Business plan creation system, business analysis method for business plan creation system, and computer program

Abstract

This involves automatically generating subsidy application documents as required by the recipient organization, by integrating a learning model—which has learned and accumulated non-standard data to be included in subsidy applications—with business analysis. [Solution] In a business plan creation system 1 that creates a business plan in cooperation with a learning model that holds parameters that have learned response information to the items required to be described in a subsidy application, the system includes a business analysis unit 16 that references a set of hypothetical questions and answers that combine questions and answers regarding fundraising, and uses a learning model 2C that holds parameters that have learned response information to the non-standard data items required in a subsidy application to acquire answers to questions and financial information from a user terminal 3, and performs business analysis using the learning model 2C based on the questions and answers.

Description

Technical Field

[0001] The present invention relates to a business plan creation system, a business analysis method of the business plan creation system, and a computer program.

Background Art

[0002] Fundraising such as subsidies is important for business management. For this, it is important to create a business plan for applying for subsidies. In addition, investors, partner companies, etc. are always looking for excellent venture companies, etc. Therefore, business operators also need to create a business plan for fundraising.

[0003] However, even if business operators (especially small and medium-sized enterprises, etc.) try to organize the current situation and issues for subsidy applications, they cannot investigate and organize them as desired.

[0004] Also, they do not know how to formulate revenue forecasts and KPIs (business goals) for subsidy applications. Furthermore, although they want to raise subsidies, there is no material to convince others of the attractiveness and growth potential of the business.

[0005] Managers of businesses with dozens of employees are occupied with daily various work responses and do not have time to create a business plan.

[0006] On the other hand, if there is no comprehensive material for explaining the business to external parties such as subsidy applications, investors, or partner companies, opportunities for fundraising may be missed.

[0007] For the company side, they want to quickly summarize a business plan without much effort. In contrast, external parties such as subsidy applicants, investors, or partner companies think that they will provide funds if there is a more comprehensive business plan.

[0008] For example, Patent Document 1 is disclosed to solve such problems. The skill analysis program of Patent Document 1 provides a skill analysis method for clarifying important skill fields for a company.

[0009] This skill analysis method involves selecting several companies with improved performance from a large number of companies, and calculating the rate of increase in skill levels (skill improvement rate) for each skill area within each of these companies. Then, the average skill improvement rate for each skill area is determined, and this average is set as the standard skill improvement rate for that skill area. The skill improvement rate in each skill area of ​​the company requesting the skill analysis is then compared to the standard skill improvement rate to identify the skill areas that are important to the company but where there is a deficiency, and a plan is developed to address those deficiencies. [Prior art documents] [Patent Documents]

[0010] [Patent Document 1] Japanese Patent Publication No. 2003-281326 [Overview of the Initiative] [Problems that the invention aims to solve]

[0011] However, the skills analysis program described in Patent Document 1 does not include instructions for creating business plans or documents explaining the future prospects of the business. Therefore, there is a problem in that the plan formulated using Patent Document 1 does not lead to a subsidy application.

[0012] Furthermore, conventional subsidy application systems employ a configuration where the server device that stores and analyzes customer management information does not interact with the server device that analyzes the management information of a specific company, and instead provides a unique information processing service.

[0013] Therefore, simply inputting prompts from user devices operated by businesses to some of the generating AI is unlikely to automatically generate the optimal business plan for each business to submit to eligible subsidy recipients. The current challenge is that, in order to develop a truly effective subsidy application service, it is necessary to collaborate with these server devices and perform service processing based on distinctive routines.

[0014] Thus, the information planned for conventional subsidy application systems is primarily biased towards financial information, and no system has been proposed to handle the non-quantitative information that the reviewers of subsidy applications want to know—specifically, information that cannot be expressed in numbers or statistics, such as a company's values, social significance, and future potential.

[0015] Therefore, even if financial information is excellent, it has been pointed out that there is a challenge in automatically generating compelling subsidy applications that reflect non-quantitative information such as corporate philosophy and vision, founding story, social impact, customer voices and testimonials, future outlook and growth strategy, and organizational culture and values.

[0016] Furthermore, conventional chatbots were limited to automating the handling of inquiries, and lacked a mechanism for utilizing the obtained information for creating business plans or marketing analysis. In other words, evaluating and improving the quality of conversations was subjective, making it difficult to identify conversations that led to conversions.

[0017] This invention was made in view of the above problems, and aims to automatically introduce a storytelling structure that enhances the appeal of application documents, even if the person in charge at a company wishing to apply for a subsidy is not familiar with the application procedures or the details of document writing, and to automatically generate a business plan that conforms to the secretariat's review criteria, which include customer information and management analysis information required by the subsidy recipient, by analyzing the business situation of the business using the system, including unstructured data. [Means for solving the problem]

[0018] To solve the above problems, one embodiment of the present invention is a business plan creation system that includes a user terminal that instructs the automatic generation of a subsidy application form to be submitted to a predetermined subsidy application recipient, and a server device that provides a service for creating a business plan for fundraising in cooperation with a learning model that holds parameters learned from response information to the items to be described in the subsidy application form, wherein the server device includes a database that stores the application guidelines for the business plan requested by the predetermined subsidy application recipient and customer management information of the service users, a business analysis unit that generates questions corresponding to each item to be described in the business plan based on the application guidelines stored in the database, presents them to the user terminal via a chatbot function, receives the answers entered from the user terminal, inputs the answers and the questions into a learning model to analyze business performance, and a creation unit that inputs the analysis results of the business analysis unit, the customer management information, the application guidelines, and a conversation score evaluated based on conversation patterns of successful cases for the answers into the learning model to instruct the creation of the business plan and acquires the business plan generated based on the parameters held by the learning model.

[0019] Another embodiment of the present invention is a business analysis method for a business plan creation system, which includes a user terminal that instructs the automatic generation of a subsidy application to be submitted to a predetermined subsidy recipient, and a server device that provides a function to create a business plan for fundraising and a chatbot function in conjunction with a learning model that holds parameters learned from response information to non-standard data items requested by the subsidy application, wherein the server device includes a database that stores the application guidelines for business plans requested by the predetermined subsidy recipient and customer management information for users of the service, and based on the application guidelines stored in the database The system is characterized by comprising: a business analysis step which generates questions corresponding to each item described in the business plan, presents them to the user terminal via a chatbot function, receives the answers entered from the user terminal, inputs the answers and the questions into a learning model to analyze business performance; and a creation step which inputs the analysis results from the business analysis step, the customer management information, the public offering guidelines, and a conversation score evaluated based on conversation patterns of successful cases for the answers into the learning model to instruct the creation of the business plan, and obtains the business plan generated based on the parameters held by the learning model.

[0020] Another embodiment of the present invention is a computer program that creates a business plan for fundraising in conjunction with a learning model that holds parameters learned from response information to non-standard data items required by a subsidy application, characterized in that the computer is instructed to perform a business analysis step in which it generates questions corresponding to each item of the business plan based on the application guidelines stored in a database, presents them to a user terminal via a chatbot function, receives the answers entered from the user terminal, inputs the answers and the questions into the learning model to analyze business performance, and inputs the analysis results of the business analysis step, customer management information, application guidelines, and a conversation score evaluated based on conversation patterns of successful cases for the answers into the learning model, instructs the learning model to create the business plan, and obtains the business plan generated based on the parameters held by the learning model. [Effects of the Invention]

[0021] As described above, according to the present invention, without the person in charge of the company who wants to apply for a subsidy being proficient in the application procedures and the details of the description in the application documents, a storytelling structure for enhancing the persuasive power in the application documents is actively introduced, and while improving the persuasiveness and empathy, a subsidy application document that can improve the adoption rate is automatically generated according to the form of the application destination, and moreover, there is an effect that a system that automatically applies to the selected application destination can be freely constructed.

