KR102211278B1 - Online platform for probability based technology valuation using collective intelligence and realtime artificial intelligence - Google Patents

Abstract

Probability-based technology value evaluation and technology transaction online platform using collective intelligence and real-time artificial intelligence learning engine are presented. Probability-based technology valuation and technology transaction online platform proposed in the present invention is an evaluator recommendation module that recommends a number of evaluators with expertise matching the technical characteristics of the client company, and evaluators recommended through mobile terminals and PC-based web applications. Evaluation module that conducts qualitative evaluation including technical evaluation, business feasibility evaluation, market feasibility evaluation, business feasibility evaluation, and technology royalty evaluation. Based on the results of qualitative evaluation, the technology life, discount rate, technology contribution rate, and average sales growth rate are calculated. Collective intelligence module that determines the range of the probability distribution of the included quantitative variable values and the final technical value evaluation amount, learns in real time the rules that exist between the score of assigning qualitative variables of evaluators and the selection of the values of quantitative variables, An artificial intelligence module that automatically calculates the quantitative variable value and calculates the technical value as a probability distribution when the qualitative evaluation result value is given, a document generation module that automatically inserts the evaluation result into the final report, external viewing of the report and technology transfer It includes a technology history management and technology transaction module that performs functions related to transactions, and a mutual evaluation module that stores the satisfaction evaluation results for the evaluator and the client company after evaluation is completed.

Description

Online platform for probability based technology valuation using collective intelligence and realtime artificial intelligence}

The present invention is a platform construction technology for technology value evaluation, website application development technology that allows multiple experts to access and collaborate online, mobile application construction technology such as notification of new customer request work, real-time artificial intelligence based on large amounts of information It relates to a method and apparatus that utilizes learning technology, encoding technology for probability programs such as Monte Carlo simulation, and technology for recording and storing evaluation history and sharing reports.

Prior patents related to the present invention and an already established online system have limitations. Patents proposed as evaluation engines operate with algorithms determined in advance by programmers, and cannot automatically reflect changes in economic and technological trends over time. In the case of the preceding patent that proposed an artificial intelligence engine, a small number of samples are separated and trained separately before use.

However, due to the development of artificial intelligence technology so far, system overload can be avoided and overfitting can be avoided even if the artificial intelligence evaluation engine is updated in real time based on actual transaction data. In addition, the automatic document generation technology has greatly improved the underlying technology, such as automatically generating reports and enabling security access to authenticated users. Moreover, the future cash flow estimation method using Monte Carlo simulation is performed standalone on a PC, and a number of evaluators participate in this technique to derive their estimates as probability distributions, and then perform the Monte Carlo simulation to evaluate the probability-based technology value. There is an advantage that can be estimated. For this, we need an online platform in which a number of experts and evaluation requesting companies can cooperate, and for this, we intend to implement it using web application and mobile application development technology.

The technical task to be achieved by the present invention is a method for pursuing the specialization and efficiency of evaluation by securing objectivity of evaluation by a collective intelligence algorithm in which a large number of evaluators participate, standardizing evaluation work, and modularizing this into detailed work again. And to provide an apparatus. In addition, by allowing artificial intelligence to learn the evaluation process in real time, we intend to implement an automated valuation that always reflects the latest economic and technical trends without designating a separate sample and training, and providing it as overall consensus information of the market. .

In one aspect, the probability-based technology value evaluation and technology transaction online platform using the collective intelligence and real-time artificial intelligence learning engine proposed in the present invention is an evaluator recommendation module that recommends a number of evaluators with expertise matching the technology characteristics of the client company. , Evaluation module that conducts qualitative evaluation including technology evaluation, business feasibility evaluation, market feasibility evaluation, and technology loyalty evaluation on technology for evaluators recommended through mobile terminals and PC-based web applications, and qualitative evaluation results Based on the technology life, discount rate, technology contribution rate, and group intelligence module that determines the range of the probability distribution of the final technology valuation amount and quantitative variable values including the average growth rate of sales, the score of qualitative variables assigned by evaluators and selection of quantitative variable values An artificial intelligence module that learns the rules that exist between them in real time and automatically calculates the quantitative variable value when the qualitative evaluation result value by expert collective intelligence is given, and the evaluation result is automatically finalized. A document generation module inserted into the report, a technology history management and technology transaction module that performs functions related to external viewing of the report and technology transfer transactions, and a mutual evaluation module that stores the satisfaction evaluation results for the evaluator and the client company after completion of the evaluation. Include.

