Ai based IP value evaluation system

Abstract

To provide a system for evaluating an IP value without a subjective evaluation stage of an expert on the basis of objective input data of a general user including a nonexpert.SOLUTION: The IP value evaluation system 10 includes a value evaluation database including conformity information data, patent information data, and economic statistics information data and including statistical data and AI learning data, a collection and purification module configured to calculate and provide statistical data and AI learning data required in a process of generating AI learning data or a core variable, and an AI model for each core variable and configured to learn an AI model for each of the collection and purification module and calculate a corresponding explanatory variable matched with each core variable based on input evaluation target IP information and calculate a corresponding prediction variable value through each explanatory variable value collected or calculated by the collection and purification module and the AI model; The system includes a AI module for respectively calculating core variables and a value evaluation service module for calculating an object IP value based on the core variables and generating a value evaluation report including statistic data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an AI-based IP valuation system, which matches patent classification information with industry classification information or import / export codes, links patent statistical information, industry classification information, and company information, collects and processes various data, calculates relevant statistical data and learning data from the raw data, trains an AI model, estimates the main core variables required for value valuation through the AI ​​model, and provides the IP valuation results together with relevant statistical data. [Background technology]

[0002] Demand for IP (Intellectual Property) valuation is continuously increasing, but traditional expert-based valuation has been difficult because it is difficult to ensure consistency in valuation results due to the subjective evaluation of experts, and the pre-valuation surveys and report preparation required a considerable amount of time.

[0003] In addition, when attempting to evaluate the value of patents held by a business entity, it is generally possible to evaluate the value of a patent portfolio containing multiple patents related to the business rather than a single patent.

[0004] The challenge here is that as the number of patents in a portfolio increases, the time and resources required increase.

[0005] In addition, there were solutions that received input from experts or users on the evaluation results for the individual evaluation elements required for calculating the value evaluation, and calculated the patent value and grade based on the input data, but there were problems in that the evaluation results were calculated simply and limitedly because expert evaluation of the individual evaluation elements was required or the input data used was limited.

[0006] Furthermore, when only the evaluation results for patents are provided, non-experts have difficulty interpreting and utilizing the evaluation results. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] Managing Technology:The Technology Valuation Approach(IEEE,2007) [Non-patent document 2] A Comparative Studyon Methods of Income Approach to Technology Valuation(Journal of Supply Chain and Operations Management,Volume 10,Number 2,September 2012) [Non-patent document 3] Internal technology valuation:real world issues(International Journal of Technology Management,Vol.53,No.2-4,2011) [Non-patent document 4] Income approach to technology valuation for innovations(International Journal of Technology Management,Vol.88,No.2-4,2022) [Non-patent document 5] Review of Methods of New Technology Valuation(IEEE, 2010) Summary of the Invention [Problem to be solved by the invention]

[0008] Therefore, the technical problem that the present invention aims to solve is to provide a system that can perform IP value evaluation based on objective input data from general users, including non-experts, without the need for a subjective evaluation stage by experts.

[0009] Furthermore, the technical problem that the present invention aims to solve is to provide a system for portfolio valuation that objectively and efficiently evaluates value from the perspective of the entire portfolio without being affected by the number of individual patents, rather than the conventional evaluation methods of compiling individual evaluations of individual patents or selecting core patents based on the subjective judgment of business entities or experts.

[0010] In addition, the technical problem that this invention aims to solve is to estimate the core variable values ​​necessary for value calculation through an AI model, train the AI ​​model by utilizing various data and input data sets, and utilize the calculated values ​​of the model optimized for each core variable to improve the reliability of the system evaluation results.

[0011] The technical problem that the present invention aims to solve is to provide a system that provides related statistical data that has been utilized or processed for core variable estimation and value calculation, and that is useful for interpreting the evaluation results and providing insights into the related industrial environment. [Means for solving the problem]

[0012] The IP valuation system according to one aspect of the present invention for solving the technical problem is an AI (Artificial Intelligence)-based IP (Intellectual Property) valuation system, comprising: a valuation database including raw information, including compliance information data, patent information data, and economic statistical information data, and statistical data and AI learning data as extracted information processed from the raw information; a collection and refinement module that collects and processes the raw information, calculates and provides the statistical data and AI learning data required in the process of generating AI learning data or first to fourth core variables, and stores them in the valuation database; and a description module that trains two or more AI models for each of the core variables using the AI ​​learning data to calculate the first to fourth core variables, and matches each core variable based on input IP information to be evaluated. The AI ​​module checks the variables and calculates the predictor variable values ​​and core variable values ​​through the AI ​​model and each explanatory variable value collected or calculated by the collection and refinement module, calculating a first predictor variable and a first core variable value through a first explanatory variable set, a second predictor variable and a second core variable value through a second explanatory variable set, a third predictor variable and a third core variable value through a third explanatory variable set, and a fourth predictor variable and a fourth core variable value through a fourth explanatory variable set; and a value assessment service module calculates the value of the target IP based on the first to fourth core variable values ​​and generates a value assessment report including the IP value and the statistical data.

[0013] In this case, the first to fourth core variables are the IP economic life span, royalty, discount rate, and sales amount, respectively.

[0014] The first to fourth predictor variables are the IP economic life span influence factor, the royalty rate influence factor, the IP commercialization risk premium, and the sales growth rate, respectively.

[0015] In addition, the first to fourth predictive variables are defined and learned during the AI ​​model learning process as the difference between the expert evaluation result for the IP economic lifespan and the median TCT (Technology Cycle Time) for the patent classification information to which the target IP belongs, the ratio between the expert-evaluated royalty rate and the standard royalty rate for the industry based on the industry classification information matched with the patent classification information to which the target IP belongs, the expert-evaluated IP commercialization risk premium, and the sales growth rate by industry.

[0016] Then, the AI ​​module checks the median TCT of the patent classification information to which the target IP belongs, and calculates the first core variable by reflecting the value of the first predictor variable in the median TCT; checks the standard royalty rate for the industry based on the industry classification information matched to the patent classification information to which the target IP belongs, and calculates the second core variable by reflecting the value of the second predictor variable in the standard royalty rate; checks the cost of equity and ratio, and cost of debt and ratio for the industry based on the industry classification information matched to the patent classification information to which the target IP belongs, and calculates the third core variable by reflecting the value of the third predictor variable in the cost of equity; and, if past sales of the business entity that owns the target IP are confirmed, sets initial sales based on the past sales, and calculates the fourth core variable by reflecting the value of the fourth predictor variable in the initial sales.

[0017] In addition, if the past sales of the business entity that owns the target IP cannot be confirmed, the AI ​​module sets the initial sales amount based on the sales statistics of companies of a predetermined size in the industry.

[0018] The first set of explanatory variables for generating the first predictor variable includes the rate of increase / decrease in the number of applications by applicant (rate of increase / decrease in applications / rate of increase / decrease in applicants), TCT statistics, evaluation factors of the rating system, evaluation index scores of the rating system, the average number of U.S. patent lawsuits by patent classification information, and the average number of trials-related cases by patent classification information; the second set of explanatory variables for generating the second predictor variable includes royalty rate statistics, the average number of opinions submitted by patent classification, evaluation factors of the rating system, evaluation index scores of the rating system, and the number of divisional applications and the number of priority claims by patent classification; the third set of explanatory variables for generating the third predictor variable includes, as optimized explanatory variables for predicting the IP commercialization risk premium, sales growth rate by industry by company size, operating profit growth rate by industry by company size, evaluation factors of the rating system, evaluation index scores of the rating system, and patent concentration; and the fourth set of explanatory variables for generating the fourth predictor variable includes the number of applicants, the rate of increase / decrease in the number of applications, and the rate of increase / decrease in imports and exports.

[0019] The target IP information is the patent registration number of the target IP.

[0020] Then, the value assessment service module calculates the target IP value by the royalty exemption method based on the first to fourth core variable values.

[0021] In addition, the compliance information data includes expert IP evaluation result data and actual IP transaction information data, the patent information data includes, as patent detail information, cited / cited data by patent, application data, trial data, litigation data, registration data and family data, and as grade evaluation information, includes data on grade evaluation factors by patent and score data for the indicators represented by the evaluation factors according to the evaluation results for each factor, and the economic statistical information data includes, as economic market information, sales growth rate data by industry, sales statistical data and economic forecast data, as financial information, stock price data, bond interest rate data and corporate financial data, and as import / export information, import / export data.

[0022] In this case, the collection and refinement module includes a raw data collection unit for collecting the raw information; a pre-processing unit for performing pre-processing on the collected raw information; a basic data calculation unit for generating basic data for calculating learning data and evaluation criterion data from the pre-processed data; a learning data calculation unit for generating AI model learning data for calculating the core variables from the basic data; a statistical data calculation unit for generating statistical data for one or more of the explanatory variables, predictor variables, and core variables as statistical data generated or required in the AI ​​learning data or core variable calculation process; and an evaluation criterion data calculation unit for calculating final evaluation criterion data based on the first to fourth core variable values ​​calculated via the AI ​​module and transmitting the final evaluation criterion data to the value assessment service module.

[0023] In addition, the evaluation criteria data calculation unit finally calculates the IP economic life as a first core variable value, taking into account one or more of the remaining legal life of the target IP and the commercialization preparation period as evaluation criteria data, and calculates the corporate tax rate and corporate tax based on the calculated sales revenue.

[0024] In addition, the statistical data includes, as statistical information utilized or extracted in the process of calculating the first core variable, TCT data for the patent classification, statistical data related to trials, U.S. litigation data, and market concentration data for the industry field; as statistical information utilized or extracted in the process of calculating the second core variable, data on industry-based royalty rates, the number of opinions submitted for the patent classification, the number of divisional applications, and the number of priority claims; as statistical information utilized or extracted in the process of calculating the third core variable, data on the cost of own / owned capital for the industry and data on the own / owned capital ratio by industry, the patent concentration rate for the patent classification, and data on sales / operating profit growth rates for the industry; and as statistical information utilized or extracted in the process of calculating the fourth core variable, data on initial sales statistics according to the industry and size to which the target IP belongs, statistical data on the number of applicants and the rate of increase / decrease in the number of applications for the patent classification, and data on the rate of increase / decrease in imports and exports for the industry and item.

