KR20170098607A - Method and apparatus for providing predictive information on technology transfer using patent analysis - Google Patents

Method and apparatus for providing predictive information on technology transfer using patent analysis Download PDF

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KR20170098607A
KR20170098607A KR1020160020731A KR20160020731A KR20170098607A KR 20170098607 A KR20170098607 A KR 20170098607A KR 1020160020731 A KR1020160020731 A KR 1020160020731A KR 20160020731 A KR20160020731 A KR 20160020731A KR 20170098607 A KR20170098607 A KR 20170098607A
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박상성
전성해
최재현
장동식
이준석
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고려대학교 산학협력단
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Abstract

The present invention relates to a method and apparatus for providing technology transfer prediction information using patent analysis.
According to an embodiment of the present invention, there is provided a technology transfer prediction information providing method comprising: collecting patent data; Extracting a predetermined number of variables including the technology transfer from the collected patent data and processing the patent data; Selecting only the variables related to the technology transfer from the processed patent data; Calculating connection weights between the selected variable and the technology transfer; And generating a technology transfer prediction result based on the calculated weight value.

Description

TECHNICAL FIELD [0001] The present invention relates to a method and apparatus for providing technology transfer prediction information using patent analysis,

The present invention relates to a technology transfer prediction information providing method and apparatus, and more particularly, to a technology transfer prediction information providing method and apparatus using patent analysis.

Because patent information includes specific contents about technology and standardized form, various researches are done on technology prediction model using patent information. A related prior art is disclosed in Japanese Patent Laid-Open No. 10-2014-0146437.

Each year, patent applications and registered patents of all countries are increasing rapidly, but technology transfer is not proportional to the increasing trend of patent applications. As a result, each country has implemented a variety of technology commercialization projects in accordance with international trends in the institutions and technology guarantee funds of the Korean Intellectual Property Office as alternatives to such reality, but they have not contributed greatly to increase the technology transfer rate.

This is because there are not enough objective indicators to confirm the superiority of numerous patents for technology commercialization, and there is no model suitable for the current system. Therefore, there is a problem that the selection of patents for most technology commercialization depends mainly on expert opinions.

Therefore, it is necessary to study the technology transfer prediction model which is objective and economical.

It is an object of the present invention to provide a method and an apparatus for providing technology transfer prediction information objectively using analysis results of collected patent data.

According to an aspect of the present invention, there is provided a method for managing patent data, comprising: collecting patent data; Extracting a predetermined number of variables including the technology transfer from the collected patent data and processing the patent data; Selecting only the variables related to the technology transfer from the processed patent data; Calculating connection weights between the selected variable and the technology transfer; And generating technology transfer prediction results based on the calculated weights.

In order to achieve the above object, according to an embodiment of the present invention, there is provided an information processing apparatus comprising: a collecting unit for collecting patent data; A preprocessing unit for extracting a predetermined number of variables including the technology transfer from the collected patent data and processing patent data; An inter-variable relationship analyzing unit for selecting only variables associated with the technology transfer from the processed patent data; A weight calculation unit for calculating a connection weight between the selected variable and the technology transfer; A prediction model generation unit for generating a technology transfer prediction result based on the calculated weight; And a control unit for controlling the collecting unit, the preprocessing unit, the inter-variable relationship analyzing unit, the weight calculating unit, and the predictive model generating unit.

The technology transfer prediction information providing method and apparatus according to an embodiment of the present invention can provide objective technology transfer prediction information by using the analysis result of the collected patent data without depending on subjective opinion of the expert.

According to one embodiment of the present invention, it is possible to prepare a detailed technology transfer strategy by making a technology transfer prediction model for a national research institution, a national university, a private university, a joint research, and a national patent data, have.

1 is a block diagram for explaining a technology transfer prediction information providing method according to an embodiment of the present invention.
2 is a block diagram illustrating a technology transfer prediction information providing apparatus according to an embodiment of the present invention.
3 is a flowchart illustrating a technique transfer prediction information providing method according to an embodiment of the present invention.
4 is a diagram for explaining a processed form of patent data collected among technology transfer prediction information providing methods according to an embodiment of the present invention.
5 to 6 are views showing results of a social network analysis among technology transfer prediction information providing methods according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating a result of a regression analysis among technology transfer prediction information providing methods according to an embodiment of the present invention.
FIG. 8 is a diagram showing the results of a decision tree model among the technology transfer prediction information providing methods according to an embodiment of the present invention.