Brief Description of the Drawings

[0022] The drawings show specific embodiments of the present invention and include not only essential configurations of the invention but also optional and preferred embodiments. [Figure 1] It is a block diagram showing the functions of a business plan creation system according to an embodiment of the present invention. [Figure 2] It is a configuration diagram showing the server configuration of a business plan creation system according to an embodiment of the present invention. [Figure 3] It is a configuration diagram showing an example of the hardware configuration of the main server of a business plan creation system according to an embodiment of the present invention. [Figure 4] It is a configuration diagram showing an example of the hardware configuration of a server related to customer management of a business plan creation system according to an embodiment of the present invention. [Figure 5] It is a configuration diagram showing an example of the hardware configuration of another server related to customer management of a business plan creation system according to an embodiment of the present invention. [Figure 6] It is a configuration diagram showing an example of the hardware configuration of a server related to the AI chatbot of a business plan creation system according to an embodiment of the present invention. [Figure 7] It is an explanatory diagram explaining the data configuration according to an embodiment of the present invention. [Figure 8] It is a flowchart explaining the process selection according to an embodiment of the present invention. [Figure 9] It is an explanatory diagram explaining the TOP screen according to an embodiment of the present invention. [Figure 10] This is a flowchart illustrating the operation of an AI chatbot processing method according to one embodiment of the present invention. [Figure 11] This is an explanatory diagram illustrating an example screen of AI chatbot processing using one embodiment of the present invention. [Figure 12] This is an explanatory diagram illustrating another example screen for AI chatbot processing using one embodiment of the present invention. [Figure 13] This is an explanatory diagram illustrating another example screen for AI chatbot processing using one embodiment of the present invention. [Figure 14] This is a flowchart illustrating the business analysis process using one embodiment of the present invention. [Figure 15] This is an explanatory diagram illustrating an example screen for business analysis processing using one embodiment of the present invention. [Figure 16] This is an explanatory diagram illustrating another example screen for business analysis processing using one embodiment of the present invention. [Figure 17] This is an explanatory diagram illustrating another example screen for business analysis processing using one embodiment of the present invention. [Figure 18] This is an explanatory diagram illustrating another example screen for business analysis processing using one embodiment of the present invention. [Figure 19] This is a flowchart illustrating the business plan creation process using one embodiment of the present invention. [Figure 20] This is an explanatory diagram illustrating an example screen for outputting a business plan using one embodiment of the present invention. [Figure 21] This is an explanatory diagram illustrating an example of the output of the business plan creation process using one embodiment of the present invention. [Figure 22] This is a flowchart illustrating the subsidy information delivery process using one embodiment of the present invention. [Figure 23] This is an explanatory diagram illustrating an example screen for the subsidy information delivery process according to one embodiment of the present invention. [Figure 24] This is an explanatory diagram illustrating another example screen for the subsidy information delivery process according to one embodiment of the present invention. [Figure 25]This is a flowchart illustrating the subsidy application review process using one embodiment of the present invention. [Figure 26] This is an explanatory diagram illustrating an example screen for the business plan review process using one embodiment of the present invention. [Figure 27] This is an explanatory diagram illustrating another example screen for the business plan review process according to one embodiment of the present invention. [Figure 28] This figure shows an example of the operation screen of a business plan creation support system according to one embodiment of the present invention. [Figure 29] This diagram shows an example of the hardware configuration of the main servers in a business plan creation support system according to one embodiment of the present invention. [Figure 30] This is a flowchart illustrating the operation of an AI chatbot processing method according to one embodiment of the present invention. [Figure 31] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 32] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 33] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 34] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 35] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 36] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 37] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 38] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 39]This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 40] This figure shows an example of a user interface screen displayed on the display unit of the user terminal shown in Figure 1. [Figure 41] Figure 1 shows an example of a screen for generating management philosophy information displayed on the user terminal's display unit. [Figure 42] Figure 1 shows an example of a screen displaying instructions for creating a business plan for subsidy applications, which is shown on the user terminal display. [Modes for carrying out the invention]

[0023] The embodiments will be described in detail below with reference to the attached drawings. In this example, publicly known technologies will not be explained. Furthermore, the structures and methods described are illustrative examples for realizing the technical idea of ​​the invention, and the technical idea of ​​the present invention is not limited to those described below. The technical idea of ​​the present invention can be modified in various ways within the scope of the claims. In particular, it should be noted that the drawings are schematic and may differ from reality. The embodiments represent the most preferred form of the invention, and the invention is not limited thereto.

[0024] Corporate fundraising methods include subsidies, grants, loans, and capital investments. Below, we will use subsidy applications as an example of fundraising. A business plan for subsidy applications is a document that clearly outlines, for example, "what the company should be like in the future," along with management strategies and action plans. It presents a concrete vision and strategy for the company's future to people both inside and outside the company, such as subsidy providers, employees, business partners, investors, and financial institutions.

[0025] Creating a business plan for a subsidy application requires a thorough examination of market trends, the company's position, the development of a business plan, and the necessary funding. This allows for a clear understanding of the current situation surrounding the company and makes it easier to identify its strengths and weaknesses in the market. Therefore, this information is crucial for developing a business strategy.

[0026] Having a solid business plan for grant applications makes it easier to create a more accurate financial plan.

[0027] [First Embodiment] First, the overall configuration of this embodiment will be described with reference to Figures 1 and 2. Figure 1 is a block diagram showing the functions of the business plan creation system according to this embodiment, and Figure 2 is a configuration diagram showing the server configuration of the business plan creation system according to this embodiment. Here, the business plan is information consisting of items such as company overview, background, management challenges, market analysis, competitor analysis, analysis methods, and problem identification.

[0028] As shown in Figure 1, the business plan creation system 1 consists of a learning model 2C, a user terminal 3, a CB information DB (hereinafter referred to as the CB information DB) 11A, a CB management unit 11B, a customer management DB 12, a customer management unit 13, an application document DB 14A, an application management unit 14B, a business plan creation / review unit 15, a management analysis unit 16, an application management DB 17, an output conversion unit 18, an application procedure unit 19, an operation unit 20, and the like.

[0029] 2 is the generation AI core unit, which consists of a verification unit 2A and a machine learning unit 2B that includes the learning model 2C. The machine learning unit 2B optimizes the parameters of the learning model 2C using training data. Here, the training data includes the application guidelines, evaluation criteria, company information, evaluation comments, and conversations obtained by users who have already used this system through question-and-answer sessions with the chatbot unit 11C when creating a subsidy application, as required by the items to be described in the subsidy application form. However, it may also be composed of other information. Furthermore, the contents of accepted and rejected subsidy applications may also be used as training data.

[0030] Furthermore, the term "approved application forms" refers to past grant applications that have been approved (in PDF or Word® format), allowing the learning model 2C to learn successful application patterns.

[0031] Furthermore, rejected applications are used by Machine Learning Unit 2B to train the learning model 2C so that it can identify failure patterns. For example, since the intended use of each grant is predetermined, the learning model 2C can detect whether there are any omissions in the application documents that the reviewers focus on during the review process.

[0032] Furthermore, the reason why machine learning unit 2B is trained on the application guidelines and review process is that the learning model 2C can identify evaluation items and scoring factors for each grant. This allows for the optimization of the output of the generated application documents (reflecting the reviewer's perspective).

[0033] Furthermore, the reason why the chatbot unit 11C is trained by the machine learning unit 2B with corporate philosophy, business plan, financial information (B / S), etc., that it receives from users is so that the learning model 2C can make decisions on extracting and integrating non-quantitative information.

[0034] Furthermore, the reason why machine learning unit 2B is trained on the review comments is so that the learning model 2C can incorporate any feedback from the reviewers into the generation of the next application documents.

[0035] Furthermore, the reason why the machine learning unit 2B trains on the template structure is to determine whether the trained model 2C conforms to the format set by the application recipient.

[0036] Furthermore, the learning model 2C retains the parameters learned by the machine learning unit 2B and performs inference processing on the input information. Specifically, this inference processing includes inference processing related to natural language generation, image generation, and structured data generation.

[0037] The verification unit 2A evaluates the accuracy, validity, and safety of the output of the learning model 2C. The verification unit 2A may also provide feedback to the machine learning unit 2B based on the evaluation results. Furthermore, this system includes a function to attach documents to the application form specified by the application recipient. These documents, while not directly focused on by the recipient's examiner, serve as a way for the company to showcase itself—in other words, a presentation (a good example is the response provided in a simulated interview with the examiner).

[0038] In this embodiment, the learning model 2C is GPT®, Gemini®, or similar software that generates answers to questions (prompts, attached documents). The functions of the learning model 2C will be described later.

[0039] Thus, this system is characterized by the fact that, in order to facilitate the routine of creating subsidy applications, the training data is composed of diverse information sources, is structured and pre-processed rather than simply a collection of documents, and plays a role in directly contributing to improved output quality. Structured and pre-processed means that, for example, when machine learning is performed on company name, management philosophy, history, and founding year, a hierarchy such as company name → history → founding year → management philosophy (this hierarchy is just one example) is set up, and the information associated with it is linked and stored.

[0040] In preparing the subsidy application form below, we will assume that the above-mentioned learning process has been performed and will explain the details of the subsidy application process primarily using learning model 2C.

[0041] User terminal 3 is a device operated by the user applying for the subsidy. This user terminal 3 is a mobile device such as a smartphone, or a PC, and in addition to a CPU and communication device (not shown), it is equipped with an input unit 31 for inputting operations, a display unit 32 for displaying characters, images, etc. Of course, the input unit 31 and the display unit 32 may be an integrated touch panel.

