KR Patent Evaluation Model
Structural equation evaluation modeling
- Determination of
- Expert brainstorming
- Determination of evaluation elements
for each evaluation index
- Delphi expert
questionnaire surveyAnalysis of feasibility
- Patent evaluation
- Structural equation modeling
Expert brainstorming step
Determination and definition of evaluation index
Determination of evaluation elements for questionnaire with satisfactory quantification, objectivity and completeness
Delphi expert questionnaire survey step
In order to collect experts' opinions, it is performed in three rounds with guaranteed anonymity.
In the second and third questionnaire surveys, the previous questionnaire survey is analyzed, so that statistical information on a median and an
interquartile range is provided to the experts as feedback.
Questionnaire survey feasibility analysis step
||When 30 persons were surveyed, CVR >= 0.33
|Verification of reliability
||A value of 0 to 1, with a greater value representing higher reliability.
|Verification of feasibility
||Degree of convergence and degree of consensus
Steps of construction of structural equation evaluation modeling
Definition of structural equation modeling
Structural Equation Modeling (SEM) is a model in which Confirmatory Factor Analysis (CFA) is combined with path analysis.
A causal relationship between dependent variables as well as a relationship between a plurality of independent variables and a plurality of dependent
variables can be analyzed at the same time.
|Number of dismissed invalidation trials
||Number of priorities claimed in a divisional application
||Number of field responses
||Number of papers and foreign patents among cited references of a forward citation patent
||Number of papers/foreign patents among prior documents
||Change in ownership
||Presence of earlier publication
||Number of countries with foreign family patents
US, EU Patent Evaluation Model
US/EU SMART3 Models applied Multiple Regression Analysis
When analyzing relationships between one dependent and multiple independent variables, multiple regression checks for the following models when
there are K independent variables.
(Y : dependent variable, X : independent variable, β : coefficient of independent variable, ε : error)
Multiple regression models use X and Y sets given in the study to produce an optimal β set and ε, which are used to generate the independent variables X,
given in the actual evaluation.