[0025] The AI ​​module includes a learning data preprocessing unit that performs preprocessing on AI learning data; an AI learning unit that trains one or more AI models for each of the first to fourth core variables using the AI ​​learning data; a learning optimization unit that sets one or more AI models optimized according to performance index results for each core variable based on verification results for the AI ​​model predicted values; and a core variable calculation unit that calculates core variables using explanatory variables matched for each core variable and predictive variables calculated through the AI ​​model.

[0026] In addition, the core variable calculation unit, in the case of an IP portfolio having two or more IPs to be evaluated, sets the average of the median TCT values ​​for the patent classification information of each individual patent as the reference TCT of the target IP portfolio, calculates IP economic lifespan influence factors for each individual patent, and sets the influence factor with the largest value as the first predictor variable for the target portfolio, calculates a first core variable for the target portfolio by reflecting the value of the first predictor variable for the target portfolio in the reference TCT of the target portfolio, sets the industry according to user-input information, or sets the industry with the largest median sales of small companies by industry among the industries according to the industrial classification information matched with the patent classification information of each individual patent as the representative industry of the target portfolio, and sets the reference royalty rate of the representative industry of the target portfolio as the reference royalty rate of the target portfolio, calculates influence factors for the royalty rate for each individual patent, and sets the largest value the influencing factor is set as a second predictor variable for the target portfolio, the second core variable for the target portfolio is calculated by reflecting the value of the second predictor variable for the target portfolio in the standard royalty rate of the target portfolio, the IP commercialization risk premium for each individual patent is calculated, and the smallest IP commercialization risk premium is set as a third predictor variable for the target portfolio, the third core variable for the target portfolio is calculated by reflecting the value of the third predictor variable in the equity capital cost of the weighted average capital cost of the representative industry of the target portfolio, a fourth predictor variable for the target portfolio is generated based on the patent classification information and import / export item classification information matched to the representative industry of the target portfolio, initial sales are set through the business entity's past sales information or sales statistics of the representative industry of the target portfolio, and the fourth core variable for the target portfolio is calculated by reflecting the value of the fourth predictor variable in the initial sales. [Effects of the Invention]

[0027] As described above, according to the present invention, it is possible to perform IP value evaluation based on objective input data from general users including non-experts, without the subjective evaluation process of experts.

[0028] In particular, according to the present invention, in portfolio valuation, it is possible to perform a rapid, efficient and objective valuation regardless of the number of individual patents.

[0029] Furthermore, according to the present invention, it is possible to obtain highly reliable value assessment results by generating various learning data and utilizing multiple AI models trained on the learning data.

[0030] In addition, according to the present invention, it is possible to provide the user with insight into the related industry environment and IP utilization by estimating core variables necessary for value calculation or providing related statistical data used or calculated in the value calculation process. [Brief explanation of the drawings]

[0031] [Figure 1] 1 is an overall configuration diagram of an IP value assessment system according to one aspect of the present invention. [Figure 2] FIG. 2 is a detailed block diagram of a value assessment service module according to one aspect of the present invention. [Figure 3] FIG. 2 is a detailed diagram of a collection / purification module according to one aspect of the present invention. [Figure 4] FIG. 2 is a detailed configuration diagram of an AI module according to one aspect of the present invention. [Figure 5] FIG. 1 is a detailed diagram of a stacking ensemble model according to one aspect of the present invention. [Figure 6] Among the core variables calculated for patent value assessment relating to one aspect of the present invention, examples of explanatory variable items input into the AI ​​model for calculating the first core variable, the IP economic life, and predictive variables are shown. [Figure 7] The figure shows examples of explanatory variable items and predictive variables that are input into the AI ​​model to calculate the royalty rate, which is the second core variable among the core variables calculated for patent value evaluation related to one aspect of the present invention. [Figure 8]The figure shows examples of explanatory variable items and predictive variables that are input into the AI ​​model to calculate the discount rate, which is the third core variable among the core variables calculated for patent value assessment related to one aspect of the present invention. [Figure 9] The figure shows examples of explanatory variable items input into an AI model to calculate sales, which is the fourth core variable among the core variables calculated for patent value assessment related to one aspect of the present invention, and predictive variables. [Figure 10] FIG. 2 is a detailed block diagram of a value assessment management module according to one aspect of the present invention. [Figure 11] FIG. 2 is a detailed configuration diagram of a value assessment database according to one aspect of the present invention. [Figure 12] The overall flow of the system for evaluating IP value based on objective target IP information input by a user and generating a report according to one aspect of the present invention will now be described. [Figure 13] The overall flow of the system for calculating the final portfolio value based on objective target IP portfolio information input by the user according to one aspect of the present invention will now be described. [Figure 14] The overall flow of the system for calculating the final portfolio value based on objective target IP portfolio information input by the user according to one aspect of the present invention will now be described. [Figure 15] 1 is a diagram illustrating an example of a computer device according to one aspect of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention can be embodied in various different forms and is not limited to the embodiments described herein. In order to clearly explain the present invention in the drawings, parts unnecessary for explanation are omitted, and similar reference numerals are used to refer to similar components throughout the specification.

[0033] Throughout the specification, when a part is said to "comprise" a certain element, this means that it may further include other elements, but not to the exclusion of other elements, unless otherwise specified.

[0034] Furthermore, terms such as "... unit," "... device," and "... module" used in the specification refer to a unit that processes at least one function or operation, and may be realized by hardware, software, or a combination of hardware and software.

[0035] The apparatus described in the present invention is configured with hardware including at least one processor, memory device, communication device, etc., and stores a program that is executed in combination with the hardware at a specified location. The hardware has the configuration and performance to perform the method of the present invention. The program includes instructions that realize the operating method of the present invention described with reference to the drawings, and executes the present invention in combination with hardware such as a processor and memory device.

[0036] In this specification, "transmitting or providing" may include not only direct transmitting or providing, but also indirect transmitting or providing via other devices or using a roundabout path.

[0037] In this specification, expressions in the singular may be construed as singular or plural unless expressly stated otherwise, such as "one" or "single".

[0038] As used herein, like reference numbers refer to like elements regardless of the drawing, and "and / or" includes each and every combination of one or more of the referenced elements.

[0039] Terms including ordinal numbers such as first, second, etc. may be used to describe various components, but the components are not limited to these terms. These terms are used only to distinguish one component from another. For example, a first component can be designated a second component, and similarly, a second component can be designated a first component, without departing from the scope of the present disclosure.

[0040] In the flowcharts described herein with reference to the drawings, the order of operations may be changed, various operations may be combined, certain operations may be separated, and certain operations may not be performed.

[0041] The present invention also provides a system that applies the royalty exemption method, one of the various methodologies utilized in IP valuation. The royalty exemption method is a method for estimating the value of the IP by estimating the appropriate royalty that would be borne if the rights to the IP were not owned. In the following description, the case where the IP to be evaluated is a patent will be described as an example.

[0042] More specifically, the royalty exemption method is a patent valuation method that estimates the current value of royalties to be paid as license fees over the economic life of the patent being evaluated.

[0043] In particular, the royalty exemption method is suitable for patent valuation by start-ups or small and medium-sized enterprises that own IP but do not currently generate sales, and is also suitable for valuing R&D-calculated patents that are unlikely to be commercialized.

[0044] When using the royalty exemption method to evaluate patent value, the specific calculation formula is as follows:

number

[0045] Therefore, according to the present invention, the IP value assessment system can calculate the economic life of the target IP (hereinafter also referred to as the "first core variable"), royalty rate (hereinafter also referred to as the "second core variable"), discount rate (hereinafter also referred to as the "third core variable") and estimated sales (hereinafter also referred to as the "fourth core variable") as core variables based on the user's objective input data, and perform a value assessment of the patent to be assessed.

[0046] Hereinafter, an IP value assessment system according to one aspect of the present invention will be described in more detail with reference to the drawings.

[0047] First, as shown in FIG. 1, the IP valuation system 10 may include a valuation service module 100 , a collection / refining module 200 , an AI module 300 , a valuation management module 400 and a valuation database 500 .

[0048] The value assessment service module 100 can receive objective information data related to the IP to be assessed from a user, receive assessment criteria data generated based on the information data, calculate the IP value, and generate a report.

[0049] Specifically, the value assessment service module 100 provides an interface through which a user can input information about an IP to be assessed, and can receive the information.

[0050] The value assessment service module 100 can also receive, as objective information data, information related to the business entity that owns the IP to be assessed, that is, information related to the business scale and industry, as selected by the user.

[0051] The value assessment service module 100 can manage the value assessment attributes for the target IP, for example, attribute information such as the assessment purpose and assessment method.

[0052] The value assessment service module 100 can perform IP value assessment according to the assessment purpose and method set through the assessment criterion data calculated from the target IP information.

[0053] The value assessment service module 100 can generate an IP value assessment report including the IP value assessment result and related statistical data calculated during the value assessment process.

[0054] The collection / refining module 200 can periodically collect, refine, and process the raw information required for IP valuation to extract the required information.

[0055] Specifically, the collection / refining module 200 can collect, as compliance information, for example, IP subject matter expert valuation data, university technology transfer data, public research institute technology transfer data, and exchange IP transaction data.

[0056] In addition, the collection / refinement module 200 can collect, as patent information, for example, cited / cited data, application data, trial litigation data, registration data, family data, and classification data of the patent.

