Prior art search using international patent classification codes and all-claims-queries

In this paper, we describe the system we developed for the Intellectual Property track of the 2009 Cross-Language Evaluation Forum. The track addressed prior art search for patent applications. We used the Lucene library to conduct experiments with the traditional TF-IDF-based ranking approach, indexing both the textual content and the IPC codes assigned to each document. We formulated our queries by using the title and claims of a patent application in order to measure the (weighted) lexical overlap between topics and prior art candidates. We also formulated a language-independent query using the IPC codes of a document to improve the coverage and to obtain a more accurate ranking of candidates. Using a simple model, our system remained efficient and had a reasonably good performance score: it achieved the 6th best Mean Average Precision score out of 14 participating systems on 500 topics, and the 4th best score out of 9 participants on 10,000 topics.