Study on Artificial Intelligence Evaluation for Technology Value Assessment — Focusing on the Absence of Standards for the Use of Artificial Intelligence in Technology Value Assessment and the Verification Zone Operating System — Abstract. Technology value assessment is a procedure for determining the present and future value of intangible assets such as patents, utility models, designs, trade secrets, software, platforms, data, manufacturing structures, and business models.

Study on Artificial Intelligence Evaluation for Technology Value Assessment

In modern industry, intangible assets such as patents, algorithms, platforms, data, brands, manufacturing know-how, and business models are increasingly forming the core value of companies and individuals, rather than tangible assets such as land, buildings, and machinery.

In particular, individual inventors, early-stage founders, holders of technology at the patent application stage, and holders of convergent technologies may have substantial technology value depending on the scope of rights, feasibility of implementation, market scalability, licensing potential, OEM or ODM potential, and future business rights, even if current sales data are insufficient.

However, existing institution-centered technology value assessment, while having a certain degree of institutional public credibility, has limitations in that assessment costs are high, assessment periods are long, and the judgment standards and deduction standards for each assessment item are not clearly disclosed.

In particular, in some cases it is difficult to verify after the fact how much is deducted when certain materials are insufficient, how implementation risk, market risk, rights risk, manufacturing risk, and profit risk are reflected, how much subjective judgment by the evaluator has intervened, and through what judgment path the final assessed amount has been derived.

In such cases, the public credibility of the assessment institution and the structural reliability of the assessment result must be distinguished.

Existing appraisal institutions or evaluation institutions are also using artificial intelligence, big data, automated value calculation models, and digital evaluation systems.

Therefore, standards for the use of artificial intelligence, the scope of input materials, evaluation items, deduction standards, standards for reflecting risk factors, and verification logs must be disclosed and standardized.

In particular, in order to prevent the result of using artificial intelligence from replacing evaluator subjectivity with algorithmic opacity, a technology value AI evaluation standards document and a verification zone operating system capable of controlling and verifying the result are necessary.

This study starts from the point that technology holders and inventors need to structurally review the value of their own technologies in advance.

Technology value should not be determined merely by current sales or accounting data.

The originality of the technology, feasibility of implementation, rights value, marketability, commercialization potential, profit model, competitive advantage, scalability, risk factors, and reliability of materials must be reviewed by item.

In particular, technology at the patent application stage, technology before the prototype stage, convergent technology, and platform-type technology must not be undervalued merely because past sales data are insufficient; the technical structure, possibility of securing rights, and future commercialization potential must be reviewed together.

Study on Artificial Intelligence Evaluation for Technology Value Assessment — Focusing on the Absence of Standards for the Use of Artificial Intelligence in Technology Value Assessment and the Verification Zone Operating System — Abstract. Technology value assessment is a procedure for determining the present and future value of intangible assets such as patents, utility models, designs, trade secrets, software, platforms, data, manufacturing structures, and business models.

Therefore, verification zones are necessary in order to structurally review technology value assessment.

A verification zone is a structural evaluation area that separates the judgment elements of technology value assessment and confirms, for each item, the materials, standards, grounds for judgment, reasons for deduction, risk factors, and matters requiring supplementation.

As a result of comprehensively analyzing the nature of technology value assessment, the limitations of existing institution-centered appraisal, the absence of standards for the use of artificial intelligence, the need to verify input materials, the need to disclose deduction standards, the need for verification logs, and the need to manage professional appraisal, this study concludes that fifteen verification zones are necessary for artificial intelligence evaluation of technology value assessment.

The fifteen verification zones consist of the evaluation target identification zone, submitted materials verification zone, rights materials verification zone, technical structure verification zone, prior art and comparative materials verification zone, feasibility verification zone, marketability verification zone, commercialization potential verification zone, profit model verification zone, manufacturing and cost verification zone, risk factor verification zone, deduction standard verification zone, value range calculation verification zone, verification log zone, and professional appraisal management zone.

Here, the fifteen verification zones are not a number arbitrarily set at the starting point of the study, but are research results derived from analyzing the structural judgment elements of technology value assessment.

The results of each verification zone may be indicated as Y, M, N, or as scores, ranges, risk marks, and similar indicators.

In modern industry, intangible assets such as patents, algorithms, platforms, data, brands, manufacturing know-how, and business models are increasingly forming the core value of companies and individuals, rather than tangible assets such as land, buildings, and machinery.

Y means that the requirements of the relevant verification zone have been satisfied or that the grounds for judgment are sufficient; M means that supplementation is needed, materials are insufficient, or judgment is reserved; and N means that requirements are not satisfied, materials are absent, grounds for judgment are insufficient, or there is a structural problem.

However, the result value must not be a mere conclusion mark, and must be recorded together with the grounds for judgment, input materials, reasons for deduction, and verification logs.

Furthermore, verification zones must not remain a simple checklist used arbitrarily by individual technology holders.

Because verification zones are an operating system for standardizing technology value assessment and making it verifiable, they need to be operated and managed by an evaluation management institution or verification institution equipped with such a system.

Such institution must receive technical materials, rights materials, implementation materials, market materials, profit materials, comparative materials, and risk materials from the technology holder or inventor, perform evaluation for each verification zone, and prepare and manage the result value, grounds for judgment, reasons for deduction, matters requiring supplementation, and verification logs for each zone.

