DocumentCode :
3576371
Title :
Exploring technological trends for patent evaluation
Author :
Shuting Wang ; Wang-Chien Lee ; Zhen Lei ; Xianliang Zhang ; Yu-Hsuan Kuo
Author_Institution :
Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA
fYear :
2014
Firstpage :
277
Lastpage :
283
Abstract :
Patents are very important intangible assets that protect firm technologies and maintain market competitiveness. Thus, patent evaluation is critical for firm business strategy and innovation management. Currently patent evaluation mostly relies on some meta information of patents, such as number of forward/backward citations and number of claims. In this paper, we propose to identify patent technological trends, which carries information about technology evolution and trajectories among patents, to enable more effective and precise patent evaluation. We explore features to capture both the value of trends and the quality of patents within a trend, and perform patent evaluation to validate the extracted trends and features using patents in the United States Patent and Trademark Office (USPTO) dataset. Experimental results demonstrate that the identified technological trends are able to capture patent value precisely. With the proposed trend related features extracted from our identified trends, we can improve patent evaluation performance significantly over the baseline using conventional features.
Keywords :
knowledge management; patents; trademarks; USPTO dataset; United States Patent and Trademark Office dataset; business strategy; feature extraction; firm technology protection; innovation management; intangible assets; market competitiveness; meta information; patent evaluation; patent evaluation performance improvement; patent quality; patent technological trends; patent trajectories; technology evolution; Feature extraction; Maintenance engineering; Market research; Patents; Technological innovation; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Science and Advanced Analytics (DSAA), 2014 International Conference on
Type :
conf
DOI :
10.1109/DSAA.2014.7058085
Filename :
7058085
Link To Document :
بازگشت