[0042] The CB Information DB 11A stores a table containing a set of anticipated questions and answers related to fundraising (including subsidy applications). The CB Management Unit 11B has an AI chatbot function using the learning model 2C. The CB Management Unit 11B refers to the CB Information DB 11A to receive and answer questions about fundraising from the user via the user terminal 3, scores the evaluation of the answers, and stores the resulting score in the CB Information DB 11A.

[0043] The customer management database 12 stores customer management information acquired by CRM (Customer Relationship Management) and MA (Marketing Automation), which will be described later. Customer management information will be explained in detail in Figure 7. The customer management unit 13 collects the above-mentioned customer management information and stores it in the customer management database 12, and also provides customer management information to the business plan creation / review unit 15 when creating or reviewing business plans.

[0044] The application document database 14A stores the application guidelines for business plans required for grant applications and other applications in fundraising. The application management unit 14B allows users to add new documents to the application document database 14A via the user terminal 3, and, as will be described later, manages the data of business plans created for application in the application management database 17. Furthermore, the application management unit 14B receives instructions from the user via the user terminal 3 and carries out the application procedures by the application procedure unit 19.

[0045] The business plan creation and review unit 15 uses the learning model 2C to create a business plan based on the anticipated question and answer set and scores stored in the CB information DB 11A, the customer management information stored in the customer management DB 12, and the management analysis results obtained by the management analysis unit 16, and stores the business plan in the application management DB 17.

[0046] The Management Analysis Department 16 uses the learning model 2C to generate questions to fill in each item of the application guidelines stored in the application document DB 14A with appropriate information, and stores the user's answers to those questions in the application document DB 14A, linking them to the corresponding questions.

[0047] The application management DB 17 stores the business plan data created by the business plan creation / review unit 15, linking it to information about funding sources and users. The output conversion unit 18, upon receiving an output request from a user via the user terminal 3, converts the business plan data stored in the application management DB 17 into a format such as WORD® and outputs it to the user terminal 3.

[0048] The application procedure unit 19 creates and sends application documents to the terminal to which the business plan is to be submitted, based on the business plan data stored in the application management DB 17. The operation unit 20 performs operations on the CB management unit 11B, such as editing the anticipated Q&A set stored in the CB information DB 11A and checking scores. The operation unit 20 receives operations from the user via the user terminal 3 and performs CRM and MA-related operations on the customer management unit 13. The operation unit 20 performs operations on the application management unit 14B, such as registering new application guidelines.

[0049] As shown in Figure 2, the business plan creation system 1 has a configuration in which user terminals 3, server 5, CRM server 6, MA server 7, AICB server 8, and learning model 2C are connected to network 4. User terminal 3 is a typical example of a device operated by a representative of each company.

[0050] CRM server 6 has a configuration that includes the customer management unit 13 and customer management DB 12 as shown in Figure 1, and similarly, MA server 7 has a configuration that includes the customer management unit 13 and customer management DB 12 as shown in Figure 1. Server 5 has a configuration that includes the application document DB 14A, application management unit 14B, business plan creation / review unit 15, management analysis unit 16, application management DB 17, output conversion unit 18, application procedure unit 19, operation unit 20, etc. as shown in Figure 1. AICB server (hereinafter referred to as AICB server) 8 has a configuration that includes CB information DB 11A, CB management unit 11B, etc.

[0051] Next, we will describe the individual server configurations with reference to Figures 3 to 6. Figures 3, 4, 5, and 6 are configuration diagrams showing examples of the hardware configurations of Server 5, CRM Server 6, MA Server 7, and AICB Server 8 according to this embodiment, respectively.

[0052] Server 5 is the main device of the business plan creation system 1 and consists of a CPU 51, ROM 52, RAM 53, display 54, keyboard 55, storage 56, etc. The CPU 51 controls the business plan creation support of this embodiment according to the program stored in ROM 52 (the program responsible for the operations shown in Figures 8 to 13).

[0053] ROM 52 stores the above program, and RAM 53 functions as a work area when the CPU 51 is operating. Display 54 is a device that forms the necessary screen as needed during the operation of server 5, and keyboard 55 is a device used by the server 5's operator for input. Storage 56 consists of the application document DB 14A, application management DB 17, etc., as described above, and data input / output is controlled by the CPU 51.

[0054] The CRM server 6 is a device responsible for CRM processing and consists of a CPU 61, ROM 62, RAM 63, storage 64, etc. The CPU 61 controls the operation of the entire CRM server 6 in conjunction with the server 5 according to the program stored in the ROM 62.

[0055] ROM62 stores the above program, and RAM63 functions as a work area during CPU61 operation. Storage64 consists of the CRM management DB12A mentioned above, and data input / output is controlled by CPU61.

[0056] The MA server 7 is a device responsible for MA processing and consists of a CPU 71, ROM 72, RAM 73, storage 74, etc. The CPU 71 controls the operation of the entire MA server 7 in conjunction with the server 5 according to the program stored in the ROM 72.

[0057] ROM 72 stores the above program, and RAM 73 functions as a work area during CPU 71 operation. Storage 74 consists of the MA management DB 12B and the like, and data input / output is controlled by CPU 71.

[0058] The AICB server 8 is a device responsible for AI chatbot processing and consists of a CPU 81, ROM 82, RAM 83, storage 84, and the like. The CPU 81 controls the operation of the entire AICB server 8 in conjunction with the server 5 according to the program stored in the ROM 82.

[0059] ROM82 stores the above program, and RAM83 functions as a work area during CPU81 operation. Storage84 is composed of the aforementioned CB information DB11A, and data input / output is controlled by CPU81.

[0060] Next, we will explain the configuration of each database with reference to Figure 7. In Figure 7, (A) shows the data configuration of CB Information DB11A, (B) shows CRM Management DB12A, (C) shows MA Management DB12B, (D) shows Application Document DB14A, and (E) shows Application Management DB17.

[0061] As shown in Figure 7(A), the CB information DB11A consists of a business ID that identifies a pre-registered business, a business name, a set of anticipated questions and answers, a score associated with each set of questions and answers, and the date and time of the last update.

[0062] As shown in Figure 7(B), the CRM management DB12A consists of the business ID, business name, and business-specific customer management information, sales promotion information, marketing information, contract-related information, and analytical information obtained through CRM processing.

[0063] As shown in Figure 7(C), the MA management DB12B consists of the business ID, business name, business-specific customer management information, behavioral history information, segment information, campaign information, score information and analysis information obtained from the evaluation of MA processing, etc.

[0064] In the case of a subsidy application, the application document DB14A consists of an ID that identifies the organization applying for the subsidy, the subsidy name indicating the title of the subsidy application, information on the application guidelines indicating the specific items required for the subsidy application, and input information required for each item of the application guidelines, as shown in Figure 7(D). This input information indicates the items and includes information obtained from AI chatbots, CRM, MA, homepage, financial statements, company profiles, and APIs for linking with external devices. For each item of the application guidelines that is referenced in this input information, the input information and the item are stored and managed in association.

[0065] As shown in Figure 7(E), the application management DB17 consists of the business ID, business name, business plan stored in a predetermined data format, creation date and time, etc.

[0066] Here, we will explain the processing overview of the learning model 2C. In this embodiment, when a combination of prompts and necessary materials is input in the form of a question for (1) AI chatbot analysis, (2) business analysis, and (3) creation of a business plan, the learning model 2C generates an answer in the following manner.

[0067] (1) AI chatbot analysis When a user's question is answered using a set of anticipated questions and answers, an evaluation is obtained from the user (referring to user terminal 3). This set of question, answer, and evaluation is scored. The learning model 2C is used for this scoring.

[0068] (2) Management analysis The content of the business analysis required varies depending on the application guidelines. Therefore, the learning model 2C generates a set of questions for business analysis based on the application guidelines. The sets of questions and answers submitted by users for each question in that set are then obtained for the creation of the business plan.

[0069] (3) Preparation of a business plan To briefly summarize the processing of the learning model 2C, the set of questions, answers, and scores (bad rate) from the AI ​​chatbot can measure the level of understanding required to create a business plan. The results of the business analysis, which consist of a set of questions and answers related to management, provide information that grasps the actual state of the business, including numerical data. The learning model 2C then maps the components according to the application guidelines based on the results from the AI ​​chatbot and the business analysis, generates a document based on logical structure (NLG) and applies style transfer according to the submission format, and outputs it after formatting the output format (Markdown, Word®). In addition, the judges' scoring criteria are also taken into consideration when creating this business plan.

[0070] Next, the operation will be explained with reference to Figures 8 to 27. First, the user selects a process from the TOP screen via the user terminal 3. Figure 8 is a flowchart illustrating the process selection according to this embodiment, and Figure 9 is an explanatory diagram illustrating the TOP screen according to this embodiment.

