KR102490732B1 - Device for creating customized startup materials based on artificial intelligence - Google Patents

Abstract

The present invention relates to a device for creating customized startup materials based on artificial intelligence, which supports the evaluation and modification of materials for attracting investment among start-up materials, in order to minimize a situation in which new companies (start-ups) and SMEs are unable to attract investment by omitting or incorrectly writing necessary information for attracting investment when preparing the start-up materials.

Description

Artificial intelligence-based customized startup data creation device {DEVICE FOR CREATING CUSTOMIZED STARTUP MATERIALS BASED ON ARTIFICIAL INTELLIGENCE}

The present invention relates to an artificial intelligence-based customized start-up data creation device, and specifically, when startups (start-ups) and small and medium-sized enterprises omit or incorrectly write the contents necessary for investment attraction among start-up data, they fail to attract investment. It relates to an artificial intelligence-based customized start-up data creation device for minimizing the situation.

IR (Investor Relations) data necessary for startups aims to clearly express the importance of the business while showing the business that is being operated or planned by the company that wants to attract investment in an integrated manner. In particular, in the case of start-up companies, it is common to start a business with ideas and new technologies rather than performance. For such companies, investment attraction activities (IR) are very important activities, and they can grow through capital raising through successful investment attraction activities. there is. In particular, with the emergence of new companies, an increasing number of companies receive investment after being evaluated for their technology and service differentiation, apart from securing capital through real estate and government guarantees, which are traditional methods of attracting investment.

The IR material should include the startup's strategy for making money based on market needs and opportunities, problem solving for risks, and so on. In addition, when evaluating IR data, the value of the project must be clearly evaluated. In the case of a start-up company that does not have enough valuable assets and real estate assets, it is evaluated according to the level of the founder's past activities, financial condition and IR data, and as a result, the IR data is used as data for financial statements, profit and loss calculations, and liquidity evaluation. do.

Republic of Korea Patent No. 10-2381179 (March 28, 2022)

The Top 12 Reasons Startups Fail (August 3, 2021, CBINSIGHTS) (https://www.cbinsights.com/research/startup-failure-reasons-top/)

An object of the present invention is to provide an artificial intelligence-based customized start-up data creation device to help start-ups and small and medium-sized enterprises attract investment.

An object of the present invention is to provide an artificial intelligence-based customized start-up data creation device that can prevent omission and erroneous writing of information necessary for investment attraction.

The present invention is necessary in the market, including entrepreneur analysis, market analysis, product and business plan, product and product development plan, operation plan, promotion plan, etc. An object of the present invention is to provide an artificial intelligence-based customized start-up data creation device that induces the creation of investment attraction materials and, at the same time, the creation of successful start-up materials through the evaluation of these data.

Artificial intelligence-based customized start-up data creation device according to the present invention,

1) a founder human information learning module 100 for machine learning of human information so as to present entrepreneurial suitable fields based on the founder's career information;

2) An IR data learning module (200) that presents essential criteria items necessary for IR data preparation based on machine learning data after collecting IR data of other companies that have succeeded in attracting capital by industry and field;

3) an IR writing module 300 that guides the IR writer step by step according to the essential criteria presented in the IR material learning module 200 to create IR materials suitable for the industry;

4) an IR validity evaluation module 400 that quantifies and scores the prepared IR data, analyzes the validity of the IR data, and presents an improvement plan; and

5) It is characterized by including a public and private statistics API (Application Programming Interface) linked IR design module 500 that expresses industrial statistical data necessary for market analysis in graphs.

The artificial intelligence-based customized start-up data creation device according to the present invention provides the contents required for qualitative evaluation, various business canvas models and early start-up company evaluation models commonly used in the relevant market as data for preparing IR data.

The artificial intelligence-based customized start-up data creation device according to the present invention reports a quantitative index for IR data based on data formed for each market, and based on these data, a statistical database required for IR data improvement and IR data improvement image database

1 is a diagram showing a schematic configuration of an artificial intelligence-based customized entrepreneurship data creation device according to the present invention.
2 is a diagram showing in detail the configuration of the IR creation module of the artificial intelligence-based customized entrepreneurship data creation device according to the present invention.

Advantages and features of the present invention, and methods for achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and will be implemented in various different forms.

