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Innovation and Intellectual Property Management (IIPM) Laboratory

Big data is increasingly available in all areas of manufacturing and operations. Increased data availability presents an opportunity for better decision making, to introduce the next generation of innovative and disruptive technologies. While intellectual property (IP) data is abundantly available, for many firms still remains a problem on how they can fully use this source of technical information. Firms struggle to decide how to better analyse IP data, to support and complement strategic decision making processes in the stages of technology and innovation development projects. In addition, while machine learning algorithms have widely been applied in other fields to analyse large amounts of data, they hardly been applied in the IP domain.

 

 

We aim to complement technology strategic decision making with IP analytics, which in turn improves the human judgement at the technology development process. We follow a system design approach, where we design, develop and test a series of IP Decision Support Tools (DST), which make use of deep learning algorithms, to analyse patent data and classify a technology project, which is underpinned by a technology patent, as successful or not, to go through the innovation management funnel. From the literature, a successful patent can be defined as one with a large number of forward citations, one with consecutive renewal periods and one which has been litigated in court and won. The methodology is applied to a number of case studies to test its suitability within the technology development process. After refinement, we explore a number of alternatives to expand the model such as the addition of more data sources or its application at different stages of the innovation process. This methodology also improves the data quality, and the quality and validity of patents that are granted, as it benchmarks a potential application before the patent application stage.

 

Project lead: Leonidas Aristodemou

 

Related Publications

 

  • Aristodemou, L., & Tietze, F., 2018. The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55 37-51https://doi.org/10.1016/j.wpi.2018.07.002
  • Aristodemou, L., & Tietze, F., 2018. Citations as a measure of technological impact: a review of forward citation-based measures. World Patent Information, 53 39-44. https://doi.org/10.1016/j.wpi.2018.05.001Conference

 

Conference papers

 

  • Aristodemou L., & Tietze F., 2019. Early Stage Identification of Valuable Technologies: a Deep Learning approach, In Topic: Intellectual Property and New Research Methods, European Policy for Intellectual Property (EPIP) Conference 2019, ETH Zurich & EPFL, Zurich, Switzerland
  • Jeong Y., Aristodemou L., & Tietze F., 2019. Exploring disruptive innovation opportunity using patent analysis and deep learning, In Track 1: Artificial Intelligence and Data Science, R&D Management Conference 2019L' École Polytechnique & HEC Paris, Paris, France
  • Silva, R., Koshiyama, A., & Aristodemou, L., 2019 Linking Research Entities to Industrial Sectors: a hybrid methodology applied to Brazil’s nanotechnology sector, In Data for Policy 2019: Digital Trust and Personal Data Conference, University College London, London, United Kingdom
  • Aristodemou, L., Tietze, F., & Brintrup A., 2018. Early Stage Technology Strategic Decision Making: a machine learning approach using Intellectual Property Analytics, In Track 18: Big Data Analytics for R&D Management, R&D Management Conference 2018, Politecnico di Milano, Milan, Italy
  • Aristodemou, L., Tietze, F., Athanassopoulou, N., & Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach. In Theme MC-4: Intellectual Property Management in Innovation, R&D Management Conference 2017KU Leuven, Leuven, Belgium

 

Working papers

 

  • Aristodemou, L., Tietze, F., Brintrup, A., & Deeble, S., 2019. Intellectual Property Analytics Decisions Support Tool (IPDST) for Early Stage Technology Decision Making. Centre for Technology Management (CTM) Working Paper Series, January 2019 (1), pp.1-7. https://doi.org/10.17863/CAM.35544
  • Aristodemou, L., Tietze, F., O'Leary, E., & Shaw, M., 2019. A Literature Review on Technology Development Process (TDP) Models. Centre for Technology Management (CTM) Working Paper Series, January 2019 (6), pp.1-32, Cambridge, UKhttps://doi.org/10.17863/CAM.35692
  • Aristodemou, L., & Tietze, F., 2019. Technology Strategic Decision Making (SDM): an overview of decision theories, processes and methods. Centre for Technology Management (CTM) Working Paper Series, January 2019 (5), pp.1-22, Cambridge, UKhttps://doi.org/10.17863/CAM.35691
  • Aristodemou, L., Tietze, F., Athanassopoulou, N., & Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach, Centre for Technology Management (CTM) Working Paper Series, November 2017 (5), pp.1-10, Cambridge, UK. Available at: http://doi.org/10.17863/CAM.13967
  • Aristodemou, L. & Tietze, F., 2017. A literature review on the state-of-the-art on intellectual property analytics, Centre for Technology Management (CTM) Working Paper Series, November 2017 (2), pp.1-15, Cambridge, UK. Available at:http://doi.org/10.17863/CAM.13928

Reports

  • Aristodemou, L. & Tietze, F., 2017. Exploring the Future of Patent Analytics. Centre for Technology Management (CTM) Insights Report, ISBN: 978-1-902546-84-1, Institute for Manufacturing, University of Cambridge, Cambridge, UK. Available at:https://www.ifm.eng.cam.ac.uk/insights/innovation-and-ip-management/expl... the-future-of- patent-analytics/
  • Aristodemou, L., 2015. Analysing Patent Influence and Patent Importance Across the Industrial Boundary of 3D Printing, Masters Thesis, Institute for Manufacturing, University of Cambridge

Online articles