[0071] First, when the user requests the TOP screen via the user terminal 3 (step S101), the server 5 prepares the display data for the TOP screen 91 shown in Figure 9 and sends (provides) it to the user terminal 3 (step S201). On the user terminal 3, the TOP screen 91 received from the server 5 is displayed on the display unit 32 (step S102). As shown in Figure 9, the TOP screen 91 displays four items that can be selected by the input unit 31: "Business plan creation support," "AI chatbot," "Customer management / sales promotion system," and "Subsidy information delivery."

[0072] When an item to be operated on is selected from the menu on the TOP screen 91 (step S103), the selected item is sent to the server 5 (step S104). The server 5 receives the selection instruction and determines which item has been selected (step S202). If the logout button 91A is pressed, the logout process is executed.

[0073] If the system determines that the "AI chatbot" has been selected, the process moves to the AI ​​chatbot processing shown in Figure 11. If the system determines that the "customer management and sales promotion system" has been selected, the process moves to CRM / MA processing (not shown in the diagram).

[0074] If the system determines that "Business plan creation support" has been selected, the process moves to the business plan creation support process shown in Figure 14. Furthermore, if the system determines that "Subsidy information delivery" has been selected, the process moves to the business plan creation support process shown in Figure 22.

[0075] [AI chatbot processing] Next, the AI ​​chatbot processing will be explained with reference to Figures 10 to 13. Figure 10 is a flowchart illustrating the operation of the AI ​​chatbot processing according to this embodiment, and Figures 11 to 13 are explanatory diagrams illustrating example screens of the AI ​​chatbot processing according to this embodiment.

[0076] In the AI ​​chatbot processing of step S312, as shown in Figure 10, server 5 and AICB server 8 communicate via API (steps S211 and S311), and an answer is generated from AICB server 8 using the learning model 2C in response to the user's question input, and this is displayed on user terminal 3. Furthermore, evaluation input is performed by the user, and scoring is executed (step S111). The above processing is repeated unless there is an instruction to return to the TOP screen (no route in step S112).

[0077] Then, when the user input accepts the operation of the button 93A to return to the TOP screen 93, the processing of user terminal 3 returns to step S102 described above (YES route of step S112). Then, the cooperation between server 5 and AICB server 8 is terminated, and the termination process is executed (steps S212 and S313). At this time, server 5 returns to the processing of step S201, and AICB server 8 enters a waiting state until the next cooperation.

[0078] Regarding the AI ​​chatbot, the history of each user's questions is registered in the CB information DB 11A, linked to the access date and time. As shown in Figure 11, the operator of Server 5 can display a list of the history shown in the CB information DB 11A on the display 54.

[0079] Furthermore, the operator of Server 5 associates the answers with each question and registers them in the CB information DB 11A along with the data of the last updated date and time. The operator of Server 5 can display the question and answer sets on the display 54, as shown in screen 94 in Figure 12, and can also modify them. The back button 93A remains displayed.

[0080] Figure 13 shows screen 95, which displays a list of questions and answers previously entered by the user through the AI ​​chatbot, and also shows the screen for handling the learning model 2C. The back button 93A remains visible. Although not shown in the figure, the display screen includes the date and time of the question, the content of the answer, the score evaluation (satisfactory evaluation rate), and the content of the user's feedback, allowing the user to ask further questions or request revisions based on each history. Furthermore, the output results of the AI ​​chat are automatically linked as an important source of information used when creating a business plan. This allows the user to visually grasp the history of changes in their intentions and management policies, leading to the creation of a more persuasive plan.

[0081] [Business Analysis Processing] Next, the business analysis process will be explained with reference to Figures 14 to 18. Figure 14 is a flowchart illustrating the business analysis process according to this embodiment, and Figures 15 to 18 are explanatory diagrams illustrating example screens of the business analysis process according to this embodiment.

[0082] In the business plan creation process, as shown in Figure 14, the user first performs a business analysis. The server 5 provides an initial screen to the user terminal 3 (step S221). On the user terminal 3, as shown in Figure 15, the initial screen 96 is displayed on the display unit 32, and user operations are accepted (step S121). The initial screen 96 includes a button 96A to return to the TOP screen, an AI generation button 96B to instruct the creation of a business plan using the learning model 2C, a progress rate display area 96C showing the progress rate of the business plan, and a subsidy selection area 96D for selection or upload by the user.

[0083] Here, the progress rate display area 96C is automatically calculated based on the input status of each item required in the application guidelines for the grant being applied for (e.g., company overview, challenges, solutions, market analysis, etc.). The completion status of each item is evaluated by both AI judgment (syntactic consistency, descriptive completeness, etc.) and explicit completion actions by the user, and the progress rate is updated in real time. This allows users to immediately identify any missing information and contribute to the completion of a high-quality business plan without any omissions.

[0084] In the subsidy selection area 96D, as shown in Figure 15, users can select the application guidelines from a dropdown menu. For example, the options "Manufacturing Subsidy" which shows already registered application guidelines, and "Upload Application Guidelines" which allows uploading unregistered application guidelines are displayed.

[0085] When a user selects the application guidelines to be submitted via the user terminal 3 (including uploading them) (step S122), the server 5 creates the application screen 97 shown in Figure 16 according to the selection instructions (step S222), and the display data of that application screen is provided to the user terminal 3 (step S223).

[0086] On user terminal 3, the application screen 97 received from server 5 is displayed, and user operations are accepted (step S123). On application screen 97, an application document creation button 97A and the current business plan are placed in the business plan display area 97B. Then, user operations on buttons etc. on the screen are sent to server 5 (step S124), and server 5 makes a processing determination according to the operation input (step S224).

[0087] If the result of the determination is an operation of the AI ​​generation button 96B, the process proceeds to step S225; if it is an operation of the application document creation button 97A, the process proceeds to the process shown in Figure 19; or if it is an operation of the button 96A to return to the TOP screen, the process proceeds to step S201 as described above.

[0088] In step S225, as shown in Figure 17, a business analysis screen 98 with question and answer areas 98A is created and provided to the user terminal 3. The user terminal 3 accepts user operations on this business analysis screen 98. The user terminal 3 displays the user's operation input for the question and answer areas 98A on the business analysis screen 98, and this content is shared with the server 5 (step S125). The process in step S125 is repeated until an output instruction is received from the user (no route in step S126). User operation input includes screen scrolling as well as answers (text) to questions necessary for business analysis. In this way, the data necessary for business analysis is gathered.

[0089] When the user operates the analysis result output button 99A (output instruction) on screen 99 shown in Figure 18 (YES route in step S126), a business analysis based on the answers to the questions in the question and answer area 98A is performed using the learning model 2C. The analysis results are stored in the application document DB 14A. Furthermore, the results are provided to the user terminal 3 for confirmation (step S226). In this way, the server 5 proceeds to step S201. On the user terminal 3, the business analysis results are displayed on the display unit 32 (step S127), and the process proceeds to step S102.

[0090] [Business plan creation process] Next, the business plan creation process will be explained with reference to Figures 19 to 21. Figure 19 is a flowchart illustrating the business plan creation process according to this embodiment, Figure 20 is an explanatory diagram illustrating an example screen for outputting a business plan document according to this embodiment, and Figure 21 is an explanatory diagram illustrating an example of output from the business plan creation process according to this embodiment.

[0091] When the AI ​​generation button 96B is pressed on the application screen 97 shown in Figure 16, the process shown in Figure 19 is executed. On server 5, the business analysis results are read (step S231), CRM information is read in cooperation with CRM server 6 (steps S232 and S233), and MA information is read in cooperation with MA server 7 (steps S234 and S235).

[0092] Once all the above data is collected, the application documents, namely the business plan, are created using the learning model 2C (step S236). The business plan created in this way is stored in the application management DB 17. Furthermore, it is provided to the user terminal 3 so that it can be viewed on the user terminal 3 (step S237). On the server 5, the process then moves to step S201. On the user terminal 3, as shown in Figure 20, the AI-generated business plan is displayed on the display unit 32 in the business plan display area 97B (step S131), and the process moves to step S102. Although not shown in the flowchart, an output button 97E is located there.

[0093] When the output button 97E is pressed by the user, a file 100 in Word® format is output to the user terminal 3, as shown in Figure 21. The user terminal 3 can then download the file 100 and display it on the screen using the Word® application.

[0094] Furthermore, when creating a business plan using the learning model 2C (steps S236 and S237), as will be described later, the table shown in the business plan essential review screen 102 is created and registered in the application management DB 17, as shown in Figures 26 and 27.