[0057] The collection / refining module 200 can collect economic statistical information from public data, such as sales growth rate data, sales statistical data, economic forecast data, stock price data, bond interest rate data, corporate financial data, and import / export data.

[0058] The collection / refinement module 200 can extract necessary information from the raw information, and can also extract relevant statistical information and learning information for AI learning.

[0059] Specifically, the collection / refining module 200 can extract statistical information such as IPC-specific patent citation lifespan, industry-specific standard royalty rates, industry-specific own / borrowed capital costs, and industry-specific own / borrowed capital ratios from the raw information.

[0060] In addition, the collection / refinement module 200 can extract, as AI learning data, for example, patent concentration, number of trials and lawsuits, number of prior IPs, number of technology transfers, number of licenses, depth of dependent claims of the target IP, number of claim series, number of opinions submitted, number of divisional applications, number of claims, number of citations, CAGR (Compound Annual Growth Rate) by industry, sales statistics by industry, sales growth rate by industry, import / export growth rate, economic forecast, and application growth rate by IPC.

[0061] The AI ​​module 300 may receive and preprocess the AI ​​training data from the collection / refinement module 200, and then use the training data to train an AI model for calculating core variables.

[0062] The AI ​​module 300 can calculate core variables required for value assessment based on user input data through each AI model optimized by learning.

[0063] The valuation management module 400 can perform management functions for the IP valuation system 10 .

[0064] Specifically, the valuation management module 400 can manage reference information for performing IP valuation, manage system users and the system, and perform a fee settlement process for using the IP valuation service.

[0065] The valuation database 500 can store raw information collected and information processed and extracted from the raw information.

[0066] The value assessment database 500 can store compliance information data, patent information data, and economic statistical information data collected by the collection / refining module 200, as well as statistical data and AI learning data as extracted information processed by the collection / refining module 200.

[0067] The modules 100 to 400 and the value assessment database 500 will be described below with reference to FIGS.

[0068] As shown in FIG. 2, the value assessment service module 100 may include a user interface unit 110 , an attribute management unit 120 , a value calculation unit 130 and a report generation unit 140 .

[0069] Specifically, the user interface unit 110 provides an interface through which the user can input information about the IP to be evaluated, and can additionally select or input information about the business size and industry of the business entity.

[0070] The attribute management unit 120 can manage attribute information such as IP valuation purpose, IP valuation method, etc. as IP valuation attributes. For example, the attribute management unit 120 can set IP secured loan as the valuation purpose and royalty exemption method as the valuation method.

[0071] The value calculation unit 130 receives valuation criteria data required for value calculation by a valuation method (the royalty exemption method in the present invention), and can calculate the IP value from the valuation criteria data.

[0072] Specifically, the value calculation unit 130 can calculate the value of the target IP according to the specific calculation formula of Equation 1.

[0073] The report generating unit 140 can generate an IP valuation report including not only the IP valuation result but also related statistical data calculated during the valuation process.

[0074] For example, the report generating unit 140 may first include statistics on Technology Cycle Time (TCT) for each target International Patent Classification (IPC) calculated in the process of estimating the economic lifespan of the target IP.

[0075] Specifically, the report generation unit 140 can generate a report including quantile, median, average, and tertile information as TCT statistics for the IPC based on the Korean and American patent citation lifetimes for the target IP.

[0076] In addition, the report generation unit 140 includes statistical analysis information related to the arbitration of the IPC that is extracted and utilized as learning data for calculating the economic lifespan of the IP, thereby enabling the competitive strength of the IP to be understood.

[0077] Similarly, the report generation unit 140 may include initial sales statistics information for the business entity's industry that is extracted and utilized for calculating sales, and may include information on the number of applicants and the rate of increase or decrease in the number of applications for the IPC, and information on the rate of increase or decrease in imports and exports by item that are utilized as learning data, allowing the IPC's industry trends, operating profitability, market growth trends, etc. to be confirmed.

[0078] In addition, the report generation unit 140 can provide statistical information on royalty rates for the relevant industry as a reference royalty rate used to calculate royalty rates, and can also provide statistical information on the number of opinions submitted for the IPC, divisional applications, and the number of domestic priority claims reflected in the learning data, which can be used to predict the stability of rights, research sustainability, and future development potential for the IP.

[0079] The report generating unit 140 may also provide information on the cost of debt capital and the cost of equity capital calculated from stock information, bond information, and financial information as discount rate-related statistics for the industry to be used for calculating the discount rate. The report generating unit 140 may also provide information on the operating profit growth rate and sales CAGR by size of the industry, which are extracted for calculating the discount rate and used as learning data, in comparison with the average for all industries, so that the information may be used to forecast the operating profit stability or margin rate of the industry relative to all industries.

[0080] Meanwhile, the IPC is an internationally unified patent classification system that indicates the technical field of an invention, and is an example of IP classification information according to the present invention. IP classification information is not limited to IPC, but may also include CPC (Cooperative Patent Classification) and other patent classification systems. In the following description of this specification, IPC will be used as an example of IP classification information.

[0081] Meanwhile, as shown in FIG. 3, the collection / refinement module 200 may include a raw data collection unit 210, a pre-processing unit 220, a basic data calculation unit 230, a training data calculation unit 240, a statistical data calculation unit 250, and an evaluation reference data calculation unit 260.

[0082] The raw data collection unit 210 can collect source data to be used in IP valuation, but can first collect previously conducted expert IP valuation data and actual IP transaction information data as reference information to serve as the basis for valuation.

[0083] In addition, the raw data collection unit 210 can collect IP-specific citation / cited data, application / trial / litigation / registration data, design right data, and family data as patent information from existing IP information providing systems.

[0084] And, the raw data collection unit 210 can collect rating data from the IP rating system.

[0085] The raw data collection unit 210 can collect sales growth rate data, sales statistics data, business forecast data, stock price data, bond interest rate data, corporate financial data, and import / export data as economic statistical information.

[0086] The pre-processing unit 220 can pre-process the collected source data by passing and purifying it preferentially.

[0087] The base data calculation unit 230 can generate base data for AI learning data and evaluation reference data from the collected and pre-processed data, respectively.

[0088] The learning data calculation unit 240 can generate AI model learning data from the basic data, which is used to estimate each predictive variable for calculating core variables for IP value evaluation.

[0089] For example, the learning data calculation unit 240 can generate learning data to be input into the AI ​​model to calculate the IP economic lifespan, using the difference between the expert evaluation result for the IP economic lifespan, which is the reference information, and the median TCT for the IPC (hereinafter also referred to as "factors influencing IP economic lifespan") as a predictive variable, and the rate of increase or decrease in the number of applicants / applications, patent concentration, TCT statistics, grade evaluation factors, grade evaluation index scores, US litigation statistics by IPC, and arbitration statistics by IPC as explanatory variables.

[0090] In addition, the learning data calculation unit 240 can generate learning data to be input into the AI ​​model to calculate royalty rates by using the ratio between the expert-assessed royalty rate, which is the compliant information, and the standard royalty rate for the industry (hereinafter also referred to as "factors influencing the standard royalty rate") as a predictive variable, and standard royalty rate statistics, the number of opinions submitted, grade evaluation factors, grade evaluation index scores, the number of divisional applications, the number of priority claims, the average depth of dependent claims, etc. as explanatory variables.

[0091] The learning data calculation unit 240 can generate learning data to be input into the AI ​​model for discount rate calculation, using the expert-assessed IP commercialization risk premium as a predictive variable and sales growth rate / operating profit growth rate by company size / industry, grade evaluation factors, grade evaluation index scores, patent concentration, etc. as explanatory variables.

[0092] The learning data calculation unit 240 can generate learning data to be input into the AI ​​model for calculating sales revenue, using sales growth rate as a predictive variable and the rate of increase / decrease in imports / exports by industry, the rate of increase / decrease in IPC special applications, etc. as explanatory variables.

[0093] The statistical data calculation unit 250 can calculate relevant statistical data required in the learning data generation or core variable calculation process.

[0094] The statistical data calculation unit 250 can provide statistical data for compliance information, basic statistics for core variables, and statistics by explanatory variables.

[0095] Specifically, the statistical data calculation unit 250 can calculate TCT statistics by IPC, statistical analysis information related to trials, statistical information on initial sales of business entities, number of applicants and increase / decrease rate of number of applications by IPC, information on increase / decrease rate of import / export by item obtained through matching IPC with import / export codes, cost of own / borrowed capital by industry size, own / borrowed capital ratio by industry size, patent concentration by IPC, statistical information on sales / operating profit growth rate by industry size, statistical information on standard royalty rates by industry, number of opinions submitted by IPC, statistical information on number of divisional applications and priority claims, etc.

[0096] According to the present invention, it is possible to calculate statistical information for each core variable by reflecting the results of predictor variable estimation calculated through two or more AI models for each core variable.

[0097] At this time, the statistical data calculation unit 250 may calculate statistical information for the predicted variable estimates or statistical information for each core variable, and provide the information to the user through a report.

[0098] Therefore, the user can utilize not only the median value of the core variable or core variable-related predictor variable, but also the 1st quantile, 3rd quantile, or mean information.

[0099] The evaluation criterion data calculation unit 260 can finally calculate the evaluation criterion data to be input into the value evaluation formula based on the calculated statistical data and core variables.

[0100] Specifically, the evaluation criteria data calculation unit 260 can check the remaining legal life of the target IP and compare it with the IP economic life calculated via the AI ​​module.

[0101] As a result of the comparison, the evaluation criteria data calculation unit 260 can determine the shorter remaining life as the final economic life of the target IP, or if a commercialization preparation period is required, can calculate the final economic life of the target IP to reflect that period.

[0102] In addition, the valuation criterion data calculation unit 260 can determine the corporate tax rate and determine the corporate tax expense. The valuation criterion data calculation unit 260 can apply the determined corporate tax rate based on the sales amount during the final economic life period of the target IP to calculate the final corporate tax expense.

