system

Abstract

A system is provided.SOLUTION: Means for inputting information such as a business plan, a financing target, and a scheduled IPO time by a business operator requiring financing; means for storing the input information in a database; means for analyzing the stored information by AI to clarify the direction of the business and the IPO target; means for transmitting an analysis result by the AI to an expert to request review; means for creating a final report on the basis of the review result and storing the final report in a database; and means for browsing the final report by an investor; The system includes a means for determining an investment and a means for storing the investment intention and the investment amount of the investor in a database and notifying the enterprise of them.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the process of matching businesses needing funding with investors, there are challenges such as a lack of clarity about business plans and funding goals, transparency in providing information to investors, and a lack of information about how the funds will be used. In addition, to ensure the efficiency and reliability of the entire funding process, detailed reports using expert reviews and AI analysis are necessary, but there is no system that can provide these in an integrated and seamless manner. [Means for solving the problem]

[0005] The present invention provides a means for businesses seeking fundraising to input information such as their business plan, fundraising goals, and planned IPO timing, and a means for storing the input information in a database. The system also includes a means for analyzing the stored information using AI to clarify the business's direction and IPO goals, a means for sending the AI ​​analysis results to experts for review, and a means for creating a final report based on the expert review results and storing it in a database. The system also provides a means for investors to view the final report and make investment decisions, and a means for saving their investment intentions and investment amounts in a database and notifying the business. This enables investors to receive transparent and reliable information and support their investment decision-making. The system also provides a means for re-analyzing information about projects that have reached their fundraising goals using AI to generate a detailed report on how the funds will be used, and a means for storing the detailed report in a database and notifying the business. This also enables the provision of information on how the funds will be used, improving project transparency.

[0006] "Funding" refers to the process by which a business raises the funds needed to operate and expand its business from external sources.

[0007] "Business operator" means an individual or legal entity that operates a particular business.

[0008] An "investor" is an individual or legal entity that invests its own funds in a particular business.

[0009] A "business plan" is a detailed outline of a business's goals, strategies, and how to carry them out.

[0010] "Funding target" means the total amount that a business seeks to raise through fundraising.

[0011] An IPO (Initial Public Offering) is the process by which a company makes its shares available to the public for the first time and raises funds from the capital markets.

[0012] A "database" is a system that organizes and stores data in a particular structured format.

[0013] "AI (artificial intelligence)" refers to technology used to mimic or augment human intelligence, and is used to analyze data and make predictions.

[0014] "Analysis" refers to a method for analyzing and understanding specific data or information in detail.

[0015] An "expert" is someone who has advanced knowledge and experience in a particular field.

[0016] "Review" refers to the process of expert evaluation or consideration.

[0017] "Final Report" means a report summarizing the results of the analysis and expert review.

[0018] "Notification" is the action of communicating specific information to interested parties.

[0019] A "detailed report" is a report that provides in-depth information about a particular event.

[0020] "Online" means accessible in real time via the Internet. [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 following describes an embodiment of the present invention. This system is a platform that matches investors with businesses that need funding, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. The specific operation of each component of the system is described below.

[0043] Project registration by business operators

[0044] 1. User (business operator): First, the business operator accesses the platform login page and enters the correct authentication information to log in. After successful login, the business operator can access the new project registration page.

[0045] 2. Terminal (business operator): The business operator enters the necessary information on the new project registration page, such as the business plan, target amount of fundraising, and planned IPO timing, and presses the submit button.

[0046] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[0047] AI-based business plan analysis

[0048] 1. Server (AI module): The AI ​​analysis module is launched and inputs the project information registered by the business. The AI ​​analyzes the business plan and generates text to clarify the business direction and IPO goals.

[0049] 2. Server: Sends the generated analysis results to the expert review module.

[0050] Expert review and final report preparation

[0051] 1. User (expert): The expert logs in to a dedicated dashboard and receives notifications of new AI analysis results. The expert can then check the analysis results from the dashboard.

[0052] 2. Terminal (expert): Review the analysis results and make corrections or additions as necessary.

[0053] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[0054] Investors viewing projects

[0055] 1. User (Investor): Investors access the platform and log in.

[0056] 2. Server: Validates the investor's credentials and returns the dashboard page if authentication is successful.

[0057] 3. Terminal (Investor): Go to the project list page from the dashboard and search for projects that interest you.

[0058] 4. Server: Based on the investor's request, it returns a list of publicly available projects.

[0059] 5. Terminal (Investor): The investor selects the project of interest from the displayed list of projects and opens the details page.

[0060] 6. Server: Returns detailed information and final report for selected projects.

[0061] Investor investment decisions

[0062] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[0063] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[0064] 3. Server: Receives the investor's investment intention and investment amount, stores them in the database, and sends a notification of investment completion to the business operator.

[0065] What happens if the funding goal is reached?

[0066] 1. Server: Checks information about projects that have reached their funding goal and performs AI analysis again. The AI ​​generates a detailed report on how the funds will be used.

[0067] 2. Server: Stores the generated detailed report in a database and notifies the operator.

[0068] 3. Terminal (operator): The operator checks the detailed report and initiates procedures to carry out the plan as planned.

[0069] Specific examples

[0070] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. AI analyzes the business plan, and experts review it to generate a final report. Based on this report, Investor B becomes interested in the project and, after checking the details, decides to invest. Once Investor B invests and the target amount is reached, AI again generates a detailed report on how the funds will be used and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[0071] In this way, the invention combines AI analysis with expert review to provide an integrated system for making the fundraising process transparent and efficient.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[0075] Step 2:

[0076] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[0077] Step 3:

[0078] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[0079] Step 4:

[0080] Server: Receives the transmitted information and stores it in a database.

[0081] Step 5:

[0082] Server: Sends the stored information to the AI ​​analysis module.

[0083] Step 6:

[0084] Server (AI module): Analyzes business plans and generates text to clarify business direction and IPO goals.

[0085] Step 7:

[0086] Server: Sends the generated AI analysis results to the expert review module.

[0087] Step 8:

[0088] User (expert): Logs in to a dashboard exclusively for experts and receives notifications of AI analysis results.

[0089] Step 9:

[0090] Device (Expert): Check notifications and review AI analysis results from the dashboard.

[0091] Step 10:

[0092] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[0093] Step 11:

[0094] Server: Receives the analysis results after the expert's corrections, generates a final report, and stores the generated final report in a database.

[0095] Step 12:

[0096] Server: Update the project status to "publishable" and add it to the publishing list.

[0097] Step 13:

[0098] User (Investor): Access the platform and log in by entering their credentials on the login page.

[0099] Step 14:

[0100] Server: Verifies investor credentials and returns dashboard page if successful.

[0101] Step 15:

[0102] Terminal (Investor): Access the project listing page from the dashboard and search for projects that interest you.

[0103] Step 16:

[0104] Server: Returns a list of publicly available projects based on the investor's request.

[0105] Step 17:

[0106] Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[0107] Step 18:

[0108] Server: Returns detailed information and final report for the selected project.

[0109] Step 19:

[0110] Terminal (Investor): Check the detailed report of the project and decide to invest.

[0111] Step 20:

[0112] Terminal (Investor): Enter the investment amount and press the invest button.

[0113] Step 21:

[0114] Server: Stores the investor's investment intention and investment amount in a database, and also sends a notification of investment completion to the business operator.

[0115] Step 22:

[0116] Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module.

[0117] Step 23:

[0118] Server (AI module): Generates detailed reports on how funds are being used and stores them in a database.

[0119] Step 24:

[0120] Server: Sends a detailed report to the operator.

[0121] Step 25:

[0122] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[0123] Example 1

[0124] 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."

[0125] For businesses that need to raise funds, analyzing business plans and clarifying how the funds will be used are extremely important. However, with conventional methods, analyzing business plans and explaining them to investors is cumbersome, making it difficult to raise funds effectively. It is also difficult for investors to grasp the reliability of the investment target and the specific use of funds. For this reason, a transparent and efficient system is needed to effectively match businesses with investors.

[0126] 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.

[0127] In this invention, the server includes: means for a business operator to input information such as a business plan, fundraising goals, and planned IPO timing; means for saving the input information in a database; means for analyzing the saved information using artificial intelligence to clarify the business direction and IPO goals; means for sending the results of the analysis by the artificial intelligence to an expert for review; means for creating a final report based on the expert's review and saving the final report in a database; means for an investor to view the final report and decide on an investment; means for saving the investor's investment intention and investment amount in a database and notifying the business operator; means for an investor to search for projects of interest on a project list page; and means for re-analyzing the project registration information by the business operator and generating a detailed report on how the funds will be used, thereby enabling a transparent and efficient fundraising process between business operators and investors.

[0128] A "business operator" is an individual or legal entity that needs financing and registers a business plan and enters information.

[0129] "Artificial intelligence" refers to machine learning algorithms and natural language processing engines that analyze input data and clarify business direction and IPO goals.

[0130] "Database" refers to a data storage system for storing information entered by businesses and investors, as well as analysis results and final reports.

[0131] "Review" refers to the process in which experts check the results of analysis by artificial intelligence and make corrections or additions.

[0132] The "final report" is a detailed business plan and use of funds report prepared based on the results of the AI ​​analysis and expert review.

[0133] "Investor" means an individual or legal entity that has an interest in a project and decides to invest.

[0134] "Funding target" refers to the specific amount of money that a business requires to carry out a project.

[0135] "IPO" refers to an initial public offering, where a business makes its shares publicly available on the market.

[0136] "Project Listing Page" means a web page where investors can search and view publicly available project information.

[0137] A "detailed report" is a report that uses artificial intelligence to analyze projects that have reached their funding goal and detail how the funds will be used.

[0138] A specific embodiment for implementing this invention is described below. The system of the invention matches businesses needing financing with investors, and uses a combination of AI and expert knowledge. This system consists of the following components:

[0139] Hardware and Software

[0140] 1. Server: Provides functions such as data storage, processing, analysis, notification, and AI module launch. Specifically, it includes a database, API server, AI analysis module, expert review module, and notification system.

[0141] 2. Terminal: A computer or mobile device used by an entrepreneur, professional or investor, on which a browser or dedicated application is installed.

[0142] 3. AI module: Uses a natural language processing engine (e.g., GPT-4 (registered trademark)) to analyze business plans.

[0143] 4. Database: A storage system for storing project information, analysis results, final reports, investment information, etc.

[0144] Data processing and calculation

[0145] 1. Data entry: The business operator enters information such as the business plan, fundraising target, and planned IPO timing using a browser or a dedicated application.

[0146] 2. Data storage: The server receives the entered information, generates SQL queries to store it in the database, and stores the information.

[0147] 3. Data analysis: The server sends the stored information to an AI analysis module, which analyzes the data and generates text that clarifies the business direction and IPO goals.

[0148] 4. Expert review: The server sends the AI ​​analysis results to the expert review module, where the experts review the results and make corrections if necessary.

[0149] 5. Generate Final Report: The server generates the final report and stores it in the database. The final report includes details of the business plan, fundraising goals, IPO plans, etc.

[0150] 6. Investor Access: Investors can access the project listing page and search for projects they are interested in. They can open the details page and view the final report.

[0151] 7. Investment decision: The investor enters the investment amount and decides to invest. The server receives the investment information, stores it in the database, and notifies the operator.

[0152] 8. Reaching the funding goal: The server checks the information of the projects that have reached their funding goal, performs AI analysis again, and generates a detailed report on how the funds will be used.

[0153] Specific examples

[0154] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. The data is then stored in a database via the server.

[0155] The AI ​​module analyzes the business plan and sends the results to an expert review module, where the experts review the analysis, make corrections, and create a final report, which is then stored in a database and made available to investors.

[0156] Investor B accesses the platform, becomes interested in Business A's project, and after checking the details, decides to invest. The investment information is stored in a database via the server and notified to Business A.

[0157] When the investment target amount is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[0158] Prompt Sentence Examples

[0159] Below are some example prompts to input to the generative AI model:

[0160] Analyze a business plan outline and generate a text that specifically articulates the IPO goals. Project details:

[0161] Business plan: [Detailed business plan content]

[0162] Funding goal: [target amount]

[0163] Planned IPO date: [Specific date]

[0164] In this way, this invention combines AI analysis with expert review to provide a transparent and efficient fundraising process between businesses and investors.

[0165] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0166] Step 1:

[0167] Business Login

[0168] The user (business operator) accesses the platform's login page and enters their user ID and password. The entered information is sent and reaches the server. The server compares the user ID and password with the database for authentication, and if successful, starts a login session. If the login is successful, a new project registration page is returned to the business operator's terminal.

[0169] Input: User ID, Password

[0170] Specific operations: login authentication, session start

[0171] Output: New project registration page

[0172] Step 2:

[0173] Entering and submitting project information

[0174] On the new project registration page, businesses enter their business plan, fundraising goals, and planned IPO timing. After entering this information and pressing the submit button, the information is sent to the server, which then generates an SQL query to store the received information in the database and executes the insert operation.

[0175] Input: Business plan, fundraising target, planned IPO date

[0176] Specific actions: Entering information, sending, saving to database

[0177] Output: Save information to a database

[0178] Step 3:

[0179] Sending data to the AI ​​module

[0180] The server sends the information stored in the database to an AI analysis module, which receives the project information, invokes a natural language processing engine (e.g., GPT-4) to analyze the business plan, and generates text that clarifies the business direction and IPO goals.

[0181] Input: Project information stored in the database

[0182] Specific operations: receiving and sending information, launching AI analysis module, generating text

[0183] Output: Parsed text

[0184] Step 4:

[0185] Submitting analysis results to an expert review module

[0186] The server sends the analysis results generated by the AI ​​module to the expert review module, where experts access the review dashboard to receive notifications of new analysis results, review the results, and make corrections or additions as necessary.

[0187] Input: Text of the analysis results generated by the AI

[0188] Specific actions: Submit information, expert review

[0189] Output: Corrected and confirmed results

[0190] Step 5:

[0191] Generate the final report

[0192] The server generates a final report based on the results of the expert review, which is then stored in a database by the server, and includes details of the business plan, fundraising targets, and IPO plans.

[0193] Input: Expert-reviewed text

[0194] Specific operation: Create and save the final report

[0195] Output: Final report

[0196] Step 6:

[0197] Investor Login and Project Search

[0198] A user (investor) accesses the platform and logs in. After successful login, the investor can access the project list page. The investor searches for a project of interest and sends a request to the server to display detailed information. The server returns the relevant project information and final report from the database.

[0199] Input: Investor login information, project search request

[0200] Specific operations: login authentication, project information search, information submission

[0201] Output: Project information, final report

[0202] Step 7:

[0203] Investment decisions and inputs

[0204] After checking the detailed project report, the investor decides to invest. By entering the investment amount and pressing the "Invest" button, the information is sent to the server. The server receives the investment information, stores it in a database, and sends a notification of investment completion to the business operator.

[0205] Input: investment amount, investment confirmation operation

[0206] Specific operations: receiving investment information, saving it in a database, and notifying businesses

[0207] Output: Save to database, notify business operator

[0208] Step 8:

[0209] What happens after the fundraising goal is reached?

[0210] The server checks the information of projects that have reached their funding goals. It then performs another AI analysis and generates a detailed report on how the funds will be used. This detailed report is then saved in a database by the server and notified to the project owner. The project owner then checks the detailed report and implements a specific plan for how the funds will be used.

[0211] Input: Project information that has reached its funding goal

[0212] Specific operations: Check information, reanalyze, create and save detailed reports, notify business operators

[0213] Output: Detailed report, notification to business operators

[0214] By explaining in detail the specific operations and data flow at each step, it becomes easier to understand the function of the entire system, and the content can be useful for actual implementation.

[0215] (Application example 1)

[0216] 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."

[0217] It has traditionally been difficult for businesses seeking funding to conduct accurate and reliable business plan analysis and provide transparent information to investors. It also requires a great deal of effort for investors to quickly view multiple projects and make appropriate investment decisions. To solve these challenges, a platform is needed that integrates AI technology and expert knowledge to efficiently and effectively analyze business plans and fund usage, and present the results to investors.

[0218] 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.

[0219] In this invention, the server includes means for a business operator to have an AI model analyze a business plan for a project and store the generated text in a database, means for sending the generated analysis results to an expert for review and storing the expert's review results in the database, and means for using the AI ​​model to generate prompt text for investors to view project details and make investment decisions. This enables business operators to efficiently present high-quality business plans and investors to make quick investment decisions based on reliable information.

[0220] Key Word Definitions

[0221] "Business operator" refers to a corporation or individual that has a business plan and needs to raise funds.

[0222] "Investor" means a legal entity or individual whose purpose is to provide funding to a Project.

[0223] A "business plan" is a document that details the outline, goals, progress, and projected income and expenditures of a particular project.

[0224] A "funding target" is a specific amount of money that an entity needs to carry out a project.

[0225] "Planned IPO date" refers to the time when a business plans to go public (IPO).

[0226] A "database" is a system for efficiently storing, managing, and retrieving information.

[0227] An "AI model" is a mathematical model that uses artificial intelligence algorithms to solve specific problems.

[0228] An "expert" is a person who has advanced knowledge and experience in a particular field.

[0229] A "prompt" is an instruction given to an AI model to perform a specific task.

[0230] "Online Platform" means a service that can be accessed by users via the Internet.

[0231] "Generated text" refers to the text that the AI ​​model generates as a result of analyzing the business plan.

[0232] "Analysis results" refer to the conclusions and proposals that AI draws from analyzing the business plan.

[0233] "Review results" refers to the final evaluation after experts have evaluated and corrected the analysis results generated by AI.

[0234] "Means to View Details" refers to the interface or functionality that allows a user to view detailed information about a particular project.

[0235] MODE FOR CARRYING OUT THE INVENTION

[0236] The following describes an embodiment of this invention. This system is an online platform that efficiently matches businesses needing fundraising with investors, analyzes business plans, and clarifies how funds will be used. The system combines AI analysis models and expert reviews to provide investors with highly reliable information.

[0237] Project registration by business operators

[0238] Terminal (operator)

[0239] Businesses register by entering information such as their project's business plan, fundraising goals, and planned IPO timing into the online platform, which is then stored in a database by the server.

[0240] AI-based business plan analysis

[0241] server

[0242] After the input information is saved in the database, the AI ​​analysis module is launched and analyzes the business plan. The generated text is saved back in the database as the analysis result. The AI ​​model used could be a generative AI model such as OpenAI's GPT-3.

[0243] Expert review and final report preparation

[0244] User (expert)

[0245] Experts review the results of the AI ​​analysis from a dedicated dashboard, and after the experts' evaluation and corrections are made, the results are saved on the server as a final report.

[0246] Investors can view projects and make investment decisions

[0247] Terminal (Investor)

[0248] Investors access the platform and view the details of the project. In order for investors to check the detailed information of the project, the server generates a prompt message and provides information according to the investor's request. Examples of prompt messages include the following:

[0249] Business plan for registered projects:

[0250] "This project aims to develop new markets in the field of image processing using innovative AI technology."

[0251] Prompt statement:

[0252] "Analyze this business plan and highlight key points and market value:"

[0253] Investment and fundraising completion

[0254] server

[0255] After the investor decides to invest, the server stores the investment intention and investment amount in a database and notifies the business. If the fundraising goal is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies the business.

[0256] As described above, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process. The hardware used includes servers and user devices, and the software includes the Flask framework, SQLite database, and OpenAI's GPT-3.

[0257] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0258] Program processing flow

[0259] Step 1:

[0260] Businesses log in to the platform using their terminals, input information such as the business plan, fundraising goals, and planned IPO timing for their new projects, and then submit it. This input information is received by the server and stored in the database. (Input: business plan, fundraising goals, planned IPO timing / Output: project information stored in the database)

[0261] Step 2:

[0262] The server sends the saved business plan information to the AI ​​analysis module. The AI ​​analysis module analyzes the business plan and generates text to clarify the business's direction and market value. This generated text is sent back to the server and saved in a database. (Input: Saved business plan information / Output: Text of the analysis results by AI)

[0263] Step 3:

[0264] The server sends the generated AI analysis results to an expert for review. The expert uses the user's device to check the analysis results and make corrections or additions as necessary. The corrected review results are returned to the server and stored in a database. (Input: AI analysis results / Output: Review results including corrections and additions made by the expert)

[0265] Step 4:

[0266] The server creates a final report based on the expert review results and stores it in a database. This final report is then made available to investors. (Input: Expert review results / Output: Final report)

[0267] Step 5:

[0268] Investors log in to the platform using their terminals and view project details. The server uses a generative AI model to generate prompts to provide detailed project information and a final report to investors. (Input: Viewing request from investor / Output: Generated prompts and project details)

[0269] Step 6:

[0270] The investor views the project details page and decides to invest. The investor enters the investment amount and presses the investment button. The server saves the investor's investment intention and investment amount in a database and notifies the business operator. (Input: Investor's investment intention and investment amount / Output: Investment information saved in the database and notification to the business operator)

[0271] Step 7:

[0272] If the fundraising goal is reached, the server performs another AI analysis and generates a detailed report on how the funds will be used. The generative AI model is used to create the detailed report, which is then saved in a database and notified to the project owner. (Input: Project information on the project that reached the fundraising goal / Output: Detailed report and notification to the project owner)

[0273] In this way, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process.

