System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving requests from users, a means for collecting relevant information from past successful cases and documents, a means for automatically generating a new business plan on the basis of the collected information, a means for providing feedback on the basis of review information and brush-up the business plan, and a means for providing information on subsidies and advertisement rates.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] New business planners spend a lot of time and effort creating business plans and preparing presentations, and are also burdened by repeated requests for revisions from investors and approvers. Given the current low success rate for new businesses, there is a need for support in creating efficient, high-quality business plans that utilize past success stories and lessons learned. [Means for solving the problem]

[0005] The present invention includes a means for receiving a user's request and collecting relevant information from past success stories and documents. It also includes a means for automatically generating a new business plan based on the collected information, and a means for providing feedback based on review information to refine the business plan. Additionally, it includes a means for providing information on subsidies and advertising fees. These means enable the efficient creation of highly accurate business plans, providing a system that can increase the success rate of new businesses.

[0006] "User" refers to an individual or organization that uses the System to request the creation of a new business plan.

[0007] "Request" refers to the request information that a user inputs to request the system to create a new business plan.

[0008] "Past success stories" refers to case information about business plans and business models that have been implemented in the past and have been successful.

[0009] "Documents" refers to all documents such as past business plans, review reports, pitch materials, etc.

[0010] "Means of collection" refers to the programs or algorithms used to search and retrieve the required information from databases, external APIs, etc.

[0011] A "new business plan" refers to a plan for a new business that includes market analysis, a business model, and a revenue and expenditure plan.

[0012] "Automatic generation means" refers to a program or algorithm that uses a generative AI model to automatically create a business plan based on collected information.

[0013] "Review information" refers to information on lessons learned and evaluations from past successes and failures.

[0014] "Feedback" refers to information that provides suggestions for improvement or advice regarding the generated business plan.

[0015] "Refinement measures" refers to programs or algorithms that incorporate feedback to improve and revise the contents of the business plan.

[0016] A "subsidy" refers to financial assistance provided by the government or public institutions to new businesses.

[0017] "Advertising fees" refers to the costs incurred for advertising and marketing activities when implementing a business plan.

[0018] "Means of delivery" refers to a program or algorithm that collects and provides users with up-to-date information on subsidies and advertising rates. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] System Overview

[0041] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[0042] Program processing flow (explained in natural language)

[0043] Receiving a request

[0044] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user might request, "I would like to create a business plan for a new social media app."

[0045] Collection of information

[0046] 2. The server analyzes the request received from the user and extracts the necessary keywords, such as "social media app," "market analysis," and "business model."

[0047] 3. The server uses internal databases and external APIs to search and collect past success stories, pitch videos, business plan documents, and review information, using algorithms to assess the quality and relevance of the collected data.

[0048] Generate a business plan

[0049] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) technology. The normalized data is then input into a generative AI model.

[0050] 5. The server launches the generative AI model and automatically generates a new business plan based on the input data. The business plan includes the following elements:

[0051] Business overview (e.g., concept and features of a new social media app)

[0052] Market analysis (e.g., social media market growth trends)

[0053] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0054] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[0055] Business model (e.g., monetization methods)

[0056] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0057] Providing Feedback

[0058] 6. The server reviews the generated business plan, references past success stories and reviews, and generates feedback. For example, it provides specific improvements such as, "An incentive plan in the early stages is important to increase user engagement."

[0059] 7. The server integrates the feedback and refines the generated business plan.

[0060] Providing supporting information

[0061] 8. The server collects information about the latest subsidy programs and advertising rates and provides relevant information to the user, such as "Current government subsidy programs can help cover part of the initial development costs."

[0062] Results distribution

[0063] 9. The server delivers the final, polished business plan to the user, who can then review it, download it, or make any further modifications necessary.

[0064] Specific examples

[0065] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[0069] Step 2:

[0070] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0071] Step 3:

[0072] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[0073] Step 4:

[0074] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[0075] Step 5:

[0076] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0077] Step 6:

[0078] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[0079] - Business overview (e.g., concept and features of a new social media app)

[0080] - Market analysis (e.g., social media market growth trends)

[0081] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0082] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[0083] - Business model (e.g., monetization methods)

[0084] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[0085] Step 7:

[0086] The server reviews the generated business plan and compares it with past success stories and review information to assess the plan's quality and identify areas for improvement.

[0087] Step 8:

[0088] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[0089] Step 9:

[0090] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[0091] Step 10:

[0092] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[0093] Example 1

[0094] 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."

[0095] The traditional process of creating a new business plan required a great deal of time and effort to search and analyze vast amounts of information, and to create and review documents. It also required time and effort to manually collect and consolidate the latest information on subsidies and advertising expenses. This situation could slow down the launch of a new business and lead to inaccurate plans.

[0096] 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.

[0097] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for normalizing the collected information and analyzing it using natural language processing technology, means for automatically generating a new business plan based on the normalized and analyzed data, means for reviewing the generated business plan and providing feedback based on past success stories and review information, means for integrating the generated feedback and improving the business plan, means for collecting and providing the latest information on subsidies and advertising expenses, and means for delivering the generated and improved business plan to the user, thereby enabling the rapid and highly accurate creation of new business plans.

[0098] The "means for receiving a user request" is the function of the system that receives the request via the input device or software that the user uses to request the creation of a new business plan.

[0099] "Means for collecting relevant information from past success stories and documents" refers to the system's function of collecting data such as past successful business plans, related documents, pitch videos, and review information via an internal database or external API.

[0100] "Means of normalizing collected information and analyzing it using natural language processing techniques" refers to the process of converting collected data into a consistent format and analyzing the information using natural language processing techniques.

[0101] "Means for automatically generating new business plans based on normalized and analyzed data" refers to a system function that uses normalized and analyzed information to create new business plans using generative AI models or other automated technologies.

[0102] The "means of reviewing the generated business plan and providing feedback based on past success stories and review information" refers to a process for evaluating the contents of the automatically generated business plan in comparison with past success stories and review information, and generating specific feedback.

[0103] The "means for integrating the generated feedback and refining the business plan" is a function of the system for integrating the provided feedback and further improving the generated business plan.

[0104] "Means for collecting and providing the latest information on subsidies and advertising expenditures" refers to the process of collecting the latest information on subsidy programs and advertising expenditures from government agencies and related websites and providing it to users.

[0105] "Means for delivering the generated and refined business plan to the user" refers to the system's function of generating the final, refined business plan in a format such as PDF or Word and delivering it to the user via email or other means.

[0106] System Overview

[0107] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[0108] Hardware and software used

[0109] The system consists of the following hardware and software:

[0110] Server: Equipped with high-performance computing resources and database management systems. Examples include AWS EC2 instances and MySQL databases.

[0111] Device: The device used by the user. Examples include PCs, smartphones, and tablets.

[0112] Natural language processing (NLP) software: Python libraries such as spaCy and NLTK are used.

[0113] Generative AI models: Leverage advanced AI models such as OpenAI's GPT-4.

[0114] How it works

[0115] Receiving a request

[0116] 1. A user inputs and submits a request to create a new business plan via a web interface or application. Specifically, the user enters a summary of the business idea and necessary information into a dedicated form and clicks the "Submit" button.

[0117] Collection of information

[0118] 2. The server analyzes the request received from the user and extracts the necessary keywords using natural language processing (NLP) techniques. Specifically, it uses a Python NLP library (e.g., spaCy or NLTK) to identify the main topics and themes from the request text.

[0119] 3. The server searches and collects past success stories, pitch videos, business plan documents, and review information using an internal database or external API (e.g., Google Scholar API or other business data API). Specifically, it runs an SQL query against the case study database to retrieve a list of relevant documents. It then downloads the data from the URLs of the listed documents.

[0120] Generate a business plan

[0121] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) techniques, such as data cleaning and converting it into a consistent format. This is often done using the Python pandas library.

[0122] 5. The server launches a generative AI model (e.g., GPT-4) and inputs the normalized data. It generates a specific prompt and sends it to the model.

[0123] Example: "Write a business plan for a new social media app. Include the following elements: 1. Business Overview 2. Market Analysis 3. Competitive Analysis 4. SWOT Analysis 5. Business Model 6. Income and Expenditure Plan."

[0124] 6. The server receives the generated business plan and performs additional processing to format it, such as converting the generated text to Markdown or LaTeX formatting, for visual editing.

[0125] Providing feedback

[0126] 7. The server compares the generated business plan with past success stories and review information in an internal database and generates feedback. Specifically, it uses a review algorithm to evaluate the plan's contents and extract strengths and areas for improvement. For example, it suggests specific areas for improvement, such as "An incentive plan in the early stages is important to increase user engagement."

[0127] 8. The server integrates the feedback and makes additional modifications to the generated business plan, again using the generative AI model to generate a new version of the business plan that reflects the feedback.

[0128] Providing supporting information

[0129] 9. The server collects information about the latest subsidy programs and advertising rates using web scraping and APIs. Specifically, it retrieves and analyzes the latest subsidy information from government agency websites.

[0130] 10. The server processes the collected information and provides appropriate supporting information to the user, such as integrating it into a business plan as "Current government subsidy programs can help cover some of the initial development costs."

[0131] Results distribution

[0132] 11. The server generates the final, polished business plan as a PDF or Word document and delivers it to the user. Specifically, it creates the document using a document generation library (such as Python-docx or ReportLab) and emails the user a download link.

[0133] 12. The user reviews the received business plan and downloads it or makes further edits as needed. For example, the user can open the downloaded PDF in Adobe Acrobat and edit the details.

[0134] Specific examples

[0135] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[0136] Example prompts to input to the generative AI model

[0137] Write a business plan for a new social media app. Include the following elements:

[0138] 1. Business overview

[0139] 2. Market analysis

[0140] 3. Competitive analysis

[0141] 4. SWOT analysis

[0142] 5. Business Model

[0143] 6. Income and expenditure plan

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] The flow of this system's program processing

[0146] Step 1: Receiving the request

[0147] Processing content

[0148] A user inputs and submits a request to create a new business plan via a web interface or application.

[0149] Input: The user enters a summary of their business idea and the necessary information into a dedicated form and clicks the "Submit" button.

[0150] Output: The user's request data is sent to the server.

[0151] Specific operation: When the submit button of the form is pressed, the request data is sent to the server via the HTTP protocol and saved in the database.

[0152] Step 2: Analyze the information

[0153] Processing content

[0154] The server analyzes the request received from the user and extracts the necessary keywords.

[0155] Input: The user's request data saved in step 1.

[0156] Output: Keyword list (e.g. "social media app", "market analysis", "business model").

[0157] What happens: The server uses a Python NLP library (e.g., spaCy or NLTK) to parse and extract major topics and themes from the request.

[0158] Step 3: Gather information

[0159] Processing content

[0160] The server uses internal databases and external APIs to search and collect past success stories and related information.

[0161] Input: The keyword list extracted in step 2.

[0162] Output: A list of relevant documents, videos, and review information.

[0163] What it does: Based on the information collected, the server executes SQL queries against the database to retrieve relevant data, and also calls external APIs to retrieve additional information.

[0164] Step 4: Data normalization and analysis

[0165] Processing content

[0166] The information collected by the server is normalized and analyzed using natural language processing technology.

[0167] Input: Data collected in Step 3.

[0168] Output: Normalized and parsed data.

[0169] Specific operation: The server performs data cleaning (e.g., deduplication, format standardization), normalization using Python's pandas library, and then analyzes the data again using natural language processing technology.

[0170] Step 5: Automatically generate a business plan

[0171] Processing content

[0172] The server launches the generative AI model, inputs the normalized data, and automatically generates a business plan.

[0173] Input: The normalized and analyzed data from step 4.

[0174] Output: Early stage business plan.

[0175] Specific operation: The server generates and sends a prompt to the generative AI model (e.g., GPT-4). For example, the prompt might be, "Please write a business plan for a new social media app."

[0176] Step 6: Review and generate feedback

[0177] Processing content

[0178] The server reviews the generated business plan and generates feedback.

[0179] Input: The business plan generated in step 5.

[0180] Output: A list of feedbacks.

[0181] What it does: The server compares past success stories and existing review information, and uses a review algorithm to generate a rating and feedback for the generated business plan.

[0182] Step 7: Integrate feedback and refine

[0183] Processing content

[0184] The server integrates the generated feedback and refines the business plan.

[0185] Input: Feedback generated in step 6.

[0186] Output: An improved business plan.

[0187] Specific operation: The server uses the generative AI model again to create a new version of the business plan that reflects the feedback.

[0188] Step 8: Gather supporting information

[0189] Processing content

[0190] The server collects and provides information on the latest subsidy programs and advertising rates.

[0191] Input: Data from government websites and related APIs.

[0192] Output: Updates on subsidies and advertising expenditures.

[0193] Specific operation: The server collects the necessary information through web scraping and API calls, and provides it after evaluating and analyzing its quality.

[0194] Step 9: Delivering results

[0195] Processing content

[0196] The server delivers the final, polished business plan to the user.

[0197] Input: Your refined business plan from Step 7.

[0198] Output: Business plan in PDF or Word document format sent to user.

[0199] What it does: The server creates a document using a document generation library like Python-docx or ReportLab, and emails the user a download link.

[0200] Step 10: User Verification and Download

[0201] Processing content

[0202] The user reviews the business plan they receive and downloads or further modifies it as necessary.

[0203] Input: Business plan delivered in Step 9.

[0204] Output: A revised or ready-to-use business plan.

[0205] What happens: The user opens the email they received, clicks the download link to get the business plan, and then reviews and edits it in a PDF reader or word processor.

[0206] (Application example 1)

[0207] 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."

[0208] In the past, the process of creating a new business plan required a lot of time and effort. Furthermore, there were few ways to efficiently collect and incorporate feedback from past success stories and the latest subsidy information. This led to problems such as a decline in the quality and success rate of business plans.

[0209] 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.

[0210] In this invention, the server includes means for receiving a user's request to create a business plan for a new product or service, means for collecting related information from past success stories and market research data, means for automatically generating a new business plan using a generative AI model based on the collected information, means for providing feedback from review information and past success stories to refine the business plan, and means for providing information on subsidies and advertising. This makes it possible to efficiently generate high-quality business plans that reflect the latest information and feedback.

[0211] "User" refers to the entity that uses the system to create new business plans and collect related information.

[0212] "New products and services" refers to products and services that are new and have not yet appeared in the existing market.

[0213] "Business Plan Creation Request" refers to a request sent by a User to the System to create a new business plan.

[0214] "Past success stories" refer to business models, operating methods, marketing strategies, and other events that have been implemented successfully in the past.

[0215] "Market research data" refers to data that compiles information about trends in a specific market, consumer behavior, and the competitive landscape.

[0216] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate data analysis and predictive models.

[0217] A "business plan" refers to a document that describes the management policy, strategy, revenue forecast, etc. of a new business.

[0218] "Review information" refers to evaluations and opinions on areas for improvement provided by third parties regarding past business plans and business performance.

[0219] "Feedback" refers to information such as advice, improvements, and evaluations provided regarding the generated business plan and management policy.

[0220] "Brushing up" refers to the process of making corrections and additions to existing business plans and information to improve them.

[0221] A "subsidy" refers to financial assistance provided by the government or local government for specific business activities.

[0222] "Advertising" refers to promotional activities carried out to widely publicize a particular product or service.

[0223] "Collection" refers to the act of gathering data or information needed for a specific purpose.

[0224] "Normalization" refers to the process of aligning data formats and values ​​to certain standards before performing data analysis.

[0225] "Natural language processing" refers to the technology that allows computers to analyze and generate human language.

[0226] MODE FOR CARRYING OUT THE INVENTION

[0227] System Overview

[0228] This invention is a system for efficiently creating and refining business plans for new products and services. Users submit requests to the system, and the server collects and analyzes past success stories and market research data, automatically generating a business plan using a generative AI model. It also provides feedback based on review information to refine the business plan. It also provides the latest information on subsidies and advertising.

[0229] Overview of program processing

[0230] The server implements the present invention using the following hardware and software: A smartphone, tablet, and VR goggles are used as the hardware, and Python, Flask (web framework), Google Cloud AI (for NLP processing), and MongoDB (database) are used as the software.

[0231] 1. Receiving a request:

[0232] A user inputs a request for the creation of a business plan for a new product or service into the system (application or web interface) and submits it.

[0233] For example, a user may request, "I would like to create a business plan for a new VR accessory product."

[0234] 2. Keyword extraction:

[0235] The server extracts keywords from the received request using natural language processing techniques. In this example, we use CountVectorizer from the scikit-learn library.

[0236] For example, from the input "new VR accessory product," keywords such as "VR accessory" and "new product" are extracted.

[0237] 3. Data Collection:

[0238] Based on the extracted keywords, the server collects related information using external APIs and internal databases.

[0239] For example, collect past success stories and market analysis data from the VR accessory market.

[0240] 4. Data normalization and analysis:

[0241] The collected data is normalized and analyzed using Google Cloud AI's natural language processing technology.

[0242] 5. Generate a business plan:

[0243] Based on the analysis results, a new business plan is automatically generated using a generative AI model (e.g., OpenAI GPT-4).

[0244] For example, use the following prompt: "Write a business plan for a new product to enter the VR accessories market. Include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income statement."

[0245] 6. Providing Feedback:

[0246] Provides feedback based on past success stories and review information to the generated business plan, thereby improving the quality of the business plan.

[0247] 7. Providing supporting information:

[0248] The server collects information about the latest subsidy programs and advertising strategies and provides it to users.

[0249] For example, provide information such as, "Current subsidy programs can help cover some of the initial development costs."

[0250] 8. Delivery of the refined plan:

[0251] The server delivers the final, polished business plan to the user, who can then check it, download it, or make any further modifications necessary.

[0252] This system allows users to efficiently generate high-quality business plans and incorporate the latest information and feedback. It also centrally collects and provides the latest information on subsidies and advertising, thereby increasing the chances of success for new businesses.

[0253] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0254] Step 1:

[0255] A user inputs and submits a request to create a business plan for a new product or service into an application or web interface. The input includes an outline of the new product or service, its purpose, and market target. The output is transferred to the server. Specifically, the user makes an input request saying, "I would like to create a business plan for a new VR accessory product."

