Discovery of Patent Influence with Directed Acyclic Graph Network Analysis.

In the domain of research, development and innovation, every new discovery usually relies on previous evidence as the basis of any novel argument. Research papers and patents are often used to support such results. However, their discovery—especially, discovery of patents—is not an easy task; it requires a lot of time and effort to find results that are actually helpful. The influence patents have on research, development and innovation is substantial because they aim to discover related and influential patents that can tremendously help drive new discoveries and inventions. In this paper, we present a database engineered solution, which showcases techniques that enable the patent discovery. The solution leads to meaningful and relevant discovery when looking for relevant and influential patents in a given domain to be used by researchers, inventors and businesses. We also explore techniques to visually inspect large amounts of data and find other interesting results that would be difficult to view otherwise. Evaluation results show the practicality of our solution in discovering and visualizing patent influence when conducting directed acyclic graph (DAG) analysis.