[0274] 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.

[0275] The following describes an embodiment of this invention. This system is a platform for matching businesses needing fundraising with investors, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, this system enhances the accuracy of information and decision-making support by incorporating an emotion engine that recognizes the user's emotions.

[0276] Project registration by business operators

[0277] 1. User (business operator): First, the business operator accesses the platform and enters their authentication information on the login page. After successful login, they can access the new project registration page.

[0278] 2. Terminal (business operator): The business operator enters the necessary information such as the business plan, fundraising target, and planned IPO date on the new project registration page and presses the submit button.

[0279] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[0280] Analysis by emotion engine

[0281] 1. Server (emotion engine): Analyzes the information input by the business operator and generates emotional data. For example, it determines the business operator's emotional state, such as optimistic, pessimistic, or excited.

[0282] 2. Server: Reflects the generated emotion data into the business plan and proposes revisions as necessary.

[0283] AI-based business plan analysis

[0284] 1. Server (AI module): The AI ​​analysis module analyzes the business plan and generates text to clarify the business direction and IPO goals.

[0285] 2. Server: Sends the generated analysis results to the expert review module.

[0286] Expert review and final report preparation

[0287] 1. User (expert): The expert accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data.

[0288] 2. Terminal (expert): Checks the analysis results and emotion data, and makes corrections or additions as necessary.

[0289] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[0290] Investors viewing projects

[0291] 1. User (Investor): Investor accesses the platform and enters their authentication information on the login page. If authentication is successful, they can access the dashboard page.

[0292] 2. Terminal (Investor): Go to the project list page from the dashboard and search for the project you are interested in.

[0293] 3. Server: Returns a list of publicly available projects based on the investor's request.

[0294] 4. Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[0295] 5. Server: Returns detailed information and final report for selected projects.

[0296] Sentiment analysis when investors browse projects

[0297] 1. Server (emotion engine): When investors view project reports, their reactions are analyzed in real time and emotion data is generated.

[0298] 2. Server: The generated emotion data is fed back to the business.

[0299] Investor investment decisions

[0300] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[0301] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[0302] 3. Server: Stores the received investment intention and investment amount in a database and notifies the business operator.

[0303] What happens if the funding goal is reached?

[0304] 1. Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module and sentiment engine.

[0305] 2. Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[0306] 3. Server: Notifies the operator of the generated detailed report.

[0307] Specific examples

[0308] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[0309] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[0310] In this way, the present invention provides a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[0311] The processing flow will be explained below.

[0312] Step 1:

[0313] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[0314] Step 2:

[0315] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[0316] Step 3:

[0317] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[0318] Step 4:

[0319] Server: Receives the transmitted information and stores it in a database.

[0320] Step 5:

[0321] Server: Sends the stored information to the AI ​​analysis module.

[0322] Step 6:

[0323] Server (emotion engine): Analyzes text data entered by businesses and generates emotional data. For example, it determines the emotional state of the input, such as whether the input is positive or anxious.

[0324] Step 7:

[0325] Server: Reflects the generated emotion data in the analysis of business plans and proposes revisions to the business operator as necessary.

[0326] Step 8:

[0327] Server (AI module): Analyzes business plan data and generates detailed analysis results to clarify business direction and IPO goals.

[0328] Step 9:

[0329] Server: Sends the generated analysis results to the expert review module.

[0330] Step 10:

[0331] User (expert): Logs in to an expert-only dashboard and receives notifications of new AI analysis results and emotion data.

[0332] Step 11:

[0333] Terminal (expert): Check the AI ​​analysis results and emotion data from the dashboard and review them.

[0334] Step 12:

[0335] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[0336] Step 13:

[0337] Server: Receives the analysis results after the experts' corrections, generates the final report, and stores it in the database.

[0338] Step 14:

[0339] Server: Add the final report to the list of publicly available projects and update it so that it is available for investors to view.

[0340] Step 15:

[0341] User (Investor): Access the platform and log in by entering credentials on the investor login page.

[0342] Step 16:

[0343] Server: Verifies investor credentials and returns dashboard page if successful.

[0344] Step 17:

[0345] Terminal (Investor): Go to the project listing page from the dashboard and search for the project you are interested in.

[0346] Step 18:

[0347] Server: Returns a list of publicly available projects in response to investor requests.

[0348] Step 19:

[0349] Terminal (Investor): Select the project you are interested in from the project list and open the details page.

[0350] Step 20:

[0351] Server: Returns detailed information and final report for the selected project.

[0352] Step 21:

[0353] Server (emotion engine): When investors view detailed project reports, their reactions are analyzed in real time and emotion data is generated.

[0354] Step 22:

[0355] Server: Provides feedback to the business operator on the generated emotion data.

[0356] Step 23:

[0357] Terminal (Investor): Check the detailed report of the project and decide to invest.

[0358] Step 24:

[0359] Terminal (Investor): Enter the investment amount and press the invest button.

[0360] Step 25:

[0361] Server: Stores the investor's investment intention and investment amount in a database and notifies the business operator.

[0362] Step 26:

[0363] Server: Sends information about projects that have reached their funding goal to the AI ​​analysis module and sentiment engine for further analysis.

[0364] Step 27:

[0365] Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[0366] Step 28:

[0367] Server: Sends a detailed report to the operator.

[0368] Step 29:

[0369] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[0370] Example 2

[0371] 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."

[0372] Traditional fundraising platforms do not take into account emotional data when analyzing the information entered by businesses or creating final reports, resulting in insufficient support for business planning accuracy and investor decision-making. Furthermore, the lack of feedback that reflects investors' emotional state prevents a fundraising process that takes into account the psychological aspects of investors.

[0373] 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.

[0374] In this invention, the server includes means for analyzing input information from business operators and investors using an emotion engine to generate emotion data, means for revising business plans based on the emotion data, means for performing real-time emotion analysis when investors view project reports, and means for feeding back the investor's emotion data to the business operator. This allows the emotion data to be reflected in business plans and final reports, enabling a fundraising process that takes into account the emotional state of investors.

[0375] "Business operator" refers to an entity that registers plans and information about a project requiring funding on the platform.

[0376] "Investor" means an entity that invests in a project offered on the Platform.

[0377] "Business Plan" means the information entered by an operator into the Platform, including project details, fundraising targets, and planned IPO timing.

[0378] A "fundraising goal" is a goal that indicates the specific target amount of funds required by the business operator to implement the project.

[0379] The "emotion engine" is a software system that analyzes the emotional state and generates specific emotional data based on the information that businesses and investors enter or view on the platform.

[0380] "Emotion data" is data that indicates the emotional state of businesses and investors, generated as a result of analysis by the emotion engine.

[0381] The "AI analysis module" is artificial intelligence software that analyzes input data such as business plans and clarifies project direction and IPO goals.

[0382] An "expert" is a person or group with the knowledge and experience to review the results of AI analysis and make any necessary corrections or additions.

[0383] The "Final Report" is a comprehensive report on the project that will be created based on the results of AI analysis, expert review, and emotional data.

[0384] "Ready for release" is a status that indicates that a final report has been prepared after expert review and is ready to be made public to investors.

[0385] "Project Report" is synonymous with Final Report and means a report that includes the overall content and evaluation results of the project.

[0386] A "database" is an information aggregation system managed by a server that stores and manages data such as business plans, sentiment data, final reports, and investor information.

[0387] This invention is a platform that matches businesses needing funding with investors, utilizing AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, by combining this system with an emotion engine that recognizes user emotions, the accuracy of information and decision-making support are enhanced.

[0388] System configuration

[0389] The system mainly consists of the following components:

[0390] 1. Server

[0391] 2. Terminal

[0392] 3. Users (businesses, investors, experts)

[0393] 4. Database

[0394] 5. AI Module

[0395] 6. Emotion Engine

[0396] Hardware and software used

[0397] Hardware: Server, user terminal (PC, smartphone)

[0398] Software: AI analysis module, emotion engine, database management system

[0399] Program processing overview

[0400] Project registration by business operators

[0401] The user (business operator) accesses the platform and enters authentication information on the login page. If authentication is successful, they are directed to a new project registration page. There, they enter the necessary information, such as the business plan, fundraising target, and planned IPO timing, and press the "Submit" button. The information sent from the terminal (business operator) is received by the server and stored in a database. This information is then sent to the AI ​​analysis module.

[0402] Analysis by emotion engine

[0403] The server (emotion engine) analyzes the business's input information and generates emotional data. For example, it determines whether the business is optimistic, pessimistic, excited, etc. The emotional data is reflected in the business plan and, if necessary, proposes revisions.

[0404] AI-based business plan analysis

[0405] The server (AI module) analyzes the business plan and generates text that clarifies the business direction and IPO goals. The analysis results are sent to the expert review module.

[0406] Expert review and final report preparation

[0407] The user (expert) accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data. The user (expert) checks the analysis results and emotional data on the device and makes corrections or additions as necessary. The server receives the corrected analysis results, generates a final report based on them, and stores it in the database. The project status is updated to "Ready for release."

[0408] Investors viewing projects

[0409] The user (investor) accesses the platform and enters authentication information on the login page. If authentication is successful, the user is taken to the dashboard page. On the terminal (investor), the user moves from the dashboard to the project list page and searches for a project of interest. The server returns a list of publicly available projects based on the investor's request. The terminal (investor) selects a project of interest from the list of projects displayed and opens the details page. The server returns detailed information and a final report for the selected project.

[0410] Sentiment analysis when investors browse projects

[0411] When investors view project reports, the server (emotion engine) analyzes their reactions in real time and generates emotional data, which is then fed back to the business.

[0412] Investor investment decisions

[0413] The investor checks the detailed project report on the terminal and decides to invest. He enters the investment amount and presses the "Invest" button. The server stores the received investment intention and investment amount in the database and notifies the business operator.

[0414] What happens if the funding goal is reached?

[0415] The server sends information about projects that have reached their funding goal back to the AI ​​analysis module and emotion engine. The server (AI module and emotion engine) generates a detailed report on how the funds will be used and stores it in a database. The generated detailed report is then notified to the business operator.

[0416] Specific examples

[0417] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[0418] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[0419] In this way, the present invention is a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[0420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0421] Step 1:

[0422] A user (business operator) accesses the platform and enters authentication information on the login page. The entered authentication information (username, password) is sent to the server. The server receives the authentication information and checks it against the database. If authentication is successful, the server returns access permission, and the user can access the new project registration page.

[0423] Step 2:

[0424] The terminal (business operator) moves to the new project registration page, enters the necessary information such as the business plan, fundraising target, and planned IPO timing, and presses the submit button. The input data (business plan, fundraising target, planned IPO timing) is sent to the server, which receives it and stores it in a database.

[0425] Step 3:

[0426] The server sends the stored information to the AI ​​analysis module. The business plan, fundraising goals, and planned IPO timing stored in the database are input into the AI ​​analysis module. The AI ​​analysis module analyzes the data and clarifies the business direction and IPO goals. The analysis results (business direction, IPO goals) are generated and output to the server.

[0427] Step 4:

[0428] The server (emotion engine) analyzes the business's input information and generates emotion data. The business plan and input text are sent to the emotion engine. The emotion engine uses natural language processing to generate emotion data (optimistic, pessimistic, etc.) and returns it to the server. The server reflects the generated emotion data in the business plan and suggests revisions as necessary.

[0429] Step 5:

[0430] The server sends the generated emotion data and AI analysis results to the expert review module, which then notifies the expert on their dashboard.

[0431] Step 6:

[0432] The user (expert) accesses a dedicated dashboard and receives notifications of AI analysis results and emotional data. By clicking on the notification, the AI ​​analysis results and emotional data are displayed on the device.

[0433] Step 7:

[0434] The terminal (expert) checks the analysis results and emotion data, and makes corrections or supplements as necessary. The expert checks the displayed data, enters corrections or comments, and presses the save button. The corrections and supplemental data are sent to the server.

[0435] Step 8:

[0436] The server receives the analysis results after the experts have completed the corrections and generates a final report based on them. A script is executed based on the corrected analysis results to generate a final report. The generated final report is saved in the database and the project status is updated to "Ready for release."

[0437] Step 9:

[0438] A user (investor) accesses the platform and enters their authentication information on the login page. The entered authentication information (username, password) is sent to the server and checked against the database. If authentication is successful, the server returns permission to access the dashboard page.

[0439] Step 10:

[0440] The terminal (investor) navigates from the dashboard to the project list page and searches for projects they are interested in. A project search request is sent to the server. The server retrieves projects with a "publishable" status from the database and returns the list to the terminal.

[0441] Step 11:

[0442] The terminal (investor) selects a project of interest from the displayed project list and opens the details page. The selected project ID is sent to the server. The server retrieves detailed information and a final report from the database and returns it to the terminal.

[0443] Step 12:

[0444] When investors view a project report, the server (emotion engine) analyzes their reactions in real time and generates emotion data. Data such as the investor's mouse movements and viewing time are input, and the emotion engine analyzes this to generate emotion data. The generated emotion data is returned to the server.

[0445] Step 13:

[0446] The server feeds back the generated emotion data to the business operator, who then notifies the business operator's dashboard of the generated emotion data.

[0447] Step 14:

[0448] The terminal (investor) checks the detailed report of the project and decides to invest. They enter the investment amount and press the "Invest" button. The investment amount and investment decision are sent to the server.

[0449] Step 15:

[0450] The server stores the received investment intention and investment amount in a database and notifies the business operator. The saved investment data is notified to the business operator's dashboard.

[0451] Step 16:

[0452] The server again sends information about the projects that have reached their funding goal to the AI ​​analysis module and emotion engine. It retrieves the funding status from the database and sends the data to the AI ​​module and emotion engine if the goal is reached.

[0453] Step 17:

[0454] The server (AI module and emotion engine) generates a detailed report on how the funds are being used. The data is analyzed, a detailed report is generated, and sent back to the server. The generated detailed report is stored in a database.

[0455] Step 18:

[0456] The server notifies the operator of the generated detailed report, and sends a notification to the operator's dashboard that the report has been generated, along with a link to the detailed report.

[0457] (Application example 2)

[0458] 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."

[0459] Traditional fundraising platforms often lack transparency in business plan analysis and investment decisions, and lack support for decision-making that takes sentiment data into account, which can reduce the efficiency and reliability of fundraising and create unnecessary risks and uncertainty between investors and businesses.

[0460] 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 a business operator needing fundraising to input information such as a business plan, fundraising target, and planned public offering date; means for storing the input information in a data base; means for analyzing the stored information using artificial intelligence to clarify the business's direction and public offering target; means for sending the analysis results from the artificial intelligence to an individual with specialized knowledge and requesting a review; means for creating a final report based on the review results and storing the final report in the data base; means for investors to view the final report and decide on an investment; means for storing the investor's investment intention and investment amount in the data base and notifying the business operator; means for analyzing users' (investors') reactions in real time when they view the project detail report and generating emotion data; and means for feeding the generated emotion data back to the business operator. This improves the transparency and efficiency of the fundraising process and enables decision-making support that takes emotion data into consideration.

[0461] "Fundraising" means raising funds necessary for business activities from outside sources.

[0462] "Business" refers to any person or legal entity that conducts its business and carries on activities related to that business.

[0463] A "business plan" is a document that outlines the direction and details of a business venture that a company will undertake in the future.

[0464] A "fundraising target" is a target amount set by a business to raise specific funds.

[0465] The "public offering date" is the date on which stocks or securities are scheduled to be offered for sale to the general public in the market.

[0466] "Means of input" refers to the interface or method by which a user inputs the required information into the system.

[0467] A "data base" refers to a database or server for storing and managing information.

[0468] "Artificial intelligence" is a computer system that mimics human intelligence and automatically learns and makes decisions.

[0469] "Analysis" is the process of examining data or information in detail and finding meaning.

[0470] An "individual with specialized knowledge" refers to someone who has advanced knowledge or experience in a particular field.

[0471] "Review" is the process of checking, evaluating, and providing feedback on submitted information and analysis results.

[0472] "Final Report" means a completed, formal report that reflects the results of the review.

[0473] "Investor" refers to an individual or legal entity that provides funds and manages assets.

[0474] "Investment intent" refers to an investor's willingness or willingness to invest funds in a particular project.

[0475] "Emotional data" is digital information that reflects a user's emotional state.

[0476] "Feedback means" refers to the process or method of retransmitting collected or generated information to the underlying data provider.

[0477] The system of this invention begins when a business seeking funding enters information such as their business plan, fundraising goals, and planned public offering date, and stores it in a data base. The stored information is analyzed using artificial intelligence to clarify the business's direction and public offering goals. The analysis results are sent to individuals with specialized knowledge for review. A final report is prepared based on the review results and stored in the data base. Investors can view this final report and decide to invest. The investor's investment intentions and investment amounts are stored in the data base and notified to the business. The system also includes a function to analyze investors' reactions to detailed project reports in real time and generate sentiment data. The generated sentiment data is fed back to the business.

[0478] Program processing explanation

[0479] Hardware and software used

[0480] Hardware: Smartphones, servers

[0481] Software: Application frameworks (React Native, Swift, Kotlin, etc.), server-side frameworks (Node.js, Django, etc.), artificial intelligence models (TENSORFLOW (registered trademark), PyTorch), databases (MySQL (registered trademark), PostgreSQL)

[0482] Program processing details

[0483] 1. User (business operator) enters

[0484] Businesses log in to the application using their smartphones and enter information about their new projects (business plan, fundraising target, and public offering date).

[0485] The entered information is sent to the server and stored at the data center.

[0486] 2. Analysis using artificial intelligence

[0487] The server transmits the information stored in the data base to an artificial intelligence analysis module.

[0488] An artificial intelligence analysis module (TensorFlow or PyTorch) analyzes the business plan and generates text that clarifies the business direction and public offering goals.

[0489] The generated analysis results are sent to individuals with specialized knowledge.

[0490] 3. Review by an individual with specialized knowledge

[0491] Individuals with specialized knowledge can access a dedicated dashboard to review the results of the AI ​​analysis.

[0492] The final report will be prepared after making any necessary corrections or additions.

[0493] The final report is sent to a server and stored in a data center.

[0494] 4. Investor viewing of the project

[0495] Investors access the application using their smartphones to view the project's final report.

[0496] If an investor is interested, they decide to invest, enter their investment intention and investment amount, and submit.

[0497] The server stores the investment intention and investment amount in a data base and notifies the business operator.

[0498] 5. Emotional Data Feedback

[0499] When investors view detailed project reports, the sentiment engine analyzes their reactions in real time and generates sentiment data.

[0500] The generated emotion data is fed back to the business operator.

[0501] Examples of concrete examples and prompts

[0502] As a concrete example, consider a situation where a business operator registers an environmental technology project and an investor invests in it. In this case, when the business operator enters project information through a smartphone app, the emotion engine analyzes the information and evaluates the business operator's emotional state. Next, an AI analysis module analyzes the direction of the business plan, which is then reviewed by an individual with specialized knowledge. When the investor uses the app to view the project and decides to invest, the emotion engine analyzes the investor's emotions and provides feedback on this emotional data to the business operator, streamlining the investment process.

[0503] Example prompt sentence:

[0504] Enter the details of your new environmental technology project. We'll use artificial intelligence and an emotion engine to analyze your business plan's direction and emotional state. Please describe your specific goals and plans.

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Step 1:

[0507] The user (business owner) logs in to the application using a smartphone. The user enters their authentication information, which is then received by the server and verified against the data base. If verification is successful, the user can access the new project registration page.

[0508] Input: Credentials

[0509] Output: Authentication result

[0510] Step 2:

[0511] The user (business operator) enters the necessary information such as the business plan, fundraising target, and planned public offering date on the new project registration page. The entered information is sent to the server and stored in the data base.

[0512] Input: Business plan, fundraising target, planned public offering date

[0513] Output: Save confirmation message

[0514] Step 3:

[0515] The server sends the information stored in the data center to an AI analysis module (TensorFlow or PyTorch), which analyzes the data and generates text to clarify the direction of the business plan and the goals of the public offering.

[0516] Input: Saved business plan related information

[0517] Output: Analysis results

[0518] Step 4:

[0519] The server sends the generated analysis results to individuals with specialized knowledge, who then access a dedicated dashboard to receive and review the analysis results, making corrections or additions as necessary.

[0520] Input: Analysis results

[0521] Output: Reviewed analysis results

[0522] Step 5:

[0523] Based on the analysis results, which have been revised and supplemented, an individual with specialized knowledge will create a final report, which will be sent to a server and stored in the data center.