[0256] Step 2:

[0257] Natural language processing techniques are used to extract key keywords from requests received by the server. The input is the user's request, and the output is a list of extracted keywords. For data processing, keyword extraction is performed using CountVectorizer from the scikit-learn library. Specifically, keywords such as "VR," "accessories," and "new product" are extracted from the sentence "new VR accessory product."

[0258] Step 3:

[0259] Based on the extracted keywords, the server collects related information from internal databases and external APIs. The input is a list of keywords, and the output is a set of related information. To collect data, external APIs (such as market research APIs) are used to extract appropriate data and store it in a database such as MongoDB. Specifically, market analysis and competitive information related to "VR" and "accessories" are collected.

[0260] Step 4:

[0261] The server normalizes the collected information and analyzes the data using natural language processing technology. The input is the collected raw data, and the output is the standardized and analyzed data. For data processing, Google Cloud AI's NLP technology is used to analyze the data and organize it into a unified format. Specifically, market analysis data and review information are normalized and analyzed according to the format.

[0262] Step 5:

[0263] The server uses a generative AI model to automatically generate a new business plan based on the analyzed data. The input is the analyzed data, and the output is a draft of the generated business plan. For data calculation, OpenAI's GPT-4 model is used to generate text. Specifically, the following prompt is used: "Please write a business plan for a new product to enter the VR accessories market. Please include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income and expenditure plan."

[0264] Step 6:

[0265] The server provides feedback to the generated business plan based on past success stories and review information, and refines the business plan. The input is the generated draft business plan and review information, and the output is a business plan that reflects the feedback. As a data calculation, each section of the business plan is evaluated based on the collected review information, and areas for improvement are listed and reflected.

[0266] Step 7:

[0267] The server collects information on the latest subsidy programs and advertising strategies and integrates it into a business plan. The input is the latest information obtained from the government and the market, and the output is a business plan with additional subsidy information added. Specifically, it collects information on currently available subsidy programs and adds specific information to the business plan, such as "these programs can cover part of the initial development costs."

[0268] Step 8:

[0269] The server delivers the final, polished business plan to the user. The input is the improved business plan, and the output is the provision of the business plan to the user. Specifically, the user can download the business plan and make a final check.

[0270] 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.

[0271] System Overview

[0272] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. By combining it with an emotion engine, it is possible to recognize user emotions and optimize the feedback and content of the business plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, a terminal, and a user.

[0273] Program processing flow (explained in natural language)

[0274] Receiving a request

[0275] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user may submit a request saying, "I would like to create a business plan for a new social media app."

[0276] Collection of information

[0277] 2. The server analyzes the request received from the user and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0278] 3. Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This comprehensively collects the information required to meet the user's request.

[0279] Generate a business plan

[0280] 4. The server normalizes the collected information. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[0281] 5. The server analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0282] 6. The server uses the generative AI model to automatically generate a new business plan. The generated plan includes the following elements:

[0283] - Business overview (e.g., concept and features of a new social media app)

[0284] - Market analysis (e.g., social media market growth trends)

[0285] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0286] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[0287] - Business model (e.g., monetization methods)

[0288] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[0289] Use of emotion engine

[0290] 7. The server uses the emotion engine to analyze the user's thoughts and feedback while viewing the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[0291] 8. The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "Market Analysis" section, the server will add more detailed data and specific success stories about the "Market Analysis" section.

[0292] Providing feedback

[0293] 9. The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[0294] Providing supporting information

[0295] 10. The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[0296] Results distribution

[0297] 11. The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[0298] Specific examples

[0299] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. The emotion engine then analyzes the user's emotional state based on their thoughts and feedback as they browse the plan. For example, if the emotion engine detects that the user is dissatisfied with the market analysis section, the server will further enhance the market analysis section and provide additional data. It also researches the latest government subsidy programs and provides support information on initial development costs. Ultimately, the user receives a high-quality business plan that is tailored to their emotional state, increasing the chances of success for their new venture.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[0303] Step 2:

[0304] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0305] Step 3:

[0306] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[0307] Step 4:

[0308] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[0309] Step 5:

[0310] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0311] Step 6:

[0312] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[0313] Business overview (e.g., concept and features of a new social media app)

[0314] Market analysis (e.g., social media market growth trends)

[0315] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0316] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[0317] Business model (e.g., monetization methods)

[0318] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0319] Step 7:

[0320] The server uses an emotion engine to analyze the user's thoughts and feedback as they view the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[0321] Step 8:

[0322] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "market analysis" section, the server will add more detailed data and specific success stories about the "market analysis."

[0323] Step 9:

[0324] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[0325] Step 10:

[0326] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[0327] Step 11:

[0328] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[0329] Example 2

[0330] 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."

[0331] The process of creating a new business plan requires time and effort, as it requires complex and time-consuming tasks such as information gathering, analysis, and document creation. It is also difficult to improve the quality of the business plan by incorporating user feedback in real time. Another challenge is effectively incorporating the latest information on subsidies and advertising fees.

[0332] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving a user request, a means for collecting related information from past success stories and documents, a means for searching and collecting information based on extracted keywords and topics, a means for normalizing the collected information, a means for analyzing the normalized information using natural language processing and inputting it into a generative AI model, a means for automatically generating a new business plan using the generative AI model, a means for analyzing user feedback using an emotion engine and adjusting the feedback, a means for providing feedback based on review information to refine the business plan, and a means for providing information on subsidies and advertising fees. This allows users to automatically generate efficient, high-quality new business plans and refine them while reflecting feedback in real time. Furthermore, the latest information on subsidies and advertising fees can be incorporated, increasing the feasibility of the business plan.

[0333] The "means for receiving a user's request" is a function for receiving a request for creating a new business plan sent by a user through a web interface or application.

[0334] "Means of collecting relevant information from past success stories and documents" refers to the function of collecting past business success stories and related documents through an internal database or external API.

[0335] "Means for searching and collecting information based on extracted keywords and topics" refers to a function for searching and collecting related information based on keywords and topics extracted by natural language processing.

[0336] "Means for normalizing collected information" refers to the ability to format information obtained from different formats and sources into a unified format, making it easier to analyze.

[0337] "Means for analyzing normalized information using natural language processing and inputting it into a generative AI model" refers to a function that analyzes normalized information and inputs it into a generative AI model as a necessary parameter.

[0338] "Means for automatically generating new business plans using a generative AI model" refers to a function that uses a generative AI model to automatically create new business plans based on collected and analyzed information.

[0339] "Means for analyzing user feedback using an emotion engine and adjusting the feedback" is a function that analyzes the impressions and feedback entered by the user in real time and adjusts the feedback based on the user's emotional state.

[0340] The "means of providing feedback based on review information and brushing up the business plan" is a function that generates specific feedback based on collected review information and improves the content of the business plan.

[0341] The "means for providing information on subsidies and advertising fees" is a function for collecting information on the latest subsidy programs and advertising fees and providing it to the user.

[0342] The present invention is a system that allows a user to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. The implementation method is described in detail below.

[0343] System configuration

[0344] The system is primarily composed of a server, a terminal, and a user. The server plays a central role in collecting, analyzing, generating, and providing feedback on information, while the terminal is a device used by users as an interface. Users are responsible for inputting requests for new business plans and for checking and providing feedback on the results.

[0345] Hardware and Software

[0346] Server: A high-performance server is used to execute various processes using Python. Libraries such as Spacy are used for natural language processing (NLP), and OpenAI's GPT-4 is used as the generative AI model. External APIs (e.g., YouTube API, Google Scholar API) are also used for data collection and analysis.

[0347] Device: A web browser or mobile application that users access. The user interface is created using HTML, CSS, and JavaScript.

[0348] Database: Use a relational database such as MySQL to store past success stories and related documents.

[0349] Using Natural Language Processing and Generative AI Models

[0350] The server analyzes new business plan requests received from users using natural language processing, and collects related information using an internal database and external APIs based on keywords and topics extracted from the analysis results.

[0351] The collected information is normalized and fed into a generative AI model. For example, the following prompts can be used to provide information to the generative AI model:

[0352] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[0353] Emotion Engine and Feedback

[0354] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback as they view the generated business plan. Based on this feedback, the server adjusts and refines the plan in real time.

[0355] Specific examples

[0356] For example, if a user sends a request saying, "I want to create a business plan for a new social media app," the server extracts keywords such as "social media app," "market analysis," and "competitive analysis." It then collects past success stories, pitch videos, and business plan documents, normalizes them, and inputs them into the generative AI model. The generated business plan includes sections such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan.

[0357] If a user reviews a plan and inputs feedback such as "the market analysis section is insufficient," the emotion engine will recognize this, and the server will add detailed data and concrete success stories to reinforce the market analysis section. It will also collect information on government subsidy programs and the latest advertising rates and notify the user.

[0358] This process allows users to obtain efficient and high-quality new business plans, increasing the chances of success for new businesses.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] A user enters and submits a request to create a new business plan through a web interface or application, for example, by typing "I would like to create a business plan for a new social media app" into a text box and clicking a submit button.

[0362] Input: User request text

[0363] Output: Request data to the server

[0364] Step 2:

[0365] The server parses the requests received from users and extracts the required keywords and topics, using Python and a natural language processing (NLP) library (e.g., Spacy) to identify keywords such as "social media app," "business model," and "market analysis."

[0366] Specifically, the request text is tokenized and important keywords are extracted.

[0367] Input: User request data

[0368] Output: Extracted keywords and topics

[0369] Step 3:

[0370] Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs, for example, querying past success stories from a MySQL database, collecting pitch videos through a video API, and retrieving related documents from a literature API.

[0371] Specific operations include issuing SQL queries, sending API requests, and analyzing responses.

[0372] Input: Extracted keywords and topics

[0373] Output: Related information data (past success stories, video links, literature information, etc.)

[0374] Step 4:

[0375] Normalize the information collected by the server. Convert information obtained from different formats and sources into a unified format and format it into data that is easy to analyze. Use the Python pandas library to clean, format, and eliminate duplicates from the data.

[0376] Input: Related information data

[0377] Output: Normalized information data

[0378] Step 5:

[0379] The server analyzes the normalized information using natural language processing (NLP) techniques and prepares it for input to the generative AI model. It also creates input prompts.

[0380] An example prompt is:

[0381] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[0382] As a concrete operation, the data is merged with the template to generate a prompt sentence.

[0383] Input: Normalized information data

[0384] Output: prompt statement

[0385] Step 6:

[0386] The server uses a generative AI model (e.g., GPT-4) to automatically generate a new business plan. The generated plan includes elements such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan. The generated text is embedded in a template.

[0387] Input: prompt statement

[0388] Output: Auto-generated business plan

[0389] Step 7:

[0390] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback while viewing the generated business plan. For example, if the user enters feedback such as "This market analysis is insufficient," the server recognizes that information as negative feedback.

[0391] Input: User feedback

[0392] Output: Parsed emotion data

[0393] Step 8:

[0394] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan, adding detailed market data and concrete success stories to enrich the data in order to generate a plan that satisfies the user.

[0395] Input: Parsed emotion data

[0396] Output: A polished business plan

[0397] Step 9:

[0398] The server generates feedback based on the review information and further refines the plan, specifically proposing incentive plans necessary to increase user engagement.

[0399] Input: Review information

[0400] Output: Specific feedback

[0401] Step 10:

[0402] The server collects the latest information on subsidies and advertising fees and provides it to users. The necessary information is obtained from government APIs and advertising agency APIs and incorporated into the business plan.

[0403] Input: Data from the relevant API

[0404] Output: Information about subsidies and advertising fees

[0405] Step 11:

[0406] The server delivers the final, polished business plan to the user, who can then download it in PDF format and further revise or print it as needed.

[0407] Input: Finalized business plan

[0408] Output: Plan delivered to user

[0409] (Application example 2)

[0410] 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."

[0411] When creating new business plans, conventional systems have had issues with not effectively utilizing past success stories and related information, and not providing sufficient feedback to refine the automatically generated business plan. They also lack the ability to reflect the user's feelings or provide information on the latest subsidies and advertising fees. This makes it difficult to create high-quality business plans that meet the user's needs.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0413] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for automatically generating a new business plan based on the collected information, means for providing feedback based on review information and the user's emotional state to refine the business plan, and means for providing the latest information on subsidies and advertising fees. This makes it possible to create a high-quality new business plan that utilizes past success stories and the latest information and incorporates feedback from emotional analysis.

[0414] "User" means an individual or organization that uses the System to request the creation of a new business plan and receives the results.

[0415] A "request" is information that a user sends to the system to request the creation of a new business plan.

[0416] "Past success stories" are specific examples of successful business activities or projects that have been undertaken in the past.

[0417] A "document" is a document that records data and information necessary to generate a new business plan.

[0418] "Related information" refers to information such as past success stories, market analysis, and competitive analysis that are necessary for creating a new business plan.

[0419] A "new business plan" is a document that describes a detailed plan for starting a new business.

[0420] "Automatic generation" refers to a system using programs and algorithms to create a business plan without human intervention.

[0421] "Review information" is information on evaluations and feedback given by users on business plans.

[0422] "Feedback" is information for improving the business plan based on user evaluations and impressions.

[0423] "Brushing up" means making corrections or additions to a new business plan to improve its quality and content.

[0424] The "user's emotional state" refers to the emotional state, such as satisfaction, dissatisfaction, or interest, that the user feels when using the system.

[0425] A "grant" is funding provided by a government or organization to support a new business or project.

[0426] "Advertising fees" are fees paid to promote new businesses or projects.

[0427] "Latest information" means the most recently updated information as of the present time.

[0428] "Normalization" means converting data from different sources into a unified format.

[0429] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0430] "Analysis" is the process of analyzing data and information in detail to find meaning and value.

[0431] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically create new documents or information.

[0432] The system of the present invention allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback based on reviews and the user's emotional state to refine the plan. It also provides the latest information on subsidies and advertising fees.

[0433] The server includes a means for receiving a user's request, a means for collecting relevant information from past success stories and documents, and a means for automatically generating a new business plan based on the collected information. It also includes a means for providing feedback based on review information and the user's emotional state to refine the business plan, and a means for providing the latest information on subsidies and advertising fees.

[0434] In processing the program, the server operates according to the following flow:

[0435] First, a user uses a device (e.g., a smartphone or PC) to input and submit a request to create a new business plan through a web interface or application. For example, a user might submit a request saying, "I would like to create a business plan for a virtual clothing store."

[0436] The server receives this request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords and topics. In this example, we use Python's spaCy as the NLP library. The extracted keywords include "virtual store," "clothing," and "market analysis."

[0437] The server then searches and collects relevant information based on the extracted keywords and topics using an internal database (MongoDB) and an external API (public data provider) to collect past success stories, pitch videos, business plans, and reviews.

[0438] The collected information is normalized on the server, converting data collected from different information sources into a unified format, and performing text cleaning, formatting, and deduplication.

[0439] The server then analyzes the normalized data and inputs it into a generative AI model, powered by OpenAI's GPT-3, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0440] The generated business plan will include the following elements:

[0441] Business overview (e.g., virtual clothing store concept and features)

[0442] Market analysis (e.g., the expansion trend of the virtual store market)

[0443] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0444] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new store)

[0445] Business model (e.g., monetization methods)

[0446] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0447] The impressions and feedback entered by users while viewing the business plan are analyzed by an emotion engine, which uses the IBM Watson Tone Analyzer API, allowing the user's emotional state (satisfaction, dissatisfaction, interest, doubt, etc.) to be recognized in real time.

[0448] Based on the user's emotional state detected by the emotion engine, the server adjusts the feedback and refines the business plan. For example, if the user is dissatisfied with the market analysis section, the server can provide additional data and specific success stories related to that section. The server also collects the latest information on subsidies and advertising fees and provides it to the user. This increases the feasibility of the business plan.

[0449] As a concrete example, the following prompt sentence is input to the generative AI model:

[0450] Virtual Clothing Store Business Plan

[0451] (Business overview)

[0452] Please explain the concept and features of the virtual store.

[0453] (Market analysis)

[0454] Please provide an update on the market size and competitive situation for virtual clothing stores.

[0455] (Competitive analysis)

[0456] Conduct an analysis of your major competitors, including their strengths and weaknesses.

[0457] (SWOT analysis)

[0458] Indicate the strengths, weaknesses, opportunities, and threats of your new app.

[0459] (Business Model)

[0460] Please elaborate on how you will monetize your clothing store.

[0461] (Income and expenditure plan)

[0462] Please specify your projected income and expenditure for the first year.

[0463] The final, polished business plan is delivered to the user, who can review it and download or modify it as needed.

[0464] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0465] Step 1:

[0466] This is the stage where the user enters "I would like to create a business plan for a virtual clothing store" into the request form and submits it. The text data entered by the user becomes the input, and the server receives the request and outputs it as data to be analyzed.

[0467] Step 2:

[0468] In this step, the server receives the request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords (e.g., "virtual store," "clothing," "market analysis," etc.). Text analysis is performed using an NLP library (e.g., spaCy), and the analyzed keywords are output.

[0469] Step 3:

[0470] This is the step where the server collects the necessary related information from an internal database (e.g., MongoDB) or an external API based on the keywords extracted. A request for the information to be collected is sent to the external API, and success stories, pitch videos, business documents, etc. are collected and output.

[0471] Step 4:

[0472] This is the step where the collected information is normalized on the server. Text cleaning, formatting, and deduplication are performed, and the normalized information is output as data in a unified format.

[0473] Step 5:

[0474] In this step, the server analyzes the normalized data and prepares it for input into a generative AI model (e.g., OpenAI GPT-3). Important key points and relationships are analyzed, and a dataset for AI input is created and output.

[0475] Step 6:

[0476] In this step, the server automatically generates a new business plan using the generative AI model. A prompt (e.g., "Write a business plan for a virtual clothing store") is input to the generative AI model, and the business plan is generated and output.

[0477] Step 7:

[0478] In this step, the user views the generated business plan and inputs their thoughts and feedback. The user's feedback text is sent to the server as input.

[0479] Step 8:

[0480] In this step, the server analyzes the user's feedback using an emotion engine (e.g., IBM Watson Tone Analyzer API). The analyzed emotion data is output and used as the basis for adjusting the feedback.

[0481] Step 9:

[0482] In this step, the server refines the business plan based on the results of the sentiment analysis. It adds detailed data to specific sections and outputs the refined business plan.

[0483] Step 10:

[0484] In this step, the server collects the latest information on subsidies and advertising fees and adds it to the business plan. It also collects the latest data from external APIs and the internet, integrates it into the business plan, and outputs it.

