] Output: A heatmap displayed in the user interface.

[0182] Step 7:

[0183] Artificial intelligence generates investment advice based on the acquired financial information.

[0184] Input: The latest financial information obtained.

[0185] Data processing: Applying investment advice generation algorithms to generate appropriate investment advice.

[0186] Output: The generated investment advice.

[0187] Step 8:

[0188] The server provides the generated investment advice to the user.

[0189] Input: Generated investment advice.

[0190] Data processing: Converting data into a format suitable for display on the user interface.

[0191] Output: Investment advice displayed in the user interface.

[0192] Step 9:

[0193] The server manages the user's investment portfolio and performs risk assessment and performance analysis.

[0194] Input: User's investment portfolio data.

[0195] Data processing: Applying risk assessment and performance analysis algorithms.

[0196] Output: Risk assessment results and performance analysis results.

[0197] Step 10:

[0198] The user interface is delivered with targeted advertisements.

[0199] Input: AI analysis results.

[0200] Data processing: Selecting targeted advertisements and converting them into a data format for display in the user interface.

[0201] Output: Targeted ads displayed in the user interface.

[0202] Example 2

[0203] Next, a description will be given of Example 2 of Form 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."

[0204] In conventional financial and economic information systems, it was difficult for users to visually understand the evaluation of individual stocks, making it difficult for users to make quick investment decisions. Furthermore, there was a lack of means to obtain and visualize evaluation data in real time based on user requests, which resulted in a poor user experience. Furthermore, targeted advertising based on evaluation results was not effectively delivered.

[0205] 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.

[0206] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of individual stock evaluations from the database, a means for providing the AI ​​analysis results to the user, a means for receiving a request from the user via a user interface, a means for acquiring evaluation data from the database based on the request, a means for preprocessing the acquired evaluation data and calculating an evaluation score, a means for generating a heat map based on the evaluation score, and a means for displaying the generated heat map on the user interface. This allows users to visually understand the evaluations of individual stocks and make quick investment decisions. Furthermore, acquiring and visualizing evaluation data in real time improves the user experience. Furthermore, targeted advertising based on the evaluation results can be effectively delivered.

[0207] "Financial and economic information" is a general term for data and information related to financial markets and economic activity.

[0208] A "database" is a system for efficiently storing, searching, and managing data.

[0209] "Individual stocks" refer to stocks of specific companies.

[0210] A "heat map" is a type of graph that visually represents the distribution and intensity of data using shades of color.

[0211] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0212] "User interface" refers to the means or screen through which a user interacts with a system.

[0213] A "request" refers to a request or inquiry made by a user to a system.

[0214] "Evaluation data" is data that includes evaluation information such as "Good!" or "Bad!" for individual stocks.

[0215] "Preprocessing" is the initial process used to prepare data for easier analysis.

[0216] An "evaluation score" is a numerical value calculated based on evaluation data, and indicates the evaluation of an individual stock.

[0217] "Visualization" refers to displaying data in the form of graphs, charts, etc., to make it easier to understand visually.

[0218] "Targeted advertising" refers to advertising that is delivered to a specific user demographic.

[0219] MODE FOR CARRYING OUT THE INVENTION

[0220] This invention is a system that uses artificial intelligence to compile a database of financial and economic information and perform heat map analysis of the evaluation of individual stocks. Specific embodiments of this system will be described below.

[0221] System configuration

[0222] The system consists of three main components: a server, a terminal, and a user. The server contains software for databases, artificial intelligence, data processing, and visualization. The terminal provides an interface for users to interact with the system.

[0223] Hardware and software used

[0224] The server is a computer equipped with a high-performance processor and large memory capacity. A relational database such as MySQL or PostgreSQL is used as the database management system. The artificial intelligence model is built using machine learning libraries such as TENSORFLOW (registered trademark) or Keras. Data visualization libraries such as Matplotlib and Seaborn are used for data visualization.

[0225] Program processing

[0226] The server first retrieves the rating data for individual stocks from the database. This rating data includes the "good" and "bad" ratings for each stock. The server then preprocesses the retrieved data and aggregates the rating values. For example, if a particular stock has received 100 "good" ratings and 20 "bad" ratings, the rating score for that stock will be 80.

[0227] The server generates a heat map based on the evaluation scores. This heat map displays stocks with high evaluation scores in red and stocks with low evaluation scores in blue. The generated heat map is intended to make it easier for users to visually understand the evaluation situation.

[0228] Specific examples

[0229] When a user wants to check the rating of a specific stock, they send a request from their device to the server. For example, if a user sends a request saying, "I want to check the rating of Company A's stock," the server retrieves the rating data of Company A's stock from the database and generates a heat map. The generated heat map is displayed to the user through their device.

[0230] Examples of specific prompts include:

[0231] "I would like to check the valuation of Company A's stock."

[0232] The server that receives this prompt generates a heat map based on the evaluation data of Company A's stock and displays it on the terminal. By checking the heat map, the user can visually understand the evaluation status of Company A's stock.

[0233] In this way, the present invention allows users to quickly and visually understand the valuation of individual stocks, thereby assisting them in making investment decisions.

[0234] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0235] Step 1:

[0236] A user sends a stock evaluation request from a terminal.

[0237] Specifically, the user enters "I would like to check the stock valuation of Company A" into the terminal interface and clicks the send button.

[0238] Input: User request (e.g., "I would like to check the stock valuation of Company A.")

[0239] Output: Request sent from terminal to server

[0240] Step 2:

[0241] The server receives the request and retrieves the rating data from the database.

[0242] Specifically, the server analyzes the request and sends a query to the database to obtain Company A's evaluation data.

[0243] Input: Request from terminal

[0244] Output: Rating data retrieved from the database

[0245] Step 3:

[0246] The server preprocesses the rating data and calculates the rating score.

[0247] Specifically, the server analyzes the acquired evaluation data and tallies the number of "likes" and "dislikes." For example, if Company A's stock has received 100 "likes" and 20 "dislikes," the evaluation score will be 80.

[0248] Input: Evaluation data retrieved from the database

[0249] Output: Rating score (e.g. 80)

[0250] Step 4:

[0251] The server generates a heat map based on the evaluation scores.

[0252] Specifically, the server uses a data visualization library such as Matplotlib or Seaborn to create a heat map that displays stocks with high evaluation scores in red and stocks with low evaluation scores in blue.

[0253] Input: Rating score

[0254] Output: The generated heatmap

[0255] Step 5:

[0256] The server sends the generated heat map to the device.

[0257] Specifically, the server sends the generated heat map to the terminal, allowing the user to visually check the evaluation status.

[0258] Input: The generated heatmap

[0259] Output: Send heatmap to device

[0260] Step 6:

[0261] The device displays the heat map to the user.

[0262] Specifically, the terminal displays the heat map received from the server to the user. By checking the heat map, the user can visually understand the valuation status of Company A's stock.

[0263] Input: Heatmap sent from the server

[0264] Output: Heatmap displayed to the user

[0265] (Application example 2)

[0266] Next, a description will be given of Application Example 2 of Form 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."

[0267] In conventional financial and economic information systems, there were limited ways to visually grasp the evaluation of individual stocks, making it difficult for users to understand stock performance in real time. In addition, there was a lack of a mechanism for users to evaluate specific stocks and have those evaluations reflected by other users, resulting in insufficient information sharing between users. This resulted in a decrease in the accuracy of investment decisions, making it difficult to improve the quality and efficiency of marketing department operations.

[0268] 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.

[0269] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence means for performing heat map analysis of individual stock ratings from the database, means for responding to the user with the results of the analysis performed by the artificial intelligence, means for delivering advertisements to a user interface, means for displaying ratings of stocks held by the user or stocks of interest to the user in real time as a heat map, means for visually grasping stock performance based on ratings obtained from the database, and means for users to rate specific stocks and for the ratings to be reflected in other users' ratings. This allows users to visually grasp stock performance in real time, promoting information sharing among users and improving the accuracy of investment decisions. It also improves the quality and efficiency of marketing department operations.

[0270] "Financial and economic information" refers to data, news, analytical reports, and other information related to financial markets such as stocks, bonds, foreign exchange, and commodities.

[0271] A "database" is an information accumulation system that systematically organizes and stores information so that it can be quickly searched and used as needed.

[0272] "Individual stocks" are stocks issued by a specific company and are valued based on the financial situation and performance of that specific company.

[0273] A "heat map" is a type of graph that visually represents data values ​​using shades or hues of color, and is a means of intuitively grasping the distribution and trends of data.

[0274] "Artificial intelligence" refers to a computer system that mimics human intellectual activity and has capabilities such as learning, reasoning, and recognition.

[0275] A "user interface" is a means or screen that allows a user to interact with a system or application, and is designed to improve operability and visibility.

[0276] "Advertising" is an information dissemination activity to widely publicize products and services, and is a promotional method aimed at a specific target.

[0277] "Real-time" means that data and information are processed and reflected immediately, providing the latest information without delay.

[0278] "Performance" is an indicator that shows the business results and achievements of a stock or company, and serves as a basis for investment decisions.

[0279] "Evaluation" is the act of judging the value or quality of a particular object and expressing it through scores, comments, etc.

[0280] "Information sharing" is an activity that aims to exchange information between multiple users and systems and achieve a common understanding and recognition.

[0281] A "marketing department" is a department within a company that is responsible for market research, advertising, sales promotion, and other tasks, with the aim of increasing sales of products and services.

[0282] "Quality improvement" refers to improvement activities aimed at enhancing the performance and reliability of products and services, and aims to improve customer satisfaction.

[0283] "Efficiency" refers to activities that aim to eliminate waste in business and operations and achieve high results with fewer resources and time.

[0284] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0285] System Configuration

[0286] The system consists of the following major components:

[0287] 1. Server: Equipped with artificial intelligence that compiles financial and economic information into a database and performs heat map analysis of individual stock evaluations.

[0288] 2. User device: A device such as a smartphone or tablet provides a user interface and displays heat maps in real time.

[0289] 3. Network: Connects the server and user terminals to send and receive data.

[0290] Hardware and software used

[0291] Hardware: smartphones, tablets, servers

[0292] Software: Python, Pandas, Matplotlib, Seaborn, database management system (e.g., MySQL)

[0293] Data processing and calculation

[0294] 1. Data Acquisition: The server acquires financial and economic information from the database, including stock rating data such as "good" and "bad."

[0295] 2. Data processing: Based on the acquired data, a rating score for each stock is calculated. Specifically, the score is calculated by subtracting the number of "bad" ratings from the number of "good" ratings.

[0296] 3. Data visualization: The calculated scores are visualized as a heat map, which intuitively displays the distribution and trends of data using color shading and hues.

[0297] 4. User Interface: The heat map is displayed on the user's device in real time, allowing the user to rate specific stocks, which is then sent to the server and reflected to other users.

[0298] Specific examples

[0299] For example, if a user were to use a smartphone to check the stock valuation of AAPL (Apple), the following steps would be taken:

[0300] 1. The user launches the application and selects "AAPL" stock.

[0301] 2. The server retrieves the reputation data for "AAPL" from the database and calculates the reputation score.

[0302] 3. Based on the calculated scores, a heat map is generated and displayed on the user's device.

[0303] 4. When a user gives "AAPL" a "Like!" rating, the rating is sent to the server and reflected to other users.

[0304] Prompt Sentence Examples

[0305] "Show me a heat map of AAPL stock valuation."

[0306] "View TSLA stock valuation in a heat map."

[0307] In this way, users can visually grasp stock performance in real time and encourage information sharing among users, which improves the accuracy of investment decisions and improves the quality and efficiency of marketing department work.

[0308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0309] Step 1:

[0310] The server retrieves financial and economic information from a database. Specifically, it extracts information including stock evaluation data such as "good" or "bad" from the database using queries. The input is a database query, and the output is a list of evaluation data.

[0311] Step 2:

[0312] The server calculates the evaluation score for each stock based on the acquired evaluation data. Specifically, it calculates the score by subtracting the number of "bad" comments from the number of "good" comments. The input is a list of evaluation data, and the output is the evaluation score for each stock.

[0313] Step 3:

[0314] The server generates a heatmap based on the calculated evaluation scores. Specifically, it uses Matplotlib and Seaborn to visually represent the evaluation scores using color shading and hues. The input is the evaluation scores, and the output is a heatmap image.

[0315] Step 4:

[0316] The server transmits the generated heatmap to the user terminal. Specifically, it transfers the heatmap image to the user terminal via a network. The input is the heatmap image, and the output is the transmission of image data to the user terminal.

[0317] Step 5:

[0318] The user device displays the received heatmap. Specifically, it displays the heatmap image on the screen of a smartphone or tablet. The input is the heatmap image, and the output is a visual display on the user interface.

[0319] Step 6:

[0320] A user rates a specific stock. Specifically, the user inputs a rating such as "Good!" or "Bad!" through a user interface. The input is the user's rating, and the output is the rating data.

[0321] Step 7:

[0322] The user terminal transmits the user's evaluation to the server. Specifically, the evaluation data is transferred to the server via the network. The input is the user's evaluation data, and the output is the data transmission to the server.

[0323] Step 8:

[0324] The server saves the received rating data in a database and reflects it to other users. Specifically, it adds the rating data to the database and updates the heat map again. The input is the user's rating data, and the output is the updated heat map.

[0325] Example 3

[0326] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0327] Conventional financial and economic information systems make it difficult for users to quickly and accurately obtain evaluations of individual stocks and sectors, and do not adequately deliver targeted advertisements based on user interests and behavior. This has made it difficult to improve the user experience and deliver advertisements effectively.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0329] In this invention, the server includes a means for compiling financial and economic information into a database, a generative AI model for generating evaluations of individual stocks and sectors from the database, a means for sending prompts to the generative AI model to generate search results, a means for responding to the user with the analysis results of the generative AI model, and a means for delivering advertisements to the user interface, thereby enabling users to quickly and accurately obtain evaluations of individual stocks and sectors and effectively deliver targeted advertisements based on the user's interests and behavior.

[0330] "Financial and economic information" refers to all data and information related to financial markets and the economy.

[0331] "Database creation" refers to organizing information and storing it in a database so that it can be searched and used efficiently.

[0332] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate an appropriate output for a given input.

[0333] A "prompt sentence" refers to an input sentence that prompts a generative AI model to generate a specific output.

[0334] "Search Results" refers to the information and ratings provided by a generative AI model based on a user's query.

[0335] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[0336] "Means for delivering advertisements" refers to the technology or method for displaying advertisements on the user interface.

[0337] "Targeted advertising" refers to advertising that is selected and delivered to specific users based on their interests and behavior.

[0338] "Marketing activities" refers to activities such as market research, advertising, and sales strategy development aimed at promoting products and services.

[0339] MODE FOR CARRYING OUT THE INVENTION

[0340] This invention is a system that uses a generative AI model to quickly and accurately provide evaluation results and deliver targeted advertising when a user searches for individual stocks or sectors. Specific embodiments of this system are described below.

[0341] System configuration

[0342] server

[0343] The server has a means of compiling financial and economic information into a database. The database contains detailed information on individual stocks and sectors. The server receives search queries from users and sends prompts to the generative AI model.

[0344] Generative AI Models

[0345] The generative AI model uses, for example, OpenAI's GPT-4 (registered trademark). This model generates evaluation results for individual stocks or sectors based on prompt statements sent from the server. The generated evaluation results are returned to the server.

[0346] Terminal

[0347] The terminal provides an interface for the user to input a search query. When the user inputs a keyword into the search box and clicks the search button, the terminal sends the query to the server.

[0348] User Interface

[0349] The user interface displays the ratings received from the server and also displays targeted advertisements based on the user's interests and behavior, including promotions for financial institutions and credit cards.

[0350] Specific examples

[0351] A user types "electronic parts" into the search box on their device and clicks the search button. The device sends this search query to the server. The server analyzes the received query and sends the following prompt to the generative AI model:

[0352] "A user has searched for the 'electronic components' sector. Please generate valuations for stocks in this sector."

[0353] The generative AI model generates an evaluation result based on this prompt. For example, it might say, "The electronic components sector includes ABC, DEF, and GHI. ABC has performed well recently, and its stock price is on the rise. DEF is attracting attention as it plans to release a new product. GHI is expanding its market share and is expected to grow in the long term."

[0354] The server then sends the evaluation results to the user's device and displays them on the user interface. At the same time, targeted advertisements based on the user's interests and behavior are also displayed. For example, an advertisement saying, "XYZ Securities is running a new account opening campaign!" is displayed.

[0355] In this way, users can quickly and accurately obtain evaluation results for individual stocks and sectors, and can also receive targeted advertisements based on their interests. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0356] Program processing flow

[0357] Step 1: User enters a search query

[0358] Users enter keywords for individual stocks or sectors they are interested in into the device's search box, for example, "electronic components," and click the search button.

[0359] Input: The keyword entered by the user (e.g., "electronic parts")

[0360] Output: The search query is sent to the device

[0361] Step 2: The device sends the search query to the server

[0362] The device sends the search query entered by the user to the server, transmitting the data securely using the HTTPS protocol.

[0363] Input: The search query entered by the user (e.g., "electronic parts")

[0364] Output: The search query is sent to the server

[0365] Step 3: The server receives the search query and sends a prompt to the generative AI model.

[0366] The server analyzes the search query received from the device and creates an appropriate prompt for the generative AI model, for example, "The user searched for the 'electronic components' sector. Please generate valuation results for stocks in this sector."

[0367] Input: Search query received from the device (e.g., "electronic parts")

[0368] Data processing: Analyze search queries and generate prompts

[0369] Output: The generated prompt sentence is sent to the generative AI model.

[0370] Step 4: The generative AI model generates search results and returns them to the server

[0371] A generative AI model (e.g., OpenAI's GPT-4) generates search results based on prompts received from the server. For example, the generated results might be something like, "The electronic components sector includes ABC, DEF, and GHI. ABC has performed well recently, and its stock price is on the rise. DEF is attracting attention as it plans to release a new product. GHI is expanding its market share and is expected to grow in the long term."

[0372] Input: The prompt received from the server (e.g., "The user searched for the 'Electronic Components' sector. Please generate valuation results for stocks in this sector.")

[0373] Data Calculation: Generate search results based on prompt statements

[0374] Output: The generated search results are returned to the server

[0375] Step 5: The server serves the generated search results to the user

[0376] The server provides the search results received from the generative AI model to the user, specifically by displaying the search results on the user interface of the terminal.

[0377] Input: Search results received from a generative AI model (e.g., "The electronic components sector includes ABC, DEF, and GHI. ABC has performed well recently and its stock price is trending upward. DEF has a new product launch that is attracting attention. GHI is expanding its market share and is expected to grow long-term.")

[0378] Output: Search results are displayed on the user's device

[0379] Step 6: The server delivers the ad to the user interface

[0380] The server delivers targeted advertisements to the user interface, selected based on the user's interests and behavior. For example, an advertisement saying "XYZ Securities is running a new account opening campaign!" is displayed.

[0381] Input: Advertising data based on user interests and behavior

[0382] Data processing: Selecting the right ads

[0383] Output: The selected ad is displayed in the user interface.

[0384] (Application example 3)

[0385] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0386] Conventional financial and economic information systems require a lot of time and effort for users to obtain evaluation results for individual stocks or sectors. Furthermore, they lack the ability to deliver targeted advertisements based on users' interests and behavior, resulting in low advertising effectiveness. Furthermore, they lack the ability to provide personalized information that utilizes users' search and trading history, resulting in a lack of an improved user experience.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0388] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of individual stock evaluations from the database, means for responding to the user with the analysis results of the artificial intelligence, means for delivering advertisements to a user interface, means for acquiring the user's search history and transaction history and generating evaluation results based on said history, and means for selecting and displaying targeted advertisements based on the user's interests and behavior. This allows the user to quickly and efficiently obtain evaluation results for individual stocks and sectors, and furthermore, the delivery of personalized targeted advertisements improves the effectiveness of the advertisements and enhances the user experience.

[0389] "Financial and economic information" is a general term for data and information related to financial markets and economic activity.

[0390] "Databasing" is the process of organizing information and storing it in a database so that it can be efficiently searched and used.

[0391] An "individual stock" refers to the stock of a specific company, and is the unit traded on the stock market.

[0392] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[0393] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0394] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[0395] "Ad serving" is the process of displaying advertisements to specific users.

[0396] "Search history" is a record of keywords or queries a user has searched for in the past.

[0397] "Transaction History" means a record of financial transactions that a User has made in the past.

[0398] "Evaluation results" are evaluations and conclusions generated based on data analyzed by artificial intelligence.

[0399] "Targeted advertising" is advertising selected based on a particular user's interests or behavior.

[0400] "Personalization" refers to customizing something to suit the characteristics and preferences of each individual user.

[0401] A system for implementing this invention includes a server, a user terminal, and artificial intelligence (AI). The server compiles financial and economic information into a database and acquires user search history and trading history. The user terminal provides a user interface and a means for users to search for individual stocks and sectors. The AI ​​performs heat map analysis based on the information acquired from the database and generates evaluation results.

[0402] The server is built using programming languages ​​such as Python, and uses MySQL or PostgreSQL as the database management system (DBMS). AI is implemented using machine learning libraries such as TensorFlow and PyTorch. The user interface is built using HTML, CSS, and JavaScript (registered trademark), and is designed to be intuitive for users to use.

