Logo des Repositoriums
Zur Startseite
  • English
  • Deutsch
Anmelden
  1. Startseite
  2. SuUB
  3. Dissertationen
  4. Machine learning for patent intelligence: opportunities and challenges
 
Zitierlink DOI
10.26092/elib/1940

Machine learning for patent intelligence: opportunities and challenges

Veröffentlichungsdatum
2022-12-09
Autoren
Denter, Nils  
Betreuer
Möhrle, Martin G.  
Gutachter
Kinra, Aseem  
Zusammenfassung
The analysis of large data volumes for decision-making has evolved from a sideline to a key driver of economic success in the business world of today. As being particularly relevant for technology-oriented organization, patent intelligence – the retrieval, pre-processing and analysis of patent information – has become a relevant means for organization-relevant decisions. This dissertation sheds light using techniques from machine learning for patent intelligence tasks. After summarizing the current literature streams of patent intelligence and machine learning, four publications outline opportunities and challenges that may arise from using supervised or unsupervised machine learning techniques for patent intelligence. For example, supervised machine learning may guide decision making by reducing noise in predictions, for example. Unsupervised machine learning may be useful to explore latent associations between patents when analyzing computationally challenging patent datasets. However, both techniques impose challenges regarding the complexity of the configuration space as well as the transparency and explainability of their underlying algorithms. Implications of this dissertation offer two trade-offs, i.e. in-house versus external procurement and high performance vs. low explanability, and relevant gaps need being addressed by this dissertation open up avenues of further research.
Schlagwörter
Patente

; 

Machine learning

; 

Patent intelligence

; 

Patent management

; 

Patent evaluation

; 

Big Data

; 

Deep learning
Institution
Universität Bremen  
Fachbereich
Fachbereich 07: Wirtschaftswissenschaft (FB 07)  
Dokumenttyp
Dissertation
Lizenz
https://creativecommons.org/licenses/by/4.0/
Sprache
Englisch
Dateien
Lade...
Vorschaubild
Name

NDenter_Machine_learning_for_patent_intelligence_digital_version.pdf

Size

964.62 KB

Format

Adobe PDF

Checksum

(MD5):c62e3a99834da3fad3f43b2462bdd297

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Datenschutzbestimmungen
  • Endnutzervereinbarung
  • Feedback schicken