[0524] Input: Reviewed analysis results

[0525] Output: Final report

[0526] Step 6:

[0527] An investor logs into the application using a smartphone and accesses the project listing page. The investor searches for projects of interest and views the final report.

[0528] Input: investor login information, project search query

[0529] Output: Project list, final report

[0530] Step 7:

[0531] When investors view detailed project reports, the server's emotion engine analyzes their reactions in real time and generates emotion data, which is then fed back to the business operator.

[0532] Input: Real-time investor reactions

[0533] Output: Emotion data, feedback

[0534] Step 8:

[0535] When an investor decides to invest, they enter their investment intention and investment amount and send it to the server, which stores this information in a data base and notifies the business operator.

[0536] Input: Investment intention, investment amount

[0537] Output: Save confirmation message, notification

[0538] Step 9:

[0539] If the fundraising goal is reached, the server will again send the project information to the AI ​​analysis module, which will generate a detailed report on how the funds will be used, which will be stored in the data center and notified to the business operator.

[0540] Input:Project Information

[0541] Output: Detailed report, notifications

[0542] 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.

[0543] 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.

[0544] 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.

[0545] [Second embodiment]

[0546] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0547] 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.

[0548] 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).

[0549] 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.

[0550] 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.

[0551] 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).

[0552] 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. 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.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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."

[0558] The following describes an embodiment of the present invention. This system is a platform that matches investors with businesses that need funding, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. The specific operation of each component of the system is described below.

[0559] Project registration by business operators

[0560] 1. User (business operator): First, the business operator accesses the platform login page and enters the correct authentication information to log in. After successful login, the business operator can access the new project registration page.

[0561] 2. Terminal (business operator): The business operator enters the necessary information on the new project registration page, such as the business plan, target amount of fundraising, and planned IPO timing, and presses the submit button.

[0562] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[0563] AI-based business plan analysis

[0564] 1. Server (AI module): The AI ​​analysis module is launched and inputs the project information registered by the business. The AI ​​analyzes the business plan and generates text to clarify the business direction and IPO goals.

[0565] 2. Server: Sends the generated analysis results to the expert review module.

[0566] Expert review and final report preparation

[0567] 1. User (expert): The expert logs in to a dedicated dashboard and receives notifications of new AI analysis results. The expert can then check the analysis results from the dashboard.

[0568] 2. Terminal (expert): Review the analysis results and make corrections or additions as necessary.

[0569] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[0570] Investors viewing projects

[0571] 1. User (Investor): Investors access the platform and log in.

[0572] 2. Server: Validates the investor's credentials and returns the dashboard page if authentication is successful.

[0573] 3. Terminal (Investor): Go to the project list page from the dashboard and search for projects that interest you.

[0574] 4. Server: Based on the investor's request, it returns a list of publicly available projects.

[0575] 5. Terminal (Investor): The investor selects the project of interest from the displayed list of projects and opens the details page.

[0576] 6. Server: Returns detailed information and final report for selected projects.

[0577] Investor investment decisions

[0578] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[0579] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[0580] 3. Server: Receives the investor's investment intention and investment amount, stores them in the database, and sends a notification of investment completion to the business operator.

[0581] What happens if the funding goal is reached?

[0582] 1. Server: Checks information about projects that have reached their funding goal and performs AI analysis again. The AI ​​generates a detailed report on how the funds will be used.

[0583] 2. Server: Stores the generated detailed report in a database and notifies the operator.

[0584] 3. Terminal (operator): The operator checks the detailed report and initiates procedures to carry out the plan as planned.

[0585] Specific examples

[0586] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. AI analyzes the business plan, and experts review it to generate a final report. Based on this report, Investor B becomes interested in the project and, after checking the details, decides to invest. Once Investor B invests and the target amount is reached, AI again generates a detailed report on how the funds will be used and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[0587] In this way, the invention combines AI analysis with expert review to provide an integrated system for making the fundraising process transparent and efficient.

[0588] The processing flow will be explained below.

[0589] Step 1:

[0590] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[0591] Step 2:

[0592] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[0593] Step 3:

[0594] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[0595] Step 4:

[0596] Server: Receives the transmitted information and stores it in a database.

[0597] Step 5:

[0598] Server: Sends the stored information to the AI ​​analysis module.

[0599] Step 6:

[0600] Server (AI module): Analyzes business plans and generates text to clarify business direction and IPO goals.

[0601] Step 7:

[0602] Server: Sends the generated AI analysis results to the expert review module.

[0603] Step 8:

[0604] User (expert): Logs in to a dashboard exclusively for experts and receives notifications of AI analysis results.

[0605] Step 9:

[0606] Device (Expert): Check notifications and review AI analysis results from the dashboard.

[0607] Step 10:

[0608] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[0609] Step 11:

[0610] Server: Receives the analysis results after the expert's corrections, generates a final report, and stores the generated final report in a database.

[0611] Step 12:

[0612] Server: Update the project status to "publishable" and add it to the publishing list.

[0613] Step 13:

[0614] User (Investor): Access the platform and log in by entering their credentials on the login page.

[0615] Step 14:

[0616] Server: Verifies investor credentials and returns dashboard page if successful.

[0617] Step 15:

[0618] Terminal (Investor): Access the project listing page from the dashboard and search for projects that interest you.

[0619] Step 16:

[0620] Server: Returns a list of publicly available projects based on the investor's request.

[0621] Step 17:

[0622] Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[0623] Step 18:

[0624] Server: Returns detailed information and final report for the selected project.

[0625] Step 19:

[0626] Terminal (Investor): Check the detailed report of the project and decide to invest.

[0627] Step 20:

[0628] Terminal (Investor): Enter the investment amount and press the invest button.

[0629] Step 21:

[0630] Server: Stores the investor's investment intention and investment amount in a database, and also sends a notification of investment completion to the business operator.

[0631] Step 22:

[0632] Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module.

[0633] Step 23:

[0634] Server (AI module): Generates detailed reports on how funds are being used and stores them in a database.

[0635] Step 24:

[0636] Server: Sends a detailed report to the operator.

[0637] Step 25:

[0638] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[0639] Example 1

[0640] 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."

[0641] For businesses that need to raise funds, analyzing business plans and clarifying how the funds will be used are extremely important. However, with conventional methods, analyzing business plans and explaining them to investors is cumbersome, making it difficult to raise funds effectively. It is also difficult for investors to grasp the reliability of the investment target and the specific use of funds. For this reason, a transparent and efficient system is needed to effectively match businesses with investors.

[0642] 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.

[0643] In this invention, the server includes: means for a business operator to input information such as a business plan, fundraising goals, and planned IPO timing; means for saving the input information in a database; means for analyzing the saved information using artificial intelligence to clarify the business direction and IPO goals; means for sending the results of the analysis by the artificial intelligence to an expert for review; means for creating a final report based on the expert's review and saving the final report in a database; means for an investor to view the final report and decide on an investment; means for saving the investor's investment intention and investment amount in a database and notifying the business operator; means for an investor to search for projects of interest on a project list page; and means for re-analyzing the project registration information by the business operator and generating a detailed report on how the funds will be used, thereby enabling a transparent and efficient fundraising process between business operators and investors.

[0644] A "business operator" is an individual or legal entity that needs financing and registers a business plan and enters information.

[0645] "Artificial intelligence" refers to machine learning algorithms and natural language processing engines that analyze input data and clarify business direction and IPO goals.

[0646] "Database" refers to a data storage system for storing information entered by businesses and investors, as well as analysis results and final reports.

[0647] "Review" refers to the process in which experts check the results of analysis by artificial intelligence and make corrections or additions.

[0648] The "final report" is a detailed business plan and use of funds report prepared based on the results of the AI ​​analysis and expert review.

[0649] "Investor" means an individual or legal entity that has an interest in a project and decides to invest.

[0650] "Funding target" refers to the specific amount of money that a business requires to carry out a project.

[0651] "IPO" refers to an initial public offering, where a business makes its shares publicly available on the market.

[0652] "Project Listing Page" means a web page where investors can search and view publicly available project information.

[0653] A "detailed report" is a report that uses artificial intelligence to analyze projects that have reached their funding goal and detail how the funds will be used.

[0654] A specific embodiment for implementing this invention is described below. The system of the invention matches businesses needing financing with investors, and uses a combination of AI and expert knowledge. This system consists of the following components:

[0655] Hardware and Software

[0656] 1. Server: Provides functions such as data storage, processing, analysis, notification, and AI module launch. Specifically, it includes a database, API server, AI analysis module, expert review module, and notification system.

[0657] 2. Terminal: A computer or mobile device used by an entrepreneur, professional or investor, on which a browser or dedicated application is installed.

[0658] 3. AI module: Uses a natural language processing engine (e.g., GPT-4) to analyze business plans.

[0659] 4. Database: A storage system for storing project information, analysis results, final reports, investment information, etc.

[0660] Data processing and calculation

[0661] 1. Data entry: The business operator enters information such as the business plan, fundraising target, and planned IPO timing using a browser or a dedicated application.

[0662] 2. Data storage: The server receives the entered information, generates SQL queries to store it in the database, and stores the information.

[0663] 3. Data analysis: The server sends the stored information to an AI analysis module, which analyzes the data and generates text that clarifies the business direction and IPO goals.

[0664] 4. Expert review: The server sends the AI ​​analysis results to the expert review module, where the experts review the results and make corrections if necessary.

[0665] 5. Generate Final Report: The server generates the final report and stores it in the database. The final report includes details of the business plan, fundraising goals, IPO plans, etc.

[0666] 6. Investor Access: Investors can access the project listing page and search for projects they are interested in. They can open the details page and view the final report.

[0667] 7. Investment decision: The investor enters the investment amount and decides to invest. The server receives the investment information, stores it in the database, and notifies the operator.

[0668] 8. Reaching the funding goal: The server checks the information of the projects that have reached their funding goal, performs AI analysis again, and generates a detailed report on how the funds will be used.

[0669] Specific examples

[0670] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. The data is then stored in a database via the server.

[0671] The AI ​​module analyzes the business plan and sends the results to an expert review module, where the experts review the analysis, make corrections, and create a final report, which is then stored in a database and made available to investors.

[0672] Investor B accesses the platform, becomes interested in Business A's project, and after checking the details, decides to invest. The investment information is stored in a database via the server and notified to Business A.

[0673] When the investment target amount is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[0674] Prompt Sentence Examples

[0675] Below are some example prompts to input to the generative AI model:

[0676] Analyze a business plan outline and generate a text that specifically articulates the IPO goals. Project details:

[0677] Business plan: [Detailed business plan content]

[0678] Funding goal: [target amount]

[0679] Planned IPO date: [Specific date]

[0680] In this way, this invention combines AI analysis with expert review to provide a transparent and efficient fundraising process between businesses and investors.

[0681] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0682] Step 1:

[0683] Business Login

[0684] The user (business operator) accesses the platform's login page and enters their user ID and password. The entered information is sent and reaches the server. The server compares the user ID and password with the database for authentication, and if successful, starts a login session. If the login is successful, a new project registration page is returned to the business operator's terminal.

[0685] Input: User ID, Password

[0686] Specific operations: login authentication, session start

[0687] Output: New project registration page

[0688] Step 2:

[0689] Entering and submitting project information

[0690] On the new project registration page, businesses enter their business plan, fundraising goals, and planned IPO timing. After entering this information and pressing the submit button, the information is sent to the server, which then generates an SQL query to store the received information in the database and executes the insert operation.

[0691] Input: Business plan, fundraising target, planned IPO date

[0692] Specific actions: Entering information, sending, saving to database

[0693] Output: Save information to a database

[0694] Step 3:

[0695] Sending data to the AI ​​module

[0696] The server sends the information stored in the database to an AI analysis module, which receives the project information, invokes a natural language processing engine (e.g., GPT-4) to analyze the business plan, and generates text that clarifies the business direction and IPO goals.

[0697] Input: Project information stored in the database

[0698] Specific operations: receiving and sending information, launching AI analysis module, generating text

[0699] Output: Parsed text

[0700] Step 4:

[0701] Submitting analysis results to an expert review module

[0702] The server sends the analysis results generated by the AI ​​module to the expert review module, where experts access the review dashboard to receive notifications of new analysis results, review the results, and make corrections or additions as necessary.

[0703] Input: Text of the analysis results generated by the AI

[0704] Specific actions: Submit information, expert review

[0705] Output: Corrected and confirmed results

[0706] Step 5:

[0707] Generate the final report

[0708] The server generates a final report based on the results of the expert review, which is then stored in a database by the server, and includes details of the business plan, fundraising targets, and IPO plans.

[0709] Input: Expert-reviewed text

[0710] Specific operation: Create and save the final report

[0711] Output: Final report

[0712] Step 6:

[0713] Investor Login and Project Search

[0714] A user (investor) accesses the platform and logs in. After successful login, the investor can access the project list page. The investor searches for a project of interest and sends a request to the server to display detailed information. The server returns the relevant project information and final report from the database.

[0715] Input: Investor login information, project search request

[0716] Specific operations: login authentication, project information search, information submission

[0717] Output: Project information, final report

[0718] Step 7:

[0719] Investment decisions and inputs

[0720] After checking the detailed project report, the investor decides to invest. By entering the investment amount and pressing the "Invest" button, the information is sent to the server. The server receives the investment information, stores it in a database, and sends a notification of investment completion to the business operator.

[0721] Input: investment amount, investment confirmation operation

[0722] Specific operations: receiving investment information, saving it in a database, and notifying businesses

[0723] Output: Save to database, notify business operator

[0724] Step 8:

[0725] What happens after the fundraising goal is reached?

[0726] The server checks the information of projects that have reached their funding goals. It then performs another AI analysis and generates a detailed report on how the funds will be used. This detailed report is then saved in a database by the server and notified to the project owner. The project owner then checks the detailed report and implements a specific plan for how the funds will be used.

[0727] Input: Project information that has reached its funding goal

[0728] Specific operations: Check information, reanalyze, create and save detailed reports, notify business operators

[0729] Output: Detailed report, notification to business operators

[0730] By explaining in detail the specific operations and data flow at each step, it becomes easier to understand the function of the entire system, and the content can be useful for actual implementation.

[0731] (Application example 1)

[0732] 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."

[0733] It has traditionally been difficult for businesses seeking funding to conduct accurate and reliable business plan analysis and provide transparent information to investors. It also requires a great deal of effort for investors to quickly view multiple projects and make appropriate investment decisions. To solve these challenges, a platform is needed that integrates AI technology and expert knowledge to efficiently and effectively analyze business plans and fund usage, and present the results to investors.

[0734] 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.

[0735] In this invention, the server includes means for a business operator to have an AI model analyze a business plan for a project and store the generated text in a database, means for sending the generated analysis results to an expert for review and storing the expert's review results in the database, and means for using the AI ​​model to generate prompt text for investors to view project details and make investment decisions. This enables business operators to efficiently present high-quality business plans and investors to make quick investment decisions based on reliable information.

[0736] Key Word Definitions

[0737] "Business operator" refers to a corporation or individual that has a business plan and needs to raise funds.

[0738] "Investor" means a legal entity or individual whose purpose is to provide funding to a Project.

[0739] A "business plan" is a document that details the outline, goals, progress, and projected income and expenditures of a particular project.

[0740] A "funding target" is a specific amount of money that an entity needs to carry out a project.

[0741] "Planned IPO date" refers to the time when a business plans to go public (IPO).

[0742] A "database" is a system for efficiently storing, managing, and retrieving information.

[0743] An "AI model" is a mathematical model that uses artificial intelligence algorithms to solve specific problems.

[0744] An "expert" is a person who has advanced knowledge and experience in a particular field.

[0745] A "prompt" is an instruction given to an AI model to perform a specific task.

[0746] "Online Platform" means a service that can be accessed by users via the Internet.

[0747] "Generated text" refers to the text that the AI ​​model generates as a result of analyzing the business plan.

[0748] "Analysis results" refer to the conclusions and proposals that AI draws from analyzing the business plan.

[0749] "Review results" refers to the final evaluation after experts have evaluated and corrected the analysis results generated by AI.

[0750] "Means to View Details" refers to the interface or functionality that allows a user to view detailed information about a particular project.

[0751] MODE FOR CARRYING OUT THE INVENTION

[0752] The following describes an embodiment of this invention. This system is an online platform that efficiently matches businesses needing fundraising with investors, analyzes business plans, and clarifies how funds will be used. The system combines AI analysis models and expert reviews to provide investors with highly reliable information.

[0753] Project registration by business operators

[0754] Terminal (operator)

[0755] Businesses register by entering information such as their project's business plan, fundraising goals, and planned IPO timing into the online platform, which is then stored in a database by the server.

[0756] AI-based business plan analysis

[0757] server

[0758] After the input information is saved in the database, an AI analysis module is launched to analyze the business plan. The generated text is saved back in the database as the analysis results. The AI ​​model used could be a generative AI model such as OpenAI's GPT-3.

[0759] Expert review and final report preparation

[0760] User (expert)

[0761] Experts review the results of the AI ​​analysis from a dedicated dashboard, and after the experts' evaluation and corrections are made, the results are saved on the server as a final report.

[0762] Investors can view projects and make investment decisions

[0763] Terminal (Investor)

[0764] Investors access the platform and view the details of the project. In order for investors to check the detailed information of the project, the server generates a prompt message and provides information according to the investor's request. Examples of prompt messages include the following:

[0765] Business plan for registered projects:

[0766] "This project aims to develop new markets in the field of image processing using innovative AI technology."

[0767] Prompt statement:

[0768] "Analyze this business plan and highlight key points and market value:"

[0769] Investment and fundraising completion

[0770] server

[0771] After the investor decides to invest, the server stores the investment intention and investment amount in a database and notifies the business. If the fundraising goal is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies the business.

[0772] As described above, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process. The hardware used includes servers and user devices, and the software includes the Flask framework, SQLite database, and OpenAI's GPT-3.

[0773] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0774] Program processing flow

[0775] Step 1:

[0776] Businesses log in to the platform using their terminals, input information such as the business plan, fundraising goals, and planned IPO timing for their new projects, and then submit it. This input information is received by the server and stored in the database. (Input: business plan, fundraising goals, planned IPO timing / Output: project information stored in the database)

[0777] Step 2:

[0778] The server sends the saved business plan information to the AI ​​analysis module. The AI ​​analysis module analyzes the business plan and generates text to clarify the business's direction and market value. This generated text is sent back to the server and saved in a database. (Input: Saved business plan information / Output: Text of the analysis results by AI)

[0779] Step 3:

[0780] The server sends the generated AI analysis results to an expert for review. The expert uses the user's device to check the analysis results and make corrections or additions as necessary. The corrected review results are returned to the server and stored in a database. (Input: AI analysis results / Output: Review results including corrections and additions made by the expert)

[0781] Step 4:

[0782] The server creates a final report based on the expert review results and stores it in a database. This final report is then made available to investors. (Input: Expert review results / Output: Final report)

[0783] Step 5:

[0784] Investors log in to the platform using their terminals and view project details. The server uses a generative AI model to generate prompts to provide detailed project information and a final report to investors. (Input: Viewing request from investor / Output: Generated prompts and project details)

[0785] Step 6:

[0786] The investor views the project details page and decides to invest. The investor enters the investment amount and presses the investment button. The server saves the investor's investment intention and investment amount in a database and notifies the business operator. (Input: Investor's investment intention and investment amount / Output: Investment information saved in the database and notification to the business operator)

[0787] Step 7:

[0788] If the fundraising goal is reached, the server performs another AI analysis and generates a detailed report on how the funds will be used. The generative AI model is used to create the detailed report, which is then saved in a database and notified to the project owner. (Input: Project information on the project that reached the fundraising goal / Output: Detailed report and notification to the project owner)

[0789] In this way, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process.

[0790] 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.

[0791] The following describes an embodiment of this invention. This system is a platform for matching businesses needing fundraising with investors, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, this system enhances the accuracy of information and decision-making support by incorporating an emotion engine that recognizes the user's emotions.

[0792] Project registration by business operators

[0793] 1. User (business operator): First, the business operator accesses the platform and enters their authentication information on the login page. After successful login, they can access the new project registration page.

[0794] 2. Terminal (business operator): The business operator enters the necessary information such as the business plan, fundraising target, and planned IPO date on the new project registration page and presses the submit button.

[0795] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[0796] Analysis by emotion engine

[0797] 1. Server (emotion engine): Analyzes the information input by the business operator and generates emotional data. For example, it determines the business operator's emotional state, such as optimistic, pessimistic, or excited.

[0798] 2. Server: Reflects the generated emotion data into the business plan and proposes revisions as necessary.

[0799] AI-based business plan analysis

[0800] 1. Server (AI module): The AI ​​analysis module analyzes the business plan and generates text to clarify the business direction and IPO goals.

