Patent retrieval: a literature review <s> Other sources <s> This paper presents a method for combining ::: query-relevance with information-novelty in the context ::: of text retrieval and summarization. The Maximal ::: Marginal Relevance (MMR) criterion strives to reduce ::: redundancy while maintaining query relevance in ::: re-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results ::: indicate some benefits for MMR diversity ranking ::: in document retrieval and in single document summarization. ::: The latter are borne out by the recent results of the ::: SUMMAC conference in the evaluation of summarization ::: systems. However, the clearest advantage is demonstrated ::: in constructing non-redundant multi-document ::: summaries, where MMR results are clearly superior to ::: non-MMR passage selection. <s> BIB001 </s> Patent retrieval: a literature review <s> Other sources <s> Abstract Using a novel evaluation toolkit that simulates a human reviewer in the loop, we compare the effectiveness of three machine-learning protocols for technology-assisted review as used in document review for discovery in legal proceedings. Our comparison addresses a central question in the deployment of technology-assisted review: Should training documents be selected at random, or should they be selected using one or more non-random methods, such as keyword search or active learning? On eight review tasks -- four derived from the TREC 2009 Legal Track and four derived from actual legal matters -- recall was measured as a function of human review effort. The results show that entirely non-random training methods, in which the initial training documents are selected using a simple keyword search, and subsequent training documents are selected by active learning, require substantially and significantly less human review effort (P <s> BIB002 </s> Patent retrieval: a literature review <s> Other sources <s> In this book chapter, we provide a roadmap of the sources of data on the various forms of intellectual property protection. We first explain what data is available about patents, copyrights, trademarks, and other types of intellectual property, and where to find it. Then we identify and analyze data sources specifically relating to intellectual property licensing and litigation, growing areas of research by scholars and lawyers. <s> BIB003
Other IP data sources are detailed by Schwartz and Sichelman BIB003 . These include full patent texts as well as bibliographic information from major patent offices such as the USPTO, EPO, and WIPO. Bibliographic information for patents published from 1976 to 2006 is provided through the National Bureau of Economic Research 8 (NBER) and subsequently cleaned and extended to include patents until 2013. BIB001 Patent prosecution histories are available through the Patent Application Information Retrieval 10 (PAIR). Patent assignments, filings, classifications, and petition decisions are also provided through the USPTO bulk downloads previously hosted by Google 11 and now by the USPTO. BIB002 