[0103] Hereinafter, the AI ​​module 300 that calculates the core variables utilized in the target IP value evaluation will be specifically described with reference to FIGS.

[0104] As shown in FIG. 4 , the AI ​​module 300 may include a training data preprocessing unit 310 , an AI training unit 320 , a training optimization unit 330 and a core variable calculation unit 340 .

[0105] The training data pre-processing unit 310 can perform pre-processing on the training data transmitted from the training data calculation unit 240 .

[0106] Specifically, the training data pre-processing unit 310 can perform duplicate row processing, classification processing, and normalization processes for the crystalline AI model, and can perform duplicate row processing and outlier processing for the generative AI model.

[0107] The AI ​​learning unit 320 includes two or more AI models and can learn the AI ​​models through the learning data.

[0108] Specifically, the AI ​​learning unit 320 can include one or more of a crystalline AI model and a generative AI model, and can utilize the learning data to train the AI ​​model for predictor variable estimation for core variable calculation.

[0109] For example, the AI ​​learning unit 320 can use AutoML (Automated Machine Learning) as the crystalline AI model, and may be configured as a stacking ensemble model.

[0110] As shown in Figure 5, a stacking ensemble model includes base models (level-0 models) and a meta-model (level-1 model, final model), and can use the predicted values ​​of multiple base models as training data for the final model to make predictions.

[0111] According to the present invention, for example, the base models may include a statistical-based model (model 1), a tri-based model (model 2), and a neural network model (model 3).

[0112] In this case, the statistically based model can include a K-Nearest Neighbors model, the tree based model can include a decision tree model, a random forest model, an extra tree model, XGBoost, LightGBM, and a CatBoost model, and the neural network model can include a multilayer perceptron model.

[0113] The AI ​​learning unit 320 can then train and predict for all of the above models.

[0114] The AI ​​learning unit 320 can train a base model using an input data set, and use the predicted values ​​generated through the trained base model as input data for the metamodel again to generate final predicted values ​​and perform model training.

[0115] Specifically, the AI ​​learning unit 320 can train each base model via default hyperparameters and evaluate the learning performance of various combinations of hyperparameters via random sampling on a predefined set of hyperparameters.

[0116] The AI ​​learning unit 320 can generate a weighted average for the predicted values ​​of each learned base model, and use the weighted average predicted value as new input data to generate a final predicted value through the meta-model.

[0117] In this case, the AI ​​learning unit 320 generates multiple predicted values ​​with differences in the features of the learning data, the learning evaluation index, and the initial setting value (seed), and the final predicted value can be selected as the median value.

[0118] For example, the AI ​​learning unit 320 can vary three learning datasets, three learning evaluation indices, and three initial setting values ​​to generate 27 types of predicted values, and the double median can be selected as the final predicted value.

[0119] In this case, for example, the three initial values ​​can be set to 1, 2, and 3 using any random number value, and the three evaluation indices can include RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error), and R2 (R-Squared).

[0120] Alternatively, the AI ​​learning unit 320 may include a generative model, such as a Bayesian neural network model (BNN), a sparse Gaussian process model (Sparse GP), or a variational inference-based sparse Gaussian process model (Variational Sparse GP).

[0121] Specifically, in the case of a BNN, the weights of the hidden layer are defined as latent variables, and the latent variables are random variables with arbitrary distributions. In addition, the datasets used for learning are also random variables with arbitrary distributions. In the following, the arbitrary distributions of the explanatory variable sets can be described as joint distributions combined with the latent variables.

[0122] According to one aspect of the present invention, a joint distribution to be estimated is set as a distribution in which explanatory variables and latent variables are combined, and learning can be performed to minimize the difference between any candidate distribution that can be easily used to estimate this joint distribution. For example, the learning method can be performed to maximize the evidence lower bound (ELBO) and minimize the Kullback-Leibler (KL) distance. In this case, the ELBO refers to the expected value of the difference between the joint distribution to be estimated and the candidate distribution. The KL distance refers to the conditional distribution for the latent variable, i.e., the difference between the joint distributions, with the candidate distribution and the explanatory variables fixed, and can refer to the difference between the distribution value generated by the candidate distribution when a latent variable distribution value is generated based on given training data.

[0123] The Monte Carlo method can be used for this learning, generating distribution values ​​from each of the candidate distribution and the joint distribution, defining the sum of the distribution values ​​as the expected value, fixing the parameters of the joint distribution, and finding the parameters of the candidate distribution that maximizes the expected value. Next, fixing the parameters of the candidate distribution, finding the parameters of the joint distribution that minimizes the KL distance. The joint distribution can be learned by repeating the process of fixing the parameters of the joint distribution again and retrieving the parameters of the candidate distribution that maximizes the ELBO.

[0124] Through the above process, when each parameter does not change any more or a predetermined number of iterative learning is reached, the learning is terminated and a predetermined number of predicted values ​​can be generated from the joint distribution composed of the learned parameters.

[0125] Next, in the case of a sparse Gaussian process (Sparse GP), the GP refers to a distribution for a function, which means a distribution composed of any function for given explanatory variables. The distribution has the form of a multivariate normal distribution having a mean function and a covariance function. However, since the GP is used as a prior distribution for predictor variable inference, the mean function and covariance function can be assumed to be any function composed of explanatory variables. In this case, the covariance function can be assumed as a radial kernel function. The radial kernel function may be composed of parameters that express the characteristics of data given to a function indicating the relationship between data.

[0126] When an explanatory variable is given, each predictor variable is a distribution generated by a predefined GP, which combines the GP of the explanatory variable with an arbitrary error. Therefore, even in GP, ​​the parameter of the kernel function and the arbitrary error that maximizes the distribution value of the predictor variable must be estimated. A value is generated by adding an arbitrary error assuming a normal distribution to the GP of the explanatory variable using the Monte Carlo method, and the parameter can be estimated using a numerical interpretation method for the generated value. In this case, if the amount of data is enormous, a large amount of calculation is required. Therefore, by setting a point in the data space where the data is examined, the amount of calculation can be reduced and high-speed learning can be achieved.

[0127] The variational sparse GP model is a GP that does not follow the normal distribution assumption for arbitrary errors used in the previously mentioned GP assumptions, but assumes an arbitrary distribution. In other words, it is a model that assumes that arbitrary errors of predictor variables follow an arbitrary distribution.

[0128] Therefore, to assume the arbitrary distribution, the arbitrary distribution can be estimated using the Monte Carlo method, which maximizes the ELBO and minimizes the KL distance, which is a method used in BNN learning. For inference on a huge amount of data, the method of reducing the number of calculation points, as explained in Sparse GP, reduces the amount of calculation and enables quick learning.

[0129] According to one aspect of the present invention, the AI ​​learning unit 320 includes all of the above-mentioned Bayesian neural network model (BNN), sparse Gaussian process model (Sparse GP), and variational inference-based sparse Gaussian process model (Variational Sparse GP) as generative AI models, and can generate a total of 30 predicted values, 10 for each model.

[0130] In this case, the AI ​​learning unit 320, for example, includes the stacking ensemble model and the generative AI model described above, and can generate 27 predicted values ​​through the stacking ensemble model and 30 predicted values ​​through the generative model, for a total of 57 predicted values, of which the median can be used as the final predicted value.

[0131] Then, the AI ​​learning unit 320 can train and validate the stacking ensemble model and the generative AI model against the entire training dataset, for example, in a ratio of 9:1, to proceed with learning.

[0132] Meanwhile, the AI ​​learning unit 320 can learn the stacking ensemble model or generative AI model as a crystalline model based on different learning data having different input variables.

[0133] For example, according to one aspect of the present invention, the AI ​​model can be trained using explanatory variable set A, which has a correlation with the predictor variable at a Pearson correlation coefficient significance level of less than 0.05, explanatory variable set B, which additionally includes statistical values ​​of correlated variables, and explanatory variable set C, which also reflects uncorrelated variables and includes all rating elements of the rating system.

[0134] The more information reflected in model learning, the more complex the model becomes and the lower the predictive performance may be. Conversely, if there is insufficient information reflected in model learning, the lower the predictive performance may be. Therefore, in the present invention, both loss of information and parsimony are taken into consideration, and variable groups can be divided and compared.

[0135] More specifically, in calculating the first core variable, the AI ​​learning unit 320 can utilize the explanatory variables shown in FIG. 6 to predict the economic lifespan influence factors of the target IP, which is the first predictor variable, and more specifically, can utilize three sets of explanatory variables.

[0136] In this case, each explanatory variable set may commonly include the rate of increase or decrease in the number of applications filed by the applicant, the HHI (Herfindahl-Hirschman index) by industry, scores from the rating system, TCT statistics, and US lawsuit or arbitration statistics by IPC.

[0137] The explanatory variable set A may further include the number of papers, foreign patents, total citations, length of independent claims, and number of independent claims among the prior documents, while the explanatory variable set B may further include the number of foreign patents for papers, total citations, length of independent claims, number of independent claims, and the average number of foreign patents for papers, average total citations by IPC, average length of independent claims by IPC, and average number of independent claims by IPC among the prior documents by IPC. The explanatory variable set C may further include all evaluation elements of the rating system.

[0138] In this case, the rating system (for example, SMART5) uses the following information to evaluate the IP's specification (number of independent claims, length of independent claims, average depth of dependent claims, length of description of invention, number of dependent claims, number of claim series), surge information (number of IPCs, number of drawings, number of divisional applications / priority claims, number of inventors), examination information (presence or absence of early publication, presence or absence of prioritized examination request, number of information provided, number of opinions submitted), administrative information after registration (number of annual registrations, number of changes in right holders, number of overseas family countries, number of licensees, number of financial institution pledges, number of registrations for extension of term, etc.) The ranking system is based on whether or not there is a patent, litigation information (number of invalidation trials dismissed, number of citations / withdrawals / rejections of invalidation trials, number of dismissals of trials to confirm the negative scope of rights, number of citations / withdrawals / rejections of trials to confirm the negative scope of rights, number of dismissals / withdrawals / rejections of trials to confirm the positive scope of rights, number of citations of trials to confirm the positive scope of rights, number of appeals against rejection decisions, correction trials), and citation information (total number of citations, number of non-patent literature / foreign patents among cited references of cited patents, difference between application date and citations, number of papers / foreign patents among prior literature).

