system

Abstract

Provide a system. The present invention provides a means for obtaining corporate financial data, strategic documents, and operational reports from a database; A means for analyzing the acquired data using a generative AI engine; A means for converting the results analyzed by the generative AI engine into natural language; The system includes means for transmitting the converted result to a user's terminal.

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] With the introduction of the new NISA system, the number of individual investors who are new to investing is increasing, but it is difficult to provide each individual investor with appropriate knowledge about finance and stocks. Traditional IR and PR methods make it difficult to fully convey the appeal of a company. For this reason, there is a demand for a system that can analyze corporate information from multiple angles and provide it to investors in an easy-to-understand manner. [Means for solving the problem]

[0005] The present invention provides a system that includes means for acquiring a company's financial data, strategic documents, and operational reports from a database, means for analyzing the acquired data using a generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, and means for transmitting the converted results to a user's terminal.

[0006] Furthermore, it includes a means for automatically generating the latest corporate information using a generation AI engine, creating releases and performance forecast reports, and sending the created releases and reports to a user's terminal. It also includes a means for converting the acquired corporate data into graphs and charts using a visualization module, and a means for sending the converted graphs and charts to a user's terminal, thereby enabling a visual presentation of corporate information.

[0007] "Financial data of a company" refers to information that indicates the financial status of a company, and includes financial statements such as balance sheets, income statements, and cash flow statements.

[0008] "Strategic documents" are documents that describe a company's business strategies and long-term plans, including new business plans, market entry strategies, and competitive analyses.

[0009] An "operational report" is a document that reports on the status of a company's daily operations and management activities, and includes information such as the results of business operations, KPIs (key performance indicators), problems and their solutions.

[0010] A database is a digital system that systematically organizes and stores corporate information, allowing information to be searched and retrieved through queries.

[0011] A "generative AI engine" is a program or system that uses artificial intelligence technology to analyze input data and automatically generate information according to a purpose.

[0012] An "analytical means" is a method or technique for analyzing data and making evaluations or inferences from a particular perspective.

[0013] "Means for converting into natural language" refers to a technology or method for converting the analysis results obtained from a generative AI engine into words that are easy for humans to understand.

[0014] "Terminal" means a device that a user uses to input and receive information, including a PC, smartphone, tablet, etc.

[0015] A "release" is a formal statement or report issued by a company, including financial results, public announcements, new product announcements, etc.

[0016] A "performance forecast report" is a document that contains forecasts about a company's future performance, including sales and profit forecasts, growth prospects, etc.

[0017] A "visualization module" is a program or system for transforming data into a visually understandable format, such as generating graphs, charts, dashboards, etc.

[0018] "Graphs and charts" are diagrams that visually represent data, and include bar charts, line graphs, pie charts, etc.

[0019] An "API request" is a request made to a server through an application program interface, requesting specific data or processing.

[0020] "Visual presentation" is the way information is visually represented and presented in a form that is easily understood by a user. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly includes the following elements:

[0043] 1. How to retrieve data from the database

[0044] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[0045] 2. Data analysis method using generative AI engine

[0046] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[0047] 3. Natural language generation means

[0048] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[0049] 4. Data Transmission Method

[0050] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[0051] 5. Automatic release and report generation tool

[0052] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[0053] 6. Visualization of IR information

[0054] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[0055] Specific examples

[0056] Multifaceted analysis of corporate information

[0057] 1. A user operates a terminal and requests detailed financial information about a company.

[0058] 2. The device sends this request to the server.

[0059] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[0060] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[0061] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[0062] 6. The server sends the generated results to the user's device.

[0063] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[0064] Automatic generation of releases and forecast reports

[0065] 1. The user requests the company's latest earnings forecast report from their device.

[0066] 2. The device sends this request to the server.

[0067] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[0068] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[0069] 5. The server sends the generated releases and reports to the user's terminal.

[0070] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[0071] Visualization of IR information

[0072] 1. The user requests visualization of a company's IR information through the terminal.

[0073] 2. The device sends this request to the server.

[0074] 3. The server retrieves the company's financial data and strategy documents.

[0075] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[0076] 5. The server sends the visualized data to the user's device.

[0077] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[0078] In this way, the "IR-GENAI" system of the present invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[0079] The processing flow will be explained below.

[0080] Multifaceted analysis of corporate information

[0081] Step 1:

[0082] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[0083] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[0084] Step 2:

[0085] The terminal sends the user's request to the server.

[0086] Specific operation: The data entered in the form is sent to the server as an API request.

[0087] Step 3:

[0088] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[0089] What it does: Executes a database query to retrieve the required information.

[0090] Step 4:

[0091] The server inputs the acquired data into the generation AI engine and requests analysis.

[0092] Specific operation: Call the API of the generation AI engine and send the acquired data.

[0093] Step 5:

[0094] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[0095] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[0096] Step 6:

[0097] The server receives the analysis results from the generative AI engine and sends them to the user.

[0098] Specific operation: The generated text data is received and sent to the user's device as a response.

[0099] Step 7:

[0100] The terminal displays the analysis results to the user.

[0101] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[0102] Automatic generation of releases and forecast reports

[0103] Step 1:

[0104] A user requests a company's latest performance forecast report through a terminal.

[0105] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[0106] Step 2:

[0107] The terminal sends a request to the server.

[0108] Specific operation: The data entered in the form is sent to the server as an API request.

[0109] Step 3:

[0110] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[0111] Specific operation: Query the database to obtain the latest information on the relevant company.

[0112] Step 4:

[0113] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[0114] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[0115] Step 5:

[0116] The server sends the generated releases and reports to the user.

[0117] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[0118] Step 6:

[0119] The terminal displays the generated releases and reports to the user.

[0120] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[0121] Visual presentation of IR information

[0122] Step 1:

[0123] A user requests a visual presentation of a company's investor relations information through a terminal.

[0124] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[0125] Step 2:

[0126] The terminal sends a request to the server.

[0127] Specific operation: The data entered in the form is sent to the server as an API request.

[0128] Step 3:

[0129] The server retrieves the company's financial data and strategy documents.

[0130] What it does: Executes a database query to retrieve the required information.

[0131] Step 4:

[0132] The server inputs the acquired data into the visualization module.

[0133] What you'll do: Format and input the data required for the visualization algorithm.

[0134] Step 5:

[0135] The visualization module transforms the data into graphs and charts.

[0136] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[0137] Step 6:

[0138] The server sends the visualized data to the user.

[0139] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[0140] Step 7:

[0141] The terminal displays the visualized information to the user.

[0142] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[0143] Q&A system

[0144] Step 1:

[0145] The user inputs a specific question through the terminal.

[0146] Specific behavior: The user enters a question in the text box and submits it.

[0147] Step 2:

[0148] The terminal sends a question to the server.

[0149] Specific operation: The entered text is sent to the server as an API request.

[0150] Step 3:

[0151] The server inputs the question into a natural language processing engine.

[0152] Specific operation: Calls the API of the natural language processing engine and sends a question.

[0153] Step 4:

[0154] A natural language processing engine generates the best answer to your question.

[0155] What it does: Parses the question and generates an answer by pulling information from relevant databases.

[0156] Step 5:

[0157] The server sends the generated answer to the user.

[0158] Specific operation: Receive the generated text and send it to the user's device as a response.

[0159] Step 6:

[0160] The terminal displays the answer to the user.

[0161] Specific operation: The received response is displayed on the screen in an easy-to-read format.

[0162] Evaluating E / S / G information

[0163] Step 1:

[0164] The server retrieves the company's E / S / G report.

[0165] What it does: Executes a database query to retrieve a company's E / S / G report.

[0166] Step 2:

[0167] The information acquired by the server is input into the generation AI engine, which evaluates each of the E / S / G items.

[0168] Specific operation: Format the obtained report and input it into the evaluation module of the generative AI engine.

[0169] Step 3:

[0170] A generative AI engine converts the evaluation results into natural language.

[0171] What it does: It rates each item through a rating algorithm and documents the results using a natural language generation algorithm.

[0172] Step 4:

[0173] The server sends the evaluation results to the user.

[0174] Specific operation: The generated evaluation document is received and sent to the user's terminal as a response.

[0175] Step 5:

[0176] The terminal displays the evaluation results to the user.

[0177] Specific operation: The received evaluation document is displayed on the screen in an easy-to-read format.

[0178] Example 1

[0179] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0180] Until now, providing corporate financial information and operational data in a format that is easy to understand even for novice investors required a great deal of specialized knowledge, making it difficult to obtain and interpret the information. Furthermore, creating automated releases and performance forecast reports to quickly provide the latest information, as well as visualizing the data, were time-consuming tasks. For these reasons, a system capable of efficiently obtaining, analyzing, reporting, and visualizing information was needed to provide corporate information.

[0181] 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.

[0182] In this invention, the server includes means for acquiring a company's financial information, strategic information, and operational data from a database, means for multifaceted analysis of the acquired information using a generative AI model, means for converting the results of the multifaceted analysis by the generative AI model into natural language, means for transmitting the converted results to a user's terminal, means for accepting information requests from users and transmitting them to the server, means for creating releases and performance forecast reports using the latest information automatically generated by the generative AI model, means for transmitting the created releases and performance forecast reports to the user's terminal, means for converting the acquired company data into charts and graphs using a visualization module, and means for transmitting the converted charts and graphs to the user's terminal. This enables efficient acquisition, analysis, reporting, and visualization of corporate information, making it possible to provide information that is easy to understand even for beginner investors.

[0183] "Financial information of a company" refers to data showing the business status of a company, including balance sheets, income statements, cash flow statements, and the like.

[0184] "Strategic information" refers to materials related to a company's future plans and management strategies, including a company's growth plans and competitive strategies.

[0185] "Operational Data" means data relating to the day-to-day business operations of the company, including operational results and daily performance reports.

[0186] A "database" is a collection of data organized in a certain way, a system that stores a company's financial information, strategic information, and operational data.

[0187] A "generative AI model" is a system based on artificial intelligence that has the ability to analyze input data from multiple angles and output it in natural language.

[0188] "Converting into natural language" refers to converting specialized data and analytical results into a linguistic form that is easy for the general public to understand.

[0189] A "user terminal" is a device operated by a user, and includes a PC, smartphone, tablet, etc.

[0190] An "information request" refers to an operation or request made by a user to a system for specific information.

[0191] A "release" is an official announcement or report issued by a company that contains a series of updates or news.

[0192] A "performance forecast report" is a report that predicts a company's future performance, and includes forecast data on future sales and profits.

[0193] A "visualization module" is a software or hardware feature that allows data to be visually represented, such as in graphs or charts.

[0194] "Graphing" refers to converting data into an easy-to-read format such as a graph or chart.

[0195] The "IR-GENAI" system of this invention uses advanced generative AI technology to provide corporate information in a format that is easy to understand even for beginner investors. This system involves data acquisition from a database, data analysis using a generative AI engine, natural language generation, data transmission, automatic generation of releases and performance forecast reports, and visualization of IR information.

[0196] System configuration

[0197] Hardware and Software Use

[0198] 1. Server: Processes data and interacts with the generative AI engine and visualization module.

[0199] 2. Database: A relational database such as MySQL® is used to store the company's financial, strategic, and operational data.

[0200] 3. Generative AI Engine: Uses generative AI models such as OpenAI (registered trademark) GPT-4 (registered trademark) to analyze data from multiple angles and output in natural language.

[0201] 4. Visualization module: Use visualization tools such as Tableau to transform data into graphs and charts.

[0202] 5. Terminal: A PC, smartphone, tablet, etc. that is operated by the user.

[0203] Data acquisition and processing

[0204] A user uses a terminal to request information about a particular company. The terminal sends this request to a server, which retrieves the relevant data from a database. For example, a balance sheet or profit and loss statement to understand the company's financial situation. Here is an example of the server retrieving the data using a MySQL query:

[0205] SELECT FROM Financial Data WHERE Company Name = "Company A"

[0206] Analyzing the data

[0207] The data acquired by the server is input into a generative AI engine, which analyzes it from multiple angles, including financial stability and growth potential. OpenAI GPT-4 is used as the generative AI engine. Examples of prompts generated by the generative AI engine include the following:

[0208] "Please analyze Company A's financial situation and explain it in a way that even a beginner investor can understand."

[0209] Generate and send results

[0210] The generative AI engine generates the analysis results in natural language and sends them to the user's device. For example, the following natural language sentences are generated:

[0211] "Company A has good financial stability, especially with a low debt ratio and stable cash flow."

[0212] The server sends the generated results to the user's terminal, which displays them.

[0213] Automatic generation of releases and forecast reports

[0214] When a user requests a company's latest performance forecast report, the server inputs the company's latest data and market forecast data into the AI ​​engine to generate a performance forecast report. The generated report is provided in the following format:

[0215] "Company A's next fiscal year's performance is expected to see increased revenue and profits, due to increased market share and the success of new products."

[0216] The server sends this report to the user's terminal, which displays it.

[0217] Visualization of IR information

[0218] When a user requests visualization of a company's investor relations information, the server retrieves financial data and strategic documents from the database and inputs them into the visualization module. The graphs and charts generated by the visualization module include the following examples:

[0219] -Bar chart showing sales trends

[0220] Line graph showing fluctuations in profit margins

[0221] The server transmits the visualized data to the user's terminal, which displays it.

[0222] In this way, the "IR-GENAI" system of this invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[0223] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0224] Step 1: Enter your information request

[0225] A user operates a terminal to request information about a specific company. For example, a request such as "Please display the financial data of Company A" is input into the terminal. The terminal sends this information request to the server. The input is the "company name" and "data type." The output is the requested information.

[0226] Step 2: Submitting the request

[0227] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Financial data" and sends it to the server. The input is the user's request, and the output is the request transmission.

[0228] Step 3: Retrieving data from the database

[0229] The server receives the request and accesses the database to retrieve the required data. Specifically, the server retrieves the data using a MySQL query. For example, it executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[0230] Step 4: Input data into the generative AI engine

[0231] The server inputs the acquired data into the generative AI engine. Specifically, the server sends financial data in JSON format to the generative AI engine. The input is Company A's financial data, and the output is data sent to the generative AI engine.

[0232] Step 5: Data analysis and natural language translation

[0233] The generative AI engine analyzes data from multiple perspectives and converts the results into natural language. Specifically, the generative AI engine generates natural language sentences such as, "Company A has good financial stability, with a particularly low debt ratio and stable cash flow." The input is Company A's financial data, and the output is the analysis results generated in natural language.

[0234] Step 6: Submitting the analysis results

[0235] The server receives the results from the generative AI engine and sends them to the user's device. Specifically, the server sends the generated natural language sentence to the device. The input is the analysis result generated in natural language, and the output is sent to the device.

[0236] Step 7: View the results

[0237] The terminal displays the results it receives to the user. Specifically, the screen displays the following: "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow." The input is the analysis results generated in natural language, and the output is the display to the user.

[0238] Automatic generation of releases and forecast reports

[0239] Step 1: Enter your performance forecast

[0240] A user requests the latest performance forecast report for a company from a terminal. For example, the user inputs, "Please generate the next performance forecast for Company A." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[0241] Step 2: Submitting the request

[0242] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Performance forecast" and sends it to the server. The input is the user's request, and the output is the request transmission.

[0243] Step 3: Data Acquisition

[0244] The server retrieves the latest performance data and market forecast data for a company. Specifically, the server queries the database and executes the query "SELECT FROM performance data WHERE company name = 'Company A'". The input is the request information, and the output is the performance data for Company A.

[0245] Step 4: Input to the generative AI engine

[0246] The server inputs the acquired data into the generative AI engine. Specifically, the server sends performance data and market forecast data in JSON format to the generative AI engine. The input is Company A's performance data and market forecast data, and the output is data sent to the generative AI engine.

[0247] Step 5: Generate a performance forecast report

[0248] The generative AI engine analyzes performance data and generates a report in natural language. For example, a report might be generated that states, "Company A's next fiscal year's performance is expected to increase in both sales and profits. This is attributed to increased market share and the success of new products." The input is Company A's performance data and market forecast data, and the output is a performance forecast report generated in natural language.

[0249] Step 6: Release Submission

[0250] The server sends the generated performance forecast report to the user's terminal. Specifically, the server sends the generated natural language report to the terminal. The input is the performance forecast report generated in natural language, and the output is sent to the terminal.

[0251] Step 7: View the report

[0252] The terminal displays the received report to the user. Specifically, the screen displays the following: "Company A's next fiscal year's performance is expected to increase in both sales and profits, due in part to increased market share and the success of new products." The input is a performance forecast report generated in natural language, and the output is the display to the user.

[0253] Visualization of IR information

[0254] Step 1: Enter your visualization request

[0255] A user requests visualization of a company's IR information through a terminal. For example, the user inputs, "Please display the IR information of Company A in a graph." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[0256] Step 2: Submitting the request

[0257] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: IR information" and sends it to the server. The input is the user's request, and the output is the request transmission.

[0258] Step 3: Data Acquisition

[0259] The server retrieves a company's financial data and strategic documents from a database. Specifically, the server executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[0260] Step 4: Input to the visualization module

[0261] The server inputs the acquired data into the visualization module. Specifically, the server sends the financial data of Company A to the visualization module. The input is the financial data of Company A, and the output is the data sent to the visualization module.

[0262] Step 5: Visualize the data

[0263] The visualization module converts the acquired data into graphs and charts. For example, it generates a bar chart showing the trend in sales volume and a line graph showing fluctuations in profit margins. The input is Company A's financial data, and the output is a visualized graph or chart.

[0264] Step 6: Sending visualized data

[0265] The server sends the visualized data to the user's device. Specifically, it sends the generated graphs and charts to the device. The input is the visualized data, and the output is the data sent to the device.

[0266] Step 7: View the visualization data

[0267] The visualized data received by the terminal is displayed to the user. For example, a "bar chart showing the trend in sales of Company A" or a "line graph showing fluctuations in profit margin" is displayed on the screen. The input is the visualized data, and the output is the display to the user.

[0268] (Application example 1)

[0269] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] Modern investors, especially those new to investing, face challenges in comprehensively and intuitively understanding corporate information. Technical financial data and operational reports are particularly difficult to understand, and there is a lack of tools to effectively utilize them to make investment decisions. Furthermore, there are limited visual and interactive ways to display corporate information, creating a need for new solutions.

[0271] 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.

[0272] In this invention, the server includes means for retrieving corporate financial data, strategic documents, and management reports from a database, means for analyzing the data retrieved by the generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for transmitting the converted results to a user's terminal, means for visually and interactively displaying the corporate information analyzed by the generative AI engine using a head-mounted display, and means for the user to retrieve and manipulate the corporate information by interactive manual operation or voice command through the head-mounted display. This allows even beginners to invest to comprehensively and intuitively understand corporate information and supports investment decisions.

[0273] "Financial data of a company" refers to data that represents the financial status of a company, such as a balance sheet, income statement, and cash flow statement.

[0274] A "strategic document" is a document that describes the medium- to long-term goals set by a company, as well as the plans and policies for achieving them.

[0275] An "operational report" is a report that describes a company's daily business activities, their results, problems, etc.

[0276] A "database" is a system for storing structured business information that can be efficiently accessed, managed, and updated.

[0277] A "generative AI engine" is an artificial intelligence engine that analyzes collected data and generates specific assessments and predictions.

[0278] "Natural language" is a human language that expresses the analysis results in a form that can be understood by users.

[0279] A "user terminal" is a device that a user uses to obtain and operate information, and includes, for example, a smartphone, a tablet, a computer, and the like.

[0280] A "head-mounted display" is a display device that the user wears on their head, and is a device that enables VR and AR experiences.