Hereinafter, a method and apparatus for providing technology transfer prediction information using patent analysis related to an embodiment of the present invention will be described with reference to the drawings.

As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. In this specification, the terms "comprising ", or" comprising "and the like should not be construed as necessarily including the various elements or steps described in the specification, Or may be further comprised of additional components or steps.

1 is a block diagram for explaining a technology transfer prediction information providing method according to an embodiment of the present invention.

As shown in the figure, the technology transfer prediction information providing apparatus 200 may be connected to the user terminal 100 through a network. The network includes at least one of a wired communication network, a computer network, and a wireless communication network.

The technology transfer prediction information providing apparatus 200 can extract the data stored in the patent DB 300. [ The patent DB 300 may include a specific database linked to the Patent Office database or the Patent Office database.

The user terminal 100 is a terminal connected to the technology transfer prediction information providing device 200 and capable of receiving technology transfer prediction information on a patent inputted by a user. The user terminal 100 can be a PC, a smart phone, a tablet, a notebook, a PDA, And the like.

2 is a block diagram illustrating a technology transfer prediction information providing apparatus according to an embodiment of the present invention.

As shown, the technology transfer prediction information providing apparatus 200 includes a collecting unit 210, a preprocessing unit 220, an inter-variable relationship analyzing unit 230, a weight calculating unit 240, a prediction model generating unit 250, A predictive information providing unit 260, and a control unit 270.

The collecting unit 210 may collect the entire patent data or patent data belonging to a specific group from the patent DB 300.

The preprocessing unit 220 can process the collected patent data into an analytical form. For example, the preprocessing unit 220 may extract a plurality of variables including a technology transfer parameter by text mining the collected patent data.

The inter-variable relationship analyzing unit 230 can select only variables related to the technology transfer variable from the data processed in the analytical form. The inter-variable relationship analyzer 230 analyzes the relationship between the plurality of variables extracted through the social network analysis, and selects only variables related to the technology transfer variable based on the analyzed result.

The weight calculation unit 240 may calculate the connection weights of the selected variables with the technology transfer parameter.

The prediction model generation unit 250 may generate the technology transfer prediction model based on the connection weighting result of each variable.

The prediction information providing unit 260 may provide the technology transfer prediction information on the target patent to the user terminal 100 using the generated technology transfer prediction model.

The control unit 270 includes the collecting unit 210, the preprocessing unit 220, the inter-variable relationship analyzing unit 230, the weight calculating unit 240, the prediction model generating unit 250, the prediction information providing unit 260, Can be controlled as a whole.

3 is a flowchart illustrating a technique transfer prediction information providing method according to an embodiment of the present invention.

The collecting unit 210 may collect patent data corresponding to the entire patent data or related groups from the patent database 300 (S310). The collected patent data is a document that can confirm whether or not the technology transfer is performed.

The preprocessing unit 220 preprocesses the collected patent data using a text mining technique and performs a structured data roll transformation (S320). The structured data may include a plurality of variables including a technology transfer parameter.

4 is a diagram for explaining a processed form of patent data collected among technology transfer prediction information providing methods according to an embodiment of the present invention.

As shown, a plurality of variables including technology transfer variables are arranged in the processed data.

In the following embodiments, 22 variables are extracted from collected patent data, and 22 extracted variables are used to generate a technology transfer prediction model.

The inter-variable relationship analyzing unit 230 may analyze the relationship between the extracted 22 variables (S330). For example, the inter-variable relationship analyzing unit 230 may analyze only the relationship between the 22 variables extracted through the Social Network Analysis (SNA) have.

5 shows a social network analysis graph for the 22 extracted variables.

The graph of FIG. 5 is generated by performing a correlation analysis using 22 variables and 1000 observations and obtaining a correlation coefficient (having a value between -1 and 1) between the respective variables. The correlation coefficient indicates the degree of association between two variables. The following matrix is an example of assuming that the obtained results are as follows after correlation analysis.

Figure pat00001

In order to analyze the social network, it is necessary to convert to the adjacency matrix. Therefore, it is necessary to convert the above correlation matrix to the adjacency matrix. Therefore, according to a certain criterion (this criterion can be appropriately modified according to the analysis object), the correlation coefficient can be expressed as 1 if it is more than 1, and as 0 if not, and by using this principle, A graph shown in FIG. 5 can be obtained by plotting the present example (experiment) as a graph.

FIG. 6 is a graph showing only the variables related to the technology transfer variable in the 22 variables shown in FIG.