This study criticizes the high cost, delay, unclear deduction standards, opacity of the evaluation process, limitations of signature and seal responsibility theory, limitations of absolute reliance on field inspection, limitations of undisclosed internal review, and qualification-centered structure of existing institution-centered technology value assessment.

In particular, individual inventors, early-stage founders, holders of technology at the patent application stage, and holders of convergent technologies may have substantial technology value depending on the scope of rights, feasibility of implementation, market scalability, licensing potential, OEM or ODM potential, and future business rights, even if current sales data are insufficient.

However, the purpose of this study is not simply to deny existing appraisal institutions.

The core of this study is to clarify that the reliability of technology value assessment should arise not from the name of an institution or the authority of an evaluator, but from the evaluation standards document, input materials, verification zones, deduction standards, verification logs, and professional appraisal management system.

Therefore, artificial intelligence evaluation of technology value assessment must not remain at the level of simple automatic amount calculation or platform-type document drafting.

It must become a structured evaluation management system that can be used as materials for technology holders’ preliminary value review, response to appraisal, investment, manufacturing, and licensing negotiations, policy support, and dispute response assistance.

This study demonstrates that the technology value AI evaluation standards document and verification zone operating system can become new evaluation management standards that enhance the objectivity, accessibility, repeatability, and verifiability of technology value assessment.

In modern industry, intangible assets such as patents, algorithms, platforms, data, brands, manufacturing know-how, and business models are increasingly forming the core value of companies and individuals, rather than tangible assets such as land, buildings, and machinery.

However, existing institution-centered technology value assessment, while having a certain degree of institutional public credibility, has limitations in that assessment costs are high, assessment periods are long, and the judgment standards and deduction standards for each assessment item are not clearly disclosed.

Keywords: artificial intelligence evaluation, technology value assessment, technology value AI evaluation standards document, verification zone, verification zone operating system, appraisal institution, deduction standards,

verification log, error control, professional appraisal management, intangible asset valuation, patent technology valuation, technology holder, inventor, evaluation management institution

Chapter 1 Introduction

In particular, in some cases it is difficult to verify after the fact how much is deducted when certain materials are insufficient, how implementation risk, market risk, rights risk, manufacturing risk, and profit risk are reflected, how much subjective judgment by the evaluator has intervened, and through what judgment path the final assessed amount has been derived.

Section 1 Background of the Study

In modern industry, the value of companies and individuals is increasingly determined by intangible assets such as patents, trademarks, designs, copyrights, software, data, platforms, manufacturing know-how, trade secrets, user networks, and business models, rather than by tangible assets such as land, buildings, and machinery.

In particular, in the case of technology-based companies or individual inventors, high future value may be formed according to patent rights, technical structure, scope of rights, market scalability, licensing potential, OEM or ODM potential, and commercialization strategy, even before actual sales occur.

Technology value assessment is an important procedure for explaining the value of such intangible assets and for determining the economic meaning of technology in investment attraction, technology transactions, licensing negotiations, manufacturing cooperation, policy support, financial review, and legal disputes.

In such cases, the public credibility of the assessment institution and the structural reliability of the assessment result must be distinguished.

However, existing institution-centered technology value assessment requires considerable cost and time and has a structure that is difficult for individual inventors or early-stage founders to access.

Even where existing appraisal presents a final assessed amount or comprehensive conclusion, there are cases in which it is not clearly shown which elements of the target technology were evaluated and how, which materials were reflected in which items, how much deduction was made for which risk factors, and how insufficiency of materials was handled.

In such cases, the assessment result may appear to be a document with institutional public credibility, but it is difficult to view it as a structurally verifiable document.

Existing appraisal institutions or evaluation institutions are also using artificial intelligence, big data, automated value calculation models, and digital evaluation systems.

This study starts from the need for technology holders and inventors to structurally review their technology value in advance and to structurally understand and verify the assessment results of existing appraisal institutions or evaluation institutions.

In particular, individual inventors, early-stage founders, holders of technology at the patent application stage, and holders of convergent technologies may have substantial technology value depending on the scope of rights, feasibility of implementation, market scalability, licensing potential, OEM or ODM potential, and future business rights, even if current sales data are insufficient.

Therefore, standards for the use of artificial intelligence, the scope of input materials, evaluation items, deduction standards, standards for reflecting risk factors, and verification logs must be disclosed and standardized.

Technology value assessment must secure reliability not by the name of an institution or the authority of an evaluator, but by the evaluation standards document, input materials, judgment items, deduction standards, standards for reflecting risk factors, verification logs, and error-control procedures.

Section 2 Purpose of the Study

The purpose of this study is to present artificial intelligence-based evaluation standards by which technology holders and inventors can structurally review their own technology value in advance, and to derive the verification zone operating system necessary for such structural review.

In particular, in order to prevent the result of using artificial intelligence from replacing evaluator subjectivity with algorithmic opacity, a technology value AI evaluation standards document and a verification zone operating system capable of controlling and verifying the result are necessary.

This study does not merely explain the possibility that artificial intelligence can assist technology value assessment.

On the premise that existing appraisal institutions or evaluation institutions also use artificial intelligence, big data, automated value calculation models, and digital evaluation systems, this study raises the issue that, if the standards for such use are unclear, new opacity rather than objectivity of evaluation may arise.

It is clear that artificial intelligence can increase the speed of evaluation.

This study starts from the point that technology holders and inventors need to structurally review the value of their own technologies in advance.

