Evaluating the effectiveness of keyword search strategy for patent identification

Xie, Zhongquan; Miyazaki, Kôji

doi:10.1016/j.wpi.2012.10.005

Cited by 60 publications

(30 citation statements)

References 32 publications

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“…Secondly, tracking and identifying emerging technologies is even more challenging for patent identification. This goal can potentially be achieved using an archive of reliable keywords (Xie and Miyzaki, 2013;Rizzi et al, 2014). However, one of the problems with a keywords-based approach is inconsistency in terminology usage by firms, inventors and patent attorneys.…”

Section: Discussionmentioning

confidence: 99%

Technology competition in the internal combustion engine waste heat recovery: a patent landscape analysis

Karvonen

Kapoor

Uusitalo

et al. 2016

Journal of Cleaner Production

130

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Section: Discussionmentioning

confidence: 99%

Technology competition in the internal combustion engine waste heat recovery: a patent landscape analysis

Karvonen

Kapoor

Uusitalo

et al. 2016

Journal of Cleaner Production

130

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“…We used text data composed of title, abstract, and keywords, following existing studies (e.g., Xie andMiyazaki 2013, Noh et al 2015). For example, Noh et al (2015) showed that using abstract data rather than description data was effective in their text mining-based patent analysis.…”

Section: Collection Of Scientific and News Datamentioning

confidence: 99%

Data-Driven Understanding of Smart Service Systems Through Text Mining

2018

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Abstract. Smart service systems are everywhere, in homes and in the transportation, energy, and healthcare sectors. However, such systems have yet to be fully understood in the literature. Given the widespread applications of and research on smart service systems, we used text mining to develop a unified understanding of such systems in a data-driven way. Specifically, we used a combination of metrics and machine learning algorithms to preprocess and analyze text data related to smart service systems, including text from the scientific literature and news articles. By analyzing 5,378 scientific articles and 1,234 news articles, we identify important keywords, 16 research topics, 4 technology factors, and 13 application areas. We define "smart service system" based on the analytics results. Furthermore, we discuss the theoretical and methodological implications of our work, such as the 5Cs (connection, collection, computation, and communications for co-creation) of smart service systems and the text mining approach to understand service research topics. We believe this work, which aims to establish common ground for understanding these systems across multiple disciplinary perspectives, will encourage further research and development of modern service systems.

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“…For instance, Tseng et al [12] represented a series of text mining techniques employing the analytical process on keyword extraction and clustering analysis to create visualized patent map for further patent analysis. Some studies compared several keyword selection criteria by employing keyword frequencies in documents, variances of keyword frequencies across patent documents, and TF-IDF values [15,16], while others explore the different parts of patents' textual documents and extract keywords, such as titles, abstracts, claims, and descriptions [17].…”

Section: Related Workmentioning

confidence: 99%

On the Patent Claim Eligibility Prediction Using Text Mining Techniques

Lai¹,

Hwang²,

Wei³

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model.

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Evaluating the effectiveness of keyword search strategy for patent identification

Cited by 60 publications

References 32 publications

Technology competition in the internal combustion engine waste heat recovery: a patent landscape analysis

Technology competition in the internal combustion engine waste heat recovery: a patent landscape analysis

Data-Driven Understanding of Smart Service Systems Through Text Mining

On the Patent Claim Eligibility Prediction Using Text Mining Techniques

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