[0801] 2. Server: Sends the generated analysis results to the expert review module.

[0802] Expert review and final report preparation

[0803] 1. User (expert): The expert accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data.

[0804] 2. Terminal (expert): Checks the analysis results and emotion data, and makes corrections or additions as necessary.

[0805] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[0806] Investors viewing projects

[0807] 1. User (Investor): Investor accesses the platform and enters their authentication information on the login page. If authentication is successful, they can access the dashboard page.

[0808] 2. Terminal (Investor): Go to the project list page from the dashboard and search for the project you are interested in.

[0809] 3. Server: Returns a list of publicly available projects based on the investor's request.

[0810] 4. Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[0811] 5. Server: Returns detailed information and final report for selected projects.

[0812] Sentiment analysis when investors browse projects

[0813] 1. Server (emotion engine): When investors view project reports, their reactions are analyzed in real time and emotion data is generated.

[0814] 2. Server: The generated emotion data is fed back to the business.

[0815] Investor investment decisions

[0816] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[0817] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[0818] 3. Server: Stores the received investment intention and investment amount in a database and notifies the business operator.

[0819] What happens if the funding goal is reached?

[0820] 1. Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module and sentiment engine.

[0821] 2. Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[0822] 3. Server: Notifies the operator of the generated detailed report.

[0823] Specific examples

[0824] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[0825] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[0826] In this way, the present invention provides a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[0827] The processing flow will be explained below.

[0828] Step 1:

[0829] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[0830] Step 2:

[0831] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[0832] Step 3:

[0833] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[0834] Step 4:

[0835] Server: Receives the transmitted information and stores it in a database.

[0836] Step 5:

[0837] Server: Sends the stored information to the AI ​​analysis module.

[0838] Step 6:

[0839] Server (emotion engine): Analyzes text data entered by businesses and generates emotional data. For example, it determines the emotional state of the input, such as whether the input is positive or anxious.

[0840] Step 7:

[0841] Server: Reflects the generated emotion data in the analysis of business plans and proposes revisions to the business operator as necessary.

[0842] Step 8:

[0843] Server (AI module): Analyzes business plan data and generates detailed analysis results to clarify business direction and IPO goals.

[0844] Step 9:

[0845] Server: Sends the generated analysis results to the expert review module.

[0846] Step 10:

[0847] User (expert): Logs in to an expert-only dashboard and receives notifications of new AI analysis results and emotion data.

[0848] Step 11:

[0849] Terminal (expert): Check the AI ​​analysis results and emotion data from the dashboard and review them.

[0850] Step 12:

[0851] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[0852] Step 13:

[0853] Server: Receives the analysis results after the experts' corrections, generates the final report, and stores it in the database.

[0854] Step 14:

[0855] Server: Add the final report to the list of publicly available projects and update it so that it is available for investors to view.

[0856] Step 15:

[0857] User (Investor): Access the platform and log in by entering credentials on the investor login page.

[0858] Step 16:

[0859] Server: Verifies investor credentials and returns dashboard page if successful.

[0860] Step 17:

[0861] Terminal (Investor): Go to the project listing page from the dashboard and search for the project you are interested in.

[0862] Step 18:

[0863] Server: Returns a list of publicly available projects in response to investor requests.

[0864] Step 19:

[0865] Terminal (Investor): Select the project you are interested in from the project list and open the details page.

[0866] Step 20:

[0867] Server: Returns detailed information and final report for the selected project.

[0868] Step 21:

[0869] Server (emotion engine): When investors view detailed project reports, their reactions are analyzed in real time and emotion data is generated.

[0870] Step 22:

[0871] Server: Provides feedback to the business operator on the generated emotion data.

[0872] Step 23:

[0873] Terminal (Investor): Check the detailed report of the project and decide to invest.

[0874] Step 24:

[0875] Terminal (Investor): Enter the investment amount and press the invest button.

[0876] Step 25:

[0877] Server: Stores the investor's investment intention and investment amount in a database and notifies the business operator.

[0878] Step 26:

[0879] Server: Sends information about projects that have reached their funding goal to the AI ​​analysis module and sentiment engine for further analysis.

[0880] Step 27:

[0881] Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[0882] Step 28:

[0883] Server: Sends a detailed report to the operator.

[0884] Step 29:

[0885] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[0886] Example 2

[0887] 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."

[0888] Traditional fundraising platforms do not take into account emotional data when analyzing the information entered by businesses or creating final reports, resulting in insufficient support for business planning accuracy and investor decision-making. Furthermore, the lack of feedback that reflects investors' emotional state prevents a fundraising process that takes into account the psychological aspects of investors.

[0889] 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.

[0890] In this invention, the server includes means for analyzing input information from business operators and investors using an emotion engine to generate emotion data, means for revising business plans based on the emotion data, means for performing real-time emotion analysis when investors view project reports, and means for feeding back the investor's emotion data to the business operator. This allows the emotion data to be reflected in business plans and final reports, enabling a fundraising process that takes into account the emotional state of investors.

[0891] "Business operator" refers to an entity that registers plans and information about a project requiring funding on the platform.

[0892] "Investor" means an entity that invests in a project offered on the Platform.

[0893] "Business Plan" means the information entered by an operator into the Platform, including project details, fundraising targets, and planned IPO timing.

[0894] A "fundraising goal" is a goal that indicates the specific target amount of funds required by the business operator to implement the project.

[0895] The "emotion engine" is a software system that analyzes the emotional state and generates specific emotional data based on the information that businesses and investors enter or view on the platform.

[0896] "Emotion data" is data that indicates the emotional state of businesses and investors, generated as a result of analysis by the emotion engine.

[0897] The "AI analysis module" is artificial intelligence software that analyzes input data such as business plans and clarifies project direction and IPO goals.

[0898] An "expert" is a person or group with the knowledge and experience to review the results of AI analysis and make any necessary corrections or additions.

[0899] The "Final Report" is a comprehensive report on the project that will be created based on the results of AI analysis, expert review, and emotional data.

[0900] "Ready for release" is a status that indicates that a final report has been prepared after expert review and is ready to be made public to investors.

[0901] "Project Report" is synonymous with Final Report and means a report that includes the overall content and evaluation results of the project.

[0902] A "database" is an information aggregation system managed by a server that stores and manages data such as business plans, sentiment data, final reports, and investor information.

[0903] This invention is a platform that matches businesses needing funding with investors, utilizing AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, by combining this system with an emotion engine that recognizes user emotions, the accuracy of information and decision-making support are enhanced.

[0904] System configuration

[0905] The system mainly consists of the following components:

[0906] 1. Server

[0907] 2. Terminal

[0908] 3. Users (businesses, investors, experts)

[0909] 4. Database

[0910] 5. AI Module

[0911] 6. Emotion Engine

[0912] Hardware and software used

[0913] Hardware: Server, user terminal (PC, smartphone)

[0914] Software: AI analysis module, emotion engine, database management system

[0915] Program processing overview

[0916] Project registration by business operators

[0917] The user (business operator) accesses the platform and enters authentication information on the login page. If authentication is successful, they are directed to a new project registration page. There, they enter the necessary information, such as the business plan, fundraising target, and planned IPO timing, and press the "Submit" button. The information sent from the terminal (business operator) is received by the server and stored in a database. This information is then sent to the AI ​​analysis module.

[0918] Analysis by emotion engine

[0919] The server (emotion engine) analyzes the business's input information and generates emotional data. For example, it determines whether the business is optimistic, pessimistic, excited, etc. The emotional data is reflected in the business plan and, if necessary, proposes revisions.

[0920] AI-based business plan analysis

[0921] The server (AI module) analyzes the business plan and generates text that clarifies the business direction and IPO goals. The analysis results are sent to the expert review module.

[0922] Expert review and final report preparation

[0923] The user (expert) accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data. The user (expert) checks the analysis results and emotional data on the device and makes corrections or additions as necessary. The server receives the corrected analysis results, generates a final report based on them, and stores it in the database. The project status is updated to "Ready for release."

[0924] Investors viewing projects

[0925] The user (investor) accesses the platform and enters authentication information on the login page. If authentication is successful, the user is taken to the dashboard page. On the terminal (investor), the user moves from the dashboard to the project list page and searches for a project of interest. The server returns a list of publicly available projects based on the investor's request. The terminal (investor) selects a project of interest from the list of projects displayed and opens the details page. The server returns detailed information and a final report for the selected project.

[0926] Sentiment analysis when investors browse projects

[0927] When investors view project reports, the server (emotion engine) analyzes their reactions in real time and generates emotional data, which is then fed back to the business.

[0928] Investor investment decisions

[0929] The investor checks the detailed project report on the terminal and decides to invest. He enters the investment amount and presses the "Invest" button. The server stores the received investment intention and investment amount in the database and notifies the business operator.

[0930] What happens if the funding goal is reached?

[0931] The server sends information about projects that have reached their funding goal back to the AI ​​analysis module and emotion engine. The server (AI module and emotion engine) generates a detailed report on how the funds will be used and stores it in a database. The generated detailed report is then notified to the business operator.

[0932] Specific examples

[0933] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[0934] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[0935] In this way, the present invention is a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[0936] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0937] Step 1:

[0938] A user (business operator) accesses the platform and enters authentication information on the login page. The entered authentication information (username, password) is sent to the server. The server receives the authentication information and checks it against the database. If authentication is successful, the server returns access permission, and the user can access the new project registration page.

[0939] Step 2:

[0940] The terminal (business operator) moves to the new project registration page, enters the necessary information such as the business plan, fundraising target, and planned IPO timing, and presses the submit button. The input data (business plan, fundraising target, planned IPO timing) is sent to the server, which receives it and stores it in a database.

[0941] Step 3:

[0942] The server sends the stored information to the AI ​​analysis module. The business plan, fundraising goals, and planned IPO timing stored in the database are input into the AI ​​analysis module. The AI ​​analysis module analyzes the data and clarifies the business direction and IPO goals. The analysis results (business direction, IPO goals) are generated and output to the server.

[0943] Step 4:

[0944] The server (emotion engine) analyzes the business's input information and generates emotion data. The business plan and input text are sent to the emotion engine. The emotion engine uses natural language processing to generate emotion data (optimistic, pessimistic, etc.) and returns it to the server. The server reflects the generated emotion data in the business plan and suggests revisions as necessary.

[0945] Step 5:

[0946] The server sends the generated emotion data and AI analysis results to the expert review module, which then notifies the expert on their dashboard.

[0947] Step 6:

[0948] The user (expert) accesses a dedicated dashboard and receives notifications of AI analysis results and emotional data. By clicking on the notification, the AI ​​analysis results and emotional data are displayed on the device.

[0949] Step 7:

[0950] The terminal (expert) checks the analysis results and emotion data, and makes corrections or supplements as necessary. The expert checks the displayed data, enters corrections or comments, and presses the save button. The corrections and supplemental data are sent to the server.

[0951] Step 8:

[0952] The server receives the analysis results after the experts have completed the corrections and generates a final report based on them. A script is executed based on the corrected analysis results to generate a final report. The generated final report is saved in the database and the project status is updated to "Ready for release."

[0953] Step 9:

[0954] A user (investor) accesses the platform and enters their authentication information on the login page. The entered authentication information (username, password) is sent to the server and checked against the database. If authentication is successful, the server returns permission to access the dashboard page.

[0955] Step 10:

[0956] The terminal (investor) navigates from the dashboard to the project list page and searches for projects they are interested in. A project search request is sent to the server. The server retrieves projects with a "publishable" status from the database and returns the list to the terminal.

[0957] Step 11:

[0958] The terminal (investor) selects a project of interest from the displayed project list and opens the details page. The selected project ID is sent to the server. The server retrieves detailed information and a final report from the database and returns it to the terminal.

[0959] Step 12:

[0960] When investors view a project report, the server (emotion engine) analyzes their reactions in real time and generates emotion data. Data such as the investor's mouse movements and viewing time are input, and the emotion engine analyzes this to generate emotion data. The generated emotion data is returned to the server.

[0961] Step 13:

[0962] The server feeds back the generated emotion data to the business operator, who then notifies the business operator's dashboard of the generated emotion data.

[0963] Step 14:

[0964] The terminal (investor) checks the detailed report of the project and decides to invest. They enter the investment amount and press the "Invest" button. The investment amount and investment decision are sent to the server.

[0965] Step 15:

[0966] The server stores the received investment intention and investment amount in a database and notifies the business operator. The saved investment data is notified to the business operator's dashboard.

[0967] Step 16:

[0968] The server again sends information about the projects that have reached their funding goal to the AI ​​analysis module and emotion engine. It retrieves the funding status from the database and sends the data to the AI ​​module and emotion engine if the goal is reached.

[0969] Step 17:

[0970] The server (AI module and emotion engine) generates a detailed report on how the funds are being used. The data is analyzed, a detailed report is generated, and sent back to the server. The generated detailed report is stored in a database.

[0971] Step 18:

[0972] The server notifies the operator of the generated detailed report, and sends a notification to the operator's dashboard that the report has been generated, along with a link to the detailed report.

[0973] (Application example 2)

[0974] 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."

[0975] Traditional fundraising platforms often lack transparency in business plan analysis and investment decisions, and lack support for decision-making that takes sentiment data into account, which can reduce the efficiency and reliability of fundraising and create unnecessary risks and uncertainty between investors and businesses.

[0976] 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 a business operator needing fundraising to input information such as a business plan, fundraising target, and planned public offering date; means for storing the input information in a data base; means for analyzing the stored information using artificial intelligence to clarify the business's direction and public offering target; means for sending the analysis results from the artificial intelligence to an individual with specialized knowledge and requesting a review; means for creating a final report based on the review results and storing the final report in the data base; means for investors to view the final report and decide on an investment; means for storing the investor's investment intention and investment amount in the data base and notifying the business operator; means for analyzing users' (investors') reactions in real time when they view the project detail report and generating emotion data; and means for feeding the generated emotion data back to the business operator. This improves the transparency and efficiency of the fundraising process and enables decision-making support that takes emotion data into consideration.

[0977] "Fundraising" means raising funds necessary for business activities from outside sources.

[0978] "Business" refers to any person or legal entity that conducts its business and carries on activities related to that business.

[0979] A "business plan" is a document that outlines the direction and details of a business venture that a company will undertake in the future.

[0980] A "fundraising target" is a target amount set by a business to raise specific funds.

[0981] The "public offering date" is the date on which stocks or securities are scheduled to be offered for sale to the general public in the market.

[0982] "Means of input" refers to the interface or method by which a user inputs the required information into the system.

[0983] A "data base" refers to a database or server for storing and managing information.

[0984] "Artificial intelligence" is a computer system that mimics human intelligence and automatically learns and makes decisions.

[0985] "Analysis" is the process of examining data or information in detail and finding meaning.

[0986] An "individual with specialized knowledge" refers to someone who has advanced knowledge or experience in a particular field.

[0987] "Review" is the process of checking, evaluating, and providing feedback on submitted information and analysis results.

[0988] "Final Report" means a completed, formal report that reflects the results of the review.

[0989] "Investor" refers to an individual or legal entity that provides funds and manages assets.

[0990] "Investment intent" refers to an investor's willingness or willingness to invest funds in a particular project.

[0991] "Emotional data" is digital information that reflects a user's emotional state.

[0992] "Feedback means" refers to the process or method of retransmitting collected or generated information to the underlying data provider.

[0993] The system of this invention begins when a business seeking funding enters information such as their business plan, fundraising goals, and planned public offering date, and stores it in a data base. The stored information is analyzed using artificial intelligence to clarify the business's direction and public offering goals. The analysis results are sent to individuals with specialized knowledge for review. A final report is prepared based on the review results and stored in the data base. Investors can view this final report and decide to invest. The investor's investment intentions and investment amounts are stored in the data base and notified to the business. The system also includes a function to analyze investors' reactions to detailed project reports in real time and generate sentiment data. The generated sentiment data is fed back to the business.

[0994] Program processing explanation

[0995] Hardware and software used

[0996] Hardware: Smartphones, servers

[0997] Software: Application frameworks (React Native, Swift, Kotlin, etc.), server-side frameworks (Node.js, Django, etc.), artificial intelligence models (TensorFlow, PyTorch), databases (MySQL, PostgreSQL)

[0998] Program processing details

[0999] 1. User (business operator) enters

[1000] Businesses log in to the application using their smartphones and enter information about their new projects (business plan, fundraising target, and public offering date).

[1001] The entered information is sent to the server and stored at the data center.

[1002] 2. Analysis using artificial intelligence

[1003] The server transmits the information stored in the data base to an artificial intelligence analysis module.

[1004] An artificial intelligence analysis module (TensorFlow or PyTorch) analyzes the business plan and generates text that clarifies the business direction and public offering goals.

[1005] The generated analysis results are sent to individuals with specialized knowledge.

[1006] 3. Review by an individual with specialized knowledge

[1007] Individuals with specialized knowledge can access a dedicated dashboard to review the results of the AI ​​analysis.

[1008] The final report will be prepared after making any necessary corrections or additions.

[1009] The final report is sent to a server and stored in a data center.

[1010] 4. Investor viewing of the project

[1011] Investors access the application using their smartphones to view the project's final report.

[1012] If an investor is interested, they decide to invest, enter their investment intention and investment amount, and submit.

[1013] The server stores the investment intention and investment amount in a data base and notifies the business operator.

[1014] 5. Emotional Data Feedback

[1015] When investors view detailed project reports, the sentiment engine analyzes their reactions in real time and generates sentiment data.

[1016] The generated emotion data is fed back to the business operator.

[1017] Examples of concrete examples and prompts

[1018] As a concrete example, consider a situation where a business operator registers an environmental technology project and an investor invests in it. In this case, when the business operator enters project information through a smartphone app, the emotion engine analyzes the information and evaluates the business operator's emotional state. Next, an AI analysis module analyzes the direction of the business plan, which is then reviewed by an individual with specialized knowledge. When the investor uses the app to view the project and decides to invest, the emotion engine analyzes the investor's emotions and provides feedback on this emotional data to the business operator, streamlining the investment process.

[1019] Example prompt sentence:

[1020] Enter the details of your new environmental technology project. We'll use artificial intelligence and an emotion engine to analyze your business plan's direction and emotional state. Please describe your specific goals and plans.

[1021] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1022] Step 1:

[1023] The user (business owner) logs in to the application using a smartphone. The user enters their authentication information, which is then received by the server and verified against the data base. If verification is successful, the user can access the new project registration page.

[1024] Input: Credentials

[1025] Output: Authentication result

[1026] Step 2:

[1027] The user (business operator) enters the necessary information such as the business plan, fundraising target, and planned public offering date on the new project registration page. The entered information is sent to the server and stored in the data base.

[1028] Input: Business plan, fundraising target, planned public offering date

[1029] Output: Save confirmation message

[1030] Step 3:

[1031] The server sends the information stored in the data center to an AI analysis module (TensorFlow or PyTorch), which analyzes the data and generates text to clarify the direction of the business plan and the goals of the public offering.

[1032] Input: Saved business plan related information

[1033] Output: Analysis results

[1034] Step 4:

[1035] The server sends the generated analysis results to individuals with specialized knowledge, who then access a dedicated dashboard to receive and review the analysis results, making corrections or additions as necessary.

[1036] Input: Analysis results

[1037] Output: Reviewed analysis results

[1038] Step 5:

[1039] Based on the analysis results, which have been revised and supplemented, an individual with specialized knowledge will create a final report, which will be sent to a server and stored in the data center.

[1040] Input: Reviewed analysis results

[1041] Output: Final report

[1042] Step 6:

[1043] An investor logs into the application using a smartphone and accesses the project listing page. The investor searches for projects of interest and views the final report.

[1044] Input: investor login information, project search query

[1045] Output: Project list, final report

[1046] Step 7:

[1047] When investors view detailed project reports, the server's emotion engine analyzes their reactions in real time and generates emotion data, which is then fed back to the business operator.

[1048] Input: Real-time investor reactions

[1049] Output: Emotion data, feedback

[1050] Step 8:

[1051] When an investor decides to invest, they enter their investment intention and investment amount and send it to the server, which stores this information in a data base and notifies the business operator.

[1052] Input: Investment intention, investment amount

[1053] Output: Save confirmation message, notification

[1054] Step 9:

[1055] If the fundraising goal is reached, the server will again send the project information to the AI ​​analysis module, which will generate a detailed report on how the funds will be used, which will be stored in the data center and notified to the business operator.

[1056] Input:Project Information

[1057] Output: Detailed report, notifications

[1058] 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.

[1059] 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.

[1060] 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.

[1061] [Third embodiment]

[1062] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1063] 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.

[1064] 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).

[1065] 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.

[1066] 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.

[1067] 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).

[1068] 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. 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.

[1069] 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.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] 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."

[1074] The following describes an embodiment of the present invention. This system is a platform that matches investors with businesses that need funding, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. The specific operation of each component of the system is described below.

