Patent retrieval: a literature review <s> Pseudo-relevance feedback (PRF) <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> Pseudo-relevance feedback (PRF) <s> Patent retrieval is a recall-oriented search task where the objective is to find all possible relevant documents. Queries in patent retrieval are typically very long since they take the form of a patent claim or even a full patent application in the case of prior-art patent search. Nevertheless, there is generally a significant mismatch between the query and the relevant documents, often leading to low retrieval effectiveness. Some previous work has tried to address this mismatch through the application of query expansion (QE) techniques which have generally showed effectiveness for many other retrieval tasks. However, results of QE on patent search have been found to be very disappointing. We present a review of previous investigations of QE in patent retrieval, and explore some of these techniques on a prior-art patent search task. In addition, a novel method for QE using automatically generated synonyms set is presented. While previous QE techniques fail to improve over baseline retrieval, our new approach show statistically better retrieval precision over the baseline, although not for recall. In addition, it proves to be significantly more efficient than existing techniques. An extensive analysis to the results is presented which seeks to better understand situations where these QE techniques succeed or fail. <s> BIB002 </s> Patent retrieval: a literature review <s> Pseudo-relevance feedback (PRF) <s> Queries in patent prior art search are full patent applications and much longer than standard ad hoc search and web search topics. Standard information retrieval (IR) techniques are not entirely effective for patent prior art search because of ambiguous terms in these massive queries. Reducing patent queries by extracting key terms has been shown to be ineffective mainly because it is not clear what the focus of the query is. An optimal query reduction algorithm must thus seek to retain the useful terms for retrieval favouring recall of relevant patents, but remove terms which impair IR effectiveness. We propose a new query reduction technique decomposing a patent application into constituent text segments and computing the Language Modeling (LM) similarities by calculating the probability of generating each segment from the top ranked documents. We reduce a patent query by removing the least similar segments from the query, hypothesising that removal of these segments can increase the precision of retrieval, while still retaining the useful context to achieve high recall. Experiments on the patent prior art search collection CLEF-IP 2010 show that the proposed method outperforms standard pseudo-relevance feedback (PRF) and a naive method of query reduction based on removal of unit frequency terms (UFTs). <s> BIB003 </s> Patent retrieval: a literature review <s> Pseudo-relevance feedback (PRF) <s> Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation. In this paper, we first reduce the query document by taking the first claim of the document itself. We then build a query-specific patent lexicon based on definitions of the International Patent Classification (IPC). We study how to expand queries by selecting expansion terms from the lexicon that are focused on the query topic. The key problem is how to capture whether an expansion term is focused on the query topic or not. We address this problem by exploiting proximity information. We assign high weights to expansion terms appearing closer to query terms based on the intuition that terms closer to query terms are more likely to be related to the query topic. Experimental results on two patent retrieval datasets show that the proposed method is effective and robust for query expansion, significantly outperforming the standard pseudo relevance feedback (PRF) and existing baselines in patent retrieval. <s> BIB004 </s> Patent retrieval: a literature review <s> Pseudo-relevance feedback (PRF) <s> In this comprehensive new study, we evaluate all substantive decisions rendered by any court in every patent case filed in 2008 and 2009 — decisions made between 2009 and 2013. We assess the outcome of litigation by technology and industry. We relate the outcomes of those cases to a host of variables, including variables related to the parties, the patents, and the courts in which those cases were litigated. We find dramatic differences in the outcomes of patent litigation by both technology and industry. For example, owners of patents in the pharmaceutical industry fare much better in dispositive litigation rulings than do owners of patents in the computer & electronics industry, and chemistry patents have much greater success in litigation than their software or biotech counterparts. Our results provide an important window into both patent litigation and the industry-specific battles over patent reform. And they suggest that the traditional narrative of industry-specific patent disputes, which pits the IT industries against the life sciences, is incomplete. <s> BIB005 </s> Patent retrieval: a literature review <s> Pseudo-relevance feedback (PRF) <s> Patents