[0281] A "visualization module" is a software component for transforming enterprise data into visual formats such as graphs and charts.

[0282] "Interactive manual manipulation or voice command" refers to a method by which a user manipulates data through a head-mounted display, including hand gestures and voice input.

[0283] This invention is a system that uses a generative AI model to analyze data such as a company's financial data, strategic documents, and operational reports, and provides information to investors in a visual and interactive format. The system includes a server, a user terminal, a head-mounted display, and a generative AI engine. Detailed embodiments of this system are described below.

[0284] 1. Data Acquisition and Analysis

[0285] The server first retrieves the company's financial data, strategy documents, and operational reports from a database. The database stores the company's balance sheet, income statement, cash flow statement, strategy documents, and operational reports. To retrieve this data, the server fetches the data via an API or similar.

[0286] The server then inputs the acquired data into a generative AI engine, which analyzes it from multiple perspectives. The generative AI engine analyzes aspects such as financial stability, growth potential, and profitability, and generates an evaluation for each. The analysis results are converted into natural language and output in a format that is easy for users to understand.

[0287] 2. Send to the user device

[0288] The generated natural language results are sent from the server to the user's device, which can be a smartphone, tablet, or PC, where the results are displayed.

[0289] 3. Visualization and Interaction with Head-Mounted Displays

[0290] The analysis results are also displayed visually and interactively using a head-mounted display (HMD). Users can wear the HMD and check company information in a VR or AR environment. HMDs such as Oculus Rift and HTC Vive are used. The visualization module converts the analysis results from the generative AI engine into graphs and charts, which are then displayed within the HMD.

[0291] Users can use manual or voice commands to interactively manipulate and gain a deeper understanding of company information. The head-mounted display interface allows easy access to detailed company financial information, and instant access to releases and earnings forecasts.

[0292] Specific examples

[0293] For example, if a user issues a voice command such as "Show me the latest earnings forecast for Company A," the server retrieves the latest information for Company A from the database. The generative AI engine then analyzes the information and generates a result in natural language. This result is displayed visually in the HMD, such as "Company A's next fiscal year's earnings are expected to increase in both sales and profits, driven by increased market share and the success of new products." Additionally, a bar chart showing sales trends and a line graph showing fluctuations in profit margins are also displayed visually.

[0294] Prompt Sentence Examples

[0295] Possible prompts for a generative AI model include:

[0296] "Analyze the following financial data: revenue: 20 million, profit: 3 million, expenses: 17 million, assets: 50 million, liabilities: 30 million"

[0297] In this way, the present invention supports investors, especially beginners, by analyzing company information from multiple angles and providing it to users in natural language and visual formats.

[0298] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0299] Step 1:

[0300] A user requests detailed financial information of a company through a device such as a head-mounted display or smartphone. This request is sent from the device to a server. The input data is the company ID and name, and the output is a request to the server.

[0301] Step 2:

[0302] The server retrieves a company's financial data, strategy documents, and management reports from the database based on user requests. The input data is the company's ID and name, and the data retrieved from the database is balance sheets, income statements, cash flow statements, etc. The output is these datasets.

[0303] Step 3:

[0304] The server inputs the acquired company data into the generation AI engine for multifaceted analysis. The input data is the company's financial data, strategy documents, and management reports, and the generation AI engine analyzes them to evaluate financial stability, growth potential, profitability, etc. The output is the analysis results.

[0305] Step 4:

[0306] The generative AI engine converts the analysis results into natural language. The input data is analyzed company information, and the output is the analysis results in natural language format.

[0307] Step 5:

[0308] The server sends the generated natural language results to the user's terminal. The input data is the analysis result of the natural language format, and the output is the data delivery to the user's terminal.

[0309] Step 6:

[0310] The device displays the received analysis results to the user. On smartphones and tablets, the results are displayed in text format. The input data is the analysis results in natural language format, and the output is the information displayed on the device screen.

[0311] Step 7:

[0312] When using a head-mounted display, the server converts the analysis results into graphs and charts using a visualization module, which then visually displays them within the HMD. The input data are the analysis results in natural language format and graph or chart data, and the output is the visualized information within the HMD.

[0313] Step 8:

[0314] Users can obtain and operate corporate information through interactive manual operations or voice commands via a head-mounted display. The input data are user operations and commands, and the output is updating the displayed information or obtaining detailed information.

[0315] This process allows even novice investors to comprehensively and intuitively understand company information and supports investment decisions.

[0316] 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.

[0317] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to provide information based on the emotional state of each individual investor. This system mainly includes the following elements:

[0318] 1. How to retrieve data from the database

[0319] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[0320] 2. Data analysis method using generative AI engine

[0321] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[0322] 3. Natural language generation means

[0323] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[0324] 4. Data Transmission Method

[0325] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[0326] 5. Automatic release and report generation tool

[0327] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[0328] 6. Visualization of IR information

[0329] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[0330] 7. Emotion Recognition Method Using Emotion Engine

[0331] The emotion engine analyzes the text and voice input by the user through the device and recognizes the user's emotions. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral) through text and voice analysis.

[0332] 8. Emotion-based information regulation

[0333] The server adjusts the information and analysis results provided by the generative AI engine based on the user's recognized emotions. For example, if the user is expressing negative emotions, it can reduce the user's stress by emphasizing positive information.

[0334] 9. Visual displays of emotions

[0335] It provides an interface for visually displaying the user's emotional state and tracks emotional fluctuations, allowing the user to understand their own emotional fluctuations.

[0336] Specific examples

[0337] Multifaceted analysis of corporate information and emotion recognition

[0338] 1. A user operates a terminal and enters a request to obtain detailed financial information about a company.

[0339] 2. The device sends this request to the server.

[0340] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[0341] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[0342] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[0343] 6. The server sends the generated results to the user's device.

[0344] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[0345] Automatic generation of releases and earnings forecasts with emotion recognition

[0346] 1. The user requests the company's latest earnings forecast report from their device.

[0347] 2. The device sends a request to the server.

[0348] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[0349] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[0350] 5. The server sends the generated releases and reports to the user's terminal.

[0351] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[0352] IR Information Visualization and Emotion Recognition

[0353] 1. The user requests visualization of a company's IR information through the terminal.

[0354] 2. The device sends a request to the server.

[0355] 3. The server retrieves the company's financial data and strategy documents.

[0356] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[0357] 5. The server sends the visualized data to the user.

[0358] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[0359] Emotion recognition and information regulation

[0360] 1. When users view company information through their devices, they can enter comments and feedback.

[0361] 2. The device sends comments and feedback to the server.

[0362] 3. The server sends these inputs to the emotion engine, which analyzes the emotional state.

[0363] 4. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[0364] 5. Based on the emotional state, the server adjusts the generative AI engine to provide the most appropriate information for the user.

[0365] 6. The device will visually display emotional fluctuations, allowing users to understand their own emotional state.

[0366] In this way, the "IR-GENAI" system of the present invention combines generative AI technology with an emotion engine to analyze corporate information from multiple angles and provide information based on the user's emotional state. This makes it possible to provide information that is easy to understand even for beginner investors and reduces the psychological burden.

[0367] The processing flow will be explained below.

[0368] Multifaceted analysis of corporate information and emotion recognition

[0369] Step 1:

[0370] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[0371] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[0372] Step 2:

[0373] The terminal sends the user's request to the server.

[0374] Specific operation: The data entered in the form is sent to the server as an API request.

[0375] Step 3:

[0376] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[0377] What it does: Executes a database query to retrieve the required information.

[0378] Step 4:

[0379] The server inputs the acquired data into the generation AI engine and requests analysis.

[0380] Specific operation: Call the API of the generation AI engine and send the acquired data.

[0381] Step 5:

[0382] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[0383] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[0384] Step 6:

[0385] The server receives the analysis results from the generative AI engine and sends them to the user.

[0386] Specific operation: The generated text data is received and sent to the user's device as a response.

[0387] Step 7:

[0388] The terminal displays the analysis results to the user.

[0389] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[0390] Step 8:

[0391] The user enters comments and feedback about the analysis results.

[0392] Specific action: Enter text into the input field and press the submit button.

[0393] Step 9:

[0394] The device sends comments and feedback to the server.

[0395] Specific operation: The entered text is sent to the server as an API request.

[0396] Step 10:

[0397] The server sends the comments and feedback to the emotion engine, which analyzes the emotional state.

[0398] Specific operation: Call the emotion engine API and send input data.

[0399] Step 11:

[0400] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[0401] What it does: Uses text analysis algorithms to determine user sentiment.

[0402] Step 12:

[0403] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[0404] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[0405] Step 13:

[0406] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[0407] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[0408] Automatic generation of releases and earnings forecasts with emotion recognition

[0409] Step 1:

[0410] A user requests the latest business forecast report of a company from a terminal.

[0411] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[0412] Step 2:

[0413] The terminal sends a request to the server.

[0414] Specific operation: The data entered in the form is sent to the server as an API request.

[0415] Step 3:

[0416] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[0417] Specific operation: Query the database to obtain the latest information on the relevant company.

[0418] Step 4:

[0419] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[0420] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[0421] Step 5:

[0422] The server sends the generated releases and reports to the user.

[0423] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[0424] Step 6:

[0425] The terminal displays the generated releases and reports to the user.

[0426] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[0427] Step 7:

[0428] Users enter comments and feedback on the report.

[0429] Specific action: Enter text into the input field and press the submit button.

[0430] Step 8:

[0431] The device sends comments and feedback to the server.

[0432] Specific operation: The entered text is sent to the server as an API request.

[0433] Step 9:

[0434] The server sends these inputs to the emotion engine, which analyzes the emotional state.

[0435] Specific operation: Call the emotion engine API and send input data.

[0436] Step 10:

[0437] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[0438] What it does: Uses text analysis algorithms to determine user sentiment.

[0439] Step 11:

[0440] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[0441] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[0442] Step 12:

[0443] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[0444] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[0445] Visual presentation of IR information and emotion recognition

[0446] Step 1:

[0447] The user requests visualization of a company's IR information through the terminal.

[0448] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[0449] Step 2:

[0450] The terminal sends a request to the server.

[0451] Specific operation: The data entered in the form is sent to the server as an API request.

[0452] Step 3:

[0453] The server retrieves the company's financial data and strategy documents.

[0454] What it does: Executes a database query to retrieve the required information.

[0455] Step 4:

[0456] The server inputs the acquired data into the visualization module.

[0457] What you'll do: Format and input the data required for the visualization algorithm.

[0458] Step 5:

[0459] The visualization module transforms the data into graphs and charts.

[0460] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[0461] Step 6:

[0462] The server sends the visualized data to the user.

[0463] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[0464] Step 7:

[0465] The terminal displays the visualized information to the user.

[0466] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[0467] Step 8:

[0468] The user inputs their thoughts on the chart or graph.

[0469] Specific action: Enter text into the input field and press the submit button.

[0470] Step 9:

[0471] The device sends the feedback to the server.

[0472] Specific operation: The entered text is sent to the server as an API request.

[0473] Step 10:

[0474] The server sends the sentiment to the emotion engine, which analyzes the emotional state.

[0475] Specific operation: Call the emotion engine API and send input data.

[0476] Step 11:

[0477] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[0478] What it does: Uses text analysis algorithms to determine user sentiment.

[0479] Step 12:

[0480] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[0481] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[0482] Step 13:

[0483] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[0484] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[0485] Example 2

[0486] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0487] Providing corporate information in an easy-to-understand manner, even for novice investors, requires advanced data analysis and natural language generation technology. However, conventional systems produce data analysis results that are technical and difficult for beginners to understand. Furthermore, the information provided does not take into account the user's emotional state, making it difficult to improve the user experience. Furthermore, efficient methods are needed for visualizing the generated information and automatically generating releases and reports.

[0488] 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.

[0489] In this invention, the server includes: means for retrieving corporate financial data, strategic documents, and management reports from a database; means for analyzing the retrieved data using a generative AI model; means for converting the results of the analysis by the generative AI model into natural language; means for transmitting the converted results to a user's terminal; means for analyzing text and voice input by the user through the terminal and recognizing the user's emotions using an emotion engine; and means for adjusting the information and analysis results provided by the generative AI model based on the recognized user emotions. This enables multifaceted analysis of corporate information, making it easy to understand even for beginner investors, and providing information based on the emotional state of each individual user. Furthermore, efficient information provision through visualization and automatic generation can be achieved.

[0490] "Corporate financial data" refers to data that shows the economic status of a company, and primarily includes balance sheets, income statements, cash flow statements, etc.

[0491] A "strategic document" is a document that outlines the company's business policies for the medium to long term.

[0492] An "operational report" is a report that records the performance and results of a company's day-to-day operations.

[0493] A "database" is a system for efficiently storing and managing structured data.

[0494] A "generative AI model" is a type of artificial intelligence technology that learns from large amounts of data and generates text and data for various tasks.

[0495] "Natural language conversion" is the process of converting specialized data and analytical results into text that is easy for the general public to understand.

[0496] "Terminal" means a device used by a user to input or receive information, including, for example, a personal computer or smartphone.

[0497] An "emotion engine" is a type of artificial intelligence technology that analyzes a user's emotional state from text and voice.

[0498] A "visualization module" is software or tools for converting data into a visual format such as a graph or chart.

[0499] A "release or report" is a written document that summarizes a company's important information, results, forecasts, etc.

[0500] "Recognizing user emotions" is the process of identifying emotions from information entered by the user, and is performed using an emotion engine.

[0501] "Adjusting information or analysis results" means changing the content or presentation of the information or analysis results provided based on the user's emotional state.

[0502] The purpose of the system of this invention is to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly combines a server, a terminal, a generative AI model, an emotion engine, and a visualization module. The details of each element and their operation are explained below.

[0503] System configuration

[0504] 1. Server

[0505] The server is a central management system that retrieves the company's financial data, strategy documents, and operational reports from the database. The server collects this data and sends it to the generative AI model and emotion engine.

[0506] The server uses specific software and hardware, such as: a database system (e.g., MySQL), a generative AI model (e.g., GPT-4), and an emotion engine (e.g., IBM Watson® Tone Analyzer).

[0507] 2. Terminal

[0508] A terminal is a device through which a user inputs information and receives results, and includes, for example, a PC, a smartphone, or a tablet.

[0509] The terminal sends the input request to the server as an HTTP request, and also has the function of displaying the results received from the server.

[0510] 3. Generative AI Models

[0511] A generative AI model is an engine that analyzes collected data and converts it into a format that is easy to understand in natural language. For example, advanced generative AI techniques such as GPT-4 are used.

[0512] The generative AI model expresses the results of its analysis of financial data in natural language and provides them to users.

[0513] 4. Emotion Engine

[0514] The emotion engine is a technology that analyzes text and speech input from users to recognize their emotional state, and adjusts the information and analysis results provided by the generative AI model based on that emotional state.

[0515] For example, if the user is expressing negative emotions, more positive expressions are emphasized and presented.

[0516] 5. Visualization Module

[0517] The visualization module is a tool for converting acquired data into graphs and charts, which provides a visually understandable view of the data.

[0518] Libraries such as D3.js and Chart.js are used for data visualization.

[0519] Specific examples

[0520] Multifaceted analysis of corporate information and emotion recognition

[0521] 1. The user operates the terminal and enters, "I would like to obtain the latest financial information for Company A."

[0522] 2. The device sends this request to the server as an HTTP request.

[0523] Specific request example: GET / fetchData?company=CompanyA&type=financial

[0524] 3. The server accesses the database to retrieve Company A's financial information, strategy documents, and operational reports.

[0525] Specific query example: SELECT FROM financial_reports WHERE company_name = 'Company A'

[0526] 4. The server inputs the acquired data into the generative AI model. When generating a prompt, input the following:

[0527] "Please analyze Company A's financial stability, growth potential, and profitability."

[0528] 5. The generative AI model analyzes the data and outputs assessment results in natural language regarding financial stability and growth potential.

[0529] Example output: "Company A has good financial stability, and is particularly praised for its low debt ratio and stable cash flow."

[0530] 6. The server sends the generated results to the user's terminal, which displays the results to the user.

[0531] In this way, the system analyzes corporate information from multiple angles and provides it in an easy-to-understand manner in natural language.Furthermore, by using an emotion engine, it is possible to provide information based on the user's emotional state.

[0532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0533] Step 1:

[0534] A user inputs a request to obtain business information.

[0535] The user enters "I would like to obtain the latest financial information of Company A" into the input field on the terminal.

[0536] Input: Text request to get company information.

[0537] Output: The request is saved to the device.

[0538] Step 2:

[0539] The terminal sends a request to the server.

[0540] The terminal sends the user's input request to the server as an HTTP request.

[0541] Input: User requested text.

[0542] Output: HTTP request (for example, GET / fetchData?company=CompanyA&type=financial).

[0543] Step 3:

[0544] The server retrieves the data of the specified company from the database.

[0545] The server accesses the database to obtain Company A's financial information, strategy documents, and operational reports.

[0546] Input: HTTP request.

[0547] Output: Company financial data, strategy documents, operational reports.

[0548] Specific query example: SELECT FROM financial_reports WHERE company_name = 'Company A'.

[0549] Step 4:

[0550] The server inputs the acquired data into a generative AI model.

[0551] The server converts the acquired data into the input format for the generative AI model.

[0552] Inputs: Company financial data, strategy documents, operational reports.

[0553] Output: The prompt statement.

[0554] Specific prompt: "Analyze Company A's financial stability, growth potential, and profitability."

[0555] Step 5:

[0556] Generative AI models analyze data and generate results in natural language.

[0557] The generative AI model analyzes input data and generates assessment results in natural language regarding financial stability and growth potential.

[0558] Input: Prompt statement and company data.

[0559] Output: Parsing results in natural language.

[0560] Example output: "Company A has good financial stability, and is particularly praised for its low debt ratio and stable cash flow."

[0561] Step 6:

[0562] The server transmits the generated results to the terminal.

[0563] The server receives the output of the generative AI model and sends it to the terminal as an HTTP response.

[0564] Input: Natural language parsing results.

[0565] Output: HTTP response (e.g., {"result": "Company A's financial stability is good..."}).

[0566] Step 7:

[0567] The terminal displays the results to the user.

[0568] The terminal displays the received results to the user.

[0569] Input: HTTP response.

[0570] Output: Display of results (e.g., "Company A's financial stability is good...").

[0571] Step 8:

[0572] The server receives comments and feedback entered by the user.

[0573] The user enters feedback in the comment field on the device and presses the send button, which sends the feedback to the server.

[0574] Input: User comments and feedback.

[0575] Output: The feedback is saved on the server.

[0576] Step 9:

[0577] The server analyzes the user's emotional state using an emotion engine.

[0578] The server sends the received feedback to the emotion engine to analyze the emotional state.

[0579] Input: User feedback.

[0580] Output: Sentiment analysis result (e.g. "positive", "negative", "neutral").

[0581] Step 10:

[0582] The server adjusts the information based on the emotional state and provides appropriate information to the user.

[0583] Based on the results of the emotion analysis, the server sends another prompt to the generative AI model, which then generates more appropriate information, which the server then sends to the device.

[0584] Input: Sentiment analysis results.

[0585] Output: Adjusted natural language information.

[0586] Specific Regeneration Outcome: "Company A has low debt and stable income."

[0587] (Application example 2)

[0588] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0589] Financial product fraud is a serious problem, especially for novice investors. Fraudulent messages and investment guides often contain well-crafted fraudulent information, putting many users at high risk of being deceived. Furthermore, novice investors often have difficulty understanding specialized information such as corporate financial data and strategic documents, and are easily influenced by their emotions, making it difficult for them to make accurate investment decisions. To address these issues, a system is needed that adjusts information provision based on the user's emotional state and reduces the risk of fraud.

