PatSearch: an integrated framework for patentability retrieval

Zhang, Longhui; Liu, Zheng; Li, Lei; Shen, Chao; Li, Tao

doi:10.1007/s10115-017-1127-0

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…These query-based methods aim to retrieve all documents that are relevant to a given patent application according to a query. Hence, the accuracy and coverage of retrieval results highly depend on the query (Zhang et al, 2018), which can be formed by a variety of items, such as keywords, citations, authors, granted year, application date or combinations of them. The core technique that lies in the query-based methods is query reformulationconverting the input query into new and more searchable queries (Alberts et al, 2017;Shalaby and Zadrozny, 2019).…”

Section: The Problem Of Retrieving Ictc Patents By Using Query-based Methodsmentioning

confidence: 99%

“…However, gathering the corpus of ICTC patents may not achieve good performance by using the query-based methods, because it is extremely challenging to accurately represent and widely cover the ICTC patents by man-made queries. The patent retrieval tasks, including priorart search, patentability search and infringement search (Zhang et al, 2018), aim to return a wide coverage of patent documents that are relevant to a patent application according to a query, helping potential patentees check and analyze relevant information before the patent application is granted. Therefore, the queries are frequently used to represent a specific patent application rather than a set of patents.…”

Section: The Problem Of Retrieving Ictc Patents By Using Query-based Methodsmentioning

confidence: 99%

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Screening patents of ICT in construction using deep learning and NLP techniques

Shen

Liu

et al. 2020

ECAM

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PurposeThis study proposes an approach to solve the fundamental problem in using query-based methods (i.e. searching engines and patent retrieval tools) to screen patents of information and communication technology in construction (ICTC). The fundamental problem is that ICTC incorporates various techniques and thus cannot be simply represented by man-made queries. To investigate this concern, this study develops a binary classifier by utilizing deep learning and NLP techniques to automatically identify whether a patent is relevant to ICTC, thus accurately screening a corpus of ICTC patents.Design/methodology/approachThis study employs NLP techniques to convert the textual data of patents into numerical vectors. Then, a supervised deep learning model is developed to learn the relations between the input vectors and outputs.FindingsThe validation results indicate that (1) the proposed approach has a better performance in screening ICTC patents than traditional machine learning methods; (2) besides the United States Patent and Trademark Office (USPTO) that provides structured and well-written patents, the approach could also accurately screen patents form Derwent Innovations Index (DIX), in which patents are written in different genres.Practical implicationsThis study contributes a specific collection for ICTC patents, which is not provided by the patent offices.Social implicationsThe proposed approach contributes an alternative manner in gathering a corpus of patents for domains like ICTC that neither exists as a searchable classification in patent offices, nor is accurately represented by man-made queries.Originality/valueA deep learning model with two layers of neurons is developed to learn the non-linear relations between the input features and outputs providing better performance than traditional machine learning models. This study uses advanced NLP techniques lemmatization and part-of-speech POS to process textual data of ICTC patents. This study contributes specific collection for ICTC patents which is not provided by the patent offices.

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Section: The Problem Of Retrieving Ictc Patents By Using Query-based Methodsmentioning

confidence: 99%

Section: The Problem Of Retrieving Ictc Patents By Using Query-based Methodsmentioning

confidence: 99%

Screening patents of ICT in construction using deep learning and NLP techniques

Shen

Liu

et al. 2020

ECAM

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“…Thus, patent texts tend to contain neologisms and abstract words that might even be unique in the corpus. To account for this variety in a keyword search is especially tedious and prone to errors as the examiner has to search for synonyms at different levels of abstraction or rely on a thesaurus, which would then need to be kept up-to-date [72]. Even the BOW approach could in this case only capture the similarity between the patent texts if there is overlap between the words in the context around a synonym.…”

Section: Combining Bow Features With Word2vec Embeddingsmentioning

confidence: 99%

“…For example, a manually formulated query can be improved by automatically including synonyms for the keywords using a thesaurus [34,36,37,63,70]. A potential drawback of such an approach, however, is that the thesaurus itself has to be manually curated and extended [72]. Another line of research focuses on pseudo-relevance feedback, where, given an initial search, the first k search results are used to identify additional keywords that can be used to extend the original query [18,19,38].…”

Section: Related Workmentioning

confidence: 99%

Automating the search for a patent’s prior art with a full text similarity search

Helmers

Horn

Biegler³

et al. 2019

PLoS ONE

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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.

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Semantic Similarity Calculation of TCM Patents in Intelligent Retrieval Based on Deep Learning

Deng

Chen

2019

Advances on P2P, Parallel, Grid, Cloud and Internet Computing

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PatSearch: an integrated framework for patentability retrieval

Cited by 10 publications

References 26 publications

Screening patents of ICT in construction using deep learning and NLP techniques

Screening patents of ICT in construction using deep learning and NLP techniques

Automating the search for a patent’s prior art with a full text similarity search

Semantic Similarity Calculation of TCM Patents in Intelligent Retrieval Based on Deep Learning

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