the 1st global tech mining conference, atlanta, usa analyzing technology evolution of graphene...

The 1st Global Tech Mining Conference, Atlanta, USA

Analyzing Technology Evolution of Graphene Sensor Based on Patent Documents

Fang Shu1, Hu Zhengyin1, Pang Hongshen1, Zhang Xian1

1Chengdu Branch of the National Science Library, Chinese Academy of Sciences, Chengdu, 610041, China

OUTLINE

Backgrounds

Methods

Empirical analysis (graphene sensor)

Conclusion and Further Works

Acknowledgement

Backgrounds

Our Aims:

Classify the patents by technology evolution trees

Try to find emerging technology

Help to find the important patents

Backgrounds

Young’s Work:

Young, Jong & Sang (2008) proposed a method of patent analysis for forecasting emerging technology, including:

building a set of patent documents;

extracting technology keywords;

clustering the patent documents;

forming a semantic network of technology keywords;

drawing technology evolution map.

Backgrounds

Advantage of Young’s Method

simple operation ;

clear interpretation of the content ;

focusing on technical points ;

reflect the evolution of related technology clearly.

Backgrounds

Disadvantage of Young’s Method

suspicion of circular reasoning ；

Ignoring distribution feature and semantic relations

of items;

k-Means clustering is not good for small sample.

Methods

Our improved method:

Methods

Our improved method:

Firstly, build a set of patent documents;

Secondly, extract keywords of technology;

Thirdly, cluster the patent documents;

This is the core improvement.

Methods

Our clustering method:

Considering the distribution feature of patent classifications:

fij: the frequency of feature item i appears in the

document j; N:number of all documents in the collection; ni: the number of documents including feature item i.

Methods

Our clustering method:

Considering the semantic relations between patent classifications:

L: the total number of feature items in the document j;

θim : semantic similarity value between feature item i and other feature item m.

Methods

Our improved method:

Fourthly, form semantic network of keywords;

Lastly, draw technology evolution map.

Empirical analysis

Firstly, build a set of patent documents. Retrieval policy :

Empirical analysis

Secondly, extract keywords of technology.(see table 2).

Empirical analysis

Thirdly, cluster the patent documents. Fourthly, form semantic network of keywords.

Empirical analysis

Finally, draw technology evolution map.

Empirical analysis

Find important patent documents:

Empirical analysis

Compared with Young’s method A semantic network of keywords of graphene sensor

(Young’s method)

Empirical analysis

Compared with Young’s method A technology evolution map(Young’s method)

Conclusion

Our new method has the following advantages:

Avoiding the defect of circular reasoning;

Considering the distribution features and the

semantic features at the same time when clustering;

Using hierarchical clustering which is more suitable

for small samples.

Further Works

Hope to formulate common standard that

helps experts to pick out keywords more

accurately ;

Try another methods to build semantic

relations or concept hierarchies of terms;

Try to apply the semantic relations of terms for further technology mining.

Acknowledgement

Thanks for funding of “Intellectual property rights Information portal of CAS”

Thanks for the experts, including: Prof. Jinhui liu, Prof. Guoshen Chen, Prof. Ge lv,etc.

Thank You for the attention!