In this specification, this embodiment is provided to complete the disclosure of the present invention, and to completely inform those skilled in the art of the scope of the invention to which the present invention belongs. And the invention is only defined by the scope of the claims. Thus, in some embodiments, well-known components, well-known operations and well-known techniques have not been described in detail in order to avoid obscuring the interpretation of the present invention.

Like reference numbers designate like elements throughout the specification. And, the terms used (mentioned) in this specification are for describing the embodiments and are not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. Also, elements and operations referred to as 'include (or include)' do not exclude the presence or addition of one or more other elements and operations.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used in a meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless they are defined.

Among startup data, writing IR data can reflect a lot of subjective judgment of the company, and as of 2021, 38% could not find an investor, and 35% of them appeared because they misjudged the market's requirements. In other words, it can be said that there are many cases where founders of new companies make their own IR data incorrectly, and many IR or startup data creators make subjective settings about their company's business performance, thereby reducing the reliability of IR data. The background is that while preparing IR data, it is not using verified market analysis data.

Therefore, the present invention acquires and simulates business portfolios of not only IR data-producing companies but also related markets and competitors, and presents a performance simulation engine based on this information.

According to the present invention, the artificial intelligence-based customized start-up data creation device receives computerized investment attraction data (hereinafter referred to as IR data) into a computing device, adjusts the items of the IR data to the type of industry and market, and converts the contents of the IR data into text. By using tect mining and data mining techniques, qualitative data is converted into quantitative data, and various market models used for business model evaluation are quantified to score IR data, and at the same time, each IR data It refers to a device that advises on data that are missing in the IR data but need to be corrected among each IR data.

Contents that require qualitative evaluation, various business canvas models commonly used in the relevant market, and evaluation of early start-up company evaluation models are provided as materials for preparing IR materials among start-up materials. Based on the data formed for each market in this way, the quantitative index for IR data is reported. In addition, based on these data, a statistical database required for IR data improvement and an image database for IR data improvement are provided.

Artificial intelligence-based customized start-up data creation device according to the present invention,

1) a founder human information learning module 100 for machine learning of human information so as to present entrepreneurial suitable fields based on the founder's career information;

2) An IR data learning module (200) that presents essential criteria items necessary for IR data preparation based on machine learning data after collecting IR data of other companies that have succeeded in attracting capital by industry and field;

3) IR preparation module 300 for guiding IR or start-up data creators step by step according to the essential criteria presented in the IR data learning module 200 to create IR data suitable for the industry;

4) an IR validity evaluation module 400 that quantifies and scores the prepared IR data, analyzes the validity of the IR data, and presents an improvement plan; and

5) It includes a public and private statistics API (Application Programming Interface) linked IR design module 500 that expresses industrial statistical data necessary for market analysis in graphs.

The IR data learning module (200) is a module that presents essential criteria for preparing IR data based on the learned data after collecting IR data from other companies that have successfully attracted investment by industry and field. It is a module 200 that collects IR data of other scattered companies and performs machine learning by industry field, type, table of contents-list, and content (context).

Based on the information learned in this way, typical measures such as market information (needs and requirements), market analysis (market size, pain point, requirements), and market forecasts such as profit rate and sales of the company to prepare IR data Suggests essential criteria items for presenting Required criterion items are presented using the LDA (Latent Dirichlet Allocation) method, which is a learning method based on natural language processing.

Using this LDA method has the advantage of minimizing omissions in IR writing by providing standard items suitable for the IR field that the founder is writing.

The IR preparation module 300 evaluates whether the IR or start-up data writer has entered the content for the essential criteria items, and if the content for the corresponding mandatory criteria items input by the IR or start-up material writer exists, the market database, the similarity database , Based on the evaluation database, the basic form is created by evaluating each through verification of the basic data, market data, and profit and loss data of the contents entered by the IR or start-up data creator. If the content for the required criteria item does not exist because the IR or start-up data writer has not entered the content for the required criteria item, the IR preparation module 300 fills the basic form with a basic template, and the IR or start-up data writer When you enter your financial data, it validates your P&L data to fill out the main form.

The IR validity evaluation module 400, which quantifies and scores the IR data created in the IR preparation module 300, quantitatively evaluates the IR data created through the IR data learning module 200 and the IR preparation module 300. As a module, based on the technology and patent assets of the company, it is analyzed to what extent the industry to which the company belongs matches the needs of the market, and the level of difficulty of the technology is analyzed to determine what value it creates based on the influence it has on the industry. It is a comprehensive evaluation module that conducts evaluation and market value evaluation based on these technologies in a scoring method.