[0403] The server retrieves the user's search history and trading history and inputs this data into the AI. The AI ​​generates evaluation results for individual stocks and sectors based on this data and provides them to the user. The evaluation results are displayed visually as heat maps. In addition, the server selects targeted advertisements based on the user's interests and behavior and displays them on the user interface.

[0404] For example, if a user searches for the "electronic components" sector, the AI ​​will generate an evaluation result that says, "The electronic components sector is currently on an upward trend," and provide it to the user. In addition, targeted advertisements for "new investment plans from securities companies" will be displayed.

[0405] Example prompts to input to a generative AI model:

[0406] When a user searches for the "electronic components" sector, generate valuation results for stocks in that sector, and select and display targeted ads based on the user's interests and behavior.

[0407] This system allows users to quickly and efficiently obtain evaluation results for individual stocks and sectors, and also delivers personalized targeted advertisements, improving the effectiveness of advertisements and improving the user experience.

[0408] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0409] Step 1:

[0410] The server creates a database of financial and economic information. Specifically, the server collects data related to financial markets and economic activity and stores it in a database management system such as MySQL or PostgreSQL. The input is financial and economic information, and the output is the information stored in the database.

[0411] Step 2:

[0412] Users use their devices to search for individual stocks or sectors. Specifically, they input search keywords through a browser on their smartphone or computer. The input is the user's search keyword, and the output is a search query.

[0413] Step 3:

[0414] The server retrieves the user's search history and transaction history. Specifically, the server retrieves the user's past search keywords and transaction data from the database. The input is the user ID, and the output is the search history and transaction history.

[0415] Step 4:

[0416] The server inputs the acquired search history and transaction history into the AI. Specifically, the server inputs this data into an AI model implemented using machine learning libraries such as TensorFlow and PyTorch. The input is the search history and transaction history, and the output is the input data for the AI ​​model.

[0417] Step 5:

[0418] AI generates evaluation results for individual stocks or sectors based on input data. Specifically, AI performs heat map analysis to generate evaluation results. The input is the input data for the AI ​​model, and the output is the evaluation results.

[0419] Step 6:

[0420] The server provides the AI ​​evaluation results to the user. Specifically, the server sends the evaluation results to the user's device and displays them on the user interface. The input is the evaluation result, and the output is the evaluation result displayed on the user's device.

[0421] Step 7:

[0422] The server selects targeted ads based on the user's interests and behavior. Specifically, the server selects appropriate ads based on the user's search history and transaction history. The input is the search history and transaction history, and the output is the selected ad.

[0423] Step 8:

[0424] The server displays the selected targeted advertisement on the user interface. Specifically, the server transmits advertisement data to the user terminal and displays it on the user interface. The input is the selected advertisement, and the output is the advertisement displayed on the user terminal.

[0425] 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.

[0426] "Example 1"

[0427] One embodiment of the present invention is a system incorporating an emotion engine. The system recognizes a user's emotion and selects and delivers advertisements to a user interface based on that emotion. Specifically, the system infers the user's emotion from the user's actions and reactions when using the system. For example, if a user has a positive reaction to a particular stock, the system selects and delivers advertisements related to that stock to the user interface.

[0428] "Example 2"

[0429] The sentiment engine can also adjust the ratings of individual stocks based on user sentiment.

[0430] Specifically, if a user has a negative reaction to a particular stock, the rating of that stock will be lowered, allowing for a more personalized service that reflects the user's sentiment.

[0431] "Example 3"

[0432] Furthermore, the emotion engine can adjust the way ads are delivered based on the user's emotions. For example, if a user has a positive reaction to a particular ad, that ad can be displayed more frequently. On the other hand, if a user has a negative reaction to a particular ad, that ad can be hidden. This allows for ad delivery that takes user emotions into maximum consideration.

[0433] "Example 4"

[0434] In this way, a system incorporating an emotion engine can recognize user emotions and adjust the selection and delivery of advertisements and the evaluation of individual stocks based on those emotions, thereby providing users with a more beneficial and satisfying service.

[0435] The processing flow of each embodiment will be described below.

[0436] "Example 1"

[0437] Step 1: The emotion engine collects the user's actions and reactions when using the system. Step 2: The emotion engine estimates the user's emotions from the collected data.

[0438] Step 3: Based on the estimated sentiment, the sentiment engine selects an ad.

[0439] Step 4: Serving the selected advertisement in the user interface.

[0440] "Example 2"

[0441] Step 1: The sentiment engine collects user reactions to a particular stock.

[0442] Step 2: The emotion engine estimates the user's emotions from the collected responses.

[0443] Step 3: Based on the estimated sentiment, the sentiment engine adjusts the ratings of individual stocks.

[0444] "Example 3"

[0445] Step 1: The sentiment engine collects user responses to specific ads.

[0446] Step 2: The emotion engine estimates the user's emotions from the collected responses.

[0447] Step 3: Based on the estimated sentiment, the sentiment engine adjusts how the ad is delivered.

[0448] "Example 4"

[0449] Step 1: The emotion engine recognizes the user's emotion.

[0450] Step 2: Based on the perceived sentiment, the sentiment engine adjusts ad selection and delivery, as well as individual stock ratings.

[0451] Step 3: Provide the adjusted results to the user.

[0452] Example 1

[0453] Next, a description will be given of Example 1 of Form 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."

[0454] In conventional financial and economic information systems, data collection and management were dispersed, making efficient data analysis and ad delivery difficult. Furthermore, ad delivery did not take into account user sentiment, limiting the effectiveness of advertising. This created the challenge of being unable to improve the quality and efficiency of marketing operations.

[0455] 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.

[0456] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of individual stock evaluations from the database, a means for providing the AI ​​analysis results to the user, a means for monitoring the user's behavior and reactions and estimating their emotions, a means for selecting advertisements based on their emotions, and a means for delivering the advertisements to a user interface. This enables unified management of financial and economic information and efficient data analysis, and also enables delivery of targeted advertisements that take user emotions into consideration. This enables improved quality and efficiency in marketing operations.

[0457] "Financial and economic information" refers to information related to economic activity, such as corporate financial data, stock price indices, and trends in financial markets.

[0458] "Database creation" means storing collected information in a database that allows it to be managed centrally and searched and used efficiently.

[0459] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[0460] "Artificial intelligence" is a technology that allows computers to imitate human intelligence and perform learning and reasoning.

[0461] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[0462] "Advertising delivery" means displaying advertisements to specific users.

[0463] "User behavior and responses" refers to data such as clicks, browsing history, and input content when users use the system.

[0464] "Emotion estimation" means predicting a user's emotional state at a given time based on their actions and reactions.

[0465] "Targeted advertising" is advertising selected based on a particular user's interests.

[0466] "Marketing activities" include activities such as market research, advertising, and formulating sales strategies aimed at promoting products and services.

[0467] This invention is a system for storing financial and economic information in a database and delivering advertisements that take into account the emotions of users. A specific embodiment of this system will be described below.

[0468] Data collection and database creation

[0469] To collect financial and economic information, the server obtains data from financial information websites on the Internet. Specifically, it uses APIs to collect financial data from listed companies and stock indexes (Nikkei Average, TOPIX, etc.). Examples of APIs that can be used include general financial information APIs.

[0470] The server uses a database management system (DBMS) to centrally manage the collected data. Specifically, using database software such as MySQL or PostgreSQL, it creates tables with columns such as company name, fiscal year, sales, and net profit, and stores the data.

[0471] Heatmap analysis and artificial intelligence

[0472] The server performs heat map analysis of the evaluation of individual stocks based on financial and economic information stored in the database. Heat map analysis uses shades of color to visually represent the distribution and trends of data, allowing users to visually grasp the trends in the data.

[0473] The server uses artificial intelligence to perform heat map analysis. The AI ​​uses machine learning models to learn data patterns, which then automatically evaluates individual stocks and provides the analysis results to the user.

[0474] User emotion recognition and ad delivery

[0475] The device monitors the user's behavior and reactions to infer emotions. Specifically, it collects data such as the user's clicks, browsing history, and input content and sends it to the emotion engine. The emotion engine then uses machine learning models to infer the user's emotions.

[0476] The server selects appropriate advertisements based on the user's emotional data received from the emotion engine. To select advertisements, it searches for highly relevant advertisements in the advertisement database and selects the best advertisements using a scoring system.

[0477] The server delivers the selected ad to the user interface using the WebSocket and HTTP protocols for real-time data transmission, which then displays the ad in the user's browser.

[0478] Examples and prompts

[0479] Specific examples

[0480] The server uses a common financial information API to retrieve the latest financial results data of listed companies and stores it in a MySQL database. A cron job is run every day at 9:00 AM to update the data.

[0481] If a user has a positive reaction to a particular stock, the device sends that information to the sentiment engine, which then selects relevant ads and delivers them to the user's browser via WebSocket.

[0482] Prompt Sentence Examples

[0483] "Generate a program that collects financial data from listed companies and stores it in a MySQL database."

[0484] "Generate a program for a system that recognizes user emotions and selects and delivers ads based on those emotions."

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1:

[0487] The server uses a financial information API to collect financial data from listed companies and stock indexes (such as the Nikkei Average and TOPIX). Specifically, it sends an HTTP request using an API key and receives data in JSON format. The input is the API request, and the output is the JSON data of the acquired financial and economic information.

[0488] Step 2:

[0489] The server analyzes the acquired JSON data and extracts the necessary information, such as company name, fiscal year, sales, and net profit. The input is JSON data, and the output is a list of extracted financial and economic information.

[0490] Step 3:

[0491] The server stores the extracted data in a MySQL database. Specifically, it inserts the data into a table with columns such as company name, fiscal year, sales, and net profit. The input is a list of financial and economic information, and the output is the data stored in the database.

[0492] Step 4:

[0493] The server sets up a cron job to periodically update the data. Specifically, it retrieves the latest data at 9:00 AM every day and compares it with the existing data to update it. The input is the latest financial and economic information, and the output is the updated database.

[0494] Step 5:

[0495] The device monitors the user's behavior and reactions and collects data to infer emotions. Specifically, it collects user clicks, browsing history, input content, etc. The input is the user's behavior data, and the output is the collected behavior data.

[0496] Step 6:

[0497] The device sends the collected behavioral data to the emotion engine, which uses a machine learning model to estimate the user's emotions. The input is the behavioral data, and the output is the estimated emotion data.

[0498] Step 7:

[0499] The server selects appropriate advertisements based on the emotion data received from the emotion engine. Specifically, it searches for relevant advertisements in the advertisement database and selects the best one using a scoring system. The input is the emotion data, and the output is the selected advertisement.

[0500] Step 8:

[0501] The server delivers the selected ad to the user interface by transmitting the ad data in real time using WebSockets and displaying it in the user's browser. The input is the selected ad, and the output is the ad displayed in the user's browser.

[0502] (Application example 1)

[0503] Next, a description will be given of Application Example 1 of Embodiment 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."

[0504] Conventional advertising delivery systems that use financial and economic information deliver ads without considering the user's emotions, resulting in ads that often do not match the user's interests. This results in low advertising effectiveness and low user satisfaction. Furthermore, when providing the results of analysis of financial and economic information to users, the system is unable to reflect the user's emotions, preventing an improvement in the user experience.

[0505] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence that performs heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the analysis results of the artificial intelligence, an emotion engine that recognizes the user's emotions and selects advertisements based on those emotions, and means for delivering advertisements to the user interface. This makes it possible to deliver advertisements based on the user's emotions, thereby increasing the effectiveness of advertisements and improving the user experience.

[0506] "Financial and economic information" is a general term for data related to financial markets, such as financial results data of listed companies and stock price indices (e.g., Nikkei Average, TOPIX).

[0507] "Database creation" is the process of storing collected data in a database that allows it to be managed centrally and efficiently searched and used.

[0508] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[0509] "Artificial intelligence" refers to computer systems that mimic human intelligence and analyze data and make predictions.

[0510] An "emotion engine" is a system that infers emotions from a user's actions and reactions and selects appropriate actions based on those emotions.

[0511] "User interface" is a general term for the screens and operating means that users use to interact with a system.

[0512] "Ad serving" is the process of displaying advertisements to specific users.

[0513] "Targeted advertising" is advertising selected based on a particular user's interests.

[0514] "Marketing activities" include activities such as market research, advertising, and formulating sales strategies for the purpose of promoting products and services.

[0515] A system for carrying out this invention is configured as follows: A server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the results of the analysis by the artificial intelligence, an emotion engine for recognizing user emotions and selecting advertisements based on those emotions, and means for delivering advertisements to a user interface.

[0516] Hardware and software used

[0517] Hardware: Servers, smartphones

[0518] Software: Python, SentimentAnalyzer (sentiment analysis library), FinancialDatabase (financial database management system), AdSelector (advertising selection system)

[0519] Data processing and calculation

[0520] 1. Collection and database of financial and economic information:

[0521] The server uses APIs to collect financial and economic information and stores it in the FinancialDatabase, which allows for centralized management of information such as financial statements and stock indexes for listed companies.

[0522] 2. Heatmap analysis:

[0523] The server performs heat map analysis of individual stock valuations based on data stored in the FinancialDatabase, and uses artificial intelligence to visually represent the distribution and trends of the data.

[0524] 3. Emotion Engine:

[0525] The server uses the SentimentAnalyzer to infer sentiment from user behavior and reactions, for example, recognizing a user's positive reaction to a particular stock.

[0526] 4. Ad Selection and Delivery:

[0527] The server uses the AdSelector to select the best ad based on the user's sentiment, and the selected ad is delivered to the user interface.

[0528] Specific examples

[0529] For example, if a user browses information about a particular stock on their smartphone and has a positive reaction to that stock, the server will recognize that emotion and select and deliver advertisements for related financial products. In this way, advertisements can be delivered based on the user's emotions, increasing the effectiveness of the advertisements.

[0530] Prompt Sentence Examples

[0531] “If a user responds positively to a particular stock, generate a Python program that selects ads related to that stock and delivers them to the user interface.”

[0532] The above is a specific embodiment for carrying out the present invention.

[0533] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0534] Step 1:

[0535] The server collects financial and economic information. Specifically, it uses APIs to obtain financial data of listed companies and stock indexes (Nikkei Average, TOPIX, etc.). The input is the financial and economic information obtained from the API, and the output is a dataset containing this information.

[0536] Step 2:

[0537] The server stores the collected financial and economic information in the FinancialDatabase. The input is the dataset acquired in step 1, and the output is the financial and economic information stored in the database. Specifically, it connects to the database and saves the data in an appropriate format.

[0538] Step 3:

[0539] The server performs heat map analysis of the evaluation of individual stocks based on the data stored in the FinancialDatabase. The input is the data of individual stocks obtained from the database, and the output is the evaluation results in heat map format. Specifically, it uses artificial intelligence to visually represent the distribution and trends of the data using shades of color.

[0540] Step 4:

[0541] The server collects user behavior and reactions. Specifically, it records the stock information viewed by users on their smartphones and their reactions (e.g., positive or negative). The input is user behavior data, and the output is a dataset containing this behavior data.

[0542] Step 5:

[0543] The server uses SentimentAnalyzer to estimate emotions from user behavior data. The input is the user behavior data collected in step 4, and the output is the estimated user emotion (e.g., positive, negative). Specifically, it applies a sentiment analysis algorithm to classify the user emotion.

[0544] Step 6:

[0545] The server uses AdSelector to select the optimal ad based on the user's emotions. The input is the user's emotions estimated in step 5 and the ad data stored in the FinancialDatabase, and the output is the selected ad. Specifically, it searches the database for ads corresponding to the emotions and selects the optimal ad.

[0546] Step 7:

[0547] The server delivers the selected advertisement to the user interface. The input is the advertisement selected in step 6, and the output is the advertisement displayed on the user's smartphone. Specifically, the advertisement data is sent to the user interface and displayed.

[0548] The above are the specific processing steps for carrying out the present invention.

[0549] Example 2

[0550] Next, a description will be given of Example 2 of Form 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."

[0551] In conventional financial and economic information systems, it was difficult to provide personalized evaluations that reflected the user's emotions when providing evaluations of individual stocks to users. In addition, there was a lack of means to display evaluation results in an easy-to-understand visual format, making it difficult for users to intuitively understand the information. Furthermore, in terms of advertisement delivery, targeting based on user emotions and evaluation results was insufficient.

[0552] 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.

[0553] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence means for performing heat map analysis of individual stock ratings from the database, means for responding to the user with the analysis results of the artificial intelligence, sentiment analysis means for adjusting the ratings of individual stocks based on the user's sentiment, means for generating a new heat map based on the adjusted rating data, and means for delivering advertisements to the user interface. This makes it possible to provide personalized ratings that reflect the user's sentiment, realize a visually easy-to-understand heat map display, and further improve the accuracy of targeted advertising.

[0554] "Financial and economic information" refers to data, news, and analytical information related to financial markets such as stocks, bonds, foreign exchange, and commodities.

[0555] A "database" refers to an information management system that systematically organizes and stores information so that it can be searched and used efficiently.

[0556] "Individual stocks" refer to shares of a specific company and are generally financial products traded on a stock exchange.

[0557] "Valuation" refers to determining the value or performance of individual stocks based on specific criteria.

[0558] A "heat map" is a type of graph format that visually represents data values ​​using shades or hues of color.

[0559] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0560] "User" refers to any individual or legal entity that uses the System.

[0561] "Emotion analysis" refers to the technology of identifying emotions from text data or audio data and evaluating the type and intensity of those emotions.

[0562] "Advertising delivery" refers to displaying or sending advertisements to specific targets.

[0563] "Targeted advertising" refers to a method of delivering optimized advertisements based on specific user demographics or behavioral patterns.

[0564] "Personalization" refers to optimizing services and content to suit the characteristics and preferences of individual users.

[0565] This invention is a system that uses artificial intelligence to create a database of financial and economic information and perform heat map analysis of individual stock evaluations. It also has the function of adjusting the evaluation of individual stocks based on user sentiment and regenerating a heat map based on the adjusted evaluation data. This system also includes a means for delivering advertisements to the user interface.

[0566] Hardware and software used

[0567] server

[0568] The server manages financial and economic information using a database management system (DBMS). Specifically, it uses common DBMSs such as MySQL and PostgreSQL. It also uses Python libraries such as Pandas, Matplotlib, and Seaborn for data analysis. It uses natural language processing libraries such as TextBlob and VADER for sentiment analysis.

[0569] Terminal

[0570] A terminal is a device through which a user accesses the system, and includes a PC, smartphone, tablet, etc. The terminal communicates with the server via a web browser and displays the user interface.

[0571] User

[0572] Users can use the system to obtain evaluation information for individual stocks and input their feelings, and can make investment decisions based on the evaluation results.

[0573] Data processing and calculation

[0574] Retrieving information from a database

[0575] The server retrieves individual stock evaluation data from the database using an SQL query, for example "SELECT stock_id, good_count, bad_count FROM stock_evaluations;".

[0576] Preparing for heatmap analysis

[0577] The server converts the retrieved data into a Pandas data frame, aggregates the number of "good" and "bad" ratings for each stock, and then creates a dataset for the heatmap.

[0578] Generate heatmaps

[0579] The server generates the heatmap using the Seaborn library and sends the generated heatmap image to the device, which displays it to the user.

[0580] Rating adjustment by emotion engine

[0581] A user inputs their sentiment about a particular stock, for example, commenting, "Stock A has no future prospects." The device sends this comment to the server, which uses the TextBlob library to analyze the comment and detect negative sentiment. It then adjusts the rating of the corresponding stock.

[0582] Regenerating the heatmap

[0583] The server regenerates a heat map based on the adjusted evaluation data and transmits it to the device, which then displays the regenerated heat map to the user.

[0584] Specific examples

[0585] Example 1: Generating a heatmap

[0586] If a user gives 100 thumbs up to stock A and 50 thumbs down to stock B, the server aggregates this data and generates a heat map, with stock A shown in red and stock B in blue.

[0587] Example 2: Using the Emotion Engine

[0588] If a user comments on Stock C, "This stock has no future prospects," the device sends this comment to the server. The server uses TextBlob to analyze the comment and detects negative sentiment. As a result, Stock C's rating drops and its color on the heatmap changes to blue.

[0589] Prompt Sentence Examples

[0590] "Please explain a system that analyzes user sentiment towards a particular stock and reflects it in a heatmap."

[0591] By inputting this prompt into a generative AI model, a detailed description of the system can be obtained.

[0592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0593] Step 1:

[0594] The server obtains the evaluation data of individual stocks from the database.

[0595] Input: Database connection information and SQL query (e.g., "SELECT stock_id, good_count, bad_count FROM stock_evaluations;")

[0596] Data processing: Execute SQL queries to obtain valuation data for individual stocks.

[0597] Output: The obtained rating data (e.g., {'stock_id': [1, 2, 3], 'good_count': [100, 50, 30], 'bad_count': [10, 20, 5]})

[0598] Step 2:

[0599] The server converts the acquired data into a Pandas data frame and tally the number of "good" and "bad" ratings for each stock.

[0600] Input: Acquired evaluation data

[0601] Data processing: Convert into a Pandas data frame and aggregate the rating numbers for each stock.

