System and method for providing customized startup funding information based on multidimensional enterprise readiness (6-RL) analysis

Abstract

The present invention relates to a system and method for providing financing information for startups, and more specifically, to a system and method for selecting and providing customized financing information that considers the actual readiness and physical feasibility of individual companies by utilizing a multidimensional Enterprise Readiness (6-RL) index that analyzes the capabilities of startups from various angles.

Description

Technology Field

[0001] The present invention relates to a system and method for providing financing information for startups, and more specifically, to a system and method for selecting and providing customized financing information that considers the actual readiness and physical feasibility of individual companies by utilizing a multidimensional Enterprise Readiness (6-RL) index that analyzes the capabilities of startups from various angles.

[0002] In one embodiment, the multidimensional enterprise readiness may be composed of multiple readiness axes including Technology Readiness Level (TRL), Manufacturing Readiness Level (MRL) or Service Readiness Level (SRL), Business Readiness Level (BRL), Marketing Readiness Level (MKRL), Global Expansion Readiness Level (GERL), and Team Building Readiness Level (TBRL). The multiple readiness axes may be used as technical standards to normalize evaluation items scattered across different support project announcements into the same comparison axis, and accordingly, the system can determine the substantive suitability of funding information by comparing the required level of the support project and the current level of the startup on the same standard framework. Background Technology

[0003] For startups, securing timely funding is crucial for commercialization and sustainable growth. While funding options include government support programs, venture capital investment, and bank loans, relevant information is fragmented across various institutions, requiring startups to spend significant time and money searching for and analyzing suitable resources.

[0004] Conventional methods of providing information rely primarily on fragmentary keyword matching based on industry, business history, and region, which limits the ability to accurately assess whether a company actually meets support requirements or determines its level of preparedness. In particular, the failure to consider practical feasibility—such as the administrative burden of offline document submission during the fundraising process or physical distance constraints for face-to-face evaluations—leads to low accuracy in information matching.

[0005] Furthermore, despite the fact that fundraising involves a step-by-step process rather than a one-time event, current systems merely list announcement information, making them insufficient for companies to systematically manage their progress and predict the probability of success in subsequent stages. Therefore, there is a need for a technological alternative capable of multidimensionally diagnosing corporate capabilities, providing practically achievable customized information that takes into account physical constraints, and efficiently managing the entire process.

[0006] Furthermore, support programs may differ in the required level of preparation and evaluation perspectives depending on their type, such as R&D support, commercialization support, product or service development support, marketing support, and export support. For instance, some programs may place relatively greater emphasis on prototype completeness and the level of technology implementation, while others may prioritize customer acquisition, market response, organizational capabilities, or readiness for overseas expansion. However, conventional information provision methods fail to convert these heterogeneous evaluation criteria into common standards for mutual comparison, making it difficult to determine, based on consistent criteria, which support program is substantially suitable for a startup. Therefore, a technical processing system is required that normalizes the evaluation criteria of multiple support programs around a common readiness axis and allows for the comparison of the normalized results with the startup's current status.

[0007] Furthermore, even if the funding information is documented to be satisfactory, the effectiveness of providing such information may be significantly diminished if physical feasibility is lacking—such as the quality of original documentation actually secured by the startup, accessibility to offline evaluation venues, and the time available to process documents within the submission deadline. Therefore, this physical feasibility needs to be reflected not merely as reference information, but as a processing variable to determine whether to recommend the funding information and to prioritize it. The problem to be solved

[0008] The problem that the present invention aims to solve is to quantify the actual readiness of a company by precisely diagnosing the capabilities of a startup company in six dimensions—technology, manufacturing or service, business, marketing, overseas expansion, and team building—and calculating a multidimensional corporate readiness.

[0009] Here, the aforementioned six dimensions are not arbitrarily listed items, but can be used as normalization axes to align evaluation items included in multiple support program announcements on a common standard. In other words, the task is not limited to simply scoring the startups themselves, but aims to quantitatively identify the difference between the level of support program requirements and the level of corporate readiness by rearranging evaluation items extracted from support program announcements with different formats and evaluation systems into the aforementioned six dimensions and comparing the rearranged results with the readiness of the startups on the same comparison structure. Accordingly, the system can provide a processing structure that goes beyond fragmentary keyword searches or industry-based classification to compare the suitability between multiple support programs and multiple startups on the same axis.

[0010] Furthermore, the present invention aims to select and provide financing information with high feasibility by going beyond simple compliance with documentary conditions and incorporating constraints of the offline environment into the matching logic, such as the actual amount of subsidies received, the status of securing physical proof, the travel distance to the evaluation location, and the physical time required for document preparation.

[0011] Furthermore, the present invention aims to provide a system and method that supports startups in making strategic decisions for successful fundraising by structuring the entire fundraising process into a management funnel and calculating conversion probabilities at each stage based on the company's history of active responses.

[0012] The problems of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0013] According to an embodiment of the present invention for solving the above problem

[0014] A communication unit that transmits and receives data with external agency servers and user terminals;

[0015] A storage unit for storing the collected data and calculation results; and

[0016] A control unit that controls the communication unit and the storage unit and performs a procedure for providing customized financing information based on multidimensional enterprise readiness analysis; comprising

[0017] The above control unit is,

[0018] A data collection module that extracts eligibility requirements and evaluation criteria by analyzing support project announcements collected through the above communication unit using language processing technology;

[0019] An indicator definition module that classifies and maps the above-mentioned extracted evaluation items based on pre-set multidimensional enterprise readiness standards;

[0020] A diagnostic evaluation module that calculates an initial score based on self-diagnosis data entered by a startup and readjusts the said initial score by reflecting offline empirical data;

[0021] An information matching module that calculates the mapping suitability by calculating the difference between the required readiness of the above support project and the readjusted readiness of the above startup, and physical limit requirements; and

[0022] The present invention is characterized by executing a funnel management module that calculates the probability of transitioning to a stage by calculating the number of days of stalled funding screening of the aforementioned startup and the history of defensive actions against institutional requests.

[0023] The above control unit dynamically controls the data collection cycle of the above communication unit, and

[0024] The control unit sets a reference period for data collection based on the current available power index of the system driving the communication unit, and

[0025] An urgency indicator is extracted from the information collected from the aforementioned external agency server, including the frequency of correction notices within a pre-set standard period and the shortage index of remaining seats for face-to-face consultation reservations.

[0026] It is characterized by calculating and obtaining a preparation burden indicator, including the estimated number of days required for registered service of original documents and the number of original document items requiring corporate seal impression, through text analysis of the collected announcements above.

[0027] The above control unit is,

[0028] A primary correction is made to shorten the set standard cycle as the frequency of correction announcements and the remaining seat shortage index of the above-acquired urgency indicator increase, and

[0029] As the estimated number of days and the number of original document items of the above-calculated preparation burden indicator increase, the above-calculated primary corrected cycle is proportionally extended to determine the final data collection cycle,

[0030] If the final data collection cycle determined above is less than a preset lower limit, the cycle is forcibly fixed to the said lower limit to prevent system overload, and if the available power index is less than a minimum threshold, the calculation of the said urgency index and the said preparation burden index is omitted and the collection cycle is forcibly fixed to a preset upper limit, thereby

[0031] It is characterized by reducing the communication computation load of the server by blocking meaningless real-time data updates for support projects that do not meet the physical lead time of offline document preparation, and

[0032] The above diagnostic evaluation module is,

[0033] After setting the above initial score as a reference value, the number of discrepancies where data between submitted evidence contradicts each other and the number of reproduction failures in offline field demonstrations are identified, and the reduction range is determined according to the identified number of discrepancies and the number of reproduction failures, and a reduction correction rate is applied to the above initial score.

[0034] In applying the above reduction correction rate, the cumulative number of on-site inspection participations of expert evaluators evaluating the field demonstration is retrieved from a database, and as the retrieved cumulative number of on-site inspection participations increases beyond a preset threshold, the reduction range is reduced by mitigating the correction to reflect flexibility based on field experience.

[0035] The above diagnostic evaluation module is,

[0036] With respect to the score with the above reduction range mitigated and corrected, verify the number of supporting documents whose originals have been verified through notarization or an institutional seal, and the duration for which the above field demonstration was operated without interruption, and sum the additional points calculated as the number of supporting documents and the duration increase,

[0037] It is characterized by processing and reflecting an increase rate slowdown operation so that, from the section where the number of supporting documents and the duration exceed a preset marginal utility threshold, the magnitude of the bonus points granted per unit value exceeding the threshold is gradually reduced compared to the previous stage.

[0038] The above diagnostic evaluation module is,

[0039] If the final readjustment score calculated by reflecting the above increase rate slowdown calculation exceeds 100 points, it is limited to 100 points, and if it is less than 0 points, it is fixed at 0 points, and

[0040] If the cumulative number of on-site inspection participations by the aforementioned expert evaluator is less than the aforementioned preset threshold, the application of the mitigation correction logic for the aforementioned reduction range is excluded, and the reduction is applied as is, thereby,

[0041] It can be characterized by refining subjective response data based on simple surveys, which reflects the psychological overestimation of startups, into quantitative and empirical data that reflects the feasibility of actual fundraising in an offline environment. Effects of the invention

[0043] According to the present invention, by precisely diagnosing the capabilities of a startup from various angles through multidimensional business readiness analysis, data errors resulting from subjective self-assessment can be mitigated and a highly reliable business profile can be constructed.

[0044] Furthermore, by comparing the aforementioned calculated readiness with the required level of support projects and simultaneously reflecting practical implementation constraints, such as physical travel distance and administrative processing time, in the calculation, it provides effective customized information to startups and contributes to reducing the resources required for information search and analysis.

[0045] Furthermore, by managing the financing process as a funnel structure and reflecting companies' complementary response efforts in the probability, it induces active participation and prevents stagnation at each stage, thereby helping to improve the overall financing success rate.

[0046] In one embodiment, the system can verify its effectiveness through comparative operation targeting a plurality of groups of startups. For example, refined funding information based on the results of the multidimensional corporate readiness analysis and offline feasibility reflection can be provided to some of the groups of startups, while general announcement-listed information can be provided to the remaining groups. Subsequently, for each group, the number of information obtained, the number of actual applications received, the time taken to receive applications, the attendance rate for face-to-face evaluations, and the conversion rate to funding success can be compared. In this case, the system can operate in a way that increases the proportion of practically applicable information, reduces the proportion of meaningless support attempts, and improves conversion efficiency at each funding stage.

[0047] Consequently, the present invention provides useful effects by enhancing the consistency of information provision, reducing administrative waste caused by indiscriminate support, efficiently operating system resources, and establishing a foundation for securing stable funds for the growth of startups.

[0048] The effects according to the present invention are not limited to those exemplified above, and a wider variety of effects are included within the present invention. Brief explanation of the drawing

[0049] Figure 1 illustrates an organic flowchart between all components according to the present invention. FIG. 2 illustrates a dynamic control flowchart of the data collection cycle of a communication unit according to the present invention. Figure 3 illustrates a flowchart of readjustment of readiness scores of a diagnostic evaluation module according to the present invention. Figure 4 illustrates a flowchart of the calculation of matching suitability of an information matching module according to the present invention. Figure 5 illustrates a flowchart for calculating the step transition probability of a funnel management module according to the present invention. Specific details for implementing the invention

[0050] Hereinafter, various embodiments are described in more detail with reference to the attached drawings. The embodiments described in this specification may be modified in various ways. Specific embodiments may be depicted in the drawings and described in detail in the detailed description. However, specific embodiments disclosed in the attached drawings are intended only to facilitate understanding of various embodiments. Accordingly, the technical concept is not limited by specific embodiments disclosed in the attached drawings, and it should be understood that it includes all equivalents or substitutions that fall within the spirit and scope of the invention.

[0051] Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but these components are not limited by the aforementioned terms. The aforementioned terms are used solely for the purpose of distinguishing one component from another.