The evaluator recommendation module compares the database in which the subscription information of the client company is stored and the database in which the subscription information of professional evaluators, including patent attorneys, technology valuation agents, and technology credit evaluators, is stored to match evaluators in related technology fields, and for subsequent evaluation. From the request case, the evaluator recommendation is made according to the technical agreement score generated based on the satisfaction survey of the requesting company generated after the evaluation is completed.

The evaluation module is structured to match the table of contents of the report and input the evaluation results and explanations based on a standardized evaluation form used in the field including evaluation of technology, rights, marketability, business, and loyalty.

After completing the qualitative evaluation, the evaluation module selects a probability parameter value including maximum, minimum, and average and a probability distribution when setting the quantitative variable value including discount rate and technical life based on the result of the qualitative evaluation. Probability values are synthesized and used for valuation, and Monte Carlo simulation is conducted to determine the volatility of the technological value generated by the probability distribution of each variable, and the section estimation of the net present value (NPV), sensitivity analysis, Conduct a stress analysis.

The artificial intelligence module evolves into a technology valuation engine that reflects changes in economic and technical trends by learning the evaluation process of multiple experts who participated in the evaluation and updating the learning results in real time.

The technology history management and technology transaction module generates abbreviated history information by encrypting the core contents of the report by a hash function, and stores it in a distributed network so that domestic and global investors, banks, and other companies who have obtained identity authentication can access it if necessary. Support to view the report.

In another aspect, the probability-based technology value evaluation and technology transaction method using the collective intelligence and real-time artificial intelligence learning engine proposed in the present invention provides evaluators with expertise matching the technology characteristics of the client company through the evaluator recommendation module. Performing a qualitative evaluation through the evaluation module, including the stage of recommending a large number of people, the technicality of the techniques for the recommended evaluators, the business feasibility evaluation, the marketability evaluation and the business feasibility evaluation, and the technology royalty evaluation The step of determining the range of the probability distribution of the final technology valuation amount and quantitative variable values including technology life, discount rate, technology contribution rate, and average growth rate of sales through the collective intelligence module. Learning the rules that exist between the selection of quantitative variable values in real time, automatically calculating the quantitative variable value and calculating the technical value as a probability distribution when the qualitative evaluation result value by expert collective intelligence is given, document creation module The step of automatically inserting the evaluation result into the final report through the technology history management and technology transaction module, and the step of performing functions related to external viewing of the report and technology transfer transaction through the technology history management module, and the evaluator after completion of the evaluation through the mutual evaluation module. It includes the step of storing the evaluation result of satisfaction with the company name.

According to embodiments of the present invention, a large number of expert groups, such as patent attorneys, technology credit appraisers, and technology appraisers involved in technology valuation, can collaborate online, thereby excluding subjectivity of evaluation and contributing to securing objectivity. The technology valuation amount estimated on the basis of probability conveys broader future value generation information than point estimates, so the information value increases, and commercial banks and investors do not directly evaluate it, but a third-party expert assessor evaluates it. The fairness of the evaluation and the risk of evaluation can be diversified. In addition, since the evaluation process is transparently exposed, the satisfaction and reliability of the customer requesting the evaluation can be expected to increase, and by managing the report containing the evaluation result, the certified counterpart can access the report at any time. Due to such transparency, objectivity, and specialization and efficiency of work, it is judged that the technology valuation market is highly likely to become a valuation platform not only in Korea, but also overseas, such as the United States and Japan. Therefore, I believe that there is enough success even if it is fostered as a global patent technology valuation network platform.

