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Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities

Hei Chia Wang (), Yung Chang Chi () and Ping Lun Hsin ()
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Hei Chia Wang: Department of Industrial and Information Management and Institute of Information Management, National Cheng Kung University, Tainan 701, Taiwan
Yung Chang Chi: Department of Industrial and Information Management and Institute of Information Management, National Cheng Kung University, Tainan 701, Taiwan
Ping Lun Hsin: Department of Industrial and Information Management and Institute of Information Management, National Cheng Kung University, Tainan 701, Taiwan

Sustainability, 2018, vol. 10, issue 10, 1-18

Abstract: With the advent of the knowledge economy, firms often compete for intellectual property rights. Being the first to acquire high-potential patents can assist firms in achieving future competitive advantages. To identify patents capable of being developed, firms often search for a focus by using existing patent documents. Because of the rapid development of technology, the number of patent documents is immense. A prominent topic among current firms is how to use this large number of patent documents to discover new business opportunities while avoiding conflicts with existing patents. In the search for technological opportunities, a crucial task is to present results in the form of an easily understood visualization. Currently, natural language processing can help in achieving this goal. In natural language processing, word sense disambiguation (WSD) is the problem of determining which “sense” (meaning) of a word is activated in a given context. Given a word and its possible senses, as defined by a dictionary, we classify the occurrence of a word in context into one or more of its sense classes. The features of the context (such as neighboring words) provide evidence for these classifications. The current method for patent document analysis warrants improvement in areas, such as the analysis of many dimensions and the development of recommendation methods. This study proposes a visualization method that supports semantics, reduces the number of dimensions formed by terms, and can easily be understood by users. Since polysemous words occur frequently in patent documents, we also propose a WSD method to decrease the calculated degrees of distortion between terms. An analysis of outlier distributions is used to construct a patent map capable of distinguishing similar patents. During the development of new strategies, the constructed patent map can assist firms in understanding patent distributions in commercial areas, thereby preventing patent infringement caused by the development of similar technologies. Subsequently, technological opportunities can be recommended according to the patent map, aiding firms in assessing relevant patents in commercial areas early and sustainably achieving future competitive advantages.

Keywords: text mining; patent mapping; technological opportunities; competitive advantage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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