The public and private statistical API-linked IR design module (500) is a model that expresses industrial statistical data necessary for market analysis in graphs, etc., using data collected for AI learning and statistical data using public and private APIs, In addition to the company that prepares IR data, it is possible to prepare IR data that reflects the situation of other competitor companies and the market.

As shown in detail in FIG. 2, the IR creation module 300 of the artificial intelligence-based customized entrepreneurship data creation device according to the present invention includes first and second input devices 310 and 320, first and second calculation devices ( 330 and 340), a learning storage device 350 and an output device 360.

The artificial intelligence-based customized start-up data creation device according to the present invention is connected through various networks including the Internet, and the first and second computing devices 330 and 340 include a central processing unit (CPU) and a GPU ( It is a device operated by programming in the Graphic Processing Unit (Graphic Processing Unit).

The first input device 310 receives the history of the founder and/or company from the IR or start-up data creator, and receives the reference data stored in the learning storage device 350 to suit the user who wants to create IR data. Catalog, default table of contents and input form according to industry differences are created and provided to IR authors.

At this time, when the IR or start-up data creator inputs the content into the generated input form into the first input device 310, the first arithmetic unit 330 verifies the basic data for the IR data and verifies the market data to determine whether or not the content is missing. , Correlation with actual market data, etc., and also by verifying the profit and loss data, when the IR or start-up data writer enters financial data by mistake or intentionally deceiving the data, after verifying the profit and loss data, the basic form can be written

If the IR or start-up data creator does not enter content in the form created by the first input device 310, the default table of contents and input form are filled with the default template, and then when the IR or start-up data writer inputs financial data , In the first arithmetic unit 330, when an IR or startup data creator inputs financial data, data that is mistakenly or intentionally deceived may be checked to verify profit and loss data, and then a basic form may be created.

Therefore, in the artificial intelligence-based customized start-up data creation device according to the present invention, even if the IR or start-up data creator does not input content into the form generated by the first input device 310, the already verified basic data and market data Based on this, it is possible to verify the profit and loss data by inputting the company's own portfolio, that is, financial data from the first input device 310 to the first calculation device 330.

The second input device 320 is a device for the IR or startup data creator to modify, if necessary, the IR data of the basic form prepared by the first calculation device 330 .

The first input device 310 and the second input device 320 include computer devices using a keyboard, mouse, and touch panel.

The second arithmetic device 340 is a device for verifying data modified by an IR or entrepreneurship data creator through the second input device 320 .

The first arithmetic unit 330 and the second arithmetic unit 340 are calculation methods for basic data and market data, based on data from domestic and foreign statistical archives, the start-up market definition and market size estimation index “TAM” (Total Available Market)-SAM (Serviceable Available Market)-SOM (Serviceable Obtainable Market, base market/revenue market)” are used.

The first calculation device 330 receives the company's financial data from the first input device 310, performs profit and loss analysis (profit and loss data verification), and performs successful verification using Monte Carlo simulation. can do. In addition, for other contents, a comparison model using game theory and machine learning methods such as Latent Dirichlet Allocation (LDA) topic modeling using Natural Language Processing (NLP) techniques was established, and IR data was converted to AI. (Artificial Intelligence).

In addition, the second arithmetic device 340 receives data corrected by the IR or start-up data creator from the second input device 320, performs profit and loss analysis (profit and loss data verification), and Monte Carlo simulation ) can be used to perform successful verification. In addition, for other contents, a comparison model using game theory and machine learning methods such as Latent Dirichlet Allocation (LDA) topic modeling using Natural Language Processing (NLP) techniques was established, and IR data was converted to AI. (Artificial Intelligence).

The areas and main contents of the data analyzed and verified by AI in the first arithmetic unit 330 and the second arithmetic unit 340 of the artificial intelligence-based customized start-up data creation device according to the present invention are as described in Table 1, but It is not limited.

area Main Content Founder Analysis 1. Analysis of founder history and career details
2. Start-up and job suitability analysis market analysis 1. Characteristics of the market
2. Market Size and Growth
3. Pain points for customers and managers
4. Competitors Product and Business Plan 1. Product planning
2. Pricing Policy
3. Distribution and sales plan
4. Sales Promotion Plan Product and product development planning 1. Development goals
2. Development plan operation plan 1. Operations and cost planning
2. Personnel recruitment and utilization plan
3. Financing and Profit and Loss Estimation
4. Economic Analysis Implementation plans 1. Propulsion system
2. Scheduling
3. Risk management plan
4. Exit strategy

The learning storage device 350 includes both a physical storage device such as a hard disk drive (HDD), a solid state drive (SDD), and other memory devices, and a storage device through a cloud.