[0052] In this specification, terms such as “comprising” or “having” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof. When a component is described as being “connected” or “connected” to another component, it should be understood that it may be directly connected to or connected to that other component, or that there may be other components in between. On the other hand, when a component is described as being “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

[0053] Meanwhile, a "module" or "part" for a component as used in this specification performs at least one function or operation. Furthermore, a "module" or "part" may perform a function or operation by hardware, software, or a combination of hardware and software. Additionally, a plurality of "modules" or a plurality of "parts," excluding a "module" or "part" that must be performed on specific hardware or on at least one processor, may be integrated into at least one module. A singular expression includes a plural expression unless the context clearly indicates otherwise.

[0054] In addition, power, power transmission, and control therefor for the following assembly configurations and embodiments, including "by control," follow conventional technology including terminals, applications, hardware control modules, etc., so they are omitted to avoid redundancy.

[0055] In addition, the operation embodiments and configurations described in a general manner without being explained in detail below follow the prior art and are omitted in order to focus on describing the purpose of the present invention and the resulting effects.

[0056] Furthermore, in describing the present invention, if it is determined that a detailed description of related known functions or configurations may unnecessarily obscure the essence of the invention, such detailed description is abbreviated or omitted.

[0057] A customized startup funding information provision system based on multidimensional enterprise readiness analysis according to one embodiment of the present invention includes a communication unit (110) that performs data transmission and reception with an external server and terminal, a storage unit (120) that stores system operation and calculation results, and a control unit (130) that controls the operation of the entire system. In addition, the control unit (130) includes and executes a data collection module (131), an indicator definition module (132), a diagnostic evaluation module (133), a requirements review module (134), an information matching module (135), and a funnel management module (136) as sub-software components to perform detailed data calculations and logical processing. Below, the definitions of each hardware unit and software module and the specific methods for performing their functions will be described in detail.

[0058] In one embodiment, the multidimensional enterprise readiness is not defined simply by an arbitrary internal classification table, but can be established based on external criteria or operational criteria corresponding to each readiness axis. For example, the technology maturity level may correspond to technology readiness criteria for judging the level of technology implementation and prototype completion in stages, and the manufacturing readiness level or the service readiness level may correspond to manufacturing or service readiness criteria for judging the state of manufacturing process readiness or the state of service operation readiness in stages. Additionally, the business readiness level may reflect the readiness for commercialization or the progress of customer validation, the marketing readiness level may reflect the level of initial market entry and customer acquisition, the overseas expansion readiness level may reflect the progress of the overseas market entry stage, and the team building readiness level may reflect role assignment, the suitability of personnel, and organizational operational capabilities. Accordingly, the six dimensions can be used as a standard system for comparing mutually heterogeneous announcement evaluation items on a unified axis.

[0059] The communication unit (110) is a hardware component for transmitting and receiving data with an external agency server, a user terminal, and a professional evaluator terminal. The communication unit (110) collects support project announcements and evaluation indicator data from an external agency server and transmits analyzed customized funding matching information and funnel management notifications to the terminal of a startup company. The communication unit (110) operates by variably switching between active mode and standby mode according to the dynamic control logic of the data collection cycle of the control unit (130). The communication unit (110) obtains announcement data and geographical update information by calling the webpage of the managing agency, an external public database, or a location-based application program interface. Based on the available power capacity index, the frequency of correction announcements, and the physical document preparation burden index, the communication unit (110) operates by shortening the data collection cycle from 60 minutes to 15 minutes when the urgency is high, and by extending the collection cycle to 180 minutes when the preparation burden is excessive, thereby preventing system load.

[0060] The storage unit (120) is a hardware component that stores basic data required for system operation and result data generated according to process execution. The storage unit (120) classifies collected support project announcements, multidimensional enterprise readiness mapping tables, self-diagnosis data of startups, on-site inspection data of professional evaluators, and calculated matching suitability and funnel conversion probability data, and stores and manages them in a database format. The storage unit (120) receives and obtains data flowing in from the outside through the communication unit (110) and result values ​​calculated by the control unit (130) and lower-level software modules via an internal system bus. To manage the long-term history of a specific startup, the storage unit (120) stores suitability scores of past supported projects, evaluator feedback, and data on the number of days of stagnation at each funnel stage in a time series, and provides this history as basic reference data during subsequent matching to enhance the consistency of the analysis.

[0061] The control unit (130) is a central processing component that controls the overall operation of the communication unit (110) and the storage unit (120) and executes subordinate software modules. The control unit (130) monitors the computational resources and power status of the entire system and oversees a series of logical data processing flows ranging from data collection to indicator definition, diagnostic evaluation, matching, and funnel management. The control unit (130) obtains an available power index from the power management module of the server hardware and obtains the computation progress status and step-by-step calculation results of each subordinate module by extracting them from internal memory. In the process of determining the collection cycle of the communication unit (110), if the control unit (130) detects that the system's available power has dropped below a specific lower limit, it switches the non-essential computation of the subordinate module to a standby state and forcibly fixes the collection cycle to a maximum upper limit to suppress the overload of computational resources.

[0062] The data collection module (131) is executed within the control unit (130) and is a software component that extracts unstructured support project announcements and evaluation information received through the communication unit (110) into structured data. The data collection module (131) analyzes the text included in the announcement using language processing technology and optical character recognition technology to identify and separate evaluation items, support conditions, qualification requirements, preferential treatment, and a list of required submission documents. The data collection module (131) obtains requirement data by analyzing the body text of the collected announcement and table and image data within the attachments. By extracting specific keywords such as postal submission, corporate seal stamping, and verification against original from the announcement attachments, the data collection module (131) derives the offline administrative processing elements and estimated time required that the startup company must actually bear as quantified structured data.

[0063] The indicator definition module (132) is a software component that classifies and maps various evaluation items collected from the data collection module (131) based on multidimensional corporate readiness standards. The indicator definition module (132) converts the collected evaluation items of the support project into standardized indicators by linking them with six pre-established dimensions: technology readiness, manufacturing readiness or service readiness, business readiness, marketing readiness, overseas expansion readiness, and team building readiness. The indicator definition module (132) receives and obtains evaluation requirement keywords and item-specific scoring criteria data extracted from the data collection module (131).

[0064] In one embodiment, the indicator definition module can classify each evaluation item by referring to a mapping table that stores the correspondence between the evaluation item and six readiness axes. The mapping table may be constructed in advance based on evaluation items extracted from multiple support project announcements, and may be corrected by reflecting the results of reviewing the corresponding axes for each item by multiple evaluators or operators. For example, if an evaluation item encompasses both the level of technology implementation and the level of market validation, the indicator definition module may divide and reflect the evaluation item in the technology maturity and marketing readiness, respectively, or designate a primary axis and additionally reflect a secondary axis according to the primary judgment purpose in the context of the evaluation item. Furthermore, the indicator definition module may correct the mapping table by reflecting the weight of points assigned to each item, the frequency of repeated appearance, and information on the degree of agreement among multiple evaluators. Accordingly, the indicator definition module can normalize evaluation items existing in announcements of different formats into a unified readiness indicator by reflecting both the meaning of the items and the scoring structure, rather than simply substituting them.

[0065] The above indicator definition module (132) maps the prototype completeness item of the announcement of the managing organization to the technology maturity level, the initial customer acquisition rate item to the marketing readiness level, and the key personnel industry experience item to the team building readiness level, respectively, thereby reconstructing announcements with different formats and standards into a unified standard multidimensional indicator.

[0066] The above diagnostic evaluation module (133) is a software component that calculates an initial score based on the self-diagnosis data of a startup and readjusts the score based on empirical data. To mitigate psychological overestimation that may be involved in the self-diagnosis data, the above diagnostic evaluation module (133) deducts points by applying the number of discrepancies between evidence and the number of failures to reproduce the field demonstration. In addition, it adds points by reflecting the number of original verified documents and the duration of the field demonstration, but performs logical operations so that the rate of increase gradually slows down after a certain level. The above diagnostic evaluation module (133) receives self-diagnosis response values ​​from a user terminal and obtains actual data, such as the number of discrepancies and the number of failures to reproduce, through a professional evaluator terminal and document cross-verification logic.

[0067] In one embodiment, the diagnostic evaluation module calculates the difference between an initial score based on self-diagnosis and a recalculated score reflecting offline actual measurement data, and if the difference exceeds a preset deviation standard, it may provide difference notification information to a user terminal or a professional evaluator terminal. The difference notification information may include which readiness axis the significant deviation occurred on, and whether the cause of the deviation is document inconsistency, demonstration failure, or insufficient original evidence. Additionally, if a request is received from a startup or operator in response to the difference notification, the diagnostic evaluation module may request an additional review from a professional evaluator and recalculate the recalculated score by reflecting the results of the additional review. In this case, the additional review may include the submission of supplementary documents, additional on-site demonstrations, or procedures to reconfirm the results of the existing on-site inspection. Accordingly, the diagnostic evaluation module may not simply subtract or add the deviation between the self-diagnosis result and the on-site inspection result, but may also perform operational procedures to identify the cause of the difference and induce re-evaluation.

[0068] The above diagnostic evaluation module (133) lowers the score for a startup company with an initial self-diagnosis score of 85 points by reflecting one case of document discrepancy and two cases of reproduction failure. At this time, if the evaluator has a high number of cumulative on-site inspection participations, the reduction is partially mitigated, and a reduced bonus point is given for three cases of original evidence to calculate a reasonable final score of 75 points that reflects the characteristics of the real environment.

[0069] The above-mentioned requirements review module (134) is a software component that primarily determines whether a specific support program is eligible for application based on the basic information of the startup company and a readjusted readiness score. The above-mentioned requirements review module (134) compares the disqualification reasons and basic qualification requirements, such as the year, industry, region, and debt ratio extracted by the above-mentioned data collection module (131), with the basic specifications of the above-mentioned startup company to filter out programs that are fundamentally impossible to support from the recommendation candidate pool. The above-mentioned requirements review module (134) obtains corporate identification information, financial data, and intellectual property rights holding status, etc., which are entered by the startup company when signing up or collected through public data linkage. If a specific support program has mandatory requirements such as being within 3 years of establishment and venture company certification, the above-mentioned requirements review module (134) cross-verifies the startup company's business registration date and certificate holding database to determine whether the requirements are met, and if the requirements are not met, it immediately filters them to prevent the waste of subsequent computational resources.

[0070] The information matching module (135) is a software component that calculates a customized funding information matching suitability by comprehensively considering the geographical limitations and physical readiness of startups for support projects that have passed the requirements review module (134). The information matching module (135) calculates a basic suitability in proportion to the actual amount of funds that can be received and the number of supporting documents held, and lowers the suitability based on the difference from the required readiness and the number of public transportation transfers. Furthermore, if the burden of document processing, such as notarization, exceeds the remaining business days, the calculated suitability is forcibly lowered to a pre-set lower limit. The information matching module (135) obtains the required readiness from the indicator definition module (132), the startup readiness from the diagnostic evaluation module (133), public transportation route data through a location-based interface, and data on the calculation of remaining business days based on the current date.

[0071] In one embodiment, the information matching module may be configured to increase the basic suitability as the number of existing supporting documents and the number of internal personnel available for attendance increase together, but to gradually decrease the contribution of the additional increase after exceeding a certain level. Furthermore, the number of public transportation transfers may not be used as a sole criterion, but may be interpreted by supplementarily reflecting information on regional areas, total travel time, round-trip travel time, and accessibility of the evaluation location. Moreover, if the estimated administrative processing burden corresponding to the number of documents requiring notarization approaches the actual number of working days available for preparation, the suitability may be adjusted downward in stages; and if the estimated administrative processing burden exceeds the actual number of working days available for preparation, the suitability may be further adjusted downward to a lower limit or a level near the lower limit. Accordingly, the information matching module may perform a multi-stage filtering procedure that sequentially reflects the document preparation status, personnel capacity, mobility accessibility, and actual administrative processing feasibility, rather than a simple match or mismatch determination.

