↓ Skip to main content

PLOS

Quantitative Determination of Technological Improvement from Patent Data

Overview of attention for article published in PLOS ONE, April 2015
Altmetric Badge

Mentioned by

news
4 news outlets
twitter
23 X users
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
71 Dimensions

Readers on

mendeley
122 Mendeley
Title
Quantitative Determination of Technological Improvement from Patent Data
Published in
PLOS ONE, April 2015
DOI 10.1371/journal.pone.0121635
Pubmed ID
Authors

Christopher L. Benson, Christopher L. Magee

Abstract

The results in this paper establish that information contained in patents in a technological domain is strongly correlated with the rate of technological progress in that domain. The importance of patents in a domain, the recency of patents in a domain and the immediacy of patents in a domain are all strongly correlated with increases in the rate of performance improvement in the domain of interest. A patent metric that combines both importance and immediacy is not only highly correlated (r = 0.76, p = 2.6*10-6) with the performance improvement rate but the correlation is also very robust to domain selection and appears to have good predictive power for more than ten years into the future. Linear regressions with all three causal concepts indicate realistic value in practical use to estimate the important performance improvement rate of a technological domain.

X Demographics

X Demographics

The data shown below were collected from the profiles of 23 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Netherlands 2 2%
United Kingdom 1 <1%
Unknown 116 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 26%
Researcher 20 16%
Student > Master 18 15%
Other 10 8%
Student > Doctoral Student 5 4%
Other 15 12%
Unknown 22 18%
Readers by discipline Count As %
Engineering 24 20%
Business, Management and Accounting 17 14%
Computer Science 13 11%
Economics, Econometrics and Finance 13 11%
Agricultural and Biological Sciences 4 3%
Other 21 17%
Unknown 30 25%