[0139] The rating system assigns a score to the target IP based on the value of each of the 32 evaluation elements, and assigns a rating according to the score range.

[0140] In addition, in calculating the second core variable, the AI ​​learning unit 320 can utilize the explanatory variables illustrated in Figure 7 to predict the influencing factors for the second predictor variable, the reference royalty rate, and more specifically, can utilize three sets of explanatory variables.

[0141] In this case, each explanatory variable set can commonly include the number of divisional applications / priority claims, the number of opinions submitted, sales growth rate by industry, royalty rate statistics, IPC-specific trial statistics, and evaluation scores of the rating system.

[0142] The explanatory variable set A may further include cited filing date difference, average dependent claim depth, number of independent claims, independent claim length, and TCT statistics, while the explanatory variable set B may further include cited filing date difference, average dependent claim depth, number of independent claims, independent claim length, TCT statistics, average number of divisional priority claims by IPC, average number of arguments submitted by IPC, average cited filing date difference by IPC, average dependent claim depth by IPC, average number of independent claims by IPC, and average independent claim length by IPC. The explanatory variable set C may further include all evaluation factors of the rating system.

[0143] Then, in calculating the third core variable, the AI ​​learning unit 320 can utilize the explanatory variables shown in FIG. 8 as the third predictor variables for predicting the IP commercialization risk premium, and more specifically, can utilize a set of three explanatory variables.

[0144] In this case, each explanatory variable set may commonly include sales growth rate by industry and company size, operating profit growth rate by industry and company size, patent concentration, and evaluation score of the rating system.

[0145] The explanatory variable set A may further include the length of the independent claim, the number of foreign patent citations of the cited patent, the business climate index by industry, and the trial statistics by IPC, the explanatory variable set B may further include the length of the independent claim, the number of foreign patent citations of the cited patent, the business climate index by industry, the average length of the independent claim by IPC, the average number of foreign patent citations of the cited patent by IPC, and the trial statistics by IPC, and the explanatory variable set C may further include all the evaluation elements of the rating system.

[0146] Meanwhile, the AI ​​learning unit 320 can include an ARIMA (Autoregressive Integrated Moving Average) model or an ETS (Exponential Smoothing) model in calculating the fourth core variable, and can perform learning for estimating the sales growth rate, which is the fourth predictive variable.

[0147] The AI ​​learning unit 320 can utilize the explanatory variables shown in Figure 9 when calculating the fourth core variable. More specifically, the AI ​​learning unit 320 can incorporate information on the number of IPC special applicants and the rate of change in the number of applications, the IPC special sales growth rate, and the rate of change in imports and exports into the learning data to learn a model for estimating sales growth rate. However, the learning can be performed by dividing the training set and validation set into an 8:2 ratio.

[0148] In this case, the ARIMA model can include ARIMA, SARIMA (Seasonal ARIMA), ARIMAX (Autoregressive Integrated Moving Average Exogenous), and SARIMAX (Seasonal Autoregressive Integrated Moving Average Exogenous) models.

[0149] And the ETS model can include Holt-Winter's seasonal technique, Holt-Winters damped technique, Damped trend technique, and Simple Exponential Smoothing (SES).

[0150] The AI ​​learning unit 320 can evaluate the performance of the model through the absolute value of the CAGR difference between the predicted value and the actual value obtained through the validation set, the mean absolute error (MAE), and the mean squared error (MSE).

[0151] The learning optimization unit 330 can set an optimal parameter combination for each AI model for calculating core variables based on the learning of the AI ​​model by the AI ​​learning unit 320 and the verification results of the model prediction values.

[0152] The learning optimization unit 330 can also remove training data features that are less important as a result of evaluating the performance of each base model in the stacking ensemble model, and can perform additional hyperparameter tuning for models that perform well.

[0153] The learning optimization unit 330 uses the modified Akaike's Information Criterion (AICc) from among the possible parameter combinations to derive the combination that minimizes AICc and selects the optimal parameter combination, where the parameters of each model in the ARIMA model are AR order p, MA order q, difference d, seasonal AR order P, seasonal MA order Q, and seasonal difference D.

[0154] The learning optimization unit 330 also uses the L-BFGS-B (quasi-Newton Method) to adapt the parameters of each model in the ETS model, which are alpha (smoothing_level), beta (smoothing_trend), initial_level (l_0), initial_trend (b_0), gamma (seasonality), phi (damping_trend), s_0, s1, s2, and s3 (initial_seasons).

[0155] Specifically, in order to calculate the economic lifespan of the target IP, which is the first core variable, the learning optimization unit 330 sets the applicant's application number increase / decrease rate (application increase / decrease rate / applicant increase / decrease rate), TCT statistics, evaluation factors of the rating system, evaluation index scores of the rating system, average number of US patent lawsuits by IPC, and average number of trial-related cases by IPC as the first explanatory variable set as optimized explanatory variables for predicting factors influencing the economic lifespan, and can set two or more AI models optimized according to the performance index results.

[0156] In addition, for example, in order to calculate the royalty rate of the target IP, which is the second core variable, the learning optimization unit 330 can set royalty rate statistics, the average number of opinions submitted by IPC, the evaluation factors of the rating system, the evaluation index scores of the rating system, and the number of divisional applications and priority claims by IPC as the second explanatory variable set as optimized explanatory variables for predicting the influencing factors on the standard royalty rate, and can set two or more AI models optimized according to the performance index results.

[0157] Then, in order to calculate the discount rate, which is the third core variable, the learning optimization unit 330 can set the sales growth rate by industry and company size, the operating profit growth rate by industry and company size, the evaluation factors of the rating system, the evaluation index scores of the rating system, and the patent concentration as the third explanatory variable set as optimized explanatory variables for predicting the IP commercialization risk premium, and can set two or more AI models optimized according to the performance index results.

[0158] In addition, in order to calculate the fourth core variable, sales, the learning optimization unit 330 can set the number of applicants, the rate of change in the number of applications, and the rate of change in imports and exports as the fourth explanatory variable set as optimized explanatory variables for estimating sales growth rate, and can set two or more optimized AI models depending on the performance index results.

[0159] The core variable calculation unit 340 can calculate each core variable for valuing the target IP.

[0160] The core variable calculation unit 340 can generate predictive variables through two or more AI models optimized for each core variable, and calculate each core variable based on the predictive variables.

[0161] Specifically, the core variable calculation unit 340 can calculate first to fourth core variable values, but can preferentially calculate first to fourth predictor variable values ​​for each of the core variables.

[0162] That is, the core variable calculation unit 340 may check the first explanatory variable set to calculate a first predictor variable value, and may calculate a first core variable value based on the first predictor variable value.

[0163] In the same manner, the core variable calculation unit 340 can sequentially calculate second predictor variables and second core variable values ​​through the second set of explanatory variables, calculate third predictor variables and third core variable values ​​through the third set of explanatory variables, and then calculate fourth predictor variables and fourth core variable values ​​by checking the fourth set of explanatory variables.

[0164] On the other hand, when the core variable calculation unit 340 obtains two or more first to fourth predictor variable values ​​through two or more AI models for each of the first to fourth core variables, it may select a specific value according to the first to fourth criteria, or obtain a statistically processed value for each result.

[0165] More specifically, a first core variable according to one aspect of the present invention is the economic life of the target IP.

[0166] The core variable calculation unit 340 can calculate the influencing factors on the IP economic lifespan as the first predictor variables through the first explanatory variable set and the AI ​​model set by the learning optimization unit 330.

[0167] The core variable calculation unit 340 can check the median TCT of the IPC to which the target IP belongs, and can calculate the first core variable, i.e., the economic lifespan of the target IP, by reflecting the first predictor variable value in the median TCT.

[0168] A second core variable according to one aspect of the present invention is the royalty rate.

[0169] The core variable calculation unit 340 can calculate the influence factors on the royalty rate as the second predictor variables through the second explanatory variable set and the AI ​​model set by the learning optimization unit 330.

[0170] The core variable calculation unit 340 can set an industry type based on the industry classification information that matches the IP classification information of the target IP, check the standard royalty rate value for the industry type, and calculate the second core variable, i.e., the final royalty rate of the target IP, by reflecting the second predictor variable value in the standard royalty rate value.

[0171] And, the third core variable according to one aspect of the present invention is the discount rate.

[0172] The core variable calculation unit 340 can calculate the IP commercialization risk premium as a third predictor variable through the third explanatory variable set and the AI ​​model set by the learning optimization unit 330.

[0173] The core variable calculation unit 340 can calculate a weighted average cost of capital (WACC) by reflecting the third predictor variable value, the IP commercialization risk premium, in the equity cost, and calculate the weighted average cost of capital into a third core variable, the discount rate, and the specific formula is as follows:

number

[0174] At this time, K d is the cost of borrowed capital, K e is the cost of equity capital, T is the corporate tax rate, E is equity capital, D is debt,

number

number

[0175] Meanwhile, the cost of equity capital can be calculated through the listed company CAPM (Capital Asset Pricing Model), but in the case of an unlisted company, a size risk premium can be added. The core variable calculation unit 340 adds the calculated IP commercialization risk premium to calculate the cost of equity capital (K e ) and the final discount rate can be calculated using equation 2.

number

[0176] In this case, the cost of equity capital for listed companies, i.e., CAPM, is as follows:

number

[0177] The statistical data calculation unit 250 can calculate market risk premiums and the like based on financial information such as stock price data, bond interest rate data, and corporate financial data. For example, the statistical data calculation unit 250 can calculate the market expected return (E(R m )) and use the expected return to calculate the risk-free interest rate (R f ) can be calculated.