[0485] Step 11:

[0486] This is the step where the server delivers the final polished business plan to the user. The generated business plan is sent to the user's device, where the user can review and download it.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] [Second embodiment]

[0491] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0492] 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.

[0493] 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).

[0494] 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.

[0495] 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.

[0496] 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).

[0497] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] 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."

[0503] System Overview

[0504] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[0505] Program processing flow (explained in natural language)

[0506] Receiving a request

[0507] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user might request, "I would like to create a business plan for a new social media app."

[0508] Collection of information

[0509] 2. The server analyzes the request received from the user and extracts the necessary keywords, such as "social media app," "market analysis," and "business model."

[0510] 3. The server uses internal databases and external APIs to search and collect past success stories, pitch videos, business plan documents, and review information, using algorithms to evaluate the quality and relevance of the collected data.

[0511] Generate a business plan

[0512] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) technology. The normalized data is then input into a generative AI model.

[0513] 5. The server launches the generative AI model and automatically generates a new business plan based on the input data. The business plan includes the following elements:

[0514] Business overview (e.g., concept and features of a new social media app)

[0515] Market analysis (e.g., social media market growth trends)

[0516] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0517] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[0518] Business model (e.g., monetization methods)

[0519] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0520] Providing feedback

[0521] 6. The server reviews the generated business plan, references past success stories and reviews, and generates feedback. For example, it provides specific improvements such as, "An incentive plan in the early stages is important to increase user engagement."

[0522] 7. The server integrates the feedback and refines the generated business plan.

[0523] Providing supporting information

[0524] 8. The server collects information about the latest subsidy programs and advertising rates and provides relevant information to the user, such as "Current government subsidy programs can help cover part of the initial development costs."

[0525] Results distribution

[0526] 9. The server delivers the final, polished business plan to the user, who can then review it, download it, or make any further modifications necessary.

[0527] Specific examples

[0528] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[0532] Step 2:

[0533] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0534] Step 3:

[0535] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[0536] Step 4:

[0537] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[0538] Step 5:

[0539] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0540] Step 6:

[0541] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[0542] - Business overview (e.g., concept and features of a new social media app)

[0543] - Market analysis (e.g., social media market growth trends)

[0544] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0545] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[0546] - Business model (e.g., monetization methods)

[0547] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[0548] Step 7:

[0549] The server reviews the generated business plan and compares it with past success stories and review information to assess the plan's quality and identify areas for improvement.

[0550] Step 8:

[0551] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[0552] Step 9:

[0553] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[0554] Step 10:

[0555] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[0556] Example 1

[0557] 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."

[0558] The traditional process of creating a new business plan required a great deal of time and effort to search and analyze vast amounts of information, and to create and review documents. It also required time and effort to manually collect and consolidate the latest information on subsidies and advertising expenses. This situation could slow down the launch of a new business and lead to inaccurate plans.

[0559] 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.

[0560] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for normalizing the collected information and analyzing it using natural language processing technology, means for automatically generating a new business plan based on the normalized and analyzed data, means for reviewing the generated business plan and providing feedback based on past success stories and review information, means for integrating the generated feedback and improving the business plan, means for collecting and providing the latest information on subsidies and advertising expenses, and means for delivering the generated and improved business plan to the user, thereby enabling the rapid and highly accurate creation of new business plans.

[0561] The "means for receiving a user request" is the function of the system that receives the request via the input device or software that the user uses to request the creation of a new business plan.

[0562] "Means for collecting relevant information from past success stories and documents" refers to the system's function of collecting data such as past successful business plans, related documents, pitch videos, and review information via an internal database or external API.

[0563] "Means of normalizing collected information and analyzing it using natural language processing techniques" refers to the process of converting collected data into a consistent format and analyzing the information using natural language processing techniques.

[0564] "Means for automatically generating new business plans based on normalized and analyzed data" refers to a system function that uses normalized and analyzed information to create new business plans using generative AI models or other automated technologies.

[0565] The "means of reviewing the generated business plan and providing feedback based on past success stories and review information" refers to a process for evaluating the contents of the automatically generated business plan in comparison with past success stories and review information, and generating specific feedback.

[0566] The "means for integrating the generated feedback and refining the business plan" is a function of the system for integrating the provided feedback and further improving the generated business plan.

[0567] "Means for collecting and providing the latest information on subsidies and advertising expenditures" refers to the process of collecting the latest information on subsidy programs and advertising expenditures from government agencies and related websites and providing it to users.

[0568] "Means for delivering the generated and refined business plan to the user" refers to the system's function of generating the final, refined business plan in a format such as PDF or Word and delivering it to the user via email or other means.

[0569] System Overview

[0570] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[0571] Hardware and software used

[0572] The system consists of the following hardware and software:

[0573] Server: Equipped with high-performance computing resources and database management systems. Examples include AWS EC2 instances and MySQL databases.

[0574] Device: The device used by the user. Examples include PCs, smartphones, and tablets.

[0575] Natural language processing (NLP) software: Python libraries such as spaCy and NLTK are used.

[0576] Generative AI models: Leverage advanced AI models such as OpenAI's GPT-4.

[0577] How it works

[0578] Receiving a request

[0579] 1. A user inputs and submits a request to create a new business plan via a web interface or application. Specifically, the user enters a summary of the business idea and necessary information into a dedicated form and clicks the "Submit" button.

[0580] Collection of information

[0581] 2. The server analyzes the request received from the user and extracts the necessary keywords using natural language processing (NLP) techniques. Specifically, it uses a Python NLP library (e.g., spaCy or NLTK) to identify the main topics and themes from the request text.

[0582] 3. The server searches and collects past success stories, pitch videos, business plan documents, and review information using an internal database or external API (e.g., Google Scholar API or other business data API). Specifically, it runs an SQL query against the case study database to retrieve a list of relevant documents. It then downloads the data from the URLs of the listed documents.

[0583] Generate a business plan

[0584] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) techniques, such as data cleaning and converting it into a consistent format. This is often done using the Python pandas library.

[0585] 5. The server launches a generative AI model (e.g., GPT-4) and inputs the normalized data. It generates a specific prompt and sends it to the model.

[0586] Example: "Write a business plan for a new social media app. Include the following elements: 1. Business Overview 2. Market Analysis 3. Competitive Analysis 4. SWOT Analysis 5. Business Model 6. Income and Expenditure Plan."

[0587] 6. The server receives the generated business plan and performs additional processing to format it, such as converting the generated text to Markdown or LaTeX formatting, for visual editing.

[0588] Providing Feedback

[0589] 7. The server compares the generated business plan with past success stories and review information in an internal database and generates feedback. Specifically, it uses a review algorithm to evaluate the plan's contents and extract strengths and areas for improvement. For example, it suggests specific areas for improvement, such as "An incentive plan in the early stages is important to increase user engagement."

[0590] 8. The server integrates the feedback and makes additional modifications to the generated business plan, again using the generative AI model to generate a new version of the business plan that reflects the feedback.

[0591] Providing supporting information

[0592] 9. The server collects information about the latest subsidy programs and advertising rates using web scraping and APIs. Specifically, it retrieves and analyzes the latest subsidy information from government agency websites.

[0593] 10. The server processes the collected information and provides appropriate supporting information to the user, such as integrating it into a business plan as "Current government subsidy programs can help cover some of the initial development costs."

[0594] Results distribution

[0595] 11. The server generates the final, polished business plan as a PDF or Word document and delivers it to the user. Specifically, it creates the document using a document generation library (such as Python-docx or ReportLab) and emails the user a download link.

[0596] 12. The user reviews the received business plan and downloads it or makes further edits as needed. For example, the user can open the downloaded PDF in Adobe Acrobat and edit the details.

[0597] Specific examples

[0598] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[0599] Example prompts to input to the generative AI model

[0600] Write a business plan for a new social media app. Include the following elements:

[0601] 1. Business overview

[0602] 2. Market analysis

[0603] 3. Competitive analysis

[0604] 4. SWOT analysis

[0605] 5. Business Model

[0606] 6. Income and expenditure plan

[0607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0608] The flow of this system's program processing

[0609] Step 1: Receiving the request

[0610] Processing content

[0611] A user inputs and submits a request to create a new business plan via a web interface or application.

[0612] Input: The user enters a summary of their business idea and the necessary information into a dedicated form and clicks the "Submit" button.

[0613] Output: The user's request data is sent to the server.

[0614] Specific operation: When the submit button of the form is pressed, the request data is sent to the server via the HTTP protocol and saved in the database.

[0615] Step 2: Analyze the information

[0616] Processing content

[0617] The server analyzes the request received from the user and extracts the necessary keywords.

[0618] Input: The user's request data saved in step 1.

[0619] Output: Keyword list (e.g. "social media app", "market analysis", "business model").

[0620] What happens: The server uses a Python NLP library (e.g., spaCy or NLTK) to parse and extract major topics and themes from the request.

[0621] Step 3: Gather information

[0622] Processing content

[0623] The server uses internal databases and external APIs to search and collect past success stories and related information.

[0624] Input: The keyword list extracted in step 2.

[0625] Output: A list of relevant documents, videos, and review information.

[0626] What it does: Based on the information collected by the server, it executes SQL queries against the database to retrieve relevant data, and also calls external APIs to retrieve additional information.

[0627] Step 4: Data normalization and analysis

[0628] Processing content

[0629] The information collected by the server is normalized and analyzed using natural language processing technology.

[0630] Input: Data collected in Step 3.

[0631] Output: Normalized and parsed data.

[0632] Specific operation: The server performs data cleaning (e.g., deduplication, format standardization), normalization using Python's pandas library, and then analyzes the data again using natural language processing technology.

[0633] Step 5: Automatically generate a business plan

[0634] Processing content

[0635] The server launches the generative AI model, inputs the normalized data, and automatically generates a business plan.

[0636] Input: The normalized and analyzed data from step 4.

[0637] Output: Early stage business plan.

[0638] Specific operation: The server generates and sends a prompt to the generative AI model (e.g., GPT-4). For example, the prompt might be, "Please write a business plan for a new social media app."

[0639] Step 6: Review and generate feedback

[0640] Processing content

[0641] The server reviews the generated business plan and generates feedback.

[0642] Input: The business plan generated in step 5.

[0643] Output: A list of feedbacks.

[0644] What it does: The server compares past success stories and existing review information, and uses a review algorithm to generate a rating and feedback for the generated business plan.

[0645] Step 7: Integrate feedback and refine

[0646] Processing content

[0647] The server integrates the generated feedback and refines the business plan.

[0648] Input: Feedback generated in step 6.

[0649] Output: An improved business plan.

[0650] Specific operation: The server uses the generative AI model again to create a new version of the business plan that reflects the feedback.

[0651] Step 8: Gather supporting information

[0652] Processing content

[0653] The server collects and provides information on the latest subsidy programs and advertising rates.

[0654] Input: Data from government websites and related APIs.

[0655] Output: Updates on subsidies and advertising expenditures.

[0656] Specific operation: The server collects the necessary information through web scraping and API calls, and provides it after evaluating and analyzing its quality.

[0657] Step 9: Delivering results

[0658] Processing content

[0659] The server delivers the final, polished business plan to the user.

[0660] Input: Your refined business plan from Step 7.

[0661] Output: Business plan in PDF or Word document format sent to user.

[0662] What it does: The server creates a document using a document generation library like Python-docx or ReportLab, and emails the user a download link.

[0663] Step 10: User Verification and Download

[0664] Processing content

[0665] The user reviews the business plan they receive and downloads or further modifies it as necessary.

[0666] Input: Business plan delivered in Step 9.

[0667] Output: A revised or ready-to-use business plan.

[0668] What happens: The user opens the email they received, clicks the download link to get the business plan, and then reviews and edits it in a PDF reader or word processor.

[0669] (Application example 1)

[0670] 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."

[0671] In the past, the process of creating a new business plan required a lot of time and effort. Furthermore, there were few ways to efficiently collect and incorporate feedback from past success stories and the latest subsidy information. This led to problems such as a decline in the quality and success rate of business plans.

[0672] 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.

[0673] In this invention, the server includes means for receiving a user's request to create a business plan for a new product or service, means for collecting related information from past success stories and market research data, means for automatically generating a new business plan using a generative AI model based on the collected information, means for providing feedback from review information and past success stories to refine the business plan, and means for providing information on subsidies and advertising. This makes it possible to efficiently generate high-quality business plans that reflect the latest information and feedback.

[0674] "User" refers to the entity that uses the system to create new business plans and collect related information.

[0675] "New products and services" refers to products and services that are new and have not yet appeared in the existing market.

[0676] "Business Plan Creation Request" refers to a request sent by a User to the System to create a new business plan.

[0677] "Past success stories" refer to business models, operating methods, marketing strategies, and other events that have been implemented successfully in the past.

[0678] "Market research data" refers to data that compiles information about trends in a specific market, consumer behavior, and the competitive landscape.

[0679] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate data analysis and predictive models.

[0680] A "business plan" refers to a document that describes the management policy, strategy, revenue forecast, etc. of a new business.

[0681] "Review information" refers to evaluations and opinions on areas for improvement provided by third parties regarding past business plans and business performance.

[0682] "Feedback" refers to information such as advice, improvements, and evaluations provided regarding the generated business plan and management policy.

[0683] "Brushing up" refers to the process of making corrections or additions to existing business plans and information to improve them.

[0684] A "subsidy" refers to financial assistance provided by the government or local government for specific business activities.

[0685] "Advertising" refers to promotional activities carried out to widely publicize a particular product or service.

[0686] "Collection" refers to the act of gathering data or information needed for a specific purpose.

[0687] "Normalization" refers to the process of aligning data formats and values ​​to certain standards before performing data analysis.

[0688] "Natural language processing" refers to the technology that allows computers to analyze and generate human language.

[0689] MODE FOR CARRYING OUT THE INVENTION

[0690] System Overview

[0691] This invention is a system for efficiently creating and refining business plans for new products and services. Users submit requests to the system, and the server collects and analyzes past success stories and market research data, automatically generating a business plan using a generative AI model. It also provides feedback based on review information to refine the business plan. It also provides the latest information on subsidies and advertising.

[0692] Overview of program processing

[0693] The server implements the present invention using the following hardware and software: A smartphone, tablet, and VR goggles are used as the hardware, and Python, Flask (web framework), Google Cloud AI (for NLP processing), and MongoDB (database) are used as the software.

[0694] 1. Receiving a request:

[0695] A user inputs a request for the creation of a business plan for a new product or service into the system (application or web interface) and submits it.

[0696] For example, a user may request, "I would like to create a business plan for a new VR accessory product."

[0697] 2. Keyword extraction:

[0698] The server extracts keywords from the received request using natural language processing techniques. In this example, we use CountVectorizer from the scikit-learn library.

[0699] For example, from the input "new VR accessory product," keywords such as "VR accessory" and "new product" are extracted.

[0700] 3. Data Collection:

[0701] Based on the extracted keywords, the server collects related information using external APIs and internal databases.

[0702] For example, collect past success stories and market analysis data from the VR accessory market.

[0703] 4. Data normalization and analysis:

[0704] The collected data is normalized and analyzed using Google Cloud AI's natural language processing technology.

[0705] 5. Generate a business plan:

[0706] Based on the analysis results, a new business plan is automatically generated using a generative AI model (e.g., OpenAI GPT-4).

[0707] For example, use the following prompt: "Write a business plan for a new product to enter the VR accessories market. Include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income statement."

[0708] 6. Providing Feedback:

[0709] Provides feedback based on past success stories and review information to the generated business plan, thereby improving the quality of the business plan.

[0710] 7. Providing supporting information:

[0711] The server collects information about the latest subsidy programs and advertising strategies and provides it to users.

[0712] For example, provide information such as, "Current subsidy programs can help cover some of the initial development costs."

[0713] 8. Delivery of the refined plan:

[0714] The server delivers the final, polished business plan to the user, who can then check it, download it, or make any further modifications necessary.

[0715] This system allows users to efficiently generate high-quality business plans and incorporate the latest information and feedback. It also centrally collects and provides the latest information on subsidies and advertising, thereby increasing the chances of success for new businesses.

[0716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0717] Step 1:

[0718] A user inputs and submits a request to create a business plan for a new product or service into an application or web interface. The input includes an outline of the new product or service, its purpose, and market target. The output is transferred to the server. Specifically, the user makes an input request saying, "I would like to create a business plan for a new VR accessory product."

[0719] Step 2:

[0720] Natural language processing techniques are used to extract key keywords from requests received by the server. The input is the user's request, and the output is a list of extracted keywords. For data processing, keyword extraction is performed using CountVectorizer from the scikit-learn library. Specifically, keywords such as "VR," "accessories," and "new product" are extracted from the sentence "new VR accessory product."

[0721] Step 3:

[0722] Based on the extracted keywords, the server collects related information from internal databases and external APIs. The input is a list of keywords, and the output is a set of related information. To collect data, external APIs (such as market research APIs) are used to extract appropriate data and store it in a database such as MongoDB. Specifically, market analysis and competitive information related to "VR" and "accessories" are collected.

[0723] Step 4:

[0724] The server normalizes the collected information and analyzes the data using natural language processing technology. The input is the collected raw data, and the output is the standardized and analyzed data. For data processing, Google Cloud AI's NLP technology is used to analyze the data and organize it into a unified format. Specifically, market analysis data and review information are normalized and analyzed according to the format.

[0725] Step 5:

[0726] The server uses a generative AI model to automatically generate a new business plan based on the analyzed data. The input is the analyzed data, and the output is a draft of the generated business plan. For data calculation, OpenAI's GPT-4 model is used to generate text. Specifically, the following prompt is used: "Please write a business plan for a new product to enter the VR accessories market. Please include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income and expenditure plan."

[0727] Step 6:

[0728] The server provides feedback to the generated business plan based on past success stories and review information, and refines the business plan. The input is the generated draft business plan and review information, and the output is a business plan that reflects the feedback. As a data calculation, each section of the business plan is evaluated based on the collected review information, and areas for improvement are listed and reflected.

[0729] Step 7:

[0730] The server collects information on the latest subsidy programs and advertising strategies and integrates it into a business plan. The input is the latest information obtained from the government and the market, and the output is a business plan with additional subsidy information added. Specifically, it collects information on currently available subsidy programs and adds specific information to the business plan, such as "these programs can cover part of the initial development costs."