However, artificial intelligence evaluation without standards may become not rapid evaluation, but rapid opaque evaluation.

If it is not recorded what input materials artificial intelligence used, by what standards it selected evaluation items, what deduction standards it applied, how it reflected risk factors, and through what judgment path the final value range was derived, the evaluation result remains a conclusion generated by an algorithm rather than a verifiable document.

In addition, this study reexamines the elements that constitute the reliability logic of existing appraisal institutions.

The theory of evaluator signature and seal responsibility should be redefined not as an absolute guarantee of the assessment result, but as confirmation of procedural compliance.

Technology value should not be determined merely by current sales or accounting data.

Field inspection should be converted into an optional procedure in an environment where digital material verification and forgery or alteration detection are possible.

The discretion and internal know-how of evaluators must be externalized into standard data, deduction tables, risk adjustment tables, sensitivity analysis, and verification logs.

Undisclosed internal review committees must be converted into data-based simulations and recordable review procedures.

The originality of the technology, feasibility of implementation, rights value, marketability, commercialization potential, profit model, competitive advantage, scalability, risk factors, and reliability of materials must be reviewed by item.

Ultimately, this study considers that the reliability of technology value assessment should be found in evaluation standards, input materials, judgment items, deduction standards, verification logs, and error-control procedures, rather than in the name of the evaluation institution.

Furthermore, this study aims to clarify that technology value AI evaluation is not completed by an evaluation standards document alone, but needs to be managed by an evaluation management institution or verification institution equipped with a verification zone operating system.

Section 3 Scope and Method of the Study

This study focuses, among technology value assessments, on the valuation of intangible assets such as patent technologies, utility models, designs, trade secrets, software, platforms, data, manufacturing structures, and business models.

In particular, technology at the patent application stage, technology before the prototype stage, convergent technology, and platform-type technology must not be undervalued merely because past sales data are insufficient; the technical structure, possibility of securing rights, and future commercialization potential must be reviewed together.

The research method combines literature-based, structural, and institutional analysis methods.

First, the nature of technology value assessment is reviewed.

It clarifies that technology value is not merely current sales or market price, but is formed by the combination of technological originality, feasibility of implementation, rights value, marketability, commercialization potential, profit model, competitive advantage, scalability, risk factors, and material reliability.

Therefore, verification zones are necessary in order to structurally review technology value assessment.

Next, the limitations of existing institution-centered appraisal are reviewed.

In particular, this study analyzes high cost, delayed evaluation, unclear deduction standards, opacity of the evaluation process, absence of standards for the use of artificial intelligence, limitations of signature and seal responsibility theory, problems of absolute reliance on field inspection, internal discretion and undisclosed review structure, qualification-centered monopoly structure, and undervaluation of early-stage and convergent technologies.

Then, the concept of artificial intelligence-based technology value assessment is defined.

A verification zone is a structural evaluation area that separates the judgment elements of technology value assessment and confirms, for each item, the materials, standards, grounds for judgment, reasons for deduction, risk factors, and matters requiring supplementation.

Artificial intelligence evaluation is not simple automatic amount calculation, but a structural evaluation that includes classification of input materials, judgment by evaluation item, indication of reasons for deduction, preparation of verification logs, and error-control procedures.

Finally, this study presents fifteen verification zones as the operating structure of technology value assessment.

The fifteen verification zones decompose the evaluation process from identification of the evaluation target to management of professional appraisal, and allow the correspondence among materials, standards, judgments, deductions, and conclusions to be confirmed for each verification zone.

In this study, the fifteen verification zones are not a number arbitrarily set from the beginning, but are research results derived from analyzing the structural judgment elements of technology value assessment.

Chapter 2 Need for Prior Review of Technology Value by Technology Holders

As a result of comprehensively analyzing the nature of technology value assessment, the limitations of existing institution-centered appraisal, the absence of standards for the use of artificial intelligence, the need to verify input materials, the need to disclose deduction standards, the need for verification logs, and the need to manage professional appraisal, this study concludes that fifteen verification zones are necessary for artificial intelligence evaluation of technology value assessment.

Section 1 Meaning of Technology Value

Technology value does not mean only the scientific excellence of the technology itself.

Technology value includes the possibility that the technology can be converted into economic benefits in the market.

However, existing institution-centered technology value assessment, while having a certain degree of institutional public credibility, has limitations in that assessment costs are high, assessment periods are long, and the judgment standards and deduction standards for each assessment item are not clearly disclosed.

The fifteen verification zones consist of the evaluation target identification zone, submitted materials verification zone, rights materials verification zone, technical structure verification zone, prior art and comparative materials verification zone, feasibility verification zone, marketability verification zone, commercialization potential verification zone, profit model verification zone, manufacturing and cost verification zone, risk factor verification zone, deduction standard verification zone, value range calculation verification zone, verification log zone, and professional appraisal management zone.

Therefore, technology value assessment must comprehensively judge the originality of the technology, feasibility of implementation, rights value, marketability, commercialization potential, profit model, competitive advantage, scalability, risk factors, and reliability of materials.

If a technology is original but difficult to implement, impossible to manufacture, limited in market, or weak in rights protection, its economic value may be reduced.

Conversely, even if the technical structure appears simple, high technology value may be formed if manufacturing is easy, the market is large, the technology is protected by patent rights, repeated sales are possible, and expansion into various industries is possible.

Technology value arises from a combined structure of multiple elements.