[0590] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring corporate financial data, strategic documents, and management reports from a database, means for analyzing the acquired data using a generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for analyzing text such as investment guidance received by the user using an emotion engine to recognize the emotional state, means for the generative AI engine to adjust the analysis results based on the emotional state, means for visually displaying the emotional state and the adjusted analysis results, means for assessing and warning about the possibility of fraud, and means for generating and proposing safe investment proposals. This makes it easy for even beginners to understand investments to provide accurate information and prevent fraud.

[0591] 1. "Financial data" refers to data that shows a company's financial status, such as its balance sheet, income statement, and cash flow statement.

[0592] 2. "Strategic documents" are documents that describe a company's medium- to long-term management strategies, policies, and business plans.

[0593] 3. An "operational report" is a report summarizing the company's daily business operations and results.

[0594] 4. A "generative AI engine" is an artificial intelligence engine that analyzes large amounts of data and generates output in natural language.

[0595] 5. "Emotion engine" is a data analysis engine that analyzes the user's emotions from input text and voice data and recognizes their emotional state.

[0596] 6. "Means for converting to natural language" refers to the process by which the generative AI engine converts the analysis results into a natural language format that is easy for humans to understand.

[0597] 7. "Emotional state" refers to the user's emotions expressed as positive, negative, neutral, etc.

[0598] 8. "Visual display" refers to the presentation of analytical results or emotional states in a visual format, such as graphs or charts.

[0599] 9. "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0600] 10. "Means for assessing the likelihood of fraud" refers to the process of using an emotion engine or generative AI to diagnose the riskiness of texts received by users, such as investment guides.

[0601] 11. "Means for generating safe investment proposals" means the process of using generative AI to suggest reliable investment options to users.

[0602] In this invention, the following system configuration and processing procedures are required to realize the "Fraud Safeguard AI" security application specialized in countering financial product fraud.

[0603] 1. Overall structure

[0604] This system mainly consists of the following hardware and software components.

[0605] Hardware: Smartphone

[0606] Software: Python, Transformers library (Hugging Face), requests module, proprietary emotion recognition module, data visualization module

[0607] 2. Data acquisition and analysis

[0608] The server first receives text such as investment information or messages received by the user, then uses an emotion engine to analyze the emotional state of the text (positive, negative, neutral), and evaluates the likelihood of fraud if the emotional state is highly negative.

[0609] 3. Emotion recognition and information provision

[0610] After the text is sentiment-recognized, the generative AI engine provides appropriate information to the user based on the results. For example, if the sentiment is highly negative, the generative AI engine will generate a warning message and suggest safe investment ideas to the user, thereby reducing the risk of fraud.

[0611] 4. Information Visualization

[0612] The server converts the acquired company's financial data and strategic documents into graphs and charts. The converted visual information is sent to the user's device and displayed in an easy-to-understand format, allowing the user to visually understand the information.

[0613] Specific examples

[0614] For example, if a user receives a suspicious investment offer email, they can input the email contents into the application. The sentiment engine analyzes the email contents and, if it detects a high degree of negativity, the generative AI engine will instead generate a safe investment proposal and present it to the user along with a warning. At this time, the company's financial stability and growth potential are visualized as graphs, allowing the user to intuitively understand the information.

[0615] Input prompt example

[0616] "Perform sentiment analysis of Company A's investment information to detect potential fraud. We will then provide you with a highly secure investment proposal. The investment proposal you entered is: {email_content}"

[0617] This system is easy to understand even for those new to investing, and it provides accurate information and prevents fraud.

[0618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0619] Step 1:

[0620] The user inputs the investment information and message into the terminal. This input data is in text format.

[0621] Step 2:

[0622] The device sends the input text data to the server, where it is properly formatted and ready for sentiment analysis.

[0623] Step 3:

[0624] The server inputs the received text data into the emotion engine, which analyzes the user's emotional state (positive, negative, neutral). The emotion engine analyzes the text and outputs the emotional state as a numerical value.

[0625] Step 4:

[0626] The server evaluates the sentiment analysis results obtained from the emotion engine, and if the result is highly negative, it flags the possibility of fraud. Based on the sentiment analysis results, the server starts the fraud evaluation process.

[0627] Step 5:

[0628] If fraud is determined to be likely, the server generates a prompt to feed into the generative AI engine, which includes a recognized negative sentiment.

[0629] Step 6:

[0630] The generative AI engine receives the prompts and generates safe investment proposals. During this process, the generative AI engine analyzes accumulated corporate financial data and strategic documents to output reliable investment proposals.

[0631] Step 7:

[0632] The server converts the generated safe investment proposals into natural language and creates text data for presenting them to the user in a format that is easy to understand. The server uses a natural language generation engine to convert the generated investment proposals into a user-friendly format.

[0633] Step 8:

[0634] The server sends the generated text data to the user's device, including the results of the sentiment analysis and fraud assessment.

[0635] Step 9:

[0636] The terminal displays the received data to the user, where visual graphs and charts are also displayed along with warning messages, allowing the user to intuitively understand their emotional state, fraud assessment, and alternative investment suggestions.

[0637] Through the above steps, the user can confirm the safety of the investment guide and reduce the risk by obtaining alternatives.

[0638] 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.

[0639] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] 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.

[0641] [Second embodiment]

[0642] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0654] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly includes the following elements:

[0655] 1. How to retrieve data from the database

[0656] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[0657] 2. Data analysis method using generative AI engine

[0658] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[0659] 3. Natural language generation means

[0660] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[0661] 4. Data Transmission Method

[0662] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[0663] 5. Automatic release and report generation tool

[0664] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[0665] 6. Visualization of IR information

[0666] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[0667] Specific examples

[0668] Multifaceted analysis of corporate information

[0669] 1. A user operates a terminal and requests detailed financial information about a company.

[0670] 2. The device sends this request to the server.

[0671] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[0672] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[0673] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[0674] 6. The server sends the generated results to the user's device.

[0675] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[0676] Automatic generation of releases and forecast reports

[0677] 1. The user requests the company's latest earnings forecast report from their device.

[0678] 2. The device sends this request to the server.

[0679] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[0680] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[0681] 5. The server sends the generated releases and reports to the user's terminal.

[0682] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[0683] Visualization of IR information

[0684] 1. The user requests visualization of a company's IR information through the terminal.

[0685] 2. The device sends this request to the server.

[0686] 3. The server retrieves the company's financial data and strategy documents.

[0687] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[0688] 5. The server sends the visualized data to the user's device.

[0689] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[0690] In this way, the "IR-GENAI" system of the present invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[0691] The processing flow will be explained below.

[0692] Multifaceted analysis of corporate information

[0693] Step 1:

[0694] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[0695] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[0696] Step 2:

[0697] The terminal sends the user's request to the server.

[0698] Specific operation: The data entered in the form is sent to the server as an API request.

[0699] Step 3:

[0700] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[0701] What it does: Executes a database query to retrieve the required information.

[0702] Step 4:

[0703] The server inputs the acquired data into the generation AI engine and requests analysis.

[0704] Specific operation: Call the API of the generation AI engine and send the acquired data.

[0705] Step 5:

[0706] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[0707] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[0708] Step 6:

[0709] The server receives the analysis results from the generative AI engine and sends them to the user.

[0710] Specific operation: The generated text data is received and sent to the user's device as a response.

[0711] Step 7:

[0712] The terminal displays the analysis results to the user.

[0713] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[0714] Automatic generation of releases and forecast reports

[0715] Step 1:

[0716] A user requests a company's latest performance forecast report through a terminal.

[0717] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[0718] Step 2:

[0719] The terminal sends a request to the server.

[0720] Specific operation: The data entered in the form is sent to the server as an API request.

[0721] Step 3:

[0722] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[0723] Specific operation: Query the database to obtain the latest information on the relevant company.

[0724] Step 4:

[0725] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[0726] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[0727] Step 5:

[0728] The server sends the generated releases and reports to the user.

[0729] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[0730] Step 6:

[0731] The terminal displays the generated releases and reports to the user.

[0732] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[0733] Visual presentation of IR information

[0734] Step 1:

[0735] A user requests a visual presentation of a company's investor relations information through a terminal.

[0736] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[0737] Step 2:

[0738] The terminal sends a request to the server.

[0739] Specific operation: The data entered in the form is sent to the server as an API request.

[0740] Step 3:

[0741] The server retrieves the company's financial data and strategy documents.

[0742] What it does: Executes a database query to retrieve the required information.

[0743] Step 4:

[0744] The server inputs the acquired data into the visualization module.

[0745] What you'll do: Format and input the data required for the visualization algorithm.

[0746] Step 5:

[0747] The visualization module transforms the data into graphs and charts.

[0748] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[0749] Step 6:

[0750] The server sends the visualized data to the user.

[0751] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[0752] Step 7:

[0753] The terminal displays the visualized information to the user.

[0754] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[0755] Q&A system

[0756] Step 1:

[0757] The user inputs a specific question through the terminal.

[0758] Specific behavior: The user enters a question in the text box and submits it.

[0759] Step 2:

[0760] The terminal sends a question to the server.

[0761] Specific operation: The entered text is sent to the server as an API request.

[0762] Step 3:

[0763] The server inputs the question into a natural language processing engine.

[0764] Specific operation: Calls the API of the natural language processing engine and sends a question.

[0765] Step 4:

[0766] A natural language processing engine generates the best answer to your question.

[0767] What it does: Parses the question and generates an answer by pulling information from relevant databases.

[0768] Step 5:

[0769] The server sends the generated answer to the user.

[0770] Specific operation: Receive the generated text and send it to the user's device as a response.

[0771] Step 6:

[0772] The terminal displays the answer to the user.

[0773] Specific operation: The received response is displayed on the screen in an easy-to-read format.

[0774] Evaluating E / S / G information

[0775] Step 1:

[0776] The server retrieves the company's E / S / G report.

[0777] What it does: Executes a database query to retrieve a company's E / S / G report.

[0778] Step 2:

[0779] The information acquired by the server is input into the generation AI engine, which evaluates each of the E / S / G items.

[0780] Specific operation: Format the obtained report and input it into the evaluation module of the generative AI engine.

[0781] Step 3:

[0782] A generative AI engine converts the evaluation results into natural language.

[0783] What it does: It rates each item through a rating algorithm and documents the results using a natural language generation algorithm.

[0784] Step 4:

[0785] The server sends the evaluation results to the user.

[0786] Specific operation: The generated evaluation document is received and sent to the user's terminal as a response.

[0787] Step 5:

[0788] The terminal displays the evaluation results to the user.

[0789] Specific operation: The received evaluation document is displayed on the screen in an easy-to-read format.

[0790] Example 1

[0791] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0792] Until now, providing corporate financial information and operational data in a format that is easy to understand even for novice investors required a great deal of specialized knowledge, making it difficult to obtain and interpret the information. Furthermore, creating automated releases and performance forecast reports to quickly provide the latest information, as well as visualizing the data, were time-consuming tasks. For these reasons, a system capable of efficiently obtaining, analyzing, reporting, and visualizing information was needed to provide corporate information.

[0793] 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.

[0794] In this invention, the server includes means for acquiring a company's financial information, strategic information, and operational data from a database, means for multifaceted analysis of the acquired information using a generative AI model, means for converting the results of the multifaceted analysis by the generative AI model into natural language, means for transmitting the converted results to a user's terminal, means for accepting information requests from users and transmitting them to the server, means for creating releases and performance forecast reports using the latest information automatically generated by the generative AI model, means for transmitting the created releases and performance forecast reports to the user's terminal, means for converting the acquired company data into charts and graphs using a visualization module, and means for transmitting the converted charts and graphs to the user's terminal. This enables efficient acquisition, analysis, reporting, and visualization of corporate information, making it possible to provide information that is easy to understand even for beginner investors.

[0795] "Financial information of a company" refers to data showing the business status of a company, including balance sheets, income statements, cash flow statements, and the like.

[0796] "Strategic information" refers to materials related to a company's future plans and management strategies, including a company's growth plans and competitive strategies.

[0797] "Operational Data" means data relating to the day-to-day business operations of the company, including operational results and daily performance reports.

[0798] A "database" is a collection of data organized in a certain way, a system that stores a company's financial information, strategic information, and operational data.

[0799] A "generative AI model" is a system based on artificial intelligence that has the ability to analyze input data from multiple angles and output it in natural language.

[0800] "Converting into natural language" refers to converting specialized data and analytical results into a linguistic form that is easy for the general public to understand.

[0801] A "user terminal" is a device operated by a user, and includes a PC, smartphone, tablet, etc.

[0802] An "information request" refers to an operation or request made by a user to a system for specific information.

[0803] A "release" is an official announcement or report issued by a company that contains a series of updates or news.

[0804] A "performance forecast report" is a report that predicts a company's future performance, and includes forecast data on future sales and profits.

[0805] A "visualization module" is a software or hardware feature that allows data to be visually represented, such as in graphs or charts.

[0806] "Graphing" refers to converting data into an easy-to-read format such as a graph or chart.

[0807] The "IR-GENAI" system of this invention uses advanced generative AI technology to provide corporate information in a format that is easy to understand even for beginner investors. This system involves data acquisition from a database, data analysis using a generative AI engine, natural language generation, data transmission, automatic generation of releases and performance forecast reports, and visualization of IR information.

[0808] System configuration

[0809] Hardware and Software Use

[0810] 1. Server: Processes data and interacts with the generative AI engine and visualization module.

[0811] 2. Database: Use a relational database such as MySQL to store the company's financial, strategic, and operational data.

[0812] 3. Generative AI engine: Uses generative AI models such as OpenAI GPT-4 to analyze data from multiple angles and output in natural language.

[0813] 4. Visualization module: Use visualization tools such as Tableau to transform data into graphs and charts.

[0814] 5. Terminal: A PC, smartphone, tablet, etc. that is operated by the user.

[0815] Data acquisition and processing

[0816] A user uses a terminal to request information about a particular company. The terminal sends this request to a server, which retrieves the relevant data from a database. For example, a balance sheet or profit and loss statement to understand the company's financial situation. Here is an example of the server retrieving the data using a MySQL query:

[0817] SELECT FROM Financial Data WHERE Company Name = "Company A"

[0818] Analyzing the data

[0819] The data acquired by the server is input into a generative AI engine, which analyzes it from multiple angles, including financial stability and growth potential. OpenAI GPT-4 is used as the generative AI engine. Examples of prompts generated by the generative AI engine include the following:

[0820] "Please analyze Company A's financial situation and explain it in a way that even a beginner investor can understand."

[0821] Generate and send results

[0822] The generative AI engine generates the analysis results in natural language and sends them to the user's device. For example, the following natural language sentences are generated:

[0823] "Company A has good financial stability, especially with a low debt ratio and stable cash flow."

[0824] The server sends the generated results to the user's terminal, which displays them.

[0825] Automatic generation of releases and forecast reports

[0826] When a user requests a company's latest performance forecast report, the server inputs the company's latest data and market forecast data into the AI ​​engine to generate a performance forecast report. The generated report is provided in the following format:

[0827] "Company A's next fiscal year's performance is expected to see increased revenue and profits, due to increased market share and the success of new products."

[0828] The server sends this report to the user's terminal, which displays it.

[0829] Visualization of IR information

[0830] When a user requests visualization of a company's investor relations information, the server retrieves financial data and strategic documents from the database and inputs them into the visualization module. The graphs and charts generated by the visualization module include the following examples:

[0831] -Bar chart showing sales trends

[0832] Line graph showing fluctuations in profit margins

[0833] The server transmits the visualized data to the user's terminal, which displays it.

[0834] In this way, the "IR-GENAI" system of this invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[0835] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0836] Step 1: Enter your information request

[0837] A user operates a terminal to request information about a specific company. For example, a request such as "Please display the financial data of Company A" is input into the terminal. The terminal sends this information request to the server. The input is the "company name" and "data type." The output is the requested information.

[0838] Step 2: Submitting the request

[0839] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Financial data" and sends it to the server. The input is the user's request, and the output is the request transmission.

[0840] Step 3: Retrieving data from the database

[0841] The server receives the request and accesses the database to retrieve the required data. Specifically, the server retrieves the data using a MySQL query. For example, it executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[0842] Step 4: Input data into the generative AI engine

[0843] The server inputs the acquired data into the generative AI engine. Specifically, the server sends financial data in JSON format to the generative AI engine. The input is Company A's financial data, and the output is data sent to the generative AI engine.

[0844] Step 5: Data analysis and natural language translation

[0845] The generative AI engine analyzes data from multiple perspectives and converts the results into natural language. Specifically, the generative AI engine generates natural language sentences such as, "Company A has good financial stability, with a particularly low debt ratio and stable cash flow." The input is Company A's financial data, and the output is the analysis results generated in natural language.

[0846] Step 6: Submitting the analysis results

[0847] The server receives the results from the generative AI engine and sends them to the user's device. Specifically, the server sends the generated natural language sentence to the device. The input is the analysis result generated in natural language, and the output is sent to the device.

[0848] Step 7: View the results

[0849] The terminal displays the results it receives to the user. Specifically, the screen displays the following: "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow." The input is the analysis results generated in natural language, and the output is the display to the user.

[0850] Automatic generation of releases and forecast reports

[0851] Step 1: Enter your performance forecast

[0852] A user requests the latest performance forecast report for a company from a terminal. For example, the user inputs, "Please generate the next performance forecast for Company A." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[0853] Step 2: Submitting the request

[0854] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Performance forecast" and sends it to the server. The input is the user's request, and the output is the request transmission.

[0855] Step 3: Data Acquisition

[0856] The server retrieves the latest performance data and market forecast data for a company. Specifically, the server queries the database and executes the query "SELECT FROM performance data WHERE company name = 'Company A'". The input is the request information, and the output is the performance data for Company A.

[0857] Step 4: Input to the generative AI engine

[0858] The server inputs the acquired data into the generative AI engine. Specifically, the server sends performance data and market forecast data in JSON format to the generative AI engine. The input is Company A's performance data and market forecast data, and the output is data sent to the generative AI engine.

[0859] Step 5: Generate a performance forecast report

[0860] The generative AI engine analyzes performance data and generates a report in natural language. For example, a report might be generated that states, "Company A's next fiscal year's performance is expected to increase in both sales and profits. This is attributed to increased market share and the success of new products." The input is Company A's performance data and market forecast data, and the output is a performance forecast report generated in natural language.

[0861] Step 6: Release Submission

[0862] The server sends the generated performance forecast report to the user's terminal. Specifically, the server sends the generated natural language report to the terminal. The input is the performance forecast report generated in natural language, and the output is sent to the terminal.

[0863] Step 7: View the report

[0864] The terminal displays the received report to the user. Specifically, the screen displays the following: "Company A's next fiscal year's performance is expected to increase in both sales and profits, due in part to increased market share and the success of new products." The input is a performance forecast report generated in natural language, and the output is the display to the user.

[0865] Visualization of IR information

[0866] Step 1: Enter your visualization request

[0867] A user requests visualization of a company's IR information through a terminal. For example, the user inputs, "Please display the IR information of Company A in a graph." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[0868] Step 2: Submitting the request

[0869] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: IR information" and sends it to the server. The input is the user's request, and the output is the request transmission.

[0870] Step 3: Data Acquisition

[0871] The server retrieves a company's financial data and strategic documents from a database. Specifically, the server executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[0872] Step 4: Input to the visualization module

[0873] The server inputs the acquired data into the visualization module. Specifically, the server sends the financial data of Company A to the visualization module. The input is the financial data of Company A, and the output is the data sent to the visualization module.