[0602] Output: Aggregated data frame (e.g. {'stock_id': [1, 2, 3], 'total_evaluation': [90, 30, 25]})

[0603] Step 3:

[0604] The server uses the Seaborn library to generate the heatmap.

[0605] Input: Aggregated data frame

[0606] Data Computation: Generate a heatmap using the Seaborn library.

[0607] Output: Generated heatmap image (e.g. heatmap.png)

[0608] Step 4:

[0609] The server transmits the generated heatmap image to the terminal.

[0610] Input: Generated heatmap image

[0611] Data processing: Converting image data into a format for sending to the terminal.

[0612] Output: Heatmap image sent to the device

[0613] Step 5:

[0614] The terminal displays the received heat map image to the user.

[0615] Input: Received heatmap image

[0616] Data processing: Converting image data into a format for display on the user interface.

[0617] Output: Heatmap image displayed to the user

[0618] Step 6:

[0619] A user inputs their sentiment for a particular stock.

[0620] Input: User's emotional comment (e.g., "Stock A has no future prospects")

[0621] Data processing: Emotional comments are entered into the terminal as text data.

[0622] Output: Emotional comments entered into the device

[0623] Step 7:

[0624] The terminal transmits the user's emotional comment to the server.

[0625] Input: User's emotional comment

[0626] Data processing: Convert the emotional comments into a format for sending to the server.

[0627] Output: Emotion comment sent to the server

[0628] Step 8:

[0629] The server uses the TextBlob library to parse sentiment comments and detect negative sentiment.

[0630] Input: User's emotional comment

[0631] Data Computation: Sentiment analysis is performed using the TextBlob library.

[0632] Output: Analysis results (e.g., negative sentiment detection)

[0633] Step 9:

[0634] The server adjusts the rating of the relevant stock based on the sentiment analysis results.

[0635] Input: Sentiment analysis results and evaluation data

[0636] Data processing: Adjusting the rating data (e.g., decreasing the rating if negative sentiment is detected).

[0637] Output: Adjusted evaluation data (e.g., {'stock_id': [1, 2, 3], 'total_evaluation': [80, 30, 25]})

[0638] Step 10:

[0639] The server generates a new heat map based on the adjusted evaluation data and transmits it to the terminal.

[0640] Input: Adjusted evaluation data

[0641] Data calculation: Generate the heatmap again using the Seaborn library.

[0642] Output: A regenerated heatmap image (e.g. heatmap_updated.png)

[0643] Step 11:

[0644] The terminal displays the regenerated heatmap image to the user.

[0645] Input: Regenerated heatmap image

[0646] Data processing: Converting image data into a format for display on the user interface.

[0647] Output: The regenerated heatmap image displayed to the user

[0648] (Application example 2)

[0649] Next, a description will be given of Application Example 2 of Form 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."

[0650] Conventional financial and economic information systems were unable to reflect users' emotions when evaluating individual stocks, making it difficult to provide personalized investment advice. Furthermore, there was a lack of a way to evaluate the stock prices related to products users were considering purchasing in real time and provide this information in a visually easy-to-understand format. This meant that users were unable to obtain sufficient information when making investment decisions, increasing investment risk.

[0651] 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.

[0652] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the results of the analysis by the AI, an emotion engine for adjusting the evaluations of individual stocks based on the user's emotions, a means for delivering advertisements to a user interface, and a means for evaluating in real time stock prices related to products the user is considering purchasing and visualizing them as a heat map. This makes it possible to provide personalized investment advice that reflects the user's emotions and to provide the user with real-time, visually easy-to-understand stock prices related to products the user is considering purchasing.

[0653] "Financial and economic information" is a general term for data and information related to financial markets and the economy, such as stock prices, exchange rates, interest rates, and economic indicators.

[0654] "Databasing" is the process of organizing information and storing it in a database so that it can be efficiently searched and used.

[0655] "Individual stocks" are stocks issued by specific companies and refer to the stock prices of individual companies.

[0656] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[0657] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0658] An "emotion engine" is a technology that analyzes the user's emotions and adjusts the system's behavior and output based on the results.

[0659] "User interface" is a general term for the screens and operating means that users use to interact with a system.

[0660] "Means for delivering advertisements" refers to the technology or method for displaying or transmitting advertisements to specific targets.

[0661] "Real-time" refers to data and information being processed immediately and provided without delay.

[0662] "Visualization" refers to the display of data or information in a visual format, such as a graph or chart.

[0663] A system for carrying out this invention has the following configuration: a server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the results of the analysis by the artificial intelligence, an emotion engine for adjusting the evaluation of individual stocks based on the user's emotions, means for delivering advertisements to a user interface, and means for evaluating in real time stock prices related to products that the user is considering purchasing and visualizing them as a heat map.

[0664] System program processing explanation

[0665] Hardware and Software

[0666] Hardware: Servers, smartphones, tablets, PCs

[0667] Software: Python, Pandas, Seaborn, TextBlob

[0668] Data processing and calculation

[0669] 1. Data Acquisition: The server creates a database of financial and economic information, including stock prices, exchange rates, interest rates, and economic indicators.

[0670] 2. Heatmap generation: The server generates a heatmap based on stock price data using artificial intelligence to analyze the heatmap of individual stock evaluations from the database. It is visualized using the Seaborn library.

[0671] 3. Sentiment Engine: The server uses a sentiment engine to adjust the ratings of individual stocks based on user sentiment. It uses the TextBlob library to perform sentiment analysis and adjusts stock ratings based on user feedback.

[0672] 4. Real-time evaluation: The server evaluates the stock prices related to the products the user is considering purchasing in real time and visualizes them as a heat map, allowing the user to instantly check the evaluation of the related stock prices.

[0673] Specific examples

[0674] A user opens the "Smart Investment Advisor" app on their smartphone and checks the stock prices related to the product they are considering purchasing. The app displays stock price ratings in real time as a heat map and adjusts the ratings based on the user's sentiment. For example, if a user has a negative reaction to a particular stock, the rating of that stock will be lowered.

[0675] Prompt Sentence Examples

[0676] "Evaluate the stock prices of items you are considering buying in real time and visualize them as a heat map. Also, adjust stock evaluations based on user sentiment and provide personalized investment advice."

[0677] In this way, the embodiments for carrying out the invention have been specifically shown.

[0678] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0679] Step 1:

[0680] The server creates a database of financial and economic information. Specifically, it collects data on stock prices, exchange rates, interest rates, economic indicators, etc. and stores it in the database. The input is data related to financial markets and the economy, and the output is an organized database.

[0681] Step 2:

[0682] The server uses artificial intelligence to analyze the evaluations of individual stocks from the database and generates a heat map based on stock price data. Specifically, it processes the data using the Pandas library and visualizes the heat map using the Seaborn library. The input is stock price data obtained from the database, and the output is the evaluation results visualized as a heat map.

[0683] Step 3:

[0684] The server uses a sentiment engine that adjusts individual stock ratings based on user sentiment. Specifically, it uses the TextBlob library to parse user feedback and perform sentiment analysis. The input is the user feedback, and the output is the adjusted stock rating.

[0685] Step 4:

[0686] The server evaluates the stock prices related to the products the user is considering purchasing in real time and visualizes them as a heat map. Specifically, it obtains real-time data and immediately updates the heat map. The input is stock price data obtained in real time, and the output is the updated heat map.

[0687] Step 5:

[0688] The server responds to the user with the results of the AI ​​analysis. Specifically, the server displays a heat map or adjusted stock price valuation through a user interface. The input is the heat map or adjusted stock price valuation, and the output is visualized information provided to the user.

[0689] Step 6:

[0690] The server delivers advertisements to the user interface, specifically displaying targeted advertisements based on user behavior and ratings. The input is user behavior data and rating data, and the output is the advertisement displayed to the user.

[0691] Example 3

[0692] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0693] Conventional financial and economic information systems have struggled to provide users with the fast and accurate evaluations of individual stocks and sector analyses they require. Furthermore, ad delivery based on user interests and behavior was not adequately implemented, leaving a need for improved user experience. Furthermore, there was a lack of technology to adjust ad delivery methods based on user emotions, making it difficult to maximize the effectiveness of advertising.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0695] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence means for performing heat map analysis of individual stock evaluations from the database, a means for providing the user with the results of the analysis performed by the artificial intelligence, a means for delivering advertisements to a user interface, a means for collecting user behavior data and analyzing it with an emotion engine, and a means for selecting and displaying advertisements to the user based on the analysis results. This makes it possible to quickly and accurately provide evaluations of individual stocks and sectors desired by users and deliver advertisements based on the user's interests and behavior. Furthermore, by adjusting the method of delivering advertisements based on the user's emotions, the effectiveness of advertisements can be maximized and the user experience can be improved.

[0696] "Financial and economic information" refers to information such as data related to financial markets, economic indicators, price information on individual stocks, and sector analysis.

[0697] "Database means" refers to technologies and systems that systematically collect, organize, and store financial and economic information, and enable it to be quickly searched and retrieved as needed.

[0698] "Artificial intelligence" refers to a system that uses technologies such as machine learning and natural language processing to analyze large amounts of data and generate and provide the information users want.

[0699] "Heat map analysis" refers to an analytical technique that uses shades of color to visually represent data distribution and trends.

[0700] "Means of responding to users" refers to the interface and communication means used to provide users with the analysis results generated by artificial intelligence.

[0701] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[0702] "Means for delivering advertisements" refers to the technology or system for displaying advertisements on the user interface.

[0703] "Behavioral data" refers to data about user behavior, such as user search history, click data, and viewing time.

[0704] An "emotion engine" refers to a technology or system that analyzes user behavioral data and estimates the user's emotional state.

[0705] "Means for selecting advertisements based on analysis results" refers to technologies and systems for selecting and delivering the most suitable advertisements to users based on the analysis results of the emotion engine.

[0706] This invention is a system that compiles financial and economic information into a database, evaluates individual stocks using artificial intelligence, and provides the results to users. It also collects user behavior data and analyzes it with an emotion engine to deliver optimal advertisements to users.

[0707] The server uses a database management system (e.g., MySQL or MongoDB) to store financial and economic information in a database, which allows for the systematic collection, organization, and storage of data related to financial markets, economic indicators, individual stock price information, sector analysis, and other information.

[0708] The server inputs the data retrieved from the database into artificial intelligence (e.g., machine learning models or natural language processing models) and performs heat map analysis of the evaluation of individual stocks. Specifically, a generative AI model (e.g., OpenAI's GPT-4) is used to generate the information the user is looking for. For example, if a user searches for the "electronic components" sector, the server generates evaluation results and market price trends for stocks in that sector.

[0709] The generated analysis results are sent from the server to the terminal and provided to the user. The terminal displays the received analysis results on the user interface, allowing the user to check the evaluation results for stocks in the "Electronic Components" sector.

[0710] The server also collects user behavior data (e.g., search history, click data, viewing time, etc.) and analyzes it using an emotion engine (e.g., Affectiva's emotion recognition technology). Based on the analysis results, the server selects the most suitable advertisement for the user and sends it to the device. The device then displays the received advertisement on the user interface, allowing the user to view advertisements based on their interests.

[0711] As a concrete example, the prompt text when a user searches the "electronic parts" sector is as follows:

[0712] Example prompt sentence:

[0713] A user has searched the "Electronic Components" sector. Please provide the following information:

[0714] 1. Evaluation results of major stocks in the electronic components sector

[0715] 2. Current market price of each stock and price trends over the past year

[0716] 3. Analysis of future prospects for each stock

[0717] In this way, the server can use the generative AI model to quickly and accurately provide the information the user wants, and can improve the user experience by adjusting the advertisement delivery method using the user's behavioral data and emotion engine. The flow of the identification process in Example 3 will be described with reference to Figure 21.

[0718] Step 1:

[0719] A user enters a search query.

[0720] A user enters a search query such as "electronic parts" into the search bar of a device and clicks the search button. The input is the text data "electronic parts." The output is the transmission of the search query.

[0721] Step 2:

[0722] The device sends a search query to the server.

[0723] The terminal sends the search query entered by the user to the server as an HTTP request. The input is the user's search query "electronic parts," and the output is the request sent to the server.

[0724] Step 3:

[0725] A server receives a search query and retrieves relevant data from a database.

[0726] The server receives the HTTP request and executes a database query to retrieve data on stocks related to the "electronic components" sector. The input is the search query "electronic components" and the output is the retrieval of the relevant stock data.

[0727] Step 4:

[0728] The server inputs the acquired data into a generative AI model.

[0729] The server converts the acquired stock data into a prompt and sends it to the generative AI model. Specifically, it generates the prompt, "Please provide the valuation results of major stocks in the electronic components sector." The input is the stock data, and the output is the generated prompt and input to the AI ​​model.

[0730] Step 5:

[0731] The generative AI model generates the analysis results.

[0732] The generative AI model analyzes the prompt text and generates the relevant stock valuation results and market price trends. The input is the prompt text, and the output is the analysis results.

[0733] Step 6:

[0734] The server transmits the generated analysis results to the terminal.

[0735] The server sends the analysis results obtained from the generative AI model to the terminal as an HTTP response. The input is the analysis results, and the output is the response sent to the terminal.

[0736] Step 7:

[0737] The terminal displays the analysis results to the user.

[0738] The terminal displays the received analysis results on a user interface, specifically in the search result area of ​​the web page. The input is the analysis results, and the output is what is displayed to the user.

[0739] Step 8:

[0740] The server collects user behavior data and analyzes it using an emotion engine.

[0741] The server collects user behavioral data such as search history and click data, and analyzes the user's emotions using an emotion engine. The input is behavioral data, and the output is the emotion analysis results.

[0742] Step 9:

[0743] The server selects an advertisement based on the analysis results and sends it to the device.

[0744] The server selects an advertisement appropriate for the user based on the analysis results of the emotion engine and sends it to the device as an HTTP response. The input is the emotion analysis result, and the output is the selection of an advertisement and its transmission to the device.

[0745] Step 10:

[0746] The terminal displays the advertisement to the user.

[0747] The terminal displays the received advertisement on the user interface. Specifically, the advertisement is displayed in the advertisement area of ​​the web page. The input is the advertisement data, and the output is the advertisement displayed to the user.

[0748] (Application example 3)

[0749] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0750] In conventional financial and economic information systems, the information provided when users searched for individual stocks or sectors was limited, and advertisements were not delivered based on user emotions. This made it difficult to increase user satisfaction, and the effectiveness of advertisements was limited. In addition, because advertisements were not delivered taking user emotions into consideration, there were problems with low ad click-through rates and conversion rates.

[0751] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0752] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the analysis results of the AI, a means for delivering advertisements to a user interface, an emotion engine for analyzing user emotions, and a means for adjusting the advertisement delivery method based on the emotion engine. This makes it possible to provide evaluation results for individual stocks or sectors searched by the user and to deliver advertisements based on the user's emotions.

[0753] "Financial and economic information" refers to data and information related to financial markets and the economy.

[0754] "Means of database creation" refers to methods and technologies for systematically organizing information and storing it in a database.

[0755] "Individual stocks" refer to stocks of specific companies.

[0756] "Heat map analysis" is a method of visually representing data, which uses shades of color to show the distribution and intensity of data.

[0757] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0758] "Means of responding to users" refers to methods and technologies for providing appropriate information in response to user inquiries.

[0759] "User interface" refers to the screens and methods of operation that allow users to interact with a system.

[0760] "Means of delivering advertisements" refers to the methods and technologies used to display a particular advertisement to a user.

[0761] An "emotion engine" refers to technology for analyzing user emotions and adjusting system behavior based on the results.

[0762] "Means for adjusting ad delivery" refers to methods or technologies for changing the frequency or content of ads based on user sentiment or behavior.

[0763] A system for carrying out this invention is configured as follows: A server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the analysis results of the artificial intelligence, means for delivering advertisements to a user interface, an emotion engine for analyzing user emotions, and means for adjusting the advertisement delivery method based on the emotion engine.

[0764] Program processing explanation

[0765] Hardware and software used

[0766] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0767] Software: Database management systems (e.g., MySQL), sentiment analysis libraries (e.g., TextBlob), AI stock valuation models, ad management systems

[0768] Data processing and calculation

[0769] 1. Database of financial and economic information:

[0770] The server collects and stores data related to financial markets and the economy in a database, including stock price data, company information, and economic indicators.

[0771] 2. Individual Stock Valuation:

[0772] The server uses the AI ​​model to evaluate individual stocks based on the information retrieved from the database, and the evaluation results are visualized in a heat map format and provided to the user.

[0773] 3. Answer to the user:

[0774] When a user searches for a specific individual stock or sector, the server generates an evaluation result using an AI model and responds to the user.

[0775] 4. Serving Advertisements:

[0776] The server displays advertisements on the user interface, such as targeted advertisements for securities, banking, cards, etc., selected based on the user's search history and behavior.

[0777] 5. Sentiment analysis with emotion engine:

[0778] The server inputs the user's response to the ad as text and uses a sentiment analysis library to determine positive, negative, or neutral sentiment.

[0779] 6. Adjusting Ad Delivery Methods:

[0780] The server then adjusts the frequency and content of ads based on the results of the sentiment analysis: ads that elicit positive responses are shown more frequently, and ads that elicit negative responses are not shown at all.

[0781] Specific examples

[0782] When a user searches for the "electronic components" sector, the server provides AI-based evaluations of stocks in that sector.

[0783] If a user responds positively to an ad by saying "I like this ad," the server will display that ad more frequently.

[0784] Prompt Sentence Examples

[0785] If a user searches for the "electronic components" sector, the AI ​​should provide a rating of stocks in that sector. Also, if a user responds positively to an ad by saying "I like this ad," the AI ​​should show that ad more frequently.

[0786] In this way, it is possible to provide evaluation results for individual stocks and sectors that users search for, as well as deliver advertisements based on user sentiment.

[0787] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0788] Step 1:

[0789] The server collects data related to financial markets and the economy and stores it in a database. This includes stock price data, company information, economic indicators, etc. The input is financial and economic information, and the output is the information stored in the database. Specifically, it retrieves financial data through an API and stores it in a database management system (e.g., MySQL).

[0790] Step 2:

[0791] The server uses an AI model to evaluate individual stocks based on information obtained from the database. The input is financial and economic information obtained from the database, and the output is the evaluation results. Specifically, the data is input into the AI ​​model, and the evaluation results are generated in the form of a heat map.

[0792] Step 3:

[0793] When a user searches for a specific individual stock or sector, the server generates an evaluation result using an AI model and responds to the user. The input is the user's search query, and the output is the evaluation result. Specifically, the server receives the user's search query, generates an evaluation result using the AI ​​model, and displays it on the user interface.

[0794] Step 4:

[0795] The server displays advertisements on the user interface. The input is the user's search history and behavioral data, and the output is the advertisement to be displayed. Specifically, the server uses an advertisement management system to select an appropriate advertisement and display it on the user interface.

[0796] Step 5:

[0797] The server inputs the user's response to the ad as text and uses a sentiment analysis library to determine whether the emotion is positive, negative, or neutral. The input is the user's response to the ad, and the output is the sentiment analysis result. Specifically, the server receives the user's response in text format and uses a sentiment analysis library (e.g., TextBlob) to determine the emotion.

[0798] Step 6:

[0799] The server adjusts the frequency and content of advertisements based on the results of the sentiment analysis. The input is the sentiment analysis result, and the output is an adjusted advertisement delivery method. Specifically, the server adjusts the advertisement management system based on the sentiment analysis result to display advertisements that elicit positive responses more frequently and not display advertisements that elicit negative responses.

[0800] 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.

[0801] 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> ) 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.

[0802] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0803] 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.

[0804] [Second embodiment]

[0805] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0806] 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.

[0807] 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).

[0808] 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.

[0809] 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.

[0810] 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).

[0811] 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.

[0812] 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.

[0813] 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.

[0814] The processor 28 executes the 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.

[0815] 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.

[0816] 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.

[0817] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0818] "Example 1"

[0819] As one embodiment of the present invention, a means for creating a database of financial and economic information is provided. Specifically, a database is constructed that collects financial data of listed companies, information such as the Nikkei average and TOPIX, and manages this information in an integrated manner.

[0820] "Example 2"

[0821] In addition, AI is provided that performs heat map analysis of the ratings of individual stocks from the database. This AI analyzes the "good!" and "bad!" ratings of individual stocks based on the information in the database and visualizes them as a heat map. For example, if a particular stock has received many "good!" ratings, that stock will be displayed in red on the heat map.

[0822] "Example 3"

[0823] In addition, a means is provided for users to receive answers based on the results of AI analysis. When a user searches for an individual stock or sector, the AI ​​generates answers based on the search results and provides them to the user. For example, if a user searches for the "electronic components" sector, the AI ​​will provide an answer based on the evaluation of stocks in that sector.

[0824] "Example 4"

[0825] Additionally, a means is provided for delivering advertisements to the user interface. As a user uses the system, advertisements are displayed on the user interface. These advertisements may be targeted advertisements for securities, banking, cards, etc., and are selected based on the user's interests and behavior.

[0826] The processing flow of each embodiment will be described below.

[0827] "Example 1"

[0828] Step 1: Collect financial and economic information. Specifically, collect financial data of listed companies, Nikkei average, TOPIX, etc. from the Internet.

[0829] Step 2: The collected financial and economic information is stored in a database. This database allows for centralized management of the information.

