Osmat Azzam Jefferson

Transparency tools in gene patenting for informing policy and practice

The Supreme Courts decision in Myriad highlights the need for tools enabling nuanced and precise analysis of gene patents at the global level.

The ownership question of plant gene and genome intellectual properties

The restructuring of the crop agriculture industry over the past two decades has enabled patent holders to exclude, prevent and deter others from using certain research tools and delay or block further follow-on inventions.

Gene patent practice across plant and human genomes.

The uses of genetic sequences to inform, enable or create products or services for human biomedicine are substantially different from their uses in crop-based agriculture.

Mapping the global influence of published research on industry and innovation

Public research is critical to the economy and society. However, tangible economic and social impact only occurs when research outputs are combined, used and reused with other elements and capabilities, to deliver a product, practice, or service. Assessing the context and influence of scholarship during the dynamic process of innovation rather than measuring ex post impact, may improve performance. With this aim, we integrated and interconnected scholarly citations with global patent literature, and here we offer new tools enabling diverse stakeholders to freely evaluate the influence published research has on the generation and potential use of inventions as reflected by the patent system. We outline an evolving toolkit, Lens Influence Mapping, that allows assessment of individual scholarly works and aggregated outputs of authors for influence on industry and enterprise as measured by citations within patents. This performance measure, applied at many levels and normalized by either research disciplines or technology fields of use, may expose and highlight institutional strength and practices, and guide their future partnerships

Automated Categorisation of Patent Claims that Reference Human Genome Sequences

Debates on gene patents have necessitated the analysis of patents that disclose and reference human sequences. In this study, we built an automated classifier that assigns sequences to one of nine predefined categories according to their functional roles in patent claims by applying natural language processing and supervised learning techniques. To improve its correctness, we experimented with various feature mappings, resulting in the maximal accuracy of 79%.