1 is a view showing the configuration of an online platform for probability-based technology value evaluation and technology transaction using collective intelligence and real-time artificial intelligence learning engine according to an embodiment of the present invention.
2 is a view for explaining the operation process of the technology value evaluation and technology transaction online platform according to an embodiment of the present invention.
3 is a diagram showing a practical guide for technology value evaluation of the Ministry of Commerce, Industry and Energy as a standardized evaluation form according to an embodiment of the present invention.
4 is a diagram showing probability-based estimation (Monte Carlo simulation) of a net present value (NPV) according to an embodiment of the present invention.
5 is a diagram showing an analysis of the technology value influence (spider chart) of major variables according to an embodiment of the present invention.
6 is a diagram illustrating an analysis of the influence of a major variable on a valuation (stress analysis) in an extreme scenario according to an embodiment of the present invention.

With the advent of the 4th industrial revolution, the competition for high-tech new technology development is becoming more intense. However, as important as the development of technology is the valuation of technology. This is because high-tech companies require enormous investments in technology development, so technology mortgage loans and investment attraction are necessary, and technology valuation is involved to prove that they are excellent technologies. In particular, in the case of technology mortgage loans, 45 trillion won was executed in Korea as of the end of 2016, and about 20,000 cases were evaluated annually during the loan process. However, problems such as lack of evaluation personnel and lack of quality, transparency and objectivity due to the lack of evaluation personnel and the lack of objectivity are problematic, so the government authorities and commercial banks are struggling to prepare countermeasures, but there are no clear measures. Accordingly, the present invention seeks for the specialization and efficiency of evaluation by securing objectivity of evaluation by a collective intelligence algorithm in which a large number of evaluators participate, standardizing evaluation work, and modularizing this into detailed work again. In addition, by allowing artificial intelligence to learn the evaluation process in real time, we intend to implement an automated valuation that always reflects the latest economic and technical trends without the need to designate a separate sample and train, and provide it as overall market consensus information. The evaluation results are encrypted and distributed, and the authenticated visitor accesses the report in both domestic and global environments and tries to increase the reliability of the technology based on this, and promote technology mortgage loans, investment attraction, and technology transfer transactions. . Since there is no case in the world that simultaneously provides collective intelligence and real-time learned artificial intelligence for technology valuation, we intend to establish itself as a standard in the global technology valuation market in the future. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a view showing the configuration of an online platform for probability-based technology value evaluation and technology transaction using collective intelligence and real-time artificial intelligence learning engine according to an embodiment of the present invention.

Probability-based technology value evaluation and technology transaction using the proposed collective intelligence and real-time artificial intelligence learning engine, the online platform 100 is an evaluator recommendation module 110, an evaluation module 120, a collective intelligence module 130, an artificial intelligence module. 140, a document generation module 150, technology history management, technology transaction module 160, and a mutual evaluation module 170.

The evaluator recommendation module 110 recommends a number of evaluators with expertise matching the technical characteristics of the client company. The database in which the subscription information of the requesting company is stored and the database in which the subscription information of professional evaluators, including patent attorneys, technology valuation agents, and technology credit evaluators are stored are matched to match evaluators in related technical fields, and evaluation from subsequent evaluation requests Evaluators are recommended according to the technical agreement score generated based on the satisfaction survey of the client company generated after completion. Evaluators selected by a number of recommendations receive an alarm that an evaluation request has arrived through a mobile terminal or a PC-based web application, and perform evaluation.

The evaluation module 120 performs a qualitative evaluation, including technology evaluation, business feasibility evaluation, marketability evaluation and business feasibility evaluation, and technology royalty evaluation for the evaluators recommended through mobile terminals and PC-based web applications. Based on the standardized evaluation form used in the field including technology, rights, marketability, business, and loyalty evaluation, the evaluation results and explanations are input, and the structure is structured to match the table of contents of the report. After completing the qualitative evaluation, when setting the quantitative variable values including discount rate and technical life based on the result of the qualitative evaluation, select probability parameter values including maximum, minimum, and average and probability distribution, and synthesize the rater's probability value. It is used for valuation, and Monte Carlo simulation is conducted to determine the volatility of the technology value generated by the probability distribution of each variable, and the section estimation of the net present value (NPV), sensitivity analysis, and stress analysis are performed. Conduct.

The collective intelligence module 130 determines a range of a probability distribution of a final technology value evaluation amount and a quantitative variable value including a technology life, a discount rate, a technology contribution rate, and an average increase rate of sales based on the result of the qualitative evaluation.