[0095] [Subsidy information delivery processing] Next, the subsidy information delivery process will be explained using Figures 22 to 24. Figure 22 is a flowchart illustrating the subsidy information delivery process according to this embodiment, Figure 23 is an explanatory diagram illustrating an example screen of the subsidy information delivery process according to this embodiment, and Figure 24 is an explanatory diagram illustrating another example screen of the subsidy information delivery process according to this embodiment.

[0096] In the subsidy information delivery process, as shown in Figures 22 and 23, first, a search screen 101 for searching for the names of available subsidies is created on the server 5 and provided to the user terminal 3 (step S241). The user terminal 3 displays the search screen 101 and accepts user input (step S141). For search operations, as shown in Figure 23, there are two methods: entering search keywords in the search area 101A through user operation on the search screen 101, and selecting the desired region of prefecture using a pull-down menu.

[0097] On server 5, for example, if a region is selected by the user (step S142), a list 101C is created as a search result for that selected region, listing the names of subsidies published in that region. As a search result, list 101C is provided to user terminal 3 (step S242). On user terminal 3, as shown in Figure 24, list 101C is displayed (step S143), and the user can select the names of the subsidies they wish to apply for, for example, by checking checkboxes, with no limit on the number of selections. Furthermore, the application button 101B can be used at any time after the check operation (step S144).

[0098] When user terminal 3 receives an instruction to operate the application button 101B (YES route in step S145), the name of the subsidy checked in the checkbox is transmitted to server 5 (step S146). Server 5 then executes an application procedure to send the already created business plan in a specified file format to the institution of the transmitted subsidy name (step S243).

[0099] When the application is completed on server 5, a notification of application completion is sent to user terminal 3 (step S244). User terminal 3 receives the application completion notification (step S147). The application completion notification may be displayed directly on the screen, or it may be sent as an email to the registered email address.

[0100] [Subsidy application review process] Next, the subsidy application review process will be explained with reference to Figures 25 to 27. Figure 25 is a flowchart illustrating the subsidy application review process according to this embodiment, and Figures 26 and 27 are explanatory diagrams illustrating example screens for the business plan review process according to this embodiment.

[0101] In this embodiment, it is possible to easily revise an already prepared business plan when applying for a subsidy. First, the header table data stored in a tabular format as shown in Figure 26 is read from the application management DB 17 and provided to the user terminal 3 (steps S251 and S252). As shown in Figure 26, the header table is configured such that each item of the business plan is associated with the input information that serves as the basis for creating that business plan.

[0102] The input information specifically includes website information (HP), CRM / MA information, AI chatbot information, financial statements, company profile information, and APIs for linking with external functions such as CRM servers and MA servers. The items include company overview, background, management challenges, market analysis, competitor analysis, analysis methods, and identification of challenges. For example, sub-items in the company overview include basic information and management philosophy.

[0103] Then, the user terminal 3 receives the header table provided by the server 5 and displays it on the screen as shown in Figures 26 and 27 (step S151). When the user selects an item they want to review, that item is provided by the server 5 and displayed in detail (steps S152 and S253).

[0104] Then, when a user instructs a correction (YES route in step S153), the correction is reflected (steps S154 and S254). Until a correction instruction is received, the process returns to step S152 (NO route in step S153). For this review and correction, processing is executed according to the user's operation via user terminal 3. If it is CB information, the correction is made in cooperation with AICB server 8; if it is CRM information, the correction is made in cooperation with CRM server 6; and if it is MA information, the correction is made in cooperation with MA server 7. Then, it is possible to recreate and reconfirm the business plan as described above. Furthermore, by being able to review and correct in this way, it is possible to refine the plan.

[0105] Here, I will provide some additional explanation about CRM and MA. CRM, which stands for "Customer Relationship Management" in Japanese, is a marketing method for managing and analyzing customer information and building and maintaining good relationships with customers. The purpose of CRM is to maximize LTV (Customer Lifetime Value) by making appropriate approaches to each individual customer, thereby improving the company's profits.

[0106] In a narrower sense, CRM refers to customer management systems and tools. By utilizing CRM, customer information can be analyzed in real time, and services and products can be delivered to each individual at the best possible time, resulting in effective marketing. As a result, it is expected to lead to improved customer satisfaction, increased repeat purchases, enhanced brand reputation, and increased sales.

[0107] Furthermore, MA stands for "a system that automates tasks related to marketing initiatives to streamline marketing activities." In business, while many tools (MA tools) have been developed to realize these marketing activities, it has become common to refer to the tools themselves as MA. With the spread of the internet and smartphones, the channels for lead acquisition have become more complex, and there is a need to nurture leads according to their needs. Against this backdrop, marketing initiatives have also diversified, and there are limits to what can be managed and implemented manually. This is where MA (Marketing Automation) comes in.

[0108] Furthermore, the following supplementary explanation is provided regarding the business plan created by Business Plan Creation System 1. The items include company overview, background, management challenges, market analysis, competitor analysis, analytical methods, and identification of challenges. The sub-items for company overview include basic information, management philosophy, management policies, added value, types of businesses and services of trading partners, number of trading partners, sales overview, and products and services.

[0109] The background section includes the company's history of operations, achievements, setbacks, crises faced, and the circumstances leading to the subsidy application. The management challenges section includes the company's current financial situation, sales, selling and administrative expenses, asset status, and methods for revising the business plan. The market analysis section includes market characteristics, current market share, current trading area, market size, number of potential customers, market growth rate, personas, and customer needs.

[0110] The competitive analysis sub-items include information on other companies in the market area, points of contention for market share, and points of differentiation from competitors. The analytical methodology sub-items include SWOT analysis, VRIO analysis, 5 Forces analysis, STP analysis, 3C analysis, and 4P analysis. The challenge sub-item includes conducting analysis from the perspective of management challenges and customer competition.

[0111] Furthermore, while the creation of an estimate is required for subsidy applications, the business plan creation system 1 can automatically create one. Specifically, the basis for the AI's ability to automatically create an estimate from the business plan is that it references CRM / MA data as a reference for estimate data, and the customer's requests are referenced from the AI ​​chatbot history and the automatically created business plan for subsidy applications. Based on the above, server 5 automatically creates the requirements for obtaining an estimate from the business plan in cooperation with the AI ​​model, learning model 2C.

[0112] Furthermore, each fundraising effort requires the submission of business results to investors, funders, and grant secretariats. Various public programs (loans and grant programs) have specific formats for reporting performance. Therefore, by applying the ability to summarize the application guidelines in the business plan creation system 1 and include headings and details in the business plan, performance reports are also output from the CRM and chatbot databases. The system is then configured to allow users to select the appropriate public format for submitting business results to investors, funders, and grant secretariats.

[0113] When obtaining quotations, it may be possible to refer to the Legal Affairs Bureau database for basic company information such as the corporate number, company name, and representative of the company to which the requirements specification is submitted, while also requiring prospective participating companies to submit a complete commercial history certificate to verify their actual status.

[0114] [Effects of the First Embodiment] As explained above, according to this embodiment, even those who are not good at understanding the application guidelines for subsidies or the systems for fundraising through loans or equity can use AI to provide input support and business analysis, and automatically generate a business plan that meets the secretariat's review criteria.

[0115] [Second Embodiment] Figure 28 shows an example of the operation screen of a business plan creation support system according to one embodiment of the present invention. The difference between the screen configuration shown in Figure 28 and the screen configuration shown in Figure 9 is that the business analysis system is integrated into the system.

[0116] [System configuration of this embodiment] Figure 29 is a configuration diagram showing an example of the main server hardware configuration of a business plan creation support system according to one embodiment of the present invention.

[0117] In Figure 29, 11C is the chatbot unit, which, following instructions from the CB management unit 11B, displays a chat screen on the display unit 32 in conjunction with the user's operation instructions on the display screen shown in Figure 28, providing answers to user questions and advice regarding subsidy applications. The information received is stored as customer information in the CB information DB 11A.

[0118] Here, the CRM data structure includes columns designed for reporting grant performance, allowing it to identify whether each transaction record is related to a specific grant. This feature is intended to allow the system to be used independently, even if the customer does not have their own CRM.

[0119] Server 5 automatically collects publicly available subsidy information by crawling or scraping the web. This process is designed to mimic how a human would search for something like "Minato Ward Small and Medium-Sized Enterprise Subsidy" on a search engine.

[0120] From the text information in the customer information acquired by CRM server 6, the learning model 2C extracts the following key data (1) to (4).

[0121] (1) Subsidy amount (2) Eligible expenses (3)Application deadline (4) Subsidy rate (e.g., 2 / 3, 1 / 2) Here, the data (1) to (4) extracted by the learning model 2C are reflected in the data table in storage 64 within the system.