[0830] "Example 2"

[0831] Step 1: Obtain individual stock information from the database.

[0832] Step 2: Input the information about individual stocks into an artificial intelligence system that performs heat map analysis.

[0833] Step 3: Based on the input information, the artificial intelligence analyzes the "good!" or "bad!" ratings of individual stocks and visualizes them as a heat map.

[0834] "Example 3"

[0835] Step 1: User searches for an individual stock or sector.

[0836] Step 2: Feed the search results into artificial intelligence.

[0837] Step 3: The AI ​​generates an answer based on the search results entered and provides it to the user.

[0838] "Example 4"

[0839] Step 1: Analyze user behavior and interests as they use the system.

[0840] Step 2: Based on the analysis results, target advertisements for securities, banking, cards, etc. are selected. Step 3: The selected advertisements are displayed on the user interface.

[0841] Example 1

[0842] Next, a description will be given of Example 1 of Form 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."

[0843] In conventional financial and economic information management systems, the series of processes, such as information collection, formatting, normalization, database storage, user request processing, data search and retrieval, and data display, are often not carried out efficiently. Another issue is that it is difficult to provide users with the information they need quickly and accurately. Furthermore, there is a lack of means to effectively utilize collected data and improve the quality and efficiency of marketing department operations.

[0844] 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.

[0845] In this invention, the server includes means for collecting financial and economic information using public APIs on the Internet or web scraping technology, means for formatting and normalizing the collected data, and means for storing the formatted and normalized data in a database. This enables efficient collection and management of financial and economic information. It also includes means for users to access the database through their terminals and obtain the necessary financial and economic information, means for the server to receive user requests, search for and obtain the relevant data from the database, and means for displaying the obtained data to the user. This enables users to quickly and accurately obtain the information they need. Furthermore, by including means for delivering targeted advertisements based on the results of analysis using artificial intelligence, it is possible to improve the quality and efficiency of marketing department operations.

[0846] "Financial and economic information" refers to information related to financial markets and economic activity, such as corporate financial data, stock price indexes, and economic indicators.

[0847] "Database creation" is the process of organizing collected data and storing it in a database in a format that is easy to search and manage.

[0848] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[0849] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[0850] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[0851] "Ad serving" is the process of displaying advertisements to specific users.

[0852] A "public API" is an application programming interface that is made publicly available for access by external developers.

[0853] "Web scraping" is the technique of automatically extracting data from websites.

[0854] "Format" is the process of converting collected data into a certain format.

[0855] "Normalization" is the process of standardizing data to ensure consistency and accuracy.

[0856] "Request processing" is the process of receiving a request from a user and providing data in response to that request.

[0857] "Targeted advertising" refers to advertising delivered to a specific user demographic.

[0858] MODE FOR CARRYING OUT THE INVENTION

[0859] The present invention is a system for efficiently collecting, managing, and analyzing financial and economic information, and providing the information to users quickly and accurately. A specific embodiment of this system will be described below.

[0860] Data collection

[0861] The server collects financial and economic information using public APIs on the Internet and web scraping technology. Specifically, it uses Python's BeautifulSoup and Selenium to obtain information such as financial results data of listed companies, stock indexes, and economic indicators. For example, the server visits "https: / / example.com / financials / companyA" and extracts data from specific HTML tags.

[0862] Data Shaping and Normalization

[0863] The server formats and normalizes the collected data. Using the Python Pandas library, it converts the collected data into a DataFrame and converts data in different formats into a unified format. For example, it standardizes dates to YYYY-MM-DD format and aligns the number of decimal points for numeric data.

[0864] Storage in the database

[0865] The server stores the formatted and normalized data in a database. MySQL or PostgreSQL is used as the database. The server generates an SQL query and inserts the data into the appropriate table. For example, it executes the SQL query "INSERT INTO financials (company, date, revenue, profit) VALUES ('CompanyA', '2023-03-31', 3000000000, 500000000)".

[0866] Processing user requests

[0867] A user accesses the web interface through a terminal and requests the necessary financial and economic information. For example, if a user wants to search for "Company A's financial results data for the first quarter of 2023," the user enters "Company A 2023 Q1 financial results data" in the search bar and clicks the search button.

[0868] Searching and retrieving data

[0869] The server receives the user's request and searches the database for the relevant data. The server generates an SQL query and executes it against the database. For example, it executes the SQL query "SELECT FROM financials WHERE company='CompanyA' AND date='2023-03-31'".

[0870] Viewing Data

[0871] The server converts the acquired data into HTML format and sends it to the user's browser. The user can then view the information displayed in the browser, such as "Company A's financial results for Q1 2023: Sales of 30 billion yen, Profit of 5 billion yen."

[0872] Specific examples

[0873] As a concrete example, consider a case where a user wants to retrieve "Company A's financial results data for the first quarter of 2023." The user logs into the web interface and types "Company A 2023 Q1 financial results data" into the search bar. The server receives this request, searches the database for the relevant data, and displays it to the user.

[0874] Examples of prompts to be input to a generative AI model include:

[0875] Prompt: "Please retrieve Company A's financial results for the first quarter of 2023."

[0876] By feeding this prompt into a generative AI model, the model can generate the appropriate API requests and database queries to automate the process of retrieving the required data.

[0877] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0878] Step 1:

[0879] The server collects financial and economic information using public APIs on the Internet and web scraping technology. Specifically, the server uses Python's BeautifulSoup and Selenium to obtain information such as financial results data of listed companies, stock indexes, and economic indicators. For example, the server visits "https: / / example.com / financials / companyA" and extracts data from specific HTML tags.

[0880] Enter: Website URL

[0881] Output: Extracted raw data (HTML format)

[0882] Step 2:

[0883] The server formats and normalizes the collected data. Using the Python Pandas library, the collected data is converted into a DataFrame and data in different formats is converted into a unified format. For example, dates are unified into the YYYY-MM-DD format, and the number of decimal points for numeric data is adjusted.

[0884] Input: Extracted raw data (HTML format)

[0885] Output: Reformatted and normalized data (DataFrame format)

[0886] Step 3:

[0887] The server stores the formatted and normalized data in a database. MySQL or PostgreSQL is used as the database. The server generates an SQL query and inserts the data into the appropriate table. For example, it executes the SQL query "INSERT INTO financials (company, date, revenue, profit) VALUES ('CompanyA', '2023-03-31', 3000000000, 500000000)".

[0888] Input: Reformatted and normalized data (DataFrame format)

[0889] Output: Data stored in the database

[0890] Step 4:

[0891] A user accesses the web interface through a terminal and requests the necessary financial and economic information. For example, if a user wants to search for "Company A's financial results data for the first quarter of 2023," he or she enters "Company A 2023 Q1 financial results data" in the search bar and clicks the search button.

[0892] Input: User's search query ("Company A 2023 Q1 financial results data")

[0893] Output: Request to server

[0894] Step 5:

[0895] The server receives the user's request and searches the database for the relevant data. The server generates an SQL query and executes it against the database. For example, it executes the SQL query "SELECT FROM financials WHERE company='CompanyA' AND date='2023-03-31'".

[0896] Input: User request

[0897] Output: Search results (matching data)

[0898] Step 6:

[0899] The server converts the acquired data into HTML format and sends it to the user's browser. The user can then view the information displayed in the browser, such as "Company A's financial results for Q1 2023: Sales of 30 billion yen, Profit of 5 billion yen."

[0900] Input: Search results (relevant data)

[0901] Output: The data that is displayed in the user's browser

[0902] (Application example 1)

[0903] Next, a description will be given of Application Example 1 of Form 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."

[0904] With conventional financial and economic information systems, it was difficult to centrally manage information such as financial results data for listed companies, the Nikkei average, and TOPIX, making it difficult for users to obtain the latest financial information in real time. Furthermore, the system lacked investment advice and portfolio management functions, providing insufficient support for users to make appropriate investment decisions. Furthermore, the targeting of ad delivery was inaccurate, reducing the operational efficiency of the marketing department.

[0905] 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.

[0906] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) that performs heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the analysis results of the AI, a means for delivering advertisements to a user interface, a means for providing financial and economic information in real time, a means for generating investment advice, and a means for managing an investment portfolio and performing risk assessment and performance analysis. This allows users to obtain the latest financial information in real time and receive support for making appropriate investment decisions. Furthermore, the targeting of advertisement delivery is more accurate, improving the operational efficiency of the marketing department.

[0907] "Financial and economic information" refers to various data related to financial markets, including financial results data of listed companies and stock price indices (Nikkei Average, TOPIX, etc.).

[0908] "Database creation" is the process of storing collected information in a database that allows it to be managed centrally and efficiently searched and used.

[0909] "Heat map analysis" is a method of visually representing data, using shades of color to show the distribution and trends of data.

[0910] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[0911] A "user interface" is the means or screen through which a user interacts with a system.

[0912] "Ad serving" is the process of displaying advertisements to specific targets.

[0913] "Real-time provision" means obtaining information instantly and providing it to users without delay.

[0914] "Investment Advice" means providing advice or recommendations to assist users in making investment decisions.

[0915] An "investment portfolio" is a combination of multiple investment assets held by a user.

[0916] "Risk assessment" is the process of analyzing and evaluating the risks associated with an investment.

[0917] "Performance analysis" is an analysis used to evaluate the results and effectiveness of an investment.

[0918] To implement the present invention, the following system configuration and program are required.

[0919] System Configuration

[0920] 1. Server: Collects financial and economic information and stores it in a database.

[0921] 2. Device: The smartphone or computer used by the user.

[0922] 3. Artificial Intelligence (AI): Retrieves information from the database and generates heat map analysis and investment advice.

[0923] 4. User Interface (UI): The screen through which users view and interact with information.

[0924] Program processing

[0925] Data collection and database creation

[0926] The server uses APIs to collect financial and economic information such as financial results data for listed companies, the Nikkei average, and TOPIX, etc. The collected data is stored in a database such as SQLite.

[0927] Heatmap Analysis

[0928] The AI ​​retrieves individual stock evaluation data from the database and performs heat map analysis, which visually shows the distribution and trends of the data.

[0929] Real-time information provision

[0930] The server provides the collected financial and economic information to users in real time, allowing them to instantly access the latest information via their smartphones or computers.

[0931] Investment advice generation

[0932] AI generates investment advice based on the collected data, which users can use to make appropriate investment decisions.

[0933] Portfolio Management

[0934] The server manages users' investment portfolios, performs risk assessments and performance analysis, and helps users understand their investment status.

[0935] Ad serving

[0936] The user interface will be delivered with targeted advertising, which will be precisely targeted based on the results of analysis using artificial intelligence.

[0937] Specific examples

[0938] Let's say a user opens the app and checks the latest Nikkei 225. They speak the following prompt:

[0939] Please tell me the latest value of the Nikkei average.

[0940] The server receives this prompt and retrieves the latest information from the database and provides it to the user, allowing the user to check the latest financial information in real time and make appropriate investment decisions.

[0941] In this way, the present invention realizes a system that provides users with financial and economic information in real time and supports investment advice and portfolio management.

[0942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0943] Step 1:

[0944] The server uses an API to collect financial and economic information such as financial results data for listed companies, the Nikkei average, and TOPIX.

[0945] Input: Financial and economic information obtained from the API.

[0946] Data processing: Convert the acquired data from JSON format to an appropriate data format.

[0947] Output: Transformed financial and economic information data.

[0948] Step 2:

[0949] The server stores the collected financial and economic information in a database such as SQLite.

[0950] Input: Transformed financial and economic information data.

[0951] Data processing: Generates SQL queries to store in the database.

[0952] Output: Financial and economic information stored in a database.

[0953] Step 3:

[0954] Users launch the application via their smartphone or computer and enter a prompt to check their latest financial information.

[0955] Input: User prompt (e.g. "What is the latest Nikkei average?").

[0956] Data processing: Analyze the prompt and extract the necessary information.

[0957] Output: Parsed information request.

[0958] Step 4:

[0959] The server retrieves the latest financial information from a database based on the parsed information request.

[0960] Input: Parsed information request.

[0961] Data processing: Generate SQL queries to retrieve the required information from the database.

[0962] Output: The latest financial information obtained.

[0963] Step 5:

[0964] The artificial intelligence performs heat map analysis based on the acquired financial information.

[0965] Input: The latest financial information obtained.

[0966] Data processing: Apply heat map analysis algorithms to visualize data distributions and trends.

[0967] Output: Heatmap analysis results.

[0968] Step 6:

[0969] The server provides the heat map analysis results to the user.

[0970] Input: Heatmap analysis results.

[0971] Data processing: Converting data into a format suitable for display on the user interface.

[0972] Output: A heatmap displayed in the user interface.

[0973] Step 7:

[0974] Artificial intelligence generates investment advice based on the acquired financial information.

[0975] Input: The latest financial information obtained.

[0976] Data processing: Applying investment advice generation algorithms to generate appropriate investment advice.

[0977] Output: The generated investment advice.

[0978] Step 8:

[0979] The server provides the generated investment advice to the user.

[0980] Input: Generated investment advice.

[0981] Data processing: Converting data into a format suitable for display on the user interface.

[0982] Output: Investment advice displayed in the user interface.

[0983] Step 9:

[0984] The server manages the user's investment portfolio and performs risk assessment and performance analysis.

[0985] Input: User's investment portfolio data.

[0986] Data processing: Applying risk assessment and performance analysis algorithms.

[0987] Output: Risk assessment results and performance analysis results.

[0988] Step 10:

[0989] The user interface is delivered with targeted advertisements.

[0990] Input: AI analysis results.

[0991] Data processing: Selecting targeted advertisements and converting them into a data format for display in the user interface.

[0992] Output: Targeted ads displayed in the user interface.

[0993] Example 2

[0994] Next, a description will be given of Example 2 of Form 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."

[0995] In conventional financial and economic information systems, it was difficult for users to visually understand the evaluation of individual stocks, making it difficult for users to make quick investment decisions. Furthermore, there was a lack of means to obtain and visualize evaluation data in real time based on user requests, which resulted in a poor user experience. Furthermore, targeted advertising based on evaluation results was not effectively delivered.

[0996] 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.

[0997] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of individual stock evaluations from the database, a means for providing the AI ​​analysis results to the user, a means for receiving a request from the user via a user interface, a means for acquiring evaluation data from the database based on the request, a means for preprocessing the acquired evaluation data and calculating an evaluation score, a means for generating a heat map based on the evaluation score, and a means for displaying the generated heat map on the user interface. This allows users to visually understand the evaluations of individual stocks and make quick investment decisions. Furthermore, acquiring and visualizing evaluation data in real time improves the user experience. Furthermore, targeted advertising based on the evaluation results can be effectively delivered.

[0998] "Financial and economic information" is a general term for data and information related to financial markets and economic activity.

[0999] A "database" is a system for efficiently storing, searching, and managing data.

[1000] "Individual stocks" refer to stocks of specific companies.

[1001] A "heat map" is a type of graph that visually represents the distribution and intensity of data using shades of color.

[1002] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1003] "User interface" refers to the means or screen through which a user interacts with a system.

[1004] A "request" refers to a request or inquiry made by a user to a system.

[1005] "Evaluation data" is data that includes evaluation information such as "Good!" or "Bad!" for individual stocks.

[1006] "Preprocessing" is the initial process used to prepare data for easier analysis.

[1007] An "evaluation score" is a numerical value calculated based on evaluation data, and indicates the evaluation of an individual stock.

[1008] "Visualization" refers to displaying data in the form of graphs, charts, etc., to make it easier to understand visually.

[1009] "Targeted advertising" refers to advertising that is delivered to a specific user demographic.

[1010] MODE FOR CARRYING OUT THE INVENTION

[1011] This invention is a system that uses artificial intelligence to compile a database of financial and economic information and perform heat map analysis of the evaluation of individual stocks. Specific embodiments of this system will be described below.

[1012] System configuration

[1013] The system consists of three main components: a server, a terminal, and a user. The server contains software for databases, artificial intelligence, data processing, and visualization. The terminal provides an interface for users to interact with the system.

[1014] Hardware and software used

[1015] The server is a computer equipped with a high-performance processor and large memory capacity. A relational database such as MySQL or PostgreSQL is used as the database management system. The artificial intelligence model is built using machine learning libraries such as TensorFlow and Keras. Data visualization libraries such as Matplotlib and Seaborn are used for data visualization.

[1016] Program processing

[1017] The server first retrieves the rating data for individual stocks from the database. This rating data includes the "good" and "bad" ratings for each stock. The server then preprocesses the retrieved data and aggregates the rating values. For example, if a particular stock has received 100 "good" ratings and 20 "bad" ratings, the rating score for that stock will be 80.

[1018] The server generates a heat map based on the evaluation scores. This heat map displays stocks with high evaluation scores in red and stocks with low evaluation scores in blue. The generated heat map is intended to make it easier for users to visually understand the evaluation situation.

[1019] Specific examples

[1020] When a user wants to check the rating of a specific stock, they send a request from their device to the server. For example, if a user sends a request saying, "I want to check the rating of Company A's stock," the server retrieves the rating data of Company A's stock from the database and generates a heat map. The generated heat map is displayed to the user through their device.

[1021] Examples of specific prompts include:

[1022] "I would like to check the valuation of Company A's stock."

[1023] The server that receives this prompt generates a heat map based on the evaluation data of Company A's stock and displays it on the terminal. By checking the heat map, the user can visually understand the evaluation status of Company A's stock.

[1024] In this way, the present invention allows users to quickly and visually understand the valuation of individual stocks, thereby assisting them in making investment decisions.

[1025] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1026] Step 1:

[1027] A user sends a stock evaluation request from a terminal.

[1028] Specifically, the user enters "I would like to check the stock valuation of Company A" into the terminal interface and clicks the send button.

[1029] Input: User request (e.g., "I would like to check the stock valuation of Company A.")

[1030] Output: Request sent from terminal to server

[1031] Step 2:

[1032] The server receives the request and retrieves the rating data from the database.

[1033] Specifically, the server analyzes the request and sends a query to the database to obtain Company A's evaluation data.

[1034] Input: Request from terminal

[1035] Output: Rating data retrieved from the database

[1036] Step 3:

[1037] The server preprocesses the rating data and calculates the rating score.

[1038] Specifically, the server analyzes the acquired evaluation data and tallies the number of "likes" and "dislikes." For example, if Company A's stock has received 100 "likes" and 20 "dislikes," the evaluation score will be 80.

[1039] Input: Evaluation data retrieved from the database

[1040] Output: Rating score (e.g. 80)

[1041] Step 4:

[1042] The server generates a heat map based on the evaluation scores.

[1043] Specifically, the server uses a data visualization library such as Matplotlib or Seaborn to create a heat map that displays stocks with high evaluation scores in red and stocks with low evaluation scores in blue.

[1044] Input: Rating score

[1045] Output: The generated heatmap

[1046] Step 5:

[1047] The server sends the generated heat map to the device.

[1048] Specifically, the server sends the generated heat map to the terminal, allowing the user to visually check the evaluation status.

[1049] Input: The generated heatmap

[1050] Output: Send heatmap to device

[1051] Step 6:

[1052] The device displays the heat map to the user.

[1053] Specifically, the terminal displays the heat map received from the server to the user. By checking the heat map, the user can visually understand the valuation status of Company A's stock.

[1054] Input: Heatmap sent from the server

[1055] Output: Heatmap displayed to the user

[1056] (Application example 2)

[1057] Next, a description will be given of Application Example 2 of Form 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."

[1058] In conventional financial and economic information systems, there were limited ways to visually grasp the evaluation of individual stocks, making it difficult for users to understand stock performance in real time. In addition, there was a lack of a mechanism for users to evaluate specific stocks and have those evaluations reflected by other users, resulting in insufficient information sharing between users. This resulted in a decrease in the accuracy of investment decisions, making it difficult to improve the quality and efficiency of marketing department operations.

[1059] 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.

[1060] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence means for performing heat map analysis of individual stock ratings from the database, means for responding to the user with the results of the analysis performed by the artificial intelligence, means for delivering advertisements to a user interface, means for displaying ratings of stocks held by the user or stocks of interest to the user in real time as a heat map, means for visually grasping stock performance based on ratings obtained from the database, and means for users to rate specific stocks and for the ratings to be reflected in other users' ratings. This allows users to visually grasp stock performance in real time, promoting information sharing among users and improving the accuracy of investment decisions. It also improves the quality and efficiency of marketing department operations.

[1061] "Financial and economic information" refers to data, news, analytical reports, and other information related to financial markets such as stocks, bonds, foreign exchange, and commodities.

[1062] A "database" is an information accumulation system that systematically organizes and stores information so that it can be quickly searched and used as needed.

[1063] "Individual stocks" are stocks issued by a specific company and are valued based on the financial situation and performance of that specific company.

[1064] A "heat map" is a type of graph that visually represents data values ​​using shades or hues of color, and is a means of intuitively grasping the distribution and trends of data.

[1065] "Artificial intelligence" refers to a computer system that mimics human intellectual activity and has capabilities such as learning, reasoning, and recognition.

[1066] A "user interface" is a means or screen that allows a user to interact with a system or application, and is designed to improve operability and visibility.

[1067] "Advertising" is an information dissemination activity to widely publicize products and services, and is a promotional method aimed at a specific target.

[1068] "Real-time" means that data and information are processed and reflected immediately, providing the latest information without delay.

