More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.
Most research concerned with facilitating and improving the search for a patent's prior art has focused on automatically composing and extending the search queries. For example, a manually formulated query can be improved by automatically including synonyms for the keywords using a thesaurus @cite_34 @cite_22 @cite_44 @cite_59 @cite_41 . A potential drawback of such an approach, however, is that the thesaurus itself has to be manually curated and extended @cite_76 . Another line of research focuses on pseudo-relevance feedback, where, given an initial search, the first @math search results are used to identify additional keywords that can be used to extend the original query @cite_65 @cite_39 @cite_49 . Similarly, past queries @cite_21 or meta data such as citations can be used to augment the search query @cite_6 @cite_0 @cite_43 . A recent study has also examined the possibility of using the language model @cite_9 @cite_16 @cite_26 to automatically identify relevant words in the search results that can be used to extend the query @cite_46 .
@cite_26 @cite_22 @cite_41 @cite_46 @cite_9 @cite_21 @cite_65 @cite_6 @cite_39 @cite_44 @cite_0 @cite_43 @cite_16 @cite_59 @cite_49 @cite_76 @cite_34
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Continuous space language models have recently demonstrated outstanding results across a variety of tasks. In this paper, we examine the vector-space word representations that are implicitly learned by the input-layer weights. We find that these representations are surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset. This allows vector-oriented reasoning based on the offsets between words. For example, the male female relationship is automatically learned, and with the induced vector representations, “King Man + Woman” results in a vector very close to “Queen.” We demonstrate that the word vectors capture syntactic regularities by means of syntactic analogy questions (provided with this paper), and are able to correctly answer almost 40 of the questions. We demonstrate that the word vectors capture semantic regularities by using the vector offset method to answer SemEval-2012 Task 2 questions. Remarkably, this method outperforms the best previous systems. In the patent domain Boolean retrieval is particularly common. But despite the importance of Boolean retrieval, there is not much work in current research assisting patent experts in formulating such queries. Currently, these approaches are mostly limited to the usage of standard dictionaries, such as WordNet, to provide synonymous expansion terms. In this paper we present a new approach to support patent searchers in the query generation process. We extract a lexical database, which we call PatNet, from real query sessions of patent examiners of the United Patent and Trademark Office (USPTO). PatNet provides several types of synonym relations. Further, we apply several query term expansion strategies to improve the precision measures of PatNet in suggesting expansion terms. Experiments based on real query sessions of patent examiners show a drastic increase in precision, when considering support of the synonym relations, US patent classes, and word senses. Since patent documents are important technical resources, effective patent retrieval has become more and more crucial. Unlike common information retrieval, patent retrieval is a recall-oriented retrieval, and patent query inputs are usually long. However, current patent retrieval approaches cannot effectively capture user query intents and obtain good expansion terms, which lead to low retrieval effectiveness. To address this issue, this paper proposes a novel semantic query expansion-based patent retrieval approach according to patent-specific characteristics. Firstly, patent domain features are extracted by using a domain-dependent term frequency scheme. Based on domain features, query inputs are analyzed to determine query domains. Furthermore, query domain matching is employed to generate candidate expansion terms, and semantic-based similarity computation is adopted to select expansion terms. Experiment results show that our approach achieves better retrieval performance than other state-of-art approaches. ABSTRACTQuery expansion is a well-known method for improving the performance of information retrieval systems. Pseudo-relevance feedback (PRF)-based query expansion is a type of query expansion approach that assumes the top-ranked retrieved documents are relevant. The addition of all the terms of PRF documents is not important or appropriate for expanding the original user query. Hence, the selection of proper expansion term is very important for improving retrieval system performance. Various individual query expansion term selection methods have been widely investigated for improving system performance. Every individual expansion term selection method has its own weaknesses and strengths. In order to minimize the weaknesses and utilizing the strengths of the individual method, we used multiple terms selection methods together. First, this paper explored the possibility of improving overall system performance by using individual query expansion terms selection methods. Further, ranks-aggregating method n... In the patent domain significant efforts are invested to assist researchers in formulating better queries, preferably via automated query expansion. Currently, automatic query expansion in patent search is mostly limited to computing co-occurring terms for the searchable features of the invention. Additional query terms are extracted automatically from patent documents based on entropy measures. Learning synonyms in the patent domain for automatic query expansion has been a difficult task. No dedicated sources providing synonyms for the patent domain, such as patent domain specific lexica or thesauri, are available. In this paper we focus on the highly professional search setting of patent examiners. In particular, we use query logs to learn synonyms for the patent domain. For automatic query expansion, we create term networks based on the query logs specifically for several USPTO patent classes. Experiments show good performance in automatic query expansion using these automatically generated term networks. Specifically, with a larger number of query logs for a specific patent US class available the performance of the learned term networks increases. Pseudo-relevance