[0731] Step 8:

[0732] The server delivers the final, polished business plan to the user. The input is the improved business plan, and the output is the provision of the business plan to the user. Specifically, the user can download the business plan and make a final check.

[0733] 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.

[0734] System Overview

[0735] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. By combining it with an emotion engine, it is possible to recognize user emotions and optimize the feedback and content of the business plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, a terminal, and a user.

[0736] Program processing flow (explained in natural language)

[0737] Receiving a request

[0738] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user may submit a request saying, "I would like to create a business plan for a new social media app."

[0739] Collection of information

[0740] 2. The server analyzes the request received from the user and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0741] 3. Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This comprehensively collects the information required to meet the user's request.

[0742] Generate a business plan

[0743] 4. The server normalizes the collected information. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[0744] 5. The server analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0745] 6. The server uses the generative AI model to automatically generate a new business plan. The generated plan includes the following elements:

[0746] - Business overview (e.g., concept and features of a new social media app)

[0747] - Market analysis (e.g., social media market growth trends)

[0748] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0749] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[0750] - Business model (e.g., monetization methods)

[0751] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[0752] Use of emotion engine

[0753] 7. The server uses the emotion engine to analyze the user's thoughts and feedback while viewing the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[0754] 8. The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "Market Analysis" section, the server will add more detailed data and specific success stories about the "Market Analysis" section.

[0755] Providing feedback

[0756] 9. The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[0757] Providing supporting information

[0758] 10. The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[0759] Results distribution

[0760] 11. The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[0761] Specific examples

[0762] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. The emotion engine then analyzes the user's emotional state based on their thoughts and feedback as they browse the plan. For example, if the emotion engine detects that the user is dissatisfied with the market analysis section, the server will further enhance the market analysis section and provide additional data. It also researches the latest government subsidy programs and provides support information on initial development costs. Ultimately, the user receives a high-quality business plan that is tailored to their emotional state, increasing the chances of success for their new venture.

[0763] The processing flow will be explained below.

[0764] Step 1:

[0765] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[0766] Step 2:

[0767] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0768] Step 3:

[0769] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[0770] Step 4:

[0771] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[0772] Step 5:

[0773] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0774] Step 6:

[0775] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[0776] Business overview (e.g., concept and features of a new social media app)

[0777] Market analysis (e.g., social media market growth trends)

[0778] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0779] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[0780] Business model (e.g., monetization methods)

[0781] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0782] Step 7:

[0783] The server uses an emotion engine to analyze the user's thoughts and feedback as they view the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[0784] Step 8:

[0785] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "market analysis" section, the server will add more detailed data and specific success stories about the "market analysis."

[0786] Step 9:

[0787] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[0788] Step 10:

[0789] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[0790] Step 11:

[0791] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[0792] Example 2

[0793] 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."

[0794] The process of creating a new business plan requires time and effort, as it requires complex and time-consuming tasks such as information gathering, analysis, and document creation. It is also difficult to improve the quality of the business plan by incorporating user feedback in real time. Another challenge is effectively incorporating the latest information on subsidies and advertising fees.

[0795] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving a user request, a means for collecting related information from past success stories and documents, a means for searching and collecting information based on extracted keywords and topics, a means for normalizing the collected information, a means for analyzing the normalized information using natural language processing and inputting it into a generative AI model, a means for automatically generating a new business plan using the generative AI model, a means for analyzing user feedback using an emotion engine and adjusting the feedback, a means for providing feedback based on review information to refine the business plan, and a means for providing information on subsidies and advertising fees. This allows users to automatically generate efficient, high-quality new business plans and refine them while reflecting feedback in real time. Furthermore, the latest information on subsidies and advertising fees can be incorporated, increasing the feasibility of the business plan.

[0796] The "means for receiving a user's request" is a function for receiving a request for creating a new business plan sent by a user through a web interface or application.

[0797] "Means of collecting relevant information from past success stories and documents" refers to the function of collecting past business success stories and related documents through an internal database or external API.

[0798] "Means for searching and collecting information based on extracted keywords and topics" refers to a function for searching and collecting related information based on keywords and topics extracted by natural language processing.

[0799] "Means for normalizing collected information" refers to the ability to format information obtained from different formats and sources into a unified format, making it easier to analyze.

[0800] "Means for analyzing normalized information using natural language processing and inputting it into a generative AI model" refers to a function that analyzes normalized information and inputs it into a generative AI model as a necessary parameter.

[0801] "Means for automatically generating new business plans using a generative AI model" refers to a function that uses a generative AI model to automatically create new business plans based on collected and analyzed information.

[0802] "Means for analyzing user feedback using an emotion engine and adjusting the feedback" is a function that analyzes the impressions and feedback entered by the user in real time and adjusts the feedback based on the user's emotional state.

[0803] The "means of providing feedback based on review information and brushing up the business plan" is a function that generates specific feedback based on collected review information and improves the content of the business plan.

[0804] The "means for providing information on subsidies and advertising fees" is a function for collecting information on the latest subsidy programs and advertising fees and providing it to the user.

[0805] The present invention is a system that allows a user to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. The implementation method is described in detail below.

[0806] System configuration

[0807] The system is primarily composed of a server, a terminal, and a user. The server plays a central role in collecting, analyzing, generating, and providing feedback on information, while the terminal is a device used by users as an interface. Users are responsible for inputting requests for new business plans and for checking and providing feedback on the results.

[0808] Hardware and Software

[0809] Server: A high-performance server is used to execute various processes using Python. Libraries such as Spacy are used for natural language processing (NLP), and OpenAI's GPT-4 is used as the generative AI model. External APIs (e.g., YouTube API, Google Scholar API) are also used for data collection and analysis.

[0810] Device: A web browser or mobile application that users access. The user interface is created using HTML, CSS, and JavaScript.

[0811] Database: Use a relational database such as MySQL to store past success stories and related documents.

[0812] Using Natural Language Processing and Generative AI Models

[0813] The server analyzes new business plan requests received from users using natural language processing, and collects related information using an internal database and external APIs based on keywords and topics extracted from the analysis results.

[0814] The collected information is normalized and fed into a generative AI model. For example, the following prompts can be used to provide information to the generative AI model:

[0815] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[0816] Emotion Engine and Feedback

[0817] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback as they view the generated business plan. Based on this feedback, the server adjusts and refines the content of the plan in real time.

[0818] Specific examples

[0819] For example, if a user sends a request saying, "I want to create a business plan for a new social media app," the server extracts keywords such as "social media app," "market analysis," and "competitive analysis." It then collects past success stories, pitch videos, and business plan documents, normalizes them, and inputs them into the generative AI model. The generated business plan includes sections such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan.

[0820] If a user reviews a plan and inputs feedback such as "the market analysis section is insufficient," the emotion engine will recognize this, and the server will add detailed data and concrete success stories to reinforce the market analysis section. It will also collect information on government subsidy programs and the latest advertising rates and notify the user.

[0821] This process allows users to obtain efficient and high-quality new business plans, increasing the chances of success for new businesses.

[0822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0823] Step 1:

[0824] A user enters and submits a request to create a new business plan through a web interface or application, for example, by typing "I would like to create a business plan for a new social media app" into a text box and clicking a submit button.

[0825] Input: User request text

[0826] Output: Request data to the server

[0827] Step 2:

[0828] The server parses the requests received from users and extracts the required keywords and topics, using Python and a natural language processing (NLP) library (e.g., Spacy) to identify keywords such as "social media app," "business model," and "market analysis."

[0829] Specifically, the request text is tokenized and important keywords are extracted.

[0830] Input: User request data

[0831] Output: Extracted keywords and topics

[0832] Step 3:

[0833] Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs, for example, querying past success stories from a MySQL database, collecting pitch videos through a video API, and retrieving related documents from a literature API.

[0834] Specific operations include issuing SQL queries, sending API requests, and analyzing responses.

[0835] Input: Extracted keywords and topics

[0836] Output: Related information data (past success stories, video links, literature information, etc.)

[0837] Step 4:

[0838] Normalize the information collected by the server. Convert information obtained from different formats and sources into a unified format and organize it into data that is easy to analyze. Use the Python pandas library to clean, organize, and eliminate duplicates from the data.

[0839] Input: Related information data

[0840] Output: Normalized information data

[0841] Step 5:

[0842] The server analyzes the normalized information using natural language processing (NLP) techniques and prepares it for input to the generative AI model. It also creates input prompts.

[0843] An example prompt is:

[0844] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[0845] As a concrete operation, the data is merged with the template to generate a prompt sentence.

[0846] Input: Normalized information data

[0847] Output: prompt statement

[0848] Step 6:

[0849] The server uses a generative AI model (e.g., GPT-4) to automatically generate a new business plan. The generated plan includes elements such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan. The generated text is embedded in a template.

[0850] Input: prompt statement

[0851] Output: Auto-generated business plan

[0852] Step 7:

[0853] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback while viewing the generated business plan. For example, if the user enters feedback such as "This market analysis is insufficient," the server recognizes that information as negative feedback.

[0854] Input: User feedback

[0855] Output: Parsed emotion data

[0856] Step 8:

[0857] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan, adding detailed market data and concrete success stories to enrich the data in order to generate a plan that satisfies the user.

[0858] Input: Parsed emotion data

[0859] Output: A polished business plan

[0860] Step 9:

[0861] The server generates feedback based on the review information and further refines the plan, specifically proposing incentive plans necessary to increase user engagement.

[0862] Input: Review information

[0863] Output: Specific feedback

[0864] Step 10:

[0865] The server collects the latest information on subsidies and advertising fees and provides it to users. The necessary information is obtained from government APIs and advertising agency APIs and incorporated into the business plan.

[0866] Input: Data from the relevant API

[0867] Output: Information about subsidies and advertising fees

[0868] Step 11:

[0869] The server delivers the final, polished business plan to the user, who can then download it in PDF format and further revise or print it as needed.

[0870] Input: Finalized business plan

[0871] Output: Plan delivered to user

[0872] (Application example 2)

[0873] 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."

[0874] When creating new business plans, conventional systems have had issues with not effectively utilizing past success stories and related information, and not providing sufficient feedback to refine the automatically generated business plan. They also lack the ability to reflect the user's feelings or provide information on the latest subsidies and advertising fees. This makes it difficult to create high-quality business plans that meet the user's needs.

[0875] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0876] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for automatically generating a new business plan based on the collected information, means for providing feedback based on review information and the user's emotional state to refine the business plan, and means for providing the latest information on subsidies and advertising fees. This makes it possible to create a high-quality new business plan that utilizes past success stories and the latest information and incorporates feedback from emotional analysis.

[0877] "User" means an individual or organization that uses the System to request the creation of a new business plan and receives the results.

[0878] A "request" is information that a user sends to the system to request the creation of a new business plan.

[0879] "Past success stories" are specific examples of successful business activities or projects that have been undertaken in the past.

[0880] A "document" is a document that records data and information necessary to generate a new business plan.

[0881] "Related information" refers to information such as past success stories, market analysis, and competitive analysis that are necessary for creating a new business plan.

[0882] A "new business plan" is a document that describes a detailed plan for starting a new business.

[0883] "Automatic generation" refers to a system using programs and algorithms to create a business plan without human intervention.

[0884] "Review information" is information on evaluations and feedback given by users on business plans.

[0885] "Feedback" is information for improving the business plan based on user evaluations and impressions.

[0886] "Brushing up" means making corrections or additions to a new business plan to improve its quality and content.

[0887] The "user's emotional state" refers to the emotional state, such as satisfaction, dissatisfaction, or interest, that the user feels when using the system.

[0888] A "grant" is funding provided by a government or organization to support a new business or project.

[0889] "Advertising fees" are fees paid to promote new businesses or projects.

[0890] "Latest information" means the most recently updated information as of the present time.

[0891] "Normalization" means converting data from different sources into a unified format.

[0892] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0893] "Analysis" is the process of analyzing data and information in detail to find meaning and value.

[0894] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically create new documents or information.

[0895] The system of the present invention allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback based on reviews and the user's emotional state to refine the plan. It also provides the latest information on subsidies and advertising fees.

[0896] The server includes a means for receiving a user's request, a means for collecting relevant information from past success stories and documents, and a means for automatically generating a new business plan based on the collected information. It also includes a means for providing feedback based on review information and the user's emotional state to refine the business plan, and a means for providing the latest information on subsidies and advertising fees.

[0897] In processing the program, the server operates according to the following flow:

[0898] First, a user uses a device (e.g., a smartphone or PC) to input and send a request to create a new business plan through a web interface or application. For example, a user might send a request saying, "I want to create a business plan for a virtual clothing store."

[0899] The server receives this request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords and topics. In this example, we use Python's spaCy as the NLP library. The extracted keywords include "virtual store," "clothing," and "market analysis."

[0900] The server then searches and collects relevant information based on the extracted keywords and topics using an internal database (MongoDB) and an external API (public data provider) to collect past success stories, pitch videos, business plans, and reviews.

[0901] The collected information is normalized on the server, converting data collected from different information sources into a unified format, and performing text cleaning, formatting, and deduplication.

[0902] The server then analyzes the normalized data and inputs it into a generative AI model, powered by OpenAI's GPT-3, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[0903] The generated business plan will include the following elements:

[0904] Business overview (e.g., virtual clothing store concept and features)

[0905] Market analysis (e.g., the expansion trend of the virtual store market)

[0906] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0907] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new store)

[0908] Business model (e.g., monetization methods)

[0909] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0910] The impressions and feedback entered by users while viewing the business plan are analyzed by an emotion engine, which uses the IBM Watson Tone Analyzer API, allowing the user's emotional state (satisfaction, dissatisfaction, interest, doubt, etc.) to be recognized in real time.

[0911] Based on the user's emotional state detected by the emotion engine, the server adjusts the feedback and refines the business plan. For example, if the user is dissatisfied with the market analysis section, the server can provide additional data and specific success stories related to that section. The server also collects the latest information on subsidies and advertising fees and provides it to the user. This increases the feasibility of the business plan.

[0912] As a concrete example, the following prompt sentence is input to the generative AI model:

[0913] Virtual Clothing Store Business Plan

[0914] (Business overview)

[0915] Please explain the concept and features of the virtual store.

[0916] (Market analysis)

[0917] Please provide an update on the market size and competitive situation for virtual clothing stores.

[0918] (Competitive analysis)

[0919] Conduct an analysis of your major competitors, including their strengths and weaknesses.

[0920] (SWOT analysis)

[0921] Indicate the strengths, weaknesses, opportunities, and threats of your new app.

[0922] (Business Model)

[0923] Please elaborate on how you will monetize your clothing store.

[0924] (Income and expenditure plan)

[0925] Please specify your projected income and expenditure for the first year.

[0926] The final, polished business plan is delivered to the user, who can review it and download or modify it as needed.

[0927] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0928] Step 1:

[0929] This is the stage where the user enters "I would like to create a business plan for a virtual clothing store" into the request form and submits it. The text data entered by the user becomes the input, and the server receives the request and outputs it as data to be analyzed.

[0930] Step 2:

[0931] In this step, the server receives the request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords (e.g., "virtual store," "clothing," "market analysis," etc.). Text analysis is performed using an NLP library (e.g., spaCy), and the analyzed keywords are output.

[0932] Step 3:

[0933] This is the step where the server collects the necessary related information from an internal database (e.g., MongoDB) or an external API based on the keywords extracted. A request for the information to be collected is sent to the external API, and success stories, pitch videos, business documents, etc. are collected and output.

[0934] Step 4:

[0935] This is the step where the collected information is normalized on the server. Text cleaning, formatting, and deduplication are performed, and the normalized information is output as data in a unified format.

[0936] Step 5:

[0937] This is the step where the server analyzes the normalized data and prepares it for input into a generative AI model (e.g., OpenAI GPT-3). Important key points and relationships are analyzed, and a dataset for AI input is created and output.

[0938] Step 6:

[0939] In this step, the server automatically generates a new business plan using the generative AI model. A prompt (e.g., "Write a business plan for a virtual clothing store") is input to the generative AI model, and the business plan is generated and output.

[0940] Step 7:

[0941] In this step, the user views the generated business plan and inputs their thoughts and feedback. The user's feedback text is sent to the server as input.

[0942] Step 8:

[0943] In this step, the server analyzes the user's feedback using an emotion engine (e.g., IBM Watson Tone Analyzer API). The analyzed emotion data is output and used as the basis for adjusting the feedback.

[0944] Step 9:

[0945] In this step, the server refines the business plan based on the results of the sentiment analysis. It adds detailed data to specific sections and outputs the refined business plan.

[0946] Step 10:

[0947] In this step, the server collects the latest information on subsidies and advertising fees and adds it to the business plan. It also collects the latest data from external APIs and the internet, integrates it into the business plan, and outputs it.

[0948] Step 11:

[0949] This is the step where the server delivers the final polished business plan to the user. The generated business plan is sent to the user's device, where the user can review and download it.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] [Third embodiment]

[0954] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0955] 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.

[0956] 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).

[0957] 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.

[0958] 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.

[0959] 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).

[0960] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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."

[0966] System Overview

[0967] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[0968] Program processing flow (explained in natural language)

[0969] Receiving a request

[0970] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user might request, "I would like to create a business plan for a new social media app."

[0971] Collection of information

[0972] 2. The server analyzes the request received from the user and extracts the necessary keywords, such as "social media app," "market analysis," and "business model."

[0973] 3. The server uses internal databases and external APIs to search and collect past success stories, pitch videos, business plan documents, and review information, using algorithms to evaluate the quality and relevance of the collected data.

[0974] Generate a business plan

[0975] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) technology. The normalized data is then input into a generative AI model.

[0976] 5. The server launches the generative AI model and automatically generates a new business plan based on the input data. The business plan includes the following elements:

[0977] Business overview (e.g., concept and features of a new social media app)

[0978] Market analysis (e.g., social media market growth trends)

[0979] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[0980] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[0981] Business model (e.g., monetization methods)

[0982] Income and expenditure plan (e.g., first year's expected income and expenditure)

[0983] Providing Feedback

[0984] 6. The server reviews the generated business plan, references past success stories and reviews, and generates feedback. For example, it provides specific improvements such as, "An incentive plan in the early stages is important to increase user engagement."

[0985] 7. The server integrates the feedback and refines the generated business plan.

