Abstract:Each Intellectual Property (IP) Office has faced common big challenges: promoting the international work sharing in the examination process and developing an environment to access foreign patent documents written in languages other than its native language. As a means of resolving such challenges, the Japan Patent Office (JPO) has actively utilized machine translation. JPO released “Machine Translation Platform (MTP)” adopting the neural machine translation engine in May, 2019. MTP enables not only accurate dissemination of JPO’s high-quality examination results to foreign IP Offices for supporting Japanese users to smoothly acquire patent rights, but also improvement of the accessibility to foreign patent documents including Chinese patents that gains importance today. In this presentation, initiatives of machine translation by JPO including “MTP” and those by foreign IP offices are introduced.
Bio:Yohei Matsutani has served as Deputy Director at the Patent Information Policy Planning Office, Policy Planning and Coordination Department in the Japan Patent Office since July 2018. He engages in planning policies related to the translation of patent information. He joined the JPO as a patent examiner in 2004. In his 15 years’ career in the JPO, he has been involved in the patent examination of medical devices, analytical instruments.
Christof Monz is an associate professor in computer science at the Informatics Institute, University of Amsterdam. His research interests lie in the area of multilingual natural language processing and machine translation in particular. Prior to joining the University of Amsterdam he worked as a lecturer at Queen Mary University of London and as a post-doctoral research fellow at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He received a PhD in Computer Science from the University of Amsterdam in 2003.
Aurélie Névéol is a Senior Staff Scientist at the Centre National pour la Recherche Scientifique (CNRS). She received an MSc in Linguistics in 2002 and a PhD in Computer Science in 2005. She has more than 10 years experience in biomedical Natural Language Processing Research and has addressed the analysis of biomedical text from the litterature and from Electronic Health Reccords in French and in English. Recently, she has been focusing on clinical NLP for languages other than English. She has contributed to the development of representations of clinical information to support information extraction from EHR text, which can then be used for high throughput phenotyping. In the course of her work she has also contributed to the evaluation of research methods and workflows through her participation in the H2020 MIROR project and international evaluation campaigns such as CLEF eHealth and the biomedical task at WMT.