Patent retrieval: a literature review <s> Metadata-based methods <s> A patent collection provides a great test-bed for cluster-based information retrieval. International Patent Classification (IPC) system provides a hierarchical taxonomy with 5 levels of specificity. We regard IPC codes of patent applications as cluster information, manually assigned by patent officers according to their subjects. Such manual cluster provides advantages over auto-matically built clusters using document term similarities. There are previous researches that successfully apply cluster-based retrieval models using language modeling. We develop cluster-based language models that employ advantages of having manually clustered documents. <s> BIB001 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized at the Humboldt University for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend. <s> BIB002 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Searches on patents to determine prior art violations are often cumbersome and require extensive manpower to accomplish successfully. When time is constrained, an automatically generated list of candidate patents may decrease search costs and improve search efficiency. We examine whether semantic relations inferred from the pseudo-hierarchy of patent classifications can contribute to the recognition of related patents. We examine a similarity measure for hierarchically-ordered patent classes and subclasses and return a ranked list of candidate patents, using a similarity measure that has demonstrated its effectiveness when applied to WordNet ontologies. We then demonstrate that this ranked list of candidate patents allows us to better constrain the effort needed to examine for prior art violations on a target patent. <s> BIB003 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> The objective of the 2009 CLEF-IP Track was to find documents that constitute prior art for a given patent. We explored a wide range of simple preprocessing and post-processing strategies, using Mean Average Precision (MAP) for evaluation purposes. Once determined the best document representation, we tuned a classical Information Retrieval engine in order to perform the retrieval step. Finally, we explored two different post-processing strategies. In our experiments, using the complete IPC codes for filtering purposes led to greater improvements than using 4-digits IPC codes. The second postprocessing strategy was to exploit the citations of retrieved patents in order to boost scores of cited patents. Combining all selected strategies, we computed optimal runs that reached a MAP of 0.122 for the training set, and a MAP of 0.129 for the official 2009 CLEF-IP XL set. <s> BIB004 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> This technical note presents the system built for the IP track of CLEF 2010 based on PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS), the modular search infrastructure initially realized for CLEF IP 2009. We largely reused the system of the previous CLEF IP but at a relatively smaller scale and with the improvement of three main components: • A new citation mining tool based on Conditional Random Fields (CRF). • A key-term extraction module developed for technical and scientific documents and adapted to patent document structures using a vast ranges of metrics, features and a bagged decision tree. • An improvement of our multi-domain terminological database called GRISP. We used the Okapi BM25 and the Indri retrieval models for the prior art task and a KNN model for the automatic classification task under the IPC subclasses. In both tasks, specific final re-ranking techniques were used, including multiple regression models based on SVM. Although the Prior Art task was more challenging and we used a more limited number of retrieval models, we maintained similar results as last year. We performed, however, miserably at the classification task, and we consider that an instance-based KNN algorithm is not competitive with standard classifiers based on preliminary large scale training. <s> BIB005 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Patent classification systems are used to help scrutinize patent applications for possible violations of the novelty and non-obviousness/inventive steps of a patentability test. There are several different patent classification systems in use today, each with a different underlying philosophy and approach. We compare the two most widely-used patent classification systems -- the IPC and USPC -- and examine their ability to help re-rank patents based on similarity. We observed a significant improvement in MAP, Recall@100, and nDCG when using these systems to re-rank our retrieved document set, demonstrating their overall utility in patent searches. <s> BIB006 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Patent prior-art search is concerned with finding all filed patents relevant to a given patent application. We report a comparison between two search approaches representing the state-of-the-art in patent prior-art search. The first approach uses simple and straightforward information retrieval (IR) techniques, while the second uses much more sophisticated techniques which try to model the steps taken by a patent examiner in patent search. Experiments show that the retrieval effectiveness using both techniques is statistically indistinguishable when patent applications contain some initial citations. However, the advanced search technique is statistically better when no initial citations are provided. Our findings suggest that less time and effort can be exerted by applying simple IR approaches when initial citations are provided. <s> BIB007 