[1075] Project registration by business operators

[1076] 1. User (business operator): First, the business operator accesses the platform login page and enters the correct authentication information to log in. After successful login, the business operator can access the new project registration page.

[1077] 2. Terminal (business operator): The business operator enters the necessary information on the new project registration page, such as the business plan, target amount of fundraising, and planned IPO timing, and presses the submit button.

[1078] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[1079] AI-based business plan analysis

[1080] 1. Server (AI module): The AI ​​analysis module is launched and inputs the project information registered by the business. The AI ​​analyzes the business plan and generates text to clarify the business direction and IPO goals.

[1081] 2. Server: Sends the generated analysis results to the expert review module.

[1082] Expert review and final report preparation

[1083] 1. User (expert): The expert logs in to a dedicated dashboard and receives notifications of new AI analysis results. The expert can then check the analysis results from the dashboard.

[1084] 2. Terminal (expert): Review the analysis results and make corrections or additions as necessary.

[1085] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[1086] Investors viewing projects

[1087] 1. User (Investor): Investors access the platform and log in.

[1088] 2. Server: Validates the investor's credentials and returns the dashboard page if authentication is successful.

[1089] 3. Terminal (Investor): Go to the project list page from the dashboard and search for projects that interest you.

[1090] 4. Server: Based on the investor's request, it returns a list of publicly available projects.

[1091] 5. Terminal (Investor): The investor selects the project of interest from the displayed list of projects and opens the details page.

[1092] 6. Server: Returns detailed information and final report for selected projects.

[1093] Investor investment decisions

[1094] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[1095] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[1096] 3. Server: Receives the investor's investment intention and investment amount, stores them in the database, and sends a notification of investment completion to the business operator.

[1097] What happens if the funding goal is reached?

[1098] 1. Server: Checks information about projects that have reached their funding goal and performs AI analysis again. The AI ​​generates a detailed report on how the funds will be used.

[1099] 2. Server: Stores the generated detailed report in a database and notifies the operator.

[1100] 3. Terminal (operator): The operator checks the detailed report and initiates procedures to carry out the plan as planned.

[1101] Specific examples

[1102] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. AI analyzes the business plan, and experts review it to generate a final report. Based on this report, Investor B becomes interested in the project and, after checking the details, decides to invest. Once Investor B invests and the target amount is reached, AI again generates a detailed report on how the funds will be used and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[1103] In this way, the invention combines AI analysis with expert review to provide an integrated system for making the fundraising process transparent and efficient.

[1104] The processing flow will be explained below.

[1105] Step 1:

[1106] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[1107] Step 2:

[1108] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[1109] Step 3:

[1110] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[1111] Step 4:

[1112] Server: Receives the transmitted information and stores it in a database.

[1113] Step 5:

[1114] Server: Sends the stored information to the AI ​​analysis module.

[1115] Step 6:

[1116] Server (AI module): Analyzes business plans and generates text to clarify business direction and IPO goals.

[1117] Step 7:

[1118] Server: Sends the generated AI analysis results to the expert review module.

[1119] Step 8:

[1120] User (expert): Logs in to a dashboard exclusively for experts and receives notifications of AI analysis results.

[1121] Step 9:

[1122] Device (Expert): Check notifications and review AI analysis results from the dashboard.

[1123] Step 10:

[1124] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[1125] Step 11:

[1126] Server: Receives the analysis results after the expert's corrections, generates a final report, and stores the generated final report in a database.

[1127] Step 12:

[1128] Server: Update the project status to "publishable" and add it to the publishing list.

[1129] Step 13:

[1130] User (Investor): Access the platform and log in by entering their credentials on the login page.

[1131] Step 14:

[1132] Server: Verifies investor credentials and returns dashboard page if successful.

[1133] Step 15:

[1134] Terminal (Investor): Access the project listing page from the dashboard and search for projects that interest you.

[1135] Step 16:

[1136] Server: Returns a list of publicly available projects based on the investor's request.

[1137] Step 17:

[1138] Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[1139] Step 18:

[1140] Server: Returns detailed information and final report for the selected project.

[1141] Step 19:

[1142] Terminal (Investor): Check the detailed report of the project and decide to invest.

[1143] Step 20:

[1144] Terminal (Investor): Enter the investment amount and press the invest button.

[1145] Step 21:

[1146] Server: Stores the investor's investment intention and investment amount in a database, and also sends a notification of investment completion to the business operator.

[1147] Step 22:

[1148] Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module.

[1149] Step 23:

[1150] Server (AI module): Generates detailed reports on how funds are being used and stores them in a database.

[1151] Step 24:

[1152] Server: Sends a detailed report to the operator.

[1153] Step 25:

[1154] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[1155] Example 1

[1156] 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."

[1157] For businesses that need to raise funds, analyzing business plans and clarifying how the funds will be used are extremely important. However, with conventional methods, analyzing business plans and explaining them to investors is cumbersome, making it difficult to raise funds effectively. It is also difficult for investors to grasp the reliability of the investment target and the specific use of funds. For this reason, a transparent and efficient system is needed to effectively match businesses with investors.

[1158] 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.

[1159] In this invention, the server includes: means for a business operator to input information such as a business plan, fundraising goals, and planned IPO timing; means for saving the input information in a database; means for analyzing the saved information using artificial intelligence to clarify the business direction and IPO goals; means for sending the results of the analysis by the artificial intelligence to an expert for review; means for creating a final report based on the expert's review and saving the final report in a database; means for an investor to view the final report and decide on an investment; means for saving the investor's investment intention and investment amount in a database and notifying the business operator; means for an investor to search for projects of interest on a project list page; and means for re-analyzing the project registration information by the business operator and generating a detailed report on how the funds will be used, thereby enabling a transparent and efficient fundraising process between business operators and investors.

[1160] A "business operator" is an individual or legal entity that needs financing and registers a business plan and enters information.

[1161] "Artificial intelligence" refers to machine learning algorithms and natural language processing engines that analyze input data and clarify business direction and IPO goals.

[1162] "Database" refers to a data storage system for storing information entered by businesses and investors, as well as analysis results and final reports.

[1163] "Review" refers to the process in which experts check the results of analysis by artificial intelligence and make corrections or additions.

[1164] The "final report" is a detailed business plan and use of funds report prepared based on the results of the AI ​​analysis and expert review.

[1165] "Investor" means an individual or legal entity that has an interest in a project and decides to invest.

[1166] "Funding target" refers to the specific amount of money that a business requires to carry out a project.

[1167] "IPO" refers to an initial public offering, where a business makes its shares publicly available on the market.

[1168] "Project Listing Page" means a web page where investors can search and view publicly available project information.

[1169] A "detailed report" is a report that uses artificial intelligence to analyze projects that have reached their funding goal and detail how the funds will be used.

[1170] A specific embodiment for implementing this invention is described below. The system of the invention matches businesses needing financing with investors, and uses a combination of AI and expert knowledge. This system consists of the following components:

[1171] Hardware and Software

[1172] 1. Server: Provides functions such as data storage, processing, analysis, notification, and AI module launch. Specifically, it includes a database, API server, AI analysis module, expert review module, and notification system.

[1173] 2. Terminal: A computer or mobile device used by an entrepreneur, professional or investor, on which a browser or dedicated application is installed.

[1174] 3. AI module: Uses a natural language processing engine (e.g., GPT-4) to analyze business plans.

[1175] 4. Database: A storage system for storing project information, analysis results, final reports, investment information, etc.

[1176] Data processing and calculation

[1177] 1. Data entry: The business operator enters information such as the business plan, fundraising target, and planned IPO timing using a browser or a dedicated application.

[1178] 2. Data storage: The server receives the entered information, generates SQL queries to store it in the database, and stores the information.

[1179] 3. Data analysis: The server sends the stored information to an AI analysis module, which analyzes the data and generates text that clarifies the business direction and IPO goals.

[1180] 4. Expert review: The server sends the AI ​​analysis results to the expert review module, where the experts review the results and make corrections if necessary.

[1181] 5. Generate Final Report: The server generates the final report and stores it in the database. The final report includes details of the business plan, fundraising goals, IPO plans, etc.

[1182] 6. Investor Access: Investors can access the project listing page and search for projects they are interested in. They can open the details page and view the final report.

[1183] 7. Investment decision: The investor enters the investment amount and decides to invest. The server receives the investment information, stores it in the database, and notifies the operator.

[1184] 8. Reaching the funding goal: The server checks the information of the projects that have reached their funding goal, performs AI analysis again, and generates a detailed report on how the funds will be used.

[1185] Specific examples

[1186] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. The data is then stored in a database via the server.

[1187] The AI ​​module analyzes the business plan and sends the results to an expert review module, where the experts review the analysis, make corrections, and create a final report, which is then stored in a database and made available to investors.

[1188] Investor B accesses the platform, becomes interested in Business A's project, and after checking the details, decides to invest. The investment information is stored in a database via the server and notified to Business A.

[1189] When the investment target amount is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[1190] Prompt Sentence Examples

[1191] Below are some example prompts to input to the generative AI model:

[1192] Analyze a business plan outline and generate a text that specifically articulates the IPO goals. Project details:

[1193] Business plan: [Detailed business plan content]

[1194] Funding goal: [target amount]

[1195] Planned IPO date: [Specific date]

[1196] In this way, this invention combines AI analysis with expert review to provide a transparent and efficient fundraising process between businesses and investors.

[1197] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1198] Step 1:

[1199] Business Login

[1200] The user (business operator) accesses the platform's login page and enters their user ID and password. The entered information is sent and reaches the server. The server compares the user ID and password with the database for authentication, and if successful, starts a login session. If the login is successful, a new project registration page is returned to the business operator's terminal.

[1201] Input: User ID, Password

[1202] Specific operations: login authentication, session start

[1203] Output: New project registration page

[1204] Step 2:

[1205] Entering and submitting project information

[1206] On the new project registration page, businesses enter their business plan, fundraising goals, and planned IPO timing. After entering this information and pressing the submit button, the information is sent to the server, which then generates an SQL query to store the received information in the database and executes the insert operation.

[1207] Input: Business plan, fundraising target, planned IPO date

[1208] Specific actions: Entering information, sending, saving to database

[1209] Output: Save information to a database

[1210] Step 3:

[1211] Sending data to the AI ​​module

[1212] The server sends the information stored in the database to an AI analysis module, which receives the project information, invokes a natural language processing engine (e.g., GPT-4) to analyze the business plan, and generates text that clarifies the business direction and IPO goals.

[1213] Input: Project information stored in the database

[1214] Specific operations: receiving and sending information, launching AI analysis module, generating text

[1215] Output: Parsed text

[1216] Step 4:

[1217] Submitting analysis results to an expert review module

[1218] The server sends the analysis results generated by the AI ​​module to the expert review module, where experts access the review dashboard to receive notifications of new analysis results, review the results, and make corrections or additions as necessary.

[1219] Input: Text of the analysis results generated by the AI

[1220] Specific actions: Submit information, expert review

[1221] Output: Corrected and confirmed results

[1222] Step 5:

[1223] Generate the final report

[1224] The server generates a final report based on the results of the expert review, which is then stored in a database by the server, and includes details of the business plan, fundraising targets, and IPO plans.

[1225] Input: Expert-reviewed text

[1226] Specific operation: Create and save the final report

[1227] Output: Final report

[1228] Step 6:

[1229] Investor Login and Project Search

[1230] A user (investor) accesses the platform and logs in. After successful login, the investor can access the project list page. The investor searches for a project of interest and sends a request to the server to display detailed information. The server returns the relevant project information and final report from the database.

[1231] Input: Investor login information, project search request

[1232] Specific operations: login authentication, project information search, information submission

[1233] Output: Project information, final report

[1234] Step 7:

[1235] Investment decisions and inputs

[1236] After checking the detailed project report, the investor decides to invest. By entering the investment amount and pressing the "Invest" button, the information is sent to the server. The server receives the investment information, stores it in a database, and sends a notification of investment completion to the business operator.

[1237] Input: investment amount, investment confirmation operation

[1238] Specific operations: receiving investment information, saving it in a database, and notifying businesses

[1239] Output: Save to database, notify business operator

[1240] Step 8:

[1241] What happens after the fundraising goal is reached?

[1242] The server checks the information of projects that have reached their funding goals. It then performs another AI analysis and generates a detailed report on how the funds will be used. This detailed report is then saved in a database by the server and notified to the project owner. The project owner then checks the detailed report and implements a specific plan for how the funds will be used.

[1243] Input: Project information that has reached its funding goal

[1244] Specific operations: Check information, reanalyze, create and save detailed reports, notify business operators

[1245] Output: Detailed report, notification to business operators

[1246] By explaining in detail the specific operations and data flow at each step, it becomes easier to understand the function of the entire system, and the content can be useful for actual implementation.

[1247] (Application example 1)

[1248] 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."

[1249] It has traditionally been difficult for businesses seeking funding to conduct accurate and reliable business plan analysis and provide transparent information to investors. It also requires a great deal of effort for investors to quickly view multiple projects and make appropriate investment decisions. To solve these challenges, a platform is needed that integrates AI technology and expert knowledge to efficiently and effectively analyze business plans and fund usage, and present the results to investors.

[1250] 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.

[1251] In this invention, the server includes means for a business operator to have an AI model analyze a business plan for a project and store the generated text in a database, means for sending the generated analysis results to an expert for review and storing the expert's review results in the database, and means for using the AI ​​model to generate prompt text for investors to view project details and make investment decisions. This enables business operators to efficiently present high-quality business plans and investors to make quick investment decisions based on reliable information.

[1252] Key Word Definitions

[1253] "Business operator" refers to a corporation or individual that has a business plan and needs to raise funds.

[1254] "Investor" means a legal entity or individual whose purpose is to provide funding to a Project.

[1255] A "business plan" is a document that details the outline, goals, progress, and projected income and expenditures of a particular project.

[1256] A "funding target" is a specific amount of money that an entity needs to carry out a project.

[1257] "Planned IPO date" refers to the time when a business plans to go public (IPO).

[1258] A "database" is a system for efficiently storing, managing, and retrieving information.

[1259] An "AI model" is a mathematical model that uses artificial intelligence algorithms to solve specific problems.

[1260] An "expert" is a person who has advanced knowledge and experience in a particular field.

[1261] A "prompt" is an instruction given to an AI model to perform a specific task.

[1262] "Online Platform" means a service that can be accessed by users via the Internet.

[1263] "Generated text" refers to the text that the AI ​​model generates as a result of analyzing the business plan.

[1264] "Analysis results" refer to the conclusions and proposals that AI draws from analyzing the business plan.

[1265] "Review results" refers to the final evaluation after experts have evaluated and corrected the analysis results generated by AI.

[1266] "Means to View Details" refers to the interface or functionality that allows a user to view detailed information about a particular project.

[1267] MODE FOR CARRYING OUT THE INVENTION

[1268] The following describes an embodiment of this invention. This system is an online platform that efficiently matches businesses needing fundraising with investors, analyzes business plans, and clarifies how funds will be used. The system combines AI analysis models and expert reviews to provide investors with highly reliable information.

[1269] Project registration by business operators

[1270] Terminal (operator)

[1271] Businesses register by entering information such as their project's business plan, fundraising goals, and planned IPO timing into the online platform, which is then stored in a database by the server.

[1272] AI-based business plan analysis

[1273] server

[1274] After the input information is saved in the database, an AI analysis module is launched to analyze the business plan. The generated text is saved back in the database as the analysis results. The AI ​​model used could be a generative AI model such as OpenAI's GPT-3.

[1275] Expert review and final report preparation

[1276] User (expert)

[1277] Experts review the results of the AI ​​analysis from a dedicated dashboard, and after the experts' evaluation and corrections are made, the results are saved on the server as a final report.

[1278] Investors can view projects and make investment decisions

[1279] Terminal (Investor)

[1280] Investors access the platform and view the details of the project. In order for investors to check the detailed information of the project, the server generates a prompt message and provides information according to the investor's request. Examples of prompt messages include the following:

[1281] Business plan for registered projects:

[1282] "This project aims to develop new markets in the field of image processing using innovative AI technology."

[1283] Prompt statement:

[1284] "Analyze this business plan and highlight key points and market value:"

[1285] Investment and fundraising completion

[1286] server

[1287] After the investor decides to invest, the server stores the investment intention and investment amount in a database and notifies the business. If the fundraising goal is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies the business.

[1288] As described above, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process. The hardware used includes servers and user devices, and the software includes the Flask framework, SQLite database, and OpenAI's GPT-3.

[1289] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1290] Program processing flow

[1291] Step 1:

[1292] Businesses log in to the platform using their terminals, input information such as the business plan, fundraising goals, and planned IPO timing for their new projects, and then submit it. This input information is received by the server and stored in the database. (Input: business plan, fundraising goals, planned IPO timing / Output: project information stored in the database)

[1293] Step 2:

[1294] The server sends the saved business plan information to the AI ​​analysis module. The AI ​​analysis module analyzes the business plan and generates text to clarify the business's direction and market value. This generated text is sent back to the server and saved in a database. (Input: Saved business plan information / Output: Text of the analysis results by AI)

[1295] Step 3:

[1296] The server sends the generated AI analysis results to an expert for review. The expert uses the user's device to check the analysis results and make corrections or additions as necessary. The corrected review results are returned to the server and stored in a database. (Input: AI analysis results / Output: Review results including corrections and additions made by the expert)

[1297] Step 4:

[1298] The server creates a final report based on the expert review results and stores it in a database. This final report is then made available to investors. (Input: Expert review results / Output: Final report)

[1299] Step 5:

[1300] Investors log in to the platform using their terminals and view project details. The server uses a generative AI model to generate prompts to provide detailed project information and a final report to investors. (Input: Viewing request from investor / Output: Generated prompts and project details)

[1301] Step 6:

[1302] The investor views the project details page and decides to invest. The investor enters the investment amount and presses the investment button. The server saves the investor's investment intention and investment amount in a database and notifies the business operator. (Input: Investor's investment intention and investment amount / Output: Investment information saved in the database and notification to the business operator)

[1303] Step 7:

[1304] If the fundraising goal is reached, the server performs another AI analysis and generates a detailed report on how the funds will be used. The generative AI model is used to create the detailed report, which is then saved in a database and notified to the project owner. (Input: Project information on the project that reached the fundraising goal / Output: Detailed report and notification to the project owner)

[1305] In this way, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process.

[1306] 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.

[1307] The following describes an embodiment of this invention. This system is a platform for matching businesses needing fundraising with investors, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, this system enhances the accuracy of information and decision-making support by incorporating an emotion engine that recognizes the user's emotions.

[1308] Project registration by business operators

[1309] 1. User (business operator): First, the business operator accesses the platform and enters their authentication information on the login page. After successful login, they can access the new project registration page.

[1310] 2. Terminal (business operator): The business operator enters the necessary information such as the business plan, fundraising target, and planned IPO date on the new project registration page and presses the submit button.

[1311] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[1312] Analysis by emotion engine

[1313] 1. Server (emotion engine): Analyzes the information input by the business operator and generates emotional data. For example, it determines the business operator's emotional state, such as optimistic, pessimistic, or excited.

[1314] 2. Server: Reflects the generated emotion data into the business plan and proposes revisions as necessary.

[1315] AI-based business plan analysis

[1316] 1. Server (AI module): The AI ​​analysis module analyzes the business plan and generates text to clarify the business direction and IPO goals.

[1317] 2. Server: Sends the generated analysis results to the expert review module.

[1318] Expert review and final report preparation

[1319] 1. User (expert): The expert accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data.

[1320] 2. Terminal (expert): Checks the analysis results and emotion data, and makes corrections or additions as necessary.

[1321] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[1322] Investors viewing projects

[1323] 1. User (Investor): Investor accesses the platform and enters their authentication information on the login page. If authentication is successful, they can access the dashboard page.

[1324] 2. Terminal (Investor): Go to the project list page from the dashboard and search for the project you are interested in.

[1325] 3. Server: Returns a list of publicly available projects based on the investor's request.

[1326] 4. Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[1327] 5. Server: Returns detailed information and final report for selected projects.

[1328] Sentiment analysis when investors browse projects

[1329] 1. Server (emotion engine): When investors view project reports, their reactions are analyzed in real time and emotion data is generated.

[1330] 2. Server: The generated emotion data is fed back to the business.

[1331] Investor investment decisions

[1332] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[1333] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[1334] 3. Server: Stores the received investment intention and investment amount in a database and notifies the business operator.

[1335] What happens if the funding goal is reached?

[1336] 1. Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module and sentiment engine.

[1337] 2. Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[1338] 3. Server: Notifies the operator of the generated detailed report.

[1339] Specific examples

[1340] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[1341] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[1342] In this way, the present invention provides a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[1343] The processing flow will be explained below.