The five selected variables are selected from nodes (variables) with transfer and connection lines in the social network analysis graph. That is, in FIG. 5, there are five nodes (own_ci, own_nci, new, right, own_cid) directly related to the transfer.

Since the directions of the arrows on the graph point to each other, they can be called undirected networks and are related to each other.

In the case of a non-directional network, if there is a connection from node i to j, then the network with the connection from j to i is called the undirected network, otherwise (ie the connection from node i to j , But not from j to i) can be referred to as a directed network. This is based on the diagonal elements in the matrix, and because each element is symmetrical to each other, it can be understood as a matrix component.

The weighting calculation unit 240 may calculate the connection weights of the five variables using the regression analysis. The connection weight is an indicator of how much influence the variable has on technology transfer.

The regression model can be expressed as shown in Equation 1 below.

Figure pat00002

The symbols used in Equation (1) can be defined as follows.

- Transfer (dependent variable Y) - means transfer of technology

- X (Independent variable) - Factors affecting technology transfer (ie, 5 variables)

- Meaning of intercept of β0-regression model

- βr - the slope of each independent variable

FIG. 7 is a diagram illustrating a result of a regression analysis among technology transfer prediction information providing methods according to an embodiment of the present invention.

Here, Beta is an indicator of the direct impact of each variable on technology transfer.

The beta value can be obtained in the following manner.

Suppose you have the following matrix.

Figure pat00003

The above multivariate regression equation can be expressed by Equation (2).

Figure pat00004

the value of? i can be obtained by using the equation (3).

Figure pat00005

In Fig. 7, the p-value is a probability that the hypothesis is correct when it is assumed that there is no hypothesis (null hypothesis - no meaningful difference) and the hypothesis is correct. In other words, the smaller the p-value, the lower the probability of supporting the null hypothesis, thus supporting the opposite hypothesis, and the larger the p-value, the more the null hypothesis is supported. Generally, there is a significant difference if the p-value is less than 0.05.

According to one embodiment of the present invention, the selected variables can also consider the indirect strength that affects the technology transfer through other variables in addition to the intensity directly affecting the technology transfer.

For example, node new is directly associated with transfer, but on the other hand new is associated with right, own_cid. On the other side, own_ci is also connected to own_nci. This is directly related to the transfer, but it can also be connected via other nodes. Therefore, in order to calculate this intensity in an indirect connection, rather than a direct connection, an indirect intensity can be calculated in one embodiment of the present invention in the following manner.

When new is used as an independent variable to obtain indirect strength and right is used as a dependent variable, a regression model can be generated to obtain the following equation (4).

Figure pat00006

Therefore, when new is used as an independent variable, the slope to the right is 0.976, and since the regression coefficient (β) is used as a measure of association between two nodes, the association between new and right is 0.976 by applying the same principle.

Therefore, the connection weights of the final new can be obtained as shown in Equation (5).

Figure pat00007

The prediction model generation unit 250 may generate the technology transfer prediction model using the above regression analysis result (S340).

For example, the prediction model generation unit 250 may generate a decision tree model using the regression analysis results. The decision tree model is a machine learning model in which the homogeneous ones are subdivided.

The reason for using the decision tree model is to find out what kind of structure the dependent variable and the independent variable have if there is a structural relation. If the regression results show that the β values are not included in the significance level, the causal relationship between the dependent variable and the independent variable is not established. However, if there is a causal relation, it is easier to judge by determining the importance of the relationship.

When constructing a decision tree model, the Gini Index can be calculated using Equation 6 below.

Figure pat00008

In Equation (6)

Figure pat00009
Figure pat00010
Is the probability that an observation in each measure belongs to the jth category of the dependent variable. In the present embodiment, if the dependent variable is categorized, the technology transfer can be set to 1, and the non-transfer to zero. In this case, the optimal separation between the independent variable and the variable that minimizes the Gini index is selected as a child node, and the amount of reduction of the Gini index can be calculated according to the following equation (7).

Figure pat00011

G is the Gini index (before division), GL is the left Gini index of the child node, and GR is the right Gini index of the child node.

Figure pat00012
Figure pat00013
The child node can be set to the maximum.

If a decision tree model is generated according to the above method, the result of decision tree model as shown in FIG. 8 can be obtained.

In this embodiment, since new (novelty) has the most important influence on technology transfer, it can be simplified as shown in FIG.

8, patent data with new < 0.756 can be expected to be technology transfer.