[0122] In this state, when a user (person in charge) on user terminal 3 selects a specific subsidy from the subsidy list and presses the application button to create a business plan, server 5 detects the status of the application button press and the process starts. Triggered by this application action, CRM server 6 sets a flag in the related transaction record linked to the customer management information it manages, indicating which subsidy it is associated with. If the user does not apply for a subsidy, CRM server 6 continues to store the related transaction record in the database without setting the flag.

[0123] On the other hand, when a user presses the application button displayed on the display unit 32 of the user terminal 3, the corresponding transaction record with the flag set is imported for subsequent processing.

[0124] This automates the process of collecting and updating subsidy information, preventing applications from being missed.

[0125] Furthermore, integration with CRM functions provided by CRM Server 6 ensures consistency between application details and performance reports.

[0126] Furthermore, even if user terminal 3 does not have a CRM installed, user terminal 3 can use the CRM server 6 as its own CRM function resource.

[0127] Furthermore, Server 5 can improve the traceability of subsidy utilization by linking application history and transaction history as a system.

[0128] [Example of business plan creation process using an AI chatbot used by a company] The chatbot function provided by the AICB server 8 in this embodiment is intended to function as a "first-line contact point" for handling inquiries on a company's website, etc. This makes it possible to reduce the workload by automating inquiries that were previously handled manually.

[0129] Furthermore, the information obtained through the chatbot function will ultimately be used directly in creating the business plan.

[0130] Furthermore, the storage 84 managed by the AICB server 8 has pre-loaded fixed information such as the company's website URL, customer information, product and service information, and founding history.

[0131] As a result, the AICB server 8 automatically generates anticipated questions and answers from the customer operating the user terminal 3.

[0132] Furthermore, AICB Server 8 can acquire and analyze the chatbot's history. Here, inquiries from prospective customers (price, delivery time, differentiating points, etc.) are considered "offers" indicating interest in the product / service, and AICB Server 8 manages them as extremely important data.

[0133] The AICB server 8 then acquires the conversation history with prospective customers obtained via the chatbot and manages and analyzes it in conjunction with the CRM. It also stores data such as the content of the chatbot inquiries, user information, the status of the conversation, and the circumstances leading to lost deals or requests for quotes in the database.

[0134] For example, it becomes possible to present a new service before its official release via a chatbot and conduct a proof-of-concept (POC) to test market reactions.

[0135] Specifically, the following chatbot function will be incorporated into the business plan creation system 1. If positive responses such as "Tell me more" are received from customers, it can be considered as evidence to promote commercialization.

[0136] Note that in order for the chatbot to automatically generate questions and answers, the user needs to input the information source as part of the initial setup.

[0137] Here, the user operating user terminal 3 registers links to web pages containing company information, product information, etc., with the AICB server 8. Currently, up to three web page links can be registered with the AICB server 8, but this number can be increased as needed.

[0138] Furthermore, even if the information provided by user terminal 3 on network 4 is scattered across multiple pages, the AICB server 8 can read the information by the user appropriately entering those links.

[0139] The chatbot function provided by Business Plan Creation System 1 can autonomously learn and improve upon "good conversations" that lead to conversions.

[0140] First, the AICB server 8 can be configured to automatically generate sets of anticipated questions and answers from information such as web pages published by companies, and to automatically replace conversations deemed to have poor performance (for example, the worst 20%) with better ones.

[0141] On the other hand, this system also manages information (non-structured data) provided as feedback from customers via the chatbot function as an evaluation metric.

[0142] For example, based on the chatbot's responses, this system suggests to user terminal 3 that conversations that the customer has clearly rated as "Bad" be replaced with other undesirable conversations.

[0143] Similarly, if the chatbot's response indicates that the customer answered a question but the conversation ended without continuing, it can be determined that the answer was insufficient.

[0144] This allows companies' chatbot services to dynamically generate responses based on context, rather than always returning fixed answers.

[0145] Specifically, when a user asks a vague question like "price," the system extracts relevant answers from a pre-prepared set of anticipated questions and combines (merges) them to generate a single answer.

[0146] Because the answers are not fixed, responses not included in the anticipated Q&A list may be generated. The evaluation of the generated responses depends on the subsequent development of the conversation. If the conversation continues based on the responses generated in this way and leads to more specific questions (for example, "How much money is needed to create a business plan?"), the response generation process will be evaluated as "good."

[0147] The AICB server 8 can store this sequence of conversations as data, for example in storage 84, and analyze it to identify which question-and-answer combinations contributed to the conversion.

[0148] Furthermore, the chatbot functionality service can also be provided as a function of Server 5, as shown in Figure 29.

[0149] [Examples of dialogue evaluation and scoring processes] The CB Management Unit 11B of this system analyzes the content of conversations in detail, based on the chatbot's conversation history stored in a database, and provides a process to quantify (score) the quality of the conversations and the evaluation of new services.

[0150] Here, the scoring criteria are based on past success stories (e.g., conversations that led to a sale, conversations that resulted in a request for a quote). In other words, by comparing the conversation patterns of success stories, the effectiveness of individual conversations (such as the keywords they contained; for example, if the phrase "That's amazing" was used, a higher number of "amazing"s would result in a higher conversation score) can be evaluated.

[0151] On the other hand, CB Management Department 11B can suggest modifications and improvements to question-and-answer pairs that receive low scores, so as to better meet the customer's intent. For example, in response to the question, "Is it free to use?", instead of simply offering a free plan, the quality of the conversation can be improved by providing additional information (offers) such as details of paid plans and contract types. Here, the evaluation criteria are set so that the score fluctuates depending on the purpose of the conversation and is not treated as a fixed value.

[0152] In this system, the term "score" as an evaluation metric for conversations is not simply a numerical score, but rather an indicator used to determine whether a conversation is leading to a conversion (such as obtaining an inquiry or a request for a quote).

[0153] Furthermore, the definition of a conversation that leads to conversion is that specific requests from customers, such as "Please tell me," "Can you do that?", and "Please give me a quote," are positioned as important conversations (love letters) that lead to conversion. More specifically, the indicator is not simply a numerical score, but rather an evaluation of the qualitative flow of the conversation, whether it is moving towards the final goal (e.g., requesting a brochure, requesting a quote). Just as a good salesperson elicits all of the customer's needs and makes a great proposal, the AI ​​chatbot will also consider conversations that elicit the maximum amount of the customer's needs as the "correct" answer and will give it high marks.

[0154] [Business plan creation process] As the primary point of contact with customers, this system provides tools to automate a company's profit-generating activities (from acquiring prospective customers to closing deals). Therefore, the CB Information DB11A is equipped with storage 64 for accumulating CRM data.

[0155] Specifically, the system will be configured to store all customer inquiries from user terminal 3, responses to those inquiries, and concrete results such as "Please give me a brochure" or "Please give me a quote" as records in storage 64 of the CRM.

[0156] This will enable the Management Analysis Department 16 to visualize and graph the conversion rate from the above-mentioned inquiries to quotations.

[0157] Specifically, the Management Analysis Department 16 will acquire three types of data as input information. Here, the three types of data are: (1) Fixed Q&A information at initial setup This includes information that is fundamental to the business, such as the company's basic information, philosophy, and history of independence. (2) CRM data obtained from AI chatbot This includes customer interaction history and proof-of-concept (POC) information obtained through the AI ​​chatbot. (3) Financial Statement Information This includes information uploaded from user terminal 3 in PDF or other formats, such as a complete set of declaration forms (BS / PL, details of selling, general and administrative expenses, etc.).

[0158] [The process from analysis to business plan creation] When the user presses the "Business Analysis Button" displayed on the display unit 32 of the user terminal 3, the system 1 reads the information from (1) to (3) above, and the business analysis unit 16 automatically starts the analysis.

[0159] The Management Analysis Department 16 conducts its analysis using fixed headings (keywords) such as "philosophy," "added value," and "customers."

[0160] In this process, if the management analysis department 16 finds that there is insufficient information during the analysis, the AICB server 8 automatically generates additional questions based on CRM data and publicly available information, thereby improving the accuracy of the analysis.

[0161] In this system, AICB Server 8 analyzes lengthy documents such as the application guidelines (approximately 60 pages), determines whether the content should be included in the business analysis, and extracts the information. AICB Server 8 then compares the extracted information with the evaluation criteria required by the subsidy application documents, and examines the content while organizing it into a business plan.

[0162] In this system, commands to the AICB server 8 are standardized, and documents are read in blocks such as pages. The system automatically creates headings that align with the themes requested by the management analysis department 16 and generates the content.

[0163] This eliminates concerns that users operating user terminal 3, who are unfamiliar with business analysis, might be satisfied with the initial analysis results generated by AICB server 8 and not continue with a detailed discussion.