[1344] Step 1:

[1345] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[1346] Step 2:

[1347] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[1348] Step 3:

[1349] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[1350] Step 4:

[1351] Server: Receives the transmitted information and stores it in a database.

[1352] Step 5:

[1353] Server: Sends the stored information to the AI ​​analysis module.

[1354] Step 6:

[1355] Server (emotion engine): Analyzes text data entered by businesses and generates emotional data. For example, it determines the emotional state of the input, such as whether the input is positive or anxious.

[1356] Step 7:

[1357] Server: Reflects the generated emotion data in the analysis of business plans and proposes revisions to the business operator as necessary.

[1358] Step 8:

[1359] Server (AI module): Analyzes business plan data and generates detailed analysis results to clarify business direction and IPO goals.

[1360] Step 9:

[1361] Server: Sends the generated analysis results to the expert review module.

[1362] Step 10:

[1363] User (expert): Logs in to an expert-only dashboard and receives notifications of new AI analysis results and emotion data.

[1364] Step 11:

[1365] Terminal (expert): Check the AI ​​analysis results and emotion data from the dashboard and review them.

[1366] Step 12:

[1367] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[1368] Step 13:

[1369] Server: Receives the analysis results after the experts' corrections, generates the final report, and stores it in the database.

[1370] Step 14:

[1371] Server: Add the final report to the list of publicly available projects and update it so that it is available for investors to view.

[1372] Step 15:

[1373] User (Investor): Access the platform and log in by entering credentials on the investor login page.

[1374] Step 16:

[1375] Server: Verifies investor credentials and returns dashboard page if successful.

[1376] Step 17:

[1377] Terminal (Investor): Go to the project listing page from the dashboard and search for the project you are interested in.

[1378] Step 18:

[1379] Server: Returns a list of publicly available projects in response to investor requests.

[1380] Step 19:

[1381] Terminal (Investor): Select the project you are interested in from the project list and open the details page.

[1382] Step 20:

[1383] Server: Returns detailed information and final report for the selected project.

[1384] Step 21:

[1385] Server (emotion engine): When investors view detailed project reports, their reactions are analyzed in real time and emotion data is generated.

[1386] Step 22:

[1387] Server: Provides feedback to the business operator on the generated emotion data.

[1388] Step 23:

[1389] Terminal (Investor): Check the detailed report of the project and decide to invest.

[1390] Step 24:

[1391] Terminal (Investor): Enter the investment amount and press the invest button.

[1392] Step 25:

[1393] Server: Stores the investor's investment intention and investment amount in a database and notifies the business operator.

[1394] Step 26:

[1395] Server: Sends information about projects that have reached their funding goal to the AI ​​analysis module and sentiment engine for further analysis.

[1396] Step 27:

[1397] Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[1398] Step 28:

[1399] Server: Sends a detailed report to the operator.

[1400] Step 29:

[1401] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[1402] Example 2

[1403] 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."

[1404] Traditional fundraising platforms do not take into account emotional data when analyzing the information entered by businesses or creating final reports, resulting in insufficient support for business planning accuracy and investor decision-making. Furthermore, the lack of feedback that reflects investors' emotional state prevents a fundraising process that takes into account the psychological aspects of investors.

[1405] 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.

[1406] In this invention, the server includes means for analyzing input information from business operators and investors using an emotion engine to generate emotion data, means for revising business plans based on the emotion data, means for performing real-time emotion analysis when investors view project reports, and means for feeding back the investor's emotion data to the business operator. This allows the emotion data to be reflected in business plans and final reports, enabling a fundraising process that takes into account the emotional state of investors.

[1407] "Business operator" refers to an entity that registers plans and information about a project requiring funding on the platform.

[1408] "Investor" means an entity that invests in a project offered on the Platform.

[1409] "Business Plan" means the information entered by an operator into the Platform, including project details, fundraising targets, and planned IPO timing.

[1410] A "fundraising goal" is a goal that indicates the specific target amount of funds required by the business operator to implement the project.

[1411] The "emotion engine" is a software system that analyzes the emotional state and generates specific emotional data based on the information that businesses and investors enter or view on the platform.

[1412] "Emotion data" is data that indicates the emotional state of businesses and investors, generated as a result of analysis by the emotion engine.

[1413] The "AI analysis module" is artificial intelligence software that analyzes input data such as business plans and clarifies project direction and IPO goals.

[1414] An "expert" is a person or group with the knowledge and experience to review the results of AI analysis and make any necessary corrections or additions.

[1415] The "Final Report" is a comprehensive report on the project that will be created based on the results of AI analysis, expert review, and emotional data.

[1416] "Ready for release" is a status that indicates that a final report has been prepared after expert review and is ready to be made public to investors.

[1417] "Project Report" is synonymous with Final Report and means a report that includes the overall content and evaluation results of the project.

[1418] A "database" is an information aggregation system managed by a server that stores and manages data such as business plans, sentiment data, final reports, and investor information.

[1419] This invention is a platform that matches businesses needing funding with investors, utilizing AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, by combining this system with an emotion engine that recognizes user emotions, the accuracy of information and decision-making support are enhanced.

[1420] System configuration

[1421] The system mainly consists of the following components:

[1422] 1. Server

[1423] 2. Terminal

[1424] 3. Users (businesses, investors, experts)

[1425] 4. Database

[1426] 5. AI Module

[1427] 6. Emotion Engine

[1428] Hardware and software used

[1429] Hardware: Server, user terminal (PC, smartphone)

[1430] Software: AI analysis module, emotion engine, database management system

[1431] Program processing overview

[1432] Project registration by business operators

[1433] The user (business operator) accesses the platform and enters authentication information on the login page. If authentication is successful, they are directed to a new project registration page. There, they enter the necessary information, such as the business plan, fundraising target, and planned IPO timing, and press the "Submit" button. The information sent from the terminal (business operator) is received by the server and stored in a database. This information is then sent to the AI ​​analysis module.

[1434] Analysis by emotion engine

[1435] The server (emotion engine) analyzes the business's input information and generates emotional data. For example, it determines whether the business is optimistic, pessimistic, excited, etc. The emotional data is reflected in the business plan and, if necessary, proposes revisions.

[1436] AI-based business plan analysis

[1437] The server (AI module) analyzes the business plan and generates text that clarifies the business direction and IPO goals. The analysis results are sent to the expert review module.

[1438] Expert review and final report preparation

[1439] The user (expert) accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data. The user (expert) checks the analysis results and emotional data on the device and makes corrections or additions as necessary. The server receives the corrected analysis results, generates a final report based on them, and stores it in the database. The project status is updated to "Ready for release."

[1440] Investors viewing projects

[1441] The user (investor) accesses the platform and enters authentication information on the login page. If authentication is successful, the user is taken to the dashboard page. On the terminal (investor), the user moves from the dashboard to the project list page and searches for a project of interest. The server returns a list of publicly available projects based on the investor's request. The terminal (investor) selects a project of interest from the list of projects displayed and opens the details page. The server returns detailed information and a final report for the selected project.

[1442] Sentiment analysis when investors browse projects

[1443] When investors view project reports, the server (emotion engine) analyzes their reactions in real time and generates emotional data, which is then fed back to the business.

[1444] Investor investment decisions

[1445] The investor checks the detailed project report on the terminal and decides to invest. He enters the investment amount and presses the "Invest" button. The server stores the received investment intention and investment amount in the database and notifies the business operator.

[1446] What happens if the funding goal is reached?

[1447] The server sends information about projects that have reached their funding goal back to the AI ​​analysis module and emotion engine. The server (AI module and emotion engine) generates a detailed report on how the funds will be used and stores it in a database. The generated detailed report is then notified to the business operator.

[1448] Specific examples

[1449] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[1450] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[1451] In this way, the present invention is a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[1452] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1453] Step 1:

[1454] A user (business operator) accesses the platform and enters authentication information on the login page. The entered authentication information (username, password) is sent to the server. The server receives the authentication information and checks it against the database. If authentication is successful, the server returns access permission, and the user can access the new project registration page.

[1455] Step 2:

[1456] The terminal (business operator) moves to the new project registration page, enters the necessary information such as the business plan, fundraising target, and planned IPO timing, and presses the submit button. The input data (business plan, fundraising target, planned IPO timing) is sent to the server, which receives it and stores it in a database.

[1457] Step 3:

[1458] The server sends the stored information to the AI ​​analysis module. The business plan, fundraising goals, and planned IPO timing stored in the database are input into the AI ​​analysis module. The AI ​​analysis module analyzes the data and clarifies the business direction and IPO goals. The analysis results (business direction, IPO goals) are generated and output to the server.

[1459] Step 4:

[1460] The server (emotion engine) analyzes the business's input information and generates emotion data. The business plan and input text are sent to the emotion engine. The emotion engine uses natural language processing to generate emotion data (optimistic, pessimistic, etc.) and returns it to the server. The server reflects the generated emotion data in the business plan and suggests revisions as necessary.

[1461] Step 5:

[1462] The server sends the generated emotion data and AI analysis results to the expert review module, which then notifies the expert on their dashboard.

[1463] Step 6:

[1464] The user (expert) accesses a dedicated dashboard and receives notifications of AI analysis results and emotional data. By clicking on the notification, the AI ​​analysis results and emotional data are displayed on the device.

[1465] Step 7:

[1466] The terminal (expert) checks the analysis results and emotion data, and makes corrections or supplements as necessary. The expert checks the displayed data, enters corrections or comments, and presses the save button. The corrections and supplemental data are sent to the server.

[1467] Step 8:

[1468] The server receives the analysis results after the experts have completed the corrections and generates a final report based on them. A script is executed based on the corrected analysis results to generate a final report. The generated final report is saved in the database and the project status is updated to "Ready for release."

[1469] Step 9:

[1470] A user (investor) accesses the platform and enters their authentication information on the login page. The entered authentication information (username, password) is sent to the server and checked against the database. If authentication is successful, the server returns permission to access the dashboard page.

[1471] Step 10:

[1472] The terminal (investor) navigates from the dashboard to the project list page and searches for projects they are interested in. A project search request is sent to the server. The server retrieves projects with a "publishable" status from the database and returns the list to the terminal.

[1473] Step 11:

[1474] The terminal (investor) selects a project of interest from the displayed project list and opens the details page. The selected project ID is sent to the server. The server retrieves detailed information and a final report from the database and returns it to the terminal.

[1475] Step 12:

[1476] When investors view a project report, the server (emotion engine) analyzes their reactions in real time and generates emotion data. Data such as the investor's mouse movements and viewing time are input, and the emotion engine analyzes this to generate emotion data. The generated emotion data is returned to the server.

[1477] Step 13:

[1478] The server feeds back the generated emotion data to the business operator, who then notifies the business operator's dashboard of the generated emotion data.

[1479] Step 14:

[1480] The terminal (investor) checks the detailed report of the project and decides to invest. They enter the investment amount and press the "Invest" button. The investment amount and investment decision are sent to the server.

[1481] Step 15:

[1482] The server stores the received investment intention and investment amount in a database and notifies the business operator. The saved investment data is notified to the business operator's dashboard.

[1483] Step 16:

[1484] The server again sends information about the projects that have reached their funding goal to the AI ​​analysis module and emotion engine. It retrieves the funding status from the database and sends the data to the AI ​​module and emotion engine if the goal is reached.

[1485] Step 17:

[1486] The server (AI module and emotion engine) generates a detailed report on how the funds are being used. The data is analyzed, a detailed report is generated, and sent back to the server. The generated detailed report is stored in a database.

[1487] Step 18:

[1488] The server notifies the operator of the generated detailed report, and sends a notification to the operator's dashboard that the report has been generated, along with a link to the detailed report.

[1489] (Application example 2)

[1490] 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."

[1491] Traditional fundraising platforms often lack transparency in business plan analysis and investment decisions, and lack support for decision-making that takes sentiment data into account, which can reduce the efficiency and reliability of fundraising and create unnecessary risks and uncertainty between investors and businesses.

[1492] 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 a business operator needing fundraising to input information such as a business plan, fundraising target, and planned public offering date; means for storing the input information in a data base; means for analyzing the stored information using artificial intelligence to clarify the business's direction and public offering target; means for sending the analysis results from the artificial intelligence to an individual with specialized knowledge and requesting a review; means for creating a final report based on the review results and storing the final report in the data base; means for investors to view the final report and decide on an investment; means for storing the investor's investment intention and investment amount in the data base and notifying the business operator; means for analyzing users' (investors') reactions in real time when they view the project detail report and generating emotion data; and means for feeding the generated emotion data back to the business operator. This improves the transparency and efficiency of the fundraising process and enables decision-making support that takes emotion data into consideration.

[1493] "Fundraising" means raising funds necessary for business activities from outside sources.

[1494] "Business" refers to any person or legal entity that conducts its business and carries on activities related to that business.

[1495] A "business plan" is a document that outlines the direction and details of a business venture that a company will undertake in the future.

[1496] A "fundraising target" is a target amount set by a business to raise specific funds.

[1497] The "public offering date" is the date on which stocks or securities are scheduled to be offered for sale to the general public in the market.

[1498] "Means of input" refers to the interface or method by which a user inputs the required information into the system.

[1499] A "data base" refers to a database or server for storing and managing information.

[1500] "Artificial intelligence" is a computer system that mimics human intelligence and automatically learns and makes decisions.

[1501] "Analysis" is the process of examining data or information in detail and finding meaning.

[1502] An "individual with specialized knowledge" refers to someone who has advanced knowledge or experience in a particular field.

[1503] "Review" is the process of checking, evaluating, and providing feedback on submitted information and analysis results.

[1504] "Final Report" means a completed, formal report that reflects the results of the review.

[1505] "Investor" refers to an individual or legal entity that provides funds and manages assets.

[1506] "Investment intent" refers to an investor's willingness or willingness to invest funds in a particular project.

[1507] "Emotional data" is digital information that reflects a user's emotional state.

[1508] "Feedback means" refers to the process or method of retransmitting collected or generated information to the underlying data provider.

[1509] The system of this invention begins when a business seeking funding enters information such as their business plan, fundraising goals, and planned public offering date, and stores it in a data base. The stored information is analyzed using artificial intelligence to clarify the business's direction and public offering goals. The analysis results are sent to individuals with specialized knowledge for review. A final report is prepared based on the review results and stored in the data base. Investors can view this final report and decide to invest. The investor's investment intentions and investment amounts are stored in the data base and notified to the business. The system also includes a function to analyze investors' reactions to detailed project reports in real time and generate sentiment data. The generated sentiment data is fed back to the business.

[1510] Program processing explanation

[1511] Hardware and software used

[1512] Hardware: Smartphones, servers

[1513] Software: Application frameworks (React Native, Swift, Kotlin, etc.), server-side frameworks (Node.js, Django, etc.), artificial intelligence models (TensorFlow, PyTorch), databases (MySQL, PostgreSQL)

[1514] Program processing details

[1515] 1. User (business operator) enters

[1516] Businesses log in to the application using their smartphones and enter information about their new projects (business plan, fundraising target, and public offering date).

[1517] The entered information is sent to the server and stored at the data center.

[1518] 2. Analysis using artificial intelligence

[1519] The server transmits the information stored in the data base to an artificial intelligence analysis module.

[1520] An artificial intelligence analysis module (TensorFlow or PyTorch) analyzes the business plan and generates text that clarifies the business direction and public offering goals.

[1521] The generated analysis results are sent to individuals with specialized knowledge.

[1522] 3. Review by an individual with specialized knowledge

[1523] Individuals with specialized knowledge can access a dedicated dashboard to review the results of the AI ​​analysis.

[1524] The final report will be prepared after making any necessary corrections or additions.

[1525] The final report is sent to a server and stored in a data center.

[1526] 4. Investor viewing of the project

[1527] Investors access the application using their smartphones to view the project's final report.

[1528] If an investor is interested, they decide to invest, enter their investment intention and investment amount, and submit.

[1529] The server stores the investment intention and investment amount in a data base and notifies the business operator.

[1530] 5. Emotional Data Feedback

[1531] When investors view detailed project reports, the sentiment engine analyzes their reactions in real time and generates sentiment data.

[1532] The generated emotion data is fed back to the business operator.

[1533] Examples of concrete examples and prompts

[1534] As a concrete example, consider a situation where a business operator registers an environmental technology project and an investor invests in it. In this case, when the business operator enters project information through a smartphone app, the emotion engine analyzes the information and evaluates the business operator's emotional state. Next, an AI analysis module analyzes the direction of the business plan, which is then reviewed by an individual with specialized knowledge. When the investor uses the app to view the project and decides to invest, the emotion engine analyzes the investor's emotions and provides feedback on this emotional data to the business operator, streamlining the investment process.

[1535] Example prompt sentence:

[1536] Enter the details of your new environmental technology project. We'll use artificial intelligence and an emotion engine to analyze your business plan's direction and emotional state. Please describe your specific goals and plans.

[1537] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1538] Step 1:

[1539] The user (business owner) logs in to the application using a smartphone. The user enters their authentication information, which is then received by the server and verified against the data base. If verification is successful, the user can access the new project registration page.

[1540] Input: Credentials

[1541] Output: Authentication result

[1542] Step 2:

[1543] The user (business operator) enters the necessary information such as the business plan, fundraising target, and planned public offering date on the new project registration page. The entered information is sent to the server and stored in the data base.

[1544] Input: Business plan, fundraising target, planned public offering date

[1545] Output: Save confirmation message

[1546] Step 3:

[1547] The server sends the information stored in the data center to an AI analysis module (TensorFlow or PyTorch), which analyzes the data and generates text to clarify the direction of the business plan and the goals of the public offering.

[1548] Input: Saved business plan related information

[1549] Output: Analysis results

[1550] Step 4:

[1551] The server sends the generated analysis results to individuals with specialized knowledge, who then access a dedicated dashboard to receive and review the analysis results, making corrections or additions as necessary.

[1552] Input: Analysis results

[1553] Output: Reviewed analysis results

[1554] Step 5:

[1555] Based on the analysis results, which have been revised and supplemented, an individual with specialized knowledge will create a final report, which will be sent to a server and stored in the data center.

[1556] Input: Reviewed analysis results

[1557] Output: Final report

[1558] Step 6:

[1559] An investor logs into the application using a smartphone and accesses the project listing page. The investor searches for projects of interest and views the final report.

[1560] Input: investor login information, project search query

[1561] Output: Project list, final report

[1562] Step 7:

[1563] When investors view detailed project reports, the server's emotion engine analyzes their reactions in real time and generates emotion data, which is then fed back to the business operator.

[1564] Input: Real-time investor reactions

[1565] Output: Emotion data, feedback

[1566] Step 8:

[1567] When an investor decides to invest, they enter their investment intention and investment amount and send it to the server, which stores this information in a data base and notifies the business operator.

[1568] Input: Investment intention, investment amount

[1569] Output: Save confirmation message, notification

[1570] Step 9:

[1571] If the fundraising goal is reached, the server will again send the project information to the AI ​​analysis module, which will generate a detailed report on how the funds will be used, which will be stored in the data center and notified to the business operator.

[1572] Input:Project Information

[1573] Output: Detailed report, notifications

[1574] 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.

[1575] 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.

[1576] 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.

[1577] [Fourth embodiment]

[1578] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1579] 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.

[1580] 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).

[1581] 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.

[1582] 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.

[1583] 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).

[1584] 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. 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.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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."

[1591] The following describes an embodiment of the present invention. This system is a platform that matches investors with businesses that need funding, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. The specific operation of each component of the system is described below.

[1592] Project registration by business operators

[1593] 1. User (business operator): First, the business operator accesses the platform login page and enters the correct authentication information to log in. After successful login, the business operator can access the new project registration page.

[1594] 2. Terminal (business operator): The business operator enters the necessary information on the new project registration page, such as the business plan, target amount of fundraising, and planned IPO timing, and presses the submit button.

[1595] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[1596] AI-based business plan analysis

[1597] 1. Server (AI module): The AI ​​analysis module is launched and inputs the project information registered by the business. The AI ​​analyzes the business plan and generates text to clarify the business direction and IPO goals.

[1598] 2. Server: Sends the generated analysis results to the expert review module.

[1599] Expert review and final report preparation

[1600] 1. User (expert): The expert logs in to a dedicated dashboard and receives notifications of new AI analysis results. The expert can then check the analysis results from the dashboard.

[1601] 2. Terminal (expert): Review the analysis results and make corrections or additions as necessary.

[1602] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[1603] Investors viewing projects

[1604] 1. User (Investor): Investors access the platform and log in.

[1605] 2. Server: Validates the investor's credentials and returns the dashboard page if authentication is successful.

[1606] 3. Terminal (Investor): Go to the project list page from the dashboard and search for projects that interest you.

[1607] 4. Server: Based on the investor's request, it returns a list of publicly available projects.

[1608] 5. Terminal (Investor): The investor selects the project of interest from the displayed list of projects and opens the details page.

