Patent data represent a significant source of information on innovation, knowledge production, and the evolution of technology through networks of citations, co-invention and co-assignment. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in knowledge production and diffusion. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventors and assignees on about 8.5 million patents found in the European Patent Office (EPO), under the Patent Cooperation Treaty (PCT), and in the US Patent and Trademark Office (USPTO). We show this disambiguation is consistent with a number of ground-truth benchmarks of both assignees and inventors, significantly outperforming the use of undisambiguated names to identify unique entities. A significant benefit of this work is the high quality assignee disambiguation with coverage across the world coupled with an inventor disambiguation (that is competitive with other state of the art approaches) in multiple patent offices.