[1069] "Performance" is an indicator that shows the business results and achievements of a stock or company, and serves as a basis for investment decisions.

[1070] "Evaluation" is the act of judging the value or quality of a particular object and expressing it through scores, comments, etc.

[1071] "Information sharing" is an activity that aims to exchange information between multiple users and systems and achieve a common understanding and recognition.

[1072] A "marketing department" is a department within a company that is responsible for market research, advertising, sales promotion, and other tasks, with the aim of increasing sales of products and services.

[1073] "Quality improvement" refers to improvement activities aimed at enhancing the performance and reliability of products and services, and aims to improve customer satisfaction.

[1074] "Efficiency" refers to activities that aim to eliminate waste in business and operations and achieve high results with fewer resources and time.

[1075] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1076] System Configuration

[1077] The system consists of the following major components:

[1078] 1. Server: Equipped with artificial intelligence that compiles financial and economic information into a database and performs heat map analysis of individual stock evaluations.

[1079] 2. User device: A device such as a smartphone or tablet provides a user interface and displays heat maps in real time.

[1080] 3. Network: Connects the server and user terminals to send and receive data.

[1081] Hardware and software used

[1082] Hardware: smartphones, tablets, servers

[1083] Software: Python, Pandas, Matplotlib, Seaborn, database management system (e.g., MySQL)

[1084] Data processing and calculation

[1085] 1. Data Acquisition: The server acquires financial and economic information from the database, including stock rating data such as "good" and "bad."

[1086] 2. Data processing: Based on the acquired data, a rating score for each stock is calculated. Specifically, the score is calculated by subtracting the number of "bad" ratings from the number of "good" ratings.

[1087] 3. Data visualization: The calculated scores are visualized as a heat map, which intuitively displays the distribution and trends of data using color shading and hues.

[1088] 4. User Interface: The heat map is displayed on the user's device in real time, allowing the user to rate specific stocks, which is then sent to the server and reflected to other users.

[1089] Specific examples

[1090] For example, if a user were to use a smartphone to check the stock valuation of AAPL (Apple), the following steps would be taken:

[1091] 1. The user launches the application and selects "AAPL" stock.

[1092] 2. The server retrieves the reputation data for "AAPL" from the database and calculates the reputation score.

[1093] 3. Based on the calculated scores, a heat map is generated and displayed on the user's device.

[1094] 4. When a user gives "AAPL" a "Like!" rating, the rating is sent to the server and reflected to other users.

[1095] Prompt Sentence Examples

[1096] "Show me a heat map of AAPL stock valuation."

[1097] "View TSLA stock valuation in a heat map."

[1098] In this way, users can visually grasp stock performance in real time and encourage information sharing among users, which improves the accuracy of investment decisions and improves the quality and efficiency of marketing department work.

[1099] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1100] Step 1:

[1101] The server retrieves financial and economic information from a database. Specifically, it extracts information including stock evaluation data such as "good" or "bad" from the database using queries. The input is a database query, and the output is a list of evaluation data.

[1102] Step 2:

[1103] The server calculates the evaluation score for each stock based on the acquired evaluation data. Specifically, it calculates the score by subtracting the number of "bad" comments from the number of "good" comments. The input is a list of evaluation data, and the output is the evaluation score for each stock.

[1104] Step 3:

[1105] The server generates a heatmap based on the calculated evaluation scores. Specifically, it uses Matplotlib and Seaborn to visually represent the evaluation scores using color shading and hues. The input is the evaluation scores, and the output is a heatmap image.

[1106] Step 4:

[1107] The server transmits the generated heatmap to the user terminal. Specifically, it transfers the heatmap image to the user terminal via a network. The input is the heatmap image, and the output is the transmission of image data to the user terminal.

[1108] Step 5:

[1109] The user device displays the received heatmap. Specifically, it displays the heatmap image on the screen of a smartphone or tablet. The input is the heatmap image, and the output is a visual display on the user interface.

[1110] Step 6:

[1111] A user rates a specific stock. Specifically, the user inputs a rating such as "Good!" or "Bad!" through a user interface. The input is the user's rating, and the output is the rating data.

[1112] Step 7:

[1113] The user terminal transmits the user's evaluation to the server. Specifically, the evaluation data is transferred to the server via the network. The input is the user's evaluation data, and the output is the data transmission to the server.

[1114] Step 8:

[1115] The server saves the received rating data in a database and reflects it to other users. Specifically, it adds the rating data to the database and updates the heat map again. The input is the user's rating data, and the output is the updated heat map.

[1116] Example 3

[1117] Next, a description will be given of Example 3 of Form Example 3. 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."

[1118] Conventional financial and economic information systems make it difficult for users to quickly and accurately obtain evaluations of individual stocks and sectors, and do not adequately deliver targeted advertisements based on user interests and behavior. This has made it difficult to improve the user experience and deliver advertisements effectively.

[1119] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1120] In this invention, the server includes a means for compiling financial and economic information into a database, a generative AI model for generating evaluations of individual stocks and sectors from the database, a means for sending prompts to the generative AI model to generate search results, a means for responding to the user with the analysis results of the generative AI model, and a means for delivering advertisements to the user interface, thereby enabling users to quickly and accurately obtain evaluations of individual stocks and sectors and effectively deliver targeted advertisements based on the user's interests and behavior.

[1121] "Financial and economic information" refers to all data and information related to financial markets and the economy.

[1122] "Database creation" refers to organizing information and storing it in a database so that it can be searched and used efficiently.

[1123] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate an appropriate output for a given input.

[1124] A "prompt sentence" refers to an input sentence that prompts a generative AI model to generate a specific output.

[1125] "Search Results" refers to the information and ratings provided by a generative AI model based on a user's query.

[1126] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[1127] "Means for delivering advertisements" refers to the technology or method for displaying advertisements on the user interface.

[1128] "Targeted advertising" refers to advertising that is selected and delivered to specific users based on their interests and behavior.

[1129] "Marketing activities" refers to activities such as market research, advertising, and sales strategy development aimed at promoting products and services.

[1130] MODE FOR CARRYING OUT THE INVENTION

[1131] This invention is a system that uses a generative AI model to quickly and accurately provide evaluation results and deliver targeted advertising when a user searches for individual stocks or sectors. Specific embodiments of this system are described below.

[1132] System configuration

[1133] server

[1134] The server has a means of compiling financial and economic information into a database. The database contains detailed information on individual stocks and sectors. The server receives search queries from users and sends prompts to the generative AI model.

[1135] Generative AI Models

[1136] The generative AI model uses, for example, OpenAI's GPT-4. This model generates evaluation results for individual stocks or sectors based on prompts sent from the server. The generated evaluation results are returned to the server.

[1137] Terminal

[1138] The terminal provides an interface for the user to input a search query. When the user inputs a keyword into the search box and clicks the search button, the terminal sends the query to the server.

[1139] User Interface

[1140] The user interface displays the ratings received from the server and also displays targeted advertisements based on the user's interests and behavior, including promotions for financial institutions and credit cards.

[1141] Specific examples

[1142] A user types "electronic parts" into the search box on their device and clicks the search button. The device sends this search query to the server. The server analyzes the received query and sends the following prompt to the generative AI model:

[1143] "A user has searched for the 'electronic components' sector. Please generate valuations for stocks in this sector."

[1144] The generative AI model generates an evaluation result based on this prompt. For example, it might say, "The electronic components sector includes ABC, DEF, and GHI. ABC has performed well recently, and its stock price is on the rise. DEF is attracting attention as it plans to release a new product. GHI is expanding its market share and is expected to grow in the long term."

[1145] The server then sends the evaluation results to the user's device and displays them on the user interface. At the same time, targeted advertisements based on the user's interests and behavior are also displayed. For example, an advertisement saying, "XYZ Securities is running a new account opening campaign!" is displayed.

[1146] In this way, users can quickly and accurately obtain evaluation results for individual stocks and sectors, and can also receive targeted advertisements based on their interests. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1147] Program processing flow

[1148] Step 1: User enters a search query

[1149] Users enter keywords for individual stocks or sectors they are interested in into the device's search box, for example, "electronic components," and click the search button.

[1150] Input: The keyword entered by the user (e.g., "electronic parts")

[1151] Output: The search query is sent to the device

[1152] Step 2: The device sends the search query to the server

[1153] The device sends the search query entered by the user to the server, transmitting the data securely using the HTTPS protocol.

[1154] Input: The search query entered by the user (e.g., "electronic parts")

[1155] Output: The search query is sent to the server

[1156] Step 3: The server receives the search query and sends a prompt to the generative AI model.

[1157] The server analyzes the search query received from the device and creates an appropriate prompt for the generative AI model, for example, "The user searched for the 'electronic components' sector. Please generate valuation results for stocks in this sector."

[1158] Input: Search query received from the device (e.g., "electronic parts")

[1159] Data processing: Analyze search queries and generate prompts

[1160] Output: The generated prompt sentence is sent to the generative AI model.

[1161] Step 4: The generative AI model generates search results and returns them to the server

[1162] A generative AI model (e.g., OpenAI's GPT-4) generates search results based on prompts received from the server. For example, the generated results might be something like, "The electronic components sector includes ABC, DEF, and GHI. ABC has performed well recently, and its stock price is on the rise. DEF is attracting attention as it plans to release a new product. GHI is expanding its market share and is expected to grow in the long term."

[1163] Input: The prompt received from the server (e.g., "The user searched for the 'Electronic Components' sector. Please generate valuation results for stocks in this sector.")

[1164] Data Calculation: Generate search results based on prompt statements

[1165] Output: The generated search results are returned to the server

[1166] Step 5: The server serves the generated search results to the user

[1167] The server provides the search results received from the generative AI model to the user, specifically by displaying the search results on the user interface of the terminal.

[1168] Input: Search results received from a generative AI model (e.g., "The electronic components sector includes ABC, DEF, and GHI. ABC has performed well recently and its stock price is trending upward. DEF has a new product launch that is attracting attention. GHI is expanding its market share and is expected to grow long-term.")

[1169] Output: Search results are displayed on the user's device

[1170] Step 6: The server delivers the ad to the user interface

[1171] The server delivers targeted advertisements to the user interface, selected based on the user's interests and behavior. For example, an advertisement saying "XYZ Securities is running a new account opening campaign!" is displayed.

[1172] Input: Advertising data based on user interests and behavior

[1173] Data processing: Selecting the right ads

[1174] Output: The selected ad is displayed in the user interface.

[1175] (Application example 3)

[1176] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1177] Conventional financial and economic information systems require a lot of time and effort for users to obtain evaluation results for individual stocks or sectors. Furthermore, they lack the ability to deliver targeted advertisements based on users' interests and behavior, resulting in low advertising effectiveness. Furthermore, they lack the ability to provide personalized information that utilizes users' search and trading history, resulting in a lack of an improved user experience.

[1178] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1179] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of individual stock evaluations from the database, means for responding to the user with the analysis results of the artificial intelligence, means for delivering advertisements to a user interface, means for acquiring the user's search history and transaction history and generating evaluation results based on said history, and means for selecting and displaying targeted advertisements based on the user's interests and behavior. This allows the user to quickly and efficiently obtain evaluation results for individual stocks and sectors, and furthermore, the delivery of personalized targeted advertisements improves the effectiveness of the advertisements and enhances the user experience.

[1180] "Financial and economic information" is a general term for data and information related to financial markets and economic activity.

[1181] "Databasing" is the process of organizing information and storing it in a database so that it can be efficiently searched and used.

[1182] An "individual stock" refers to the stock of a specific company, and is the unit traded on the stock market.

[1183] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[1184] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1185] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[1186] "Ad serving" is the process of displaying advertisements to specific users.

[1187] "Search history" is a record of keywords or queries a user has searched for in the past.

[1188] "Transaction History" means a record of financial transactions that a User has made in the past.

[1189] "Evaluation results" are evaluations and conclusions generated based on data analyzed by artificial intelligence.

[1190] "Targeted advertising" is advertising selected based on a particular user's interests or behavior.

[1191] "Personalization" refers to customizing something to suit the characteristics and preferences of each individual user.

[1192] A system for implementing this invention includes a server, a user terminal, and artificial intelligence (AI). The server compiles financial and economic information into a database and acquires user search history and trading history. The user terminal provides a user interface and a means for users to search for individual stocks and sectors. The AI ​​performs heat map analysis based on the information acquired from the database and generates evaluation results.

[1193] The server is built using a programming language such as Python, and uses MySQL or PostgreSQL as the database management system (DBMS). AI is implemented using machine learning libraries such as TensorFlow and PyTorch. The user interface is built using HTML, CSS, and JavaScript, and is designed to be intuitive for users.

[1194] The server retrieves the user's search history and trading history and inputs this data into the AI. The AI ​​generates evaluation results for individual stocks and sectors based on this data and provides them to the user. The evaluation results are displayed visually as heat maps. In addition, the server selects targeted advertisements based on the user's interests and behavior and displays them on the user interface.

[1195] For example, if a user searches for the "electronic components" sector, the AI ​​will generate an evaluation result that says, "The electronic components sector is currently on an upward trend," and provide it to the user. In addition, targeted advertisements for "new investment plans from securities companies" will be displayed.

[1196] Example prompts to input to a generative AI model:

[1197] When a user searches for the "electronic components" sector, generate valuation results for stocks in that sector, and select and display targeted ads based on the user's interests and behavior.

[1198] This system allows users to quickly and efficiently obtain evaluation results for individual stocks and sectors, and also delivers personalized targeted advertisements, improving the effectiveness of advertisements and improving the user experience.

[1199] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1200] Step 1:

[1201] The server creates a database of financial and economic information. Specifically, the server collects data related to financial markets and economic activity and stores it in a database management system such as MySQL or PostgreSQL. The input is financial and economic information, and the output is the information stored in the database.

[1202] Step 2:

[1203] Users use their devices to search for individual stocks or sectors. Specifically, they input search keywords through a browser on their smartphone or computer. The input is the user's search keyword, and the output is a search query.

[1204] Step 3:

[1205] The server retrieves the user's search history and transaction history. Specifically, the server retrieves the user's past search keywords and transaction data from the database. The input is the user ID, and the output is the search history and transaction history.

[1206] Step 4:

[1207] The server inputs the acquired search history and transaction history into the AI. Specifically, the server inputs this data into an AI model implemented using machine learning libraries such as TensorFlow and PyTorch. The input is the search history and transaction history, and the output is the input data for the AI ​​model.

[1208] Step 5:

[1209] AI generates evaluation results for individual stocks or sectors based on input data. Specifically, AI performs heat map analysis to generate evaluation results. The input is the input data for the AI ​​model, and the output is the evaluation results.

[1210] Step 6:

[1211] The server provides the AI ​​evaluation results to the user. Specifically, the server sends the evaluation results to the user's device and displays them on the user interface. The input is the evaluation result, and the output is the evaluation result displayed on the user's device.

[1212] Step 7:

[1213] The server selects targeted ads based on the user's interests and behavior. Specifically, the server selects appropriate ads based on the user's search history and transaction history. The input is the search history and transaction history, and the output is the selected ad.

[1214] Step 8:

[1215] The server displays the selected targeted advertisement on the user interface. Specifically, the server transmits advertisement data to the user terminal and displays it on the user interface. The input is the selected advertisement, and the output is the advertisement displayed on the user terminal.

[1216] 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.

[1217] "Example 1"

[1218] One embodiment of the present invention is a system incorporating an emotion engine. The system recognizes a user's emotion and selects and delivers advertisements to a user interface based on that emotion. Specifically, the system infers the user's emotion from the user's actions and reactions when using the system. For example, if a user has a positive reaction to a particular stock, the system selects and delivers advertisements related to that stock to the user interface.

[1219] "Example 2"

[1220] The sentiment engine can also adjust the ratings of individual stocks based on user sentiment. For example, if a user has a negative reaction to a particular stock, the rating of that stock will be lowered. This allows for a more personalized service that reflects user sentiment.

[1221] "Example 3"

[1222] Furthermore, the emotion engine can adjust the way ads are delivered based on the user's emotions. For example, if a user has a positive reaction to a particular ad, that ad can be displayed more frequently. On the other hand, if a user has a negative reaction to a particular ad, that ad can be hidden. This allows for ad delivery that takes user emotions into maximum consideration.

[1223] "Example 4"

[1224] In this way, a system incorporating an emotion engine can recognize user emotions and adjust the selection and delivery of advertisements and the evaluation of individual stocks based on those emotions, thereby providing users with a more beneficial and satisfying service.

[1225] The processing flow of each embodiment will be described below.

[1226] "Example 1"

[1227] Step 1: The emotion engine collects the user's actions and reactions when using the system. Step 2: The emotion engine estimates the user's emotions from the collected data.

[1228] Step 3: Based on the estimated sentiment, the sentiment engine selects an ad.

[1229] Step 4: Serving the selected advertisement in the user interface.

[1230] "Example 2"

[1231] Step 1: The sentiment engine collects user reactions to a particular stock.

[1232] Step 2: The emotion engine estimates the user's emotions from the collected responses.

[1233] Step 3: Based on the estimated sentiment, the sentiment engine adjusts the ratings of individual stocks.

[1234] "Example 3"

[1235] Step 1: The sentiment engine collects user responses to specific ads.

[1236] Step 2: The emotion engine estimates the user's emotions from the collected responses.

[1237] Step 3: Based on the estimated sentiment, the sentiment engine adjusts how the ad is delivered.

[1238] "Example 4"

[1239] Step 1: The emotion engine recognizes the user's emotion.

[1240] Step 2: Based on the perceived sentiment, the sentiment engine adjusts ad selection and delivery, as well as individual stock ratings.

[1241] Step 3: Provide the adjusted results to the user.

[1242] Example 1

[1243] Next, a description will be given of Example 1 of Form 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."

[1244] In conventional financial and economic information systems, data collection and management were dispersed, making efficient data analysis and ad delivery difficult. Furthermore, ad delivery did not take into account user sentiment, limiting the effectiveness of advertising. This created the challenge of being unable to improve the quality and efficiency of marketing operations.

[1245] 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.

[1246] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of individual stock evaluations from the database, a means for providing the AI ​​analysis results to the user, a means for monitoring the user's behavior and reactions and estimating their emotions, a means for selecting advertisements based on their emotions, and a means for delivering the advertisements to a user interface. This enables unified management of financial and economic information and efficient data analysis, and also enables delivery of targeted advertisements that take user emotions into consideration. This enables improved quality and efficiency in marketing operations.

[1247] "Financial and economic information" refers to information related to economic activity, such as corporate financial data, stock price indices, and trends in financial markets.

[1248] "Database creation" means storing collected information in a database that allows it to be managed centrally and searched and used efficiently.

[1249] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[1250] "Artificial intelligence" is a technology that allows computers to imitate human intelligence and perform learning and reasoning.

[1251] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[1252] "Advertising delivery" means displaying advertisements to specific users.

[1253] "User behavior and responses" refers to data such as clicks, browsing history, and input content when users use the system.

[1254] "Emotion estimation" means predicting a user's emotional state at a given time based on their actions and reactions.

[1255] "Targeted advertising" is advertising selected based on a particular user's interests.

[1256] "Marketing activities" include activities such as market research, advertising, and formulating sales strategies aimed at promoting products and services.

[1257] This invention is a system for storing financial and economic information in a database and delivering advertisements that take into account the emotions of users. A specific embodiment of this system will be described below.

[1258] Data collection and database creation

[1259] To collect financial and economic information, the server obtains data from financial information websites on the Internet. Specifically, it uses APIs to collect financial data from listed companies and stock indexes (Nikkei Average, TOPIX, etc.). Examples of APIs that can be used include general financial information APIs.

[1260] The server uses a database management system (DBMS) to centrally manage the collected data. Specifically, using database software such as MySQL or PostgreSQL, it creates tables with columns such as company name, fiscal year, sales, and net profit, and stores the data.

[1261] Heatmap analysis and artificial intelligence

[1262] The server performs heat map analysis of the evaluation of individual stocks based on financial and economic information stored in the database. Heat map analysis uses shades of color to visually represent the distribution and trends of data, allowing users to visually grasp the trends in the data.

[1263] The server uses artificial intelligence to perform heat map analysis. The AI ​​uses machine learning models to learn data patterns, which then automatically evaluates individual stocks and provides the analysis results to the user.

[1264] User emotion recognition and ad delivery

[1265] The device monitors the user's behavior and reactions to infer emotions. Specifically, it collects data such as the user's clicks, browsing history, and input content and sends it to the emotion engine. The emotion engine then uses machine learning models to infer the user's emotions.

[1266] The server selects appropriate advertisements based on the user's emotional data received from the emotion engine. To select advertisements, it searches for highly relevant advertisements in the advertisement database and selects the best advertisements using a scoring system.

[1267] The server delivers the selected ad to the user interface using the WebSocket and HTTP protocols for real-time data transmission, which then displays the ad in the user's browser.

[1268] Examples and prompts

[1269] Specific examples

[1270] The server uses a common financial information API to retrieve the latest financial results data of listed companies and stores it in a MySQL database. A cron job is run every day at 9:00 AM to update the data.

[1271] If a user has a positive reaction to a particular stock, the device sends that information to the sentiment engine, which then selects relevant ads and delivers them to the user's browser via WebSocket.