The artificial intelligence module 140 learns in real time the rules that exist between the evaluators' qualitative variable assignment score and the selection of the quantitative variable value, and automatically calculates the quantitative variable value when the qualitative evaluation result value by expert collective intelligence is given. And the technology value is also calculated as a probability distribution. The artificial intelligence module 140 learns the evaluation process of a plurality of experts who participated in the evaluation, updates the learning results in real time, and evolves into a technology value evaluation engine reflecting changes in economic and technical trends.

The document generation module 150 automatically inserts the evaluation result into the final report. After the evaluation is completed, the result is received from the evaluation module and automatically generates a valuation result report.

The technology history management and technology transaction module 160 performs functions related to external viewing of reports and technology transfer transactions. The core contents of the report are encrypted by a hash function to generate abbreviated history information, and distributed and stored in the network to provide support for domestic and global investors, banks, and other companies to access and view the report if necessary.

The mutual evaluation module 170 stores the satisfaction evaluation result for the evaluator and the client company after completion of the evaluation. The result of mutual evaluation of the technology evaluation requesting company and the evaluator (eg, patent attorney, technology valuation agent, technology credit evaluator) is surveyed and stored.

2 is a view for explaining the operation process of the technology value evaluation and technology transaction online platform according to an embodiment of the present invention.

The present invention relates to an online platform for technology valuation and technology transaction, and more particularly, as an online platform built in which a number of professional evaluators required for technology valuation participate, and their evaluation mechanism is built to learn by artificial intelligence in real time. In 2, the evaluator recommendation module 210 is operated to recommend a number of evaluators with expertise that best matches the technical characteristics of the client company.

The selected evaluator connects to the evaluation module 220 through a mobile terminal and a PC-based web application, and performs qualitative evaluation such as technical feasibility, business feasibility evaluation, market feasibility evaluation and feasibility evaluation, and technology royalty evaluation first. According to an embodiment of the present invention, a qualitative evaluation result is evaluated on an appropriate interval measure such as a 7 point scale.

When the qualitative evaluation is completed, based on this, the evaluators determine the probability range of the quantitative variable values such as technology life, discount rate, technology contribution rate, and average sales growth rate, and the collective intelligence module 230 determines the quantitative variable assigned by each participating evaluator. Based on the value, the probability distribution of each variable in technology valuation is estimated as a range, and the final technology valuation amount is also estimated as a probability distribution.

The artificial intelligence module 204 learns, in real time, an implicit rule that exists between the evaluators' qualitative variable assignment score and the selection of the quantitative variable value in the collective intelligence module 230. Based on this inference engine, when a qualitative evaluation result value by expert collective intelligence is given, the quantitative variable value is automatically calculated, and the technology value is also calculated as a probability distribution. Based on the probability range of the main variables by the expert collective intelligence, a simulation is performed to estimate the valuation section, and artificial intelligence learning is performed in real time, and based on this, valuation is automatically performed in parallel.

The evaluation result is automatically inserted into the final report by the document generation module 205. Functions for external viewing of the report, technology transfer transaction, etc. are performed in the history management module 260, and the mutual evaluation module 270 stores the satisfaction evaluation results for the evaluator and the client company after the evaluation is completed.

The evaluator recommendation module is a recommendation system that matches the evaluators in the closest technology field by matching the database containing the subscription information of the evaluation client and the database containing the subscription information of professional evaluators such as patent attorneys, technology valuation agents, and technology credit evaluators. In addition, the evaluation requesting company enters several technology keywords that describe the requested technology. However, the initial recommendation is randomly recommended within the same technology group, but from subsequent evaluation requests, the evaluator recommendation is made according to the technical agreement score generated based on the satisfaction survey of the evaluation requesting company generated after the evaluation is completed. Let's do it.

Through a mobile terminal or a PC-based web application, evaluators selected by a number of recommendations receive an alarm that an evaluation request has arrived and perform evaluation.

The evaluation module is based on standardized evaluation forms widely used in the field such as technology, rights, marketability, business feasibility, and loyalty evaluation (e.g. Drawing 1, Ministry of Commerce, Industry and Energy's technology value evaluation guideline, etc.), based on evaluation results and qualitative evaluation As a module for inputting text containing explanations, it has been structured to match the table of contents of the report exactly one-to-one with the automatic generation of the report in the document generation module in mind, and a function that allows you to request other experts by separating only some modules. Combines.

