Screening early stage ideas in technology development processes: a text mining and k-nearest neighbours approach using patent information

ABSTRACT

Applying previous idea screening approaches to large amounts of early stage ideas is recognised as challenging since they rely heavily on manual tasks and human judgments. Considering that technological factors are more important than others in early phases of technology development processes, we propose a machine learning approach to screening ideas by linking the contents of ideas implied in patented inventions and the technological value of the ideas. At the heart of the proposed approach are the text mining technique, to construct keyword vectors from patents, and the k-nearest neighbours algorithm, to capture the relationships between the keyword vectors and the numbers of forward citations of the patents. Integration of these methods makes it possible to assess large amounts of early stage ideas in terms of their potential technological value. A case study of pharmaceutical technology shows that our approach is useful for filtering out ideas of little technological value.

KEYWORDS:

• Idea screening
• patent information
• text mining
• k-nearest neighbours algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Han-Gyun Woo is an associate professor of the School of Management Engineering at Ulsan National Institute of Science and Technology (UNIST). He received a BBA and MBA from Seoul National University (SNU), and a PhD in business administration from Georgia State University. His research interests are organisational silence and creativity, collaborative innovation, and technology strategy.

Jaesun Yeom is a PhD student of the School of Management Engineering at UNIST. He holds a BS in technology management from UNIST. His research interests are sentiment analysis, text mining, and recommendation systems.

Changyong Lee is currently an associate professor of the School of Management Engineering at UNIST. He received a BS in computer science and industrial engineering from Korea Advanced Institute of Science and Technology (KAIST), and a PhD in industrial engineering from SNU. Prior to joining UNIST, he had worked at Korea Institute for Science and Technology Information (KISTI) as a senior researcher and at the Centre for Technology Management (CTM), University of Cambridge, as a visiting scholar. His research interests include data mining and machine learning, technology management, and service science.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIP) [No. 2014R1A1A1005931], [No. 2017R1C1B2011434].