[1272] Prompt Sentence Examples

[1273] "Generate a program that collects financial data from listed companies and stores it in a MySQL database."

[1274] "Generate a program for a system that recognizes user emotions and selects and delivers ads based on those emotions."

[1275] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1276] Step 1:

[1277] The server uses a financial information API to collect financial data from listed companies and stock indexes (such as the Nikkei Average and TOPIX). Specifically, it sends an HTTP request using an API key and receives data in JSON format. The input is the API request, and the output is the JSON data of the acquired financial and economic information.

[1278] Step 2:

[1279] The server analyzes the acquired JSON data and extracts the necessary information, such as company name, fiscal year, sales, and net profit. The input is JSON data, and the output is a list of extracted financial and economic information.

[1280] Step 3:

[1281] The server stores the extracted data in a MySQL database. Specifically, it inserts the data into a table with columns such as company name, fiscal year, sales, and net profit. The input is a list of financial and economic information, and the output is the data stored in the database.

[1282] Step 4:

[1283] The server sets up a cron job to periodically update the data. Specifically, it retrieves the latest data at 9:00 AM every day and compares it with the existing data to update it. The input is the latest financial and economic information, and the output is the updated database.

[1284] Step 5:

[1285] The device monitors the user's behavior and reactions and collects data to infer emotions. Specifically, it collects user clicks, browsing history, input content, etc. The input is the user's behavior data, and the output is the collected behavior data.

[1286] Step 6:

[1287] The device sends the collected behavioral data to the emotion engine, which uses a machine learning model to estimate the user's emotions. The input is the behavioral data, and the output is the estimated emotion data.

[1288] Step 7:

[1289] The server selects appropriate advertisements based on the emotion data received from the emotion engine. Specifically, it searches for relevant advertisements in the advertisement database and selects the best one using a scoring system. The input is the emotion data, and the output is the selected advertisement.

[1290] Step 8:

[1291] The server delivers the selected ad to the user interface by transmitting the ad data in real time using WebSockets and displaying it in the user's browser. The input is the selected ad, and the output is the ad displayed in the user's browser.

[1292] (Application example 1)

[1293] Next, a description will be given of Application Example 1 of Form 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."

[1294] Conventional advertising delivery systems that use financial and economic information deliver ads without considering the user's emotions, resulting in ads that often do not match the user's interests. This results in low advertising effectiveness and low user satisfaction. Furthermore, when providing the results of analysis of financial and economic information to users, the system is unable to reflect the user's emotions, preventing an improvement in the user experience.

[1295] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence that performs heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the analysis results of the artificial intelligence, an emotion engine that recognizes the user's emotions and selects advertisements based on those emotions, and means for delivering advertisements to the user interface. This makes it possible to deliver advertisements based on the user's emotions, thereby increasing the effectiveness of advertisements and improving the user experience.

[1296] "Financial and economic information" is a general term for data related to financial markets, such as financial results data of listed companies and stock price indices (e.g., Nikkei Average, TOPIX).

[1297] "Database creation" is the process of storing collected data in a database that allows it to be managed centrally and efficiently searched and used.

[1298] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[1299] "Artificial intelligence" refers to computer systems that mimic human intelligence and analyze data and make predictions.

[1300] An "emotion engine" is a system that infers emotions from a user's actions and reactions and selects appropriate actions based on those emotions.

[1301] "User interface" is a general term for the screens and operating means that users use to interact with a system.

[1302] "Ad serving" is the process of displaying advertisements to specific users.

[1303] "Targeted advertising" is advertising selected based on a particular user's interests.

[1304] "Marketing activities" include activities such as market research, advertising, and formulating sales strategies for the purpose of promoting products and services.

[1305] A system for carrying out this invention is configured as follows: A server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the results of the analysis by the artificial intelligence, an emotion engine for recognizing user emotions and selecting advertisements based on those emotions, and means for delivering advertisements to a user interface.

[1306] Hardware and software used

[1307] Hardware: Servers, smartphones

[1308] Software: Python, SentimentAnalyzer (sentiment analysis library), FinancialDatabase (financial database management system), AdSelector (advertising selection system)

[1309] Data processing and calculation

[1310] 1. Collection and database of financial and economic information:

[1311] The server uses APIs to collect financial and economic information and stores it in the FinancialDatabase, which allows for centralized management of information such as financial statements and stock indexes for listed companies.

[1312] 2. Heatmap analysis:

[1313] The server performs heat map analysis of individual stock valuations based on data stored in the FinancialDatabase, and uses artificial intelligence to visually represent the distribution and trends of the data.

[1314] 3. Emotion Engine:

[1315] The server uses the SentimentAnalyzer to infer sentiment from user behavior and reactions, for example, recognizing a user's positive reaction to a particular stock.

[1316] 4. Ad Selection and Delivery:

[1317] The server uses the AdSelector to select the best ad based on the user's sentiment, and the selected ad is delivered to the user interface.

[1318] Specific examples

[1319] For example, if a user browses information about a particular stock on their smartphone and has a positive reaction to that stock, the server will recognize that emotion and select and deliver advertisements for related financial products. In this way, advertisements can be delivered based on the user's emotions, increasing the effectiveness of the advertisements.

[1320] Prompt Sentence Examples

[1321] “If a user responds positively to a particular stock, generate a Python program that selects ads related to that stock and delivers them to the user interface.”

[1322] The above is a specific embodiment for carrying out the present invention.

[1323] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1324] Step 1:

[1325] The server collects financial and economic information. Specifically, it uses APIs to obtain financial data of listed companies and stock indexes (Nikkei Average, TOPIX, etc.). The input is the financial and economic information obtained from the API, and the output is a dataset containing this information.

[1326] Step 2:

[1327] The server stores the collected financial and economic information in the FinancialDatabase. The input is the dataset acquired in step 1, and the output is the financial and economic information stored in the database. Specifically, it connects to the database and saves the data in an appropriate format.

[1328] Step 3:

[1329] The server performs heat map analysis of the evaluation of individual stocks based on the data stored in the FinancialDatabase. The input is the data of individual stocks obtained from the database, and the output is the evaluation results in heat map format. Specifically, it uses artificial intelligence to visually represent the distribution and trends of the data using shades of color.

[1330] Step 4:

[1331] The server collects user behavior and reactions. Specifically, it records the stock information viewed by users on their smartphones and their reactions (e.g., positive or negative). The input is user behavior data, and the output is a dataset containing this behavior data.

[1332] Step 5:

[1333] The server uses SentimentAnalyzer to estimate emotions from user behavior data. The input is the user behavior data collected in step 4, and the output is the estimated user emotion (e.g., positive, negative). Specifically, it applies a sentiment analysis algorithm to classify the user emotion.

[1334] Step 6:

[1335] The server uses AdSelector to select the optimal ad based on the user's emotions. The input is the user's emotions estimated in step 5 and the ad data stored in the FinancialDatabase, and the output is the selected ad. Specifically, it searches the database for ads corresponding to the emotions and selects the optimal ad.

[1336] Step 7:

[1337] The server delivers the selected advertisement to the user interface. The input is the advertisement selected in step 6, and the output is the advertisement displayed on the user's smartphone. Specifically, the advertisement data is sent to the user interface and displayed.

[1338] The above are the specific processing steps for carrying out the present invention.

[1339] Example 2

[1340] Next, a description will be given of Example 2 of Form 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."

[1341] In conventional financial and economic information systems, it was difficult to provide personalized evaluations that reflected the user's emotions when providing evaluations of individual stocks to users. In addition, there was a lack of means to display evaluation results in an easy-to-understand visual format, making it difficult for users to intuitively understand the information. Furthermore, in terms of advertisement delivery, targeting based on user emotions and evaluation results was insufficient.

[1342] 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.

[1343] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence means for performing heat map analysis of individual stock ratings from the database, means for responding to the user with the analysis results of the artificial intelligence, sentiment analysis means for adjusting the ratings of individual stocks based on the user's sentiment, means for generating a new heat map based on the adjusted rating data, and means for delivering advertisements to the user interface. This makes it possible to provide personalized ratings that reflect the user's sentiment, realize a visually easy-to-understand heat map display, and further improve the accuracy of targeted advertising.

[1344] "Financial and economic information" refers to data, news, and analytical information related to financial markets such as stocks, bonds, foreign exchange, and commodities.

[1345] A "database" refers to an information management system that systematically organizes and stores information so that it can be searched and used efficiently.

[1346] "Individual stocks" refer to shares of a specific company and are generally financial products traded on a stock exchange.

[1347] "Valuation" refers to determining the value or performance of individual stocks based on specific criteria.

[1348] A "heat map" is a type of graph format that visually represents data values ​​using shades or hues of color.

[1349] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1350] "User" refers to any individual or legal entity that uses the System.

[1351] "Emotion analysis" refers to the technology of identifying emotions from text data or audio data and evaluating the type and intensity of those emotions.

[1352] "Advertising delivery" refers to displaying or sending advertisements to specific targets.

[1353] "Targeted advertising" refers to a method of delivering optimized advertisements based on specific user demographics or behavioral patterns.

[1354] "Personalization" refers to optimizing services and content to suit the characteristics and preferences of individual users.

[1355] This invention is a system that uses artificial intelligence to create a database of financial and economic information and perform heat map analysis of individual stock evaluations. It also has the function of adjusting the evaluation of individual stocks based on user sentiment and regenerating a heat map based on the adjusted evaluation data. This system also includes a means for delivering advertisements to the user interface.

[1356] Hardware and software used

[1357] server

[1358] The server manages financial and economic information using a database management system (DBMS). Specifically, it uses common DBMSs such as MySQL and PostgreSQL. It also uses Python libraries such as Pandas, Matplotlib, and Seaborn for data analysis. It uses natural language processing libraries such as TextBlob and VADER for sentiment analysis.

[1359] Terminal

[1360] A terminal is a device through which a user accesses the system, and includes a PC, smartphone, tablet, etc. The terminal communicates with the server via a web browser and displays the user interface.

[1361] User

[1362] Users can use the system to obtain evaluation information for individual stocks and input their feelings, and can make investment decisions based on the evaluation results.

[1363] Data processing and calculation

[1364] Retrieving information from a database

[1365] The server retrieves individual stock evaluation data from the database using an SQL query, for example "SELECT stock_id, good_count, bad_count FROM stock_evaluations;".

[1366] Preparing for heatmap analysis

[1367] The server converts the retrieved data into a Pandas data frame, aggregates the number of "good" and "bad" ratings for each stock, and then creates a dataset for the heatmap.

[1368] Generate heatmaps

[1369] The server generates the heatmap using the Seaborn library and sends the generated heatmap image to the device, which displays it to the user.

[1370] Rating adjustment by emotion engine

[1371] A user inputs their sentiment about a particular stock, for example, commenting, "Stock A has no future prospects." The device sends this comment to the server, which uses the TextBlob library to analyze the comment and detect negative sentiment. It then adjusts the rating of the corresponding stock.

[1372] Regenerating the heatmap

[1373] The server regenerates a heat map based on the adjusted evaluation data and transmits it to the device, which then displays the regenerated heat map to the user.

[1374] Specific examples

[1375] Example 1: Generating a heatmap

[1376] If a user gives 100 thumbs up to stock A and 50 thumbs down to stock B, the server aggregates this data and generates a heat map, with stock A shown in red and stock B in blue.

[1377] Example 2: Using the Emotion Engine

[1378] If a user comments on Stock C, "This stock has no future prospects," the device sends this comment to the server. The server uses TextBlob to analyze the comment and detects negative sentiment. As a result, Stock C's rating drops and its color on the heatmap changes to blue.

[1379] Prompt Sentence Examples

[1380] "Please explain a system that analyzes user sentiment towards a particular stock and reflects it in a heatmap."

[1381] By inputting this prompt into a generative AI model, a detailed description of the system can be obtained.

[1382] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1383] Step 1:

[1384] The server obtains the evaluation data of individual stocks from the database.

[1385] Input: Database connection information and SQL query (e.g., "SELECT stock_id, good_count, bad_count FROM stock_evaluations;")

[1386] Data processing: Execute SQL queries to obtain valuation data for individual stocks.

[1387] Output: The obtained rating data (e.g., {'stock_id': [1, 2, 3], 'good_count': [100, 50, 30], 'bad_count': [10, 20, 5]})

[1388] Step 2:

[1389] The server converts the acquired data into a Pandas data frame and tally the number of "good" and "bad" ratings for each stock.

[1390] Input: Acquired evaluation data

[1391] Data processing: Convert into a Pandas data frame and aggregate the rating numbers for each stock.

[1392] Output: Aggregated data frame (e.g. {'stock_id': [1, 2, 3], 'total_evaluation': [90, 30, 25]})

[1393] Step 3:

[1394] The server uses the Seaborn library to generate the heatmap.

[1395] Input: Aggregated data frame

[1396] Data Computation: Generate a heatmap using the Seaborn library.

[1397] Output: Generated heatmap image (e.g. heatmap.png)

[1398] Step 4:

[1399] The server transmits the generated heatmap image to the terminal.

[1400] Input: Generated heatmap image

[1401] Data processing: Converting image data into a format for sending to the terminal.

[1402] Output: Heatmap image sent to the device

[1403] Step 5:

[1404] The terminal displays the received heat map image to the user.

[1405] Input: Received heatmap image

[1406] Data processing: Converting image data into a format for display on the user interface.

[1407] Output: Heatmap image displayed to the user

[1408] Step 6:

[1409] A user inputs their sentiment for a particular stock.

[1410] Input: User's emotional comment (e.g., "Stock A has no future prospects")

[1411] Data processing: Emotional comments are entered into the terminal as text data.

[1412] Output: Emotional comments entered into the device

[1413] Step 7:

[1414] The terminal transmits the user's emotional comment to the server.

[1415] Input: User's emotional comment

[1416] Data processing: Convert the emotional comments into a format for sending to the server.

[1417] Output: Emotion comment sent to the server

[1418] Step 8:

[1419] The server uses the TextBlob library to parse sentiment comments and detect negative sentiment.

[1420] Input: User's emotional comment

[1421] Data Computation: Sentiment analysis is performed using the TextBlob library.

[1422] Output: Analysis results (e.g., negative sentiment detection)

[1423] Step 9:

[1424] The server adjusts the rating of the relevant stock based on the sentiment analysis results.

[1425] Input: Sentiment analysis results and evaluation data

[1426] Data processing: Adjusting the rating data (e.g., decreasing the rating if negative sentiment is detected).

[1427] Output: Adjusted evaluation data (e.g., {'stock_id': [1, 2, 3], 'total_evaluation': [80, 30, 25]})

[1428] Step 10:

[1429] The server generates a new heat map based on the adjusted evaluation data and transmits it to the terminal.

[1430] Input: Adjusted evaluation data

[1431] Data calculation: Generate the heatmap again using the Seaborn library.

[1432] Output: A regenerated heatmap image (e.g. heatmap_updated.png)

[1433] Step 11:

[1434] The terminal displays the regenerated heatmap image to the user.

[1435] Input: Regenerated heatmap image

[1436] Data processing: Converting image data into a format for display on the user interface.

[1437] Output: The regenerated heatmap image displayed to the user

[1438] (Application example 2)

[1439] Next, a description will be given of Application Example 2 of Form 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."

[1440] Conventional financial and economic information systems were unable to reflect users' emotions when evaluating individual stocks, making it difficult to provide personalized investment advice. Furthermore, there was a lack of a way to evaluate the stock prices related to products users were considering purchasing in real time and provide this information in a visually easy-to-understand format. This meant that users were unable to obtain sufficient information when making investment decisions, increasing investment risk.

[1441] 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.

[1442] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the results of the analysis by the AI, an emotion engine for adjusting the evaluations of individual stocks based on the user's emotions, a means for delivering advertisements to a user interface, and a means for evaluating in real time stock prices related to products the user is considering purchasing and visualizing them as a heat map. This makes it possible to provide personalized investment advice that reflects the user's emotions and to provide the user with real-time, visually easy-to-understand stock prices related to products the user is considering purchasing.

[1443] "Financial and economic information" is a general term for data and information related to financial markets and the economy, such as stock prices, exchange rates, interest rates, and economic indicators.

[1444] "Databasing" is the process of organizing information and storing it in a database so that it can be efficiently searched and used.

[1445] "Individual stocks" are stocks issued by specific companies and refer to the stock prices of individual companies.

[1446] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[1447] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1448] An "emotion engine" is a technology that analyzes the user's emotions and adjusts the system's behavior and output based on the results.

[1449] "User interface" is a general term for the screens and operating means that users use to interact with a system.

[1450] "Means for delivering advertisements" refers to the technology or method for displaying or transmitting advertisements to specific targets.

[1451] "Real-time" refers to data and information being processed immediately and provided without delay.

[1452] "Visualization" refers to the display of data or information in a visual format, such as a graph or chart.

[1453] A system for carrying out this invention has the following configuration: a server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the results of the analysis by the artificial intelligence, an emotion engine for adjusting the evaluation of individual stocks based on the user's emotions, means for delivering advertisements to a user interface, and means for evaluating in real time stock prices related to products that the user is considering purchasing and visualizing them as a heat map.

[1454] System program processing explanation

[1455] Hardware and Software

[1456] Hardware: Servers, smartphones, tablets, PCs

[1457] Software: Python, Pandas, Seaborn, TextBlob

[1458] Data processing and calculation

[1459] 1. Data Acquisition: The server creates a database of financial and economic information, including stock prices, exchange rates, interest rates, and economic indicators.

[1460] 2. Heatmap generation: The server generates a heatmap based on stock price data using artificial intelligence to analyze the heatmap of individual stock evaluations from the database. It is visualized using the Seaborn library.

[1461] 3. Sentiment Engine: The server uses a sentiment engine to adjust the ratings of individual stocks based on user sentiment. It uses the TextBlob library to perform sentiment analysis and adjusts stock ratings based on user feedback.

[1462] 4. Real-time evaluation: The server evaluates the stock prices related to the products the user is considering purchasing in real time and visualizes them as a heat map, allowing the user to instantly check the evaluation of the related stock prices.

[1463] Specific examples

[1464] A user opens the "Smart Investment Advisor" app on their smartphone and checks the stock prices related to the product they are considering purchasing. The app displays stock price ratings in real time as a heat map and adjusts the ratings based on the user's sentiment. For example, if a user has a negative reaction to a particular stock, the rating of that stock will be lowered.

[1465] Prompt Sentence Examples

[1466] "Evaluate the stock prices of items you are considering buying in real time and visualize them as a heat map. Also, adjust stock evaluations based on user sentiment and provide personalized investment advice."

[1467] In this way, the embodiments for carrying out the invention have been specifically shown.

[1468] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1469] Step 1:

[1470] The server creates a database of financial and economic information. Specifically, it collects data on stock prices, exchange rates, interest rates, economic indicators, etc. and stores it in the database. The input is data related to financial markets and the economy, and the output is an organized database.

[1471] Step 2:

[1472] The server uses artificial intelligence to analyze the evaluations of individual stocks from the database and generates a heat map based on stock price data. Specifically, it processes the data using the Pandas library and visualizes the heat map using the Seaborn library. The input is stock price data obtained from the database, and the output is the evaluation results visualized as a heat map.

[1473] Step 3:

[1474] The server uses a sentiment engine that adjusts individual stock ratings based on user sentiment. Specifically, it uses the TextBlob library to parse user feedback and perform sentiment analysis. The input is the user feedback, and the output is the adjusted stock rating.

[1475] Step 4:

[1476] The server evaluates the stock prices related to the products the user is considering purchasing in real time and visualizes them as a heat map. Specifically, it obtains real-time data and immediately updates the heat map. The input is stock price data obtained in real time, and the output is the updated heat map.

[1477] Step 5:

[1478] The server responds to the user with the results of the AI ​​analysis. Specifically, the server displays a heat map or adjusted stock price valuation through a user interface. The input is the heat map or adjusted stock price valuation, and the output is visualized information provided to the user.

[1479] Step 6:

[1480] The server delivers advertisements to the user interface, specifically displaying targeted advertisements based on user behavior and ratings. The input is user behavior data and rating data, and the output is the advertisement displayed to the user.

[1481] Example 3

[1482] Next, a description will be given of Example 3 of Form Example 3. 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."

[1483] Conventional financial and economic information systems have struggled to provide users with the fast and accurate evaluations of individual stocks and sector analyses they require. Furthermore, ad delivery based on user interests and behavior was not adequately implemented, leaving a need for improved user experience. Furthermore, there was a lack of technology to adjust ad delivery methods based on user emotions, making it difficult to maximize the effectiveness of advertising.

[1484] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1485] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence means for performing heat map analysis of individual stock evaluations from the database, a means for providing the user with the results of the analysis performed by the artificial intelligence, a means for delivering advertisements to a user interface, a means for collecting user behavior data and analyzing it with an emotion engine, and a means for selecting and displaying advertisements to the user based on the analysis results. This makes it possible to quickly and accurately provide evaluations of individual stocks and sectors desired by users and deliver advertisements based on the user's interests and behavior. Furthermore, by adjusting the method of delivering advertisements based on the user's emotions, the effectiveness of advertisements can be maximized and the user experience can be improved.

[1486] "Financial and economic information" refers to information such as data related to financial markets, economic indicators, price information on individual stocks, and sector analysis.

