A patent review on machine learning techniques and applications: Depicting main players, relations and technology landscapes

The increasing availability of data, promised by the 4th industrial revolution wave, is challenging companies and organizations in diverse industry sectors to extract useful and actionable information. To this end, a vast array of data management strategies and new analytical methods is becoming available to the large audience of researchers and practitioners. Although traditional statistical approaches are still applicable for different purposes, artificial intelligence techniques, particularly machine learning algorithms, are increasingly being explored and adopted to approach data analysis. Artificial intelligence becomes a necessary ingredient for technology progress. The machine learning domain, in particular, has been extensively investigated by academics, who mainly focused on algorithms and suitable applications, and it is also permeating business reality at an unprecedented rate. Against this background, instead of eliciting knowledge from academics, the proposed research adopts a patent review and analysis approach, with the specific purpose of understanding the ongoing industrial effort on the subject, and new as well as expected trends on machine learning technologies and applications. The paper analyses technological development in various industries by defining patents trend over the years and investigating the different areas of applications according to the Cooperative Patent Classification (CPC), a patent classification system jointly developed by the European and US patent authorities. Patent applicants are also investigated in order to highlight active and competitive players in the domain, as well as collaboration between different companies. Furthermore, the paper includes a patent citation network analysis, which is useful to show critical technologies developed, and to understand applicants’ behaviours, such as influences or infringement trials. Overall, the paper provides an original and “literature-complementary” outlook on the machine learning landscape, giving an understanding on industrial R&D effort in this context, delineating trends related to technology diffusion and innovation from an industrial perspective.