The prediction information providing unit 260 may generate the technology transfer prediction information of the target patent using the generated decision tree model and provide the generated information to the user terminal 100 in operation S350.

As described above, the technology transfer prediction information providing method and apparatus according to an embodiment of the present invention can provide objective technology transfer prediction information by using the analyzed result of the collected patent data without depending on subjective opinion of the expert .

According to one embodiment of the present invention, it is possible to prepare a detailed technology transfer strategy by making a technology transfer prediction model for a national research institution, a national university, a private university, a joint research, and a national patent data, have.

The above-described technology transfer prediction information providing method may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable recording medium. At this time, the computer-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination. On the other hand, the program instructions recorded on the recording medium may be those specially designed and configured for the present invention or may be those known to those skilled in the computer software.

The computer-readable recording medium includes a magnetic recording medium such as a magnetic medium such as a hard disk, a floppy disk and a magnetic tape, an optical medium such as a CD-ROM and a DVD, a magnetic disk such as a floppy disk, A hard disk drive, a magneto-optical medium, and a memory device such as a ROM, a RAM, a flash memory, and a solid state drive (SSD).

The recording medium may be a transmission medium, such as a light or metal line, a wave guide, or the like, including a carrier wave for transmitting a signal designating a program command, a data structure, and the like.

The program instructions also include machine language code, such as those generated by the compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

The method and apparatus for providing the technology transfer prediction information described above can be applied to all or some of the embodiments so that various modifications can be made to the embodiments and methods of the embodiments described above. Or may be selectively combined.

100: User terminal
200: Technology transfer prediction information providing device
210:
220:
230: Relationship between variables
240:
250: prediction model generation unit
260: prediction information providing unit
270:
300: Patent DB

Claims (10)

Collecting patent data;
Extracting a predetermined number of variables including the technology transfer from the collected patent data and processing the patent data;
Selecting only the variables related to the technology transfer from the processed patent data;
Calculating connection weights between the selected variable and the technology transfer; And
And generating a technology transfer prediction result based on the calculated weight value. The method according to claim 1, wherein the step of selecting only the variables related to the technology transfer
And analyzing a relationship between the extracted predetermined number of variables using a social analysis network analysis. 3. The method of claim 2, wherein the weighting step
And calculating a connection weight between the technology transfer and the selected variable using a regression analysis. The method of claim 3,
The connection weight calculation step may further include calculating a connection weight between the first variable and the second variable among the selected variables,
Wherein the first variable and the second variable are directly connected to each other based on the analysis result of the social analysis network. 4. The method of claim 3, wherein generating the skill transfer prediction result comprises:
And generating a decision tree model based on the result of the regression analysis. A collection unit for collecting patent data;
A preprocessing unit for extracting a predetermined number of variables including the technology transfer from the collected patent data and processing patent data;
An inter-variable relationship analyzing unit for selecting only variables associated with the technology transfer from the processed patent data;
A weight calculation unit for calculating a connection weight between the selected variable and the technology transfer;
A prediction model generation unit for generating a technology transfer prediction result based on the calculated weight; And
And a control unit for controlling the collecting unit, the preprocessing unit, the inter-variable relationship analyzing unit, the weight calculating unit, and the predictive model generating unit. 7. The apparatus of claim 6, wherein the inter-variable relationship analyzing unit
And analyzing a relationship between the extracted predetermined number of variables using a social analysis network analysis. 8. The apparatus of claim 7, wherein the weight calculation unit
And calculates a connection weight between the technology transfer and the selected variable using a regression analysis. 9. The method of claim 8,
The weight calculation unit may further include calculating a connection weight between the first variable and the second variable among the selected variables,
Wherein the first variable and the second variable are directly connected to each other based on the analysis result of the social analysis network. 9. The apparatus of claim 8, wherein the prediction model generation unit
And generating a decision tree model based on the result of the regression analysis.
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KR20190100506A (en) * 2018-02-06 2019-08-29 아주대학교산학협력단 Method for forecasting of promising patents and apparatus thereof

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KR102224684B1 (en) 2019-07-01 2021-03-09 청주대학교 산학협력단 System and method for generating prediction of technology transfer base on machine learning
KR20220108970A (en) 2021-01-28 2022-08-04 특허법인 세원 Method of risk analysis and technology transfer opportunity recommendation by technology-product-material based on machine learning or AI

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* Cited by examiner, † Cited by third party
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KR20190100506A (en) * 2018-02-06 2019-08-29 아주대학교산학협력단 Method for forecasting of promising patents and apparatus thereof

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