Here, the fifteen verification zones are not a number arbitrarily set at the starting point of the study, but are research results derived from analyzing the structural judgment elements of technology value assessment.

Technological originality creates protective value when combined with rights value; feasibility of implementation creates realizable value when combined with commercialization potential; and marketability is converted into economic value when combined with a profit model.

In addition, scalability expands future value, while risk factors and the reliability of materials function to adjust the assessed amount.

Section 2 Difference Between Technology Value Assessment and General Appraisal

The results of each verification zone may be indicated as Y, M, N, or as scores, ranges, risk marks, and similar indicators.

Technology value assessment is fundamentally different from the appraisal of tangible assets such as real estate, machinery, and inventory assets.

Tangible assets can be evaluated based on comparable transaction cases, current condition, location, years of use, depreciation, physical characteristics, and similar factors.

However, technology is not a visible object, but an intangible asset in which rights, information, structure, possibility, marketability, and future profitability are combined.

Y means that the requirements of the relevant verification zone have been satisfied or that the grounds for judgment are sufficient; M means that supplementation is needed, materials are insufficient, or judgment is reserved; and N means that requirements are not satisfied, materials are absent, grounds for judgment are insufficient, or there is a structural problem.

If this difference is not understood, procedures for tangible asset appraisal may be applied inappropriately to intangible asset valuation.

For example, if technology value is determined mainly on the basis of site visits, confirmation of physical condition, book asset value, and past sales data, the structure of patent claims, software algorithms, data assets, platform scalability, licensing potential, and future business rights may be undervalued.

In particular, technology at the patent application stage may not yet have a confirmed registration status, may not have a prototype, or may not have generated sales.

However, if the structure of the claims is specific, the difference from prior art is clear, implementation is possible, and market scalability is large, future value may exist.

Therefore, technology value assessment must not be conclusively determined based only on current materials, and future business rights and the possibility of securing rights must also be reviewed together.

Section 3 Importance of Evaluating Early-Stage and Convergent Technologies

However, the result value must not be a mere conclusion mark, and must be recorded together with the grounds for judgment, input materials, reasons for deduction, and verification logs.

Technology value assessment is not needed only for completed companies or products with confirmed sales.

Rather, technology value assessment is more important for early-stage technologies.

Individual inventors, early-stage founders, holders of technology at the patent application stage, and SMEs must explain the value of their technologies in investment attraction, manufacturing cooperation, licensing negotiations, policy support, financial review, and legal dispute response.

Early-stage technologies often lack sales data, accounting data, market data, and comparable cases.

In particular, in some cases it is difficult to verify after the fact how much is deducted when certain materials are insufficient, how implementation risk, market risk, rights risk, manufacturing risk, and profit risk are reflected, how much subjective judgment by the evaluator has intervened, and through what judgment path the final assessed amount has been derived.

Furthermore, verification zones must not remain a simple checklist used arbitrarily by individual technology holders.

Existing institution-centered evaluation may give a low evaluation or regard evaluation itself as difficult because of such insufficiency of materials.

Moreover, even where an appraisal institution uses artificial intelligence or an automated evaluation model, if the model is designed mainly around current sales data or comparative cases, the future value of early-stage technologies and convergent technologies may be structurally undervalued.

The value of early-stage technology may arise not from current sales, but from the possibility of securing rights, feasibility of implementation, market scalability, commercialization path, and profit model.

Therefore, early-stage technology assessment must not start simply from the question, “Are there sales?”

Rather, it must start from structural questions such as: Is the technical structure specific? Can it be protected by patent claims? Is it manufacturable? Does a market exist? In what way can it be monetized? What risk factors exist? What materials are insufficient?

Section 4 Structural Disadvantage of Technology Holders

Because verification zones are an operating system for standardizing technology value assessment and making it verifiable, they need to be operated and managed by an evaluation management institution or verification institution equipped with such a system.

Individual inventors, early-stage founders, and SMEs are the parties with the greatest need to explain technology value, but in many cases they are placed in the most disadvantageous position in preparing evaluation materials and responding to evaluation institutions.

They have difficulty bearing the cost of professional appraisal, difficulty fully understanding the form of materials required by evaluation institutions, and difficulty identifying in advance what reasons for deduction may exist.

As a result, technology holders undergo evaluation without properly knowing in what elements their technology has strengths, what materials are insufficient and cause deductions, and what risk factors are reflected in value assessment.

This may weaken the technology holder’s right to respond to evaluation and may become a cause of undervaluing the structural value of early-stage and convergent technologies.

Therefore, what technology holders need is not a standard that replaces high-cost appraisal, but a standard by which they can first structurally review their technology value, identify insufficient materials and risk factors, and organize evaluation materials before appraisal or investment negotiations.

The technology value AI evaluation standards document and verification zone operating system proposed in this study start from this need.

Chapter 3 Structural Limitations of Existing Institution-Centered Appraisal

Section 1 Separation of Public Credibility and Evaluation Reliability

An appraisal institution may have a certain degree of public credibility under statutes, registration systems, qualification systems, or administrative procedures.

However, public credibility arises from the status of the institution and does not automatically guarantee the accuracy of individual evaluation results.

Even an evaluation report prepared by an institution with public credibility is structurally difficult to trust if evaluation items are omitted, input materials are unclear, comparative materials are inappropriate, or risk factors are not sufficiently reflected.

This problem is the same even where an appraisal institution uses artificial intelligence.