In the evaluation module, a qualitative evaluation is completed according to a standardized evaluation form widely used in the field, such as evaluation of technology, rights, marketability, business, and loyalty (for example, Figure 1, Ministry of Commerce, Industry and Energy Technology Valuation Guidelines). Afterwards, based on this, the collective intelligence module uses probability parameter values such as maximum, minimum, and average when setting quantitative variable values such as discount rate and technical life, and appropriate probability distribution (e.g., beta distribution, normal distribution, triangle, uniform Distribution, etc.). Based on this, the participants' probability values are synthesized and used for valuation, and Monte Carlo simulation is conducted to determine the volatility of the technology value generated by the probability distribution of each variable to estimate the section of the net present value (NPV). , Sensitivity analysis, stress analysis, etc.

The artificial intelligence module learns the evaluation process of a number of experts who participated in the evaluation, and updates the previous learning result in real time based on this, without the artificial intelligence learning process through separate sample designation, and always keeps the latest economic and technical trend changes. It evolves into a reflective technology valuation engine.

After the evaluation is completed, the document generation module receives the results from the evaluation module and automatically generates a valuation result report.

When the document generation module generates the final report, the technology history management and technology transaction module encrypts the core contents of the report (e.g., evaluation serial number, corresponding patent number, list of evaluators, etc.) by a hash function to save the abbreviated history information. It is created and distributed in a private network shared by all members participating in this transaction, and when necessary, it supports domestic and global investors, banks, and other companies (technical trading) who have been authenticated to access and read the report.

The mutual evaluation module surveys and stores the results of the mutual evaluation of the technology evaluation requesting company and the evaluator (patent attorney, technology value evaluator, technology credit evaluator).

3 to 6 are examples for explaining the process of determining the probability distribution range of the final technology valuation amount and quantitative variable values including technology life, discount rate, technology contribution rate, and average increase rate of sales based on the result of qualitative evaluation.

3 is a diagram showing a practical guide for technology value evaluation of the Ministry of Commerce, Industry and Energy as a standardized evaluation form according to an embodiment of the present invention.

4 is a diagram showing probability-based estimation (Monte Carlo simulation) of a net present value (NPV) according to an embodiment of the present invention.

5 is a diagram showing an analysis of the technology value influence (spider chart) of major variables according to an embodiment of the present invention.

6 is a diagram illustrating an analysis of the influence of a major variable on a valuation (stress analysis) in an extreme scenario according to an embodiment of the present invention.

After completing the qualitative evaluation according to the standardized evaluation form widely used in the field such as technology, rights, marketability, business feasibility, and loyalty evaluation in the evaluation module (for example, the Ministry of Commerce, Industry and Energy's technology value evaluation guideline, etc.), When setting the value of quantitative variables such as discount rate and technical life based on the basis of this, it is asked to select probability parameter values such as maximum, minimum, and average and appropriate probability distribution (e.g., beta distribution, normal distribution, triangle, uniform distribution, etc.) . Based on this, the participants' probability values are synthesized and used for valuation, and Monte Carlo simulation is conducted to determine the volatility of the technology value generated by the probability distribution of each variable to estimate the section of the net present value (NPV). , Sensitivity analysis, stress analysis, etc. are performed as shown in FIGS. 3 to 6.

According to an embodiment of the present invention, an online platform is established so that a large number of expert evaluators required for technology valuation can participate, so that they can evaluate various qualitative items necessary for calculating the technology value, such as technology, rights, marketability, and business value. An evaluation form is provided, and when selecting quantitative variables necessary for calculating the technology value, such as technology life, discount rate, technology contribution rate, and cash flow growth rate, it is encouraged to select a probability distribution. By doing this, it is possible to quantitatively derive the technology value by Monte Carlo simulation by synthesizing the probability distribution presented by each evaluator in the presence of multiple evaluators.

By avoiding the technology value evaluation that relies on the existing point estimation, and by providing the technology value in a section estimation method based on the probability distribution based on the collective intelligence of experts, the uncertainty of the future value is quantified, and based on this, technology collateral loan, technology It is to ensure that the sale, investment attraction, etc. can be made reasonably.