[0874] Step 5: Visualize the data

[0875] The visualization module converts the acquired data into graphs and charts. For example, it generates a bar chart showing the trend in sales volume and a line graph showing fluctuations in profit margins. The input is Company A's financial data, and the output is a visualized graph or chart.

[0876] Step 6: Sending visualized data

[0877] The server sends the visualized data to the user's device. Specifically, it sends the generated graphs and charts to the device. The input is the visualized data, and the output is the data sent to the device.

[0878] Step 7: View the visualization data

[0879] The visualized data received by the terminal is displayed to the user. For example, a "bar chart showing the trend in sales of Company A" or a "line graph showing fluctuations in profit margin" is displayed on the screen. The input is the visualized data, and the output is the display to the user.

[0880] (Application example 1)

[0881] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0882] Modern investors, especially those new to investing, face challenges in comprehensively and intuitively understanding corporate information. Technical financial data and operational reports are particularly difficult to understand, and there is a lack of tools to effectively utilize them to make investment decisions. Furthermore, there are limited visual and interactive ways to display corporate information, creating a need for new solutions.

[0883] 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.

[0884] In this invention, the server includes means for retrieving corporate financial data, strategic documents, and management reports from a database, means for analyzing the data retrieved by the generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for transmitting the converted results to a user's terminal, means for visually and interactively displaying the corporate information analyzed by the generative AI engine using a head-mounted display, and means for the user to retrieve and manipulate the corporate information by interactive manual operation or voice command through the head-mounted display. This allows even beginners to invest to comprehensively and intuitively understand corporate information and supports investment decisions.

[0885] "Financial data of a company" refers to data that represents the financial status of a company, such as a balance sheet, income statement, and cash flow statement.

[0886] A "strategic document" is a document that describes the medium- to long-term goals set by a company, as well as the plans and policies for achieving them.

[0887] An "operational report" is a report that describes a company's daily business activities, their results, problems, etc.

[0888] A "database" is a system for storing structured business information that can be efficiently accessed, managed, and updated.

[0889] A "generative AI engine" is an artificial intelligence engine that analyzes collected data and generates specific assessments and predictions.

[0890] "Natural language" is a human language that expresses the analysis results in a form that can be understood by users.

[0891] A "user terminal" is a device that a user uses to obtain and operate information, and includes, for example, a smartphone, a tablet, a computer, and the like.

[0892] A "head-mounted display" is a display device that the user wears on their head, and is a device that enables VR and AR experiences.

[0893] A "visualization module" is a software component for transforming enterprise data into visual formats such as graphs and charts.

[0894] "Interactive manual manipulation or voice command" refers to a method by which a user manipulates data through a head-mounted display, including hand gestures and voice input.

[0895] This invention is a system that uses a generative AI model to analyze data such as a company's financial data, strategic documents, and operational reports, and provides information to investors in a visual and interactive format. The system includes a server, a user terminal, a head-mounted display, and a generative AI engine. Detailed embodiments of this system are described below.

[0896] 1. Data Acquisition and Analysis

[0897] The server first retrieves the company's financial data, strategy documents, and operational reports from a database. The database stores the company's balance sheet, income statement, cash flow statement, strategy documents, and operational reports. To retrieve this data, the server fetches the data via an API or similar.

[0898] The server then inputs the acquired data into a generative AI engine, which analyzes it from multiple perspectives. The generative AI engine analyzes aspects such as financial stability, growth potential, and profitability, and generates an evaluation for each. The analysis results are converted into natural language and output in a format that is easy for users to understand.

[0899] 2. Send to the user device

[0900] The generated natural language results are sent from the server to the user's device, which can be a smartphone, tablet, or PC, where the results are displayed.

[0901] 3. Visualization and Interaction with Head-Mounted Displays

[0902] The analysis results are also displayed visually and interactively using a head-mounted display (HMD). Users can wear the HMD and check company information in a VR or AR environment. HMDs such as Oculus Rift and HTC Vive are used. The visualization module converts the analysis results from the generative AI engine into graphs and charts, which are then displayed within the HMD.

[0903] Users can use manual or voice commands to interactively manipulate and gain a deeper understanding of company information. The head-mounted display interface allows easy access to detailed company financial information, and instant access to releases and earnings forecasts.

[0904] Specific examples

[0905] For example, if a user issues a voice command such as "Show me the latest earnings forecast for Company A," the server retrieves the latest information for Company A from the database. The generative AI engine then analyzes the information and generates a result in natural language. This result is displayed visually in the HMD, such as "Company A's next fiscal year's earnings are expected to increase in both sales and profits, driven by increased market share and the success of new products." Additionally, a bar chart showing sales trends and a line graph showing fluctuations in profit margins are also displayed visually.

[0906] Prompt Sentence Examples

[0907] Possible prompts for a generative AI model include:

[0908] "Analyze the following financial data: revenue: 20 million, profit: 3 million, expenses: 17 million, assets: 50 million, liabilities: 30 million"

[0909] In this way, the present invention supports investors, especially beginners, by analyzing company information from multiple angles and providing it to users in natural language and visual formats.

[0910] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0911] Step 1:

[0912] A user requests detailed financial information of a company through a device such as a head-mounted display or smartphone. This request is sent from the device to a server. The input data is the company ID and name, and the output is a request to the server.

[0913] Step 2:

[0914] The server retrieves a company's financial data, strategy documents, and management reports from the database based on user requests. The input data is the company's ID and name, and the data retrieved from the database is balance sheets, income statements, cash flow statements, etc. The output is these datasets.

[0915] Step 3:

[0916] The server inputs the acquired company data into the generation AI engine for multifaceted analysis. The input data is the company's financial data, strategy documents, and management reports, and the generation AI engine analyzes them to evaluate financial stability, growth potential, profitability, etc. The output is the analysis results.

[0917] Step 4:

[0918] The generative AI engine converts the analysis results into natural language. The input data is analyzed company information, and the output is the analysis results in natural language format.

[0919] Step 5:

[0920] The server sends the generated natural language results to the user's terminal. The input data is the analysis result of the natural language format, and the output is the data delivery to the user's terminal.

[0921] Step 6:

[0922] The device displays the received analysis results to the user. On smartphones and tablets, the results are displayed in text format. The input data is the analysis results in natural language format, and the output is the information displayed on the device screen.

[0923] Step 7:

[0924] When using a head-mounted display, the server converts the analysis results into graphs and charts using a visualization module, which then visually displays them within the HMD. The input data are the analysis results in natural language format and graph or chart data, and the output is the visualized information within the HMD.

[0925] Step 8:

[0926] Users can obtain and operate corporate information through interactive manual operations or voice commands via a head-mounted display. The input data are user operations and commands, and the output is updating the displayed information or obtaining detailed information.

[0927] This process allows even novice investors to comprehensively and intuitively understand company information and supports investment decisions.

[0928] 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.

[0929] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to provide information based on the emotional state of each individual investor. This system mainly includes the following elements:

[0930] 1. How to retrieve data from the database

[0931] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[0932] 2. Data analysis method using generative AI engine

[0933] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[0934] 3. Natural language generation means

[0935] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[0936] 4. Data Transmission Method

[0937] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[0938] 5. Automatic release and report generation tool

[0939] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[0940] 6. Visualization of IR information

[0941] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[0942] 7. Emotion Recognition Method Using Emotion Engine

[0943] The emotion engine analyzes the text and voice input by the user through the device and recognizes the user's emotions. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral) through text and voice analysis.

[0944] 8. Emotion-based information regulation

[0945] The server adjusts the information and analysis results provided by the generative AI engine based on the user's recognized emotions. For example, if the user is expressing negative emotions, it can reduce the user's stress by emphasizing positive information.

[0946] 9. Visual displays of emotions

[0947] It provides an interface for visually displaying the user's emotional state and tracks emotional fluctuations, allowing the user to understand their own emotional fluctuations.

[0948] Specific examples

[0949] Multifaceted analysis of corporate information and emotion recognition

[0950] 1. A user operates a terminal and enters a request to obtain detailed financial information about a company.

[0951] 2. The device sends this request to the server.

[0952] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[0953] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[0954] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[0955] 6. The server sends the generated results to the user's device.

[0956] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[0957] Automatic generation of releases and earnings forecasts with emotion recognition

[0958] 1. The user requests the company's latest earnings forecast report from their device.

[0959] 2. The device sends a request to the server.

[0960] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[0961] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[0962] 5. The server sends the generated releases and reports to the user's terminal.

[0963] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[0964] IR Information Visualization and Emotion Recognition

[0965] 1. The user requests visualization of a company's IR information through the terminal.

[0966] 2. The device sends a request to the server.

[0967] 3. The server retrieves the company's financial data and strategy documents.

[0968] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[0969] 5. The server sends the visualized data to the user.

[0970] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[0971] Emotion recognition and information regulation

[0972] 1. When users view company information through their devices, they can enter comments and feedback.

[0973] 2. The device sends comments and feedback to the server.

[0974] 3. The server sends these inputs to the emotion engine, which analyzes the emotional state.

[0975] 4. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[0976] 5. Based on the emotional state, the server adjusts the generative AI engine to provide the most appropriate information for the user.

[0977] 6. The device will visually display emotional fluctuations, allowing users to understand their own emotional state.

[0978] In this way, the "IR-GENAI" system of the present invention combines generative AI technology with an emotion engine to analyze corporate information from multiple angles and provide information based on the user's emotional state. This makes it possible to provide information that is easy to understand even for beginner investors and reduces the psychological burden.

[0979] The processing flow will be explained below.

[0980] Multifaceted analysis of corporate information and emotion recognition

[0981] Step 1:

[0982] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[0983] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[0984] Step 2:

[0985] The terminal sends the user's request to the server.

[0986] Specific operation: The data entered in the form is sent to the server as an API request.

[0987] Step 3:

[0988] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[0989] What it does: Executes a database query to retrieve the required information.

[0990] Step 4:

[0991] The server inputs the acquired data into the generation AI engine and requests analysis.

[0992] Specific operation: Call the API of the generation AI engine and send the acquired data.

[0993] Step 5:

[0994] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[0995] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[0996] Step 6:

[0997] The server receives the analysis results from the generative AI engine and sends them to the user.

[0998] Specific operation: The generated text data is received and sent to the user's device as a response.

[0999] Step 7:

[1000] The terminal displays the analysis results to the user.

[1001] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[1002] Step 8:

[1003] The user enters comments and feedback about the analysis results.

[1004] Specific action: Enter text into the input field and press the submit button.

[1005] Step 9:

[1006] The device sends comments and feedback to the server.

[1007] Specific operation: The entered text is sent to the server as an API request.

[1008] Step 10:

[1009] The server sends the comments and feedback to the emotion engine, which analyzes the emotional state.

[1010] Specific operation: Call the emotion engine API and send input data.

[1011] Step 11:

[1012] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1013] What it does: Uses text analysis algorithms to determine user sentiment.

[1014] Step 12:

[1015] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[1016] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[1017] Step 13:

[1018] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[1019] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[1020] Automatic generation of releases and earnings forecasts with emotion recognition

[1021] Step 1:

[1022] A user requests the latest business forecast report of a company from a terminal.

[1023] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[1024] Step 2:

[1025] The terminal sends a request to the server.

[1026] Specific operation: The data entered in the form is sent to the server as an API request.

[1027] Step 3:

[1028] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[1029] Specific operation: Query the database to obtain the latest information on the relevant company.

[1030] Step 4:

[1031] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[1032] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[1033] Step 5:

[1034] The server sends the generated releases and reports to the user.

[1035] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[1036] Step 6:

[1037] The terminal displays the generated releases and reports to the user.

[1038] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[1039] Step 7:

[1040] Users enter comments and feedback on the report.

[1041] Specific action: Enter text into the input field and press the submit button.

[1042] Step 8:

[1043] The device sends comments and feedback to the server.

[1044] Specific operation: The entered text is sent to the server as an API request.

[1045] Step 9:

[1046] The server sends these inputs to the emotion engine, which analyzes the emotional state.

[1047] Specific operation: Call the emotion engine API and send input data.

[1048] Step 10:

[1049] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1050] What it does: Uses text analysis algorithms to determine user sentiment.

[1051] Step 11:

[1052] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[1053] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[1054] Step 12:

[1055] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[1056] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[1057] Visual presentation of IR information and emotion recognition

[1058] Step 1:

[1059] The user requests visualization of a company's IR information through the terminal.

[1060] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[1061] Step 2:

[1062] The terminal sends a request to the server.

[1063] Specific operation: The data entered in the form is sent to the server as an API request.

[1064] Step 3:

[1065] The server retrieves the company's financial data and strategy documents.

[1066] What it does: Executes a database query to retrieve the required information.

[1067] Step 4:

[1068] The server inputs the acquired data into the visualization module.

[1069] What you'll do: Format and input the data required for the visualization algorithm.

[1070] Step 5:

[1071] The visualization module transforms the data into graphs and charts.

[1072] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[1073] Step 6:

[1074] The server sends the visualized data to the user.

[1075] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[1076] Step 7:

[1077] The terminal displays the visualized information to the user.

[1078] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[1079] Step 8:

[1080] The user inputs their thoughts on the chart or graph.

[1081] Specific action: Enter text into the input field and press the submit button.

[1082] Step 9:

[1083] The device sends the feedback to the server.

[1084] Specific operation: The entered text is sent to the server as an API request.

[1085] Step 10:

[1086] The server sends the sentiment to the emotion engine, which analyzes the emotional state.

[1087] Specific operation: Call the emotion engine API and send input data.

[1088] Step 11:

[1089] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1090] What it does: Uses text analysis algorithms to determine user sentiment.

[1091] Step 12:

[1092] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[1093] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[1094] Step 13:

[1095] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[1096] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[1097] Example 2

[1098] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1099] Providing corporate information in an easy-to-understand manner, even for novice investors, requires advanced data analysis and natural language generation technology. However, conventional systems produce data analysis results that are technical and difficult for beginners to understand. Furthermore, the information provided does not take into account the user's emotional state, making it difficult to improve the user experience. Furthermore, efficient methods are needed for visualizing the generated information and automatically generating releases and reports.

[1100] 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.

[1101] In this invention, the server includes: means for retrieving corporate financial data, strategic documents, and management reports from a database; means for analyzing the retrieved data using a generative AI model; means for converting the results of the analysis by the generative AI model into natural language; means for transmitting the converted results to a user's terminal; means for analyzing text and voice input by the user through the terminal and recognizing the user's emotions using an emotion engine; and means for adjusting the information and analysis results provided by the generative AI model based on the recognized user emotions. This enables multifaceted analysis of corporate information, making it easy to understand even for beginner investors, and providing information based on the emotional state of each individual user. Furthermore, efficient information provision through visualization and automatic generation can be achieved.

[1102] "Corporate financial data" refers to data that shows the economic status of a company, and primarily includes balance sheets, income statements, cash flow statements, etc.

[1103] A "strategic document" is a document that outlines the company's business policies for the medium to long term.

[1104] An "operational report" is a report that records the performance and results of a company's day-to-day operations.

[1105] A "database" is a system for efficiently storing and managing structured data.

[1106] A "generative AI model" is a type of artificial intelligence technology that learns from large amounts of data and generates text and data for various tasks.

[1107] "Natural language conversion" is the process of converting specialized data and analytical results into text that is easy for the general public to understand.

[1108] "Terminal" means a device used by a user to input or receive information, including, for example, a personal computer or smartphone.

[1109] An "emotion engine" is a type of artificial intelligence technology that analyzes a user's emotional state from text and voice.

[1110] A "visualization module" is software or tools for converting data into a visual format such as a graph or chart.

[1111] A "release or report" is a written document that summarizes a company's important information, results, forecasts, etc.

[1112] "Recognizing user emotions" is the process of identifying emotions from information entered by the user, and is performed using an emotion engine.

[1113] "Adjusting information or analysis results" means changing the content or presentation of the information or analysis results provided based on the user's emotional state.

[1114] The purpose of the system of this invention is to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly combines a server, a terminal, a generative AI model, an emotion engine, and a visualization module. The details of each element and their operation are explained below.

[1115] System configuration

[1116] 1. Server

[1117] The server is a central management system that retrieves the company's financial data, strategy documents, and operational reports from the database. The server collects this data and sends it to the generative AI model and emotion engine.

[1118] The server uses specific software and hardware, such as: a database system (e.g., MySQL), a generative AI model (e.g., GPT-4), and an emotion engine (e.g., IBM Watson Tone Analyzer).

[1119] 2. Terminal

[1120] A terminal is a device through which a user inputs information and receives results, and includes, for example, a PC, a smartphone, or a tablet.

[1121] The terminal sends the input request to the server as an HTTP request, and also has the function of displaying the results received from the server.

[1122] 3. Generative AI Models

[1123] A generative AI model is an engine that analyzes collected data and converts it into a format that is easy to understand in natural language. For example, advanced generative AI techniques such as GPT-4 are used.

[1124] The generative AI model expresses the results of its analysis of financial data in natural language and provides them to users.

[1125] 4. Emotion Engine

[1126] The emotion engine is a technology that analyzes text and speech input from users to recognize their emotional state, and adjusts the information and analysis results provided by the generative AI model based on that emotional state.

[1127] For example, if the user is expressing negative emotions, more positive expressions are emphasized and presented.

[1128] 5. Visualization Module

[1129] The visualization module is a tool for converting acquired data into graphs and charts, which provides a visually understandable view of the data.

[1130] Libraries such as D3.js and Chart.js are used for data visualization.

[1131] Specific examples

[1132] Multifaceted analysis of corporate information and emotion recognition

[1133] 1. The user operates the terminal and enters, "I would like to obtain the latest financial information for Company A."

[1134] 2. The device sends this request to the server as an HTTP request.

[1135] Specific request example: GET / fetchData?company=CompanyA&type=financial

[1136] 3. The server accesses the database to retrieve Company A's financial information, strategy documents, and operational reports.

[1137] Specific query example: SELECT FROM financial_reports WHERE company_name = 'Company A'

[1138] 4. The server inputs the acquired data into the generative AI model. When generating a prompt, input the following:

[1139] "Please analyze Company A's financial stability, growth potential, and profitability."

[1140] 5. The generative AI model analyzes the data and outputs assessment results in natural language regarding financial stability and growth potential.

[1141] Example output: "Company A has good financial stability, and is particularly praised for its low debt ratio and stable cash flow."

[1142] 6. The server sends the generated results to the user's terminal, which displays the results to the user.

[1143] In this way, the system analyzes corporate information from multiple angles and provides it in an easy-to-understand manner in natural language.Furthermore, by using an emotion engine, it is possible to provide information based on the user's emotional state.

[1144] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1145] Step 1:

[1146] A user inputs a request to obtain business information.

[1147] The user enters "I would like to obtain the latest financial information of Company A" into the input field on the terminal.

[1148] Input: Text request to get company information.

[1149] Output: The request is saved to the device.

[1150] Step 2:

[1151] The terminal sends a request to the server.

[1152] The terminal sends the user's input request to the server as an HTTP request.

[1153] Input: User requested text.

[1154] Output: HTTP request (for example, GET / fetchData?company=CompanyA&type=financial).

[1155] Step 3:

[1156] The server retrieves the data of the specified company from the database.

[1157] The server accesses the database to obtain Company A's financial information, strategy documents, and operational reports.

[1158] Input: HTTP request.

[1159] Output: Company financial data, strategy documents, operational reports.

[1160] Specific query example: SELECT FROM financial_reports WHERE company_name = 'Company A'.

[1161] Step 4:

[1162] The server inputs the acquired data into a generative AI model.

[1163] The server converts the acquired data into the input format for the generative AI model.