are used by legal entities to legally protect their inventions and represent a multi-billion dollar industry of licensing and litigation. In 2014, 326,033 patent applications were approved in the US alone -- a number that has doubled in the past 15 years and which makes prior art search a daunting, but necessary task in the patent application process. In this work, we seek to investigate the efficacy of prior art search strategies from the perspective of the inventor who wishes to assess the patentability of their ideas prior to writing a full application. While much of the literature inspired by the evaluation framework of the CLEF-IP competition has aimed to assist patent examiners in assessing prior art for complete patent applications, less of this work has focused on patent search with queries representing partial applications. In the (partial) patent search setting, a query is often much longer than in other standard IR tasks, e.g., the description section may contain hundreds or even thousands of words. While the length of such queries may suggest query reduction strategies to remove irrelevant terms, intentional obfuscation and general language used in patents suggests that it may help to expand queries with additionally relevant terms. To assess the trade-offs among all of these pre-application prior art search strategies, we comparatively evaluate a variety of partial application search and query reformulation methods. Among numerous findings, querying with a full description, perhaps in conjunction with generic (non-patent specific) query reduction methods, is recommended for best performance. However, we also find that querying with an abstract represents the best trade-off in terms of writing effort vs. retrieval efficacy (i.e., querying with the description sections only lead to marginal improvements) and that for such relatively short queries, generic query expansion methods help. <s> BIB006
These methods are one of the prominent techniques used for QRE. PRF starts with an initial run of the given query Q. Then, terms from top ranked results are used to select, remove, and/or expand terms in Q, assuming that these top results are relevant. PRF is thus advantageous as it works automatically without human intervention but might be computationally inefficient, especially with long queries. Table 4 shows some PRF methods along with their performance results on benchmark datasets. Despite their effectiveness and popularity, several challenges arise when it comes to PRFbased QRE BIB006 such as: (1) which part(s) of the patent application should be used as the initial query?; (2) which part(s) of the retrieved results should be used as the source of expansion and/or reduction?; BIB005 what is the best length of the expansion list in case of query expansion, or the best threshold for removing terms in case of reduction?; (4) which pseudo-relevant results are really relevant and how many of them should be used?; and (5) what is the best Table 4 Pseudo-relevance feedback patent retrieval methods relevance scoring model for the search task (e.g., BM25  , the vector space model with tf-idf weighting). Bouadjenek et al. BIB006 provided a thorough evaluation on the CLEF-IP 2010/2011 collections to address some of the above challenges. The authors explored the scenario when only partial patent application is available for prior art search (e.g., title, abstract, extended abstract, or description). The authors tested different query expansion and reduction general methods such as  and a variant of the Maximal Marginal Relevance (MMR) BIB001 . They also tested patent-specific methods utilizing synonym sets BIB002 , language models BIB003 , and IPC-based lexicon BIB004 . After experimenting various sections as sources for the initial query terms as well as expansion/reduction sources, the results showed that the description section among other sections is the best to use as the initial query in the case of both query expansion and reduction. Query reduction was not beneficial for the long description queries as it already contains good coverage of relevant terms. However, query reduction on description queries was useful as it removed many of the noisy terms. Generally, query reduction outperformed query expansion on description and extended abstract queries which indicates that, with long queries, query reduction is effective for better retrieval performance. The results also showed that generic query expansion methods such as Rocchio works generally better for query expansion than patent-specific query expansion methods. Finally, the results showed that BM25 scoring works better than the TF-IDF scoring on the long description queries for both query reduction and expansion, while TF-IDF works better than BM25 on short and medium-length title or abstract queries. Through this comprehensive experimental study, the authors did not evaluate the impact of using multiple sections in combination as sources for query expansion or reduction. More importantly, the study does not provide any insights into the respective values of number of expansion terms or term removal threshold and whether these values are somewhat deterministic or vary widely calling for interactive setting.