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Patent surrogate extraction and evaluation in the context of patent mapping

National Taiwan Normal University, Taipei, Taiwan, samtseng{at}ntnu.edu.tw

Yeong-Ming Wang

LungHwa University of Science and Technology, Guishan, Taoyuan County, Taiwan

Yu-I Lin

Taipei Municipal University of Education, Taipei, Taiwan

Chi-Jen Lin

WebGenie Information LTD., Taipei, Taiwan

Dai-Wei Juang

WebGenie Information LTD., Taipei, Taiwan

Patent documents contain important research results. They are often collectively analyzed and organized in a visual way to support decision making. However, they are lengthy and rich in technical terminology, and thus require a lot of human effort for analysis. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a summarization method for patent surrogate extraction, intended to efficiently and effectively support patent mapping, which is an important subtask of patent analysis. Six patent maps were used to evaluate its relative usefulness. The experimental results confirm that the machine generated summaries do preserve more important content words than some other patent sections or even than the full patent texts when only a few terms are to be considered for classification and mapping. The implication is that if one were to determine a patent's category based on only a few terms at a quick pace, one could begin by reading the section summaries generated automatically.

Key Words: Text mining • summarization • feature extraction • patent classification • patent clustering

This version was published on December 1, 2007

Journal of Information Science, Vol. 33, No. 6, 718-736 (2007)
DOI: 10.1177/0165551507077406


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