Prior Art Search using International Patent Classification Codes and All-Claims-Queries

In this study, we describe our system at the Intellectual Property track of the 2009 CrossLanguage Evaluation Forum campaign (CLEF-IP). The CLEF-IP track addressed prior art search for patent applications. We used the Apache Lucene IR library to conduct experiments with the traditional TF-IDF-based ranking approach, indexing both the textual content of each patent and the IPC codes assigned to each document. We formulated our queries by using all claims and the title 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. Additionally, we used the IPC taxonomy (the categories and their short descriptive texts) to create a Concept Based Query Expansion [14] model for measuring the semantic overlap between topics and prior art candidates and tried to incorporate this information to our system’s ranking process. Probably due to an insufficient length of definition texts in the IPC taxonomy (used to define the concept mapping of our model), incorporating the concept based similarity measure did not improve our performance and was thus excluded from the final submission. Using the extended boolean vector space model of Lucene, our system remained efficient and still yielded fair performance: 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.