[1164] Inputs: Company financial data, strategy documents, operational reports.

[1165] Output: The prompt statement.

[1166] Specific prompt: "Analyze Company A's financial stability, growth potential, and profitability."

[1167] Step 5:

[1168] Generative AI models analyze data and generate results in natural language.

[1169] The generative AI model analyzes input data and generates assessment results in natural language regarding financial stability and growth potential.

[1170] Input: Prompt statement and company data.

[1171] Output: Parsing results in natural language.

[1172] Example output: "Company A has good financial stability, and is particularly praised for its low debt ratio and stable cash flow."

[1173] Step 6:

[1174] The server transmits the generated results to the terminal.

[1175] The server receives the output of the generative AI model and sends it to the terminal as an HTTP response.

[1176] Input: Natural language parsing results.

[1177] Output: HTTP response (e.g., {"result": "Company A's financial stability is good..."}).

[1178] Step 7:

[1179] The terminal displays the results to the user.

[1180] The terminal displays the received results to the user.

[1181] Input: HTTP response.

[1182] Output: Display of results (e.g., "Company A's financial stability is good...").

[1183] Step 8:

[1184] The server receives comments and feedback entered by the user.

[1185] The user enters feedback in the comment field on the device and presses the send button, which sends the feedback to the server.

[1186] Input: User comments and feedback.

[1187] Output: The feedback is saved on the server.

[1188] Step 9:

[1189] The server analyzes the user's emotional state using an emotion engine.

[1190] The server sends the received feedback to the emotion engine to analyze the emotional state.

[1191] Input: User feedback.

[1192] Output: Sentiment analysis result (e.g. "positive", "negative", "neutral").

[1193] Step 10:

[1194] The server adjusts the information based on the emotional state and provides appropriate information to the user.

[1195] Based on the results of the emotion analysis, the server sends another prompt to the generative AI model, which then generates more appropriate information, which the server then sends to the device.

[1196] Input: Sentiment analysis results.

[1197] Output: Adjusted natural language information.

[1198] Specific Regeneration Outcome: "Company A has low debt and stable income."

[1199] (Application example 2)

[1200] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1201] Financial product fraud is a serious problem, especially for novice investors. Fraudulent messages and investment guides often contain well-crafted fraudulent information, putting many users at high risk of being deceived. Furthermore, novice investors often have difficulty understanding specialized information such as corporate financial data and strategic documents, and are easily influenced by their emotions, making it difficult for them to make accurate investment decisions. To address these issues, a system is needed that adjusts information provision based on the user's emotional state and reduces the risk of fraud.

[1202] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring corporate financial data, strategic documents, and management reports from a database, means for analyzing the acquired data using a generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for analyzing text such as investment guidance received by the user using an emotion engine to recognize the emotional state, means for the generative AI engine to adjust the analysis results based on the emotional state, means for visually displaying the emotional state and the adjusted analysis results, means for assessing and warning about the possibility of fraud, and means for generating and proposing safe investment proposals. This makes it easy for even beginners to understand investments to provide accurate information and prevent fraud.

[1203] 1. "Financial data" refers to data that shows a company's financial status, such as its balance sheet, income statement, and cash flow statement.

[1204] 2. "Strategic documents" are documents that describe a company's medium- to long-term management strategies, policies, and business plans.

[1205] 3. An "operational report" is a report summarizing the company's daily business operations and results.

[1206] 4. A "generative AI engine" is an artificial intelligence engine that analyzes large amounts of data and generates output in natural language.

[1207] 5. "Emotion engine" is a data analysis engine that analyzes the user's emotions from input text and voice data and recognizes their emotional state.

[1208] 6. "Means for converting to natural language" refers to the process by which the generative AI engine converts the analysis results into a natural language format that is easy for humans to understand.

[1209] 7. "Emotional state" refers to the user's emotions expressed as positive, negative, neutral, etc.

[1210] 8. "Visual display" refers to the presentation of analytical results or emotional states in a visual format, such as graphs or charts.

[1211] 9. "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1212] 10. "Means for assessing the likelihood of fraud" refers to the process of using an emotion engine or generative AI to diagnose the riskiness of texts received by users, such as investment guides.

[1213] 11. "Means for generating safe investment proposals" means the process of using generative AI to suggest reliable investment options to users.

[1214] In this invention, the following system configuration and processing procedures are required to realize the "Fraud Safeguard AI" security application specialized in countering financial product fraud.

[1215] 1. Overall structure

[1216] This system mainly consists of the following hardware and software components.

[1217] Hardware: Smartphone

[1218] Software: Python, Transformers library (Hugging Face), requests module, proprietary emotion recognition module, data visualization module

[1219] 2. Data acquisition and analysis

[1220] The server first receives text such as investment information or messages received by the user, then uses an emotion engine to analyze the emotional state of the text (positive, negative, neutral), and evaluates the likelihood of fraud if the emotional state is highly negative.

[1221] 3. Emotion recognition and information provision

[1222] After the text is sentiment-recognized, the generative AI engine provides appropriate information to the user based on the results. For example, if the sentiment is highly negative, the generative AI engine will generate a warning message and suggest safe investment ideas to the user, thereby reducing the risk of fraud.

[1223] 4. Information Visualization

[1224] The server converts the acquired company's financial data and strategic documents into graphs and charts. The converted visual information is sent to the user's device and displayed in an easy-to-understand format, allowing the user to visually understand the information.

[1225] Specific examples

[1226] For example, if a user receives a suspicious investment offer email, they can input the email contents into the application. The sentiment engine analyzes the email contents and, if it detects a high degree of negativity, the generative AI engine will instead generate a safe investment proposal and present it to the user along with a warning. At this time, the company's financial stability and growth potential are visualized as graphs, allowing the user to intuitively understand the information.

[1227] Input prompt example

[1228] "Perform sentiment analysis of Company A's investment information to detect potential fraud. We will then provide you with a highly secure investment proposal. The investment proposal you entered is: {email_content}"

[1229] This system is easy to understand even for those new to investing, and it provides accurate information and prevents fraud.

[1230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1231] Step 1:

[1232] The user inputs the investment information and message into the terminal. This input data is in text format.

[1233] Step 2:

[1234] The device sends the input text data to the server, where it is properly formatted and ready for sentiment analysis.

[1235] Step 3:

[1236] The server inputs the received text data into the emotion engine, which analyzes the user's emotional state (positive, negative, neutral). The emotion engine analyzes the text and outputs the emotional state as a numerical value.

[1237] Step 4:

[1238] The server evaluates the sentiment analysis results obtained from the emotion engine, and if the result is highly negative, it flags the possibility of fraud. Based on the sentiment analysis results, the server starts the fraud evaluation process.

[1239] Step 5:

[1240] If fraud is determined to be likely, the server generates a prompt to feed into the generative AI engine, which includes a recognized negative sentiment.

[1241] Step 6:

[1242] The generative AI engine receives the prompts and generates safe investment proposals. During this process, the generative AI engine analyzes accumulated corporate financial data and strategic documents to output reliable investment proposals.

[1243] Step 7:

[1244] The server converts the generated safe investment proposals into natural language and creates text data for presenting them to the user in a format that is easy to understand. The server uses a natural language generation engine to convert the generated investment proposals into a user-friendly format.

[1245] Step 8:

[1246] The server sends the generated text data to the user's device, including the results of the sentiment analysis and fraud assessment.

[1247] Step 9:

[1248] The terminal displays the received data to the user, where visual graphs and charts are also displayed along with warning messages, allowing the user to intuitively understand their emotional state, fraud assessment, and alternative investment suggestions.

[1249] Through the above steps, the user can confirm the safety of the investment guide and reduce the risk by obtaining alternatives.

[1250] 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.

[1251] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1252] 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.

[1253] [Third embodiment]

[1254] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1255] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1256] 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).

[1257] 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.

[1258] 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.

[1259] 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).

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1266] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly includes the following elements:

[1267] 1. How to retrieve data from the database

[1268] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[1269] 2. Data analysis method using generative AI engine

[1270] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[1271] 3. Natural language generation means

[1272] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[1273] 4. Data Transmission Method

[1274] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[1275] 5. Automatic release and report generation tool

[1276] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[1277] 6. Visualization of IR information

[1278] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[1279] Specific examples

[1280] Multifaceted analysis of corporate information

[1281] 1. A user operates a terminal and requests detailed financial information about a company.

[1282] 2. The device sends this request to the server.

[1283] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[1284] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[1285] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[1286] 6. The server sends the generated results to the user's device.

[1287] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[1288] Automatic generation of releases and forecast reports

[1289] 1. The user requests the company's latest earnings forecast report from their device.

[1290] 2. The device sends this request to the server.

[1291] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[1292] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[1293] 5. The server sends the generated releases and reports to the user's terminal.

[1294] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[1295] Visualization of IR information

[1296] 1. The user requests visualization of a company's IR information through the terminal.

[1297] 2. The device sends this request to the server.

[1298] 3. The server retrieves the company's financial data and strategy documents.

[1299] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[1300] 5. The server sends the visualized data to the user's device.

[1301] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[1302] In this way, the "IR-GENAI" system of the present invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[1303] The processing flow will be explained below.

[1304] Multifaceted analysis of corporate information

[1305] Step 1:

[1306] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[1307] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[1308] Step 2:

[1309] The terminal sends the user's request to the server.

[1310] Specific operation: The data entered in the form is sent to the server as an API request.

[1311] Step 3:

[1312] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[1313] What it does: Executes a database query to retrieve the required information.

[1314] Step 4:

[1315] The server inputs the acquired data into the generation AI engine and requests analysis.

[1316] Specific operation: Call the API of the generation AI engine and send the acquired data.

[1317] Step 5:

[1318] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[1319] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[1320] Step 6:

[1321] The server receives the analysis results from the generative AI engine and sends them to the user.

[1322] Specific operation: The generated text data is received and sent to the user's device as a response.

[1323] Step 7:

[1324] The terminal displays the analysis results to the user.

[1325] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[1326] Automatic generation of releases and forecast reports

[1327] Step 1:

[1328] A user requests a company's latest performance forecast report through a terminal.

[1329] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[1330] Step 2:

[1331] The terminal sends a request to the server.

[1332] Specific operation: The data entered in the form is sent to the server as an API request.

[1333] Step 3:

[1334] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[1335] Specific operation: Query the database to obtain the latest information on the relevant company.

[1336] Step 4:

[1337] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[1338] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[1339] Step 5:

[1340] The server sends the generated releases and reports to the user.

[1341] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[1342] Step 6:

[1343] The terminal displays the generated releases and reports to the user.

[1344] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[1345] Visual presentation of IR information

[1346] Step 1:

[1347] A user requests a visual presentation of a company's investor relations information through a terminal.

[1348] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[1349] Step 2:

[1350] The terminal sends a request to the server.

[1351] Specific operation: The data entered in the form is sent to the server as an API request.

[1352] Step 3:

[1353] The server retrieves the company's financial data and strategy documents.

[1354] What it does: Executes a database query to retrieve the required information.

[1355] Step 4:

[1356] The server inputs the acquired data into the visualization module.

[1357] What you'll do: Format and input the data required for the visualization algorithm.

[1358] Step 5:

[1359] The visualization module transforms the data into graphs and charts.

[1360] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[1361] Step 6:

[1362] The server sends the visualized data to the user.

[1363] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[1364] Step 7:

[1365] The terminal displays the visualized information to the user.

[1366] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[1367] Q&A system

[1368] Step 1:

[1369] The user inputs a specific question through the terminal.

[1370] Specific behavior: The user enters a question in the text box and submits it.

[1371] Step 2:

[1372] The terminal sends a question to the server.

[1373] Specific operation: The entered text is sent to the server as an API request.

[1374] Step 3:

[1375] The server inputs the question into a natural language processing engine.

[1376] Specific operation: Calls the API of the natural language processing engine and sends a question.

[1377] Step 4:

[1378] A natural language processing engine generates the best answer to your question.

[1379] What it does: Parses the question and generates an answer by pulling information from relevant databases.

[1380] Step 5:

[1381] The server sends the generated answer to the user.

[1382] Specific operation: Receive the generated text and send it to the user's device as a response.

[1383] Step 6:

[1384] The terminal displays the answer to the user.

[1385] Specific operation: The received response is displayed on the screen in an easy-to-read format.

[1386] Evaluating E / S / G information

[1387] Step 1:

[1388] The server retrieves the company's E / S / G report.

[1389] What it does: Executes a database query to retrieve a company's E / S / G report.

[1390] Step 2:

[1391] The information acquired by the server is input into the generation AI engine, which evaluates each of the E / S / G items.

[1392] Specific operation: Format the obtained report and input it into the evaluation module of the generative AI engine.

[1393] Step 3:

[1394] A generative AI engine converts the evaluation results into natural language.

[1395] What it does: It rates each item through a rating algorithm and documents the results using a natural language generation algorithm.

[1396] Step 4:

[1397] The server sends the evaluation results to the user.

[1398] Specific operation: The generated evaluation document is received and sent to the user's terminal as a response.

[1399] Step 5:

[1400] The terminal displays the evaluation results to the user.

[1401] Specific operation: The received evaluation document is displayed on the screen in an easy-to-read format.

[1402] Example 1

[1403] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1404] Until now, providing corporate financial information and operational data in a format that is easy to understand even for novice investors required a great deal of specialized knowledge, making it difficult to obtain and interpret the information. Furthermore, creating automated releases and performance forecast reports to quickly provide the latest information, as well as visualizing the data, were time-consuming tasks. For these reasons, a system capable of efficiently obtaining, analyzing, reporting, and visualizing information was needed to provide corporate information.

[1405] 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.

[1406] In this invention, the server includes means for acquiring a company's financial information, strategic information, and operational data from a database, means for multifaceted analysis of the acquired information using a generative AI model, means for converting the results of the multifaceted analysis by the generative AI model into natural language, means for transmitting the converted results to a user's terminal, means for accepting information requests from users and transmitting them to the server, means for creating releases and performance forecast reports using the latest information automatically generated by the generative AI model, means for transmitting the created releases and performance forecast reports to the user's terminal, means for converting the acquired company data into charts and graphs using a visualization module, and means for transmitting the converted charts and graphs to the user's terminal. This enables efficient acquisition, analysis, reporting, and visualization of corporate information, making it possible to provide information that is easy to understand even for beginner investors.

[1407] "Financial information of a company" refers to data showing the business status of a company, including balance sheets, income statements, cash flow statements, and the like.

[1408] "Strategic information" refers to materials related to a company's future plans and management strategies, including a company's growth plans and competitive strategies.

[1409] "Operational Data" means data relating to the day-to-day business operations of the company, including operational results and daily performance reports.

[1410] A "database" is a collection of data organized in a certain way, a system that stores a company's financial information, strategic information, and operational data.

[1411] A "generative AI model" is a system based on artificial intelligence that has the ability to analyze input data from multiple angles and output it in natural language.

[1412] "Converting into natural language" refers to converting specialized data and analytical results into a linguistic form that is easy for the general public to understand.

[1413] A "user terminal" is a device operated by a user, and includes a PC, smartphone, tablet, etc.

[1414] An "information request" refers to an operation or request made by a user to a system for specific information.

[1415] A "release" is an official announcement or report issued by a company that contains a series of updates or news.

[1416] A "performance forecast report" is a report that predicts a company's future performance, and includes forecast data on future sales and profits.

[1417] A "visualization module" is a software or hardware feature that allows data to be visually represented, such as in graphs or charts.

[1418] "Graphing" refers to converting data into an easy-to-read format such as a graph or chart.

[1419] The "IR-GENAI" system of this invention uses advanced generative AI technology to provide corporate information in a format that is easy to understand even for beginner investors. This system involves data acquisition from a database, data analysis using a generative AI engine, natural language generation, data transmission, automatic generation of releases and performance forecast reports, and visualization of IR information.

[1420] System configuration

[1421] Hardware and Software Use

[1422] 1. Server: Processes data and interacts with the generative AI engine and visualization module.

[1423] 2. Database: Use a relational database such as MySQL to store the company's financial, strategic, and operational data.

[1424] 3. Generative AI engine: Uses generative AI models such as OpenAI GPT-4 to analyze data from multiple angles and output in natural language.

[1425] 4. Visualization module: Use visualization tools such as Tableau to transform data into graphs and charts.

[1426] 5. Terminal: A PC, smartphone, tablet, etc. that is operated by the user.

[1427] Data acquisition and processing

[1428] A user uses a terminal to request information about a particular company. The terminal sends this request to a server, which retrieves the relevant data from a database. For example, a balance sheet or profit and loss statement to understand the company's financial situation. Here is an example of the server retrieving the data using a MySQL query:

[1429] SELECT FROM Financial Data WHERE Company Name = "Company A"

[1430] Analyzing the data

[1431] The data acquired by the server is input into a generative AI engine, which analyzes it from multiple angles, including financial stability and growth potential. OpenAI GPT-4 is used as the generative AI engine. Examples of prompts generated by the generative AI engine include the following:

[1432] "Please analyze Company A's financial situation and explain it in a way that even a beginner investor can understand."

[1433] Generate and send results

[1434] The generative AI engine generates the analysis results in natural language and sends them to the user's device. For example, the following natural language sentences are generated:

[1435] "Company A has good financial stability, especially with a low debt ratio and stable cash flow."

[1436] The server sends the generated results to the user's terminal, which displays them.

[1437] Automatic generation of releases and forecast reports

[1438] When a user requests a company's latest performance forecast report, the server inputs the company's latest data and market forecast data into the AI ​​engine to generate a performance forecast report. The generated report is provided in the following format:

[1439] "Company A's next fiscal year's performance is expected to see increased revenue and profits, due to increased market share and the success of new products."

[1440] The server sends this report to the user's terminal, which displays it.

[1441] Visualization of IR information

[1442] When a user requests visualization of a company's investor relations information, the server retrieves financial data and strategic documents from the database and inputs them into the visualization module. The graphs and charts generated by the visualization module include the following examples:

[1443] -Bar chart showing sales trends

[1444] Line graph showing fluctuations in profit margins

[1445] The server transmits the visualized data to the user's terminal, which displays it.

[1446] In this way, the "IR-GENAI" system of this invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[1447] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1448] Step 1: Enter your information request

[1449] A user operates a terminal to request information about a specific company. For example, a request such as "Please display the financial data of Company A" is input into the terminal. The terminal sends this information request to the server. The input is the "company name" and "data type." The output is the requested information.

[1450] Step 2: Submitting the request

[1451] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Financial data" and sends it to the server. The input is the user's request, and the output is the request transmission.

[1452] Step 3: Retrieving data from the database

[1453] The server receives the request and accesses the database to retrieve the required data. Specifically, the server retrieves the data using a MySQL query. For example, it executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[1454] Step 4: Input data into the generative AI engine

[1455] The server inputs the acquired data into the generative AI engine. Specifically, the server sends financial data in JSON format to the generative AI engine. The input is Company A's financial data, and the output is data sent to the generative AI engine.

[1456] Step 5: Data analysis and natural language translation

[1457] The generative AI engine analyzes data from multiple perspectives and converts the results into natural language. Specifically, the generative AI engine generates natural language sentences such as, "Company A has good financial stability, with a particularly low debt ratio and stable cash flow." The input is Company A's financial data, and the output is the analysis results generated in natural language.

[1458] Step 6: Submitting the analysis results

[1459] The server receives the results from the generative AI engine and sends them to the user's device. Specifically, the server sends the generated natural language sentence to the device. The input is the analysis result generated in natural language, and the output is sent to the device.