In addition, by allowing the artificial intelligence engine to learn in real time the entire evaluation process in which the collective intelligence of many of these experts participates, the artificial intelligence evaluation engine for the relevant technology field is continuously updated to reflect the latest trends, and through this, the expert collective intelligence is calculated Apart from the technological value, the valuation result by artificial intelligence itself is also derived in parallel. Although the relevance of the evaluation of the technology is somewhat lower than that of the expert group, broad objectivity can be secured by providing valuation results that reflect market consensus over a certain period of time for the overall technology field.

Since the entire evaluation process is promoted through a web application and a mobile terminal, the evaluation process can also be viewed by the customer requesting the evaluation, thus ensuring transparency in evaluation. In addition, through the automatic document generation function for the evaluation result, the report is automatically generated without any additional document preparation effort, so the evaluator can save the effort of writing the report. In addition, the reliability of the report can be increased by automatically including the list of many experts who participated in the report and electronic signatures when the document is automatically generated.

In addition, external investors, banks, technology trading counterparts, etc., who are authorized by using the authentication technology, can access the server to view the evaluation result report, and can easily decide whether to invest/loan/sell based on this. In the event of a transaction such as investment/loan/trading for the relevant technology, information on the transaction result is encrypted and shared to ensure transaction transparency. In addition, the stored transaction information is decoded by the artificial intelligence server to further learn the evaluation results in the actual technology transaction market after the technology value evaluation. Through this, the artificial intelligence engine can learn not only the technical characteristics and history of the entire technology from the birth to the sale, but also the technology value observed in the actual market such as the amount of the sale price and the size of loans. As a result, it is possible to further increase the accuracy of evaluation of artificial intelligence in the future technology value evaluation.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It can be implemented using one or more general purpose computers or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. Can be embodyed. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like.

As described above, although the embodiments have been described by the limited embodiments and the drawings, various modifications and variations are possible from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

Claims (12)