Such institution must receive technical materials, rights materials, implementation materials, market materials, profit materials, comparative materials, and risk materials from the technology holder or inventor, perform evaluation for each verification zone, and prepare and manage the result value, grounds for judgment, reasons for deduction, matters requiring supplementation, and verification logs for each zone.

This study criticizes the high cost, delay, unclear deduction standards, opacity of the evaluation process, limitations of signature and seal responsibility theory, limitations of absolute reliance on field inspection, limitations of undisclosed internal review, and qualification-centered structure of existing institution-centered technology value assessment.

The fact that an appraisal institution has used artificial intelligence, big data, or an automated value calculation model may show the modernity of the evaluation, but it does not by itself guarantee the reliability of the evaluation result.

It must be confirmed what input materials artificial intelligence used, by what standards it classified the materials, to which items it assigned weights, what deduction standards it applied, how it reflected risk factors, and whether the judgment path was left as a verification log.

Therefore, the public credibility of an appraisal institution and the structural reliability of an evaluation result must be distinguished.

Public credibility is a matter of institutional status, while reliability is a matter of evaluation standards and judgment paths.

However, the purpose of this study is not simply to deny existing appraisal institutions.

In the future, the reliability of technology value assessment should be judged based on “under what standards document and verification structure it was evaluated” rather than “which institution evaluated it.”

Section 2 High-Cost Structure and Accessibility Issues

Existing institution-centered technology value assessment requires considerable cost.

The core of this study is to clarify that the reliability of technology value assessment should arise not from the name of an institution or the authority of an evaluator, but from the evaluation standards document, input materials, verification zones, deduction standards, verification logs, and professional appraisal management system.

Large companies or companies that have completed investment attraction may be able to bear such costs, but individual inventors, early-stage founders, holders of technology at the patent application stage, and SMEs have difficulty bearing high appraisal costs.

However, the parties who most need technology value assessment are precisely these parties.

Individual inventors must explain their technology to manufacturers, early-stage founders must explain the value of their technology to investors, and SMEs must explain the future potential of their technology to financial institutions or policy support agencies.

In such cases, the public credibility of the assessment institution and the structural reliability of the assessment result must be distinguished.

Therefore, artificial intelligence evaluation of technology value assessment must not remain at the level of simple automatic amount calculation or platform-type document drafting.

Patent applicants need to explain that their inventions are not mere ideas but technologies with the possibility of securing rights and market value.

Existing appraisal institutions may justify high costs on the grounds of evaluator responsibility, field inspection, internal review, institutional review procedures, and similar matters.

However, in technology value assessment, all of these procedures are not always necessary.

It must become a structured evaluation management system that can be used as materials for technology holders’ preliminary value review, response to appraisal, investment, manufacturing, and licensing negotiations, policy support, and dispute response assistance.

Where input materials are submitted digitally, the technical structure and rights materials are specified in documents, and market materials and manufacturing materials are provided as data, it is possible first to classify the materials, identify insufficient materials and risk factors, and then connect only the necessary parts to professional appraisal.

Section 3 Contradiction Between Delayed Evaluation and Use of AI

This study demonstrates that the technology value AI evaluation standards document and verification zone operating system can become new evaluation management standards that enhance the objectivity, accessibility, repeatability, and verifiability of technology value assessment.

Existing institution-centered evaluation may require considerable time because it goes through many procedures such as collection of materials, review, on-site confirmation, expert meetings, preparation of an evaluation report, and internal review.

Of course, accurate evaluation requires sufficient review.

Keywords: artificial intelligence evaluation, technology value assessment, technology value AI evaluation standards document, verification zone, verification zone operating system, appraisal institution, deduction standards,

However, not every technology value assessment requires a long-term procedure.

Early-stage technology holders often need rapid preliminary evaluation.

verification log, error control, professional appraisal management, intangible asset valuation, patent technology valuation, technology holder, inventor, evaluation management institution

In investor meetings, manufacturer negotiations, patent strategy establishment, policy support applications, financial review, and legal dispute response, it must be possible to explain the structural value of the technology within a certain period.

However, if existing appraisal takes too long, technology holders may be unable to use evaluation materials at the necessary time.

Chapter 1 Introduction

If appraisal said to use artificial intelligence is still delayed, the meaning of that use is limited.

The advantage of artificial intelligence lies in its ability to quickly classify extensive input materials, arrange grounds for judgment by evaluation item, and immediately indicate insufficient materials and risk factors.

Therefore, if appraisal uses artificial intelligence, speed must be secured at least at the stages of preliminary evaluation, material organization, indication of reasons for deduction, guidance on insufficient materials, and detection of risk factors.

Section 1 Background of the Study

Section 4 Absence of Deduction Standards and Algorithmic Opacity

One of the most important problems in existing appraisal is that deduction standards are sometimes not clear.

In modern industry, the value of companies and individuals is increasingly determined by intangible assets such as patents, trademarks, designs, copyrights, software, data, platforms, manufacturing know-how, trade secrets, user networks, and business models, rather than by tangible assets such as land, buildings, and machinery.

Technology value assessment is not a procedure that evaluates only the strengths of a technology.

Technology value assessment must evaluate together insufficiency of materials, implementation risk, market risk, rights risk, manufacturing risk, certification risk, profit risk, competitive risk, and reliability of materials.