[0178] The statistical data calculation unit 250 calculates the market expected return (E(R m )) and the risk-free interest rate (R f ) to calculate the market risk premium.

[0179] Then, the statistical data calculation unit 250 can calculate the correlation coefficient between the stock index return and individual stocks, and calculate the beta by industry.

[0180] Meanwhile, the statistical data calculation unit 250 can calculate the cost of debt capital by adding an additional risk spread to the cost of debt capital of listed companies in the industry.

[0181] Specifically, the statistical data calculation unit 250 can calculate the financial costs of listed companies based on financial information such as stock price data, bond interest rate data, and corporate financial data, and can use the spreads based on the average credit grade of unguaranteed corporate bonds to calculate the additional risk spread of unlisted companies compared to the average credit grade of listed companies, and calculate the cost of borrowed capital.

[0182] A fourth core variable according to one aspect of the present invention is sales volume.

[0183] The core variable calculation unit 340 can calculate the sales growth rate as a fourth predictor variable through the fourth explanatory variable set and the AI ​​model set by the learning optimization unit 330.

[0184] The core variable calculation unit 340 can set an initial sales amount according to the industry type, and can calculate the sales amount during the economic life period by reflecting the fourth predictor variable in the initial sales amount.

[0185] Specifically, if the core variable calculation unit 340 identifies a business entity, it can set the initial sales amount based on the average past sales of the business entity. If the core variable calculation unit 340 cannot identify a business entity, it can set the initial sales amount based on sales statistics according to the business type, size, or item.

[0186] The core variable calculation unit 340 can calculate the flow of sales for the period by reflecting the calculated sales growth rate on the initial sales amount.

[0187] Meanwhile, as shown in FIG. 10, the value assessment management module 400 may include a reference information management unit 410, a user management unit 420, a payment management unit 430 and a value assessment system management unit 440.

[0188] The criteria information management unit 410 can manage evaluation criteria information such as tax rates and other related laws and regulations as criteria information required for performing IP valuation.

[0189] The user management unit 420 can manage basic information and history information for users of the system according to the present invention, the payment management unit 430 can manage payment for expenses for using the system according to the present invention, and the value assessment system management unit 440 can manage resources of the system according to the present invention.

[0190] 11 is a detailed block diagram of a value assessment database 500 according to one aspect of the present invention. As shown, the value assessment database 500 may include raw information and extracted information collected and processed by the collection / refining module 200.

[0191] First, the raw information can include compliance information data, patent information data, and economic statistics information data.

[0192] Compliance information data is information that is used as a standard in the value assessment process of the IP value assessment system or serves as a verification standard for the system assessment results using an AI model, and can include IP assessment information and transaction information that has already been completed.

[0193] In this case, the valuation information may include, as IP value valuation data completed by experts, for example, expert-valued IP economic life, expert-valued royalty rate, and expert-valued IP commercialization risk premium, and the transaction information may include, as actual IP transaction information, university technology transfer data, public research institute technology transfer data, and IP transaction data from IP exchanges.

[0194] Meanwhile, the patent information data may include patent detail information and rating information as patent-related objective data.

[0195] The detailed patent information may include cited / cited data for each patent, application data, trial data, litigation data, registration data, and family data. The grade evaluation information may include the above-mentioned grade evaluation element data and score data for the corresponding indicators that each evaluation element represents according to the evaluation results for each element as grade evaluation data performed for each patent.

[0196] Economic statistical information data may include economic market information, financial information, and import / export information necessary for IP valuation among public data generated or acquired and managed by public institutions.

[0197] The economic market information may include sales growth rate data by industry, sales statistics data, and economic forecast data, the financial information may include stock price data, bond interest rate data, and corporate financial data, and the import / export information may include import / export data.

[0198] Next, the extracted information is information extracted by processing raw information, and can include statistical data calculated in the IP valuation process and provided along with the final valuation result for the target IP, as well as AI learning data used for AI model learning.

[0199] In this case, the statistical data may include IP economic life-span related information, royalty rate related information, discount rate related information, and sales related statistical information.

[0200] Specifically, statistical information utilized or extracted in the process of calculating the economic lifespan of an IP may include, for example, patent citation lifespan (TCT) data by IPC, statistical data related to trials, US litigation data, and market concentration data for the relevant industry sector (sector).

[0201] Statistical information utilized or extracted in the royalty rate calculation process may include industry-specific standard royalty rate data, the number of opinions submitted by IPC, the number of divisional applications, and statistical data on the number of priority claims.

[0202] In addition, statistical information utilized or extracted in the discount rate calculation process may include data on the cost of own / borrowed capital by industry and data on the ratio of own / borrowed capital by industry, patent concentration by target IPC, and CAGR data on sales / operating profits by industry and company size for the last five years in the target IP business field.

[0203] In addition, the statistical information utilized or extracted in the process of calculating sales revenue may include initial sales revenue statistical data according to the industry and size to which the business entity's target IP belongs, statistical data on the number of applicants for the IPC and the rate of increase or decrease in the number of applications, and statistical data on the rate of increase or decrease in imports and exports for the industry and item.

[0204] Meanwhile, the AI ​​learning data used for AI model learning can include learning data input into the AI ​​model to calculate the first core variable, the IP economic lifespan, learning data input into the AI ​​model to calculate the royalty rate, the second core variable, learning data input into the AI ​​model to calculate the discount rate, the third core variable, and learning data input into the AI ​​model to calculate the sales amount, the fourth core variable.

[0205] Specific examples of the learning data used to calculate the first to fourth core variables are the same as those described above, and therefore will not be repeated here.

[0206] The overall flow of the process executed by the IP value assessment system according to one aspect of the present invention will be summarized and explained below with reference to FIG.

[0207] First, a user can input information about the IP to be evaluated via the value evaluation service module 100. For example, the user can input the registration number of the patent to be evaluated.

[0208] The AI ​​module 300 can calculate the first to fourth core variables for valuing the target IP, and can check the patent classification information of the patent from the patent information in the value assessment database 500 based on the registration number input preferentially, and can check the TCT statistics of the patent classification information from the statistical information in the value assessment database 500.

[0209] The AI ​​module 300 can check the first explanatory variable set value for the target IP from the value assessment database 500 and calculate the influencing factors for the IP economic lifespan, which is the first predictor variable, through the set first explanatory variable set and AI model.

[0210] The AI ​​module 300 checks the median TCT of the patent classification information, and calculates the first core variable, the IP economic life, by reflecting the first predictor variable value in the median TCT. In this case, the collection / refinement module 200 can finally compare the remaining legal life of the target IP and calculate the shorter one as the final IP economic life (S100).

[0211] The AI ​​module 300 can confirm the industry type by checking the industry classification information that matches the patent classification information, and can then check the sales statistics of the industry type from the economic statistics information in the value assessment database 500 (S101, S103, S105).

[0212] When the size and industry of the business entity are identified from the user-input information and the economic statistical information in the value assessment database 500, the AI ​​module 300 can proceed with calculating the core variables based on the identified size and industry (S107, S113).

[0213] The AI ​​module 300 can check the second explanatory set values ​​for the target IP from the value assessment database 500 and calculate the influencing factors for the royalty rate, which is the second predictor variable, through the set second explanatory variable set and AI model.

[0214] The AI ​​module 300 can check the standard royalty rate for the industry from the value assessment database 500, and calculate the final royalty rate, which is the second core variable, by reflecting the second predictor variable value in the standard royalty rate (S117).

[0215] Then, the AI ​​module 300 can check the third explanatory variable set value for the target IP from the value assessment database 500 and calculate the third predictive variable, the IP commercialization premium, through the set third explanatory variable set and the AI ​​model.

[0216] The AI ​​module 300 can reflect the value of the third predictor variable in the cost of equity capital for the industry or scale to which the target IP belongs, calculated from the economic statistical information in the value assessment database 500, and calculate the weighted average cost of capital (WACC) as the final discount rate (S115).

[0217] Then, the AI ​​module 300 can check the fourth explanatory variable set value for the target IP from the value assessment database 500 based on the mapping between the IP classification information and the import / export item classification information, and calculate the fourth predictive variable, sales growth rate, through the set fourth explanatory variable set and the AI ​​model.

[0218] When the business entity is identified and sales are confirmed, the AI ​​module 300 can set the initial sales amount using the business entity's average past sales amount, and can calculate the sales amount for the period by reflecting the fourth predictive variable in the initial sales amount (S109, S111, S121).

[0219] On the other hand, if the AI ​​module 300 cannot identify sales, it may set initial sales using sales statistics of a pre-defined company of a specific size (e.g., small) in the industry, and calculate sales reflecting the fourth predictive variable (S109, S119, S121).

[0220] The collection / refining module 200 can then calculate the final corporate tax expense according to the estimated sales amount during the economic life of the target IP, the final royalty rate, and the corporate tax rate based on the sales amount (S123).

[0221] The value assessment service module 100 can perform a value assessment on the target IP in accordance with the above-mentioned number 1 based on the final IP economic life, final royalties, final discount rate, estimated sales and final corporate tax value, as described above (S125).

[0222] The value assessment service module 100 can then generate a report including the value assessment results, compliance information extracted or utilized in the value assessment process, and statistical information on the core variables or various explanatory variables used to estimate the core variables (S127).

[0223] Meanwhile, the portfolio valuation process of the IP valuation system according to one aspect of the present invention will be summarized and explained with reference to FIGS.

[0224] First, a user can input information about an IP portfolio to be evaluated via the value evaluation service module 100. For example, a user can input the registration number of a patent to be evaluated.

