Unsupervised learning based patent landscapes using full-text patent data

Arho Suominen, Hannes Toivanen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

The complexity technologies require that companies have in-depth knowledge of the nature and effect of knowledge - its depth and breadth. Companies need to master expanding technological knowledge bases creating tensions for MOT. We examine how big data in patent landscaping creates insights into MOT. Using big data to manage Competitive Technical Intelligence, companies can foster new forms of adaptive learning processes in MOT. This however requires that managers augment human judgment with machine-learning tools, prompting challenges to management traditions. We demonstrate how unsupervised learning creates insight into MOT by identifying topical knowledge foci and showing the dynamics of knowledge domains among companies. Using unsupervised learning and network analysis; we show how a semantic analysis leads to the identification of opportunities in complex environments. We illustrate this using a case in globally operating telecommunication companies using a full-text copy of USPTO-database with approximately 6 million patents data. Our results show the landscape of the companies and the underlying knowledge embedded in the companies. We discuss how managers can evaluate their technological knowledge against competitors, balancing current needs with the adoption of new knowledge. We further discuss how a semantic analysis can lead to the discovery of latent patterns and identification of opportunities.
Original languageEnglish
Title of host publicationManagement of Engineering and Technology (PICMET), 2015 Portland International Conference on
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages2195-2203
ISBN (Electronic)978-1-8908-4331-1, 978-1-8908-4332-8
ISBN (Print)978-1-4799-1767-9
DOIs
Publication statusPublished - 24 Sep 2015
MoE publication typeA4 Article in a conference publication
EventPortland International Conference on Management of Engineering and Technology, PICMET 2015 - Portland, United States
Duration: 2 Aug 20156 Aug 2015

Conference

ConferencePortland International Conference on Management of Engineering and Technology, PICMET 2015
Abbreviated titlePICMET 2015
CountryUnited States
CityPortland
Period2/08/156/08/15

Fingerprint

Patent data
Technological knowledge
Unsupervised learning
Managers
Patents
Telecommunications
Competitors
Learning process
Domain knowledge
Knowledge base
Adaptive learning
Network analysis
Data base
Machine learning

Keywords

  • big data
  • companies
  • data mining
  • industries
  • patents
  • semantics
  • unsupervised learning

Cite this

Suominen, A., & Toivanen, H. (2015). Unsupervised learning based patent landscapes using full-text patent data. In Management of Engineering and Technology (PICMET), 2015 Portland International Conference on (pp. 2195-2203). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/PICMET.2015.7273139
Suominen, Arho ; Toivanen, Hannes. / Unsupervised learning based patent landscapes using full-text patent data. Management of Engineering and Technology (PICMET), 2015 Portland International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. pp. 2195-2203
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Suominen, A & Toivanen, H 2015, Unsupervised learning based patent landscapes using full-text patent data. in Management of Engineering and Technology (PICMET), 2015 Portland International Conference on. Institute of Electrical and Electronic Engineers IEEE, pp. 2195-2203, Portland International Conference on Management of Engineering and Technology, PICMET 2015, Portland, United States, 2/08/15. https://doi.org/10.1109/PICMET.2015.7273139

Unsupervised learning based patent landscapes using full-text patent data. / Suominen, Arho; Toivanen, Hannes.

Management of Engineering and Technology (PICMET), 2015 Portland International Conference on. Institute of Electrical and Electronic Engineers IEEE, 2015. p. 2195-2203.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Suominen A, Toivanen H. Unsupervised learning based patent landscapes using full-text patent data. In Management of Engineering and Technology (PICMET), 2015 Portland International Conference on. Institute of Electrical and Electronic Engineers IEEE. 2015. p. 2195-2203 https://doi.org/10.1109/PICMET.2015.7273139