[1460] Step 7: View the results

[1461] The terminal displays the results it receives to the user. Specifically, the screen displays the following: "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow." The input is the analysis results generated in natural language, and the output is the display to the user.

[1462] Automatic generation of releases and forecast reports

[1463] Step 1: Enter your performance forecast

[1464] A user requests the latest performance forecast report for a company from a terminal. For example, the user inputs, "Please generate the next performance forecast for Company A." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[1465] Step 2: Submitting the request

[1466] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Performance forecast" and sends it to the server. The input is the user's request, and the output is the request transmission.

[1467] Step 3: Data Acquisition

[1468] The server retrieves the latest performance data and market forecast data for a company. Specifically, the server queries the database and executes the query "SELECT FROM performance data WHERE company name = 'Company A'". The input is the request information, and the output is the performance data for Company A.

[1469] Step 4: Input to the generative AI engine

[1470] The server inputs the acquired data into the generative AI engine. Specifically, the server sends performance data and market forecast data in JSON format to the generative AI engine. The input is Company A's performance data and market forecast data, and the output is data sent to the generative AI engine.

[1471] Step 5: Generate a performance forecast report

[1472] The generative AI engine analyzes performance data and generates a report in natural language. For example, a report might be generated that states, "Company A's next fiscal year's performance is expected to increase in both sales and profits. This is attributed to increased market share and the success of new products." The input is Company A's performance data and market forecast data, and the output is a performance forecast report generated in natural language.

[1473] Step 6: Release Submission

[1474] The server sends the generated performance forecast report to the user's terminal. Specifically, the server sends the generated natural language report to the terminal. The input is the performance forecast report generated in natural language, and the output is sent to the terminal.

[1475] Step 7: View the report

[1476] The terminal displays the received report to the user. Specifically, the screen displays the following: "Company A's next fiscal year's performance is expected to increase in both sales and profits, due in part to increased market share and the success of new products." The input is a performance forecast report generated in natural language, and the output is the display to the user.

[1477] Visualization of IR information

[1478] Step 1: Enter your visualization request

[1479] A user requests visualization of a company's IR information through a terminal. For example, the user inputs, "Please display the IR information of Company A in a graph." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[1480] Step 2: Submitting the request

[1481] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: IR information" and sends it to the server. The input is the user's request, and the output is the request transmission.

[1482] Step 3: Data Acquisition

[1483] The server retrieves a company's financial data and strategic documents from a database. Specifically, the server executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[1484] Step 4: Input to the visualization module

[1485] The server inputs the acquired data into the visualization module. Specifically, the server sends the financial data of Company A to the visualization module. The input is the financial data of Company A, and the output is the data sent to the visualization module.

[1486] Step 5: Visualize the data

[1487] The visualization module converts the acquired data into graphs and charts. For example, it generates a bar chart showing the trend in sales volume and a line graph showing fluctuations in profit margins. The input is Company A's financial data, and the output is a visualized graph or chart.

[1488] Step 6: Sending visualized data

[1489] The server sends the visualized data to the user's device. Specifically, it sends the generated graphs and charts to the device. The input is the visualized data, and the output is the data sent to the device.

[1490] Step 7: View the visualization data

[1491] The visualized data received by the terminal is displayed to the user. For example, a "bar chart showing the trend in sales of Company A" or a "line graph showing fluctuations in profit margin" is displayed on the screen. The input is the visualized data, and the output is the display to the user.

[1492] (Application example 1)

[1493] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1494] Modern investors, especially those new to investing, face challenges in comprehensively and intuitively understanding corporate information. Technical financial data and operational reports are particularly difficult to understand, and there is a lack of tools to effectively utilize them to make investment decisions. Furthermore, there are limited visual and interactive ways to display corporate information, creating a need for new solutions.

[1495] 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.

[1496] In this invention, the server includes means for retrieving corporate financial data, strategic documents, and management reports from a database, means for analyzing the data retrieved by the generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for transmitting the converted results to a user's terminal, means for visually and interactively displaying the corporate information analyzed by the generative AI engine using a head-mounted display, and means for the user to retrieve and manipulate the corporate information by interactive manual operation or voice command through the head-mounted display. This allows even beginners to invest to comprehensively and intuitively understand corporate information and supports investment decisions.

[1497] "Financial data of a company" refers to data that represents the financial status of a company, such as a balance sheet, income statement, and cash flow statement.

[1498] A "strategic document" is a document that describes the medium- to long-term goals set by a company, as well as the plans and policies for achieving them.

[1499] An "operational report" is a report that describes a company's daily business activities, their results, problems, etc.

[1500] A "database" is a system for storing structured business information that can be efficiently accessed, managed, and updated.

[1501] A "generative AI engine" is an artificial intelligence engine that analyzes collected data and generates specific assessments and predictions.

[1502] "Natural language" is a human language that expresses the analysis results in a form that can be understood by users.

[1503] A "user terminal" is a device that a user uses to obtain and operate information, and includes, for example, a smartphone, a tablet, a computer, and the like.

[1504] A "head-mounted display" is a display device that the user wears on their head, and is a device that enables VR and AR experiences.

[1505] A "visualization module" is a software component for transforming enterprise data into visual formats such as graphs and charts.

[1506] "Interactive manual manipulation or voice command" refers to a method by which a user manipulates data through a head-mounted display, including hand gestures and voice input.

[1507] This invention is a system that uses a generative AI model to analyze data such as a company's financial data, strategic documents, and operational reports, and provides information to investors in a visual and interactive format. The system includes a server, a user terminal, a head-mounted display, and a generative AI engine. Detailed embodiments of this system are described below.

[1508] 1. Data Acquisition and Analysis

[1509] The server first retrieves the company's financial data, strategy documents, and operational reports from a database. The database stores the company's balance sheet, income statement, cash flow statement, strategy documents, and operational reports. To retrieve this data, the server fetches the data via an API or similar.

[1510] The server then inputs the acquired data into a generative AI engine, which analyzes it from multiple perspectives. The generative AI engine analyzes aspects such as financial stability, growth potential, and profitability, and generates an evaluation for each. The analysis results are converted into natural language and output in a format that is easy for users to understand.

[1511] 2. Send to the user device

[1512] The generated natural language results are sent from the server to the user's device, which can be a smartphone, tablet, or PC, where the results are displayed.

[1513] 3. Visualization and Interaction with Head-Mounted Displays

[1514] The analysis results are also displayed visually and interactively using a head-mounted display (HMD). Users can wear the HMD and check company information in a VR or AR environment. HMDs such as Oculus Rift and HTC Vive are used. The visualization module converts the analysis results from the generative AI engine into graphs and charts, which are then displayed within the HMD.

[1515] Users can use manual or voice commands to interactively manipulate and gain a deeper understanding of company information. The head-mounted display interface allows easy access to detailed company financial information, and instant access to releases and earnings forecasts.

[1516] Specific examples

[1517] For example, if a user issues a voice command such as "Show me the latest earnings forecast for Company A," the server retrieves the latest information for Company A from the database. The generative AI engine then analyzes the information and generates a result in natural language. This result is displayed visually in the HMD, such as "Company A's next fiscal year's earnings are expected to increase in both sales and profits, driven by increased market share and the success of new products." Additionally, a bar chart showing sales trends and a line graph showing fluctuations in profit margins are also displayed visually.

[1518] Prompt Sentence Examples

[1519] Possible prompts for a generative AI model include:

[1520] "Analyze the following financial data: revenue: 20 million, profit: 3 million, expenses: 17 million, assets: 50 million, liabilities: 30 million"

[1521] In this way, the present invention supports investors, especially beginners, by analyzing company information from multiple angles and providing it to users in natural language and visual formats.

[1522] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1523] Step 1:

[1524] A user requests detailed financial information of a company through a device such as a head-mounted display or smartphone. This request is sent from the device to a server. The input data is the company ID and name, and the output is a request to the server.

[1525] Step 2:

[1526] The server retrieves a company's financial data, strategy documents, and management reports from the database based on user requests. The input data is the company's ID and name, and the data retrieved from the database is balance sheets, income statements, cash flow statements, etc. The output is these datasets.

[1527] Step 3:

[1528] The server inputs the acquired company data into the generation AI engine for multifaceted analysis. The input data is the company's financial data, strategy documents, and management reports, and the generation AI engine analyzes them to evaluate financial stability, growth potential, profitability, etc. The output is the analysis results.

[1529] Step 4:

[1530] The generative AI engine converts the analysis results into natural language. The input data is analyzed company information, and the output is the analysis results in natural language format.

[1531] Step 5:

[1532] The server sends the generated natural language results to the user's terminal. The input data is the analysis result of the natural language format, and the output is the data delivery to the user's terminal.

[1533] Step 6:

[1534] The device displays the received analysis results to the user. On smartphones and tablets, the results are displayed in text format. The input data is the analysis results in natural language format, and the output is the information displayed on the device screen.

[1535] Step 7:

[1536] When using a head-mounted display, the server converts the analysis results into graphs and charts using a visualization module, which then visually displays them within the HMD. The input data are the analysis results in natural language format and graph or chart data, and the output is the visualized information within the HMD.

[1537] Step 8:

[1538] Users can obtain and operate corporate information through interactive manual operations or voice commands via a head-mounted display. The input data are user operations and commands, and the output is updating the displayed information or obtaining detailed information.

[1539] This process allows even novice investors to comprehensively and intuitively understand company information and supports investment decisions.

[1540] 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.

[1541] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to provide information based on the emotional state of each individual investor. This system mainly includes the following elements:

[1542] 1. How to retrieve data from the database

[1543] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[1544] 2. Data analysis method using generative AI engine

[1545] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[1546] 3. Natural language generation means

[1547] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[1548] 4. Data Transmission Method

[1549] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[1550] 5. Automatic release and report generation tool

[1551] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[1552] 6. Visualization of IR information

[1553] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[1554] 7. Emotion Recognition Method Using Emotion Engine

[1555] The emotion engine analyzes the text and voice input by the user through the device and recognizes the user's emotions. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral) through text and voice analysis.

[1556] 8. Emotion-based information regulation

[1557] The server adjusts the information and analysis results provided by the generative AI engine based on the user's recognized emotions. For example, if the user is expressing negative emotions, it can reduce the user's stress by emphasizing positive information.

[1558] 9. Visual displays of emotions

[1559] It provides an interface for visually displaying the user's emotional state and tracks emotional fluctuations, allowing the user to understand their own emotional fluctuations.

[1560] Specific examples

[1561] Multifaceted analysis of corporate information and emotion recognition

[1562] 1. A user operates a terminal and enters a request to obtain detailed financial information about a company.

[1563] 2. The device sends this request to the server.

[1564] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[1565] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[1566] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[1567] 6. The server sends the generated results to the user's device.

[1568] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[1569] Automatic generation of releases and earnings forecasts with emotion recognition

[1570] 1. The user requests the company's latest earnings forecast report from their device.

[1571] 2. The device sends a request to the server.

[1572] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[1573] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[1574] 5. The server sends the generated releases and reports to the user's terminal.

[1575] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[1576] IR Information Visualization and Emotion Recognition

[1577] 1. The user requests visualization of a company's IR information through the terminal.

[1578] 2. The device sends a request to the server.

[1579] 3. The server retrieves the company's financial data and strategy documents.

[1580] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[1581] 5. The server sends the visualized data to the user.

[1582] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[1583] Emotion recognition and information regulation

[1584] 1. When users view company information through their devices, they can enter comments and feedback.

[1585] 2. The device sends comments and feedback to the server.

[1586] 3. The server sends these inputs to the emotion engine, which analyzes the emotional state.

[1587] 4. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1588] 5. Based on the emotional state, the server adjusts the generative AI engine to provide the most appropriate information for the user.

[1589] 6. The device will visually display emotional fluctuations, allowing users to understand their own emotional state.

[1590] In this way, the "IR-GENAI" system of the present invention combines generative AI technology with an emotion engine to analyze corporate information from multiple angles and provide information based on the user's emotional state. This makes it possible to provide information that is easy to understand even for beginner investors and reduces the psychological burden.

[1591] The processing flow will be explained below.

[1592] Multifaceted analysis of corporate information and emotion recognition

[1593] Step 1:

[1594] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[1595] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[1596] Step 2:

[1597] The terminal sends the user's request to the server.

[1598] Specific operation: The data entered in the form is sent to the server as an API request.

[1599] Step 3:

[1600] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[1601] What it does: Executes a database query to retrieve the required information.

[1602] Step 4:

[1603] The server inputs the acquired data into the generation AI engine and requests analysis.

[1604] Specific operation: Call the API of the generation AI engine and send the acquired data.

[1605] Step 5:

[1606] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[1607] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[1608] Step 6:

[1609] The server receives the analysis results from the generative AI engine and sends them to the user.

[1610] Specific operation: The generated text data is received and sent to the user's device as a response.

[1611] Step 7:

[1612] The terminal displays the analysis results to the user.

[1613] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[1614] Step 8:

[1615] The user enters comments and feedback about the analysis results.

[1616] Specific action: Enter text into the input field and press the submit button.

[1617] Step 9:

[1618] The device sends comments and feedback to the server.

[1619] Specific operation: The entered text is sent to the server as an API request.

[1620] Step 10:

[1621] The server sends the comments and feedback to the emotion engine, which analyzes the emotional state.

[1622] Specific operation: Call the emotion engine API and send input data.

[1623] Step 11:

[1624] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1625] What it does: Uses text analysis algorithms to determine user sentiment.

[1626] Step 12:

[1627] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[1628] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[1629] Step 13:

[1630] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[1631] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[1632] Automatic generation of releases and earnings forecasts with emotion recognition

[1633] Step 1:

[1634] A user requests the latest business forecast report of a company from a terminal.

[1635] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[1636] Step 2:

[1637] The terminal sends a request to the server.

[1638] Specific operation: The data entered in the form is sent to the server as an API request.

[1639] Step 3:

[1640] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[1641] Specific operation: Query the database to obtain the latest information on the relevant company.

[1642] Step 4:

[1643] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[1644] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[1645] Step 5:

[1646] The server sends the generated releases and reports to the user.

[1647] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[1648] Step 6:

[1649] The terminal displays the generated releases and reports to the user.

[1650] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[1651] Step 7:

[1652] Users enter comments and feedback on the report.

[1653] Specific action: Enter text into the input field and press the submit button.

[1654] Step 8:

[1655] The device sends comments and feedback to the server.

[1656] Specific operation: The entered text is sent to the server as an API request.

[1657] Step 9:

[1658] The server sends these inputs to the emotion engine, which analyzes the emotional state.

[1659] Specific operation: Call the emotion engine API and send input data.

[1660] Step 10:

[1661] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1662] What it does: Uses text analysis algorithms to determine user sentiment.

[1663] Step 11:

[1664] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[1665] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[1666] Step 12:

[1667] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[1668] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[1669] Visual presentation of IR information and emotion recognition

[1670] Step 1:

[1671] The user requests visualization of a company's IR information through the terminal.

[1672] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[1673] Step 2:

[1674] The terminal sends a request to the server.

[1675] Specific operation: The data entered in the form is sent to the server as an API request.

[1676] Step 3:

[1677] The server retrieves the company's financial data and strategy documents.

[1678] What it does: Executes a database query to retrieve the required information.

[1679] Step 4:

[1680] The server inputs the acquired data into the visualization module.

[1681] What you'll do: Format and input the data required for the visualization algorithm.

[1682] Step 5:

[1683] The visualization module transforms the data into graphs and charts.

[1684] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[1685] Step 6:

[1686] The server sends the visualized data to the user.

[1687] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[1688] Step 7:

[1689] The terminal displays the visualized information to the user.

[1690] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[1691] Step 8:

[1692] The user inputs their thoughts on the chart or graph.

[1693] Specific action: Enter text into the input field and press the submit button.

[1694] Step 9:

[1695] The device sends the feedback to the server.

[1696] Specific operation: The entered text is sent to the server as an API request.

[1697] Step 10:

[1698] The server sends the sentiment to the emotion engine, which analyzes the emotional state.

[1699] Specific operation: Call the emotion engine API and send input data.

[1700] Step 11:

[1701] An emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[1702] What it does: Uses text analysis algorithms to determine user sentiment.

[1703] Step 12:

[1704] The server adjusts the generative AI engine based on the user's emotional state and provides the most appropriate information for the user.

[1705] Specific operation: Send emotional state data to the generative AI engine and run the resulting adjustment algorithm.

[1706] Step 13:

[1707] The device visually displays emotional fluctuations, allowing the user to understand their own emotional state.

[1708] Specific actions: Displays icons and graphs on the screen that indicate emotional state.

[1709] Example 2

[1710] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1711] Providing corporate information in an easy-to-understand manner, even for novice investors, requires advanced data analysis and natural language generation technology. However, conventional systems produce data analysis results that are technical and difficult for beginners to understand. Furthermore, the information provided does not take into account the user's emotional state, making it difficult to improve the user experience. Furthermore, efficient methods are needed for visualizing the generated information and automatically generating releases and reports.

[1712] 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.

[1713] In this invention, the server includes: means for retrieving corporate financial data, strategic documents, and management reports from a database; means for analyzing the retrieved data using a generative AI model; means for converting the results of the analysis by the generative AI model into natural language; means for transmitting the converted results to a user's terminal; means for analyzing text and voice input by the user through the terminal and recognizing the user's emotions using an emotion engine; and means for adjusting the information and analysis results provided by the generative AI model based on the recognized user emotions. This enables multifaceted analysis of corporate information, making it easy to understand even for beginner investors, and providing information based on the emotional state of each individual user. Furthermore, efficient information provision through visualization and automatic generation can be achieved.

[1714] "Corporate financial data" refers to data that shows the economic status of a company, and primarily includes balance sheets, income statements, cash flow statements, etc.

[1715] A "strategic document" is a document that outlines the company's business policies for the medium to long term.

[1716] An "operational report" is a report that records the performance and results of a company's day-to-day operations.

[1717] A "database" is a system for efficiently storing and managing structured data.

[1718] A "generative AI model" is a type of artificial intelligence technology that learns from large amounts of data and generates text and data for various tasks.

[1719] "Natural language conversion" is the process of converting specialized data and analytical results into text that is easy for the general public to understand.

[1720] "Terminal" means a device used by a user to input or receive information, including, for example, a personal computer or smartphone.

[1721] An "emotion engine" is a type of artificial intelligence technology that analyzes a user's emotional state from text and voice.

[1722] A "visualization module" is software or tools for converting data into a visual format such as a graph or chart.

[1723] A "release or report" is a written document that summarizes a company's important information, results, forecasts, etc.

[1724] "Recognizing user emotions" is the process of identifying emotions from information entered by the user, and is performed using an emotion engine.

[1725] "Adjusting information or analysis results" means changing the content or presentation of the information or analysis results provided based on the user's emotional state.

[1726] The purpose of the system of this invention is to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly combines a server, a terminal, a generative AI model, an emotion engine, and a visualization module. The details of each element and their operation are explained below.

[1727] System configuration

[1728] 1. Server

[1729] The server is a central management system that retrieves the company's financial data, strategy documents, and operational reports from the database. The server collects this data and sends it to the generative AI model and emotion engine.

[1730] The server uses specific software and hardware, such as: a database system (e.g., MySQL), a generative AI model (e.g., GPT-4), and an emotion engine (e.g., IBM Watson Tone Analyzer).

[1731] 2. Terminal

[1732] A terminal is a device through which a user inputs information and receives results, and includes, for example, a PC, a smartphone, or a tablet.

[1733] The terminal sends the input request to the server as an HTTP request, and also has the function of displaying the results received from the server.

[1734] 3. Generative AI Models

[1735] A generative AI model is an engine that analyzes collected data and converts it into a format that is easy to understand in natural language. For example, advanced generative AI techniques such as GPT-4 are used.