[0164] Furthermore, one of the functions provided by this system is to encourage user feedback and improve the accuracy of analysis by having the business plan creation system 1 present "unanswered" items to the user terminal 3.

[0165] Furthermore, once a customer's application documents pass the approval review process through this system—that is, after the subsidy has been approved by the recipient—it is possible to incorporate a function into the system that defines requirements based on the approved business plan and investment amount, and creates a request for quotation and requirements specification document.

[0166] This allows us to extract investment amounts and requirements for new businesses from the business plan and provide customers with an automated service to generate draft cost estimates.

[0167] [Example of business plan creation process] Figure 30 is a flowchart illustrating the operation of an AI chatbot processing according to one embodiment of the present invention. This processing is based on the steps shown in Figure 10 and is characterized by processing that includes chatbot function processing.

[0168] In the AI ​​chatbot processing in step S312, as shown in Figure 30, the server 5 and the AICB server 8 communicate via API (steps S211 and S311), and an answer is generated from the AICB server 8 using the learning model 2C in response to the user's question input, and this is displayed on the user terminal 3. Next, the user interface screen shown in Figure 29 is displayed on the display unit 32 of the user terminal 3, and the user presses the business analysis button (S112). When the server 5 detects that this business analysis button has been pressed (S212), the chatbot unit 11C executes AI chatbot processing (S312) to perform business analysis processing based on unstructured data acquired and stored through question and answer with the user (S213).

[0169] Meanwhile, if the AICB server 8 determines that it has all the necessary non-standard data (data) for business analysis obtained through the question-and-answer session by the chatbot unit 11C (S313), it sends the customer information to the business analysis unit 16 (S314).

[0170] Furthermore, the user inputs an evaluation and performs scoring (step S111). The above process is repeated unless there is an instruction to return to the TOP screen (NO route in step S113).

[0171] Then, when the user input is received by pressing the button 93A to return to the TOP screen 93, the processing of the user terminal 3 returns to step S102 described above (YES route of step S112).

[0172] Then, the connection between server 5 and AICB server 8 is terminated, and the termination process is executed (steps S214 and S315). At this time, server 5 returns to the process in step S201, and AICB server 8 enters a waiting state until the next connection.

[0173] Regarding the AI ​​chatbot, the history of each user's questions is registered in the CB information DB 11A, linked to the access date and time. As shown in Figure 11, the operator of Server 5 can display a list of the history shown in the CB information DB 11A on the display 54.

[0174] Furthermore, the operator of Server 5 associates the answers with each question and registers them in the CB information DB 11A along with the data of the last updated date and time. The operator of Server 5 can display the question and answer sets on the display 54, as shown in screen 94 in Figure 12, and can also modify them. The back button 93A remains displayed.

[0175] The commands to the AICB server 8 are standardized, and the system reads documents in chunks such as pages, automatically creates headings aligned with the business analysis theme, and generates the content.

[0176] This is because users unfamiliar with business analysis may be satisfied with the initial analysis results generated by the AI ​​and may not continue with a detailed discussion.

[0177] The MA server 7 may also take an approach to improve analysis accuracy by prompting user feedback by presenting "unanswered" items to the user terminal 3.

[0178] According to this embodiment, by linking multiple information analysis server devices, even those who are not good at understanding the application guidelines for subsidies or the systems for fundraising through loans or equity can automatically generate a business plan that includes customer information and management analysis information required by the subsidy recipient, in accordance with the secretariat's review criteria, while already having an analysis of the business's management situation.

[0179] [Example of operation based on the user interface screen] Figures 31 to 40 show examples of user interface screens displayed on the display unit 32 of the user terminal 3 shown in Figure 1.

[0180] Figure 31 shows the dashboard screen of the subsidy application operation screen that Server 5 presents to User Terminal 3. Initial settings include common items, business plan creation support, AI chatbot, customer management / sales promotion system, subsidy information delivery, and performance reporting items, with performance reporting being configurable.

[0181] Furthermore, the system is configured so that users can select each of the above items to display them on the corresponding hierarchical operation screens.

[0182] Furthermore, this screen operation assumes that the user account has been registered on the registration screen provided by server 5 by operating user terminal 3.

[0183] [Basic Information Registration Process] In the screen shown in Figure 31, when "Common Items" is selected, the basic information registration screen (Figures 32-34) is displayed on the display unit 32 of the user terminal 3 from the server 5. Here, in the "Your Company" item, the user can enter their company's address and website link to register it with the server 5.

[0184] Furthermore, in the "Customer" section, users can register customer information on Server 5 by entering the customer's industry / business type (major category), such as "information and communications industry," and their industry / business type (medium category), such as "information services industry."

[0185] Furthermore, users can register information about their trading partners by entering their industry / business type (subcategory), such as software industry, and their industry / business type (detailed category), such as packaged software industry, on Server 5.

[0186] Next, in the "Company Concept" section, users can register links to their company's website regarding its management philosophy and its products and services on server 5 by entering these links.

[0187] From this point onward, 16 items are set as pre-registered questions from the chatbot unit 11C to server 5.

[0188] As shown in Figure 33, for example, the first pre-consultation question is, "How did your company's representative start the business? Please tell us about the background of its founding."... The 16th question is, "Please tell us about any difficulties your company is facing in terms of sales promotion, including specific examples." Non-standard data-based questions such as these are registered in the CB Information DB11A in an updatable manner.

[0189] Therefore, by simply answering the 1st to 16th questions generated by the chatbot unit 11C presented by the server 5, the user of user terminal 3 can have information about the company's founding and challenges gathered and registered as customer information linked to the customer ID in the CB information DB 11A.

[0190] In this example, when the server 5 receives a response from the user operating user terminal 3 stating, "We haven't found a strategy to ensure our marketing efforts reach our target customers," the analysis target is narrowed down to the management analysis department 16.

[0191] Next, as itemized financial information, users can upload financial statements (files containing financial indicator information). Users can upload the current period, previous period, and the period before that to server 5.

[0192] Next, the server 5 presents a screen for the user operating the user terminal 3 to select the subsidy application business plan menu, as shown in Figure 35.

[0193] In this example, an AI generation button and a free LLM business analysis button are provided.

[0194] Since Server 5 has already selected the appropriate application destination for the company currently accessing User Terminal 3 from the publicly available subsidy application destination information based on the Q&A with User Terminal 3, clicking "Select a subsidy" will display the subsidy application chart screen shown in Figure 36 from Server 5 to User Terminal 3.

[0195] Currently, the chart shows that, as part of the procedure, phases 1-3 of the seven progress screens have been completed, and the user is now in the phase where they need to input the company profile. At this point, the user on user terminal 3 enters links to the company's management philosophy website and its products and services website as part of the company profile, and then clicks the "Next" button.

[0196] As a result, the pre-question first screen is displayed on the display unit 32 of the user terminal 3 by the server 5.

[0197] Here, the chatbot unit 11C responds to question 1 (where the question corresponds to the answer item requested by the grant application recipient) with the user's answer: "By obtaining a degree in natural language processing and working in customer management systems as a company employee, I acquired skills in dynamic information management and built my own customer management system and marketing automation."

[0198] Next, chatbot unit 11C responded to question 2 with the statement, "I felt that creating a business plan could serve as a compass for my business, and therefore I am very interested in deepening my knowledge of how to create highly accurate business plans." Questions 3 and 12 are omitted below.

[0199] The chatbot unit 11C obtains the response "Due to my low credit score, I will not be able to do business with companies that evaluate based on company size and capital strength" from the user terminal 3 in response to question 13.

[0200] Similarly, the chatbot unit 11C obtains the response "It is difficult to raise the unit price of transactions because our main business is with small and medium-sized enterprises" from the user terminal 3 in response to question 14. When the user clicks the next button, the server 5 displays the screen showing Phase 6 (Financial Information) as shown in Figure 39 to the user terminal 3.

[0201] Here, user terminal 3 selects the previous year's tax return and uploads the financial information to server 5 in the specified file format. Furthermore, when the user clicks the "Next" button, server 5 displays the Phase 7 screen shown in Figure 40 to user terminal 3.

[0202] Figure 41 shows an example of the management philosophy information generation screen displayed on the display unit 32 of the user terminal 3 shown in Figure 1. Figure 42 shows an example of the business plan creation instruction screen for subsidy applications displayed on the display unit 32 of the user terminal 3 shown in Figure 1. The following describes an example of the business plan creation process for subsidy applications in this system.

[0203] Here, the user operating user terminal 3 can choose to either press the "Start Business Analysis" button or perform business analysis using the free LLM (Lawyer for Management) tool.

