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Linking innovations and patents - a machine learning assisted method

Johansson, Mathias LU orcid ; Nyqvist, Jakob LU and Taalbi, Josef LU (2022) In SSRN:s working paper series
Abstract
This paper describes the methodology behind the matching of patents and a literature-based innovation output indicator (LBIO) collected from trade journals covering the manufacturing and ICT service sectors in Sweden 1970-2015. A combination of manual processing and simple machine learning tools has enabled the identification, classification and linking of patents that otherwise would have been very difficult for either of the methods to detect on its own.
Data generated using this method can be used to assess many aspects of the relationship between patenting, knowledge accumulation and innovation activity.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
Innovation, Patents, Machine-learning, LBIO
in
SSRN:s working paper series
pages
17 pages
publisher
Social Science Research Network (SSRN)
project
SWINNO 3.0 Significant Swedish technological Innovations from 1970 until now
language
English
LU publication?
yes
id
38d69f07-1c55-42d5-bd95-408a8bd1903e
alternative location
https://ssrn.com/abstract=4127194
date added to LUP
2022-06-03 18:28:02
date last changed
2023-04-30 12:50:20
@misc{38d69f07-1c55-42d5-bd95-408a8bd1903e,
  abstract     = {{This paper describes the methodology behind the matching of patents and a literature-based innovation output indicator (LBIO) collected from trade journals covering the manufacturing and ICT service sectors in Sweden 1970-2015. A combination of manual processing and simple machine learning tools has enabled the identification, classification and linking of patents that otherwise would have been very difficult for either of the methods to detect on its own.<br/>Data generated using this method can be used to assess many aspects of the relationship between patenting, knowledge accumulation and innovation activity.}},
  author       = {{Johansson, Mathias and Nyqvist, Jakob and Taalbi, Josef}},
  keywords     = {{Innovation; Patents; Machine-learning; LBIO}},
  language     = {{eng}},
  note         = {{Preprint}},
  publisher    = {{Social Science Research Network (SSRN)}},
  series       = {{SSRN:s working paper series}},
  title        = {{Linking innovations and patents - a machine learning assisted method}},
  url          = {{https://ssrn.com/abstract=4127194}},
  year         = {{2022}},
}