[0986] Providing supporting information

[0987] 8. The server collects information on the latest subsidy programs and advertising rates and provides relevant information to the user, such as "Current government subsidy programs can help cover part of the initial development costs."

[0988] Results distribution

[0989] 9. The server delivers the final, polished business plan to the user, who can then review it, download it, or make any further modifications necessary.

[0990] Specific examples

[0991] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[0992] The processing flow will be explained below.

[0993] Step 1:

[0994] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[0995] Step 2:

[0996] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[0997] Step 3:

[0998] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[0999] Step 4:

[1000] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[1001] Step 5:

[1002] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1003] Step 6:

[1004] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[1005] - Business overview (e.g., concept and features of a new social media app)

[1006] - Market analysis (e.g., social media market growth trends)

[1007] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1008] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[1009] - Business model (e.g., monetization methods)

[1010] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[1011] Step 7:

[1012] The server reviews the generated business plan and compares it with past success stories and review information to assess the plan's quality and identify areas for improvement.

[1013] Step 8:

[1014] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[1015] Step 9:

[1016] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[1017] Step 10:

[1018] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[1019] Example 1

[1020] 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."

[1021] The traditional process of creating a new business plan required a great deal of time and effort to search and analyze vast amounts of information, and to create and review documents. It also required time and effort to manually collect and consolidate the latest information on subsidies and advertising expenses. This situation could slow down the launch of a new business and lead to inaccurate plans.

[1022] 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.

[1023] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for normalizing the collected information and analyzing it using natural language processing technology, means for automatically generating a new business plan based on the normalized and analyzed data, means for reviewing the generated business plan and providing feedback based on past success stories and review information, means for integrating the generated feedback and improving the business plan, means for collecting and providing the latest information on subsidies and advertising expenses, and means for delivering the generated and improved business plan to the user, thereby enabling the rapid and highly accurate creation of new business plans.

[1024] The "means for receiving a user request" is the function of the system that receives the request via the input device or software that the user uses to request the creation of a new business plan.

[1025] "Means for collecting relevant information from past success stories and documents" refers to the system's function of collecting data such as past successful business plans, related documents, pitch videos, and review information via an internal database or external API.

[1026] "Means of normalizing collected information and analyzing it using natural language processing techniques" refers to the process of converting collected data into a consistent format and analyzing the information using natural language processing techniques.

[1027] "Means for automatically generating new business plans based on normalized and analyzed data" refers to a system function that uses normalized and analyzed information to create new business plans using generative AI models or other automated technologies.

[1028] The "means of reviewing the generated business plan and providing feedback based on past success stories and review information" refers to a process for evaluating the contents of the automatically generated business plan in comparison with past success stories and review information, and generating specific feedback.

[1029] The "means for integrating the generated feedback and refining the business plan" is a function of the system for integrating the provided feedback and further improving the generated business plan.

[1030] "Means for collecting and providing the latest information on subsidies and advertising expenditures" refers to the process of collecting the latest information on subsidy programs and advertising expenditures from government agencies and related websites and providing it to users.

[1031] "Means for delivering the generated and refined business plan to the user" refers to the system's function of generating the final, refined business plan in a format such as PDF or Word and delivering it to the user via email or other means.

[1032] System Overview

[1033] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[1034] Hardware and software used

[1035] The system consists of the following hardware and software:

[1036] Server: Equipped with high-performance computing resources and database management systems. Examples include AWS EC2 instances and MySQL databases.

[1037] Device: The device used by the user. Examples include PCs, smartphones, and tablets.

[1038] Natural language processing (NLP) software: Python libraries such as spaCy and NLTK are used.

[1039] Generative AI models: Leverage advanced AI models such as OpenAI's GPT-4.

[1040] How it works

[1041] Receiving a request

[1042] 1. A user inputs and submits a request to create a new business plan via a web interface or application. Specifically, the user enters a summary of the business idea and necessary information into a dedicated form and clicks the "Submit" button.

[1043] Collection of information

[1044] 2. The server analyzes the request received from the user and extracts the necessary keywords using natural language processing (NLP) techniques. Specifically, it uses a Python NLP library (e.g., spaCy or NLTK) to identify the main topics and themes from the request text.

[1045] 3. The server searches and collects past success stories, pitch videos, business plan documents, and review information using an internal database or external API (e.g., Google Scholar API or other business data API). Specifically, it runs an SQL query against the case study database to retrieve a list of relevant documents. It then downloads the data from the URLs of the listed documents.

[1046] Generate a business plan

[1047] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) techniques, such as data cleaning and converting it into a consistent format. This is often done using the Python pandas library.

[1048] 5. The server launches a generative AI model (e.g., GPT-4) and inputs the normalized data. It generates a specific prompt and sends it to the model.

[1049] Example: "Write a business plan for a new social media app. Include the following elements: 1. Business Overview 2. Market Analysis 3. Competitive Analysis 4. SWOT Analysis 5. Business Model 6. Income and Expenditure Plan."

[1050] 6. The server receives the generated business plan and performs additional processing to format it, such as converting the generated text to Markdown or LaTeX formatting, for visual editing.

[1051] Providing feedback

[1052] 7. The server compares the generated business plan with past success stories and review information in an internal database and generates feedback. Specifically, it uses a review algorithm to evaluate the plan's contents and extract strengths and areas for improvement. For example, it suggests specific areas for improvement, such as "An incentive plan in the early stages is important to increase user engagement."

[1053] 8. The server integrates the feedback and makes additional modifications to the generated business plan, again using the generative AI model to generate a new version of the business plan that reflects the feedback.

[1054] Providing supporting information

[1055] 9. The server collects information about the latest subsidy programs and advertising rates using web scraping and APIs. Specifically, it retrieves and analyzes the latest subsidy information from government agency websites.

[1056] 10. The server processes the collected information and provides appropriate supporting information to the user, such as integrating it into a business plan as "Current government subsidy programs can help cover some of the initial development costs."

[1057] Results distribution

[1058] 11. The server generates the final, polished business plan as a PDF or Word document and delivers it to the user. Specifically, it creates the document using a document generation library (such as Python-docx or ReportLab) and emails the user a download link.

[1059] 12. The user reviews the received business plan and downloads it or makes further edits as needed. For example, the user can open the downloaded PDF in Adobe Acrobat and edit the details.

[1060] Specific examples

[1061] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[1062] Example prompts to input to the generative AI model

[1063] Write a business plan for a new social media app. Include the following elements:

[1064] 1. Business overview

[1065] 2. Market analysis

[1066] 3. Competitive analysis

[1067] 4. SWOT analysis

[1068] 5. Business Model

[1069] 6. Income and expenditure plan

[1070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1071] The flow of this system's program processing

[1072] Step 1: Receiving the request

[1073] Processing content

[1074] A user inputs and submits a request to create a new business plan via a web interface or application.

[1075] Input: The user enters a summary of their business idea and the necessary information into a dedicated form and clicks the "Submit" button.

[1076] Output: The user's request data is sent to the server.

[1077] Specific operation: When the submit button of the form is pressed, the request data is sent to the server via the HTTP protocol and saved in the database.

[1078] Step 2: Analyze the information

[1079] Processing content

[1080] The server analyzes the request received from the user and extracts the necessary keywords.

[1081] Input: The user's request data saved in step 1.

[1082] Output: Keyword list (e.g. "social media app", "market analysis", "business model").

[1083] What happens: The server uses a Python NLP library (e.g., spaCy or NLTK) to parse and extract major topics and themes from the request.

[1084] Step 3: Gather information

[1085] Processing content

[1086] The server uses internal databases and external APIs to search and collect past success stories and related information.

[1087] Input: The keyword list extracted in step 2.

[1088] Output: A list of relevant documents, videos, and review information.

[1089] What it does: Based on the information collected, the server executes SQL queries against the database to retrieve relevant data, and also calls external APIs to retrieve additional information.

[1090] Step 4: Data normalization and analysis

[1091] Processing content

[1092] The information collected by the server is normalized and analyzed using natural language processing technology.

[1093] Input: Data collected in Step 3.

[1094] Output: Normalized and parsed data.

[1095] Specific operation: The server performs data cleaning (e.g., deduplication, format standardization), normalization using Python's pandas library, and then analyzes the data again using natural language processing technology.

[1096] Step 5: Automatically generate a business plan

[1097] Processing content

[1098] The server launches the generative AI model, inputs the normalized data, and automatically generates a business plan.

[1099] Input: The normalized and analyzed data from step 4.

[1100] Output: Early stage business plan.

[1101] Specific operation: The server generates and sends a prompt to the generative AI model (e.g., GPT-4). For example, the prompt might be, "Please write a business plan for a new social media app."

[1102] Step 6: Review and generate feedback

[1103] Processing content

[1104] The server reviews the generated business plan and generates feedback.

[1105] Input: The business plan generated in step 5.

[1106] Output: A list of feedbacks.

[1107] What it does: The server compares past success stories and existing review information, and uses a review algorithm to generate a rating and feedback for the generated business plan.

[1108] Step 7: Integrate feedback and refine

[1109] Processing content

[1110] The server integrates the generated feedback and refines the business plan.

[1111] Input: Feedback generated in step 6.

[1112] Output: An improved business plan.

[1113] Specific operation: The server uses the generative AI model again to create a new version of the business plan that reflects the feedback.

[1114] Step 8: Gather supporting information

[1115] Processing content

[1116] The server collects and provides information on the latest subsidy programs and advertising rates.

[1117] Input: Data from government websites and related APIs.

[1118] Output: Updates on subsidies and advertising expenditures.

[1119] Specific operation: The server collects the necessary information through web scraping and API calls, and provides it after evaluating and analyzing its quality.

[1120] Step 9: Delivering results

[1121] Processing content

[1122] The server delivers the final, polished business plan to the user.

[1123] Input: Your refined business plan from Step 7.

[1124] Output: Business plan in PDF or Word document format sent to user.

[1125] What it does: The server creates a document using a document generation library like Python-docx or ReportLab, and emails the user a download link.

[1126] Step 10: User Verification and Download

[1127] Processing content

[1128] The user reviews the business plan they receive and downloads or further modifies it as necessary.

[1129] Input: Business plan delivered in Step 9.

[1130] Output: A revised or ready-to-use business plan.

[1131] What happens: The user opens the email they received, clicks the download link to get the business plan, and then reviews and edits it in a PDF reader or word processor.

[1132] (Application example 1)

[1133] 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."

[1134] In the past, the process of creating a new business plan required a lot of time and effort. Furthermore, there were few ways to efficiently collect and incorporate feedback from past success stories and the latest subsidy information. This led to problems such as a decline in the quality and success rate of business plans.

[1135] 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.

[1136] In this invention, the server includes means for receiving a user's request to create a business plan for a new product or service, means for collecting related information from past success stories and market research data, means for automatically generating a new business plan using a generative AI model based on the collected information, means for providing feedback from review information and past success stories to refine the business plan, and means for providing information on subsidies and advertising. This makes it possible to efficiently generate high-quality business plans that reflect the latest information and feedback.

[1137] "User" refers to the entity that uses the system to create new business plans and collect related information.

[1138] "New products and services" refers to products and services that are new and have not yet appeared in the existing market.

[1139] "Business Plan Creation Request" refers to a request sent by a User to the System to create a new business plan.

[1140] "Past success stories" refer to business models, operating methods, marketing strategies, and other events that have been implemented successfully in the past.

[1141] "Market research data" refers to data that compiles information about trends in a specific market, consumer behavior, and the competitive landscape.

[1142] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate data analysis and predictive models.

[1143] A "business plan" refers to a document that describes the management policy, strategy, revenue forecast, etc. of a new business.

[1144] "Review information" refers to evaluations and opinions on areas for improvement provided by third parties regarding past business plans and business performance.

[1145] "Feedback" refers to information such as advice, improvements, and evaluations provided regarding the generated business plan and management policy.

[1146] "Brushing up" refers to the process of making corrections or additions to existing business plans and information to improve them.

[1147] A "subsidy" refers to financial assistance provided by the government or local government for specific business activities.

[1148] "Advertising" refers to promotional activities carried out to widely publicize a particular product or service.

[1149] "Collection" refers to the act of gathering data or information needed for a specific purpose.

[1150] "Normalization" refers to the process of aligning data formats and values ​​to certain standards before performing data analysis.

[1151] "Natural language processing" refers to the technology that allows computers to analyze and generate human language.

[1152] MODE FOR CARRYING OUT THE INVENTION

[1153] System Overview

[1154] This invention is a system for efficiently creating and refining business plans for new products and services. Users submit requests to the system, and the server collects and analyzes past success stories and market research data, automatically generating a business plan using a generative AI model. It also provides feedback based on review information to refine the business plan. It also provides the latest information on subsidies and advertising.

[1155] Overview of program processing

[1156] The server implements the present invention using the following hardware and software: A smartphone, tablet, and VR goggles are used as the hardware, and Python, Flask (web framework), Google Cloud AI (for NLP processing), and MongoDB (database) are used as the software.

[1157] 1. Receiving a request:

[1158] A user inputs a request for the creation of a business plan for a new product or service into the system (application or web interface) and submits it.

[1159] For example, a user may request, "I would like to create a business plan for a new VR accessory product."

[1160] 2. Keyword extraction:

[1161] The server extracts keywords from the received request using natural language processing techniques. In this example, we use CountVectorizer from the scikit-learn library.

[1162] For example, from the input "new VR accessory product," keywords such as "VR accessory" and "new product" are extracted.

[1163] 3. Data Collection:

[1164] Based on the extracted keywords, the server collects related information using external APIs and internal databases.

[1165] For example, collect past success stories and market analysis data from the VR accessory market.

[1166] 4. Data normalization and analysis:

[1167] The collected data is normalized and analyzed using Google Cloud AI's natural language processing technology.

[1168] 5. Generate a business plan:

[1169] Based on the analysis results, a new business plan is automatically generated using a generative AI model (e.g., OpenAI GPT-4).

[1170] For example, use the following prompt: "Write a business plan for a new product to enter the VR accessories market. Include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income statement."

[1171] 6. Providing Feedback:

[1172] Provides feedback based on past success stories and review information to the generated business plan, thereby improving the quality of the business plan.

[1173] 7. Providing supporting information:

[1174] The server collects information about the latest subsidy programs and advertising strategies and provides it to users.

[1175] For example, provide information such as, "Current subsidy programs can help cover some of the initial development costs."

[1176] 8. Delivery of the refined plan:

[1177] The server delivers the final, polished business plan to the user, who can then check it, download it, or make any further modifications necessary.

[1178] This system allows users to efficiently generate high-quality business plans and incorporate the latest information and feedback. It also centrally collects and provides the latest information on subsidies and advertising, thereby increasing the chances of success for new businesses.

[1179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] A user inputs and submits a request to create a business plan for a new product or service into an application or web interface. The input includes an outline of the new product or service, its purpose, and market target. The output is transferred to the server. Specifically, the user makes an input request saying, "I would like to create a business plan for a new VR accessory product."

[1182] Step 2:

[1183] Natural language processing techniques are used to extract key keywords from requests received by the server. The input is the user's request, and the output is a list of extracted keywords. For data processing, keyword extraction is performed using CountVectorizer from the scikit-learn library. Specifically, keywords such as "VR," "accessories," and "new product" are extracted from the sentence "new VR accessory product."

[1184] Step 3:

[1185] Based on the extracted keywords, the server collects related information from internal databases and external APIs. The input is a list of keywords, and the output is a set of related information. To collect data, external APIs (such as market research APIs) are used to extract appropriate data and store it in a database such as MongoDB. Specifically, market analysis and competitive information related to "VR" and "accessories" are collected.

[1186] Step 4:

[1187] The server normalizes the collected information and analyzes the data using natural language processing technology. The input is the collected raw data, and the output is the standardized and analyzed data. For data processing, Google Cloud AI's NLP technology is used to analyze the data and organize it into a unified format. Specifically, market analysis data and review information are normalized and analyzed according to the format.

[1188] Step 5:

[1189] The server uses a generative AI model to automatically generate a new business plan based on the analyzed data. The input is the analyzed data, and the output is a draft of the generated business plan. For data calculation, OpenAI's GPT-4 model is used to generate text. Specifically, the following prompt is used: "Please write a business plan for a new product to enter the VR accessories market. Please include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income and expenditure plan."

[1190] Step 6:

[1191] The server provides feedback to the generated business plan based on past success stories and review information, and refines the business plan. The input is the generated draft business plan and review information, and the output is a business plan that reflects the feedback. As a data calculation, each section of the business plan is evaluated based on the collected review information, and areas for improvement are listed and reflected.

[1192] Step 7:

[1193] The server collects information on the latest subsidy programs and advertising strategies and integrates it into a business plan. The input is the latest information obtained from the government and the market, and the output is a business plan with additional subsidy information added. Specifically, it collects information on currently available subsidy programs and adds specific information to the business plan, such as "these programs can cover part of the initial development costs."

[1194] Step 8:

[1195] The server delivers the final, polished business plan to the user. The input is the improved business plan, and the output is the provision of the business plan to the user. Specifically, the user can download the business plan and make a final check.

[1196] 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.

[1197] System Overview

[1198] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. By combining it with an emotion engine, it is possible to recognize user emotions and optimize the feedback and content of the business plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, a terminal, and a user.

[1199] Program processing flow (explained in natural language)

[1200] Receiving a request

[1201] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user may submit a request saying, "I would like to create a business plan for a new social media app."

[1202] Collection of information

[1203] 2. The server analyzes the request received from the user and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[1204] 3. Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This comprehensively collects the information required to meet the user's request.

[1205] Generate a business plan

[1206] 4. The server normalizes the collected information. Data collected from different sources is converted into a unified format and organized for easier analysis. This process includes text cleaning, formatting, and deduplication.

[1207] 5. The server analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1208] 6. The server uses the generative AI model to automatically generate a new business plan. The generated plan includes the following elements:

[1209] - Business overview (e.g., concept and features of a new social media app)

[1210] - Market analysis (e.g., social media market growth trends)

[1211] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1212] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[1213] - Business model (e.g., monetization methods)

[1214] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[1215] Use of emotion engine

[1216] 7. The server uses the emotion engine to analyze the user's thoughts and feedback while viewing the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[1217] 8. The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "Market Analysis" section, the server will add more detailed data and specific success stories about the "Market Analysis" section.