In particular, in the case of technology-based companies or individual inventors, high future value may be formed according to patent rights, technical structure, scope of rights, market scalability, licensing potential, OEM or ODM potential, and commercialization strategy, even before actual sales occur.

However, if it is not clear how much is deducted when certain materials are insufficient, in which item and how implementation risk is reflected, how insufficiency of market data affects the marketability score and value range, how unstable patent rights adjust the rights value evaluation, or whether the same risk factor is deducted twice, the evaluation result is difficult to regard as objective.

This problem is even more important in evaluations using artificial intelligence.

Technology value assessment is an important procedure for explaining the value of such intangible assets and for determining the economic meaning of technology in investment attraction, technology transactions, licensing negotiations, manufacturing cooperation, policy support, financial review, and legal disputes.

It is problematic when a human evaluator does not clearly disclose deduction standards, but it creates a greater problem when artificial intelligence or an automated evaluation model does not disclose deduction standards.

The subjective judgment of a human evaluator can at least be the subject of questions and requests for explanation, but the judgment of an algorithm is difficult to confirm externally without standards and logs.

However, existing institution-centered technology value assessment requires considerable cost and time and has a structure that is difficult for individual inventors or early-stage founders to access.

If an appraisal institution uses artificial intelligence but does not disclose deduction standards, it replaces evaluator subjectivity with the black box of an algorithm.

Section 5 Opacity of the Evaluation Process and Absence of Verification Logs

Existing institution-centered evaluation reports present a final assessed amount or comprehensive opinion, but in some cases they do not sufficiently indicate through what judgment path that conclusion was derived.

Even where existing appraisal presents a final assessed amount or comprehensive conclusion, there are cases in which it is not clearly shown which elements of the target technology were evaluated and how, which materials were reflected in which items, how much deduction was made for which risk factors, and how insufficiency of materials was handled.

Because technology value assessment is a combined evaluation of multiple items, it is difficult to verify the evaluation result if the evaluation process is not transparent.

If the evaluation process is opaque, it is difficult to know what input materials were used, what materials were omitted, which items were evaluated and which items were excluded, how risk factors were reflected, and in what parts the evaluator’s subjective judgment intervened.

In such cases, the assessment result may appear to be a document with institutional public credibility, but it is difficult to view it as a structurally verifiable document.

When artificial intelligence is involved, the problem becomes more complex.

If it cannot be confirmed what materials artificial intelligence emphasized, what materials it excluded, on what grounds it assigned scores, and on what grounds it made deductions, the evaluation result is merely a conclusion generated by an algorithm, not a verifiable document.

Existing appraisal institutions or evaluation institutions are also using artificial intelligence, big data, automated value calculation models, and digital evaluation systems.

Therefore, if an appraisal institution uses artificial intelligence, verification logs must be mandatory, not optional.

A verification log is a procedure for recording the name of the technology subject to evaluation, the list of input materials, the source and date of each material, materials used by evaluation item, scores and grounds by item, reasons for deduction, risk factors, insufficient materials, grounds for calculating the value range, evaluation limitations, conditions for reevaluation, and the version of the evaluation system.

Therefore, standards for the use of artificial intelligence, the scope of input materials, evaluation items, deduction standards, standards for reflecting risk factors, and verification logs must be disclosed and standardized.

Section 6 Limitations of Signature and Seal Responsibility Theory

The existing institution-centered appraisal system has explained the reliability of an appraisal report by adding the signature and seal of the appraiser at the end of the appraisal report and by adopting the form that the appraiser bears responsibility for the evaluation result.

However, in technology value assessment, the theory of signature and seal responsibility must be reexamined.

The responsibility of an appraiser is not an absolute guarantee that the evaluation result is always accurate.

Existing appraisal institutions or evaluation institutions are also using artificial intelligence, big data, automated value calculation models, and digital evaluation systems.

In particular, in order to prevent the result of using artificial intelligence from replacing evaluator subjectivity with algorithmic opacity, a technology value AI evaluation standards document and a verification zone operating system capable of controlling and verifying the result are necessary.

Technology value is a structural value that changes according to market conditions, rights status, manufacturability, profit model, competing technologies, regulatory environment, and the recency of materials.

Therefore, the responsibility of an appraiser should be understood not as responsibility for the absolute accuracy of a specific amount, but as procedural responsibility for whether evaluation standards were followed, whether input materials were properly confirmed, whether reasons for deduction were not omitted, whether risk factors were appropriately reflected, and whether the judgment path was left in a verifiable manner.

This study starts from the need for technology holders and inventors to structurally review their technology value in advance and to structurally understand and verify the assessment results of existing appraisal institutions or evaluation institutions.

In artificial intelligence-based technology value assessment, the center of responsibility must move from the appraiser’s seal itself to the evaluation standards document, input materials, deduction standards, error-control procedures, and verification logs.

A signature and seal must not be a device that justifies an evaluation result without verification logs, but must become an auxiliary indication confirming whether standards and procedures were complied with.

Section 7 Limitations of Absolute Reliance on Field Inspection

Technology value assessment must secure reliability not by the name of an institution or the authority of an evaluator, but by the evaluation standards document, input materials, judgment items, deduction standards, standards for reflecting risk factors, verification logs, and error-control procedures.

In existing appraisal, site visits, prototype photography, factory confirmation, and physical inspection have been regarded as important procedures.

In the evaluation of tangible assets, field inspection still has meaning.