[1487] "Database means" refers to technologies and systems that systematically collect, organize, and store financial and economic information, and enable it to be quickly searched and retrieved as needed.

[1488] "Artificial intelligence" refers to a system that uses technologies such as machine learning and natural language processing to analyze large amounts of data and generate and provide the information users want.

[1489] "Heat map analysis" refers to an analytical technique that uses shades of color to visually represent data distribution and trends.

[1490] "Means of responding to users" refers to the interface and communication means used to provide users with the analysis results generated by artificial intelligence.

[1491] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[1492] "Means for delivering advertisements" refers to the technology or system for displaying advertisements on the user interface.

[1493] "Behavioral data" refers to data about user behavior, such as user search history, click data, and viewing time.

[1494] An "emotion engine" refers to a technology or system that analyzes user behavioral data and estimates the user's emotional state.

[1495] "Means for selecting advertisements based on analysis results" refers to technologies and systems for selecting and delivering the most suitable advertisements to users based on the analysis results of the emotion engine.

[1496] This invention is a system that compiles financial and economic information into a database, evaluates individual stocks using artificial intelligence, and provides the results to users. It also collects user behavior data and analyzes it with an emotion engine to deliver optimal advertisements to users.

[1497] The server uses a database management system (e.g., MySQL or MongoDB) to store financial and economic information in a database, which allows for the systematic collection, organization, and storage of data related to financial markets, economic indicators, individual stock price information, sector analysis, and other information.

[1498] The server inputs the data retrieved from the database into artificial intelligence (e.g., machine learning models or natural language processing models) and performs heat map analysis of the evaluation of individual stocks. Specifically, a generative AI model (e.g., OpenAI's GPT-4) is used to generate the information the user is looking for. For example, if a user searches for the "electronic components" sector, the server generates evaluation results and market price trends for stocks in that sector.

[1499] The generated analysis results are sent from the server to the terminal and provided to the user. The terminal displays the received analysis results on the user interface, allowing the user to check the evaluation results for stocks in the "Electronic Components" sector.

[1500] The server also collects user behavior data (e.g., search history, click data, viewing time, etc.) and analyzes it using an emotion engine (e.g., Affectiva's emotion recognition technology). Based on the analysis results, the server selects the most suitable advertisement for the user and sends it to the device. The device then displays the received advertisement on the user interface, allowing the user to view advertisements based on their interests.

[1501] As a concrete example, the prompt text when a user searches the "electronic parts" sector is as follows:

[1502] Example prompt sentence:

[1503] A user has searched the "Electronic Components" sector. Please provide the following information:

[1504] 1. Evaluation results of major stocks in the electronic components sector

[1505] 2. Current market price of each stock and price trends over the past year

[1506] 3. Analysis of future prospects for each stock

[1507] In this way, the server can use the generative AI model to quickly and accurately provide the information the user wants, and can improve the user experience by adjusting the advertisement delivery method using the user's behavioral data and emotion engine. The flow of the identification process in Example 3 will be described with reference to Figure 21.

[1508] Step 1:

[1509] A user enters a search query.

[1510] A user enters a search query such as "electronic parts" into the search bar of a device and clicks the search button. The input is the text data "electronic parts." The output is the transmission of the search query.

[1511] Step 2:

[1512] The device sends a search query to the server.

[1513] The terminal sends the search query entered by the user to the server as an HTTP request. The input is the user's search query "electronic parts," and the output is the request sent to the server.

[1514] Step 3:

[1515] A server receives a search query and retrieves relevant data from a database.

[1516] The server receives the HTTP request and executes a database query to retrieve data on stocks related to the "electronic components" sector. The input is the search query "electronic components" and the output is the retrieval of the relevant stock data.

[1517] Step 4:

[1518] The server inputs the acquired data into a generative AI model.

[1519] The server converts the acquired stock data into a prompt and sends it to the generative AI model. Specifically, it generates the prompt, "Please provide the valuation results of major stocks in the electronic components sector." The input is the stock data, and the output is the generated prompt and input to the AI ​​model.

[1520] Step 5:

[1521] The generative AI model generates the analysis results.

[1522] The generative AI model analyzes the prompt text and generates the relevant stock valuation results and market price trends. The input is the prompt text, and the output is the analysis results.

[1523] Step 6:

[1524] The server transmits the generated analysis results to the terminal.

[1525] The server sends the analysis results obtained from the generative AI model to the terminal as an HTTP response. The input is the analysis results, and the output is the response sent to the terminal.

[1526] Step 7:

[1527] The terminal displays the analysis results to the user.

[1528] The terminal displays the received analysis results on a user interface, specifically in the search result area of ​​the web page. The input is the analysis results, and the output is what is displayed to the user.

[1529] Step 8:

[1530] The server collects user behavior data and analyzes it using an emotion engine.

[1531] The server collects user behavioral data such as search history and click data, and analyzes the user's emotions using an emotion engine. The input is behavioral data, and the output is the emotion analysis results.

[1532] Step 9:

[1533] The server selects an advertisement based on the analysis results and sends it to the device.

[1534] The server selects an advertisement appropriate for the user based on the analysis results of the emotion engine and sends it to the device as an HTTP response. The input is the emotion analysis result, and the output is the selection of an advertisement and its transmission to the device.

[1535] Step 10:

[1536] The terminal displays the advertisement to the user.

[1537] The terminal displays the received advertisement on the user interface. Specifically, the advertisement is displayed in the advertisement area of ​​the web page. The input is the advertisement data, and the output is the advertisement displayed to the user.

[1538] (Application example 3)

[1539] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1540] In conventional financial and economic information systems, the information provided when users searched for individual stocks or sectors was limited, and advertisements were not delivered based on user emotions. This made it difficult to increase user satisfaction, and the effectiveness of advertisements was limited. In addition, because advertisements were not delivered taking user emotions into consideration, there were problems with low ad click-through rates and conversion rates.

[1541] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1542] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the analysis results of the AI, a means for delivering advertisements to a user interface, an emotion engine for analyzing user emotions, and a means for adjusting the advertisement delivery method based on the emotion engine. This makes it possible to provide evaluation results for individual stocks or sectors searched by the user and to deliver advertisements based on the user's emotions.

[1543] "Financial and economic information" refers to data and information related to financial markets and the economy.

[1544] "Means of database creation" refers to methods and technologies for systematically organizing information and storing it in a database.

[1545] "Individual stocks" refer to stocks of specific companies.

[1546] "Heat map analysis" is a method of visually representing data, which uses shades of color to show the distribution and intensity of data.

[1547] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1548] "Means of responding to users" refers to methods and technologies for providing appropriate information in response to user inquiries.

[1549] "User interface" refers to the screens and methods of operation that allow users to interact with a system.

[1550] "Means of delivering advertisements" refers to the methods and technologies used to display a particular advertisement to a user.

[1551] An "emotion engine" refers to technology for analyzing user emotions and adjusting system behavior based on the results.

[1552] "Means for adjusting ad delivery" refers to methods or technologies for changing the frequency or content of ads based on user sentiment or behavior.

[1553] A system for carrying out this invention is configured as follows: A server includes means for compiling financial and economic information into a database, artificial intelligence for performing heat map analysis of the evaluations of individual stocks from the database, means for responding to the user with the analysis results of the artificial intelligence, means for delivering advertisements to a user interface, an emotion engine for analyzing user emotions, and means for adjusting the advertisement delivery method based on the emotion engine.

[1554] Program processing explanation

[1555] Hardware and software used

[1556] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1557] Software: Database management systems (e.g., MySQL), sentiment analysis libraries (e.g., TextBlob), AI stock valuation models, ad management systems

[1558] Data processing and calculation

[1559] 1. Database of financial and economic information:

[1560] The server collects and stores data related to financial markets and the economy in a database, including stock price data, company information, and economic indicators.

[1561] 2. Individual Stock Valuation:

[1562] The server uses the AI ​​model to evaluate individual stocks based on the information retrieved from the database, and the evaluation results are visualized in a heat map format and provided to the user.

[1563] 3. Answer to the user:

[1564] When a user searches for a specific individual stock or sector, the server generates an evaluation result using an AI model and responds to the user.

[1565] 4. Serving Advertisements:

[1566] The server displays advertisements on the user interface, such as targeted advertisements for securities, banking, cards, etc., selected based on the user's search history and behavior.

[1567] 5. Sentiment analysis with emotion engine:

[1568] The server inputs the user's response to the ad as text and uses a sentiment analysis library to determine positive, negative, or neutral sentiment.

[1569] 6. Adjusting Ad Delivery Methods:

[1570] The server then adjusts the frequency and content of ads based on the results of the sentiment analysis: ads that elicit positive responses are shown more frequently, and ads that elicit negative responses are not shown at all.

[1571] Specific examples

[1572] When a user searches for the "electronic components" sector, the server provides AI-based evaluations of stocks in that sector.

[1573] If a user responds positively to an ad by saying "I like this ad," the server will display that ad more frequently.

[1574] Prompt Sentence Examples

[1575] If a user searches for the "electronic components" sector, the AI ​​should provide a rating of stocks in that sector. Also, if a user responds positively to an ad by saying "I like this ad," the AI ​​should show that ad more frequently.

[1576] In this way, it is possible to provide evaluation results for individual stocks and sectors that users search for, as well as deliver advertisements based on user sentiment.

[1577] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1578] Step 1:

[1579] The server collects data related to financial markets and the economy and stores it in a database. This includes stock price data, company information, economic indicators, etc. The input is financial and economic information, and the output is the information stored in the database. Specifically, it retrieves financial data through an API and stores it in a database management system (e.g., MySQL).

[1580] Step 2:

[1581] The server uses an AI model to evaluate individual stocks based on information obtained from the database. The input is financial and economic information obtained from the database, and the output is the evaluation results. Specifically, the data is input into the AI ​​model, and the evaluation results are generated in the form of a heat map.

[1582] Step 3:

[1583] When a user searches for a specific individual stock or sector, the server generates an evaluation result using an AI model and responds to the user. The input is the user's search query, and the output is the evaluation result. Specifically, the server receives the user's search query, generates an evaluation result using the AI ​​model, and displays it on the user interface.

[1584] Step 4:

[1585] The server displays advertisements on the user interface. The input is the user's search history and behavioral data, and the output is the advertisement to be displayed. Specifically, the server uses an advertisement management system to select an appropriate advertisement and display it on the user interface.

[1586] Step 5:

[1587] The server inputs the user's response to the ad as text and uses a sentiment analysis library to determine whether the emotion is positive, negative, or neutral. The input is the user's response to the ad, and the output is the sentiment analysis result. Specifically, the server receives the user's response in text format and uses a sentiment analysis library (e.g., TextBlob) to determine the emotion.

[1588] Step 6:

[1589] The server adjusts the frequency and content of advertisements based on the results of the sentiment analysis. The input is the sentiment analysis result, and the output is an adjusted advertisement delivery method. Specifically, the server adjusts the advertisement management system based on the sentiment analysis result to display advertisements that elicit positive responses more frequently and not display advertisements that elicit negative responses.

[1590] 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.

[1591] 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> ) 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.

[1592] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1593] 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.

[1594] [Third embodiment]

[1595] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1596] 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.

[1597] 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).

[1598] 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.

[1599] 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.

[1600] 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).

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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.

[1605] 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.

[1606] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1607] "Example 1"

[1608] As one embodiment of the present invention, a means for creating a database of financial and economic information is provided. Specifically, a database is constructed that collects financial data of listed companies, information such as the Nikkei average and TOPIX, and manages this information in an integrated manner.

[1609] "Example 2"

[1610] In addition, AI is provided that performs heat map analysis of the ratings of individual stocks from the database. This AI analyzes the "good!" and "bad!" ratings of individual stocks based on the information in the database and visualizes them as a heat map. For example, if a particular stock has received many "good!" ratings, that stock will be displayed in red on the heat map.

[1611] "Example 3"

[1612] In addition, a means is provided for users to receive answers based on the results of AI analysis. When a user searches for an individual stock or sector, the AI ​​generates answers based on the search results and provides them to the user. For example, if a user searches for the "electronic components" sector, the AI ​​will provide an answer based on the evaluation of stocks in that sector.

[1613] "Example 4"

[1614] Additionally, a means is provided for delivering advertisements to the user interface. As a user uses the system, advertisements are displayed on the user interface. These advertisements may be targeted advertisements for securities, banking, cards, etc., and are selected based on the user's interests and behavior.

[1615] The processing flow of each embodiment will be described below.

[1616] "Example 1"

[1617] Step 1: Collect financial and economic information. Specifically, collect financial data of listed companies, Nikkei average, TOPIX, etc. from the Internet.

[1618] Step 2: The collected financial and economic information is stored in a database. This database allows for centralized management of the information.

[1619] "Example 2"

[1620] Step 1: Obtain individual stock information from the database.

[1621] Step 2: Input the information about individual stocks into an artificial intelligence system that performs heat map analysis.

[1622] Step 3: Based on the input information, the artificial intelligence analyzes the "good!" or "bad!" ratings of individual stocks and visualizes them as a heat map.

[1623] "Example 3"

[1624] Step 1: User searches for an individual stock or sector.

[1625] Step 2: Feed the search results into artificial intelligence.

[1626] Step 3: The AI ​​generates an answer based on the search results entered and provides it to the user.

[1627] "Example 4"

[1628] Step 1: Analyze user behavior and interests as they use the system.

[1629] Step 2: Based on the analysis results, target advertisements for securities, banking, cards, etc. are selected. Step 3: The selected advertisements are displayed on the user interface.

[1630] Example 1

[1631] Next, a description will be given of Example 1 of Form 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."

[1632] In conventional financial and economic information management systems, the series of processes, such as information collection, formatting, normalization, database storage, user request processing, data search and retrieval, and data display, are often not carried out efficiently. Another issue is that it is difficult to provide users with the information they need quickly and accurately. Furthermore, there is a lack of means to effectively utilize collected data and improve the quality and efficiency of marketing department operations.

[1633] 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.

[1634] In this invention, the server includes means for collecting financial and economic information using public APIs on the Internet or web scraping technology, means for formatting and normalizing the collected data, and means for storing the formatted and normalized data in a database. This enables efficient collection and management of financial and economic information. It also includes means for users to access the database through their terminals and obtain the necessary financial and economic information, means for the server to receive user requests, search for and obtain the relevant data from the database, and means for displaying the obtained data to the user. This enables users to quickly and accurately obtain the information they need. Furthermore, by including means for delivering targeted advertisements based on the results of analysis using artificial intelligence, it is possible to improve the quality and efficiency of marketing department operations.

[1635] "Financial and economic information" refers to information related to financial markets and economic activity, such as corporate financial data, stock price indexes, and economic indicators.

[1636] "Database creation" is the process of organizing collected data and storing it in a database in a format that is easy to search and manage.

[1637] "Artificial intelligence" is the technology that enables computer systems to mimic human intelligence and learn, reason, and self-correct.

[1638] "Heat map analysis" is an analytical technique that uses shades of color to visually represent data distribution and trends.

[1639] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[1640] "Ad serving" is the process of displaying advertisements to specific users.

[1641] A "public API" is an application programming interface that is made publicly available for access by external developers.

[1642] "Web scraping" is the technique of automatically extracting data from websites.

[1643] "Format" is the process of converting collected data into a certain format.

[1644] "Normalization" is the process of standardizing data to ensure consistency and accuracy.

[1645] "Request processing" is the process of receiving a request from a user and providing data in response to that request.

[1646] "Targeted advertising" refers to advertising delivered to a specific user demographic.

[1647] MODE FOR CARRYING OUT THE INVENTION

[1648] The present invention is a system for efficiently collecting, managing, and analyzing financial and economic information, and providing the information to users quickly and accurately. A specific embodiment of this system will be described below.

[1649] Data collection

[1650] The server collects financial and economic information using public APIs on the Internet and web scraping technology. Specifically, it uses Python's BeautifulSoup and Selenium to obtain information such as financial results data of listed companies, stock indexes, and economic indicators. For example, the server visits "https: / / example.com / financials / companyA" and extracts data from specific HTML tags.

[1651] Data Shaping and Normalization

[1652] The server formats and normalizes the collected data. Using the Python Pandas library, it converts the collected data into a DataFrame and converts data in different formats into a unified format. For example, it standardizes dates to YYYY-MM-DD format and aligns the number of decimal points for numeric data.

[1653] Storage in the database

[1654] The server stores the formatted and normalized data in a database. MySQL or PostgreSQL is used as the database. The server generates an SQL query and inserts the data into the appropriate table. For example, it executes the SQL query "INSERT INTO financials (company, date, revenue, profit) VALUES ('CompanyA', '2023-03-31', 3000000000, 500000000)".

[1655] Processing user requests

[1656] A user accesses the web interface through a terminal and requests the necessary financial and economic information. For example, if a user wants to search for "Company A's financial results data for the first quarter of 2023," the user enters "Company A 2023 Q1 financial results data" in the search bar and clicks the search button.

[1657] Searching and retrieving data

[1658] The server receives the user's request and searches the database for the relevant data. The server generates an SQL query and executes it against the database. For example, it executes the SQL query "SELECT FROM financials WHERE company='CompanyA' AND date='2023-03-31'".

[1659] Viewing Data

[1660] The server converts the acquired data into HTML format and sends it to the user's browser. The user can then view the information displayed in the browser, such as "Company A's financial results for Q1 2023: Sales of 30 billion yen, Profit of 5 billion yen."

[1661] Specific examples

[1662] As a concrete example, consider a case where a user wants to retrieve "Company A's financial results data for the first quarter of 2023." The user logs into the web interface and types "Company A 2023 Q1 financial results data" into the search bar. The server receives this request, searches the database for the relevant data, and displays it to the user.

[1663] Examples of prompts to be input to a generative AI model include:

[1664] Prompt: "Please retrieve Company A's financial results for the first quarter of 2023."

[1665] By feeding this prompt into a generative AI model, the model can generate the appropriate API requests and database queries to automate the process of retrieving the required data.

[1666] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1667] Step 1:

[1668] The server collects financial and economic information using public APIs on the Internet and web scraping technology. Specifically, the server uses Python's BeautifulSoup and Selenium to obtain information such as financial results data of listed companies, stock indexes, and economic indicators. For example, the server visits "https: / / example.com / financials / companyA" and extracts data from specific HTML tags.

[1669] Enter: Website URL

[1670] Output: Extracted raw data (HTML format)

[1671] Step 2:

[1672] The server formats and normalizes the collected data. Using the Python Pandas library, the collected data is converted into a DataFrame and data in different formats is converted into a unified format. For example, dates are unified into the YYYY-MM-DD format, and the number of decimal points for numeric data is adjusted.

[1673] Input: Extracted raw data (HTML format)

[1674] Output: Reformatted and normalized data (DataFrame format)

[1675] Step 3:

[1676] The server stores the formatted and normalized data in a database. MySQL or PostgreSQL is used as the database. The server generates an SQL query and inserts the data into the appropriate table. For example, it executes the SQL query "INSERT INTO financials (company, date, revenue, profit) VALUES ('CompanyA', '2023-03-31', 3000000000, 500000000)".

[1677] Input: Reformatted and normalized data (DataFrame format)

[1678] Output: Data stored in the database

[1679] Step 4:

[1680] A user accesses the web interface through a terminal and requests the necessary financial and economic information. For example, if a user wants to search for "Company A's financial results data for the first quarter of 2023," he or she enters "Company A 2023 Q1 financial results data" in the search bar and clicks the search button.

[1681] Input: User's search query ("Company A 2023 Q1 financial results data")

[1682] Output: Request to server

[1683] Step 5:

[1684] The server receives the user's request and searches the database for the relevant data. The server generates an SQL query and executes it against the database. For example, it executes the SQL query "SELECT FROM financials WHERE company='CompanyA' AND date='2023-03-31'".

[1685] Input: User request

[1686] Output: Search results (matching data)

[1687] Step 6:

[1688] The server converts the acquired data into HTML format and sends it to the user's browser. The user can then view the information displayed in the browser, such as "Company A's financial results for Q1 2023: Sales of 30 billion yen, Profit of 5 billion yen."

[1689] Input: Search results (relevant data)

[1690] Output: The data that is displayed in the user's browser

[1691] (Application example 1)

[1692] Next, a description will be given of Application Example 1 of Form 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."

[1693] With conventional financial and economic information systems, it was difficult to centrally manage information such as financial results data for listed companies, the Nikkei average, and TOPIX, making it difficult for users to obtain the latest financial information in real time. Furthermore, the system lacked investment advice and portfolio management functions, providing insufficient support for users to make appropriate investment decisions. Furthermore, the targeting of ad delivery was inaccurate, reducing the operational efficiency of the marketing department.

[1694] 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.

[1695] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) that performs heat map analysis of the evaluations of individual stocks from the database, a means for providing the user with the analysis results of the AI, a means for delivering advertisements to a user interface, a means for providing financial and economic information in real time, a means for generating investment advice, and a means for managing an investment portfolio and performing risk assessment and performance analysis. This allows users to obtain the latest financial information in real time and receive support for making appropriate investment decisions. Furthermore, the targeting of advertisement delivery is more accurate, improving the operational efficiency of the marketing department.

[1696] "Financial and economic information" refers to various data related to financial markets, including financial results data of listed companies and stock price indices (Nikkei Average, TOPIX, etc.).