[0225] The AI ​​module 300 can calculate the first to fourth core variables for the portfolio to evaluate the value of the target IP portfolio, and based on the preferentially entered registration number, can check the patent classification information of each individual patent from the patent information in the value assessment database 500, and can check the TCT statistics for the patent classification information of each individual patent from the statistical information in the value assessment database 500.

[0226] The AI ​​module 300 can set the average of the median TCT values ​​for each individual patent as the reference TCT value for the portfolio (S201).

[0227] At this time, the AI ​​module 300 can calculate the influence factors on the IP economic life for each individual patent that constitutes the portfolio using the method described above. Then, the AI ​​module 300 can estimate the influence factor with the largest value among the calculated values ​​as the first predictor variable for the target portfolio, i.e., the influence factor on the economic life of the target portfolio (S203).

[0228] The AI ​​module 300 may reflect the first predictor variable value for the portfolio in the reference TCT value for the portfolio, and calculate the first core variable for the portfolio, i.e., the IP economic life for the portfolio (S205).

[0229] The collection / refining module 300 can then compare the remaining legal life of each individual patent and calculate the shortest remaining legal life as the final IP economic life of the target portfolio (S207).

[0230] In other words, if the IP economic life calculated in S205 is shorter than the shortest remaining life, the calculated IP economic life is reflected as is, and if the IP economic life is longer than the shortest remaining life, the shortest remaining life replaces the IP economic life calculated in S205.

[0231] On the other hand, if the size and industry of the business entity are identified from user-input information or economic statistical information in the value assessment database 500, the AI ​​module 300 can proceed with calculating core variables for the portfolio based on the identified size and industry (S301, S303).

[0232] Furthermore, if the business entity cannot be identified, the AI ​​module 300 checks each industry using the industry classification information matched to each IP classification information of the individual IP, assumes the company size to be a small company, and checks and compares the median sales of small companies by industry from the value assessment database 500 (S301, S305, S307).The AI ​​module 300 can then set the industry with the largest median sales as the representative industry of the IP portfolio (S309).

[0233] Furthermore, the AI ​​module 300 can check the standard royalty rate for the representative industry of the IP portfolio from the value assessment database 500 and set it as the standard royalty rate for the IP portfolio (S401).

[0234] The AI ​​module 300 may calculate the influence factors on the royalty rate for each individual patent that constitutes the portfolio using the method described above. Then, the AI ​​module 300 can estimate the influence factor with the largest value among the calculated values ​​as the second predictor variable for the target portfolio, i.e., the influence factor on the royalty rate of the target portfolio (S403).

[0235] The AI ​​module 300 can calculate the final royalty rate of the portfolio by reflecting the royalty rate influencing factor value of the portfolio in the base royalty rate of the portfolio (S405).

[0236] Meanwhile, the AI ​​module 300 can calculate the weighted average cost of capital of the portfolio using the cost of equity capital / ratio and cost of debt capital / ratio for the portfolio's representative industry calculated from the economic statistical information in the value assessment database 500 (S501).

[0237] At this time, the AI ​​module 300 can calculate the IP commercialization premium for each individual IP using the method described above, and estimate the smallest IP commercialization premium among these as the third predictor variable for the target portfolio, i.e., the IP commercialization premium of the target portfolio (S503).

[0238] The AI ​​module 300 can calculate the final discount rate of the portfolio by reflecting the estimated IP commercialization premium of the portfolio in the cost of equity capital, which is a weighted average cost of capital for the portfolio industry (S505).

[0239] The AI ​​module 300 can set initial sales, and when a business entity is identified and the business entity's past sales are confirmed from the economic statistical information in the value assessment database 500, the AI ​​module 300 can assume the average of past sales (for example, the average of past sales over the past 3 to 5 years) as the initial sales for the year-end period immediately prior to the time of assessment (S601).

[0240] In addition, the AI ​​module 300 can estimate the sales growth rate of the target portfolio, which is the fourth predictive variable for the target portfolio, in the manner described above based on the IP classification information and import / export item classification information mapping based on the portfolio's representative industry (S603).

[0241] Meanwhile, if there is no business entity or sales information, the AI ​​module 300 can set the initial sales amount using sales statistics of a company of a specific size (for example, small size) that is preset in the portfolio representative industry.

[0242] The AI ​​module 300 can calculate the sales amount for the set period by reflecting the sales growth rate of the portfolio on the initial sales amount (S605).

[0243] On the other hand, if the sales growth rate is estimated as a quarterly growth rate, the AI ​​module 300 can convert the quarterly sales growth rate into an annual growth rate using a geometric mean, and similarly, the overall period is limited within the IP economic lifespan.

[0244] Then, the collection and refinement module 200 can determine a corporate tax rate based on sales over the economic life of the IP portfolio, for example, based on the average sales over the entire period (S701).

[0245] The collection and refinement module 200 can calculate the corporate tax expense by reflecting the estimated sales amount of the portfolio, the final royalty rate, and the corporate tax rate (S703).

[0246] The value assessment service module 100 can calculate the final value of the IP portfolio based on the final IP economic life, final royalties, final discount rate, estimated sales and final corporate tax value for the portfolio according to equation 1 above (S705).

[0247] The value assessment service module 100 can generate a report including not only the value assessment results but also compliance information extracted or utilized in the value assessment process for the IP portfolio, and statistical information on core variables or various explanatory variables used to estimate the core variables.

[0248] Therefore, according to the present invention, it is possible to quickly, objectively, and efficiently evaluate the value of the entire portfolio, regardless of the number of individual IPs included in the portfolio.

[0249] Furthermore, according to the present invention, patent information and economic statistical information can be used in conjunction with each other to perform IP valuation and provide statistical information, thereby improving the objectivity and reliability of the valuation results.

[0250] In addition, the present invention provides users with not only direct IP value assessment results but also related patent and industry statistical information, which is highly useful for users' understanding of the target IP-related industry, interpretation and utilization of the value assessment results.

[0251] In particular, according to the present invention, not only compliance information but also statistical information on the calculated core variables and various explanatory variables used to estimate the core variables is provided, thereby increasing the reliability and usability of the evaluation results.

[0252] In addition, according to the present invention, in calculating each core variable for IP value evaluation, data-based objective statistical data based on patent information and economic statistics information is extracted and utilized instead of the qualitative evaluation indicators that have been used in conventional expert evaluations, thereby improving the objectivity of the evaluation results.

[0253] In addition, according to the present invention, raw information can be continuously collected and processed to generate new statistical information and AI learning information, which can be used as learning data for an AI model to calculate core variables, thereby efficiently ensuring the currency and suitability of related information used in the IP valuation process.

[0254] Furthermore, according to the present invention, the estimation results of two or more AI models optimized for each core variable can be utilized when calculating the core variables, thereby increasing the objectivity and reliability of the evaluation results.

[0255] In addition, according to the present invention, when generating learning data for AI model learning, it is possible to reflect value assessment data and actual transaction data accumulated over a long period of time by multiple experts, thereby further improving the reliability of the valuation results.

[0256] 15 is a diagram illustrating an example of a computer device according to an embodiment of the present invention. The valuation process of the IP valuation system according to one aspect of the present invention can be implemented by a computer device 600 shown in FIG.

[0257] As shown in FIG. 15 , the computer device 600 may include a memory 610, a processor 620, a communication interface 630, and an input / output interface 640. The memory 610 may include computer-readable recording media such as random access memory (RAM), read-only memory (ROM), and a permanent mass storage device such as a disk drive. The ROM and the non-permanent mass storage device such as a disk drive may be included in the computer device 600 as separate permanent storage devices distinct from the memory 610. The memory 610 may also store an operating system and at least one program code. Such software components may be loaded into the memory 610 from a computer-readable recording medium separate from the memory 610. Such separate computer-readable recording media may include a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, or other computer-readable recording media. In another embodiment, the software components may be loaded into the memory 610 via the communication interface 630 rather than from a computer-readable recording medium. For example, the software components may be loaded into memory 610 of computer device 600 based on a computer program installed by a file received over network 700 .

[0258] The processor 620 may be configured to process computer program instructions by performing basic arithmetic, logic, and input / output operations. The instructions may be provided to the processor 620 by the memory 610 or the communication interface 630. For example, the processor 620 may be configured to execute received instructions according to program code stored in a storage device, such as the memory 610.

[0259] The communication interface 630 may provide a function for the computer device 600 to communicate with other devices (e.g., the storage device described above) via the network 700. For example, the processor 620 of the computer device 600 may transmit requests, commands, data, files, etc. generated by program code stored in a storage device such as the memory 610 to other devices via the network 700 under the control of the communication interface 630. Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device 600 via the communication interface 630 of the computer device 600 via the network 700. The signals, commands, data, etc. received via the communication interface 630 may be transmitted to the processor 620 or the memory 610, and the files, etc. may be stored in a storage medium (e.g., the permanent storage device described above) that the computer device 600 may further include.

[0260] The input / output interface 640 may be a means for interfacing with the input / output device 650. For example, the input device may include a device such as a microphone, keyboard, or mouse, and the output device may include a device such as a display or speaker. As another example, the input / output interface 640 may be a means for interfacing with a device that integrates input and output functions into one, such as a touch screen. The input / output device 650 may be configured as a single device together with the computer device 600.

[0261] Furthermore, in other embodiments, computing device 600 may include fewer or more components than those shown in Figure 14. However, it is not necessary to explicitly show most prior art components. For example, computing device 600 may be implemented to include at least some of the input / output devices 650 described above, or may further include other components such as a transceiver, a database, etc.

[0262] The above-described embodiments may be implemented in the form of a computer program that can be executed on a computer via various components, and such a computer program may be recorded on a computer-readable medium, which may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, and flash memory.