[0072] The above information matching module (135) controls the matching suitability of a project to be lowered to a level of 5 points so that only effective information is displayed to the user, even if the amount of support for the matched project is large and the document requirements are met, if the number of public transportation transfers is 4 or more or the time required to prepare the corporate seal exceeds the actual remaining business days until the deadline.

[0073] The above funnel management module (136) is a software component that manages the progress of a startup's fundraising in stages and dynamically calculates the probability of transitioning to the next stage based on the history of active responses. The above funnel management module (136) lowers the basic transition probability based on the number of days of stagnation at a specific stage and adjusts the probability upward when defensive actions, such as the number of replies within the deadline and the cumulative number of resubmission approvals, are identified. Additionally, it slows down the rate of increase in the probability and continuously makes additional adjustments based on whether official confirmation documents have been secured and practical rehearsals have been completed. The above funnel management module (136) obtains defensive action data by analyzing the history of sending and receiving notifications within the platform, the startup's document upload records, and the history of offline face-to-face mentoring using language processing technology.

[0074] In one embodiment, the funnel management module can manage the financing process by classifying it into an information acquisition stage, a review and execution stage, a financing success stage, an additional financing success stage, and a linked support project or follow-up investment connection stage. For each stage, the funnel management module can store management indicators such as the number of information leads secured, the number of actual receipts, the duration of the stage, the number of requests for supplementation, the number of completed supplementation, whether a presentation or due diligence is in progress, and whether the process has transitioned to the next stage. Furthermore, based on the management indicators for each stage, the funnel management module can identify which stage experiences frequent dropouts, which stage experiences response delays, and which stage is characterized by a concentration of insufficient preparation for actual operations. Accordingly, the funnel management module can perform a management function that identifies bottlenecks throughout the entire financing process, in addition to calculating conversion probabilities for each stage.

[0075] The above funnel management module (136) performs a valid management procedure in which, if a startup company that has been stuck in the waiting stage for review for more than 20 days and has a low conversion probability is identified as having responded to the request for supplementation from the managing organization within the designated period and performed two practical rehearsals, it evaluates this as a meaningful defensive action, adjusts the conversion probability upward, and reclassifies it to the intensive management stage.

[0076] In one embodiment, when an increase in the number of days of stagnation and the absence of defensive action are simultaneously confirmed, the funnel management module may not immediately terminate the process with the same treatment, but may manage it by classifying it into a candidate for failure stage, a review for forced termination stage, and a stage where forced termination is possible. For example, if the number of days of stagnation is greater than or equal to a first criterion and both the number of replies within the deadline and the number of accumulated resubmission approvals are absent, it may be classified into a candidate for failure stage; if the number of days of stagnation persists at or greater than a second criterion which is longer than the first criterion, it may be classified into a review for forced termination stage; and if the number of days of stagnation persists at or greater than a third criterion which is longer than the second criterion, it may be classified into a stage where forced termination is possible. Even in this case, if the number of accumulated official confirmation documents secured or the number of completed practical rehearsals is additionally confirmed to be above a certain level, the funnel management module may not terminate the process immediately but may perform a review or re-entry induction procedure. Accordingly, the funnel management module may not uniformly terminate the long-term stagnation state, but may vary the management intensity by stage by reflecting the existence of actual response history.

[0077] The customized startup financing information provision system based on the above-mentioned multidimensional enterprise readiness analysis goes beyond simple collection and listing of information to perform the following four major data processing processes to enhance the effectiveness of financing information and efficiently operate system resources.

[0078] First, a process is performed to dynamically control the data collection cycle of the communication unit (110) by combining the available power of the system, the urgency of external information updates, and the burden of preparing physical documents through the control unit (130).

[0079] Next, through the above diagnostic evaluation module (133), a process is performed to rationally readjust the readiness score of the startup company based on offline empirical data, such as discrepancies in evidence and results of field demonstrations.

[0080] Next, through the information matching module (135), a process is performed to calculate a feasible offline-based matching suitability by reflecting physical limitations, such as the startup's public transportation mobility restrictions and the time required to process notarized documents, beyond the document condition compliance.

[0081] Next, a process is performed to dynamically calculate the probability of transitioning to the funding stage by calculating the number of days of stagnation of the startup and the history of defensive actions against institutional inquiries during the funding screening process through the funnel management module (136).

[0082] Below, detailed judgment criteria, data acquisition methods, simulation-based threshold setting, and conditional logic operation procedures for implementing each of the above processes are described in detail.

[0083] In one embodiment, the control unit may not maintain the reference values, lower limits, upper limits, increase rate slowdown criteria, and step interval criteria used in processes 1 to 4 as fixed values, but may periodically update them based on log data accumulated during system operation. For example, the control unit may statistically analyze the announcement update history, offline evaluation attendance history, document submission processing history, deviation history of self-diagnosis and on-site inspection results, and conversion success rates at each funnel stage, and then correct the reference value table if a specific reference value is significantly inconsistent with the actual operation results. Accordingly, the system may adjust the judgment sensitivity of each process by reflecting the accumulated operation history, rather than repeatedly applying the initially set criteria as they are.

[0084] In one embodiment, the control unit (130) may not uniformly set the data collection cycle of the communication unit (110) according to a fixed timer, but may dynamically determine it by reflecting the power reserve status of the system, the variability of external announcement information, and the offline preparation burden that the startup company actually has to bear. Through this, the communication unit (110) may be controlled to be activated at a shorter cycle when the urgency of updating information is high, and to maintain a standby state at a longer cycle when the actual offline preparation burden is high.

[0085] The main judgment criteria used by the control unit (130) to determine the data collection cycle of the communication unit (110) can be defined as follows. The available power index is a value representing the ratio of the currently available surplus power to the total power that can be supplied to the system including the communication unit (110) and the server, or the level of allowable power margin, and can be used as a reference value for setting a reference cycle for data collection by the communication unit (110). The frequency of correction announcements is a value representing the number of times modifications, changes, corrections, or updates have occurred regarding announcements of the same support project or the same managing agency during a pre-set reference period, and can be used as an urgency indicator to determine the variability of the announcement information. The remaining seat shortage index is a value representing the ratio of the number of reserved seats to the total number of allocated seats in on-site briefings, face-to-face consultations, presentation evaluations, or offline reservation procedures linked to the support project, or the degree of shortage of remaining seats relative to the total number of allocated seats, and can be used as an urgency indicator to determine the degree of imminent physical closure. The above estimated number of days for registered delivery represents the estimated number of days required to pass through postal, registered, or express delivery networks to physically submit original documents to the supervising agency, and can be used as a factor to assess the burden of offline preparation. The above number of original document items represents the number of submission documents that cannot be completed through online processing alone, as they require the affixing of a corporate seal, verification against the original, official seal, handwritten signature, or direct issuance, and can be used as a factor to assess the burden of physical preparation that the above startup company must actually perform.

[0086] The above available power index can be obtained through a power management unit or power monitoring circuit equipped in the system. For example, the control unit (130) can calculate the above available power index by receiving data regarding the remaining battery level, auxiliary power reserve, or allowable power reserve relative to current power consumption. The above frequency of correction notice occurrences can be calculated by the communication unit (110) accessing the webpage, application programming interface, or notice posting server of the organizing agency and comparing the post's modification tags, correction history, whether the title has been changed, whether the attachment has been replaced, or the notice update time. The above remaining seat shortage index can be derived by the communication unit (110) collecting data regarding the total number of seats, the number of reserved seats, and the number of remaining seats from the on-site briefing or face-to-face consultation reservation system and analyzing it. The above estimated number of days required for registered mail delivery and the above number of original document items can be extracted by analyzing the collected notice text, attachments, list of submitted documents, or cautionary notes using a language processing algorithm. For example, the control unit (130) can identify sentence or table items containing phrases such as “postal submission,” “registered mail,” “verified against original,” “corporate seal impression,” “official seal required,” and “direct submission,” and based thereon, calculate the estimated number of days for registered mail delivery and the number of original document items.

[0087] The control unit (130) may first set a reference cycle corresponding to the available power index. In one embodiment, the control unit (130) may set a first reference cycle when the available power index is above a preset stable range, and may set a second reference cycle longer than the first reference cycle when the available power index is lower than the stable range. For example, the reference cycle may be set to 60 minutes when the available power index is 50 percent or more, and the reference cycle may be set to 120 minutes when the available power index is less than 50 percent. However, the above values ​​are merely examples and may be changed depending on the system's deployment environment, server capacity, or power structure.

[0088] After the above standard cycle is set, the control unit (130) can perform a cycle shortening correction based on the urgency of the external environment. Specifically, the control unit (130) checks the frequency of correction announcements and the remaining seat shortage index, and can correct the standard cycle in a direction that shortens it as the frequency of correction announcements or the remaining seat shortage index increases. At this time, since the frequency of correction announcements implies a high possibility of changes to announcement information, the control unit (130) may determine that faster information re-collection is required as correction announcements occur repeatedly during the same period. Additionally, since the remaining seat shortage index implies that the physical deadline for on-site briefings or face-to-face consultations is imminent, the control unit (130) can set the activation cycle of the communication unit (110) to be shorter as the shortage index increases. However, after the remaining seat shortage index reaches a very high level, the cycle shortening effect can be processed so that the additional increase becomes gradually gradual. This allows for the suppression of the phenomenon where the data collection cycle becomes excessively short due to a rapid increase in a single variable.

[0089] After the above-mentioned urgency correction, the control unit (130) can perform a correction to extend the period based on the offline preparation burden of the startup company. Specifically, the control unit (130) can calculate the level of preparation burden by comprehensively considering the estimated number of days required for registered mail delivery and the number of original document items. The level of preparation burden may be a value that reflects the minimum preparation time required to secure original documents, perform physical procedures such as stamping or original verification, and deliver them to the supervising agency. The control unit (130) can perform a final correction in the direction of extending the period after the above-mentioned urgency correction as the level of preparation burden increases. That is, the more offline original documents required for submission and the longer the delivery period, the lower the likelihood of actual application conversion is determined to be, even if the communication unit (110) accesses the external server excessively frequently, and thus the collection period can be extended.

[0090] The control unit (130) can determine the final data collection cycle by linking the urgency correction and the preparation burden correction. For example, if correction notices occur frequently or reserved seats are quickly exhausted, the urgency of updating information is high, so the cycle can be shortened. On the other hand, if the time required for postal submission is long and the number of original document items is large, sufficient physical time required for application preparation must be secured, so the cycle can be extended. If both the urgency and the preparation burden are high, the control unit (130) can determine the final cycle within the range of the standard cycle or the standard cycle by reflecting both the shortening effect based on the urgency and the extension effect based on the preparation burden. Through this, a complex judgment structure that considers both the necessity of updating information and the actual offline response capability can be implemented. Conversely, if both the urgency and the preparation burden are low, the control unit can maintain the standard cycle as is or adjust it slightly.

[0091] The final data collection cycle determined by the control unit (130) can be reflected as the transition time between the standby mode and the active mode of the communication unit (110). The shorter the final data collection cycle, the more frequently the communication unit (110) is activated to connect to an external agency server, and the longer the final data collection cycle, the longer the communication unit (110) maintains a standby state to suppress network usage and system resource occupancy. The control unit (130) can immediately reflect the final data collection cycle in subsequent repeated data collection operations by recording it in a timer register, a scheduling queue, or a task execution cycle table.