[1218] Providing feedback

[1219] 9. The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[1220] Providing supporting information

[1221] 10. The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[1222] Results distribution

[1223] 11. The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[1224] Specific examples

[1225] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. The emotion engine then analyzes the user's emotional state based on their thoughts and feedback as they browse the plan. For example, if the emotion engine detects that the user is dissatisfied with the market analysis section, the server will further enhance the market analysis section and provide additional data. It also researches the latest government subsidy programs and provides support information on initial development costs. Ultimately, the user receives a high-quality business plan that is tailored to their emotional state, increasing the chances of success for their new venture.

[1226] The processing flow will be explained below.

[1227] Step 1:

[1228] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[1229] Step 2:

[1230] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[1231] Step 3:

[1232] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[1233] Step 4:

[1234] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[1235] Step 5:

[1236] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1237] Step 6:

[1238] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[1239] Business overview (e.g., concept and features of a new social media app)

[1240] Market analysis (e.g., social media market growth trends)

[1241] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1242] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[1243] Business model (e.g., monetization methods)

[1244] Income and expenditure plan (e.g., first year's expected income and expenditure)

[1245] Step 7:

[1246] The server uses an emotion engine to analyze the user's thoughts and feedback as they view the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[1247] Step 8:

[1248] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "market analysis" section, the server will add more detailed data and specific success stories about the "market analysis."

[1249] Step 9:

[1250] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[1251] Step 10:

[1252] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[1253] Step 11:

[1254] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[1255] Example 2

[1256] 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."

[1257] The process of creating a new business plan requires time and effort, as it requires complex and time-consuming tasks such as information gathering, analysis, and document creation. It is also difficult to improve the quality of the business plan by incorporating user feedback in real time. Another challenge is effectively incorporating the latest information on subsidies and advertising fees.

[1258] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving a user request, a means for collecting related information from past success stories and documents, a means for searching and collecting information based on extracted keywords and topics, a means for normalizing the collected information, a means for analyzing the normalized information using natural language processing and inputting it into a generative AI model, a means for automatically generating a new business plan using the generative AI model, a means for analyzing user feedback using an emotion engine and adjusting the feedback, a means for providing feedback based on review information to refine the business plan, and a means for providing information on subsidies and advertising fees. This allows users to automatically generate efficient, high-quality new business plans and refine them while reflecting feedback in real time. Furthermore, the latest information on subsidies and advertising fees can be incorporated, increasing the feasibility of the business plan.

[1259] The "means for receiving a user's request" is a function for receiving a request for creating a new business plan sent by a user through a web interface or application.

[1260] "Means of collecting relevant information from past success stories and documents" refers to the function of collecting past business success stories and related documents through an internal database or external API.

[1261] "Means for searching and collecting information based on extracted keywords and topics" refers to a function for searching and collecting related information based on keywords and topics extracted by natural language processing.

[1262] "Means for normalizing collected information" refers to the ability to format information obtained from different formats and sources into a unified format, making it easier to analyze.

[1263] "Means for analyzing normalized information using natural language processing and inputting it into a generative AI model" refers to a function that analyzes normalized information and inputs it into a generative AI model as necessary parameters.

[1264] "Means for automatically generating new business plans using a generative AI model" refers to a function that uses a generative AI model to automatically create new business plans based on collected and analyzed information.

[1265] "Means for analyzing user feedback using an emotion engine and adjusting the feedback" is a function that analyzes the impressions and feedback entered by the user in real time and adjusts the feedback based on the user's emotional state.

[1266] The "means for providing feedback based on review information and brushing up the business plan" is a function that generates specific feedback based on collected review information and improves the content of the business plan.

[1267] The "means for providing information on subsidies and advertising fees" is a function for collecting information on the latest subsidy programs and advertising fees and providing it to the user.

[1268] The present invention is a system that allows a user to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. The implementation method is described in detail below.

[1269] System configuration

[1270] The system is primarily composed of a server, a terminal, and a user. The server plays a central role in collecting, analyzing, generating, and providing feedback on information, while the terminal is a device used by users as an interface. Users are responsible for inputting requests for new business plans and for checking and providing feedback on the results.

[1271] Hardware and Software

[1272] Server: A high-performance server is used to execute various processes using Python. Libraries such as Spacy are used for natural language processing (NLP), and OpenAI's GPT-4 is used as the generative AI model. External APIs (e.g., YouTube API, Google Scholar API) are also used for data collection and analysis.

[1273] Device: A web browser or mobile application that users access. The user interface is created using HTML, CSS, and JavaScript.

[1274] Database: Use a relational database such as MySQL to store past success stories and related documents.

[1275] Using Natural Language Processing and Generative AI Models

[1276] The server analyzes new business plan requests received from users using natural language processing, and collects related information using an internal database and external APIs based on keywords and topics extracted from the analysis results.

[1277] The collected information is normalized and fed into a generative AI model. For example, the following prompts can be used to provide information to the generative AI model:

[1278] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[1279] Emotion Engine and Feedback

[1280] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback as they view the generated business plan. Based on this feedback, the server adjusts and refines the plan in real time.

[1281] Specific examples

[1282] For example, if a user sends a request saying, "I want to create a business plan for a new social media app," the server extracts keywords such as "social media app," "market analysis," and "competitive analysis." It then collects past success stories, pitch videos, and business plan documents, normalizes them, and inputs them into the generative AI model. The generated business plan includes sections such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan.

[1283] If a user reviews a plan and inputs feedback such as "the market analysis section is insufficient," the emotion engine will recognize this, and the server will add detailed data and concrete success stories to reinforce the market analysis section. It will also collect information on government subsidy programs and the latest advertising rates and notify the user.

[1284] This process allows users to obtain efficient and high-quality new business plans, increasing the chances of success for new businesses.

[1285] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1286] Step 1:

[1287] A user enters and submits a request to create a new business plan through a web interface or application, for example, by typing "I would like to create a business plan for a new social media app" into a text box and clicking a submit button.

[1288] Input: User request text

[1289] Output: Request data to the server

[1290] Step 2:

[1291] The server parses the requests received from users and extracts the required keywords and topics, using Python and a natural language processing (NLP) library (e.g., Spacy) to identify keywords such as "social media app," "business model," and "market analysis."

[1292] Specifically, the request text is tokenized and important keywords are extracted.

[1293] Input: User request data

[1294] Output: Extracted keywords and topics

[1295] Step 3:

[1296] Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs, for example, querying past success stories from a MySQL database, collecting pitch videos through a video API, and retrieving related documents from a literature API.

[1297] Specific operations include issuing SQL queries, sending API requests, and analyzing responses.

[1298] Input: Extracted keywords and topics

[1299] Output: Related information data (past success stories, video links, literature information, etc.)

[1300] Step 4:

[1301] Normalize the information collected by the server. Convert information obtained from different formats and sources into a unified format and format it into data that is easy to analyze. Use the Python pandas library to clean, format, and eliminate duplicates from the data.

[1302] Input: Related information data

[1303] Output: Normalized information data

[1304] Step 5:

[1305] The server analyzes the normalized information using natural language processing (NLP) techniques and prepares it for input to the generative AI model. It also creates input prompts.

[1306] An example prompt is:

[1307] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[1308] As a concrete operation, the data is merged with the template to generate a prompt sentence.

[1309] Input: Normalized information data

[1310] Output: prompt statement

[1311] Step 6:

[1312] The server uses a generative AI model (e.g., GPT-4) to automatically generate a new business plan. The generated plan includes elements such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan. The generated text is embedded in a template.

[1313] Input: prompt statement

[1314] Output: Auto-generated business plan

[1315] Step 7:

[1316] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback while viewing the generated business plan. For example, if the user enters feedback such as "This market analysis is insufficient," the server recognizes that information as negative feedback.

[1317] Input: User feedback

[1318] Output: Parsed emotion data

[1319] Step 8:

[1320] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan, adding detailed market data and concrete success stories to enrich the data in order to generate a plan that satisfies the user.

[1321] Input: Parsed emotion data

[1322] Output: A polished business plan

[1323] Step 9:

[1324] The server generates feedback based on the review information and further refines the plan, specifically proposing incentive plans necessary to increase user engagement.

[1325] Input: Review information

[1326] Output: Specific feedback

[1327] Step 10:

[1328] The server collects the latest information on subsidies and advertising fees and provides it to users. The necessary information is obtained from government APIs and advertising agency APIs and incorporated into the business plan.

[1329] Input: Data from the relevant API

[1330] Output: Information about subsidies and advertising fees

[1331] Step 11:

[1332] The server delivers the final, polished business plan to the user, who can then download it in PDF format and further revise or print it as needed.

[1333] Input: Finalized business plan

[1334] Output: Plan delivered to user

[1335] (Application example 2)

[1336] 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."

[1337] When creating new business plans, conventional systems have had issues with not effectively utilizing past success stories and related information, and not providing sufficient feedback to refine the automatically generated business plan. They also lack the ability to reflect the user's feelings or provide information on the latest subsidies and advertising fees. This makes it difficult to create high-quality business plans that meet the user's needs.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1339] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for automatically generating a new business plan based on the collected information, means for providing feedback based on review information and the user's emotional state to refine the business plan, and means for providing the latest information on subsidies and advertising fees. This makes it possible to create a high-quality new business plan that utilizes past success stories and the latest information and incorporates feedback from emotional analysis.

[1340] "User" means an individual or organization that uses the System to request the creation of a new business plan and receives the results.

[1341] A "request" is information that a user sends to the system to request the creation of a new business plan.

[1342] "Past success stories" are specific examples of successful business activities or projects that have been undertaken in the past.

[1343] A "document" is a document that records data and information necessary to generate a new business plan.

[1344] "Related information" refers to information such as past success stories, market analysis, and competitive analysis that are necessary for creating a new business plan.

[1345] A "new business plan" is a document that describes a detailed plan for starting a new business.

[1346] "Automatic generation" refers to a system using programs and algorithms to create a business plan without human intervention.

[1347] "Review information" is information on evaluations and feedback given by users on business plans.

[1348] "Feedback" is information for improving the business plan based on user evaluations and impressions.

[1349] "Brushing up" means making corrections or additions to a new business plan to improve its quality and content.

[1350] The "user's emotional state" refers to the emotional state, such as satisfaction, dissatisfaction, or interest, that the user feels when using the system.

[1351] A "grant" is funding provided by a government or organization to support a new business or project.

[1352] "Advertising fees" are fees paid to promote new businesses or projects.

[1353] "Latest information" means the most recently updated information as of the present time.

[1354] "Normalization" means converting data from different sources into a unified format.

[1355] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1356] "Analysis" is the process of analyzing data and information in detail to find meaning and value.

[1357] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically create new documents or information.

[1358] The system of the present invention allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback based on reviews and the user's emotional state to refine the plan. It also provides the latest information on subsidies and advertising fees.

[1359] The server includes a means for receiving a user's request, a means for collecting relevant information from past success stories and documents, and a means for automatically generating a new business plan based on the collected information. It also includes a means for providing feedback based on review information and the user's emotional state to refine the business plan, and a means for providing the latest information on subsidies and advertising fees.

[1360] In processing the program, the server operates according to the following flow:

[1361] First, a user uses a device (e.g., a smartphone or PC) to input and send a request to create a new business plan through a web interface or application. For example, a user might send a request saying, "I want to create a business plan for a virtual clothing store."

[1362] The server receives this request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords and topics. In this example, we use Python's spaCy as the NLP library. The extracted keywords include "virtual store," "clothing," and "market analysis."

[1363] The server then searches and collects relevant information based on the extracted keywords and topics using an internal database (MongoDB) and an external API (public data provider) to collect past success stories, pitch videos, business plans, and reviews.

[1364] The collected information is normalized on the server, converting data collected from different information sources into a unified format, and performing text cleaning, formatting, and deduplication.

[1365] The server then analyzes the normalized data and inputs it into a generative AI model, powered by OpenAI's GPT-3, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1366] The generated business plan will include the following elements:

[1367] Business overview (e.g., virtual clothing store concept and features)

[1368] Market analysis (e.g., the expansion trend of the virtual store market)

[1369] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1370] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new store)

[1371] Business model (e.g., monetization methods)

[1372] Income and expenditure plan (e.g., first year's expected income and expenditure)

[1373] The impressions and feedback entered by users while viewing the business plan are analyzed by an emotion engine, which uses the IBM Watson Tone Analyzer API, allowing the user's emotional state (satisfaction, dissatisfaction, interest, doubt, etc.) to be recognized in real time.

[1374] Based on the user's emotional state detected by the emotion engine, the server adjusts the feedback and refines the business plan. For example, if the user is dissatisfied with the market analysis section, the server can provide additional data and specific success stories related to that section. The server also collects the latest information on subsidies and advertising fees and provides it to the user. This increases the feasibility of the business plan.

[1375] As a concrete example, the following prompt sentence is input to the generative AI model:

[1376] Virtual Clothing Store Business Plan

[1377] (Business overview)

[1378] Please explain the concept and features of the virtual store.

[1379] (Market analysis)

[1380] Please provide an update on the market size and competitive situation for virtual clothing stores.

[1381] (Competitive analysis)

[1382] Conduct an analysis of your major competitors, including their strengths and weaknesses.

[1383] (SWOT analysis)

[1384] Indicate the strengths, weaknesses, opportunities, and threats of your new app.

[1385] (Business Model)

[1386] Please elaborate on how you will monetize your clothing store.

[1387] (Income and expenditure plan)

[1388] Please specify your projected income and expenditure for the first year.

[1389] The final, polished business plan is delivered to the user, who can review it and download or modify it as needed.

[1390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1391] Step 1:

[1392] This is the stage where the user enters "I would like to create a business plan for a virtual clothing store" into the request form and submits it. The text data entered by the user becomes the input, and the server receives the request and outputs it as data to be analyzed.

[1393] Step 2:

[1394] In this step, the server receives the request, analyzes the request using natural language processing (NLP) technology, and extracts the necessary keywords (e.g., "virtual store," "clothing," "market analysis," etc.). Text analysis is performed using an NLP library (e.g., spaCy), and the analyzed keywords are output.

[1395] Step 3:

[1396] This is the step where the server collects the necessary related information from an internal database (e.g., MongoDB) or an external API based on the keywords extracted. A request for the information to be collected is sent to the external API, and success stories, pitch videos, business documents, etc. are collected and output.

[1397] Step 4:

[1398] This is the step where the collected information is normalized on the server. Text cleaning, formatting, and deduplication are performed, and the normalized information is output as data in a unified format.

[1399] Step 5:

[1400] This is the step where the server analyzes the normalized data and prepares it for input into a generative AI model (e.g., OpenAI GPT-3). Important key points and relationships are analyzed, and a dataset for AI input is created and output.

[1401] Step 6:

[1402] In this step, the server automatically generates a new business plan using the generative AI model. A prompt (e.g., "Write a business plan for a virtual clothing store") is input to the generative AI model, and the business plan is generated and output.

[1403] Step 7:

[1404] In this step, the user views the generated business plan and inputs their thoughts and feedback. The user's feedback text is sent to the server as input.

[1405] Step 8:

[1406] In this step, the server uses an emotion engine (e.g., IBM Watson Tone Analyzer API) to analyze the user's feedback. The analyzed emotion data is output and becomes the basis for adjusting the feedback.

[1407] Step 9:

[1408] In this step, the server refines the business plan based on the results of the sentiment analysis. It adds detailed data to specific sections and outputs the refined business plan.

[1409] Step 10:

[1410] In this step, the server collects the latest information on subsidies and advertising fees and adds it to the business plan. It also collects the latest data from external APIs and the internet, integrates it into the business plan, and outputs it.

[1411] Step 11:

[1412] This is the step where the server delivers the final, polished business plan to the user. The generated business plan is sent to the user's device, where the user can review and download it.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] [Fourth embodiment]

[1417] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1418] 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.

[1419] 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).

[1420] 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.

[1421] 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.

[1422] 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).

[1423] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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."

[1430] System Overview

[1431] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[1432] Program processing flow (explained in natural language)

[1433] Receiving a request

[1434] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user might request, "I would like to create a business plan for a new social media app."

[1435] Collection of information

[1436] 2. The server analyzes the request received from the user and extracts the necessary keywords, such as "social media app," "market analysis," and "business model."

[1437] 3. The server uses internal databases and external APIs to search and collect past success stories, pitch videos, business plan documents, and review information, using algorithms to assess the quality and relevance of the collected data.

[1438] Generate a business plan

[1439] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) technology. The normalized data is then input into a generative AI model.

[1440] 5. The server launches the generative AI model and automatically generates a new business plan based on the input data. The business plan includes the following elements:

[1441] Business overview (e.g., concept and features of a new social media app)

[1442] Market analysis (e.g., social media market growth trends)

[1443] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1444] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[1445] Business model (e.g., monetization methods)

[1446] Income and expenditure plan (e.g., first year's expected income and expenditure)

[1447] Providing Feedback

[1448] 6. The server reviews the generated business plan, references past success stories and reviews, and generates feedback. For example, it provides specific improvements such as, "An incentive plan in the early stages is important to increase user engagement."

[1449] 7. The server integrates the feedback and refines the generated business plan.

[1450] Providing supporting information

[1451] 8. The server collects information on the latest subsidy programs and advertising rates and provides relevant information to the user, such as "Current government subsidy programs can help cover part of the initial development costs."

[1452] Results distribution

[1453] 9. The server delivers the final, polished business plan to the user, who can then review it, download it, or make any further modifications necessary.

[1454] Specific examples

[1455] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[1456] The processing flow will be explained below.

[1457] Step 1:

[1458] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[1459] Step 2:

[1460] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[1461] Step 3:

[1462] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[1463] Step 4:

[1464] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[1465] Step 5:

[1466] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1467] Step 6:

[1468] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[1469] - Business overview (e.g., concept and features of a new social media app)

[1470] - Market analysis (e.g., social media market growth trends)

[1471] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1472] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[1473] - Business model (e.g., monetization methods)

[1474] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[1475] Step 7:

[1476] The server reviews the generated business plan and compares it with past success stories and review information to assess the plan's quality and identify areas for improvement.

[1477] Step 8:

[1478] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[1479] Step 9:

[1480] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[1481] Step 10:

[1482] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[1483] Example 1

[1484] 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."

