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.
 Searching for prior-art patents is an essential step for the patent examiner to validate or invalidate a patent application. In this paper, we consider the whole patent as the query, which reduces the burden on the user, and also makes many more potential search features available. We explore how to automatically transform the query patent into an effective search query, especially focusing on the effect of different patent fields. Experiments show that the background summary of a patent is the most useful source of terms for generating a query, even though most previous work used the patent claims. In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. In this paper we describe experiments conducted for CLEF- IP 2011 Prior Art Retrieval track. We examined the impact of 1) us- ing key phrase extraction to generate queries from input patent and 2) the use of citation network and (International Patent Classification) IPC class vector in ranking patents. Variations of a popular key phrase extrac- tion technique were explored for extracting and scoring terms of query patent. These terms are used as queries to retrieve similar patents. In the second approach, we use a two stage retrieval model to find similar patents. Each patent is represented as an IPC class vector. Citation net- work of patents is used to propagate these vectors from a node (patent) to its neighbors (cited patents). Similar patents are found by comparing query vector with vectors of patents in the corpus. Text based search is used to re-rank this solution set to improve precision. Two-stage sys- tem is used to retrieve and rank patents. Finally, we also extract and add citations present within the text of a query patent to the result set. Adding these citations (present in query patent text) to the results shows significant improvement in Mean Average Precision (MAP). Invalidity search poses different challenges when compared to conventional Information Retrieval problems. Presently, the success of invalidity search relies on the queries created from a patent application by the patent examiner. Since a lot of time is spent in constructing relevant queries, automatically creating them from a patent would save the examiner a lot of effort. In this paper, we address the problem of automatically creating queries from an input patent. An optimal query can be formed by extracting important keywords or phrases from a patent by using Key Phrase Extraction (KPE) techniques. Several KPE algorithms have been proposed in the literature but their performance on query construction for patents has not yet been explored. We systematically evaluate and analyze the performance of queries created by using state-of-the-art KPE techniques for invalidity search task. Our experiments show that queries formed by KPE approaches perform better than those formed by selecting phrases based on tf or tf-idf scores.  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.  A tracking control system for controlling a relative positional relation between an optical pick-up and a rotating optical disc is provided. A tracking error signal is supplied to a low pass filter, to a lead phase compensating circuit and also to a zero cross comparator which supplies its output to a pulse generating circuit. During a normal tracking mode, in which the optical pick-up is maintained in alignment with a recording track of the rotating disc, an output from the low pass filter and an output from the lead phase compensating circuit are added to define a feed-back signal which is then supplied to the optical pick-up. On the other hand, during a track jump control mode, in which the optical pick-up is caused to jump to the next adjacent recording track, with the lead compensating circuit maintained in an inhibited state, an acceleration pulse is generated from the pulse generating circuit and then a deceleration pulse opposite in polarity is generated from the pulse generating circuit in response to an output from the zero cross comparator, wherein the output of the low pass filter is added with the acceleration and deceleration pulses to define a feed-back signal to be applied to the optical pick-up. The first Clef-Ip test collection was made available in 2009 to support research in IR methods in the intellectual property domain; only one type of retrieval task Prior Art Search was given to the participants. Since then the test collection has been extended with both more content and varied types of tasks, reflecting various specific parts of patent experts' workflows. In 2013 we organized two tasks --- Passage Retrieval Starting from Claims and Structure Recognition --- on which we report in this work.