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Prior-art search is a critical step in the examination procedure of a patent application. This study explores automatic query generation from patent documents to facilitate the time-consuming and labor-intensive search for relevant patents. It is essential for this task to identify discriminative terms in different fields of a query patent, which enables us to distinguish relevant patents from non-relevant patents. To this end we investigate the distribution of terms occurring in different fields of the query patent and compare the distributions with the rest of the collection using language modeling estimation techniques. We experiment with term weighting based on the Kullback-Leibler divergence between the query patent and the collection and also with parsimonious language model estimation. Both of these techniques promote words that are common in the query patent and are rare in the collection. We also incorporate the classification assigned to patent documents into our model, to exploit available human judgements in the form of a hierarchical classification. Experimental results show that the retrieval using the generated queries is effective, particularly in terms of recall, while patent description is shown to be the most useful source for extracting query terms. <s> BIB008 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Determining requirements when searching for and retrieving relevant information suited to a user's needs has become increasingly important and difficult, partly due to the explosive growth of electronic documents. The vector space model (VSM) is a popular method in retrieval procedures. However, the weakness in traditional VSM is that the indexing vocabulary changes whenever changes occur in the document set, or the indexing vocabulary selection algorithms, or parameters of the algorithms, or if wording evolution occurs. The major objective of this research is to design a method to solve the afore-mentioned problems for patent retrieval. The proposed method utilizes the special characteristics of the patent documents, the International Patent Classification (IPC) codes, to generate the indexing vocabulary for presenting all the patent documents. The advantage of the generated indexing vocabulary is that it remains unchanged, even if the document sets, selection algorithms, and parameters are changed, or if wording evolution occurs. Comparison of the proposed method with two traditional methods (entropy and chi-square) in manual and automatic evaluations is presented to verify the feasibility and validity. The results also indicate that the IPC-based indexing vocabulary selection method achieves a higher accuracy and is more satisfactory. <s> BIB009 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> 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. <s> BIB010 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources. <s> BIB011 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> 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. <s> BIB012 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> 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. <s> BIB013 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> Patent prior art search is a task in patent retrieval with the goal of finding documents which describe prior art work related to a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Fortunately, other relevance evidence sources (i.e., classification tags and bibliographical data) provide additional details about the underlying information need. In this article, we propose a unified framework that integrates multiple relevance evidence components for query formulation. We first build a query model from the textual fields of a query patent. To overcome the term mismatch, we expand this initial query model with the term distribution of documents in the citation graph, modeling old and recent domain terminology. We build an IPC lexicon and perform query expansion using this lexicon incorporating proximity information. We performed an empirical evaluation on two patent datasets. Our results show that employing the temporal features of documents has a precision enhancing effect, while query expansion using IPC lexicon improves the recall of the final rank list. <s> BIB014 </s> Patent retrieval: a literature review <s> Metadata-based methods <s> In this paper we present a method that can be used to attain specific objectives in a typical prior art search process. The objectives are first to assist patent searchers in understanding the underlying technical concepts of a patent by identifying relevant international patent classification (IPC) codes and second to help them conduct a filtered search based on automatically selected IPCs. We view the automated selection of IPCs as a collection selection problem from the domain of distributed information retrieval (DIR) that can be addressed using existing DIR methods, which we extend and adapt for the patent domain. Our work exploits the intellectually assigned classifications codes that are used to categorize patents and to facilitate patent searches. In our method, manually assigned IPC codes of patent documents are used to cluster, distribute and index patents through hundreds or thousands of sub-collections. We propose a new multilayer collection selection method that effectively suggests classification codes exploiting the hierarchical classification schemes such as IPC/CPC. The new method in addition to utilizing the topical relevance of IPCs at a particular level of interest exploits the topical relevance of their ancestors in the IPC hierarchy and aggregates those multiple estimations of relevance to a single estimation. Experimental results on the CLEF-IP 2011 dataset show that the proposed approach outperforms state-of-art methods from the DIR domain not only in identifying relevant IPC codes but also in retrieving relevant patent documents given a patent query. <s> BIB015