Section 2 Purpose of the Study

However, in technology value assessment, especially in the valuation of intangible assets such as patents, software, platforms, data, manufacturing structures, and business models, field inspection cannot be an absolute requirement.

The essence of technology value lies not in the physical condition of a specific place, but in the combination of technical structure, scope of rights in claims, feasibility of implementation, market demand, manufacturing cost materials, profit model, competing technologies, and risk factors.

The mere fact that a site has been visited does not mean that the technical structure is accurately understood, that the rights value of the claims is confirmed, or that marketability and the profit model are verified.

Therefore, standards for the use of artificial intelligence, the scope of input materials, evaluation items, deduction standards, standards for reflecting risk factors, and verification logs must be disclosed and standardized.

The purpose of this study is to present artificial intelligence-based evaluation standards by which technology holders and inventors can structurally review their own technology value in advance, and to derive the verification zone operating system necessary for such structural review.

Therefore, in artificial intelligence-based technology value assessment, field inspection should be redefined not as a mandatory procedure in principle, but as an optional sample-confirmation procedure.

Field inspection is sufficient when high-risk materials, materials suspected of forgery or alteration, confirmation of large-scale equipment, or verification of manufacturing capability is necessary.

Requiring field inspection for all technology value assessments is inconsistent with the nature of technology value assessment and applies the procedure of tangible asset appraisal directly to intangible asset valuation.

This study does not merely explain the possibility that artificial intelligence can assist technology value assessment.

Section 8 Limitations of Discretion, Internal Review, and Qualification-Centered Structure

In existing appraisal, discount rates, marketability adjustment, technology maturity adjustment, regulatory risk, manufacturing risk, profit risk, and value-range adjustment are often determined by the evaluator’s experience and internal know-how.

Such discretion may reflect a certain degree of expertise, but if its standards are not disclosed, it is difficult to explain differences in evaluation results.

If one evaluator applies a 15 percent risk adjustment to the same technology and another evaluator applies a 25 percent risk adjustment, the difference is difficult to justify merely by saying that it is based on evaluator experience.

Because risk adjustment in technology value assessment directly affects the result, it should be connected as much as possible to standardized standards, external statistical data, similar transaction cases, success rates by technology field, patent registration possibility, market data, manufacturing cost materials, and regulatory materials.

Undisclosed internal review committees have the same problem.

If it is not recorded who adjusted which item’s score or value range in the internal review process, based on what materials and on what grounds, internal review risks functioning as an exercise of undisclosed authority rather than a transparent verification procedure.

In the field of intangible asset valuation, standards documents, verification logs, and professional appraisal management systems should become more important reliability standards than qualification monopoly.

Anyone who is equipped with evaluation standards documents, input material standards, deduction standards, verification logs, error control, and a professional appraisal management system, and who can connect with experts in necessary fields, should be able to function as a technology value evaluation management entity.

Section 9 Undervaluation of Early-Stage and Convergent Technologies

On the premise that existing appraisal institutions or evaluation institutions also use artificial intelligence, big data, automated value calculation models, and digital evaluation systems, this study raises the issue that, if the standards for such use are unclear, new opacity rather than objectivity of evaluation may arise.

Existing institution-centered evaluation may tend to undervalue technologies that lack current sales data, accounting data, and comparative cases.

However, the value of early-stage technologies and convergent technologies cannot be determined only by current sales data.

Even technology at the patent application stage may have future value if the claims are specific, there are differences from prior art, feasibility of implementation is high, and market scalability is large.

If artificial intelligence or an automated evaluation model relies excessively on past data, transaction cases, sales data, or comparable market data, early-stage technologies and convergent technologies may be more easily undervalued.

Existing industries and mature companies with abundant data occupy a favorable position in evaluation models, while individual inventors or early-stage founders with insufficient data may receive low evaluations even though they have structural value.

This is not a problem of artificial intelligence evaluation itself, but a problem of artificial intelligence evaluation without standards.

Therefore, early-stage technologies and convergent technologies require structural decomposition by component.

The configuration of the technology, scope of rights, method of implementation, manufacturability, market scalability, profit model, platform potential, possibility of repeat sales, and risk factors must be decomposed and evaluated item by item.

It is clear that artificial intelligence can increase the speed of evaluation.

Chapter 4 Artificial Intelligence-Based Technology Value Assessment and the Need for Verification Zones

Section 1 Need for Transition to Artificial Intelligence Evaluation

Today, artificial intelligence is already being used across society.

However, artificial intelligence evaluation without standards may become not rapid evaluation, but rapid opaque evaluation.

In various areas such as document drafting, translation, summarization, legal document review, patent search, market analysis, medical assistance, financial review, manufacturing management, customer response, and risk detection, artificial intelligence is used as a tool that quickly analyzes materials and organizes grounds for judgment according to repeatable standards.

There is no reason why only technology value assessment should remain in the traditional institution-centered appraisal method.

Technology value assessment is also an area that requires classification of extensive materials, judgment by item, review of risk factors, organization of comparative materials, application of deduction standards, and preparation of evaluation logs.

In particular, in order to prevent the result of using artificial intelligence from replacing evaluator subjectivity with algorithmic opacity, a technology value AI evaluation standards document and a verification zone operating system capable of controlling and verifying the result are necessary.

If it is not recorded what input materials artificial intelligence used, by what standards it selected evaluation items, what deduction standards it applied, how it reflected risk factors, and through what judgment path the final value range was derived, the evaluation result remains a conclusion generated by an algorithm rather than a verifiable document.