An evaluator recommendation module that recommends a number of evaluators with expertise matching the technical characteristics of the client company;
Qualitative evaluation results are based on the qualitative evaluation including technology evaluation, marketability evaluation, marketability evaluation, and technology royalty evaluation for technology conducted by evaluators recommended through mobile terminals and PC-based web applications. An evaluation module to obtain;
A collective intelligence module that determines a range of a probability distribution of a final technology valuation amount and quantitative variable values including technology life, discount rate, technology contribution rate, and average sales growth rate based on the result of qualitative evaluation;
It learns in real time the rules that exist between the evaluator's qualitative variable assigned score and the quantitative variable value, and automatically calculates the quantitative variable value when the qualitative evaluation result by expert collective intelligence is obtained, and the technical value is also calculated as a probability distribution Artificial intelligence module;
A document generation module that automatically inserts the evaluation results into the final report;
Technology history management and technology transaction modules that perform functions related to external viewing of reports and technology transfer transactions; And
Mutual evaluation module that stores the satisfaction evaluation results for each evaluator and the client company after evaluation is completed
Including,
The evaluator recommendation module,
Based on a plurality of technology keywords input from the client company, the evaluators are recommended according to the technical agreement score generated based on the satisfaction survey of the previous client within the same technology group,
The evaluation module,
Through the web application, it generates an alarm for the evaluation request to the recommended evaluators,
Provides a standardized evaluation form for qualitative evaluation through a web application,
When the qualitative evaluation is completed by the recommended evaluators, the qualitative evaluation result is obtained from the web application,
The recommended raters,
When receiving an alarm for an evaluation request through the web application,
Conduct a qualitative evaluation based on a standardized evaluation form provided through a web application,
Qualitative evaluation is a probability-based technology valuation and technology transaction online platform that consists of evaluation on an equal scale. delete delete The method of claim 1,
The evaluation module,
After completing the qualitative evaluation, when setting the quantitative variable values including discount rate and technical life based on the result of the qualitative evaluation, select probability parameter values including maximum, minimum, and average and probability distribution, and synthesize the rater's probability value. It is used for valuation, and Monte Carlo simulation is conducted to determine the volatility of the technology value generated by the probability distribution of each variable, and the section estimation of the net present value (NPV), sensitivity analysis, and stress analysis are performed. Conducted
Probability-based technology valuation and technology transaction online platform. The method of claim 1,
The artificial intelligence module,
Evolving into a technology value evaluation engine that reflects changes in economic and technical trends by learning the evaluation process of multiple experts participating in the evaluation and updating the learning results in real time.
Probability-based technology valuation and technology transaction online platform. The method of claim 1,
The technology history management and technology transaction module,
The core content of the report is encrypted by a hash function to generate abbreviated history information, and distributed and stored in the network to support domestic and global investors, banks, and other companies who have been authenticated when necessary to access and read the report.
Probability-based technology valuation and technology transaction online platform. Recommending a plurality of evaluators having expertise matching the technical characteristics of the client company through the evaluator recommendation module;
Obtaining a qualitative evaluation result based on a qualitative evaluation including technicality, business feasibility evaluation, marketability evaluation and business feasibility evaluation, and technology royalty evaluation for the technology conducted by the recommended evaluators through the evaluation module;
Determining a probability distribution range of a final technology value evaluation amount and quantitative variable values including technology life, discount rate, technology contribution rate, and average sales growth rate based on the result of the qualitative evaluation through the collective intelligence module;
Through the artificial intelligence module, it learns in real time the rules that exist between the evaluator's qualitative variable assignment score and the quantitative variable value, and automatically calculates and describes the quantitative variable value when the qualitative evaluation result by expert collective intelligence is obtained. Calculating a value as a probability distribution;
Automatically inserting the evaluation result into the final report through the document generation module;
Performing functions related to external viewing of the report and technology transfer transactions through technology history management and technology transaction modules; And
The step of storing the satisfaction evaluation results for each evaluator and the client company after completion of the evaluation through the mutual evaluation module
Including,
The step in recommending evaluators is:
Based on a plurality of technology keywords input from the client company, the evaluators are recommended according to the technical agreement score generated based on the satisfaction survey of the previous client within the same technology group,
The step of obtaining a qualitative evaluation result is:
Generating an alarm for an evaluation request to recommended evaluators through a web application;
Providing a standardized evaluation form for qualitative evaluation through a web application; And
When the qualitative evaluation is completed by the recommended evaluators, obtaining a qualitative evaluation result from the web application,
The recommended raters,
When receiving an alarm for an evaluation request through the web application,
Based on the standardized evaluation form provided through the web application, qualitative evaluation is performed,
Qualitative evaluation is a probability-based technology value evaluation and technology transaction method that consists of evaluation on an equality scale. delete delete The method of claim 7,
The step of conducting qualitative evaluation, including technical evaluation, business feasibility evaluation, marketability evaluation and business feasibility evaluation, and technology loyalty evaluation for the recommended evaluators through the evaluation module
After completing the qualitative evaluation, when setting the quantitative variable values including discount rate and technical life based on the result of the qualitative evaluation, select probability parameter values including maximum, minimum, and average and probability distribution, and synthesize the rater's probability value. It is used for valuation, and Monte Carlo simulation is conducted to determine the volatility of the technology value generated by the probability distribution of each variable, and the section estimation of the net present value (NPV), sensitivity analysis, and stress analysis are performed. Conducted
Probability-based technology valuation and technology transaction method. The method of claim 7,
Through the artificial intelligence module, it learns in real time the rules that exist between the evaluators' qualitative variable assignment score and the choice of the quantitative variable value, and automatically calculates the quantitative variable value when the qualitative evaluation result value is given by expert collective intelligence. The step of calculating the technology value as a probability distribution is:
Evolving into a technology value evaluation engine that reflects changes in economic and technical trends by learning the evaluation process of multiple experts participating in the evaluation and updating the learning results in real time.
Probability-based technology valuation and technology transaction method. The method of claim 7,
After completing the evaluation through the mutual evaluation module, the step of storing the satisfaction evaluation results for the evaluator and the client company is:
The core content of the report is encrypted by a hash function to generate abbreviated history information, and distributed and stored in the network to support domestic and global investors, banks, and other companies who have been authenticated when necessary to access and read the report.
Probability-based technology valuation and technology transaction method.

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