[1697] "Database creation" is the process of storing collected information in a database that allows it to be managed centrally and efficiently searched and used.

[1698] "Heat map analysis" is a method of visually representing data, using shades of color to show the distribution and trends of data.

[1699] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1700] A "user interface" is the means or screen through which a user interacts with a system.

[1701] "Ad serving" is the process of displaying advertisements to specific targets.

[1702] "Real-time provision" means obtaining information instantly and providing it to users without delay.

[1703] "Investment Advice" means providing advice or recommendations to assist users in making investment decisions.

[1704] An "investment portfolio" is a combination of multiple investment assets held by a user.

[1705] "Risk assessment" is the process of analyzing and evaluating the risks associated with an investment.

[1706] "Performance analysis" is an analysis used to evaluate the results and effectiveness of an investment.

[1707] To implement the present invention, the following system configuration and program are required.

[1708] System Configuration

[1709] 1. Server: Collects financial and economic information and stores it in a database.

[1710] 2. Device: The smartphone or computer used by the user.

[1711] 3. Artificial Intelligence (AI): Retrieves information from the database and generates heat map analysis and investment advice.

[1712] 4. User Interface (UI): The screen through which users view and interact with information.

[1713] Program processing

[1714] Data collection and database creation

[1715] The server uses APIs to collect financial and economic information such as financial results data for listed companies, the Nikkei average, and TOPIX, etc. The collected data is stored in a database such as SQLite.

[1716] Heatmap Analysis

[1717] The AI ​​retrieves individual stock evaluation data from the database and performs heat map analysis, which visually shows the distribution and trends of the data.

[1718] Real-time information provision

[1719] The server provides the collected financial and economic information to users in real time, allowing them to instantly access the latest information via their smartphones or computers.

[1720] Investment advice generation

[1721] AI generates investment advice based on the collected data, which users can use to make appropriate investment decisions.

[1722] Portfolio Management

[1723] The server manages users' investment portfolios, performs risk assessments and performance analysis, and helps users understand their investment status.

[1724] Ad serving

[1725] The user interface will be delivered with targeted advertising, which will be precisely targeted based on the results of analysis using artificial intelligence.

[1726] Specific examples

[1727] Let's say a user opens the app and checks the latest Nikkei 225. They speak the following prompt:

[1728] Please tell me the latest value of the Nikkei average.

[1729] The server receives this prompt and retrieves the latest information from the database and provides it to the user, allowing the user to check the latest financial information in real time and make appropriate investment decisions.

[1730] In this way, the present invention realizes a system that provides users with financial and economic information in real time and supports investment advice and portfolio management.

[1731] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1732] Step 1:

[1733] The server uses an API to collect financial and economic information such as financial results data for listed companies, the Nikkei average, and TOPIX.

[1734] Input: Financial and economic information obtained from the API.

[1735] Data processing: Convert the acquired data from JSON format to an appropriate data format.

[1736] Output: Transformed financial and economic information data.

[1737] Step 2:

[1738] The server stores the collected financial and economic information in a database such as SQLite.

[1739] Input: Transformed financial and economic information data.

[1740] Data processing: Generates SQL queries to store in the database.

[1741] Output: Financial and economic information stored in a database.

[1742] Step 3:

[1743] Users launch the application via their smartphone or computer and enter a prompt to check their latest financial information.

[1744] Input: User prompt (e.g. "What is the latest Nikkei average?").

[1745] Data processing: Analyze the prompt and extract the necessary information.

[1746] Output: Parsed information request.

[1747] Step 4:

[1748] The server retrieves the latest financial information from a database based on the parsed information request.

[1749] Input: Parsed information request.

[1750] Data processing: Generate SQL queries to retrieve the required information from the database.

[1751] Output: The latest financial information obtained.

[1752] Step 5:

[1753] The artificial intelligence performs heat map analysis based on the acquired financial information.

[1754] Input: The latest financial information obtained.

[1755] Data processing: Apply heat map analysis algorithms to visualize data distributions and trends.

[1756] Output: Heatmap analysis results.

[1757] Step 6:

[1758] The server provides the heat map analysis results to the user.

[1759] Input: Heatmap analysis results.

[1760] Data processing: Converting data into a format suitable for display on the user interface.

[1761] Output: A heatmap displayed in the user interface.

[1762] Step 7:

[1763] Artificial intelligence generates investment advice based on the acquired financial information.

[1764] Input: The latest financial information obtained.

[1765] Data processing: Applying investment advice generation algorithms to generate appropriate investment advice.

[1766] Output: The generated investment advice.

[1767] Step 8:

[1768] The server provides the generated investment advice to the user.

[1769] Input: Generated investment advice.

[1770] Data processing: Converting data into a format suitable for display on the user interface.

[1771] Output: Investment advice displayed in the user interface.

[1772] Step 9:

[1773] The server manages the user's investment portfolio and performs risk assessment and performance analysis.

[1774] Input: User's investment portfolio data.

[1775] Data processing: Applying risk assessment and performance analysis algorithms.

[1776] Output: Risk assessment results and performance analysis results.

[1777] Step 10:

[1778] The user interface is delivered with targeted advertisements.

[1779] Input: AI analysis results.

[1780] Data processing: Selecting targeted advertisements and converting them into a data format for display in the user interface.

[1781] Output: Targeted ads displayed in the user interface.

[1782] Example 2

[1783] Next, a description will be given of Example 2 of Form 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."

[1784] In conventional financial and economic information systems, it was difficult for users to visually understand the evaluation of individual stocks, making it difficult for users to make quick investment decisions. Furthermore, there was a lack of means to obtain and visualize evaluation data in real time based on user requests, which resulted in a poor user experience. Furthermore, targeted advertising based on evaluation results was not effectively delivered.

[1785] 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.

[1786] In this invention, the server includes a means for compiling financial and economic information into a database, an artificial intelligence (AI) for performing heat map analysis of individual stock evaluations from the database, a means for providing the AI ​​analysis results to the user, a means for receiving a request from the user via a user interface, a means for acquiring evaluation data from the database based on the request, a means for preprocessing the acquired evaluation data and calculating an evaluation score, a means for generating a heat map based on the evaluation score, and a means for displaying the generated heat map on the user interface. This allows users to visually understand the evaluations of individual stocks and make quick investment decisions. Furthermore, acquiring and visualizing evaluation data in real time improves the user experience. Furthermore, targeted advertising based on the evaluation results can be effectively delivered.

[1787] "Financial and economic information" is a general term for data and information related to financial markets and economic activity.

[1788] A "database" is a system for efficiently storing, searching, and managing data.

[1789] "Individual stocks" refer to stocks of specific companies.

[1790] A "heat map" is a type of graph that visually represents the distribution and intensity of data using shades of color.

[1791] "Artificial intelligence" refers to computer systems that mimic human intelligence and can learn and reason.

[1792] "User interface" refers to the means or screen through which a user interacts with a system.

[1793] A "request" refers to a request or inquiry made by a user to a system.

[1794] "Evaluation data" is data that includes evaluation information such as "Good!" or "Bad!" for individual stocks.

[1795] "Preprocessing" is the initial process used to prepare data for easier analysis.

[1796] An "evaluation score" is a numerical value calculated based on evaluation data, and indicates the evaluation of an individual stock.

[1797] "Visualization" refers to displaying data in the form of graphs, charts, etc., to make it easier to understand visually.

[1798] "Targeted advertising" refers to advertising that is delivered to a specific user demographic.

[1799] MODE FOR CARRYING OUT THE INVENTION

[1800] This invention is a system that uses artificial intelligence to compile a database of financial and economic information and perform heat map analysis of the evaluation of individual stocks. Specific embodiments of this system will be described below.

[1801] System configuration

[1802] The system consists of three main components: a server, a terminal, and a user. The server contains software for databases, artificial intelligence, data processing, and visualization. The terminal provides an interface for users to interact with the system.

[1803] Hardware and software used

[1804] The server is a computer equipped with a high-performance processor and large memory capacity. A relational database such as MySQL or PostgreSQL is used as the database management system. The artificial intelligence model is built using machine learning libraries such as TensorFlow and Keras. Data visualization libraries such as Matplotlib and Seaborn are used for data visualization.

[1805] Program processing

[1806] The server first retrieves the rating data for individual stocks from the database. This rating data includes the "good" and "bad" ratings for each stock. The server then preprocesses the retrieved data and aggregates the rating values. For example, if a particular stock has received 100 "good" ratings and 20 "bad" ratings, the rating score for that stock will be 80.

[1807] The server generates a heat map based on the evaluation scores. This heat map displays stocks with high evaluation scores in red and stocks with low evaluation scores in blue. The generated heat map is intended to make it easier for users to visually understand the evaluation situation.

[1808] Specific examples

[1809] When a user wants to check the rating of a specific stock, they send a request from their device to the server. For example, if a user sends a request saying, "I want to check the rating of Company A's stock," the server retrieves the rating data of Company A's stock from the database and generates a heat map. The generated heat map is displayed to the user through their device.

[1810] Examples of specific prompts include:

[1811] "I would like to check the valuation of Company A's stock."

[1812] The server that receives this prompt generates a heat map based on the evaluation data of Company A's stock and displays it on the terminal. By checking the heat map, the user can visually understand the evaluation status of Company A's stock.

[1813] In this way, the present invention allows users to quickly and visually understand the valuation of individual stocks, thereby assisting them in making investment decisions.

[1814] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1815] Step 1:

[1816] A user sends a stock evaluation request from a terminal.

[1817] Specifically, the user enters "I would like to check the stock valuation of Company A" into the terminal interface and clicks the send button.

[1818] Input: User request (e.g., "I would like to check the stock valuation of Company A.")

[1819] Output: Request sent from terminal to server

[1820] Step 2:

[1821] The server receives the request and retrieves the rating data from the database.

[1822] Specifically, the server analyzes the request and sends a query to the database to obtain Company A's evaluation data.

[1823] Input: Request from terminal

[1824] Output: Rating data retrieved from the database

[1825] Step 3:

[1826] The server preprocesses the rating data and calculates the rating score.

[1827] Specifically, the server analyzes the acquired evaluation data and tallies the number of "likes" and "dislikes." For example, if Company A's stock has received 100 "likes" and 20 "dislikes," the evaluation score will be 80.

[1828] Input: Evaluation data retrieved from the database

[1829] Output: Rating score (e.g. 80)

[1830] Step 4:

[1831] The server generates a heat map based on the evaluation scores.

[1832] Specifically, the server uses a data visualization library such as Matplotlib or Seaborn to create a heat map that displays stocks with high evaluation scores in red and stocks with low evaluation scores in blue.

[1833] Input: Rating score

[1834] Output: The generated heatmap

[1835] Step 5:

[1836] The server sends the generated heat map to the device.

[1837] Specifically, the server sends the generated heat map to the terminal, allowing the user to visually check the evaluation status.

[1838] Input: The generated heatmap

[1839] Output: Send heatmap to device

[1840] Step 6:

[1841] The device displays the heat map to the user.

[1842] Specifically, the terminal displays the heat map received from the server to the user. By checking the heat map, the user can visually understand the valuation status of Company A's stock.

[1843] Input: Heatmap sent from the server

[1844] Output: Heatmap displayed to the user

[1845] (Application example 2)

[1846] Next, a description will be given of Application Example 2 of Form 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."

[1847] In conventional financial and economic information systems, there were limited ways to visually grasp the evaluation of individual stocks, making it difficult for users to understand stock performance in real time. In addition, there was a lack of a mechanism for users to evaluate specific stocks and have those evaluations reflected by other users, resulting in insufficient information sharing between users. This resulted in a decrease in the accuracy of investment decisions, making it difficult to improve the quality and efficiency of marketing department operations.

[1848] 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.

[1849] In this invention, the server includes means for compiling financial and economic information into a database, artificial intelligence means for performing heat map analysis of individual stock ratings from the database, means for responding to the user with the results of the analysis performed by the artificial intelligence, means for delivering advertisements to a user interface, means for displaying ratings of stocks held by the user or stocks of interest to the user in real time as a heat map, means for visually grasping stock performance based on ratings obtained from the database, and means for users to rate specific stocks and for the ratings to be reflected in other users' ratings. This allows users to visually grasp stock performance in real time, promoting information sharing among users and improving the accuracy of investment decisions. It also improves the quality and efficiency of marketing department operations.

[1850] "Financial and economic information" refers to data, news, analytical reports, and other information related to financial markets such as stocks, bonds, foreign exchange, and commodities.

[1851] A "database" is an information accumulation system that systematically organizes and stores information so that it can be quickly searched and used as needed.

[1852] "Individual stocks" are stocks issued by a specific company and are valued based on the financial situation and performance of that specific company.

[1853] A "heat map" is a type of graph that visually represents data values ​​using shades or hues of color, and is a means of intuitively grasping the distribution and trends of data.

[1854] "Artificial intelligence" refers to a computer system that mimics human intellectual activity and has capabilities such as learning, reasoning, and recognition.

[1855] A "user interface" is a means or screen that allows a user to interact with a system or application, and is designed to improve operability and visibility.

[1856] "Advertising" is an information dissemination activity to widely publicize products and services, and is a promotional method aimed at a specific target.

[1857] "Real-time" means that data and information are processed and reflected immediately, providing the latest information without delay.

[1858] "Performance" is an indicator that shows the business results and achievements of a stock or company, and serves as a basis for investment decisions.

[1859] "Evaluation" is the act of judging the value or quality of a particular object and expressing it through scores, comments, etc.

[1860] "Information sharing" is an activity that aims to exchange information between multiple users and systems and achieve a common understanding and recognition.

[1861] A "marketing department" is a department within a company that is responsible for market research, advertising, sales promotion, and other tasks, with the aim of increasing sales of products and services.

[1862] "Quality improvement" refers to improvement activities aimed at enhancing the performance and reliability of products and services, and aims to improve customer satisfaction.

[1863] "Efficiency" refers to activities that aim to eliminate waste in business and operations and achieve high results with fewer resources and time.

[1864] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1865] System Configuration

[1866] The system consists of the following major components:

[1867] 1. Server: Equipped with artificial intelligence that compiles financial and economic information into a database and performs heat map analysis of individual stock evaluations.

[1868] 2. User device: A device such as a smartphone or tablet provides a user interface and displays heat maps in real time.

[1869] 3. Network: Connects the server and user terminals to send and receive data.

[1870] Hardware and software used

[1871] Hardware: smartphones, tablets, servers

[1872] Software: Python, Pandas, Matplotlib, Seaborn, database management system (e.g., MySQL)

[1873] Data processing and calculation

[1874] 1. Data Acquisition: The server acquires financial and economic information from the database, including stock rating data such as "good" and "bad."

[1875] 2. Data processing: Based on the acquired data, a rating score for each stock is calculated. Specifically, the score is calculated by subtracting the number of "bad" ratings from the number of "good" ratings.

[1876] 3. Data visualization: The calculated scores are visualized as a heat map, which intuitively displays the distribution and trends of data using color shading and hues.

[1877] 4. User Interface: The heat map is displayed on the user's device in real time, allowing the user to rate specific stocks, which is then sent to the server and reflected to other users.

[1878] Specific examples

[1879] For example, if a user were to use a smartphone to check the stock valuation of AAPL (Apple), the following steps would be taken:

[1880] 1. The user launches the application and selects "AAPL" stock.

[1881] 2. The server retrieves the reputation data for "AAPL" from the database and calculates the reputation score.

[1882] 3. Based on the calculated scores, a heat map is generated and displayed on the user's device.

[1883] 4. When a user gives "AAPL" a "Like!" rating, the rating is sent to the server and reflected to other users.

[1884] Prompt Sentence Examples

[1885] "Show me a heat map of AAPL stock valuation."

[1886] "View TSLA stock valuation in a heat map."

[1887] In this way, users can visually grasp stock performance in real time and encourage information sharing among users, which improves the accuracy of investment decisions and improves the quality and efficiency of marketing department work.

[1888] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1889] Step 1:

[1890] The server retrieves financial and economic information from a database. Specifically, it extracts information including stock evaluation data such as "good" or "bad" from the database using queries. The input is a database query, and the output is a list of evaluation data.

[1891] Step 2:

[1892] The server calculates the evaluation score for each stock based on the acquired evaluation data. Specifically, it calculates the score by subtracting the number of "bad" comments from the number of "good" comments. The input is a list of evaluation data, and the output is the evaluation score for each stock.

[1893] Step 3:

[1894] The server generates a heatmap based on the calculated evaluation scores. Specifically, it uses Matplotlib and Seaborn to visually represent the evaluation scores using color shading and hues. The input is the evaluation scores, and the output is a heatmap image.

[1895] Step 4:

[1896] The server transmits the generated heatmap to the user terminal. Specifically, it transfers the heatmap image to the user terminal via a network. The input is the heatmap image, and the output is the transmission of image data to the user terminal.

[1897] Step 5:

[1898] The user device displays the received heatmap. Specifically, it displays the heatmap image on the screen of a smartphone or tablet. The input is the heatmap image, and the output is a visual display on the user interface.

[1899] Step 6:

[1900] A user rates a specific stock. Specifically, the user inputs a rating such as "Good!" or "Bad!" through a user interface. The input is the user's rating, and the output is the rating data.

[1901] Step 7:

[1902] The user terminal transmits the user's evaluation to the server. Specifically, the evaluation data is transferred to the server via the network. The input is the user's evaluation data, and the output is the data transmission to the server.

[1903] Step 8:

[1904] The server saves the received rating data in a database and reflects it to other users. Specifically, it adds the rating data to the database and updates the heat map again. The input is the user's rating data, and the output is the updated heat map.

[1905] Example 3

[1906] Next, a third embodiment of the third embodiment will be described. 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."

[1907] Conventional financial and economic information systems make it difficult for users to quickly and accurately obtain evaluations of individual stocks and sectors, and do not adequately deliver targeted advertisements based on user interests and behavior. This has made it difficult to improve the user experience and deliver advertisements effectively.

[1908] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1909] In this invention, the server includes a means for compiling financial and economic information into a database, a generative AI model for generating evaluations of individual stocks and sectors from the database, a means for sending prompts to the generative AI model to generate search results, a means for responding to the user with the analysis results of the generative AI model, and a means for delivering advertisements to the user interface, thereby enabling users to quickly and accurately obtain evaluations of individual stocks and sectors and effectively deliver targeted advertisements based on the user's interests and behavior.

[1910] "Financial and economic information" refers to all data and information related to financial markets and the economy.

[1911] "Database creation" refers to organizing information and storing it in a database so that it can be searched and used efficiently.

[1912] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate an appropriate output for a given input.

[1913] A "prompt sentence" refers to an input sentence that prompts a generative AI model to generate a specific output.

[1914] "Search Results" refers to the information and ratings provided by a generative AI model based on a user's query.

[1915] "User interface" refers to the screens and operating means that allow a user to interact with a system.

[1916] "Means for delivering advertisements" refers to the technology or method for displaying advertisements on the user interface.

[1917] "Targeted advertising" refers to advertising that is selected and delivered to specific users based on their interests and behavior.

[1918] "Marketing activities" refers to activities such as market research, advertising, and sales strategy development aimed at promoting products and services.

[1919] MODE FOR CARRYING OUT THE INVENTION

[1920] This invention is a system that uses a generative AI model to quickly and accurately provide evaluation results and deliver targeted advertising when a user searches for individual stocks or sectors. Specific embodiments of this system are described below.

[1921] System configuration

[1922] server

[1923] The server has a means of compiling financial and economic information into a database. The database contains detailed information on individual stocks and sectors. The server receives search queries from users and sends prompts to the generative AI model.

[1924] Generative AI Models

[1925] The generative AI model uses, for example, OpenAI's GPT-4. This model generates evaluation results for individual stocks or sectors based on prompts sent from the server. The generated evaluation results are returned to the server.

[1926] Terminal

[1927] The terminal provides an interface for the user to input a search query. When the user inputs a keyword into the search box and clicks the search button, the terminal sends the query to the server.

[1928] User Interface

[1929] The user interface displays the ratings received from the server and also displays targeted advertisements based on the user's interests and behavior, including promotions for financial institutions and credit cards.

[1930] Specific examples

[1931] A user types "electronic parts" into the search box on their device and clicks the search button. The device sends this search query to the server. The server analyzes the received query and sends the following prompt to the generative AI model:

[1932] "A user has searched for the 'electronic components' sector. Please generate valuations for stocks in this sector."

[1933] The generative AI model generates an evaluation result based on this prompt. For example, it might say, "The electronic components sec...

Claims

1. A means for collecting financial and economic information using public APIs or web scraping technology on the Internet; A means to format and normalize the collected data; A means of storing the formatted and normalized data in a database; A means for searching the database for relevant data in response to a request from a user; means for transmitting the retrieved data to a user's browser; A system including:

2. A means for obtaining a search query including a name of an individual stock or sector; means for generating a response using a generative AI model, the response including information related to individual stocks or sectors included in the search query; means for transmitting the generated response to the user from which the search query originated; means for delivering targeted advertisements to the user selected based on the user's interests or behavior; A system including:

3. A means for receiving a stock evaluation request from a user; means for obtaining valuation data for the stocks related to the valuation request from a database; means for pre-processing the evaluation data and deriving an evaluation score; A means for generating a heat map based on the evaluation scores; means for transmitting the heat map to the user; A system including:

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