Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation

Changyong Lee () and Gyumin Lee ()
Additional contact information
Changyong Lee: Ulsan National Institute of Science and Technology
Gyumin Lee: Ulsan National Institute of Science and Technology

Scientometrics, 2019, vol. 121, issue 2, No 2, 603-632

Abstract: Abstract This research responds to the need for the use of quantitative data and scientific methods for technology opportunity analysis by focusing on idea generation. Interpreting innovation as a process of recombinant search, we propose a patent landscape analysis to generate ideas which are likely to have more novelty and value than others. For this, first, a patent landscape is constructed from patent classification information as a vector space model, where each position represents a configuration of technological components and corresponds to an idea and, if they exist, relevant patented inventions. Second, the novelty of ideas is assessed via the modified local outlier factor based on the distribution of existing patented inventions on the landscape. Finally, the value of ideas is estimated via naïve Bayes models based on the forward citations of existing patented inventions. In addition, this study also investigates the recombinant synergies between different technological components and the relationships between novelty and value of ideas. A case study of pharmaceutical technology shows that our approach can guide organisations towards setting up effective search strategies for new technology development.

Keywords: Technology opportunity analysis; Recombinant search; Patent landscape analysis; Idea generation; Novelty; Value; Synergy (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-019-03224-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03224-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-019-03224-7

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().