The output device 360 is a device that presents IR data prepared in a first calculation unit and modified and verified in a second calculation unit to an IR or start-up data creator, such as a display of a computer device including various mobile devices, a printer, etc. It may include, but is not limited thereto.

The manager of the artificial intelligence-based customized start-up data creation device according to the present invention may set the rules of AI machine learning as supervised learning or unsupervised learning. For example, (1) input supervising information into the reference data collected online and offline of the IR data learning module 200 in FIG. 1 and the learning storage device 350 in FIG. A form of supervised learning that learns rules and calculates indicators for all learning data based on these supervised rules; You can also select a learning type to execute learning.

The unsupervised learning form receives data other than the data extracted from the reference data of the client device and the learning storage device 350 and new data generated by using the artificial intelligence-based customized entrepreneurship data creation device according to the present invention as input data. Thus, it has an additional effect of updating the rules sequentially.

As mentioned above, about 20% of the rule data, which is a part of the rule data generated after learning, is classified into sub-data to verify new learning, and keeps overfitting from occurring. .

As a learning method, using Latent Dirichlet allocation (LDA) of Natural Language Processing (NLP), etc., the probability of occurrence of a corpus and the requirements of a model within a category entering document content, etc. The degree of implementation is learned and regularized.

Through learning using LDA, it is possible to have a learning grade of 80% or more. Here, the learning score means that an error occurs within 20% after similarity verification and model inspection for newly input IR data.

The above description is merely illustrative of the technical details of the present invention, and various modifications and variations can be made to those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain, and the scope of the technical idea of the present invention is not limited by these embodiments. The protection scope of the present invention should be construed according to the following claims, and all technical ideas within the equivalent range should be construed as being included in the scope of the present invention.

100: Founder human information learning module
200: IR data learning module
300: IR writing module
400: IR feasibility evaluation module
500: IR design module
310: first input device
320: second input device
330: first arithmetic unit
340: second arithmetic unit
350: learning storage device
360: output device

Claims (4)

A founder human information learning module that machine-learns human information to suggest entrepreneurial fields based on the founder's career history information;
An IR data learning module that presents essential criteria for preparing IR data based on machine learning data after collecting IR data from other companies that have succeeded in attracting capital by industry and field;
An IR writing module that guides the IR writer step by step to create IR materials suitable for the industry according to the essential criteria items presented in the IR material learning module;
An IR validity evaluation module that quantifies and scores the written IR data, analyzes the validity of the IR data, and suggests improvement measures; and
It includes a public and private statistics API (Application Programming Interface) interlocking IR design module that expresses industrial statistical data necessary for market analysis in graphs, etc.
The IR writing module
IR creator by receiving input from the founder or corporate history from the IR writer, receiving reference data stored in the learning storage device, and creating a default table of contents and input form according to industry-specific differences to suit the user who wants to create IR materials a first input device provided to;
When the IR author enters content into the form created by the first input device, the basic data and market data for the IR data are verified to determine whether the content is missing, correlation with actual market data is calculated, and the profit and loss data is verified. a first arithmetic unit that fills out a basic form;
a second input device configured to allow the IR preparer to modify, if necessary, the IR material in the basic form created in the first calculation device;
a second arithmetic device that receives the data corrected by the IR creator through the second input device and re-executes the verification of the profit and loss data;
a learning storage device that stores reference data, input data, and a reference model, and presents the reference model to the first computing device; and
An output device for presenting IR data prepared in the first calculation unit and modified and verified in the second calculation unit to the IR creator;
If the IR creator does not input content into the form created by the first input device, the first calculation device fills in the default table of contents and the input form as a basic template, and then, when financial data is input from the first input device, returns profit and loss data. Characterized in filling out the basic form after verification
Artificial intelligence-based customized start-up data creation device. delete delete According to claim 1,
The area of data analyzed and verified by AI in the first computing unit and the second computing unit includes founder analysis, market analysis, product and business plan, product and product development plan, operation plan, and promotion plan.
Artificial intelligence-based customized start-up data creation device.

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