[1485] The traditional process of creating a new business plan required a great deal of time and effort to search and analyze vast amounts of information, and to create and review documents. It also required time and effort to manually collect and consolidate the latest information on subsidies and advertising expenses. This situation could slow down the launch of a new business and lead to inaccurate plans.

[1486] 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.

[1487] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for normalizing the collected information and analyzing it using natural language processing technology, means for automatically generating a new business plan based on the normalized and analyzed data, means for reviewing the generated business plan and providing feedback based on past success stories and review information, means for integrating the generated feedback and improving the business plan, means for collecting and providing the latest information on subsidies and advertising expenses, and means for delivering the generated and improved business plan to the user, thereby enabling the rapid and highly accurate creation of new business plans.

[1488] The "means for receiving a user request" is the function of the system that receives the request via the input device or software that the user uses to request the creation of a new business plan.

[1489] "Means for collecting relevant information from past success stories and documents" refers to the system's function of collecting data such as past successful business plans, related documents, pitch videos, and review information via an internal database or external API.

[1490] "Means of normalizing collected information and analyzing it using natural language processing techniques" refers to the process of converting collected data into a consistent format and analyzing the information using natural language processing techniques.

[1491] "Means for automatically generating new business plans based on normalized and analyzed data" refers to a system function that uses normalized and analyzed information to create new business plans using generative AI models or other automated technologies.

[1492] The "means of reviewing the generated business plan and providing feedback based on past success stories and review information" refers to a process for evaluating the contents of the automatically generated business plan in comparison with past success stories and review information, and generating specific feedback.

[1493] The "means for integrating the generated feedback and refining the business plan" is a function of the system for integrating the provided feedback and further improving the generated business plan.

[1494] "Means for collecting and providing the latest information on subsidies and advertising expenditures" refers to the process of collecting the latest information on subsidy programs and advertising expenditures from government agencies and related websites and providing it to users.

[1495] "Means for delivering the generated and refined business plan to the user" refers to the system's function of generating the final, refined business plan in a format such as PDF or Word and delivering it to the user via email or other means.

[1496] System Overview

[1497] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from reviews to refine the plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, terminals, and users.

[1498] Hardware and software used

[1499] The system consists of the following hardware and software:

[1500] Server: Equipped with high-performance computing resources and database management systems. Examples include AWS EC2 instances and MySQL databases.

[1501] Device: The device used by the user. Examples include PCs, smartphones, and tablets.

[1502] Natural language processing (NLP) software: Python libraries such as spaCy and NLTK are used.

[1503] Generative AI models: Leverage advanced AI models such as OpenAI's GPT-4.

[1504] How it works

[1505] Receiving a request

[1506] 1. A user inputs and submits a request to create a new business plan via a web interface or application. Specifically, the user enters a summary of the business idea and necessary information into a dedicated form and clicks the "Submit" button.

[1507] Collection of information

[1508] 2. The server analyzes the request received from the user and extracts the necessary keywords using natural language processing (NLP) techniques. Specifically, it uses a Python NLP library (e.g., spaCy or NLTK) to identify the main topics and themes from the request text.

[1509] 3. The server searches and collects past success stories, pitch videos, business plan documents, and review information using an internal database or external API (e.g., Google Scholar API or other business data API). Specifically, it runs an SQL query against the case study database to retrieve a list of relevant documents. It then downloads the data from the URLs of the listed documents.

[1510] Generate a business plan

[1511] 4. The server normalizes the collected information and analyzes it using natural language processing (NLP) techniques, such as data cleaning and converting it into a consistent format. This is often done using the Python pandas library.

[1512] 5. The server launches a generative AI model (e.g., GPT-4) and inputs the normalized data. It generates a specific prompt and sends it to the model.

[1513] Example: "Write a business plan for a new social media app. Include the following elements: 1. Business Overview 2. Market Analysis 3. Competitive Analysis 4. SWOT Analysis 5. Business Model 6. Income and Expenditure Plan."

[1514] 6. The server receives the generated business plan and performs additional processing to format it, such as converting the generated text to Markdown or LaTeX formatting, for visual editing.

[1515] Providing feedback

[1516] 7. The server compares the generated business plan with past success stories and review information in an internal database and generates feedback. Specifically, it uses a review algorithm to evaluate the plan's contents and extract strengths and areas for improvement. For example, it suggests specific areas for improvement, such as "An incentive plan in the early stages is important to increase user engagement."

[1517] 8. The server integrates the feedback and makes additional modifications to the generated business plan, again using the generative AI model to generate a new version of the business plan that reflects the feedback.

[1518] Providing supporting information

[1519] 9. The server collects information about the latest subsidy programs and advertising rates using web scraping and APIs. Specifically, it retrieves and analyzes the latest subsidy information from government agency websites.

[1520] 10. The server processes the collected information and provides appropriate supporting information to the user, for example, by integrating it into the business plan as "Current government subsidy programs can help cover some of the initial development costs."

[1521] Results distribution

[1522] 11. The server generates the final, polished business plan as a PDF or Word document and delivers it to the user. Specifically, it creates the document using a document generation library (such as Python-docx or ReportLab) and emails the user a download link.

[1523] 12. The user reviews the received business plan and downloads it or makes further edits as needed. For example, the user can open the downloaded PDF in Adobe Acrobat and edit the details.

[1524] Specific examples

[1525] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. It then recommends an "incentive plan for the early stage of user engagement" based on past reviews and incorporates this information into the plan. It also researches the latest government subsidy programs and provides information on initial development costs. Ultimately, the user obtains a high-quality business plan, increasing the chances of success for the new business.

[1526] Example prompts to input to the generative AI model

[1527] Write a business plan for a new social media app. Include the following elements:

[1528] 1. Business overview

[1529] 2. Market analysis

[1530] 3. Competitive analysis

[1531] 4. SWOT analysis

[1532] 5. Business Model

[1533] 6. Income and expenditure plan

[1534] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1535] The flow of this system's program processing

[1536] Step 1: Receiving the request

[1537] Processing content

[1538] A user inputs and submits a request to create a new business plan via a web interface or application.

[1539] Input: The user enters a summary of their business idea and the necessary information into a dedicated form and clicks the "Submit" button.

[1540] Output: The user's request data is sent to the server.

[1541] Specific operation: When the submit button of the form is pressed, the request data is sent to the server via the HTTP protocol and saved in the database.

[1542] Step 2: Analyze the information

[1543] Processing content

[1544] The server analyzes the request received from the user and extracts the necessary keywords.

[1545] Input: The user's request data saved in step 1.

[1546] Output: Keyword list (e.g. "social media app", "market analysis", "business model").

[1547] What happens: The server uses a Python NLP library (e.g., spaCy or NLTK) to parse and extract major topics and themes from the request.

[1548] Step 3: Gather information

[1549] Processing content

[1550] The server uses internal databases and external APIs to search and collect past success stories and related information.

[1551] Input: The keyword list extracted in step 2.

[1552] Output: A list of relevant documents, videos, and review information.

[1553] What it does: Based on the information collected, the server executes SQL queries against the database to retrieve relevant data, and also calls external APIs to retrieve additional information.

[1554] Step 4: Data normalization and analysis

[1555] Processing content

[1556] The information collected by the server is normalized and analyzed using natural language processing technology.

[1557] Input: Data collected in Step 3.

[1558] Output: Normalized and parsed data.

[1559] Specific operation: The server performs data cleaning (e.g., deduplication, format standardization), normalization using Python's pandas library, and then analyzes the data again using natural language processing technology.

[1560] Step 5: Automatically generate a business plan

[1561] Processing content

[1562] The server launches the generative AI model, inputs the normalized data, and automatically generates a business plan.

[1563] Input: The normalized and analyzed data from step 4.

[1564] Output: Early stage business plan.

[1565] Specific operation: The server generates and sends a prompt to the generative AI model (e.g., GPT-4). For example, the prompt might be, "Please write a business plan for a new social media app."

[1566] Step 6: Review and generate feedback

[1567] Processing content

[1568] The server reviews the generated business plan and generates feedback.

[1569] Input: The business plan generated in step 5.

[1570] Output: A list of feedbacks.

[1571] What it does: The server compares past success stories and existing review information, and uses a review algorithm to generate a rating and feedback for the generated business plan.

[1572] Step 7: Integrate feedback and refine

[1573] Processing content

[1574] The server integrates the generated feedback and refines the business plan.

[1575] Input: Feedback generated in step 6.

[1576] Output: An improved business plan.

[1577] Specific operation: The server uses the generative AI model again to create a new version of the business plan that reflects the feedback.

[1578] Step 8: Gather supporting information

[1579] Processing content

[1580] The server collects and provides information on the latest subsidy programs and advertising rates.

[1581] Input: Data from government websites and related APIs.

[1582] Output: Updates on subsidies and advertising expenditures.

[1583] Specific operation: The server collects the necessary information through web scraping and API calls, and provides it after evaluating and analyzing its quality.

[1584] Step 9: Delivering results

[1585] Processing content

[1586] The server delivers the final, polished business plan to the user.

[1587] Input: Your refined business plan from Step 7.

[1588] Output: Business plan in PDF or Word document format sent to user.

[1589] What it does: The server creates a document using a document generation library like Python-docx or ReportLab, and emails the user a download link.

[1590] Step 10: User Verification and Download

[1591] Processing content

[1592] The user reviews the business plan they receive and downloads or further modifies it as necessary.

[1593] Input: Business plan delivered in Step 9.

[1594] Output: A revised or ready-to-use business plan.

[1595] What happens: The user opens the email they received, clicks the download link to get the business plan, and then reviews and edits it in a PDF reader or word processor.

[1596] (Application example 1)

[1597] 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."

[1598] In the past, the process of creating a new business plan required a lot of time and effort. Furthermore, there were few ways to efficiently collect and incorporate feedback from past success stories and the latest subsidy information. This led to problems such as a decline in the quality and success rate of business plans.

[1599] 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.

[1600] In this invention, the server includes means for receiving a user's request to create a business plan for a new product or service, means for collecting related information from past success stories and market research data, means for automatically generating a new business plan using a generative AI model based on the collected information, means for providing feedback from review information and past success stories to refine the business plan, and means for providing information on subsidies and advertising. This makes it possible to efficiently generate high-quality business plans that reflect the latest information and feedback.

[1601] "User" refers to the entity that uses the system to create new business plans and collect related information.

[1602] "New products and services" refers to products and services that are new and have not yet appeared in the existing market.

[1603] "Business Plan Creation Request" refers to a request sent by a User to the System to create a new business plan.

[1604] "Past success stories" refer to business models, operating methods, marketing strategies, and other events that have been implemented successfully in the past.

[1605] "Market research data" refers to data that compiles information about trends in a specific market, consumer behavior, and the competitive landscape.

[1606] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate data analysis and predictive models.

[1607] A "business plan" refers to a document that describes the management policy, strategy, revenue forecast, etc. of a new business.

[1608] "Review information" refers to evaluations and opinions on areas for improvement provided by third parties regarding past business plans and business performance.

[1609] "Feedback" refers to information such as advice, improvements, and evaluations provided regarding the generated business plan and management policy.

[1610] "Brushing up" refers to the process of making corrections and additions to existing business plans and information to improve them.

[1611] A "subsidy" refers to financial assistance provided by the government or local government for specific business activities.

[1612] "Advertising" refers to promotional activities carried out to widely publicize a particular product or service.

[1613] "Collection" refers to the act of gathering data or information needed for a specific purpose.

[1614] "Normalization" refers to the process of aligning data formats and values ​​to certain standards before performing data analysis.

[1615] "Natural language processing" refers to the technology that allows computers to analyze and generate human language.

[1616] MODE FOR CARRYING OUT THE INVENTION

[1617] System Overview

[1618] This invention is a system for efficiently creating and refining business plans for new products and services. Users submit requests to the system, and the server collects and analyzes past success stories and market research data, automatically generating a business plan using a generative AI model. It also provides feedback based on review information to refine the business plan. It also provides the latest information on subsidies and advertising.

[1619] Overview of program processing

[1620] The server implements the present invention using the following hardware and software: A smartphone, tablet, and VR goggles are used as the hardware, and Python, Flask (web framework), Google Cloud AI (for NLP processing), and MongoDB (database) are used as the software.

[1621] 1. Receiving a request:

[1622] A user inputs a request for the creation of a business plan for a new product or service into the system (application or web interface) and submits it.

[1623] For example, a user may request, "I would like to create a business plan for a new VR accessory product."

[1624] 2. Keyword extraction:

[1625] The server extracts keywords from the received request using natural language processing techniques. In this example, we use CountVectorizer from the scikit-learn library.

[1626] For example, from the input "new VR accessory product," keywords such as "VR accessory" and "new product" are extracted.

[1627] 3. Data Collection:

[1628] Based on the extracted keywords, the server collects related information using external APIs and internal databases.

[1629] For example, collect past success stories and market analysis data from the VR accessory market.

[1630] 4. Data normalization and analysis:

[1631] The collected data is normalized and analyzed using Google Cloud AI's natural language processing technology.

[1632] 5. Generate a business plan:

[1633] Based on the analysis results, a new business plan is automatically generated using a generative AI model (e.g., OpenAI GPT-4).

[1634] For example, use the following prompt: "Write a business plan for a new product to enter the VR accessories market. Include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income statement."

[1635] 6. Providing Feedback:

[1636] Provides feedback based on past success stories and review information to the generated business plan, thereby improving the quality of the business plan.

[1637] 7. Providing supporting information:

[1638] The server collects information about the latest subsidy programs and advertising strategies and provides it to users.

[1639] For example, provide information such as, "Current subsidy programs can help cover some of the initial development costs."

[1640] 8. Delivery of the refined plan:

[1641] The server delivers the final, polished business plan to the user, who can then check it, download it, or make any further modifications necessary.

[1642] This system allows users to efficiently generate high-quality business plans and incorporate the latest information and feedback. It also centrally collects and provides the latest information on subsidies and advertising, thereby increasing the chances of success for new businesses.

[1643] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1644] Step 1:

[1645] A user inputs and submits a request to create a business plan for a new product or service into an application or web interface. The input includes an outline of the new product or service, its purpose, and market target. The output is transferred to the server. Specifically, the user makes an input request saying, "I would like to create a business plan for a new VR accessory product."

[1646] Step 2:

[1647] Natural language processing techniques are used to extract key keywords from requests received by the server. The input is the user's request, and the output is a list of extracted keywords. For data processing, keyword extraction is performed using CountVectorizer from the scikit-learn library. Specifically, keywords such as "VR," "accessories," and "new product" are extracted from the sentence "new VR accessory product."

[1648] Step 3:

[1649] Based on the extracted keywords, the server collects related information from internal databases and external APIs. The input is a list of keywords, and the output is a set of related information. To collect data, external APIs (such as market research APIs) are used to extract appropriate data and store it in a database such as MongoDB. Specifically, market analysis and competitive information related to "VR" and "accessories" are collected.

[1650] Step 4:

[1651] The server normalizes the collected information and analyzes the data using natural language processing technology. The input is the collected raw data, and the output is the standardized and analyzed data. For data processing, Google Cloud AI's NLP technology is used to analyze the data and organize it into a unified format. Specifically, market analysis data and review information are normalized and analyzed according to the format.

[1652] Step 5:

[1653] The server uses a generative AI model to automatically generate a new business plan based on the analyzed data. The input is the analyzed data, and the output is a draft of the generated business plan. For data calculation, OpenAI's GPT-4 model is used to generate text. Specifically, the following prompt is used: "Please write a business plan for a new product to enter the VR accessories market. Please include the following elements: 1. Business overview, 2. Market analysis, 3. Competitive analysis, 4. SWOT analysis, 5. Business model, 6. Income and expenditure plan."

[1654] Step 6:

[1655] The server provides feedback to the generated business plan based on past success stories and review information, and refines the business plan. The input is the generated draft business plan and review information, and the output is a business plan that reflects the feedback. As a data calculation, each section of the business plan is evaluated based on the collected review information, and areas for improvement are listed and reflected.

[1656] Step 7:

[1657] The server collects information on the latest subsidy programs and advertising strategies and integrates it into a business plan. The input is the latest information obtained from the government and the market, and the output is a business plan with additional subsidy information added. Specifically, it collects information on currently available subsidy programs and adds specific information to the business plan, such as "these programs can cover part of the initial development costs."

[1658] Step 8:

[1659] The server delivers the final, polished business plan to the user. The input is the improved business plan, and the output is the provision of the business plan to the user. Specifically, the user can download the business plan and make a final check.

[1660] 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.

[1661] System Overview

[1662] This system allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. By combining it with an emotion engine, it is possible to recognize user emotions and optimize the feedback and content of the business plan. It also provides the latest information on subsidies and advertising fees. The system is composed of a server, a terminal, and a user.

[1663] Program processing flow (explained in natural language)

[1664] Receiving a request

[1665] 1. A user enters and submits a request to create a new business plan through a web interface or application. For example, a user may submit a request saying, "I would like to create a business plan for a new social media app."

[1666] Collection of information

[1667] 2. The server analyzes the request received from the user and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[1668] 3. Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This comprehensively collects the information required to meet the user's request.

[1669] Generate a business plan

[1670] 4. The server normalizes the collected information. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[1671] 5. The server analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1672] 6. The server uses the generative AI model to automatically generate a new business plan. The generated plan includes the following elements:

[1673] - Business overview (e.g., concept and features of a new social media app)

[1674] - Market analysis (e.g., social media market growth trends)

[1675] - Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1676] - SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats of a new app)

[1677] - Business model (e.g., monetization methods)

[1678] - Income and expenditure plan (e.g., first year's expected income and expenditure)

[1679] Use of emotion engine

[1680] 7. The server uses the emotion engine to analyze the user's thoughts and feedback while viewing the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[1681] 8. The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "Market Analysis" section, the server will add more detailed data and specific success stories about the "Market Analysis" section.

[1682] Providing feedback

[1683] 9. The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[1684] Providing supporting information

[1685] 10. The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[1686] Results distribution

[1687] 11. The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[1688] Specific examples

[1689] Specifically, if a user requests a business plan for a "new social media app," the server first extracts keywords such as "social media app," "market analysis," and "competitive analysis." Next, it collects information on past successful social media businesses and uses this information to generate a business plan using a generative AI model. The emotion engine then analyzes the user's emotional state based on their thoughts and feedback as they browse the plan. For example, if the emotion engine detects that the user is dissatisfied with the market analysis section, the server will further enhance the market analysis section and provide additional data. It also researches the latest government subsidy programs and provides support information on initial development costs. Ultimately, the user receives a high-quality business plan that is tailored to their emotional state, increasing the chances of success for their new venture.