[0263] Unless explicitly stated or contrary to the order of steps constituting the method according to the embodiments of the present invention, the steps can be performed in any suitable order. The order of the steps described above is not necessarily intended to limit the scope of the present invention. The use of all examples or exemplary terms (such as, for example, etc.) in the present invention is merely for the purpose of describing the present invention in detail and does not limit the scope of the present invention. It is understood that a person of ordinary skill in the art can make various modifications, combinations, and variations within the scope of the claims or their equivalents.

[0264] The embodiments of the present disclosure described above may be realized not only by an apparatus and a method, but also by a program that realizes functions corresponding to the configuration of the embodiments of the present disclosure or a recording medium on which the program is recorded.

[0265] Although the embodiments of the present disclosure have been described in detail above, the scope of the present disclosure is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concepts of the present disclosure defined in the following claims also fall within the scope of the present disclosure. [Explanation of symbols]

[0266] 10: IP Value Assessment System 100: Value Assessment Service Module 110: User interface unit 120: Attribute management unit 130: Value calculation unit 140: Report generation unit 200: Collection and refinement module 210: RAW data collection unit 220: Preprocessing unit 230: Basic data calculation unit 240: Learning data calculation unit 250: Statistical data calculation unit 260: Evaluation criteria data calculation unit 300: AI module 310: Learning data preprocessing unit 320: AI learning unit 330: Learning Optimization Unit 340: Core Variable Calculation Unit 400: Value assessment management module 410: Standard information management unit 420: User management unit 430: Payment management unit 440: Value assessment system management unit 500: Value assessment database 600: Computer device 610: Memory 620: Processor 630: Communication interface 640: Input / output interface 650: Input / output device

Claims

1. An AI (Artificial Intelligence)-based IP (Intellectual Property) value assessment system, a value assessment database including raw information such as compliance information data, patent information data, and economic statistical information data, and extracted information processed from the raw information such as statistical data and AI learning data; a collection and refinement module that collects and processes the raw information, calculates the statistical data and the AI ​​learning data required in the process of generating AI learning data or the first to fourth core variables, and stores them in the value assessment database; In order to calculate the first to fourth core variables, two or more AI models for each core variable are trained using the AI ​​learning data, and explanatory variables matching each core variable are confirmed based on the input evaluation target IP information. Each explanatory variable value collected or calculated by the collection and refinement module and the corresponding predictor variable value are calculated using the AI ​​model, thereby calculating each core variable value. an AI module that calculates a first predictor variable and a first core variable value via a first set of explanatory variables, a second predictor variable and a second core variable value via a second set of explanatory variables, a third predictor variable and a third core variable value via a third set of explanatory variables, and a fourth predictor variable and a fourth core variable value via a fourth set of explanatory variables; and a value assessment service module that calculates the target IP value according to the royalty exemption method based on the first to fourth core variable values, and generates a value assessment report including the IP value and the statistical data; The AI ​​module checks patent classification information to which the target IP belongs from the input evaluation target IP information, checks matching industry classification information from the checked patent classification information, Checking the median TCT of the patent classification information to which the target IP belongs, and calculating the first core variable by reflecting the first predictor variable value in the median TCT; confirming a standard royalty rate for the industry through industry classification information that matches the patent classification information to which the target IP belongs, and calculating the second core variable by reflecting the second predictor variable value on the standard royalty rate; confirming the equity capital cost and ratio and the debt capital cost and ratio of the industry through industry classification information that matches the patent classification information to which the target IP belongs, and calculating the third core variable by reflecting the third predictor variable value in the equity capital cost; a system for calculating a fourth core variable by reflecting the value of the fourth predictor variable in the initial sales amount when the past sales amount of the business entity that owns the target IP are confirmed, and setting the initial sales amount based on the past sales amount when the past sales amount of the business entity that owns the target IP are not confirmed, and setting the initial sales amount based on sales statistics by company size that are set in the industry; The first to fourth core variables are IP economic life, royalty, discount rate, and sales, respectively; The first to fourth predictor variables are respectively: The factors influencing the IP economic lifespan, the factors influencing the royalty rate, the IP commercialization risk premium, and the sales growth rate are, respectively, the following when the AI ​​model is trained: The difference between the expert evaluation result of the IP economic life and the median TCT (Technology Cycle Time) for the patent classification information to which the target IP belongs, the ratio between the expert-assessed royalty rate and the standard royalty rate for the industry based on the industry classification information that matches the patent classification information to which the target IP belongs; Expert-assessed IP commercialization risk premium, and Defined and studied by industry sales growth rate, The AI ​​module In the case of an IP portfolio with two or more IPs to be evaluated, A reference TCT value for the target IP portfolio is set from the TCT statistics for the patent classification information of each individual patent, a first predictor variable for the target portfolio is set from the IP economic lifespan influence factors calculated for each individual patent, and a first core variable for the target portfolio is calculated by reflecting the first predictor variable value for the target portfolio in the reference TCT of the target portfolio; The industry type is set according to the user-entered information, or the representative industry type of the target portfolio is set from among the industries according to the industrial classification information that matches each patent classification information of individual patents. A reference royalty rate for the representative industry of the target portfolio is set as the reference royalty rate for the target portfolio, a second predictor variable for the target portfolio is set from the royalty rate influencing factors calculated for each individual patent, the value of the second predictor variable for the target portfolio is reflected in the reference royalty rate for the target portfolio, and a second core variable for the target portfolio is calculated; A third predictor variable for the target portfolio is set from the IP commercialization risk premium calculated for each individual patent, and a third core variable for the target portfolio is calculated by reflecting the value of the third predictor variable in the cost of equity capital among the weighted average cost of capital of the representative industry of the target portfolio; A system that calculates a sales growth rate from the representative industry of the target portfolio, generates a fourth predictor variable for the target portfolio, sets initial sales through the business entity's past sales information or sales statistics for the representative industry of the target portfolio, reflects the value of the fourth predictor variable in the initial sales, and calculates a fourth core variable for the target portfolio.

2. The system of claim 1 , wherein the target IP information is a patent registration number of the target IP.

3. The compliance information data includes expert IP evaluation result data and actual IP transaction information data; The patent information data includes, as detailed patent information, cited / cited data for each patent, application data, trial data, litigation data, registration data, and family data, and, as grade evaluation information, includes data on grade evaluation factors for each patent and score data for the corresponding indicators that the evaluation factors represent according to the evaluation results for each factor; The system of claim 2, wherein the economic statistical information data includes, as economic market information, industry sales growth rate data, sales statistical data, and economic forecast data, as financial information, stock price data, bond interest rate data, and corporate financial data, and as import / export information, import / export data.

4. The collection and purification module includes: a raw data acquisition unit for collecting the raw information; a pre-processing unit for performing pre-processing on the collected raw information; a basic data calculation unit that generates basic data for calculating learning data and evaluation reference data from the preprocessed data; a learning data calculation unit that generates AI model learning data for calculating the core variables from the basic data; A statistical data calculation unit that generates statistical data for one or more of the explanatory variables, predictor variables, and core variables as AI learning data or the core variable calculation process or as required statistical data; and The system of claim 2, further comprising an evaluation criterion data calculation unit that calculates final evaluation criterion data based on the first to fourth core variable values ​​calculated through the AI ​​module and transmits the final evaluation criterion data to the value assessment service module.

5. The evaluation reference data calculation unit The system of claim 4, wherein the IP economic life is finally calculated as the first core variable value, taking into account one or more of the remaining legal life of the target IP and the preparation period for commercialization as evaluation criteria data, and the corporate tax rate and corporate tax are calculated based on the calculated sales revenue.

6. The statistical data is The statistical information utilized or extracted in the process of calculating the first core variable includes TCT data for the patent classification, statistical data related to trials, US litigation data, and market concentration data for the industry field; The statistical information utilized or extracted in the process of calculating the second core variable includes data on the industry-based royalty rate, the number of opinions submitted for the patent classification, the number of divisional applications, and the number of priority claims; The statistical information utilized or extracted in the process of calculating the third core variable includes data on the cost of own / borrowed capital of the industry, data on the ratio of own / borrowed capital by industry, patent concentration of the patent classification, and sales / operating profit growth rate data of the industry; The system of claim 4, wherein the statistical information utilized or extracted in the fourth core variable calculation process includes initial sales statistical data according to the industry and size to which the target IP belongs, statistical data on the number of applicants for the patent classification and the rate of increase or decrease in the number of applications, and data on the rate of increase or decrease in imports and exports for the industry and item.

7. The AI ​​module a training data preprocessing unit that performs preprocessing on AI training data; an AI learning unit that learns one or more AI models for each of the first to fourth core variables through the AI ​​learning data; A learning optimization unit that sets two or more AI models optimized according to performance index results for each core variable based on the verification results for the AI ​​model predicted values; and The system of claim 2 , further comprising a core variable calculation unit that calculates a core variable using explanatory variables matched for each core variable and predictive variables calculated through the AI ​​model.

8. The first explanatory variable set for generating the first predictor variable is The data includes the applicant's application number change rate (application change rate / applicant change rate), TCT statistics, evaluation factors of the rating system, evaluation index scores of the rating system, the average number of US patent litigation cases by patent classification information, and the average number of trials by patent classification information. The second explanatory variable set for generating the second predictor variable is Includes royalty rate statistics, average number of opinions submitted by patent classification, evaluation factors of the rating system, evaluation index scores of the rating system, and the number of divisional applications and priority claims by patent classification. The third explanatory variable set for generating the third predictor variable is The optimized explanatory variables for predicting the IP commercialization risk premium include sales growth rate by industry and company size, operating profit growth rate by industry and company size, evaluation factors of the rating system, evaluation index scores of the rating system, and patent concentration. The system according to any one of claims 1 to 7, wherein the fourth set of explanatory variables for generating the fourth predictor variable includes the number of applicants, the rate of change in the number of applications, and the rate of change in imports and exports.

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