Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach

Uijun Kwon and Youngjung Geum ()
Additional contact information
Uijun Kwon: Seoul National University of Science and Technology
Youngjung Geum: Seoul National University of Science and Technology

Scientometrics, 2020, vol. 125, issue 3, No 3, 1877-1897

Abstract: Abstract The identification of promising inventions is an important task in technology planning practice. Although several studies have been carried out using patent-based machine learning techniques, none of these have used the quality of knowledge accumulation as an input for identifying promising inventions, and have simply considered the number of backward citations as the link with previous knowledge. The current study therefore aims to fill this research gap by predicting promising inventions with patent-based machine learning, using the quality of knowledge accumulation as an important input variable. Eight criteria and 17 patent indicators are used as input variables, and patent forward citations are employed as the output variable. Six machine learning techniques are tested on 363,620 G06F patents filed between January 1990 and December 2009, and the results show that the quality of knowledge accumulation is the most important variable in predicting emerging inventions.

Keywords: Promising technology; Technology forecasting; Patent analysis; Machine learning; Patent indicator (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03710-3 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:125:y:2020:i:3:d:10.1007_s11192-020-03710-3

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

DOI: 10.1007/s11192-020-03710-3

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 ().