[1609] 6. Server: Returns detailed information and final report for selected projects.

[1610] Investor investment decisions

[1611] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[1612] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[1613] 3. Server: Receives the investor's investment intention and investment amount, stores them in the database, and sends a notification of investment completion to the business operator.

[1614] What happens if the funding goal is reached?

[1615] 1. Server: Checks information about projects that have reached their funding goal and performs AI analysis again. The AI ​​generates a detailed report on how the funds will be used.

[1616] 2. Server: Stores the generated detailed report in a database and notifies the operator.

[1617] 3. Terminal (operator): The operator checks the detailed report and initiates procedures to carry out the plan as planned.

[1618] Specific examples

[1619] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. AI analyzes the business plan, and experts review it to generate a final report. Based on this report, Investor B becomes interested in the project and, after checking the details, decides to invest. Once Investor B invests and the target amount is reached, AI again generates a detailed report on how the funds will be used and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[1620] In this way, the invention combines AI analysis with expert review to provide an integrated system for making the fundraising process transparent and efficient.

[1621] The processing flow will be explained below.

[1622] Step 1:

[1623] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[1624] Step 2:

[1625] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[1626] Step 3:

[1627] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[1628] Step 4:

[1629] Server: Receives the transmitted information and stores it in a database.

[1630] Step 5:

[1631] Server: Sends the stored information to the AI ​​analysis module.

[1632] Step 6:

[1633] Server (AI module): Analyzes business plans and generates text to clarify business direction and IPO goals.

[1634] Step 7:

[1635] Server: Sends the generated AI analysis results to the expert review module.

[1636] Step 8:

[1637] User (expert): Logs in to a dashboard exclusively for experts and receives notifications of AI analysis results.

[1638] Step 9:

[1639] Device (Expert): Check notifications and review AI analysis results from the dashboard.

[1640] Step 10:

[1641] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[1642] Step 11:

[1643] Server: Receives the analysis results after the expert's corrections, generates a final report, and stores the generated final report in a database.

[1644] Step 12:

[1645] Server: Update the project status to "publishable" and add it to the publishing list.

[1646] Step 13:

[1647] User (Investor): Access the platform and log in by entering their credentials on the login page.

[1648] Step 14:

[1649] Server: Verifies investor credentials and returns dashboard page if successful.

[1650] Step 15:

[1651] Terminal (Investor): Access the project listing page from the dashboard and search for projects that interest you.

[1652] Step 16:

[1653] Server: Returns a list of publicly available projects based on the investor's request.

[1654] Step 17:

[1655] Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[1656] Step 18:

[1657] Server: Returns detailed information and final report for the selected project.

[1658] Step 19:

[1659] Terminal (Investor): Check the detailed report of the project and decide to invest.

[1660] Step 20:

[1661] Terminal (Investor): Enter the investment amount and press the invest button.

[1662] Step 21:

[1663] Server: Stores the investor's investment intention and investment amount in a database, and also sends a notification of investment completion to the business operator.

[1664] Step 22:

[1665] Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module.

[1666] Step 23:

[1667] Server (AI module): Generates detailed reports on how funds are being used and stores them in a database.

[1668] Step 24:

[1669] Server: Sends a detailed report to the operator.

[1670] Step 25:

[1671] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[1672] Example 1

[1673] 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."

[1674] For businesses that need to raise funds, analyzing business plans and clarifying how the funds will be used are extremely important. However, with conventional methods, analyzing business plans and explaining them to investors is cumbersome, making it difficult to raise funds effectively. It is also difficult for investors to grasp the reliability of the investment target and the specific use of funds. For this reason, a transparent and efficient system is needed to effectively match businesses with investors.

[1675] 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.

[1676] In this invention, the server includes: means for a business operator to input information such as a business plan, fundraising goals, and planned IPO timing; means for saving the input information in a database; means for analyzing the saved information using artificial intelligence to clarify the business direction and IPO goals; means for sending the results of the analysis by the artificial intelligence to an expert for review; means for creating a final report based on the expert's review and saving the final report in a database; means for an investor to view the final report and decide on an investment; means for saving the investor's investment intention and investment amount in a database and notifying the business operator; means for an investor to search for projects of interest on a project list page; and means for re-analyzing the project registration information by the business operator and generating a detailed report on how the funds will be used, thereby enabling a transparent and efficient fundraising process between business operators and investors.

[1677] A "business operator" is an individual or legal entity that needs financing and registers a business plan and enters information.

[1678] "Artificial intelligence" refers to machine learning algorithms and natural language processing engines that analyze input data and clarify business direction and IPO goals.

[1679] "Database" refers to a data storage system for storing information entered by businesses and investors, as well as analysis results and final reports.

[1680] "Review" refers to the process in which experts check the results of analysis by artificial intelligence and make corrections or additions.

[1681] The "final report" is a detailed business plan and use of funds report prepared based on the results of the AI ​​analysis and expert review.

[1682] "Investor" means an individual or legal entity that has an interest in a project and decides to invest.

[1683] "Funding target" refers to the specific amount of money that a business requires to carry out a project.

[1684] "IPO" refers to an initial public offering, where a business makes its shares publicly available on the market.

[1685] "Project Listing Page" means a web page where investors can search and view publicly available project information.

[1686] A "detailed report" is a report that uses artificial intelligence to analyze projects that have reached their funding goal and detail how the funds will be used.

[1687] A specific embodiment for implementing this invention is described below. The system of the invention matches businesses needing financing with investors, and uses a combination of AI and expert knowledge. This system consists of the following components:

[1688] Hardware and Software

[1689] 1. Server: Provides functions such as data storage, processing, analysis, notification, and AI module launch. Specifically, it includes a database, API server, AI analysis module, expert review module, and notification system.

[1690] 2. Terminal: A computer or mobile device used by an entrepreneur, professional or investor, on which a browser or dedicated application is installed.

[1691] 3. AI module: Uses a natural language processing engine (e.g., GPT-4) to analyze business plans.

[1692] 4. Database: A storage system for storing project information, analysis results, final reports, investment information, etc.

[1693] Data processing and calculation

[1694] 1. Data entry: The business operator enters information such as the business plan, fundraising target, and planned IPO timing using a browser or a dedicated application.

[1695] 2. Data storage: The server receives the entered information, generates SQL queries to store it in the database, and stores the information.

[1696] 3. Data analysis: The server sends the stored information to an AI analysis module, which analyzes the data and generates text that clarifies the business direction and IPO goals.

[1697] 4. Expert review: The server sends the AI ​​analysis results to the expert review module, where the experts review the results and make corrections if necessary.

[1698] 5. Generate Final Report: The server generates the final report and stores it in the database. The final report includes details of the business plan, fundraising goals, IPO plans, etc.

[1699] 6. Investor Access: Investors can access the project listing page and search for projects they are interested in. They can open the details page and view the final report.

[1700] 7. Investment decision: The investor enters the investment amount and decides to invest. The server receives the investment information, stores it in the database, and notifies the operator.

[1701] 8. Reaching the funding goal: The server checks the information of the projects that have reached their funding goal, performs AI analysis again, and generates a detailed report on how the funds will be used.

[1702] Specific examples

[1703] For example, suppose Business A wants to raise funds for a new technology startup. Business A accesses the platform and registers by entering their business plan, fundraising goals, and planned IPO date. The data is then stored in a database via the server.

[1704] The AI ​​module analyzes the business plan and sends the results to an expert review module, where the experts review the analysis, make corrections, and create a final report, which is then stored in a database and made available to investors.

[1705] Investor B accesses the platform, becomes interested in Business A's project, and after checking the details, decides to invest. The investment information is stored in a database via the server and notified to Business A.

[1706] When the investment target amount is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies Business A. This allows Business A to use the funds as planned and move forward with the project.

[1707] Prompt Sentence Examples

[1708] Below are some example prompts to input to the generative AI model:

[1709] Analyze a business plan outline and generate a text that specifically articulates the IPO goals. Project details:

[1710] Business plan: [Detailed business plan content]

[1711] Funding goal: [target amount]

[1712] Planned IPO date: [Specific date]

[1713] In this way, this invention combines AI analysis with expert review to provide a transparent and efficient fundraising process between businesses and investors.

[1714] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1715] Step 1:

[1716] Business Login

[1717] The user (business operator) accesses the platform's login page and enters their user ID and password. The entered information is sent and reaches the server. The server compares the user ID and password with the database for authentication, and if successful, starts a login session. If the login is successful, a new project registration page is returned to the business operator's terminal.

[1718] Input: User ID, Password

[1719] Specific operations: login authentication, session start

[1720] Output: New project registration page

[1721] Step 2:

[1722] Entering and submitting project information

[1723] On the new project registration page, businesses enter their business plan, fundraising goals, and planned IPO timing. After entering this information and pressing the submit button, the information is sent to the server, which then generates an SQL query to store the received information in the database and executes the insert operation.

[1724] Input: Business plan, fundraising target, planned IPO date

[1725] Specific actions: Entering information, sending, saving to database

[1726] Output: Save information to a database

[1727] Step 3:

[1728] Sending data to the AI ​​module

[1729] The server sends the information stored in the database to an AI analysis module, which receives the project information, invokes a natural language processing engine (e.g., GPT-4) to analyze the business plan, and generates text that clarifies the business direction and IPO goals.

[1730] Input: Project information stored in the database

[1731] Specific operations: receiving and sending information, launching AI analysis module, generating text

[1732] Output: Parsed text

[1733] Step 4:

[1734] Submitting analysis results to an expert review module

[1735] The server sends the analysis results generated by the AI ​​module to the expert review module, where experts access the review dashboard to receive notifications of new analysis results, review the results, and make corrections or additions as necessary.

[1736] Input: Text of the analysis results generated by the AI

[1737] Specific actions: Submit information, expert review

[1738] Output: Corrected and confirmed results

[1739] Step 5:

[1740] Generate the final report

[1741] The server generates a final report based on the results of the expert review, which is then stored in a database by the server, and includes details of the business plan, fundraising targets, and IPO plans.

[1742] Input: Expert-reviewed text

[1743] Specific operation: Create and save the final report

[1744] Output: Final report

[1745] Step 6:

[1746] Investor Login and Project Search

[1747] A user (investor) accesses the platform and logs in. After successful login, the investor can access the project list page. The investor searches for a project of interest and sends a request to the server to display detailed information. The server returns the relevant project information and final report from the database.

[1748] Input: Investor login information, project search request

[1749] Specific operations: login authentication, project information search, information submission

[1750] Output: Project information, final report

[1751] Step 7:

[1752] Investment decisions and inputs

[1753] After checking the detailed project report, the investor decides to invest. By entering the investment amount and pressing the "Invest" button, the information is sent to the server. The server receives the investment information, stores it in a database, and sends a notification of investment completion to the business operator.

[1754] Input: investment amount, investment confirmation operation

[1755] Specific operations: receiving investment information, saving it in a database, and notifying businesses

[1756] Output: Save to database, notify business operator

[1757] Step 8:

[1758] What happens after the fundraising goal is reached?

[1759] The server checks the information of projects that have reached their funding goals. It then performs another AI analysis and generates a detailed report on how the funds will be used. This detailed report is then saved in a database by the server and notified to the project owner. The project owner then checks the detailed report and implements a specific plan for how the funds will be used.

[1760] Input: Project information that has reached its funding goal

[1761] Specific operations: Check information, reanalyze, create and save detailed reports, notify business operators

[1762] Output: Detailed report, notification to business operators

[1763] By explaining in detail the specific operations and data flow at each step, it becomes easier to understand the function of the entire system, and the content can be useful for actual implementation.

[1764] (Application example 1)

[1765] 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."

[1766] It has traditionally been difficult for businesses seeking funding to conduct accurate and reliable business plan analysis and provide transparent information to investors. It also requires a great deal of effort for investors to quickly view multiple projects and make appropriate investment decisions. To solve these challenges, a platform is needed that integrates AI technology and expert knowledge to efficiently and effectively analyze business plans and fund usage, and present the results to investors.

[1767] 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.

[1768] In this invention, the server includes means for a business operator to have an AI model analyze a business plan for a project and store the generated text in a database, means for sending the generated analysis results to an expert for review and storing the expert's review results in the database, and means for using the AI ​​model to generate prompt text for investors to view project details and make investment decisions. This enables business operators to efficiently present high-quality business plans and investors to make quick investment decisions based on reliable information.

[1769] Key Word Definitions

[1770] "Business operator" refers to a corporation or individual that has a business plan and needs to raise funds.

[1771] "Investor" means a legal entity or individual whose purpose is to provide funding to a Project.

[1772] A "business plan" is a document that details the outline, goals, progress, and projected income and expenditures of a particular project.

[1773] A "funding target" is a specific amount of money that an entity needs to carry out a project.

[1774] "Planned IPO date" refers to the time when a business plans to go public (IPO).

[1775] A "database" is a system for efficiently storing, managing, and retrieving information.

[1776] An "AI model" is a mathematical model that uses artificial intelligence algorithms to solve specific problems.

[1777] An "expert" is a person who has advanced knowledge and experience in a particular field.

[1778] A "prompt" is an instruction given to an AI model to perform a specific task.

[1779] "Online Platform" means a service that can be accessed by users via the Internet.

[1780] "Generated text" refers to the text that the AI ​​model generates as a result of analyzing the business plan.

[1781] "Analysis results" refer to the conclusions and proposals that AI draws from analyzing the business plan.

[1782] "Review results" refers to the final evaluation after experts have evaluated and corrected the analysis results generated by AI.

[1783] "Means to View Details" refers to the interface or functionality that allows a user to view detailed information about a particular project.

[1784] MODE FOR CARRYING OUT THE INVENTION

[1785] The following describes an embodiment of this invention. This system is an online platform that efficiently matches businesses needing fundraising with investors, analyzes business plans, and clarifies how funds will be used. The system combines AI analysis models and expert reviews to provide investors with highly reliable information.

[1786] Project registration by business operators

[1787] Terminal (operator)

[1788] Businesses register by entering information such as their project's business plan, fundraising goals, and planned IPO timing into the online platform, which is then stored in a database by the server.

[1789] AI-based business plan analysis

[1790] server

[1791] After the input information is saved in the database, an AI analysis module is launched to analyze the business plan. The generated text is saved back in the database as the analysis results. The AI ​​model used could be a generative AI model such as OpenAI's GPT-3.

[1792] Expert review and final report preparation

[1793] User (expert)

[1794] Experts review the results of the AI ​​analysis from a dedicated dashboard, and after the experts' evaluation and corrections are made, the results are saved on the server as a final report.

[1795] Investors can view projects and make investment decisions

[1796] Terminal (Investor)

[1797] Investors access the platform and view the details of the project. In order for investors to check the detailed information of the project, the server generates a prompt message and provides information according to the investor's request. Examples of prompt messages include the following:

[1798] Business plan for registered projects:

[1799] "This project aims to develop new markets in the field of image processing using innovative AI technology."

[1800] Prompt statement:

[1801] "Analyze this business plan and highlight key points and market value:"

[1802] Investment and fundraising completion

[1803] server

[1804] After the investor decides to invest, the server stores the investment intention and investment amount in a database and notifies the business. If the fundraising goal is reached, the server performs another AI analysis, generates a detailed report on how the funds will be used, and notifies the business.

[1805] As described above, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process. The hardware used includes servers and user devices, and the software includes the Flask framework, SQLite database, and OpenAI's GPT-3.

[1806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1807] Program processing flow

[1808] Step 1:

[1809] Businesses log in to the platform using their terminals, input information such as the business plan, fundraising goals, and planned IPO timing for their new projects, and then submit it. This input information is received by the server and stored in the database. (Input: business plan, fundraising goals, planned IPO timing / Output: project information stored in the database)

[1810] Step 2:

[1811] The server sends the saved business plan information to the AI ​​analysis module. The AI ​​analysis module analyzes the business plan and generates text to clarify the business's direction and market value. This generated text is sent back to the server and saved in a database. (Input: Saved business plan information / Output: Text of the analysis results by AI)

[1812] Step 3:

[1813] The server sends the generated AI analysis results to an expert for review. The expert uses the user's device to check the analysis results and make corrections or additions as necessary. The corrected review results are returned to the server and stored in a database. (Input: AI analysis results / Output: Review results including corrections and additions made by the expert)

[1814] Step 4:

[1815] The server creates a final report based on the expert review results and stores it in a database. This final report is then made available to investors. (Input: Expert review results / Output: Final report)

[1816] Step 5:

[1817] Investors log in to the platform using their terminals and view project details. The server uses a generative AI model to generate prompts to provide detailed project information and a final report to investors. (Input: Viewing request from investor / Output: Generated prompts and project details)

[1818] Step 6:

[1819] The investor views the project details page and decides to invest. The investor enters the investment amount and presses the investment button. The server saves the investor's investment intention and investment amount in a database and notifies the business operator. (Input: Investor's investment intention and investment amount / Output: Investment information saved in the database and notification to the business operator)

[1820] Step 7:

[1821] If the fundraising goal is reached, the server performs another AI analysis and generates a detailed report on how the funds will be used. The generative AI model is used to create the detailed report, which is then saved in a database and notified to the project owner. (Input: Project information on the project that reached the fundraising goal / Output: Detailed report and notification to the project owner)

[1822] In this way, the system enables businesses, experts, and investors to interact through an online platform, enabling an efficient and transparent fundraising process.

[1823] 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.

[1824] The following describes an embodiment of this invention. This system is a platform for matching businesses needing fundraising with investors, and utilizes AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, this system enhances the accuracy of information and decision-making support by incorporating an emotion engine that recognizes the user's emotions.

[1825] Project registration by business operators

[1826] 1. User (business operator): First, the business operator accesses the platform and enters their authentication information on the login page. After successful login, they can access the new project registration page.

[1827] 2. Terminal (business operator): The business operator enters the necessary information such as the business plan, fundraising target, and planned IPO date on the new project registration page and presses the submit button.

[1828] 3. Server: Receives the transmitted information and stores it in a database. Once the storage is complete, it sends the stored information to the AI ​​analysis module.

[1829] Analysis by emotion engine

[1830] 1. Server (emotion engine): Analyzes the information input by the business operator and generates emotional data. For example, it determines the business operator's emotional state, such as optimistic, pessimistic, or excited.

[1831] 2. Server: Reflects the generated emotion data into the business plan and proposes revisions as necessary.

[1832] AI-based business plan analysis

[1833] 1. Server (AI module): The AI ​​analysis module analyzes the business plan and generates text to clarify the business direction and IPO goals.

[1834] 2. Server: Sends the generated analysis results to the expert review module.

[1835] Expert review and final report preparation

[1836] 1. User (expert): The expert accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data.

[1837] 2. Terminal (expert): Checks the analysis results and emotion data, and makes corrections or additions as necessary.

[1838] 3. Server: Receives the analysis results after the experts have completed their corrections, generates a final report based on them, stores it in the database, and updates the project status to "Ready for publication."

[1839] Investors viewing projects

[1840] 1. User (Investor): Investor accesses the platform and enters their authentication information on the login page. If authentication is successful, they can access the dashboard page.

[1841] 2. Terminal (Investor): Go to the project list page from the dashboard and search for the project you are interested in.

[1842] 3. Server: Returns a list of publicly available projects based on the investor's request.

[1843] 4. Terminal (Investor): Select the project you are interested in from the list of projects displayed and open the details page.

[1844] 5. Server: Returns detailed information and final report for selected projects.

[1845] Sentiment analysis when investors browse projects

[1846] 1. Server (emotion engine): When investors view project reports, their reactions are analyzed in real time and emotion data is generated.

[1847] 2. Server: The generated emotion data is fed back to the business.

[1848] Investor investment decisions

[1849] 1. Terminal (Investor): The investor reviews the detailed project report and decides to invest.

[1850] 2. Terminal (Investor): Enter the investment amount and press the invest button.

[1851] 3. Server: Stores the received investment intention and investment amount in a database and notifies the business operator.

[1852] What happens if the funding goal is reached?

[1853] 1. Server: Sends information about projects that have reached their funding goal back to the AI ​​analysis module and sentiment engine.

[1854] 2. Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[1855] 3. Server: Notifies the operator of the generated detailed report.

[1856] Specific examples

[1857] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[1858] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[1859] In this way, the present invention provides a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[1860] The processing flow will be explained below.

[1861] Step 1:

[1862] User (business operator): Accesses the platform login page and logs in by entering a username and password.

[1863] Step 2:

[1864] Server: Receives the business's login information, verifies the authentication information in the database, and returns the project registration page if authentication is successful.

[1865] Step 3:

[1866] Terminal (business operator): Access the project registration page, enter information such as the business plan, fundraising target, and planned IPO date, and press the submit button.

[1867] Step 4:

[1868] Server: Receives the transmitted information and stores it in a database.

[1869] Step 5:

[1870] Server: Sends the stored information to the AI ​​analysis module.