[0092] The control unit (130) may further perform an exception handling routine. For example, if the period calculated as the result of the urgency correction becomes excessively short and there is a concern about system overload, the control unit (130) may limit the collection period so that it does not decrease further below a preset minimum lower limit. In one embodiment, the minimum lower limit may be set to 5 minutes. Additionally, if the available power index decreases below a preset crisis period, the control unit (130) may omit a detailed evaluation of the urgency and preparation burden and forcibly set the collection period to a maximum upper limit. In one embodiment, the crisis period may be set to less than 10 percent, and the maximum upper limit may be set to 24 hours. However, the lower limit, the crisis period, and the upper limit may also be adjusted according to system specifications and operating policies.

[0093] In one embodiment, the control unit (130) may perform a multi-condition combination judgment by pre-setting a reference value for urgency or preparation burden. For example, the criterion for determining that the urgency is high may be set to when correction notices occur three or more times within a reference period or when the remaining seat shortage index exceeds 80 percent. Additionally, the criterion for determining that the preparation burden is high may be set to when the estimated number of days for registered mail delivery is three or more, or when the number of original document items is three or more. The control unit (130) may select to shorten, maintain, or extend the data collection cycle depending on the combination state of the urgency and the preparation burden. For example, if the urgency is high and the preparation burden is low, the cycle may be shortened significantly compared to the reference cycle, and if the urgency is low and the preparation burden is high, the cycle may be extended significantly compared to the reference cycle. If both the above-mentioned urgency and the above-mentioned preparation burden are high, the period can be adjusted to the reference period or a range near it by reflecting the combined effects of both, and if both are low, the reference period can be maintained.

[0094] In one embodiment, the control unit (130) can verify the data collection cycle control logic through a plurality of simulations. For example, if the available power index is 90 percent, the frequency of correction notices is 0, the remaining seat shortage index is 10 percent, the estimated delivery time for registered mail is 1 day, and the number of original document items is 1, it is determined to be a normal state, and the standard cycle of 60 minutes can be maintained. In addition, if the available power index is 85 percent and the frequency of correction notices is 5, it is determined to be a state of high information variability, and the standard cycle can be shortened to 15 minutes. In addition, if the available power index is 80 percent and the remaining seat shortage index reaches 95 percent, it is determined to be a state where offline reservations are about to close, and the standard cycle can be shortened to 20 minutes. Conversely, if the available power index is 88 percent, the estimated delivery time for registered mail is 4 days, and the number of original document items is 5, it is determined that the preparation burden is excessive, and the above standard cycle may be extended up to 180 minutes. Additionally, if the frequency of correction notices is 4 times and the remaining seat shortage index is 90 percent, indicating high urgency, but the estimated delivery time for registered mail is 4 days and the number of original document items is 5, indicating high preparation burden, the effects of both factors may be reflected together to calculate the standard cycle or a cycle within a nearby range. Furthermore, if the available power index is 40 percent, it is determined that there is insufficient power margin, and the standard cycle may be extended to 120 minutes. However, even if the above available power index is relatively low, if both the frequency of correction notices and the remaining seat shortage index are simultaneously very high, an additional urgency correction may be applied, resulting in a cycle shorter than the above standard cycle. In addition, if the urgency is extremely high and the calculated cycle falls below the above minimum lower limit, it may be fixed at the above minimum lower limit, and if the available power index falls below the above crisis range, it may be forcibly set to the above maximum upper limit.

[0095] According to the stepwise judgment and correction procedure described above, the control unit (130) can reflect both the variability of online announcement information and the limitations of offline response of startups, thereby reducing unnecessary server access and resource waste caused by a uniform timer method. In particular, delays in obtaining information can be suppressed when announcements are frequently modified or when the offline reservation deadline is imminent, and conversely, when the physical time required for preparing and delivering original documents is long, the phenomenon of excessive data collection being repeated in a state where the actual possibility of application is low can be reduced. Accordingly, the network usage of the communication unit (110), the processing burden of the server, and the number of meaningless matching detections can be reduced, and the stability and reliability of the overall system operation can be improved by inducing the priority securing of feasible funding information.

[0096] The above reference value, lower limit, upper limit, and each judgment interval may be set based on average sampling data of multiple companies secured at the beginning of system operation, server load statistics, network usage history, or actual support project response results, and may be variably adjusted according to statistical information accumulated during system operation. Additionally, the correction range for the above urgency or the extension range for the above preparation burden may be limited so that the growth rate gradually decreases after a specific interval. For example, if the frequency of correction announcements exceeds a certain number of times, the effect of shortening the cycle for the additional increase may be reflected in a diminishing manner. Through this, it is possible to prevent the collection cycle of the above communication unit (110) from fluctuating abnormally due to a rapid increase in a specific variable.

[0097] The control unit (130) may apply step-by-step correction rules to set the data collection cycle of the communication unit (110), first setting a standard cycle based on the available power amount, then reflecting a shortening correction based on the frequency of correction notices and the remaining seat shortage index, and additionally reflecting an extension correction based on the estimated number of days for registered mail delivery and the number of original document items. In one embodiment, the control unit (130) may determine the final data collection cycle by calculating a standard cycle corresponding to the available power amount, shortening the standard cycle as the frequency of correction notices and the remaining seat shortage index increase, and extending the shortened cycle again as the estimated number of days for registered mail delivery and the number of original document items increase. Additionally, if the calculated final data collection cycle falls below a preset minimum lower limit, it may be fixed at the preset minimum lower limit, and if the available power amount falls below a preset minimum standard value, the collection cycle may be fixed at the maximum upper limit. The above correction rule is not an abstract mathematical calculation, but may be a computational flow structured into a data processing procedure that incorporates the urgency of protecting system resources and acquiring information.

[0098] To establish the above correction criteria, a comparative simulation by condition group can be performed on multiple public institution support project announcement data collected over a recent specified period. In this case, the information omission rate can be defined as the ratio of the number of correction announcements that were not reflected within the pre-set collection cycle. Table 1 below is verification data by condition group in which the frequency of correction announcements was varied from 1 to 10 times to establish the criteria for initiating correction to shorten the cycle based on the frequency of correction announcements, and the information omission rate was compared with the actual update cycle of the managing agency server in each condition group.

[0099] [Table 1: Comparative simulation data by condition group for setting criteria for the frequency of correction notices]

[0100]

[0101] As shown in Table 1 above, in the interval where the frequency of correction notices reaches 3 times, the update cycle of the supervising agency server is rapidly shortened, and a turning point can be identified where the information omission rate exceeds 30 percent when maintaining the existing fixed cycle of 60 minutes. Accordingly, the control unit (130) can set the frequency of correction notices to 3 times as the starting criterion for the cycle shortening correction.

[0102] Table 2 below is effectiveness verification data based on an overall operation scenario comparing the case where step-by-step correction rules including the above criteria are applied with the case where a fixed cycle of one hour is maintained as in the past. In this case, the above daily average number of communication calls may be the average of multiple operation conditions in which normal periods, emergency periods, and preparation burden periods are mixed.

[0103] [Table 2: Data Verification of Data Collection Effects Based on Application of Step-by-Step Correction Rules]

[0104]

[0105] The above results show that the step-by-step correction rule of the control unit (130), which reflects the frequency of correction notices, the remaining seat shortage index, the estimated number of days for registered mail delivery, and the number of original document items together, can operate in a way that significantly reduces the omission of urgent information while reducing the number of communication calls compared to a simple fixed-cycle method. Accordingly, the data collection cycle control may not be a simple arbitrary setting, but a data processing procedure set based on operational history analysis and comparison results by condition group.

[0106] In one embodiment, the diagnostic evaluation module (133) does not determine the multidimensional corporate readiness based solely on the self-diagnosis results entered by the startup, but can readjust the readiness score by reflecting the consistency of submitted documents, the stability of on-site demonstrations, the level of physical evidence verified as originals, and the on-site inspection experience of professional evaluators. Through this, the diagnostic evaluation module (133) can mitigate subjective deviations that may occur during the self-diagnosis process and derive a more reliable readiness score based on actual measurement data confirmed in an offline on-site inspection environment.

[0107] The main judgment criteria used by the above diagnostic evaluation module (133) for readjusting the readiness score can be defined as follows. The above initial score is a primary readiness score calculated by analyzing the self-diagnosis table response values ​​entered by the startup company through a user terminal according to multidimensional enterprise readiness mapping criteria, and can be used as a reference value for readjustment. The above number of discrepancies between the evidence is a value representing the number of occurrences where the company name, sales revenue, contract counterparty, technical performance figures, certification information, business implementation schedule, or personnel composition information is mutually contradictory or inconsistent among multiple documents submitted by the startup company, and can be used as a negative indicator for evaluating the consistency of the documents. The above number of failures in field demonstration reproduction is a value representing the number of times the prototype or service was interrupted, the stated performance could not be reproduced, or repeated operation failed under the same conditions during the offline evaluation, presentation evaluation, on-site inspection, or prototype verification process, and can be used as a negative indicator for evaluating the actual implementation level and execution stability. The aforementioned cumulative number of expert evaluator participations represents the cumulative history of the evaluator's participation in on-site inspections, presentation evaluations, or offline verification procedures within the same or similar support project group, and can be used as a mitigation criterion to adjust the interpretation intensity of negative indicators. The aforementioned number of verified original documents represents the number of documents whose authenticity has been secured through notarization, verification against the original, official seals from relevant agencies, electronic signature verification, or the evaluator's original verification procedures, and can be used as a positive indicator to assess the level of substantive evidence supporting the company's claims. The aforementioned field demonstration duration represents the time during which a prototype or service maintained stable operation without interruption in an offline environment, and can be used as a positive indicator to evaluate actual operational stability and continuity.

[0108] The above initial score can be calculated by correlating the multiple-choice and subjective self-diagnosis response values ​​received from the user terminal with the above multidimensional corporate readiness criteria and synthesizing the evaluation results for each item. For example, the above diagnostic evaluation module (133) can set the initial score to 85 points by analyzing self-responses regarding technology maturity, manufacturing readiness or service readiness, business readiness, marketing readiness, overseas expansion readiness, and team building readiness.

[0109] The number of discrepancies between the aforementioned evidence documents can be calculated using at least one of electronic document analysis, image-based character recognition, and language processing algorithms. For example, the diagnostic evaluation module (133) can extract key items included in business plans, financial statements, contracts, proposals, introductory materials, certificates, or externally submitted documents, and identify whether there is a discrepancy by comparing values ​​corresponding to the same items. In one embodiment, if the sales revenue in the business plan differs from the sales revenue in the financial statements, or if the time of completion of the prototype in the introductory materials differs from the schedule in the submission confirmation, the diagnostic evaluation module (133) can count this as a discrepancy.

[0110] The number of field demonstration reproduction failures and the field demonstration duration can be obtained based on direct input through a professional evaluator terminal, analysis of demonstration recording video, or analysis of prototype status monitoring data. For example, the professional evaluator may input whether operation was interrupted, an error message occurred, performance standards were not met, or whether a re-demonstration under the same conditions failed through an evaluation application, and the diagnostic evaluation module (133) can calculate the number of reproduction failures based on this. In addition, the diagnostic evaluation module (133) can derive the field demonstration duration by identifying the demonstration start time and end time, or the normal operation maintenance period.

[0111] The cumulative number of participations by the above-mentioned expert evaluator can be obtained by querying the evaluator profile database, the past on-site inspection history database, or the evaluation log information stored in the system. For example, the above-mentioned diagnostic evaluation module (133) can identify the history of the above-mentioned expert evaluator participating in on-site inspections 35 times regarding the same support project group. The above-mentioned number of verified original documents can be calculated by identifying the electronic signature verification result of the submitted document, the original comparison check information, whether it has been notarized, or the relevant agency confirmation mark. For example, the above-mentioned diagnostic evaluation module (133) can determine that there are 5 verified original documents.

[0112] The diagnostic evaluation module (133) can first set the initial score as a reference value. Subsequently, the diagnostic evaluation module (133) can identify the number of discrepancies between the evidence and the number of failures to reproduce the field demonstration, and calculate a reduction correction rate for the initial score by reflecting the identified negative indicators. As the number of discrepancies between the evidence increases or the number of failures to reproduce the field demonstration increases, the reduction correction rate may increase. Through this, the initial score may be lowered for companies with low document consistency or insufficient actual demonstration stability.