feedback (PRF) is an effective approach in Information Retrieval but unfortunately many experiments have shown that PRF is ineffective in patent retrieval. This is because the quality of initial results in the patent retrieval is poor and therefore estimating a relevance model via PRF often hurts the retrieval performance due to off-topic terms. We propose a learning to rank framework for estimating the effectiveness of a patent document in terms of its performance in PRF. Specifically, the knowledge of effective feedback documents on past queries is used to estimate effective feedback documents for new queries. This is achieved by introducing features correlated with feedback document effectiveness. We use patent-specific contents to define such features. We then apply regression to predict document effectiveness given the proposed features. We evaluated the effectiveness of the proposed method on the patent prior art search collection CLEF-IP 2010. Our experimental results show significantly improved retrieval accuracy over a PRF baseline which expands the query using all top-ranked documents. This paper proposes a method to combine text-based and citation-based retrieval methods in the invalidity patent search. Using the NTCIR-6 test collection including eight years of USPTO patents, we show the effectiveness of our method experimentally. 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). Patent prior art search is a type of search in the patent domain where documents are searched for that describe the work previously carried out related to a patent application. The goal of this search is to check whether the idea in the patent application is novel. Vocabulary mismatch is one of the main problems of patent retrieval which results in low retrievability of similar documents for a given patent application. In this paper we show how the term distribution of the cited documents in an initially retrieved ranked list can be used to address the vocabulary mismatch. We propose a method for query modeling estimation which utilizes the citation links in a pseudo relevance feedback set. We first build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. We identify the important documents in the topic dependent citation graph using a citation analysis measure. We then use the term distribution of the documents in the citation graph to estimate a query model by identifying the distinguishing terms and their respective weights. We then use these terms to expand our original query. We use CLEF-IP 2011 collection to evaluate the effectiveness of our query modeling approach for prior art search. We also study the influence of different parameters on the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly improves the recall over a state-of-the-art baseline which uses the link-based structure of the citation graph but not the term distribution of the cited documents. Prior art search or recommending citations for a patent application is a challenging task. Many approaches have been proposed and shown to be useful for prior art search. However, most of these methods do not consider the network structure for integrating and diffusion of different kinds of information present among tied patents in the citation network. In this paper, we propose a method based on a time-aware random walk on a weighted network of patent citations, the weights of which are characterized by contextual similarity relations between two nodes on the network. The goal of the random walker is to find influential documents in the citation network of a query patent, which can serve as candidates for drawing query terms and bigrams for query refinement. The experimental results on CLEF-IP datasets (CLEF-IP 2010 and CLEF-IP 2011) show the effectiveness of encoding contextual similarities (common classification codes, common inventor, and common applicant) between nodes in the citation network. Our proposed approach can achieve significantly better results in terms of recall and Mean Average Precision rates compared to strong baselines of prior art search. The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. This paper presents the experiments and results of DCU in CLEF-IP 2009. Our work applied standard information retrieval (IR) techniques to patent search. Different experiments tested various methods for the patent retrieval, including query formulation, structured index, weighted fields, document filtering, and blind relevance feedback. Some methods did not show expected good retrieval effectiveness such as blind relevance feedback, other experiments showed acceptable performance. Query formulation was the key to achieving better retrieval effectiveness, and this was performed through assigning higher weights to certain document fields. Further experiments showed that for longer queries, better results are achieved but at the expense of additional computations. For the best runs, the retrieval effectiveness is still lower than for IR applications for other domains, illustrating the difficulty of patent search. The official results have shown that among fifteen participants we achieved the seventh and the fourth ranks from the mean average precision (MAP) and recall point of view, respectively. In this paper, we investigate the influence of term selection on retrieval performance on the CLEF-IP prior art test collection, using the Description section of the patent query with Language Model (LM) and BM25 scoring functions. We find that an oracular relevance feedback system that extracts terms from the judged relevant documents far outperforms the baseline and performs twice as well on MAP as the best competitor in CLEF-IP 2010. We find a very clear term selection value threshold for use when choosing terms. We also noticed that most of the useful feedback terms are actually present in the original query and hypothesized that the baseline system could be substantially improved by removing negative query terms. We tried four simple automated approaches to identify negative terms for query reduction but we were unable to notably improve on the baseline performance with any of them. However, we show that a simple, minimal interactive relevance feedback approach where terms are selected from only the first retrieved relevant document outperforms the best result from CLEF-IP 2010 suggesting the promise of interactive methods for term selection in patent prior art search. 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.