[1736] The generative AI model expresses the results of its analysis of financial data in natural language and provides them to users.

[1737] 4. Emotion Engine

[1738] The emotion engine is a technology that analyzes text and speech input from users to recognize their emotional state, and adjusts the information and analysis results provided by the generative AI model based on that emotional state.

[1739] For example, if the user is expressing negative emotions, more positive expressions are emphasized and presented.

[1740] 5. Visualization Module

[1741] The visualization module is a tool for converting acquired data into graphs and charts, which provides a visually understandable view of the data.

[1742] Libraries such as D3.js and Chart.js are used for data visualization.

[1743] Specific examples

[1744] Multifaceted analysis of corporate information and emotion recognition

[1745] 1. The user operates the terminal and enters, "I would like to obtain the latest financial information for Company A."

[1746] 2. The device sends this request to the server as an HTTP request.

[1747] Specific request example: GET / fetchData?company=CompanyA&type=financial

[1748] 3. The server accesses the database to retrieve Company A's financial information, strategy documents, and operational reports.

[1749] Specific query example: SELECT FROM financial_reports WHERE company_name = 'Company A'

[1750] 4. The server inputs the acquired data into the generative AI model. When generating a prompt, input the following:

[1751] "Please analyze Company A's financial stability, growth potential, and profitability."

[1752] 5. The generative AI model analyzes the data and outputs assessment results in natural language regarding financial stability and growth potential.

[1753] Example output: "Company A has good financial stability, and is particularly praised for its low debt ratio and stable cash flow."

[1754] 6. The server sends the generated results to the user's terminal, which displays the results to the user.

[1755] In this way, the system analyzes corporate information from multiple angles and provides it in an easy-to-understand manner in natural language.Furthermore, by using an emotion engine, it is possible to provide information based on the user's emotional state.

[1756] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1757] Step 1:

[1758] A user inputs a request to obtain business information.

[1759] The user enters "I would like to obtain the latest financial information of Company A" into the input field on the terminal.

[1760] Input: Text request to get company information.

[1761] Output: The request is saved to the device.

[1762] Step 2:

[1763] The terminal sends a request to the server.

[1764] The terminal sends the user's input request to the server as an HTTP request.

[1765] Input: User requested text.

[1766] Output: HTTP request (for example, GET / fetchData?company=CompanyA&type=financial).

[1767] Step 3:

[1768] The server retrieves the data of the specified company from the database.

[1769] The server accesses the database to obtain Company A's financial information, strategy documents, and operational reports.

[1770] Input: HTTP request.

[1771] Output: Company financial data, strategy documents, operational reports.

[1772] Specific query example: SELECT FROM financial_reports WHERE company_name = 'Company A'.

[1773] Step 4:

[1774] The server inputs the acquired data into a generative AI model.

[1775] The server converts the acquired data into the input format for the generative AI model.

[1776] Inputs: Company financial data, strategy documents, operational reports.

[1777] Output: The prompt statement.

[1778] Specific prompt: "Analyze Company A's financial stability, growth potential, and profitability."

[1779] Step 5:

[1780] Generative AI models analyze data and generate results in natural language.

[1781] The generative AI model analyzes input data and generates assessment results in natural language regarding financial stability and growth potential.

[1782] Input: Prompt statement and company data.

[1783] Output: Parsing results in natural language.

[1784] Example output: "Company A has good financial stability, and is particularly praised for its low debt ratio and stable cash flow."

[1785] Step 6:

[1786] The server transmits the generated results to the terminal.

[1787] The server receives the output of the generative AI model and sends it to the terminal as an HTTP response.

[1788] Input: Natural language parsing results.

[1789] Output: HTTP response (e.g., {"result": "Company A's financial stability is good..."}).

[1790] Step 7:

[1791] The terminal displays the results to the user.

[1792] The terminal displays the received results to the user.

[1793] Input: HTTP response.

[1794] Output: Display of results (e.g., "Company A's financial stability is good...").

[1795] Step 8:

[1796] The server receives comments and feedback entered by the user.

[1797] The user enters feedback in the comment field on the device and presses the send button, which sends the feedback to the server.

[1798] Input: User comments and feedback.

[1799] Output: The feedback is saved on the server.

[1800] Step 9:

[1801] The server analyzes the user's emotional state using an emotion engine.

[1802] The server sends the received feedback to the emotion engine to analyze the emotional state.

[1803] Input: User feedback.

[1804] Output: Sentiment analysis result (e.g. "positive", "negative", "neutral").

[1805] Step 10:

[1806] The server adjusts the information based on the emotional state and provides appropriate information to the user.

[1807] Based on the results of the emotion analysis, the server sends another prompt to the generative AI model, which then generates more appropriate information, which the server then sends to the device.

[1808] Input: Sentiment analysis results.

[1809] Output: Adjusted natural language information.

[1810] Specific Regeneration Outcome: "Company A has low debt and stable income."

[1811] (Application example 2)

[1812] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1813] Financial product fraud is a serious problem, especially for novice investors. Fraudulent messages and investment guides often contain well-crafted fraudulent information, putting many users at high risk of being deceived. Furthermore, novice investors often have difficulty understanding specialized information such as corporate financial data and strategic documents, and are easily influenced by their emotions, making it difficult for them to make accurate investment decisions. To address these issues, a system is needed that adjusts information provision based on the user's emotional state and reduces the risk of fraud.

[1814] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring corporate financial data, strategic documents, and management reports from a database, means for analyzing the acquired data using a generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for analyzing text such as investment guidance received by the user using an emotion engine to recognize the emotional state, means for the generative AI engine to adjust the analysis results based on the emotional state, means for visually displaying the emotional state and the adjusted analysis results, means for assessing and warning about the possibility of fraud, and means for generating and proposing safe investment proposals. This makes it easy for even beginners to understand investments to provide accurate information and prevent fraud.

[1815] 1. "Financial data" refers to data that shows a company's financial status, such as its balance sheet, income statement, and cash flow statement.

[1816] 2. "Strategic documents" are documents that describe a company's medium- to long-term management strategies, policies, and business plans.

[1817] 3. An "operational report" is a report summarizing the company's daily business operations and results.

[1818] 4. A "generative AI engine" is an artificial intelligence engine that analyzes large amounts of data and generates output in natural language.

[1819] 5. "Emotion engine" is a data analysis engine that analyzes the user's emotions from input text and voice data and recognizes their emotional state.

[1820] 6. "Means for converting to natural language" refers to the process by which the generative AI engine converts the analysis results into a natural language format that is easy for humans to understand.

[1821] 7. "Emotional state" refers to the user's emotions expressed as positive, negative, neutral, etc.

[1822] 8. "Visual display" refers to the presentation of analytical results or emotional states in a visual format, such as graphs or charts.

[1823] 9. "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1824] 10. "Means for assessing the likelihood of fraud" refers to the process of using an emotion engine or generative AI to diagnose the riskiness of texts received by users, such as investment guides.

[1825] 11. "Means for generating safe investment proposals" means the process of using generative AI to suggest reliable investment options to users.

[1826] In this invention, the following system configuration and processing procedures are required to realize the "Fraud Safeguard AI" security application specialized in countering financial product fraud.

[1827] 1. Overall structure

[1828] This system mainly consists of the following hardware and software components.

[1829] Hardware: Smartphone

[1830] Software: Python, Transformers library (Hugging Face), requests module, proprietary emotion recognition module, data visualization module

[1831] 2. Data acquisition and analysis

[1832] The server first receives text such as investment information or messages received by the user, then uses an emotion engine to analyze the emotional state of the text (positive, negative, neutral), and evaluates the likelihood of fraud if the emotional state is highly negative.

[1833] 3. Emotion recognition and information provision

[1834] After the text is sentiment-recognized, the generative AI engine provides appropriate information to the user based on the results. For example, if the sentiment is highly negative, the generative AI engine will generate a warning message and suggest safe investment ideas to the user, thereby reducing the risk of fraud.

[1835] 4. Information Visualization

[1836] The server converts the acquired company's financial data and strategic documents into graphs and charts. The converted visual information is sent to the user's device and displayed in an easy-to-understand format, allowing the user to visually understand the information.

[1837] Specific examples

[1838] For example, if a user receives a suspicious investment offer email, they can input the email contents into the application. The sentiment engine analyzes the email contents and, if it detects a high degree of negativity, the generative AI engine will instead generate a safe investment proposal and present it to the user along with a warning. At this time, the company's financial stability and growth potential are visualized as graphs, allowing the user to intuitively understand the information.

[1839] Input prompt example

[1840] "Perform sentiment analysis of Company A's investment information to detect potential fraud. We will then provide you with a highly secure investment proposal. The investment proposal you entered is: {email_content}"

[1841] This system is easy to understand even for those new to investing, and it provides accurate information and prevents fraud.

[1842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1843] Step 1:

[1844] The user inputs the investment information and message into the terminal. This input data is in text format.

[1845] Step 2:

[1846] The device sends the input text data to the server, where it is properly formatted and ready for sentiment analysis.

[1847] Step 3:

[1848] The server inputs the received text data into the emotion engine, which analyzes the user's emotional state (positive, negative, neutral). The emotion engine analyzes the text and outputs the emotional state as a numerical value.

[1849] Step 4:

[1850] The server evaluates the sentiment analysis results obtained from the emotion engine, and if the result is highly negative, it flags the possibility of fraud. Based on the sentiment analysis results, the server starts the fraud evaluation process.

[1851] Step 5:

[1852] If fraud is determined to be likely, the server generates a prompt to feed into the generative AI engine, which includes a recognized negative sentiment.

[1853] Step 6:

[1854] The generative AI engine receives the prompts and generates safe investment proposals. During this process, the generative AI engine analyzes accumulated corporate financial data and strategic documents to output reliable investment proposals.

[1855] Step 7:

[1856] The server converts the generated safe investment proposals into natural language and creates text data for presenting them to the user in a format that is easy to understand. The server uses a natural language generation engine to convert the generated investment proposals into a user-friendly format.

[1857] Step 8:

[1858] The server sends the generated text data to the user's device, including the results of the sentiment analysis and fraud assessment.

[1859] Step 9:

[1860] The terminal displays the received data to the user, where visual graphs and charts are also displayed along with warning messages, allowing the user to intuitively understand their emotional state, fraud assessment, and alternative investment suggestions.

[1861] Through the above steps, the user can confirm the safety of the investment guide and reduce the risk by obtaining alternatives.

[1862] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1863] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1864] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1865] [Fourth embodiment]

[1866] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1867] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1868] 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).

[1869] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1870] 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.

[1871] 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).

[1872] 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.

[1873] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1874] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1875] 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.

[1876] 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.

[1877] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1878] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1879] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. This system mainly includes the following elements:

[1880] 1. How to retrieve data from the database

[1881] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[1882] 2. Data analysis method using generative AI engine

[1883] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[1884] 3. Natural language generation means

[1885] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[1886] 4. Data Transmission Method

[1887] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[1888] 5. Automatic release and report generation tool

[1889] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[1890] 6. Visualization of IR information

[1891] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[1892] Specific examples

[1893] Multifaceted analysis of corporate information

[1894] 1. A user operates a terminal and requests detailed financial information about a company.

[1895] 2. The device sends this request to the server.

[1896] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[1897] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[1898] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[1899] 6. The server sends the generated results to the user's device.

[1900] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[1901] Automatic generation of releases and forecast reports

[1902] 1. The user requests the company's latest earnings forecast report from their device.

[1903] 2. The device sends this request to the server.

[1904] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[1905] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[1906] 5. The server sends the generated releases and reports to the user's terminal.

[1907] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[1908] Visualization of IR information

[1909] 1. The user requests visualization of a company's IR information through the terminal.

[1910] 2. The device sends this request to the server.

[1911] 3. The server retrieves the company's financial data and strategy documents.

[1912] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[1913] 5. The server sends the visualized data to the user's device.

[1914] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[1915] In this way, the "IR-GENAI" system of the present invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[1916] The processing flow will be explained below.

[1917] Multifaceted analysis of corporate information

[1918] Step 1:

[1919] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[1920] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[1921] Step 2:

[1922] The terminal sends the user's request to the server.

[1923] Specific operation: The data entered in the form is sent to the server as an API request.

[1924] Step 3:

[1925] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[1926] What it does: Executes a database query to retrieve the required information.

[1927] Step 4:

[1928] The server inputs the acquired data into the generation AI engine and requests analysis.

[1929] Specific operation: Call the API of the generation AI engine and send the acquired data.

[1930] Step 5:

[1931] The generative AI engine analyzes a company's financial stability and growth potential based on financial data and generates results in natural language.

[1932] Specific operation: Each viewpoint is evaluated using a data analysis algorithm, and a document is created using a natural language generation algorithm.

[1933] Step 6:

[1934] The server receives the analysis results from the generative AI engine and sends them to the user.

[1935] Specific operation: The generated text data is received and sent to the user's device as a response.

[1936] Step 7:

[1937] The terminal displays the analysis results to the user.

[1938] Specific operation: The received text is formatted into an easy-to-read format and displayed on the screen.

[1939] Automatic generation of releases and forecast reports

[1940] Step 1:

[1941] A user requests a company's latest performance forecast report through a terminal.

[1942] Specific operation: The user selects the company name and the type of report (e.g., quarterly report, earnings forecast).

[1943] Step 2:

[1944] The terminal sends a request to the server.

[1945] Specific operation: The data entered in the form is sent to the server as an API request.

[1946] Step 3:

[1947] The server prepares the latest company information (financial data, market trend data, etc.) to be input into the generative AI engine.

[1948] Specific operation: Query the database to obtain the latest information on the relevant company.

[1949] Step 4:

[1950] The generative AI engine uses a natural language generation engine to automatically generate releases and performance forecast reports.

[1951] Specific operation: Analyzes data and generates a document according to the specified type of report format.

[1952] Step 5:

[1953] The server sends the generated releases and reports to the user.

[1954] Specific operation: Receive the generated document and send it to the user's terminal as a response.

[1955] Step 6:

[1956] The terminal displays the generated releases and reports to the user.

[1957] Specific operation: The received document is formatted into an easy-to-read format and displayed on the screen.

[1958] Visual presentation of IR information

[1959] Step 1:

[1960] A user requests a visual presentation of a company's investor relations information through a terminal.

[1961] Specific operation: The user selects the company name and the type of visualization (e.g., sales trends, profit margin fluctuations).

[1962] Step 2:

[1963] The terminal sends a request to the server.

[1964] Specific operation: The data entered in the form is sent to the server as an API request.

[1965] Step 3:

[1966] The server retrieves the company's financial data and strategy documents.

[1967] What it does: Executes a database query to retrieve the required information.

[1968] Step 4:

[1969] The server inputs the acquired data into the visualization module.

[1970] What you'll do: Format and input the data required for the visualization algorithm.

[1971] Step 5:

[1972] The visualization module transforms the data into graphs and charts.

[1973] Specific behavior: Analyze data and generate graphs and charts in specified formats.

[1974] Step 6:

[1975] The server sends the visualized data to the user.

[1976] Specific operation: Receive the generated graphs and charts and send them to the user's device as a response.

[1977] Step 7:

[1978] The terminal displays the visualized information to the user.

[1979] Specific operation: Display received graphs and charts on the screen in an easy-to-read format.

[1980] Q&A system

[1981] Step 1:

[1982] The user inputs a specific question through the terminal.

[1983] Specific behavior: The user enters a question in the text box and submits it.

[1984] Step 2:

[1985] The terminal sends a question to the server.

[1986] Specific operation: The entered text is sent to the server as an API request.

[1987] Step 3:

[1988] The server inputs the question into a natural language processing engine.

[1989] Specific operation: Calls the API of the natural language processing engine and sends a question.

[1990] Step 4:

[1991] A natural language processing engine generates the best answer to your question.

[1992] What it does: Parses the question and generates an answer by pulling information from relevant databases.

[1993] Step 5:

[1994] The server sends the generated answer to the user.

[1995] Specific operation: Receive the generated text and send it to the user's device as a response.

[1996] Step 6:

[1997] The terminal displays the answer to the user.

[1998] Specific operation: The received response is displayed on the screen in an easy-to-read format.

[1999] Evaluating E / S / G information

[2000] Step 1:

[2001] The server retrieves the company's E / S / G report.

[2002] What it does: Executes a database query to retrieve a company's E / S / G report.

[2003] Step 2:

[2004] The information acquired by the server is input into the generation AI engine, which evaluates each of the E / S / G items.

[2005] Specific operation: Format the obtained report and input it into the evaluation module of the generative AI engine.

[2006] Step 3:

[2007] A generative AI engine converts the evaluation results into natural language.

[2008] What it does: It rates each item through a rating algorithm and documents the results using a natural language generation algorithm.

[2009] Step 4:

[2010] The server sends the evaluation results to the user.

[2011] Specific operation: The generated evaluation document is received and sent to the user's terminal as a response.

[2012] Step 5:

[2013] The terminal displays the evaluation results to the user.

[2014] Specific operation: The received evaluation document is displayed on the screen in an easy-to-read format.

[2015] Example 1

[2016] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2017] Until now, providing corporate financial information and operational data in a format that is easy to understand even for novice investors required a great deal of specialized knowledge, making it difficult to obtain and interpret the information. Furthermore, creating automated releases and performance forecast reports to quickly provide the latest information, as well as visualizing the data, were time-consuming tasks. For these reasons, a system capable of efficiently obtaining, analyzing, reporting, and visualizing information was needed to provide corporate information.

[2018] 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.

[2019] In this invention, the server includes means for acquiring a company's financial information, strategic information, and operational data from a database, means for multifaceted analysis of the acquired information using a generative AI model, means for converting the results of the multifaceted analysis by the generative AI model into natural language, means for transmitting the converted results to a user's terminal, means for accepting information requests from users and transmitting them to the server, means for creating releases and performance forecast reports using the latest information automatically generated by the generative AI model, means for transmitting the created releases and performance forecast reports to the user's terminal, means for converting the acquired company data into charts and graphs using a visualization module, and means for transmitting the converted charts and graphs to the user's terminal. This enables efficient acquisition, analysis, reporting, and visualization of corporate information, making it possible to provide information that is easy to understand even for beginner investors.

[2020] "Financial information of a company" refers to data showing the business status of a company, including balance sheets, income statements, cash flow statements, and the like.

[2021] "Strategic information" refers to materials related to a company's future plans and management strategies, including a company's growth plans and competitive strategies.

[2022] "Operational Data" means data relating to the day-to-day business operations of the company, including operational results and daily performance reports.

[2023] A "database" is a collection of data organized in a certain way, a system that stores a company's financial information, strategic information, and operational data.

[2024] A "generative AI model" is a system based on artificial intelligence that has the ability to analyze input data from multiple angles and output it in natural language.

[2025] "Converting into natural language" refers to converting specialized data and analytical results into a linguistic form that is easy for the general public to understand.

[2026] A "user terminal" is a device operated by a user, and includes a PC, smartphone, tablet, etc.

[2027] An "information request" refers to an operation or request made by a user to a system for specific information.

[2028] A "release" is an official announcement or report issued by a company that contains a series of updates or news.

[2029] A "performance forecast report" is a report that predicts a company's future performance, and includes forecast data on future sales and profits.

[2030] A "visualization module" is a software or hardware feature that allows data to be visually represented, such as in graphs or charts.

[2031] "Graphing" refers to converting data into an easy-to-read format such as a graph or chart.