[0113] The above diagnostic evaluation module (133) can adjust the application intensity of the above reduction correction rate by further verifying the cumulative number of expert evaluator participations. Specifically, as the cumulative number of expert evaluator participations increases, the above reduction correction rate may be gradually relaxed. This is because expert evaluators with extensive field experience are less likely to over-interpret minor formal errors or temporary demonstration variations as an overall lack of readiness. However, the extent of relaxation may be limited to gradually decrease after the cumulative number of participations exceeds a certain level. This prevents the above reduction correction rate from being relaxed indefinitely.

[0114] When the above reduction correction rate is calculated, the diagnostic evaluation module (133) can calculate a first correction score by applying the above reduction correction rate to the above initial score. For example, if the above initial score is 85 points, the number of discrepancies between the evidence is 1, and the number of failures to reproduce the field demonstration is 2, the above diagnostic evaluation module (133) can first calculate a basic reduction correction rate based on negative indicators. Subsequently, if the cumulative number of expert evaluator participations is confirmed to be 35, the above diagnostic evaluation module (133) can apply a relaxed basic reduction correction rate to correct the above initial score to 74.8 points. However, the above figures are merely examples, and the actual reduction criteria may vary depending on the evaluation policy for each support project group.

[0115] After the above first correction score is calculated, the above diagnostic evaluation module (133) can calculate bonus points by reflecting the degree of combination of the number of original verified documents and the duration of the field demonstration. That is, as the number of original verified documents increases and the duration of the field demonstration increases together, it is determined that the substantive basis for the company's claims and actual operational stability are secured together, and thus a larger bonus point can be awarded. However, from the point where the number of original verified documents or the duration of the field demonstration exceeds a certain level, the increase in bonus points for the additional increase can be processed to gradually decrease. Through this, it is possible to prevent the readiness score from rising excessively merely because the demonstration time, which has already been maintained sufficiently stably, has been extended, or because the evidence of the same meaning is excessively repeated.

[0116] For example, the diagnostic evaluation module (133) can calculate bonus points by reflecting the degree of combination of the two positive indicators when the number of original verified documents is 5 and the duration of the on-site demonstration is 15 minutes. In this case, for sections where the number of original verified documents exceeds 3 or the duration of the on-site demonstration exceeds 10 minutes, the increase in bonus points for the additional increase can be applied lower than in the previous section. For example, the diagnostic evaluation module (133) can determine the final readjustment score to be 81.8 points by granting a total of 7 bonus points to the first adjustment score of 74.8 points. However, the above figures are merely examples, and the actual bonus criteria may be adjusted according to the support project group or evaluation policy.

[0117] The diagnostic evaluation module (133) can calculate a final readjustment score by reflecting negative indicators and positive indicators in a mutual checks and balances structure. For example, if the negative indicator is high and the positive indicator is low, the initial score may be significantly lowered. Conversely, if the negative indicator is low and the positive indicator is high, an additive correction within a limited range may be applied to the initial score. Additionally, if both the negative indicator and the positive indicator are high, the diagnostic evaluation module (133) may first apply a subtraction correction based on the negative indicator, and then additionally reflect an additive correction based on the original verification completion document and the stable demonstration maintenance time to derive the final score. On the other hand, if both the negative indicator and the positive indicator are low, the subtraction and addition ranges are applied restrictively, so that a score similar to the initial score is maintained.

[0118] The diagnostic evaluation module (133) may further perform an exception handling routine. For example, if the recalculated score calculated after the subtraction correction and addition correction is less than 0 points, it may be fixed at 0 points, and if it exceeds 100 points, it may be limited to an upper limit of 100 points. Additionally, if the cumulative number of expert evaluator participations is less than a preset minimum standard, the application of the logic for mitigating the subtraction correction rate may be restricted or excluded. For example, if the cumulative number of expert evaluator participations is less than 3, the diagnostic evaluation module (133) may omit the application of mitigation of the subtraction correction rate, taking into account the possibility of judgment deviation due to lack of experience.

[0119] The aforementioned final readjustment score may be stored or updated in the system database as multidimensional corporate readiness profile information for the relevant startup. Subsequently, the final readjustment score may be used as reference data for information matching procedures, eligibility review procedures, and financing suitability calculation procedures performed in subsequent stages. In other words, the final readjustment score is not limited to a simple diagnostic result but can be utilized as an input value for practical support program recommendations and matching decisions.

[0120] In one embodiment, the diagnostic evaluation module (133) can verify the readjustment procedure through a plurality of simulation scenarios. For example, even if the initial score is 90 points, if there are 2 discrepancies between the evidence, 3 failures in reproducing the field demonstration, and only 1 original verified document, the diagnostic evaluation module (133) can drastically reduce the readiness score and classify the company as having low practical feasibility. Conversely, even if the initial score is at the 80-point level, if there are no discrepancies between the evidence, sufficient time for maintaining the stable demonstration, and multiple original verified documents are secured, the diagnostic evaluation module (133) can grant additional points within a certain range and reclassify the company as having high financing response capability. Furthermore, even if the initial score is high, if field reproduction failures are repeated, the score is automatically lowered, and even if the initial score is somewhat low, if physical evidence and stable demonstrations are sufficiently secured, some of the score may be recovered.

[0121] According to the score readjustment procedure described above, the diagnostic evaluation module (133), unlike the conventional method that relies solely on the self-diagnosis results of a startup, can improve the reliability of readiness information by reflecting the consistency of submitted documents, the authenticity of physical evidence, the stability of on-site demonstrations, and the on-site inspection experience of professional evaluators. In particular, since feasibility and the ability to respond to on-site inspections, which are difficult to ascertain from abstract survey responses alone, can be reflected through a quantitative judgment procedure, the suitability of subsequent support program matching can be increased and administrative waste caused by indiscriminate applications for support programs can be reduced. Furthermore, the quality of the company readiness information stored on the platform can be improved by systematically correcting the difference between subjective self-evaluations and objective actual measurement data regarding the same company.

[0122] The above criteria for mitigating reduction, the above criteria for slowing down the increase in addition, and the above criteria for upper and lower limits of scores may be set based on the on-site inspection history, evaluation result statistics, and support project selection results during the initial operation of the system, and may be variably adjusted according to evaluation logs accumulated during the operation of the system. For example, the mitigation of the reduction correction rate may be set to begin when the cumulative number of participations by the above professional evaluators exceeds a certain number, and the increase in additional points may be set to be applied at a lower rate than the previous range from the point when the number of original verification documents exceeds 3 or the duration of the above field demonstration exceeds 10 minutes. Through this, the above diagnostic evaluation module (133) can prevent the phenomenon in which the readjustment score is abnormally overcalculated or undercalculated solely by an increase in a specific variable.

[0123] The above diagnostic evaluation module (133) may apply a step-by-step readjustment rule that sets an initial self-diagnosis-based score as a reference value, first reflects a reduction correction based on the number of discrepancies between evidence and the number of failures to reproduce the field demonstration, gradually mitigates the reduction range based on the cumulative number of expert evaluators participating, and then performs an increase correction based on the combined degree of the number of original verified evidence documents and the duration of the field demonstration. At this time, the increase correction may be set so that the increase rate gradually decreases after a certain level. The above readjustment rule may be a procedure for correcting subjective deviations that may occur during the self-diagnosis process and for generating a readiness score based on offline actual measurement data.

[0124] To establish the criteria for slowing down the increase in the above-mentioned bonus, a simulation can be performed for multiple groups of companies belonging to similar initial score ranges, varying the number of verified original documents from 1 to 10 and comparing the change in the actual face-to-face evaluation pass rate. Table 3 below is comparative data for each condition group analyzing the correspondence between the number of verified original documents and the actual face-to-face evaluation pass rate.

[0125] [Table 3: Data on Setting Criteria for Slowing Growth Rate of Additional Points Based on the Number of Originally Verified Supporting Documents]

[0126]

[0127] As shown in Table 3 above, the increase in the pass rate of the actual face-to-face evaluation is significant until the number of original verified documents reaches 3, but from the 4th or higher range, a tendency can be observed where the increase in the pass rate due to securing additional documents decreases rapidly. Accordingly, the diagnostic evaluation module (133) can set the number of original verified documents to 3 as the starting criterion for slowing down the increase rate.

[0128] Table 4 below shows the effectiveness verification data comparing the case where the above readjustment rule is applied with the conventional method using only simple self-diagnosis scores. In this case, the occurrence rate of score overestimation can be defined as the ratio in which the deviation between the platform-calculated score and the actual offline evaluation result exceeds a pre-set allowable range. Additionally, the correlation coefficient can be calculated by comparing the correspondence between the platform-calculated score and the actual offline evaluation result.

[0129] [Table 4: Data verifying the effect of applying readiness score rebalancing rules]

[0130]

[0131] The above results show that the diagnostic evaluation module (133) can reflect actual-measure-based readiness information, which is difficult to reflect solely by self-diagnosis scores, into the database through a readjustment procedure that reflects evidence discrepancies, demonstration failures, original verification documents, and stable demonstration maintenance time together. Accordingly, the readjustment rule may be a data processing procedure to improve the quality of multidimensional enterprise readiness data accumulated on the platform.

[0132] In one embodiment, the information matching module (135) does not determine the suitability between a startup and a support program based solely on whether the industry matches, technology field keywords, or formal qualification requirements are met, but rather calculates the suitability of funding information by reflecting the offline response capabilities, physical mobility, and the ability to prepare documents within the deadline that the startup can actually perform. Through this, the information matching module (135) can downgrade or exclude support programs that appear eligible for application on paper but are actually difficult to attend face-to-face evaluations, prepare original documents, or submit within the deadline, and conversely, can prioritize recommending support programs that are practically manageable.

[0133] The main judgment criteria used by the information matching module (135) to calculate the matching suitability of funding information can be defined as follows. The above-mentioned actual amount of funds is a value representing the actual amount of funds that can be received by subtracting the self-contribution, mandatory guarantee costs, stamp duty, notarization costs, or other essential expenditure items that the startup company is obligated to bear from the total support funds of the support project, and can be used as a criterion for judging the effective financial value provided by the support project to the startup company. The above-mentioned number of supporting documents is a value representing the number of original or verified documents that the startup company can submit immediately without separate reissuance or additional verification procedures, and can be used as a criterion for judging ease of preparation. The above-mentioned number of internal personnel available to attend is a value representing the number of personnel who can actually attend the on-site briefing, presentation evaluation, face-to-face consultation, or on-site evaluation procedure among the representative, employees, or internal participants of the startup company capable of performing support project response tasks, and can be used as a criterion for judging offline response capacity. The difference between the aforementioned required readiness level and the startup readiness level represents the gap between the multidimensional corporate readiness level required by the support program and the startup's current multidimensional corporate readiness level, and can be used as a criterion to evaluate the degree of alignment between the support program's required level and the startup's current capabilities. The aforementioned number of public transportation transfers represents the number of transfers required to travel from the startup's business premises, headquarters, or actual starting point to the designated evaluation venue, information session venue, or reception venue, and can be used as a criterion to evaluate physical accessibility. The aforementioned number of documents requiring notarization represents the number of submitted documents requiring notarization, verification against originals, corporate seal confirmation, verification by relevant agencies, or face-to-face administrative procedures, and can be used as a criterion to assess the burden of offline preparation.The above number of actual workdays available for preparation is a value representing the number of workdays actually available, excluding weekends, public holidays, and schedules where work cannot be performed, during the period from the time of checking the announcement to the deadline, and can be used as a criterion for determining the feasibility of physical preparation within the deadline.