Patents are not only textual documents, they contain lot of non-textual metadata and bibliographic information as well (e.g., citations, tables, formulas, drawings, classification, etc.). Combining metadata analysis with text-based PR has shown improvements in performance in the literature BIB011 19, BIB002 BIB005 BIB012 . Metadata features are also language-independent making them advantageous when used for CLIR. Table 6 shows some metadata-based methods along with their performance results on benchmark datasets Citation-based: The use of citation analysis for better patent retrieval is the most heavily reported technique of metadata-based methods. Naively incorporating citations from topic patent applications as prior art proved to be effective, eliminating the need for deeper citation analysis BIB007 . However, citation extraction from patent texts is challenging because there is no standard writing style for patent references. Lopez and Romary BIB005 developed a tool for citation mining which identifies, parses, normalizes, and consolidates patent citations. As citations might not be always available in all scenarios (e.g., related work search, technology survey), more mature techniques are needed. Fujii [19] proposed using PageRank  and document popularity as an additional scoring to re-rank query top results returned using claims-based queries. The results of applying popularity scoring on the English subset of NTCIR-6 improved MAP and recall over the raw text-based scoring. Incorporating PageRank, though intuitive, poses many challenges, especially because patent documents have references to the non-patent literature which would produce incomplete citation graph. Mahdabi and Crestani BIB012 extended their query modeling technique in Mahdabi et al. BIB008 by incorporating term distributions of the PRF results as well as their citations in calculating the query language model. The authors first construct a query-specific citation graph using PRF results and their citations and assign a score for each of them using PageRank. Then, a query model is estimated from term distributions of the documents in the citation graph constrained by their respective PageRank. Finally, query expansion is performed using the estimated query model. Experiments on the CLEF-IP 2011 collection showed improved recall performance with no change in precision, which indicates the usefulness of using cited documents vocabulary for query expansion. Best improvements were achieved using the top 30 PRF documents, 2-levels citation graph, and 100 expansion terms. However, we can notice two main computational challenges using this technique in real-time setting: (1) computing the PageRank of the 2-level citations graph and (2) estimating the query model from top PRF documents as well as documents in the citation graph. Classification-based: these methods utilize classification information of the topic patent and the retrieved documents to improve the performance of patent retrieval BIB009 BIB015 BIB003 BIB006 BIB001 . The naive use of IPC classification is to filter retrieved documents to keep only ones that share the same IPC classification code at some level (e.g., same subclass) with the topic patent BIB004  . The more sophisticated use of classification information was introduced by Verma and Varma BIB010 who proposed a new representation of patent documents based on IPC classifications. The method utilizes IPC codes assigned to the corpus patents as well as codes of their citing documents to form an IPC class vector. First, the vector is initialized from patent's IPC code, then codes of citing patents are propagated over multiple iterations. The most similar patents are retrieved using cosine similarity between IPC class vectors and re-ranked using text-based search utilizing the top 20 tf-idf topic patent terms. Experiments on the CLEF-IP 2011 collection showed improved recall but low MAP scores. The instability of the patent classification system poses a real challenge when it comes to incorporating classification metadata into PR systems. Over time, new classes are added to the classification hierarchy and existing classes are expanded. In order to do reliable search based on classification codes, these changes must be accounted for periodically. Moreover, patents are assigned to multiple classification codes; however, almost all previous research considered only the primary class but not secondary classifications which might, if utilized, improve the retrieval performance. Hybrid: these methods utilize various sources of metadata to improve PR performance. Mahdabi and Crestani BIB013 built upon previous work in Mahdabi et al. BIB008 and Mahdabi and Crestani BIB012 and proposed a query expansion method that utilizes time-aware random walk on a weighted patent citations network. Citation weights are derived from various metadata (e.g., classification codes, inventors, assignee, etc.). Citations with higher weights are considered more influential when performing query expansion. Experiments on the CLEF-IP 2010/2011 collections show improved recall and MAP. Mahdabi and Crestani BIB014 proposed building a query-specific lexicon from IPC definition pages and using it for query expansion. Unfortunately, the lexicon would be helpful only if the query represents a complete patent document with IPC codes assigned to it which is not always the case, especially at the early stages of the patent life cycle.