[2032] The "IR-GENAI" system of this invention uses advanced generative AI technology to provide corporate information in a format that is easy to understand even for beginner investors. This system involves data acquisition from a database, data analysis using a generative AI engine, natural language generation, data transmission, automatic generation of releases and performance forecast reports, and visualization of IR information.

[2033] System configuration

[2034] Hardware and Software Use

[2035] 1. Server: Processes data and interacts with the generative AI engine and visualization module.

[2036] 2. Database: Use a relational database such as MySQL to store the company's financial, strategic, and operational data.

[2037] 3. Generative AI engine: Uses generative AI models such as OpenAI GPT-4 to analyze data from multiple angles and output in natural language.

[2038] 4. Visualization module: Use visualization tools such as Tableau to transform data into graphs and charts.

[2039] 5. Terminal: A PC, smartphone, tablet, etc. that is operated by the user.

[2040] Data acquisition and processing

[2041] A user uses a terminal to request information about a particular company. The terminal sends this request to a server, which retrieves the relevant data from a database. For example, a balance sheet or profit and loss statement to understand the company's financial situation. Here is an example of the server retrieving the data using a MySQL query:

[2042] SELECT FROM Financial Data WHERE Company Name = "Company A"

[2043] Analyzing the data

[2044] The data acquired by the server is input into a generative AI engine, which analyzes it from multiple angles, including financial stability and growth potential. OpenAI GPT-4 is used as the generative AI engine. Examples of prompts generated by the generative AI engine include the following:

[2045] "Please analyze Company A's financial situation and explain it in a way that even a beginner investor can understand."

[2046] Generate and send results

[2047] The generative AI engine generates the analysis results in natural language and sends them to the user's device. For example, the following natural language sentences are generated:

[2048] "Company A has good financial stability, especially with a low debt ratio and stable cash flow."

[2049] The server sends the generated results to the user's terminal, which displays them.

[2050] Automatic generation of releases and forecast reports

[2051] When a user requests a company's latest performance forecast report, the server inputs the company's latest data and market forecast data into the AI ​​engine to generate a performance forecast report. The generated report is provided in the following format:

[2052] "Company A's next fiscal year's performance is expected to see increased revenue and profits, due to increased market share and the success of new products."

[2053] The server sends this report to the user's terminal, which displays it.

[2054] Visualization of IR information

[2055] When a user requests visualization of a company's investor relations information, the server retrieves financial data and strategic documents from the database and inputs them into the visualization module. The graphs and charts generated by the visualization module include the following examples:

[2056] -Bar chart showing sales trends

[2057] Line graph showing fluctuations in profit margins

[2058] The server transmits the visualized data to the user's terminal, which displays it.

[2059] In this way, the "IR-GENAI" system of this invention utilizes generative AI technology to analyze corporate information from multiple angles and provide it in natural language and visual formats, thereby providing information that is easy to understand even for beginner investors.

[2060] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2061] Step 1: Enter your information request

[2062] A user operates a terminal to request information about a specific company. For example, a request such as "Please display the financial data of Company A" is input into the terminal. The terminal sends this information request to the server. The input is the "company name" and "data type." The output is the requested information.

[2063] Step 2: Submitting the request

[2064] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Financial data" and sends it to the server. The input is the user's request, and the output is the request transmission.

[2065] Step 3: Retrieving data from the database

[2066] The server receives the request and accesses the database to retrieve the required data. Specifically, the server retrieves the data using a MySQL query. For example, it executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[2067] Step 4: Input data into the generative AI engine

[2068] The server inputs the acquired data into the generative AI engine. Specifically, the server sends financial data in JSON format to the generative AI engine. The input is Company A's financial data, and the output is data sent to the generative AI engine.

[2069] Step 5: Data analysis and natural language translation

[2070] The generative AI engine analyzes data from multiple perspectives and converts the results into natural language. Specifically, the generative AI engine generates natural language sentences such as, "Company A has good financial stability, with a particularly low debt ratio and stable cash flow." The input is Company A's financial data, and the output is the analysis results generated in natural language.

[2071] Step 6: Submitting the analysis results

[2072] The server receives the results from the generative AI engine and sends them to the user's device. Specifically, the server sends the generated natural language sentence to the device. The input is the analysis result generated in natural language, and the output is sent to the device.

[2073] Step 7: View the results

[2074] The terminal displays the results it receives to the user. Specifically, the screen displays the following: "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow." The input is the analysis results generated in natural language, and the output is the display to the user.

[2075] Automatic generation of releases and forecast reports

[2076] Step 1: Enter your performance forecast

[2077] A user requests the latest performance forecast report for a company from a terminal. For example, the user inputs, "Please generate the next performance forecast for Company A." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[2078] Step 2: Submitting the request

[2079] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: Performance forecast" and sends it to the server. The input is the user's request, and the output is the request transmission.

[2080] Step 3: Data Acquisition

[2081] The server retrieves the latest performance data and market forecast data for a company. Specifically, the server queries the database and executes the query "SELECT FROM performance data WHERE company name = 'Company A'". The input is the request information, and the output is the performance data for Company A.

[2082] Step 4: Input to the generative AI engine

[2083] The server inputs the acquired data into the generative AI engine. Specifically, the server sends performance data and market forecast data in JSON format to the generative AI engine. The input is Company A's performance data and market forecast data, and the output is data sent to the generative AI engine.

[2084] Step 5: Generate a performance forecast report

[2085] The generative AI engine analyzes performance data and generates a report in natural language. For example, a report might be generated that states, "Company A's next fiscal year's performance is expected to increase in both sales and profits. This is attributed to increased market share and the success of new products." The input is Company A's performance data and market forecast data, and the output is a performance forecast report generated in natural language.

[2086] Step 6: Release Submission

[2087] The server sends the generated performance forecast report to the user's terminal. Specifically, the server sends the generated natural language report to the terminal. The input is the performance forecast report generated in natural language, and the output is sent to the terminal.

[2088] Step 7: View the report

[2089] The terminal displays the received report to the user. Specifically, the screen displays the following: "Company A's next fiscal year's performance is expected to increase in both sales and profits, due in part to increased market share and the success of new products." The input is a performance forecast report generated in natural language, and the output is the display to the user.

[2090] Visualization of IR information

[2091] Step 1: Enter your visualization request

[2092] A user requests visualization of a company's IR information through a terminal. For example, the user inputs, "Please display the IR information of Company A in a graph." The terminal sends this information request to the server. The inputs are "company name" and "data type." The output is the requested information.

[2093] Step 2: Submitting the request

[2094] The terminal sends the user's request to the server. Specifically, the terminal generates a request containing the information "Company name: Company A" and "Data type: IR information" and sends it to the server. The input is the user's request, and the output is the request transmission.

[2095] Step 3: Data Acquisition

[2096] The server retrieves a company's financial data and strategic documents from a database. Specifically, the server executes the query "SELECT FROM financial data WHERE company name = 'Company A'". The input is the request information, and the output is Company A's financial data.

[2097] Step 4: Input to the visualization module

[2098] The server inputs the acquired data into the visualization module. Specifically, the server sends the financial data of Company A to the visualization module. The input is the financial data of Company A, and the output is the data sent to the visualization module.

[2099] Step 5: Visualize the data

[2100] The visualization module converts the acquired data into graphs and charts. For example, it generates a bar chart showing the trend in sales volume and a line graph showing fluctuations in profit margins. The input is Company A's financial data, and the output is a visualized graph or chart.

[2101] Step 6: Sending visualized data

[2102] The server sends the visualized data to the user's device. Specifically, it sends the generated graphs and charts to the device. The input is the visualized data, and the output is the data sent to the device.

[2103] Step 7: View the visualization data

[2104] The visualized data received by the terminal is displayed to the user. For example, a "bar chart showing the trend in sales of Company A" or a "line graph showing fluctuations in profit margin" is displayed on the screen. The input is the visualized data, and the output is the display to the user.

[2105] (Application example 1)

[2106] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2107] Modern investors, especially those new to investing, face challenges in comprehensively and intuitively understanding corporate information. Technical financial data and operational reports are particularly difficult to understand, and there is a lack of tools to effectively utilize them to make investment decisions. Furthermore, there are limited visual and interactive ways to display corporate information, creating a need for new solutions.

[2108] 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.

[2109] In this invention, the server includes means for retrieving corporate financial data, strategic documents, and management reports from a database, means for analyzing the data retrieved by the generative AI engine, means for converting the results of the analysis by the generative AI engine into natural language, means for transmitting the converted results to a user's terminal, means for visually and interactively displaying the corporate information analyzed by the generative AI engine using a head-mounted display, and means for the user to retrieve and manipulate the corporate information by interactive manual operation or voice command through the head-mounted display. This allows even beginners to invest to comprehensively and intuitively understand corporate information and supports investment decisions.

[2110] "Financial data of a company" refers to data that represents the financial status of a company, such as a balance sheet, income statement, and cash flow statement.

[2111] A "strategic document" is a document that describes the medium- to long-term goals set by a company, as well as the plans and policies for achieving them.

[2112] An "operational report" is a report that describes a company's daily business activities, their results, problems, etc.

[2113] A "database" is a system for storing structured business information that can be efficiently accessed, managed, and updated.

[2114] A "generative AI engine" is an artificial intelligence engine that analyzes collected data and generates specific assessments and predictions.

[2115] "Natural language" is a human language that expresses the analysis results in a form that can be understood by users.

[2116] A "user terminal" is a device that a user uses to obtain and operate information, and includes, for example, a smartphone, a tablet, a computer, and the like.

[2117] A "head-mounted display" is a display device that the user wears on their head, and is a device that enables VR and AR experiences.

[2118] A "visualization module" is a software component for transforming enterprise data into visual formats such as graphs and charts.

[2119] "Interactive manual manipulation or voice command" refers to a method by which a user manipulates data through a head-mounted display, including hand gestures and voice input.

[2120] This invention is a system that uses a generative AI model to analyze data such as a company's financial data, strategic documents, and operational reports, and provides information to investors in a visual and interactive format. The system includes a server, a user terminal, a head-mounted display, and a generative AI engine. Detailed embodiments of this system are described below.

[2121] 1. Data Acquisition and Analysis

[2122] The server first retrieves the company's financial data, strategy documents, and operational reports from a database. The database stores the company's balance sheet, income statement, cash flow statement, strategy documents, and operational reports. To retrieve this data, the server fetches the data via an API or similar.

[2123] The server then inputs the acquired data into a generative AI engine, which analyzes it from multiple perspectives. The generative AI engine analyzes aspects such as financial stability, growth potential, and profitability, and generates an evaluation for each. The analysis results are converted into natural language and output in a format that is easy for users to understand.

[2124] 2. Send to the user device

[2125] The generated natural language results are sent from the server to the user's device, which can be a smartphone, tablet, or PC, where the results are displayed.

[2126] 3. Visualization and Interaction with Head-Mounted Displays

[2127] The analysis results are also displayed visually and interactively using a head-mounted display (HMD). Users can wear the HMD and check company information in a VR or AR environment. HMDs such as Oculus Rift and HTC Vive are used. The visualization module converts the analysis results from the generative AI engine into graphs and charts, which are then displayed within the HMD.

[2128] Users can use manual or voice commands to interactively manipulate and gain a deeper understanding of company information. The head-mounted display interface allows easy access to detailed company financial information, and instant access to releases and earnings forecasts.

[2129] Specific examples

[2130] For example, if a user issues a voice command such as "Show me the latest earnings forecast for Company A," the server retrieves the latest information for Company A from the database. The generative AI engine then analyzes the information and generates a result in natural language. This result is displayed visually in the HMD, such as "Company A's next fiscal year's earnings are expected to increase in both sales and profits, driven by increased market share and the success of new products." Additionally, a bar chart showing sales trends and a line graph showing fluctuations in profit margins are also displayed visually.

[2131] Prompt Sentence Examples

[2132] Possible prompts for a generative AI model include:

[2133] "Analyze the following financial data: revenue: 20 million, profit: 3 million, expenses: 17 million, assets: 50 million, liabilities: 30 million"

[2134] In this way, the present invention supports investors, especially beginners, by analyzing company information from multiple angles and providing it to users in natural language and visual formats.

[2135] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2136] Step 1:

[2137] A user requests detailed financial information of a company through a device such as a head-mounted display or smartphone. This request is sent from the device to a server. The input data is the company ID and name, and the output is a request to the server.

[2138] Step 2:

[2139] The server retrieves a company's financial data, strategy documents, and management reports from the database based on user requests. The input data is the company's ID and name, and the data retrieved from the database is balance sheets, income statements, cash flow statements, etc. The output is these datasets.

[2140] Step 3:

[2141] The server inputs the acquired company data into the generation AI engine for multifaceted analysis. The input data is the company's financial data, strategy documents, and management reports, and the generation AI engine analyzes them to evaluate financial stability, growth potential, profitability, etc. The output is the analysis results.

[2142] Step 4:

[2143] The generative AI engine converts the analysis results into natural language. The input data is analyzed company information, and the output is the analysis results in natural language format.

[2144] Step 5:

[2145] The server sends the generated natural language results to the user's terminal. The input data is the analysis result of the natural language format, and the output is the data delivery to the user's terminal.

[2146] Step 6:

[2147] The device displays the received analysis results to the user. On smartphones and tablets, the results are displayed in text format. The input data is the analysis results in natural language format, and the output is the information displayed on the device screen.

[2148] Step 7:

[2149] When using a head-mounted display, the server converts the analysis results into graphs and charts using a visualization module, which then visually displays them within the HMD. The input data are the analysis results in natural language format and graph or chart data, and the output is the visualized information within the HMD.

[2150] Step 8:

[2151] Users can obtain and operate corporate information through interactive manual operations or voice commands via a head-mounted display. The input data are user operations and commands, and the output is updating the displayed information or obtaining detailed information.

[2152] This process allows even novice investors to comprehensively and intuitively understand company information and supports investment decisions.

[2153] 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.

[2154] The "IR-GENAI" system of this invention utilizes advanced generative AI technology to provide corporate information in an easy-to-understand manner even for beginner investors. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it becomes possible to provide information based on the emotional state of each individual investor. This system mainly includes the following elements:

[2155] 1. How to retrieve data from the database

[2156] The server retrieves the company's financial data, strategy documents, and operational reports from a database that stores balance sheets, income statements, cash flow statements, corporate strategy documents, and operational results.

[2157] 2. Data analysis method using generative AI engine

[2158] The data acquired by the server is input into the generative AI engine, which analyzes it from multiple perspectives, such as financial stability, growth potential, and profitability, and generates an evaluation for each.

[2159] 3. Natural language generation means

[2160] The generative AI engine outputs results in natural language based on the analysis, providing specialized data and evaluation results in a format that is easy to understand even for beginner investors.

[2161] 4. Data Transmission Method

[2162] The server sends the generated natural language results to the user's terminal, where the information requested by the user is displayed in an appropriate format.

[2163] 5. Automatic release and report generation tool

[2164] Users can use their devices to generate the latest company information, which is then automatically generated by an AI engine to create releases and performance forecast reports, providing fast and accurate information.

[2165] 6. Visualization of IR information

[2166] The server converts the company's data into graphs and charts using a visualization module and sends them to the user's device, providing information in a visually understandable way.

[2167] 7. Emotion Recognition Method Using Emotion Engine

[2168] The emotion engine analyzes the text and voice input by the user through the device and recognizes the user's emotions. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral) through text and voice analysis.

[2169] 8. Emotion-based information regulation

[2170] The server adjusts the information and analysis results provided by the generative AI engine based on the user's recognized emotions. For example, if the user is expressing negative emotions, it can reduce the user's stress by emphasizing positive information.

[2171] 9. Visual displays of emotions

[2172] It provides an interface for visually displaying the user's emotional state and tracks emotional fluctuations, allowing the user to understand their own emotional fluctuations.

[2173] Specific examples

[2174] Multifaceted analysis of corporate information and emotion recognition

[2175] 1. A user operates a terminal and enters a request to obtain detailed financial information about a company.

[2176] 2. The device sends this request to the server.

[2177] 3. The server retrieves financial data, strategy documents, and operational reports for the specified company from the database.

[2178] 4. The data acquired by the server is input into a generative AI engine to analyze financial stability and growth potential.

[2179] 5. The generative AI engine converts the analysis results into natural language and outputs them in an easy-to-understand format.

[2180] 6. The server sends the generated results to the user's device.

[2181] 7. The terminal displays the results to the user. For example, the screen might say, "Company A's financial stability is good, and it is particularly praised for its low debt ratio and stable cash flow."

[2182] Automatic generation of releases and earnings forecasts with emotion recognition

[2183] 1. The user requests the company's latest earnings forecast report from their device.

[2184] 2. The device sends a request to the server.

[2185] 3. The server inputs the company's latest performance data and market forecast data into the generation AI engine.

[2186] 4. The generative AI engine generates a performance forecast report in natural language based on this data.

[2187] 5. The server sends the generated releases and reports to the user's terminal.

[2188] 6. The device displays the report to the user. For example, it might say, "Company A's next fiscal year is expected to see increased sales and profits, driven by increased market share and the success of new products."

[2189] IR Information Visualization and Emotion Recognition

[2190] 1. The user requests visualization of a company's IR information through the terminal.

[2191] 2. The device sends a request to the server.

[2192] 3. The server retrieves the company's financial data and strategy documents.

[2193] 4. The data acquired by the server is input into the visualization module and converted into graphs and charts.

[2194] 5. The server sends the visualized data to the user.

[2195] 6. The device displays the visualized information to the user, for example, a bar chart showing sales figures over time or a line graph showing profit margin fluctuations.

[2196] Emotion recognition and information regulation

[2197] 1. When users view company information through their devices, they can enter comments and feedback.

[2198] 2. The device sends comments and feedback to the server.

[2199] 3. The server sends these inputs to the emotion engine, which analyzes the emotional state.

[2200] 4. The emotion engine identifies the user's emotional state (e.g., positive, negative, neutral).

[2201] 5. Based on the emotional state, the server adjusts the generative AI engine to provide the most appropriate information for the user.

[2202] 6. The device will visually display emotional fluctuations, allowing users to understand their own emotional state.

[2203] In this way, the "IR-GENAI" system of the present invention combines generative AI technology with an emotion engine to analyze corporate information from multiple angles and provide information based on the user's emotional state. This makes it possible to provide information that is easy to understand even for beginner investors and reduces the psychological burden.

[2204] The processing flow will be explained below.

[2205] Multifaceted analysis of corporate information and emotion recognition

[2206] Step 1:

[2207] A user operates a terminal and inputs a request to obtain detailed financial information of a company.

[2208] Specific operation: The user selects the company name and the perspective to analyze (e.g., financial stability, growth potential).

[2209] Step 2:

[2210] The terminal sends the user's request to the server.

[2211] Specific operation: The data entered in the form is sent to the server as an API request.

[2212] Step 3:

[2213] The server retrieves financial data, strategy documents, and operational reports of the designated company from the database.

[2214] What it does: Executes a database query to retrieve the required information.

[2215] Step 4:

[2216] The server inputs the acquired data into the generation AI engine and requests analysis.

[2217] Specific operation: Call the API of the generation AI engine and send the...

Claims

1. A means of retrieving company financial data, strategic documents, and operational reports from the database; A means for analyzing the acquired data using a generative AI engine; A means for converting the results analyzed by the generative AI engine into natural language; The system includes means for transmitting the converted result to a user's terminal.

2. The latest company information is automatically generated by an AI engine, and a means of creating releases and performance forecast reports is provided.

2. The system according to claim 1, further comprising means for transmitting the prepared releases and reports to a user terminal.

3. A means for converting the acquired company data into graphs and charts through a visualization module; 2. The system of claim 1, further comprising means for transmitting the converted graph or chart to a user terminal.

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