[0134] The above-mentioned actual amount of funds can be obtained by analyzing the support project announcement, budget calculation details, or support condition documents. For example, the above-mentioned information matching module (135) can calculate the above-mentioned actual amount of funds by identifying phrases related to the total support amount, self-contribution ratio, mandatory fees, guarantee insurance subscription costs, and notarization costs. The above-mentioned number of existing supporting documents can be obtained by querying the document database of the startup company stored in the system, the supporting document storage box, or submission history information. For example, the above-mentioned number of existing supporting documents can be calculated by counting only documents that have a remaining validity period and can be submitted immediately among business registration certificates, financial statements, contracts, certificates, tax-related proofs, or patent-related proofs. The above-mentioned number of internal personnel available for attendance can be obtained by analyzing the internal schedule, registration information of eligible participants, or personnel allocation information. For example, the information matching module (135) can derive the number of internal personnel who can attend by identifying representatives and personnel in charge who can actually attend without overlapping with the pre-deadline briefing or evaluation schedule.

[0135] The difference value between the required readiness level and the startup readiness level can be obtained by comparing the required readiness level information set for each support project with the multidimensional corporate readiness information of the startup calculated in the preceding procedure. For example, the information matching module (135) can calculate the difference value by comparing the required level of the support project with the current level of the startup regarding at least some items among technology maturity, manufacturing readiness or service readiness, business readiness, marketing readiness, overseas expansion readiness, and team building readiness, and by synthesizing the differences. The number of public transportation transfers can be obtained by inputting the address information of the startup and the address information of the evaluation location into a location-based service or map application interface and analyzing the route. For example, the information matching module (135) can calculate the minimum transfer route based on public transportation and set the number of transfers required therefrom as the number of public transportation transfers. The number of documents requiring notarization can be obtained by analyzing the main body of the support project announcement, the list of submitted documents, or the notice of precautions document. For example, the information matching module (135) can calculate the number of documents requiring notarization by identifying items containing phrases such as “notarization required,” “verified against original,” “corporate seal affixed,” “direct submission,” and “submission of original.” The actual number of workdays available for preparation can be calculated by reflecting information on the announcement posting date, announcement confirmation date, deadline, and public holidays. For example, the information matching module (135) can calculate the actual number of workdays available for preparation by counting only the days during which actual work can be performed within the entire period from the current date to the deadline.

[0136] The information matching module (135) can first calculate a basic suitability by reflecting the combination of the actual amount of funds available for receipt, the number of existing supporting documents, and the number of internal personnel available for attendance. Specifically, the basic suitability may increase as it is determined that the effective value of the support project is higher when the actual amount of funds available for receipt is larger. Additionally, the basic suitability may increase as it is determined that the likelihood of the startup company performing offline procedures quickly without separate additional preparation is higher when the number of existing supporting documents is large and the number of internal personnel available for attendance is sufficient. However, after the number of existing supporting documents or the number of internal personnel available for attendance exceeds a certain level, the increase in suitability due to additional increases may be reflected to gradually decrease. Through this, it is possible to prevent the suitability from rising excessively even when there are an excessive number of documents with the same utility or when additional personnel are added while sufficient response personnel are secured above a certain level.

[0137] In one embodiment, the information matching module (135) can calculate a basic suitability score of 85 points by reflecting the fact that the actual amount of funds that can be received is 80 million won, the number of existing supporting documents is 3, and the number of internal personnel who can attend is 2. However, the above figure is merely one embodiment, and the actual score conversion criteria may vary depending on the characteristics of the support project group and the operation policy.

[0138] After the above basic suitability is calculated, the information matching module (135) may lower the above basic suitability by reflecting the difference between the required readiness and the startup readiness and the number of public transportation transfers. Specifically, the suitability may be lowered as it is determined that the greater the difference, the greater the discrepancy between the level required by the support project and the current level of the startup. Additionally, the suitability may be further lowered as it is determined that the greater the number of public transportation transfers, the greater the physical burden of movement required to reach the evaluation venue or briefing venue. Accordingly, the information matching module (135) may calculate a secondary suitability that reflects the actual capability level required by the support project, the current responsiveness of the startup, and offline accessibility.

[0139] In one embodiment, when the difference between the required readiness and the startup readiness is 15 points and the number of public transportation transfers is 3 times, the information matching module (135) can correct the secondary readiness to 65 points by performing the downward adjustment on the basic readiness of 85 points. However, the above figures are also merely examples.

[0140] After the above secondary suitability is calculated, the information matching module (135) may further adjust the secondary suitability by reflecting the relationship between the expected processing burden corresponding to the number of documents requiring notarization and the actual available business days for preparation. Specifically, as the number of documents requiring notarization increases, the time and effort required for face-to-face administrative processing increase, and as the actual available business days for preparation decrease, the possibility of responding within the deadline may decrease. Therefore, as the expected processing burden corresponding to the number of documents requiring notarization increases relative to the actual available business days for preparation, the information matching module (135) may gradually lower the secondary suitability. Furthermore, if it is determined that the expected processing burden exceeds the actual available business days for preparation, the information matching module (135) may further lower the secondary suitability to a pre-set lower limit or to a level near the lower limit. Through this, support projects for which actual physical submission is practically difficult, even if formal eligibility requirements are met, can be prevented from being exposed in the top recommendation list.

[0141] In one embodiment, if the number of documents requiring notarization is 2 and the actual available workday for preparation is 1 day, the information matching module (135) may determine that the burden of performing the notarization procedure exceeds the available preparation period. In this case, the information matching module (135) may further lower the secondary suitability score of 65 points to the lower limit of 5 points to determine the final matching suitability. However, the lower limit and the judgment criteria are merely examples and may vary depending on the actual operation policy.

[0142] The information matching module (135) can reflect the final matching suitability in determining the recommendation ranking of customized funding information provided to startups. For example, if the final matching suitability is greater than or equal to a pre-set recommendation standard, it can be included in the priority recommendation list, and if it is less than the recommendation standard, it can be placed in a lower priority or excluded from the recommendation target. Accordingly, the information matching module (135) can provide funding information sorted in order of high likelihood of actual application and offline response, rather than simply listing support project information.

[0143] The information matching module (135) may perform additional exception handling routines. For example, if the support project is conducted entirely online and non-face-to-face, and attendance at offline briefings, attendance at presentation evaluations, submission of original documents, or notarization procedures are not required, the information matching module (135) may omit the application of all or part of the downward adjustment logic based on the number of public transportation transfers and the number of documents requiring notarization. This prevents offline constraints from being unreasonably reflected in non-face-to-face projects. Additionally, if the actual available work days for preparation are sufficiently long and the number of documents requiring notarization is very small, the information matching module (135) may apply the additional downward adjustment step restrictively or omit it.

[0144] The information matching module (135) can determine the final matching suitability based on the combination of various conditions. For example, if the actual amount of funds that can be received is high, the number of existing supporting documents and the number of internal personnel available for attendance are sufficient, and the difference value and the number of public transportation transfers are small, the information matching module (135) can determine that it is a support project with high feasibility and calculate a high level of suitability. Conversely, even if the basic suitability is high, if the processing burden corresponding to the number of documents requiring notarization exceeds the actual number of working days available for preparation, the information matching module (135) can determine that it is a support project where physical acceptance is difficult and significantly lower the suitability. Additionally, if the basic conditions are favorable but the difference value is large and the number of transfers is high, the information matching module (135) can calculate an intermediate level of suitability by acknowledging the possibility of formal application while reflecting the disadvantages of the actual face-to-face procedure. On the other hand, if both the above-mentioned upward and downward factors are unfavorable, the information matching module (135) may lower the recommendation priority or exclude it from the recommendation target.

[0145] In one embodiment, the information matching module (135) can verify the suitability calculation procedure through a plurality of simulation scenarios. For example, if the actual amount of funds available for receipt is high, the document preparation status and the attendance status of available personnel are good, and the number of transfers is low, the final matching suitability is calculated to be high, and the candidate may be a priority recommendation candidate. Conversely, even if the actual amount of funds available for receipt is high, if the difference between the required preparation level and the startup company preparation level is large, the number of public transportation transfers is high, and the processing burden corresponding to the number of documents requiring notarization is excessive, the final matching suitability is significantly lowered, and the candidate may be excluded from the recommendation candidate. Furthermore, even if the number of existing supporting documents and the number of available internal personnel are sufficient, if the actual work days available for preparation are very short and there are many face-to-face administrative procedures, the final matching suitability may be adjusted to a level near the lower limit.

[0146] According to the above procedure, the information matching module (135) can reduce the problem of support programs that only meet formal conditions being indiscriminately recommended by reflecting the geographical constraints, manpower capacity, document readiness, and physical administrative processing capabilities of the startup. In particular, it can prevent the waste of time and resources of the startup by filtering out in advance evaluation locations that are difficult to visit in reality, submission of original documents that are difficult to prepare within the deadline, or programs with significant differences in the level of readiness. Furthermore, the information matching module (135) can concentrate system computational resources on valid information with high final matching suitability, thereby improving the recommendation reliability and operational efficiency of the platform as a whole.

[0147] The criteria for slowing the rate of increase for the aforementioned upward factors, the criteria for downward adjustment, the lower limit, and the recommendation criteria may be set based on the support project response history, recommendation success rate, statistics on the causes of application failure, or user feedback data during the initial stages of system operation, and may be variably adjusted according to log data accumulated during system operation. For example, in one embodiment, from the point when the number of existing supporting documents exceeds 5 or the number of internal personnel available for attendance exceeds 3, the contribution to the increase in suitability for the additional increase may be reflected at a lower level than in the previous range. Additionally, in one embodiment, the lower limit may be set to 5 points. However, the above figures may be changed depending on the operating environment and the characteristics of the support project group.

[0148] The information matching module (135) may apply a step-by-step judgment rule to calculate the matching suitability of funding information by calculating a basic suitability by reflecting the degree of combination of the actual amount of funds that can be received, the number of existing supporting documents, and the number of internal personnel who can attend, then lowering the basic suitability based on the difference between the required readiness and the start-up company readiness and the number of public transportation transfers, and finally further adjusting the final suitability by reflecting the relationship between the expected administrative processing burden corresponding to the number of documents requiring notarization and the actual number of working days available for preparation. At this time, if the expected administrative processing burden approaches or exceeds the actual number of working days available for preparation, the final suitability may be further lowered to a lower limit value or a level near the lower limit value. The judgment rule may be a structure that implements the actual offline feasibility of the start-up company through a data filtering procedure, going beyond the matching of conditions on paper.

[0149] In order to establish criteria for strengthening penalty points based on the number of public transportation transfers mentioned above, a simulation can be performed by varying the number of transfers from 0 to 9 based on movement scenarios within the same or similar regional areas, and comparing the actual offline evaluation attendance rate and dropout rate in each condition group. Table 5 below is comparative data by condition group analyzing the change in physical accessibility according to the number of transfers mentioned above.

[0150] [Table 5: Comparative simulation data by condition group for setting criteria for the number of public transportation transfers]

[0151]

[0152] As shown in Table 5 above, in sections where the number of transfers is 3 or less, the attendance rate is relatively maintained, but from the section with 4 or more transfers, a discontinued section can be identified where the attendance rate drops sharply and the dropout rate increases significantly. Accordingly, the information matching module (135) can set the number of transfers to 3 as the starting criterion for strengthening penalty points, and the section with 4 or more transfers as the section with the most significant downward penalty. However, the number of transfers can be reflected as an auxiliary factor along with the regional area, total travel time, or round-trip travel time.

[0153] Table 6 below is the effectiveness verification data comparing the case where the above-mentioned step-by-step judgment rules are applied with the conventional method of performing matching based on keywords and formal conditions. In this case, the above-mentioned actual support rate can be defined as the percentage of startups among the recommended support programs that actually initiated the application process, and the above-mentioned face-to-face evaluation non-attendance rate can be defined as the percentage of those who failed to attend the scheduled offline evaluation after passing the document screening.

