Patents and knowledge diffusion:The impact of Machine Translation

One of the main rationales for the patent system’s existence is to encourage knowledge diffusion and follow-on innovation through the full disclosure of the technical knowledge embodied in a patented invention. Yet, economists and legal scholars cast doubts on the validity of the disclosure theory and stress that inventors do not learn their science from reading patents. The empirical evidence on the actual benefits of the disclosure function is, indeed, limited. The present paper aims at expanding our understanding of how information spreads via patent disclosure and exploits recent improvements in machine translation (MT) to identify the effect of broader access to patented knowledge. More specifically, the paper uses a unique natural experiment. In September 2013, Google launched a major upgrade of its Google Patents service and added patent applications from the China National Intellectual Property Agency(CNIPA) to its searchable patent database. To do so, Google used its own neural machine translation service to translate patent documents previously available to the general public only in Chinese. Using a difference-in-differences approach, we show that the translation of the Chinese patents into English resulted in an increase in citations received from patents filed by U.S. inventors, compared to a suitable control group composed of patents which Google translated only in 2016. Our results suggest that increased access to patented knowledge promotes technological progress. This finding seems to confirm the beneficial effect of patent disclosur