Artificial intelligence can perform these tasks quickly.

However, the transition to artificial intelligence evaluation must not be a method in which artificial intelligence arbitrarily calculates the assessment amount.

In addition, this study reexamines the elements that constitute the reliability logic of existing appraisal institutions.

Just as an existing appraisal institution cannot secure reliability if it uses artificial intelligence without standards, a new artificial intelligence evaluation system must not be operated without standards.

Artificial intelligence evaluation must be performed in accordance with a technology value AI evaluation standards document and must include input materials, evaluation items, judgment standards, deduction standards, verification logs, and error-control procedures.

Section 2 Existing Evaluation Institutions’ Use of Artificial Intelligence and Absence of Standards

The theory of evaluator signature and seal responsibility should be redefined not as an absolute guarantee of the assessment result, but as confirmation of procedural compliance.

Existing appraisal institutions or evaluation institutions are also using artificial intelligence, big data, automated value calculation models, and digital evaluation systems.

What is more important than the fact that artificial intelligence was used is the standard by which that artificial intelligence operated.

It must be confirmed what input materials were reflected, what materials were excluded, what items were weighted, in what items deductions were made, how risk factors were reflected, and through what formula and judgment path the final value range was derived.

The use of artificial intelligence without standards does not eliminate evaluator subjectivity, but may produce the result of transferring that subjectivity into algorithmic opacity.

Therefore, artificial intelligence-based technology value assessment must necessarily include an evaluation standards document, input material standards, item-specific judgment standards, deduction standards, verification logs, error-control procedures, and a professional appraisal management system.

Section 3 Concept of Verification Zones in Technology Value Assessment

A verification zone is an evaluation area that structurally separates the judgment elements of technology value assessment and confirms the materials, standards, grounds for judgment, reasons for deduction, risk factors, and matters requiring supplementation for each item.

A verification zone does not immediately present a single final assessed amount; rather, it divides the subject of evaluation into multiple judgment areas and confirms whether materials and judgments correspond in each area.

For technology value assessment, the evaluation target must be clearly specified, the existence and reliability of submitted materials must be confirmed, and rights materials, technical structure, prior art and comparative materials, feasibility of implementation, marketability, commercialization potential, profit model, manufacturing and cost, risk factors, deduction standards, value range calculation, and verification logs must be reviewed by item.

Field inspection should be converted into an optional procedure in an environment where digital material verification and forgery or alteration detection are possible.

Each of these judgment areas becomes a verification zone.

A verification zone is not a simple checklist item.

Each verification zone has an independent object of judgment and judgment standards, and the result value of each zone affects the overall technology value assessment.

For example, if materials are insufficient in the rights materials verification zone, the rights value evaluation may decrease, and if objective materials are insufficient in the feasibility verification zone, this may affect commercialization potential and risk factor evaluation.

If there is no deduction standard verification zone, it is difficult for the technology holder to confirm why the evaluation result was lowered.

The discretion and internal know-how of evaluators must be externalized into standard data, deduction tables, risk adjustment tables, sensitivity analysis, and verification logs.

Section 4 Reasons Why Verification Zones Are Necessary

The first reason verification zones are necessary is to enable technology holders to review value in advance.

Undisclosed internal review committees must be converted into data-based simulations and recordable review procedures.

Before appraisal or investment negotiations, technology holders must be able to confirm in which items their technology has strengths, what materials are insufficient, and what risk factors affect value assessment.

The second reason is to structurally understand and respond to the evaluation results of existing appraisal institutions or evaluation institutions.

Even if an evaluation institution presents a low assessed amount, it must be possible to distinguish whether the reason is insufficiency of rights materials, insufficiency of implementation materials, insufficiency of market data, insufficiency of manufacturing cost materials, or uncertainty of the profit model.

Ultimately, this study considers that the reliability of technology value assessment should be found in evaluation standards, input materials, judgment items, deduction standards, verification logs, and error-control procedures, rather than in the name of the evaluation institution.

Verification zones make such distinctions possible.

The third reason is to control the results of the use of artificial intelligence.

This study starts from the point that technology holders and inventors need to structurally review the value of their own technologies in advance.

Even if artificial intelligence automatically calculates an appraisal amount or value range, it is difficult to trust the evaluation result unless the result is classified by verification zone and the grounds for judgment and reasons for deduction for each zone are recorded in a verification log.

The fourth reason is to reduce professional appraisal costs.

A method in which experts comprehensively evaluate all items from the beginning maintains a high-cost structure.

By contrast, if insufficient materials and disputed items are first identified through verification zones, only the necessary parts can be connected to experts.

This is a method that reduces cost and time while maintaining expertise.

The fifth reason is to disclose evaluation standards and make them repeatable.

If the same materials and the same standards are applied, similar evaluation results should be produced.

If different results are produced, it must be possible to confirm where differences arose in input materials, deduction standards, or risk adjustment.

Verification zones and verification logs are devices that secure such repeatability and verifiability.

Section 5 Need for a Verification Zone Operating System

Verification zones must not remain a checklist used arbitrarily by individual technology holders.

Verification zones are an operating system for standardizing structural judgments concerning technology value assessment.

Therefore, the establishment of verification zones, classification of input materials, zone-specific judgment, assignment of result values, indication of reasons for deduction, calculation of value ranges, preparation of verification logs, and connection to professional appraisal must be operated according to certain standards.