[0154] [Table 6: Verification Data on the Effect of Applying Matching Fit Calculation Rules Reflecting Physical Constraints]

[0155]

[0156] The above results show that the information matching module (135) can lower the likelihood of a project being formally applicable but actually executed through a procedure that considers the actual amount of funds that can be received, the number of internal personnel available for attendance, the number of transfers, and the burden of processing notarized documents. In particular, if the expected administrative processing burden is greater than the actual number of working days available for preparation, the suitability can be further lowered to increase the effectiveness of the recommended information. Accordingly, the judgment rule of the information matching module (135) may be a data processing procedure to reduce matching errors resulting from simple information listing and to prioritize the allocation of computational resources to information with a high probability of success.

[0157] In one embodiment, the funnel management module (136) does not simply display the funding progress status of a startup company based on stage name or elapsed time, but can calculate the probability of transitioning to the next stage by reflecting the degree of stagnation at the current stage, the history of responding to institutions, and the level of readiness for actual operations. Through this, the funnel management module (136) can quantitatively distinguish differences based on the actual level of response even among startup companies belonging to the same funnel stage, provide guidance for improvement to companies with a low probability of transitioning to the next stage, and support the preparation of subsequent procedures for companies with a high probability of transitioning to the next stage.

[0158] The main judgment criteria used by the funnel management module (136) to calculate the stage transition probability can be defined as follows. The number of days of stagnation is a value representing the cumulative number of days a startup has remained without moving to the next stage after entering a specific stage of the current funding management funnel, and can be used as a criterion for judging the degree of delay in progress. The number of replies within the deadline is a value representing the number of times a reply or response was completed within the designated deadline regarding inquiries, requests for supplementation, requests for modification, or requests for additional submission received from a managing agency, investment agency, or evaluation agency, and can be used as a defensive action indicator to judge the timeliness of the agency's response. The number of cumulative resubmission approvals is a value representing the number of times data was modified or supplemented and resubmitted after receiving a rejection or supplementation notice, and the resubmission was accepted or approved, and can be used as a defensive action indicator to judge problem-solving ability and response continuity. The cumulative number of official confirmation documents secured above is a value representing the number of official documents secured, such as receipts, evaluation notices, interview schedule notices, requests for supplementation, draft agreements, confirmations, or other documents issued by the managing organization or investment institution, and can be used as a practical preparation indicator to verify the objective status of progress. The number of practical rehearsal completions above is a value representing the number of presentation practice, demonstration practice, Q&A practice, or mock checks completed by the startup in preparation for on-site inspection, presentation evaluation, demo day, investment briefing, or face-to-face interview, and can be used as a practical preparation indicator to judge the level of readiness for actual response.

[0159] The above number of days of stagnation can be calculated by comparing the time of entry into the funnel stage recorded in the system with the current time. For example, the funnel management module (136) can derive the above number of days of stagnation by calculating the number of days elapsed since the point when the startup company entered the “document supplementation stage,” “presentation evaluation waiting stage,” or “investment review stage.” The above number of replies within the deadline and the above number of accumulated resubmission approvals can be calculated by analyzing email sending and receiving history, institutional linkage system logs, user upload document history, or supplementation completion notification documents. For example, the above funnel management module (136) can determine whether a reply was made within the deadline by comparing the time of inquiry reply with deadline information, and if an approval notification is received after resubmission, it can count this as the above number of accumulated resubmission approvals. The above number of accumulated official confirmation documents can be calculated through at least one of document parsing, character recognition, issuance number verification, or relevant agency verification procedures. For example, the funnel management module (136) can verify whether the document was issued by an actual institution by analyzing the issuance number, seal, confirmation mark, or electronic signature verification information included in the official document. The number of times the practical rehearsal was completed can be calculated by analyzing the mentoring log, internal mock presentation records, presentation material rehearsal registration information, venue rental history, or user input logs.

[0160] The funnel management module (136) can first calculate the probability of transitioning to a basic stage by reflecting the number of days of stagnation. Specifically, since an increase in the number of days of stagnation implies a longer time spent at the current stage, the funnel management module (136) can gradually lower the probability of transitioning to the next stage. This allows for the reflection of the risk of delay that naturally accumulates over time. However, the extent of the downward adjustment may vary depending on the characteristics of the stage, the average processing time of the institution, or the operational schedule of the support project. For example, since the document review stage and the presentation evaluation waiting stage may have different typical allowable stagnation periods, the funnel management module (136) can apply different standard intervals for each stage type.

[0161] In one embodiment, if the startup company is currently staying in the funnel stage for 15 days, the funnel management module (136) may lower the probability of transitioning to the basic stage to 40 percent to reflect the increase in the number of stagnation days. However, the above figure is merely one embodiment, and the actual criteria for lowering may vary depending on the stage operation policy.

[0162] After the above basic stage conversion probability is calculated, the funnel management module (136) can perform a first correction to restore the decreased conversion probability by reflecting the combined level of the number of replies within the deadline and the number of accumulated resubmission approvals. Specifically, as the number of replies within the deadline and the number of accumulated resubmission approvals increase together, it can be determined that the startup company is actively responding to the institution's requirements and increasing the possibility of progressing to the next stage through substantial modifications and supplements even after a problem occurs. Accordingly, the funnel management module (136) can correct the decreased basic stage conversion probability upward. However, if the number of replies within the deadline or the number of accumulated resubmission approvals exceeds a certain level, the recovery effect based on the additional increase can be processed to gradually decrease. Through this, it is possible to prevent the conversion probability from rising indefinitely even if repeated replies or resubmissions accumulate.

[0163] In one embodiment, if the number of replies within the deadline is 2 and the cumulative number of resubmission approvals is 1, the funnel management module (136) may adjust the basic stage transition probability of 40 percent upward to 60 percent by reflecting the overall level of the defense action. However, the above figures are also merely one embodiment.

[0164] After the above first correction is performed, the funnel management module (136) can further correct the stage transition probability by reflecting the combined degree of the number of accumulated official confirmation documents and the number of times the actual rehearsal is completed. Specifically, an increase in the number of accumulated official confirmation documents means that objective evidence showing that the procedure with the institution is actually proceeding is being accumulated, and an increase in the number of times the actual rehearsal is completed means that the startup company is performing preparations for subsequent screening, presentation, or due diligence procedures. Therefore, as the number of accumulated official confirmation documents and the number of times the actual rehearsal is completed increase together, the funnel management module (136) can further correct the stage transition probability upward. However, from the point where the number of accumulated official confirmation documents or the number of times the actual rehearsal is completed exceeds a certain level, the upward correction range according to the additional increase can be processed to gradually decrease. Through this, it is possible to prevent the transition probability from rising excessively even when official documents of the same type are excessively accumulated or the number of rehearsals increases repeatedly.

[0165] In one embodiment, when the cumulative number of official confirmation documents secured is 3 and the number of actual rehearsals completed is 2, the funnel management module (136) can further increase the stage transition probability after the first correction from 60 percent to 82 percent by reflecting the degree of combination of the two actual preparation indicators. However, the above figures are also merely examples, and the actual probability calculation criteria may vary depending on the type of funnel stage and the operation policy.

[0166] The funnel management module (136) can calculate the probability of transitioning to the final stage by combining the stagnation period, the level of defensive action, and the level of readiness for actual operation. For example, if the stagnation period is short and both the level of defensive action and the level of readiness for actual operation are high, the funnel management module (136) can calculate a high probability of transitioning to the stage corresponding to a pre-set upper probability range. Conversely, even if the stagnation period is long, if the number of replies within the deadline, the cumulative number of resubmission approvals, the cumulative number of official confirmation documents secured, and the number of completed actual operation rehearsals are all above a certain level, the funnel management module (136) can determine that active response is continuing despite the stagnation state and can restore the lowered probability to a certain range. Additionally, even if the stagnation period is short, if both the level of defensive action and the level of readiness for actual operation are low, the probability of transitioning to the stage can be adjusted downward to a middle or lower range by determining that the possibility of responding to subsequent procedures is low. On the other hand, if the above stagnation period is long and both the above defense action level and the above operational readiness level are low, the funnel management module (136) can lower the stage transition probability to a level corresponding to a preset warning interval or failure candidate interval.

[0167] The funnel management module (136) may perform additional exception handling routines. For example, if the number of days of stagnation exceeds a preset maximum standard and both the number of replies within the deadline and the cumulative number of resubmission approvals are absent, the funnel management module (136) may lower the stage transition probability to a preset minimum level or lower and classify the corresponding funnel stage as a failure candidate stage, a review stage, or a re-entry induction stage. This allows companies that remain in a stagnant state for a long period without taking effective action to be identified as separate management targets. Additionally, if the cumulative number of official confirmation documents secured or the number of practical rehearsals completed is entered at an abnormally high level, the funnel management module (136) may limit the upward correction range by reviewing whether there is duplicate document submission or repeated registration of the same rehearsal.

[0168] Apart from the calculation of the stage transition probability mentioned above, the funnel management module (136) may perform additional management routines. For example, if the combined degree of the number of accumulated official confirmation documents secured and the number of times practical rehearsals are completed exceeds a preset management standard, the funnel management module (136) may automatically classify the startup as a target for intensive management, a target for priority support, or a target for recommendation to prepare for a subsequent stage. This is a subsequent management function distinct from the logic that calculates the stage transition probability itself, and can be used as a procedure to link more detailed notifications, document checks, presentation material supplementation, or schedule management services to startups with high probability.

[0169] The funnel management module (136) can utilize the calculated final stage conversion probability as an indicator to visualize the progress of the startup's fundraising. For example, if the stage conversion probability is above a pre-set upper range, it can provide a notification for preparing the next stage, a notification for checking presentation materials, or a notification for confirming the schedule; if the stage conversion probability is below a pre-set warning range, it can provide a guide for supplementary response, encouragement to respond to inquiries, or a recommendation to perform a rehearsal. Accordingly, the funnel management module (136) can operate as an auxiliary means to induce the startup's fundraising response actions, going beyond a simple notification function.

[0170] In one embodiment, the funnel management module (136) can verify the step transition probability calculation procedure through a plurality of simulation scenarios. For example, if the stagnation period is short, the number of replies within the deadline and the cumulative number of resubmission approvals are sufficient, and the cumulative number of official confirmation documents secured and the number of actual rehearsals completed are also good, the final step transition probability is calculated to be high and can be classified into an upper range. Conversely, if the stagnation period is long, the level of defensive action is low, and the level of actual preparation is insufficient, the final step transition probability may be lowered to a warning range or a failure candidate range. Additionally, even if the stagnation period is somewhat long, if the inquiry replies, resubmission approvals, securing of official confirmation documents, and rehearsal execution are continuously carried out, the step transition probability may be recovered to a certain level and classified as a state where there is a possibility of proceeding to a subsequent stage.

[0171] According to the above procedure, the funnel management module (136) can calculate the possibility of transitioning to a stage more realistically by reflecting both the risk of delay over time and the level of active response of the startup company at each stage of the funding funnel. In particular, by reflecting offline response history, such as the response to the inquiry, the approval of resubmission, the securing of official confirmation documents, and the practical rehearsal, into the probability of transitioning to a stage, it is possible to specifically distinguish the difference in the actual progress among startup companies existing under the same stage name. Accordingly, the funnel management module (136) can manage companies whose funding response is stagnant and companies that have a high probability of entering the next stage differently, and can contribute to enhancing the possibility of successful funding by inducing voluntary response activities of the startup company.

