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Research Summary: Data Science & IP

RESEARCH AREA: Computational & Empirical IP Research
This area of research applies computational data science (big data) and machine learning techniques to conduct empirical-based IP research. This area of research is focused on the development and application of computational data science & artificial intelligence to solve data intensive problems of public and private interest. The research projects in this area typically lie at the interface between engineering, law, and management sciences. The overall objective is to apply computational data science in order to conduct evidence-based (empirical) studies to guide public policy and/or private strategy. Examples areas of research include:

Computational Data Science & IP
IP Informatics & Computational Analytics
Automated Patent Search Algorithm Development
Patent Landscape Studies
Model-Based Patent Valuation, Licensing & Technology Transfer
Empirical Legal Research (Impact Empirical Analysis of IP Decisions)
Machine Learning & IP
Medical Device Development, Law & Regulation
Patentability of Computer-Implemented & Biotech Inventions
Intellectual Capital & IP Strategy & Management
Technology & Innovation Management: Competitive Strategy & Disruptive Innovation

For additional details visit the LML, University of Cambridge, UK