[1690] The processing flow will be explained below.

[1691] Step 1:

[1692] A user enters and submits a request to create a new business plan through a web interface or application. For example, a user submits a request saying, "I would like to create a business plan for a new social media app."

[1693] Step 2:

[1694] The server analyzes the requests received from users and extracts the necessary keywords and topics. Here, natural language processing (NLP) technology is used to analyze the request content and identify related keywords such as "social media app," "business model," and "market analysis."

[1695] Step 3:

[1696] Based on the keywords and topics extracted by the server, the server searches and collects related information using an internal database or external API. Specifically, it collects past success stories, pitch videos, business plan documents, and review information. This allows it to comprehensively collect the information required for the user's request.

[1697] Step 4:

[1698] The server normalizes the information collected. This process converts data collected from different sources into a unified format and organizes it for easier analysis. This process includes text cleaning, formatting, and deduplication.

[1699] Step 5:

[1700] The server then analyzes the normalized data using natural language processing (NLP) techniques and feeds it into a generative AI model, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1701] Step 6:

[1702] The server uses the generative AI model to automatically generate a new business plan, which includes the following elements:

[1703] Business overview (e.g., concept and features of a new social media app)

[1704] Market analysis (e.g., social media market growth trends)

[1705] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1706] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new app)

[1707] Business model (e.g., monetization methods)

[1708] Income and expenditure plan (e.g., first year's expected income and expenditure)

[1709] Step 7:

[1710] The server uses an emotion engine to analyze the user's thoughts and feedback as they view the generated business plan. Based on this information, the server recognizes the user's emotional state (e.g., satisfaction, dissatisfaction, interest, doubt, etc.) in real time.

[1711] Step 8:

[1712] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan. For example, if the user is dissatisfied with the "market analysis" section, the server will add more detailed data and specific success stories about the "market analysis."

[1713] Step 9:

[1714] The server generates feedback based on the review information and refines the business plan. Specifically, it provides specific improvements, such as "An incentive plan in the early stages is important to increase user engagement." This feedback is reflected in the plan.

[1715] Step 10:

[1716] The server collects the latest information on subsidies and advertising fees and provides it to users, thereby suggesting concrete measures to increase the feasibility of business plans.

[1717] Step 11:

[1718] The server delivers the final, polished business plan to the user, who can then review it and download or further revise it as needed.

[1719] Example 2

[1720] 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."

[1721] The process of creating a new business plan requires time and effort, as it requires complex and time-consuming tasks such as information gathering, analysis, and document creation. It is also difficult to improve the quality of the business plan by incorporating user feedback in real time. Another challenge is effectively incorporating the latest information on subsidies and advertising fees.

[1722] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving a user request, a means for collecting related information from past success stories and documents, a means for searching and collecting information based on extracted keywords and topics, a means for normalizing the collected information, a means for analyzing the normalized information using natural language processing and inputting it into a generative AI model, a means for automatically generating a new business plan using the generative AI model, a means for analyzing user feedback using an emotion engine and adjusting the feedback, a means for providing feedback based on review information to refine the business plan, and a means for providing information on subsidies and advertising fees. This allows users to automatically generate efficient, high-quality new business plans and refine them while reflecting feedback in real time. Furthermore, the latest information on subsidies and advertising fees can be incorporated, increasing the feasibility of the business plan.

[1723] The "means for receiving a user's request" is a function for receiving a request for creating a new business plan sent by a user through a web interface or application.

[1724] "Means of collecting relevant information from past success stories and documents" refers to the function of collecting past business success stories and related documents through an internal database or external API.

[1725] "Means for searching and collecting information based on extracted keywords and topics" refers to a function for searching and collecting related information based on keywords and topics extracted by natural language processing.

[1726] "Means for normalizing collected information" refers to the ability to format information obtained from different formats and sources into a unified format, making it easier to analyze.

[1727] "Means for analyzing normalized information using natural language processing and inputting it into a generative AI model" refers to a function that analyzes normalized information and inputs it into a generative AI model as a necessary parameter.

[1728] "Means for automatically generating new business plans using a generative AI model" refers to a function that uses a generative AI model to automatically create new business plans based on collected and analyzed information.

[1729] "Means for analyzing user feedback using an emotion engine and adjusting the feedback" is a function that analyzes the impressions and feedback entered by the user in real time and adjusts the feedback based on the user's emotional state.

[1730] The "means of providing feedback based on review information and brushing up the business plan" is a function that generates specific feedback based on collected review information and improves the content of the business plan.

[1731] The "means for providing information on subsidies and advertising fees" is a function for collecting information on the latest subsidy programs and advertising fees and providing it to the user.

[1732] The present invention is a system that allows a user to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback from review information to refine the plan. The implementation method is described in detail below.

[1733] System configuration

[1734] The system is primarily composed of a server, a terminal, and a user. The server plays a central role in collecting, analyzing, generating, and providing feedback on information, while the terminal is a device used by users as an interface. Users are responsible for inputting requests for new business plans and for checking and providing feedback on the results.

[1735] Hardware and Software

[1736] Server: A high-performance server is used to execute various processes using Python. Libraries such as Spacy are used for natural language processing (NLP), and OpenAI's GPT-4 is used as the generative AI model. External APIs (e.g., YouTube API, Google Scholar API) are also used for data collection and analysis.

[1737] Device: A web browser or mobile application that users access. The user interface is created using HTML, CSS, and JavaScript.

[1738] Database: Use a relational database such as MySQL to store past success stories and related documents.

[1739] Using Natural Language Processing and Generative AI Models

[1740] The server analyzes new business plan requests received from users using natural language processing, and collects related information using an internal database and external APIs based on keywords and topics extracted from the analysis results.

[1741] The collected information is normalized and fed into a generative AI model. For example, the following prompts can be used to provide information to the generative AI model:

[1742] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[1743] Emotion Engine and Feedback

[1744] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback as they view the generated business plan. Based on this feedback, the server adjusts and refines the plan in real time.

[1745] Specific examples

[1746] For example, if a user sends a request saying, "I want to create a business plan for a new social media app," the server extracts keywords such as "social media app," "market analysis," and "competitive analysis." It then collects past success stories, pitch videos, and business plan documents, normalizes them, and inputs them into the generative AI model. The generated business plan includes sections such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan.

[1747] If a user reviews a plan and inputs feedback such as "the market analysis section is insufficient," the emotion engine will recognize this, and the server will add detailed data and concrete success stories to reinforce the market analysis section. It will also collect information on government subsidy programs and the latest advertising rates and notify the user.

[1748] This process allows users to obtain efficient and high-quality new business plans, increasing the chances of success for new businesses.

[1749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1750] Step 1:

[1751] A user enters and submits a request to create a new business plan through a web interface or application, for example, by typing "I would like to create a business plan for a new social media app" into a text box and clicking a submit button.

[1752] Input: User request text

[1753] Output: Request data to the server

[1754] Step 2:

[1755] The server parses the requests received from users and extracts the required keywords and topics, using Python and a natural language processing (NLP) library (e.g., Spacy) to identify keywords such as "social media app," "business model," and "market analysis."

[1756] Specifically, the request text is tokenized and important keywords are extracted.

[1757] Input: User request data

[1758] Output: Extracted keywords and topics

[1759] Step 3:

[1760] Based on the keywords and topics extracted by the server, the server searches and collects related information using internal databases and external APIs, for example, querying past success stories from a MySQL database, collecting pitch videos through a video API, and retrieving related documents from a literature API.

[1761] Specific operations include issuing SQL queries, sending API requests, and analyzing responses.

[1762] Input: Extracted keywords and topics

[1763] Output: Related information data (past success stories, video links, literature information, etc.)

[1764] Step 4:

[1765] Normalize the information collected by the server. Convert information obtained from different formats and sources into a unified format and organize it into data that is easy to analyze. Use the Python pandas library to clean, organize, and eliminate duplicates from the data.

[1766] Input: Related information data

[1767] Output: Normalized information data

[1768] Step 5:

[1769] The server analyzes the normalized information using natural language processing (NLP) techniques and prepares it for input to the generative AI model. It also creates input prompts.

[1770] An example prompt is:

[1771] "Write a business plan for a new social media app. Use the following information: Market analysis: {market analysis data}, Competitive analysis: {competitive analysis data}, Business model: {business model data}, ..."

[1772] As a concrete operation, the data is merged with the template to generate a prompt sentence.

[1773] Input: Normalized information data

[1774] Output: prompt statement

[1775] Step 6:

[1776] The server uses a generative AI model (e.g., GPT-4) to automatically generate a new business plan. The generated plan includes elements such as a business overview, market analysis, competitive analysis, SWOT analysis, business model, and income and expenditure plan. The generated text is embedded in a template.

[1777] Input: prompt statement

[1778] Output: Auto-generated business plan

[1779] Step 7:

[1780] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze the user's thoughts and feedback while viewing the generated business plan. For example, if the user enters feedback such as "This market analysis is insufficient," the server recognizes that information as negative feedback.

[1781] Input: User feedback

[1782] Output: Parsed emotion data

[1783] Step 8:

[1784] The server adjusts the feedback based on the user's emotional state and refines the content of the business plan, adding detailed market data and concrete success stories to enrich the data in order to generate a plan that satisfies the user.

[1785] Input: Parsed emotion data

[1786] Output: A polished business plan

[1787] Step 9:

[1788] The server generates feedback based on the review information and further refines the plan, specifically proposing incentive plans necessary to increase user engagement.

[1789] Input: Review information

[1790] Output: Specific feedback

[1791] Step 10:

[1792] The server collects the latest information on subsidies and advertising fees and provides it to users. The necessary information is obtained from government APIs and advertising agency APIs and incorporated into the business plan.

[1793] Input: Data from the relevant API

[1794] Output: Information about subsidies and advertising fees

[1795] Step 11:

[1796] The server delivers the final, polished business plan to the user, who can then download it in PDF format and further revise or print it as needed.

[1797] Input: Finalized business plan

[1798] Output: Plan delivered to user

[1799] (Application example 2)

[1800] 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."

[1801] When creating new business plans, conventional systems have had issues with not effectively utilizing past success stories and related information, and not providing sufficient feedback to refine the automatically generated business plan. They also lack the ability to reflect the user's feelings or provide information on the latest subsidies and advertising fees. This makes it difficult to create high-quality business plans that meet the user's needs.

[1802] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1803] In this invention, the server includes means for receiving user requests, means for collecting related information from past success stories and documents, means for automatically generating a new business plan based on the collected information, means for providing feedback based on review information and the user's emotional state to refine the business plan, and means for providing the latest information on subsidies and advertising fees. This makes it possible to create a high-quality new business plan that utilizes past success stories and the latest information and incorporates feedback from emotional analysis.

[1804] "User" means an individual or organization that uses the System to request the creation of a new business plan and receives the results.

[1805] A "request" is information that a user sends to the system to request the creation of a new business plan.

[1806] "Past success stories" are specific examples of successful business activities or projects that have been undertaken in the past.

[1807] A "document" is a document that records data and information necessary to generate a new business plan.

[1808] "Related information" refers to information such as past success stories, market analysis, and competitive analysis that are necessary for creating a new business plan.

[1809] A "new business plan" is a document that describes a detailed plan for starting a new business.

[1810] "Automatic generation" refers to a system using programs and algorithms to create a business plan without human intervention.

[1811] "Review information" is information on evaluations and feedback given by users on business plans.

[1812] "Feedback" is information for improving the business plan based on user evaluations and impressions.

[1813] "Brushing up" means making corrections or additions to a new business plan to improve its quality and content.

[1814] The "user's emotional state" refers to the emotional state, such as satisfaction, dissatisfaction, or interest, that the user feels when using the system.

[1815] A "grant" is funding provided by a government or organization to support a new business or project.

[1816] "Advertising fees" are fees paid to promote new businesses or projects.

[1817] "Latest information" means the most recently updated information as of the present time.

[1818] "Normalization" means converting data from different sources into a unified format.

[1819] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1820] "Analysis" is the process of analyzing data and information in detail to find meaning and value.

[1821] A "generative AI model" is an algorithm or program that uses artificial intelligence to automatically create new documents or information.

[1822] The system of the present invention allows users to request the creation of a new business plan, collects information from past success stories and documents, automatically generates a business plan based on that information, and provides feedback based on reviews and the user's emotional state to refine the plan. It also provides the latest information on subsidies and advertising fees.

[1823] The server includes a means for receiving a user's request, a means for collecting relevant information from past success stories and documents, and a means for automatically generating a new business plan based on the collected information. It also includes a means for providing feedback based on review information and the user's emotional state to refine the business plan, and a means for providing the latest information on subsidies and advertising fees.

[1824] In processing the program, the server operates according to the following flow:

[1825] First, a user uses a device (e.g., a smartphone or PC) to input and submit a request to create a new business plan through a web interface or application. For example, a user might submit a request saying, "I would like to create a business plan for a virtual clothing store."

[1826] The server receives this request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords and topics. In this example, we use Python's spaCy as the NLP library. The extracted keywords include "virtual store," "clothing," and "market analysis."

[1827] The server then searches and collects relevant information based on the extracted keywords and topics using an internal database (MongoDB) and an external API (public data provider) to collect past success stories, pitch videos, business plans, and reviews.

[1828] The collected information is normalized on the server, converting data collected from different information sources into a unified format, and performing text cleaning, formatting, and deduplication.

[1829] The server then analyzes the normalized data and inputs it into a generative AI model, powered by OpenAI's GPT-3, which analyzes the key points and relationships of each piece of information and assigns them appropriately to each section of the business plan.

[1830] The generated business plan will include the following elements:

[1831] Business overview (e.g., virtual clothing store concept and features)

[1832] Market analysis (e.g., the expansion trend of the virtual store market)

[1833] Competitive analysis (e.g., current major competitors and their strengths and weaknesses)

[1834] SWOT analysis (e.g., strengths, weaknesses, opportunities, and threats for a new store)

[1835] Business model (e.g., monetization methods)

[1836] Income and expenditure plan (e.g., first year's expected income and expenditure)

[1837] The impressions and feedback entered by users while viewing the business plan are analyzed by an emotion engine, which uses the IBM Watson Tone Analyzer API, allowing the user's emotional state (satisfaction, dissatisfaction, interest, doubt, etc.) to be recognized in real time.

[1838] Based on the user's emotional state detected by the emotion engine, the server adjusts the feedback and refines the business plan. For example, if the user is dissatisfied with the market analysis section, the server can provide additional data and specific success stories related to that section. The server also collects the latest information on subsidies and advertising fees and provides it to the user. This increases the feasibility of the business plan.

[1839] As a concrete example, the following prompt sentence is input to the generative AI model:

[1840] Virtual Clothing Store Business Plan

[1841] (Business overview)

[1842] Please explain the concept and features of the virtual store.

[1843] (Market analysis)

[1844] Please provide an update on the market size and competitive situation for virtual clothing stores.

[1845] (Competitive analysis)

[1846] Conduct an analysis of your major competitors, including their strengths and weaknesses.

[1847] (SWOT analysis)

[1848] Indicate the strengths, weaknesses, opportunities, and threats of your new app.

[1849] (Business Model)

[1850] Please elaborate on how you will monetize your clothing store.

[1851] (Income and expenditure plan)

[1852] Please specify your projected income and expenditure for the first year.

[1853] The final, polished business plan is delivered to the user, who can review it and download or modify it as needed.

[1854] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1855] Step 1:

[1856] This is the stage where the user enters "I would like to create a business plan for a virtual clothing store" into the request form and submits it. The text data entered by the user becomes the input, and the server receives the request and outputs it as data to be analyzed.

[1857] Step 2:

[1858] In this step, the server receives the request, analyzes the request using natural language processing (NLP) techniques, and extracts the necessary keywords (e.g., "virtual store," "clothing," "market analysis," etc.). Text analysis is performed using an NLP library (e.g., spaCy), and the analyzed keywords are output.

[1859] Step 3:

[1860] This is the step where the server collects the necessary related information from an internal database (e.g., MongoDB) or an external API based on the keywords extracted. A request for the information to be collected is sent to the external API, and success stories, pitch videos, business documents, etc. are collected and output.

[1861] Step 4:

[1862] This is the step where the collected information is normalized on the server. Text cleaning, formatting, and deduplication are performed, and the normalized information is output as data in a unified format.

[1863] Step 5:

[1864] This is the step where the server analyzes the normalized data and prepares it for input into a generative AI model (e.g., OpenAI GPT-3). Important key points and relationships are analyzed, and a dataset for AI input is created and output.

[1865] Step 6:

[1866] In this step, the server automatically generates a new business plan using the generative AI model. A prompt (e.g., "Write a business plan for a virtual clothing store") is input to the generative AI model, and the business plan is generated and output.

[1867] Step 7:

[1868] In this step, the user views the generated business plan and inputs their thoughts and feedback. The user's feedback text is sent to the server as input.

[1869] Step 8:

[1870] In this step, the server analyzes the user's feedback using an emotion engine (e.g., IBM Watson Tone Analyzer API). The analyzed emotion data is output and used as the basis for adjusting the feedback.

[1871] Step 9:

[1872] In this step, the server refines the business plan based on the results of the sentiment analysis. It adds detailed data to specific sections and outputs the refined business plan.

[1873] Step 10:

[1874] In this step, the server collects the latest information on subsidies and advertising fees and adds it to the business plan. It also collects the latest data from external APIs and the internet, integrates it into the business plan, and outputs it.

[1875] Step 11:

[1876] This is the step where the server delivers the final polished business plan to the user. The generated business plan is sent to the user's device, where the user can review and download it.

[1877] 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.

[1878] 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.

[1879] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1880] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1881] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotion...

Claims

1. means for receiving a user request; A means of gathering relevant information from past success stories and documents; A means to automatically generate a new business plan based on the collected information, A means to provide feedback based on review information and refine business plans, a means of providing information on subsidies and advertising fees; A system including:

2. The system of claim 1 , further comprising means for normalizing the information on past success stories and documents and analyzing it using natural language processing.

3. 10. The system of claim 1, further comprising means for integrating the collected information and feedback and delivering the generated business plan to the user.

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