[0172] The recovery correction criteria for the above defensive action, the additional correction criteria for the above operational readiness, the criteria for slowing the rate of increase, the warning interval, the failure candidate interval, and the intensive management criteria may be set based on the history of financing response, phased transition success rates, statistics on processing supplementary requests, history of passing presentation evaluations, or user feedback data during the initial stages of system operation, and may be variably adjusted according to log data accumulated during system operation. For example, in one embodiment, from the point when the number of replies within the deadline exceeds 3 or the accumulated number of resubmission approvals exceeds 2, the recovery correction range for the additional increase may be reflected at a lower level than in the previous interval. Also, in one embodiment, from the point when the number of completed operational rehearsals exceeds 3, the upward correction range due to additional rehearsals may be gradually reduced. Additionally, in one embodiment, if the accumulated number of secured official confirmation documents is 2 or more and the number of completed operational rehearsals is 2 or more, it may be classified as an intensive management target. However, the above figures may change depending on the operating environment and the characteristics of the funnel stage.

[0173] The above funnel management module (136) may apply a step-by-step judgment rule to calculate the probability of transitioning to the next stage in the funding management funnel by lowering the basic transition probability according to the number of days of stagnation, then restoring the lowered probability by reflecting the combined level of the number of replies within the deadline and the number of cumulative resubmission approvals, and subsequently applying an additional correction by reflecting the combined degree of the number of cumulative official confirmation documents secured and the number of times actual rehearsals are completed. At this time, the upward correction range according to the additional increase in the defensive action or the actual readiness level may be set to gradually decrease after a certain level. In addition, if the number of days of stagnation accumulates for a long period and the defensive action is absent, the step transition probability may be classified downward into a failure candidate section or a forced termination review section. The above judgment rule may be a data processing procedure that reflects the variability of the funding screening process in conjunction with the startup's active response history and actual readiness history.

[0174] To establish the above failure candidate criteria and forced termination review criteria, the number of days of stagnation at a specific funnel stage can be varied from 5 to 50, and a simulation can be performed for each condition group to compare the probability of transitioning to the next stage autonomously without external intervention and the frequency of defensive actions in each condition group. Table 7 below is comparative data analyzing the change in the possibility of transitioning to the next stage according to the above number of days of stagnation.

[0175] [Table 7: Comparative simulation data by condition group for setting congestion days criteria]

[0176]

[0177] As shown in Table 7 above, if a specific stage remains stagnant for 30 days or more without defensive action, a failure candidate section where the probability of self-transition decreases to 2 percent or less may be identified, and if it persists for 35 days or more, a forced termination review section where the probability of self-transition converges to less than 1 percent may be identified. Additionally, if it persists for 40 days or more, the probability of self-transition may appear to be at a level of virtually 0 percent. Accordingly, the funnel management module (136) can set 30 days as the failure candidate stage classification standard, 35 days or more as the forced termination review standard, and 40 days or more as the forced termination possible section, respectively.

[0178] Table 8 below is the effectiveness verification data comparing the case where the above judgment rule is applied with the conventional method that simply displays the progress status. In this case, the timely response rate can be defined as the rate at which a response to a request for supplementation or inquiry is completed within the deadline, and the rate of inactive stage data occurrence can be defined as the rate of funnel stage data that is maintained without updating for a long period.

[0179] [Table 8: Verification Data on the Effect of Applying Rules for Calculating Transition Probabilities of Defense Action Reflection Phases]

[0180]

[0181] The above results show that the funnel management module (136) can reflect the actual level of response of the startup company in the stage transition probability by reflecting not only the number of days of stagnation but also the number of replies within the deadline, the number of resubmission approvals, the level of securing official confirmation documents, and the level of actual rehearsal performance. In addition, by introducing the failure candidate criteria and forced termination review criteria, inactive funnel data in a long-term stagnant state can be identified, and a separate warning or re-entry induction procedure can be performed. Accordingly, the judgment rule of the funnel management module (136) may be a data processing procedure to go beyond simple status notifications to induce active behavior of the startup company and maintain the up-to-date nature of the funnel data.

[0182] An integrated embodiment in which the hardware and software components of the customized startup financing information provision system based on multidimensional corporate readiness analysis according to the present invention and four major data processing processes operate in organic linkage is described in detail.

[0183] The control unit (130) monitors the support project database of an external supervising agency while the system's available power is at a stable level of 80%. At this time, it identifies that there have been 4 correction announcements in a specific research and development support project announcement and that the shortage index of remaining seats for offline on-site briefings has reached 90%. The control unit (130) determines that the urgency of updating information is high and performs a first process of controlling the communication unit (110) to shorten the data collection cycle from the basic 60 minutes to 20 minutes. The announcement text collected by the communication unit (110) at the shortened cycle is transmitted to the data collection module (131), and essential qualification requirements and submission document items are extracted as structured data through language processing technology and optical character recognition technology. Subsequently, the indicator definition module (132) maps the extracted items to multidimensional corporate readiness standards, such as technology maturity and business readiness, and stores them in the storage unit (120).

[0184] Meanwhile, a specific startup (hereinafter 'Startup A') inputs self-diagnosis data through a user terminal, and the diagnostic evaluation module (133) calculates an initial score of 88 points based on this. However, through cross-verification of documents accumulated in the system, one discrepancy between Startup A's past business plan and financial statements is identified. The diagnostic evaluation module (133) applies the discrepancy as a deduction factor, but considering that the evaluator's accumulated number of due diligence participation cases is 40, the deduction range is mitigated and the score is initially lowered to 80 points. Subsequently, as four original supporting documents that have been notarized are confirmed, additional points are awarded, but a second process is applied to slow down the increase in additional points starting from the fourth case, which exceeds three cases, to derive a final readjusted score of 85 points and update this to the storage unit (120).

[0185] Subsequently, the requirements review module (134) checks whether the basic eligibility requirements for the previously collected research and development support project are met based on the business history and financial status of startup company A. After passing the requirements, the information matching module (135) performs a third process to calculate the offline-based matching suitability by reflecting the physical environment of startup company A. A high basic suitability is derived because the actual amount of funds that can be received is high at 100 million won and there are 4 existing supporting documents. However, the suitability is slightly lowered as it is confirmed that the number of public transportation transfers from startup company A's business location to the evaluation location is 3. Furthermore, the estimated physical time for processing essential documents requiring a corporate seal and verification against the original is calculated as 2 days, and the number of remaining business days until the actual deadline is confirmed to be 3 days. Since the estimated processing time does not exceed the number of remaining business days, it does not fall under the filtering condition for physical submission impossibility, thus avoiding the forced lowering of the lower limit value mentioned above, and is classified as valid and reasonable recommendation information with a final suitability score of 78 points and transmitted to the terminal of startup company A.

[0186] When startup company A applies for the relevant support program and enters the document screening waiting funnel stage, the funnel management module (136) operates to perform the fourth process of calculating the stage transition probability. As 15 days pass in the screening waiting state, the basic stage transition probability drops to 40%. However, one record of startup company A completing a reply within the deadline regarding the organization's request for additional data submission and two records of performing offline practical rehearsals registered in the system are identified. The funnel management module (136) adjusts the dropped transition probability upward to 75% by reflecting these defensive actions and practical readiness. In particular, as the defensive action satisfies the pre-set synergy addition criteria, startup company A is automatically classified as a target for intensive management, and a notification for linking subsequent mentoring to further increase the success rate is sent through the communication unit (110).

[0187] As such, the present invention organically connects a series of processes—ranging from data collection (first process), objective score readjustment (second process), feasible matching (third process), and posterior probability management (fourth process)—through each hardware and software component. Through this, moving beyond a mere listing of information, the physical limitations and uncertainties of offline financing are replaced by a data processing stage, thereby enhancing system computational efficiency and ensuring the validity of providing practical information.

[0188] Although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. Various modifications are possible by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention. Explanation of the symbols

[0189] Communication unit (110) Storage unit (120) control unit (130) Data collection module (131) Indicator definition module (132) Diagnostic evaluation module (133) Requirements review module (134) Information matching module (135) Funnel management module (136)

Claims

Claim 1 A communication unit that transmits and receives data with external agency servers and user terminals; a storage unit that stores collected data and calculation results; and a control unit that controls the communication unit and the storage unit and performs a procedure for providing customized financing information based on multidimensional enterprise readiness analysis; wherein the control unit includes: a data collection module that analyzes support project announcements collected through the communication unit using language processing technology to extract eligibility requirements and evaluation items; an indicator definition module that classifies and maps the extracted evaluation items according to pre-set multidimensional enterprise readiness standards; a diagnostic evaluation module that calculates an initial score based on self-diagnosis data input by the startup and readjusts the initial score by reflecting offline verification data; a requirements review module that reviews whether the eligibility requirements and disqualification criteria of the support project are met based on the basic information of the startup and the readjusted readiness score to make a preliminary determination of whether an application is possible; and an information matching module that calculates the mapping suitability by calculating the difference between the required readiness of the support project and the readjusted readiness of the startup and the physical limit requirements. A customized startup funding information provision system based on multidimensional corporate readiness (6-RL) analysis, characterized by executing a funnel management module that calculates a stage transition probability by calculating the number of days of delay in the funding screening of the startup and the history of defensive actions against institutional requests, wherein the control unit dynamically controls the data collection cycle of the communication unit, and the control unit sets a standard cycle for data collection based on the current available power index of the system driving the communication unit, extracts an urgency index including the frequency of correction notices occurring within a preset standard period and the shortage index of remaining seats for face-to-face consultation reservations from information collected from the external institution server, and calculates and obtains a preparation burden index including the estimated number of days required for registered delivery of original documents and the number of original document items requiring corporate seal impression through text analysis of the collected notice. Claim 2 delete Claim 3 In claim 1, the control unit first corrects the set reference cycle as the frequency of correction announcements and the remaining seat shortage index of the acquired urgency index increase, and determines the final data collection cycle by proportionally extending the first corrected cycle as the estimated number of days and the number of original document items of the calculated preparation burden index increase, and if the determined final data collection cycle is less than a preset lower limit value, forcibly fixes the cycle to the preset lower limit value to prevent system overload, and if the available power index is less than a minimum reference value, omits the calculation of the urgency index and the preparation burden index and forcibly fixes the collection cycle to a preset upper limit value, thereby blocking meaningless real-time data updates for support projects that do not meet the physical lead time for offline document preparation, thereby reducing the communication computation load of the server. Claim 4 In claim 1, the diagnostic evaluation module sets the initial score as a reference value, identifies the number of discrepancies where data between submitted evidence contradicts each other and the number of reproduction failures in offline field demonstrations, determines the reduction range according to the identified number of discrepancies and the number of reproduction failures, applies a reduction correction rate to the initial score, and in applying the reduction correction rate, retrieves the cumulative number of due diligence participations of professional evaluators evaluating the field demonstrations from a database, and applies a mitigation correction so that the reduction range is reduced by reflecting flexibility based on field experience as the retrieved cumulative number of due diligence participations increases beyond a preset reference value, thereby providing a customized startup funding information system based on multidimensional enterprise readiness (6-RL) analysis. Claim 5 In claim 4, the diagnostic evaluation module confirms the number of supporting documents whose originals have been verified through notarization or an institutional seal and the duration for which the field demonstration was operated without interruption for the score where the reduction range has been mitigated and corrected, and sums the additional points calculated as the number of supporting documents and the duration increase, wherein from the section where the number of supporting documents and the duration exceed a preset marginal utility threshold, the increase rate slowdown operation is processed and reflected so that the size of the additional points granted per unit value exceeding the threshold is gradually reduced compared to the previous stage, thereby providing a customized startup funding information system based on multidimensional corporate readiness (6-RL) analysis. Claim 6 In claim 5, the diagnostic evaluation module is characterized by limiting the final readjustment score calculated by reflecting the increase rate slowdown calculation to 100 points if it exceeds 100 points and fixing it to 0 points if it is less than 0 points, and by excluding the application of the mitigation correction logic for the reduction rate and applying the reduction as is if the cumulative number of due diligence participations of the expert evaluators queried is less than the preset threshold, thereby refining subjective response data based on a simple survey reflecting the psychological overestimation of the startup into quantitative and empirical data reflecting the feasibility of actual fundraising in an offline environment.

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