[1871] Step 6:

[1872] Server (emotion engine): Analyzes text data entered by businesses and generates emotional data. For example, it determines the emotional state of the input, such as whether the input is positive or anxious.

[1873] Step 7:

[1874] Server: Reflects the generated emotion data in the analysis of business plans and proposes revisions to the business operator as necessary.

[1875] Step 8:

[1876] Server (AI module): Analyzes business plan data and generates detailed analysis results to clarify business direction and IPO goals.

[1877] Step 9:

[1878] Server: Sends the generated analysis results to the expert review module.

[1879] Step 10:

[1880] User (expert): Logs in to an expert-only dashboard and receives notifications of new AI analysis results and emotion data.

[1881] Step 11:

[1882] Terminal (expert): Check the AI ​​analysis results and emotion data from the dashboard and review them.

[1883] Step 12:

[1884] User (expert): Corrects or supplements the AI ​​analysis results as needed and finalizes the final report.

[1885] Step 13:

[1886] Server: Receives the analysis results after the experts' corrections, generates the final report, and stores it in the database.

[1887] Step 14:

[1888] Server: Add the final report to the list of publicly available projects and update it so that it is available for investors to view.

[1889] Step 15:

[1890] User (Investor): Access the platform and log in by entering credentials on the investor login page.

[1891] Step 16:

[1892] Server: Verifies investor credentials and returns dashboard page if successful.

[1893] Step 17:

[1894] Terminal (Investor): Go to the project listing page from the dashboard and search for the project you are interested in.

[1895] Step 18:

[1896] Server: Returns a list of publicly available projects in response to investor requests.

[1897] Step 19:

[1898] Terminal (Investor): Select the project you are interested in from the project list and open the details page.

[1899] Step 20:

[1900] Server: Returns detailed information and final report for the selected project.

[1901] Step 21:

[1902] Server (emotion engine): When investors view detailed project reports, their reactions are analyzed in real time and emotion data is generated.

[1903] Step 22:

[1904] Server: Provides feedback to the business operator on the generated emotion data.

[1905] Step 23:

[1906] Terminal (Investor): Check the detailed report of the project and decide to invest.

[1907] Step 24:

[1908] Terminal (Investor): Enter the investment amount and press the invest button.

[1909] Step 25:

[1910] Server: Stores the investor's investment intention and investment amount in a database and notifies the business operator.

[1911] Step 26:

[1912] Server: Sends information about projects that have reached their funding goal to the AI ​​analysis module and sentiment engine for further analysis.

[1913] Step 27:

[1914] Server (AI module and emotion engine): Generates detailed reports on how funds are being spent and stores them in a database.

[1915] Step 28:

[1916] Server: Sends a detailed report to the operator.

[1917] Step 29:

[1918] Terminal (operator): Check the detailed report, use the funds as planned, and begin the process of moving forward with the project.

[1919] Example 2

[1920] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1921] Traditional fundraising platforms do not take into account emotional data when analyzing the information entered by businesses or creating final reports, resulting in insufficient support for business planning accuracy and investor decision-making. Furthermore, the lack of feedback that reflects investors' emotional state prevents a fundraising process that takes into account the psychological aspects of investors.

[1922] 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.

[1923] In this invention, the server includes means for analyzing input information from business operators and investors using an emotion engine to generate emotion data, means for revising business plans based on the emotion data, means for performing real-time emotion analysis when investors view project reports, and means for feeding back the investor's emotion data to the business operator. This allows the emotion data to be reflected in business plans and final reports, enabling a fundraising process that takes into account the emotional state of investors.

[1924] "Business operator" refers to an entity that registers plans and information about a project requiring funding on the platform.

[1925] "Investor" means an entity that invests in a project offered on the Platform.

[1926] "Business Plan" means the information entered by an operator into the Platform, including project details, fundraising targets, and planned IPO timing.

[1927] A "fundraising goal" is a goal that indicates the specific target amount of funds required by the business operator to implement the project.

[1928] The "emotion engine" is a software system that analyzes the emotional state and generates specific emotional data based on the information that businesses and investors enter or view on the platform.

[1929] "Emotion data" is data that indicates the emotional state of businesses and investors, generated as a result of analysis by the emotion engine.

[1930] The "AI analysis module" is artificial intelligence software that analyzes input data such as business plans and clarifies project direction and IPO goals.

[1931] An "expert" is a person or group with the knowledge and experience to review the results of AI analysis and make any necessary corrections or additions.

[1932] The "Final Report" is a comprehensive report on the project that will be created based on the results of AI analysis, expert review, and emotional data.

[1933] "Ready for release" is a status that indicates that a final report has been prepared after expert review and is ready to be made public to investors.

[1934] "Project Report" is synonymous with Final Report and means a report that includes the overall content and evaluation results of the project.

[1935] A "database" is an information aggregation system managed by a server that stores and manages data such as business plans, sentiment data, final reports, and investor information.

[1936] This invention is a platform that matches businesses needing funding with investors, utilizing AI and expert knowledge to analyze business plans and clarify how funds will be used. Furthermore, by combining this system with an emotion engine that recognizes user emotions, the accuracy of information and decision-making support are enhanced.

[1937] System configuration

[1938] The system mainly consists of the following components:

[1939] 1. Server

[1940] 2. Terminal

[1941] 3. Users (businesses, investors, experts)

[1942] 4. Database

[1943] 5. AI Module

[1944] 6. Emotion Engine

[1945] Hardware and software used

[1946] Hardware: Server, user terminal (PC, smartphone)

[1947] Software: AI analysis module, emotion engine, database management system

[1948] Program processing overview

[1949] Project registration by business operators

[1950] The user (business operator) accesses the platform and enters authentication information on the login page. If authentication is successful, they are directed to a new project registration page. There, they enter the necessary information, such as the business plan, fundraising target, and planned IPO timing, and press the "Submit" button. The information sent from the terminal (business operator) is received by the server and stored in a database. This information is then sent to the AI ​​analysis module.

[1951] Analysis by emotion engine

[1952] The server (emotion engine) analyzes the business's input information and generates emotional data. For example, it determines whether the business is optimistic, pessimistic, excited, etc. The emotional data is reflected in the business plan and, if necessary, proposes revisions.

[1953] AI-based business plan analysis

[1954] The server (AI module) analyzes the business plan and generates text that clarifies the business direction and IPO goals. The analysis results are sent to the expert review module.

[1955] Expert review and final report preparation

[1956] The user (expert) accesses a dedicated dashboard and receives notifications of the AI ​​analysis results and emotional data. The user (expert) checks the analysis results and emotional data on the device and makes corrections or additions as necessary. The server receives the corrected analysis results, generates a final report based on them, and stores it in the database. The project status is updated to "Ready for release."

[1957] Investors viewing projects

[1958] The user (investor) accesses the platform and enters authentication information on the login page. If authentication is successful, the user is taken to the dashboard page. On the terminal (investor), the user moves from the dashboard to the project list page and searches for a project of interest. The server returns a list of publicly available projects based on the investor's request. The terminal (investor) selects a project of interest from the list of projects displayed and opens the details page. The server returns detailed information and a final report for the selected project.

[1959] Sentiment analysis when investors browse projects

[1960] When investors view project reports, the server (emotion engine) analyzes their reactions in real time and generates emotional data, which is then fed back to the business.

[1961] Investor investment decisions

[1962] The investor checks the detailed project report on the terminal and decides to invest. He enters the investment amount and presses the "Invest" button. The server stores the received investment intention and investment amount in the database and notifies the business operator.

[1963] What happens if the funding goal is reached?

[1964] The server sends information about projects that have reached their funding goal back to the AI ​​analysis module and emotion engine. The server (AI module and emotion engine) generates a detailed report on how the funds will be used and stores it in a database. The generated detailed report is then notified to the business operator.

[1965] Specific examples

[1966] For example, Business A launches a new project related to environmental technology and registers information on the platform with the aim of raising funds. The information entered by Business A is first analyzed by an emotion engine, which generates optimistic emotion data. This emotion data is then reflected in AI analysis, clarifying the direction of the business plan. Experts then review the content and complete the final report.

[1967] Next, Investor B becomes interested in this project and views a detailed report. As Investor B reads the report, the emotion engine analyzes his reaction and feeds that emotional data back to Business A. Investor B ultimately decides to invest, inputs and submits the investment amount. When the fundraising goal is reached, the system again uses the emotion engine and AI analysis to provide a detailed report on how the funds will be used and notifies Business A. This enables Business A to use the funds as planned and deploy new environmental technologies.

[1968] In this way, the present invention is a system that makes the fundraising process more transparent and efficient by incorporating emotion engine data into AI analysis and expert review.

[1969] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1970] Step 1:

[1971] A user (business operator) accesses the platform and enters authentication information on the login page. The entered authentication information (username, password) is sent to the server. The server receives the authentication information and checks it against the database. If authentication is successful, the server returns access permission, and the user can access the new project registration page.

[1972] Step 2:

[1973] The terminal (business operator) moves to the new project registration page, enters the necessary information such as the business plan, fundraising target, and planned IPO timing, and presses the submit button. The input data (business plan, fundraising target, planned IPO timing) is sent to the server, which receives it and stores it in a database.

[1974] Step 3:

[1975] The server sends the stored information to the AI ​​analysis module. The business plan, fundraising goals, and planned IPO timing stored in the database are input into the AI ​​analysis module. The AI ​​analysis module analyzes the data and clarifies the business direction and IPO goals. The analysis results (business direction, IPO goals) are generated and output to the server.

[1976] Step 4:

[1977] The server (emotion engine) analyzes the business's input information and generates emotion data. The business plan and input text are sent to the emotion engine. The emotion engine uses natural language processing to generate emotion data (optimistic, pessimistic, etc.) and returns it to the server. The server reflects the generated emotion data in the business plan and suggests revisions as necessary.

[1978] Step 5:

[1979] The server sends the generated emotion data and AI analysis results to the expert review module, which then notifies the expert on their dashboard.

[1980] Step 6:

[1981] The user (expert) accesses a dedicated dashboard and receives notifications of AI analysis results and emotional data. By clicking on the notification, the AI ​​analysis results and emotional data are displayed on the device.

[1982] Step 7:

[1983] The terminal (expert) checks the analysis results and emotion data, and makes corrections or supplements as necessary. The expert checks the displayed data, enters corrections or comments, and presses the save button. The corrections and supplemental data are sent to the server.

[1984] Step 8:

[1985] The server receives the analysis results after the experts have completed the corrections and generates a final report based on them. A script is executed based on the corrected analysis results to generate a final report. The generated final report is saved in the database and the project status is updated to "Ready for release."

[1986] Step 9:

[1987] A user (investor) accesses the platform and enters their authentication information on the login page. The entered authentication information (username, password) is sent to the server and checked against the database. If authentication is successful, the server returns permission to access the dashboard page.

[1988] Step 10:

[1989] The terminal (investor) navigates from the dashboard to the project list page and searches for projects they are interested in. A project search request is sent to the server. The server retrieves projects with a "publishable" status from the database and returns the list to the terminal.

[1990] Step 11:

[1991] The terminal (investor) selects a project of interest from the displayed project list and opens the details page. The selected project ID is sent to the server. The server retrieves detailed information and a final report from the database and returns it to the terminal.

[1992] Step 12:

[1993] When investors view a project report, the server (emotion engine) analyzes their reactions in real time and generates emotion data. Data such as the investor's mouse movements and viewing time are input, and the emotion engine analyzes this to generate emotion data. The generated emotion data is returned to the server.

[1994] Step 13:

[1995] The server feeds back the generated emotion data to the business operator, who then notifies the business operator's dashboard of the generated emotion data.

[1996] Step 14:

[1997] The terminal (investor) checks the detailed report of the project and decides to invest. They enter the investment amount and press the "Invest" button. The investment amount and investment decision are sent to the server.

[1998] Step 15:

[1999] The server stores the received investment intention and investment amount in a database and notifies the business operator. The saved investment data is notified to the business operator's dashboard.

[2000] Step 16:

[2001] The server again sends information about the projects that have reached their funding goal to the AI ​​analysis module and emotion engine. It retrieves the funding status from the database and sends the data to the AI ​​module and emotion engine if the goal is reached.

[2002] Step 17:

[2003] The server (AI module and emotion engine) generates a detailed report on how the funds are being used. The data is analyzed, a detailed report is generated, and sent back to the server. The generated detailed report is stored in a database.

[2004] Step 18:

[2005] The server notifies the operator of the generated detailed report, and sends a notification to the operator's dashboard that the report has been generated, along with a link to the detailed report.

[2006] (Application example 2)

[2007] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2008] Traditional fundraising platforms often lack transparency in business plan analysis and investment decisions, and lack support for decision-making that takes sentiment data into account, which can reduce the efficiency and reliability of fundraising and create unnecessary risks and uncertainty between investors and businesses.

[2009] 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 a business operator needing fundraising to input information such as a business plan, fundraising target, and planned public offering date; means for storing the input information in a data base; means for analyzing the stored information using artificial intelligence to clarify the business's direction and public offering target; means for sending the analysis results from the artificial intelligence to an individual with specialized knowledge and requesting a review; means for creating a final report based on the review results and storing the final report in the data base; means for investors to view the final report and decide on an investment; means for storing the investor's investment intention and investment amount in the data base and notifying the business operator; means for analyzing users' (investors') reactions in real time when they view the project detail report and generating emotion data; and means for feeding the generated emotion data back to the business operator. This improves the transparency and efficiency of the fundraising process and enables decision-making support that takes emotion data into consideration.

[2010] "Fundraising" means raising funds necessary for business activities from outside sources.

[2011] "Business" refers to any person or legal entity that conducts its business and carries on activities related to that business.

[2012] A "business plan" is a document that outlines the direction and details of a business venture that a company will undertake in the future.

[2013] A "fundraising target" is a target amount set by a business to raise specific funds.

[2014] The "public offering date" is the date on which stocks or securities are scheduled to be offered for sale to the general public in the market.

[2015] "Means of input" refers to the interface or method by which a user inputs the required information into the system.

[2016] A "data base" refers to a database or server for storing and managing information.

[2017] "Artificial intelligence" is a computer system that mimics human intelligence and automatically learns and makes decisions.

[2018] "Analysis" is the process of examining data or information in detail and finding meaning.

[2019] An "individual with specialized knowledge" refers to someone who has advanced knowledge or experience in a particular field.

[2020] "Review" is the process of checking, evaluating, and providing feedback on submitted information and analysis results.

[2021] "Final Report" means a completed, formal report that reflects the results of the review.

[2022] "Investor" refers to an individual or legal entity that provides funds and manages assets.

[2023] "Investment intent" refers to an investor's willingness or willingness to invest funds in a particular project.

[2024] "Emotional data" is digital information that reflects a user's emotional state.

[2025] "Feedback means" refers to the process or method of retransmitting collected or generated information to the underlying data provider.

[2026] The system of this invention begins when a business seeking funding enters information such as their business plan, fundraising goals, and planned public offering date, and stores it in a data base. The stored information is analyzed using artificial intelligence to clarify the business's direction and public offering goals. The analysis results are sent to individuals with specialized knowledge for review. A final report is prepared based on the review results and stored in the data base. Investors can view this final report and decide to invest. The investor's investment intentions and investment amounts are stored in the data base and notified to the business. The system also includes a function to analyze investors' reactions to detailed project reports in real time and generate sentiment data. The generated sentiment data is fed back to the business.

[2027] Program processing explanation

[2028] Hardware and software used

[2029] Hardware: Smartphones, servers

[2030] Software: Application frameworks (React Native, Swift, Kotlin, etc.), server-side frameworks (Node.js, Django, etc.), artificial intelligence models (TensorFlow, PyTorch), databases (MySQL, PostgreSQL)

[2031] Program processing details

[2032] 1. User (business operator) enters

[2033] Businesses log in to the application using their smartphones and enter information about their new projects (business plan, fundraising target, and public offering date).

[2034] The entered information is sent to the server and stored at the data center.

[2035] 2. Analysis using artificial intelligence

[2036] The server transmits the information stored in the data base to an artificial intelligence analysis module.

[2037] An artificial intelligence analysis module (TensorFlow or PyTorch) analyzes the business plan and generates text that clarifies the business direction and public offering goals.

[2038] The generated analysis results are sent to individuals with specialized knowledge.

[2039] 3. Review by an individual with specialized knowledge

[2040] Individuals with specialized knowledge can access a dedicated dashboard to review the results of the AI ​​analysis.

[2041] The final report will be prepared after making any necessary corrections or additions.

[2042] The final report is sent to a server and stored in a data center.

[2043] 4. Investor viewing of the project

[2044] Investors access the application using their smartphones to view the project's final report.

[2045] If an investor is interested, they decide to invest, enter their investment intention and investment amount, and submit.

[2046] The server stores the investment intention and investment amount in a data base and notifies the business operator.

[2047] 5. Emotional Data Feedback

[2048] When investors view detailed project reports, the sentiment engine analyzes their reactions in real time and generates sentiment data.

[2049] The generated emotion data is fed back to the business operator.

[2050] Examples of concrete examples and prompts

[2051] As a concrete example, consider a situation where a business operator registers an environmental technology project and an investor invests in it. In this case, when the business operator enters project information through a smartphone app, the emotion engine analyzes the information and evaluates the business operator's emotional state. Next, an AI analysis module analyzes the direction of the business plan, which is then reviewed by an individual with specialized knowledge. When the investor uses the app to view the project and decides to invest, the emotion engine analyzes the investor's emotions and provides feedback on this emotional data to the business operator, streamlining the investment process.

[2052] Example prompt sentence:

[2053] Enter the details of your new environmental technology project. We'll use artificial intelligence and an emotion engine to analyze your business plan's direction and emotional state. Please describe your specific goals and plans.

[2054] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2055] Step 1:

[2056] The user (business owner) logs in to the application using a smartphone. The user enters their authentication information, which is then received by the server and verified against the data base. If verification is successful, the user can access the new project registration page.

[2057] Input: Credentials

[2058] Output: Authentication result

[2059] Step 2:

[2060] The user (business operator) enters the necessary information such as the business plan, fundraising target, and planned public offering date on the new project registration page. The entered information is sent to the server and stored in the data base.

[2061] Input: Business plan, fundraising target, planned public offering date

[2062] Output: Save confirmation message

[2063] Step 3:

[2064] The server sends the information stored in the data center to an AI analysis module (TensorFlow or PyTorch), which analyzes the data and generates text to clarify the direction of the business plan and the goals of the public offering.

[2065] Input: Saved business plan related information

[2066] Output: Analysis results

[2067] Step 4:

[2068] The server sends the generated analysis results to individuals with specialized knowledge, who then access a dedicated dashboard to receive and review the analysis results, making corrections or additions as necessary.

[2069] Input: Analysis results

[2070] Output: Reviewed analysis results

[2071] Step 5:

[2072] Based on the analysis results, which have been revised and supplemented, an individual with specialized knowledge will create a final report, which will be sent to a server and stored in the data center.

[2073] Input: Reviewed analysis results

[2074] Output: Final report

[2075] Step 6:

[2076] An investor logs into the application using a smartphone and accesses the project listing page. The investor searches for projects of interest and views the final report.

[2077] Input: investor login information, project search query

[2078] Output: Project list, final report

[2079] Step 7:

[2080] When investors view detailed project reports, the server's emotion engine analyzes their reactions in real time and generates emotion data, which is then fed back to the business operator.

[2081] Input: Real-time investor reactions

[2082] Output: Emotion data, feedback

[2083] Step 8:

[2084] When an investor decides to invest, they enter their investment intention and investment amount and send it to the server, which stores this information in a data base and notifies the business operator.

[2085] Input: Investment intention, investment amount

[2086] Output: Save confirmation message, notification

[2087] Step 9:

[2088] If the fundraising goal is reached, the server will again send the project information to the AI ​​analysis module, which will generate a detailed report on how the funds will be used, which will be stored in the data center and notified to the business operator.

[2089] Input:Project Information

[2090] Output: Detailed report, notifications

[2091] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2092] 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 summariza...

Claims

1. A means for businesses needing fundraising to input information such as their business plans, fundraising goals, and planned IPO timing; means for storing the input information in a database; The stored information will be analyzed using AI to clarify the business direction and IPO goals, A means for sending the analysis results by the AI ​​to an expert and requesting their review; a means for creating a final report based on the review results and storing the final report in a database; A means for investors to view the final report and make investment decisions; A means for storing the investor's investment intention and investment amount in a database and notifying the business operator; A system including:

2. A method to use AI to analyze information about projects that have reached their funding goal and generate detailed reports on how the funds will be used. a means for storing the detailed report in a database and notifying the business operator; The system of claim 1 further comprising:

3. 2. The system of claim 1, wherein project registration, report creation, investment decisions, and detailed reports on fund usage are all performed online.

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