[0204] For example, if the "Start Business Analysis" button is selected, the business analysis unit 16 generates a business philosophy to be responded to the selected subsidy application recipient, as shown in Figure 41, based on the customer information stored in the CB information DB 11A and the information that the user has answered sequentially from Phase 1 described above.

[0205] When user terminal 3 selects the business plan creation support shown in Figure 31, the screen shown in Figure 42 is displayed from server 5. On this screen, the user can choose whether to create a business plan for subsidy application based on AI generation or by performing business analysis using free LLM.

[0206] For example, if you choose the AI-generated option from the above choices, you will be providing a paid service, and since the system is based on the premise of generating revenue through subscriptions, a detailed business plan for subsidy applications will be generated.

[0207] On the other hand, when using the free LLM for business analysis, it generates a business plan for subsidy applications, focusing on the general answer items required by the recipient, based on the information the user provides in response to the questions.

[0208] [Effects of the second embodiment] According to this embodiment, in a business plan service for subsidy applications, the chatbot can be made to behave like a concierge interacting with the user. The complex process of creating subsidy application documents can be easily simplified by extracting non-standard data obtained from the chatbot's question-and-answer session, such as the company's founding history, and automatically generating compelling documents that fit the required format of the subsidy application and are more likely to pass the review process. In other words, a smooth conversation can be expected that cannot be obtained with so-called template-based, formulaic responses, and even staff members unfamiliar with subsidy applications can respond naturally, allowing them to obtain and scrutinize the non-standard data that should be included in the subsidy application documents.

[0209] Furthermore, the chatbot will behave like a concierge to the user, providing flexible and accurate questions that are relevant to the context. This will enable smooth and natural responses that cannot be obtained with so-called template-based, pre-defined questions, and will allow the system to acquire non-standard data necessary for subsidy applications from users.

[0210] Although each embodiment has been described in detail above, the invention is not limited to any particular embodiment, and various modifications and changes are possible within the scope described in the claims. Furthermore, it is possible to combine all or more of the components of the embodiments described above.

[0211] Furthermore, the configurations of the various functions, processes, and databases described above are merely examples. The configurations of the various functions, processes, and databases can be changed to the optimal configuration from the standpoint of the performance, processing efficiency, and communication efficiency of the hardware and software provided by these devices.

[0212] Furthermore, the configuration of the database (schema, etc.) that stores the various types of data mentioned above can be flexibly modified from the perspective of efficient resource utilization, improved processing efficiency, improved access efficiency, and improved search efficiency.

[0213] Specifically, the subject of the system or method in this disclosure is a computer.

[0214] The functions of the subject of the apparatus or method described herein are realized by the computer executing a computer program. The computer comprises, as its main hardware configuration, a processor that operates according to the computer program. The processor may be of any type as long as it can realize its functions by executing the computer program.

[0215] A processor consists of one or more electronic circuits, including semiconductor integrated circuits (ICs, LSIs, etc.). Computer programs are recorded on non-temporary recording media such as ROMs, optical discs, and hard disk drives that are readable by the computer. Computer programs may be pre-stored on the recording media or supplied to the recording media via a wide-area communication network, including the Internet. [Explanation of Symbols]

[0216] 1. Business plan creation system 2. Generation AI Core 2C Learning Model 3. User terminals 5 Servers 6 CRM Server 7 MA Server 8 AICB Servers 11A CB information DB 11B CB Management Department 12 Customer management DB 13 Customer Management Department 14A Application Documents Database 14B Application Management Department 15. Business Plan Creation and Review Department 16. Management Analysis Department 17 Application management DB 18 Output Conversion Section 19. Application Procedures Department 20 Control section

Claims

1. A business plan creation system comprising a user terminal that instructs the automatic generation of a subsidy application form to be submitted to a designated subsidy application recipient, and a server device that provides a service for creating a business plan for fundraising by linking with a learning model that holds parameters learned from response information to the items required by the subsidy application form, The server device is A database that stores the application guidelines for the business plan required by the aforementioned designated subsidy application recipient and customer management information for the service, A business analysis unit generates questions corresponding to each item in the business plan based on the application guidelines stored in the database, presents them to the user terminal via a chatbot function, receives the answers entered from the user terminal, and inputs the answers and questions into a learning model to analyze business performance. A creation unit inputs the analysis results of the management analysis unit, the customer management information, the application guidelines, and a conversation score evaluated based on conversation patterns of successful cases for the responses into the learning model, instructs the creation of the business plan, and obtains the business plan generated based on the parameters held by the learning model. A business plan creation system characterized by having the following features.

2. In the business plan creation system described in claim 1, The business plan creation system is characterized by the following: the business analysis department extracts necessary question items based on the public offering guidelines and customer management information, presents the question items to the user terminal via a chatbot function, receives the answers entered from the user terminal, and inputs the non-quantitative data contained in the answers into the learning model to analyze the business situation.

3. In the business plan creation system described in claim 1, The business plan creation system is characterized in that the database includes the customer management information related to CRM (Customer Relationship Management).

4. In the business plan creation system described in claim 1, A business plan creation system characterized in that the database includes the customer management information related to MA (Marketing Automation).

5. In the business plan creation system described in claim 1, The business plan creation system is characterized in that the creation unit creates a performance table in which numerical data is entered into each item table to be described in the business plan, based on the customer management information and the analysis targets of the management analysis unit, presents it to the user terminal, and accepts the selection of items to be reviewed from the user terminal.

6. In the business plan creation system described in claim 5, The creation unit is characterized by modifying the information corresponding to the selected item from the customer management information stored in the database and the analysis targets of the business analysis unit in accordance with the operation of the user terminal.

7. In the business plan creation system described in claim 1, Furthermore, the business plan creation system is characterized by having an output unit that converts the business plan created by the creation unit into a file and outputs it to the user terminal.

8. In the business plan creation system described in claim 1, The aforementioned database is a business plan creation system characterized by adding and storing unregistered application guidelines in response to operations on the user terminal.

9. In the business plan creation system described in claim 1, Furthermore, the business plan creation system is characterized by having an application procedure unit that connects to a server to which funding applications are submitted and uses the business plan created in the creation unit to submit an application to the server.

10. In the business plan creation system described in claim 1, A business plan creation system characterized in that the aforementioned funding is in the form of subsidies, grants, loans, or investments.

11. In the business plan creation system described in claim 1, A business plan creation system characterized in that the items to be included in the business plan include one or more of the following: company overview, background, management challenges, market analysis, competitor analysis, analytical methods, and problem identification.

12. In the business plan creation system described in claim 1, A business plan creation system characterized by having a management unit for managing the aforementioned conversation score.

13. In the business plan creation system described in claim 1, The learning model is a business plan creation system characterized by storing the following as parameters: approved subsidy applications, rejected subsidy applications, application guidelines, evaluation criteria for each application destination, company information applying for the subsidy, and evaluation comments for reviewing the subsidy application.

14. A business analysis method for a business plan creation system, comprising a user terminal that instructs the automatic generation of a subsidy application form to be submitted to a designated subsidy application recipient, and a server device that provides a function to create a business plan for fundraising and a chatbot function in conjunction with a learning model that holds parameters learned from response information to non-standard data items required by the subsidy application form, wherein the system includes a user terminal that instructs the automatic generation of a subsidy application form to be submitted to a designated subsidy application recipient, and a server device that provides a function to create a business plan for fundraising and a chatbot function, The server device is The system includes a database that stores the application guidelines for the business plan required by the aforementioned designated subsidy recipient and customer management information for users of the service, A business analysis step involves generating questions corresponding to each item in the business plan based on the application guidelines stored in the database, presenting them to the user terminal via a chatbot function, receiving the answers entered from the user terminal, and inputting the answers and questions into a learning model to analyze business performance. A creation step involves inputting the analysis results from the aforementioned business analysis step, the customer management information, the public offering guidelines, and a conversation score evaluated based on conversation patterns of successful cases for the responses into the learning model, instructing it to create the business plan, and obtaining the business plan generated based on the parameters held by the learning model. A business plan creation system and its management analysis method characterized by comprising the following features.

15. A computer program that generates a business plan for fundraising by linking it with a learning model that stores parameters learned from response information to non-standard data items required by a subsidy application, To the aforementioned computer, A business analysis step involves generating questions corresponding to each item in the business plan based on the application guidelines stored in the database, presenting them to the user terminal via a chatbot function, receiving the answers entered from the user terminal, and inputting the answers and questions into a learning model to analyze business performance. A creation step involves inputting the analysis results from the aforementioned business analysis step, customer management information, application guidelines, and a conversation score evaluated based on conversation patterns of successful cases for the aforementioned responses into the learning model, instructing it to create the business plan, and obtaining the business plan generated based on the parameters held by the learning model. A computer program characterized by causing the execution of a specific action.

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