Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/179519
Authors: 
Billington, Stephen D.
Hanna, Alan J.
Year of Publication: 
2018
Series/Report no.: 
QUCEH Working Paper Series 2018-06
Abstract: 
Patent studies inform our understanding of innovation. Any study of patenting involves classifying patent data according to a chosen taxonomy. The literature has produced numerous taxonomies, which means patents are being classified differently across studies. This potential inconsistency is compounded by a lack of documentation provided on existing taxonomies, making them diffcult to replicate. Because of this, we develop a new patent taxonomy using machine learning techniques, and propose a new methodology to automate patent classification. We contrast existing taxonomies with our own upon a widely used patent dataset. In a regression analysis of patent classes upon patent characteristics, we show that classification bias exists: the size, statistical significance, and direction of association of coefficients depend upon how a patent dataset has been classified. We recommend investigators adopt our approach to ensure future studies are comparable and replicable.
Subjects: 
Innovation
Invention
Machine Learning
Patents
Patent Classification
Taxonomy
Economic History
JEL: 
K11
N24
N74
O31
O33
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
File
Size
498.57 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.