A patent search and classification system
Proceedings of the fourth ACM conference on Digital libraries
Enhancing patent retrieval by citation analysis
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic query generation for patent search
Proceedings of the 18th ACM conference on Information and knowledge management
Ranking structured documents: a large margin based approach for patent prior art search
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploring structured documents and query formulation techniques for patent retrieval
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Development of a Patent Retrieval and Analysis Platform - A hybrid approach
Expert Systems with Applications: An International Journal
An IPC-based vector space model for patent retrieval
Information Processing and Management: an International Journal
Patent query reduction using pseudo relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management
Improving retrievability of patents in prior-art search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
PatentMiner: topic-driven patent analysis and mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommending citations: translating papers into references
Proceedings of the 21st ACM international conference on Information and knowledge management
A User-Friendly Patent Search Paradigm
IEEE Transactions on Knowledge and Data Engineering
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Patent citation recommendation and prior patent search, critical for patent filing and patent examination, have become increasingly difficult due to the rapidly growing number of patents. Unlike paper citations that focus on reference comprehensiveness, patent citations tend to be more parsimonious and refer only to those prior patents bearing significant technological and/or economic value, as they define the scope of the citing patent and thus have significant legal and economic implications. Based on the insight that patent citations are important information reflecting the value of cited patents to the citing patent, we propose a heterogeneous patent citation-bibliographic network that combines patent citations (reflecting value relation) and bibliographic information (reflecting similarity relation) together. From this network, we extract various features that reflect the value of a prior patent to a query patent with regard to the context of the query patent such as its assignee, classifications, etc. We then propose a two-stage framework for patent citation recommendation. Our idea is that by exploiting those context-specific value measures of candidate patents to the query patent, the proposed framework is able to make effective patent citation recommendations. We evaluate the proposed context-guided value-driven framework using a collection of 1.8M U.S. patents. Experimental results validate our ideas and show that those value-driven features are very effective and significantly outperform two state-of-the-art methods in terms of both the precision and recall rates.