Proposal of two-stage patent retrieval method considering the claim structure
ACM Transactions on Asian Language Information Processing (TALIP)
Cluster-based patent retrieval
Information Processing and Management: an International Journal
Enhancing patent retrieval by citation analysis
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
On the role of classification in patent invalidity searches
Proceedings of the 2nd international workshop on Patent information retrieval
Comparison of IPC and USPC classification systems in patent prior art searches
PaIR '10 Proceedings of the 3rd international workshop on Patent information retrieval
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
An IPC-based vector space model for patent retrieval
Information Processing and Management: an International Journal
Cluster-based patent retrieval using international patent classification system
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
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Finding similar patents is a challenging task in patent information retrieval. A patent application is often a starting point to find similar inventions. Keyword search for similar patents requires significant domain expertise and may not fetch relevant results. We propose a novel representation for patents and use a two stage approach to find similar patents. Each patent is represented as an IPC class vector. Citation network of patents is used to propagate these vectors from a node (patent) to its neighbors (cited patents). Thus, each patent is represented as a weighted combination of its IPC information as well as of its neighbors. A query patent is represented as a vector using its IPC information and similar patents can be simply found by comparing this vector with vectors of patents in the corpus. Text based search is used to re-rank this solution set to improve precision. We experiment with two similarity measures and re-ranking strategies to empirically show that our representation is effective in improving both precision and recall of queries of CLEF-2011 dataset.