Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining

Hido, Shohei; Suzuki, Shinichi; Nishiyama, Risa; Imamichi, Takashi; Takahashi, Rikiya; Nasukawa, Tetsuya; Idé, Tsuyoshi; Kanehira, Yusuke; Yohda, Rinju; Ueno, Takeshi; Tajima, Akira; Watanabe, Toshiya

doi:10.2197/ipsjjip.20.655

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…In our proposed framework, the cost of a vertex (patent) is defined based on the content and citation counts of the corresponding patent. It is interesting to extend it using external resources, such as patent examination results [10], patent maintenance decisions [12], and court judgments [17]. These resources explicitly indicate the relative importance of the patents, and hence are helpful to refine the definition of the cost.…”

Section: Discussionmentioning

confidence: 99%

PatentDom

Zhang

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grasp the linkage between different technologies in order to better understand the technical trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing the relationships between technologies. In this paper, we propose a unified framework, named PatentDom, to identify important patents related to key techniques from a large number of patent documents. The framework integrates different types of patent information, including patent content, citations of patents, and temporal relations, and provides a concise yet comprehensive technology summary. The identified key patents enable a variety of patent-related analytical applications, e.g., outlining the technology evolution of a particular domain, tracing a given technique to prior technologies, and mining the technical connection of two given patent documents. Empirical analysis and extensive case studies on a collection of US patent documents demonstrate the efficacy of our proposed framework.

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Section: Discussionmentioning

confidence: 99%

PatentDom

Zhang

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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“…Quality assessment based on the lexical features of the patent text was also explored in the literature Jin et al (2011);Liu et al (2011);Hido et al (2012). Liu et al (2011) proposed a graphical model to estimate patent quality as a latent variable.…”

Section: Patent Quality Assessmentmentioning

confidence: 99%

“…Results showed high correlation between assigned ranks and forward citations count. Hido et al (2012) proposed a scoring model which assigned a patentability score to each patent and thus can be utilized to determine whether it will be granted. First, the authors extracted textual features such as word frequency, word age, and syntactic complexity (e.g., number of sentences).…”

Section: Patent Quality Assessmentmentioning

confidence: 99%

Patent retrieval: a literature review

Shalaby¹,

Zadrozny²

2019

Knowl Inf Syst

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With the ever increasing number of filed patent applications every year, the need for effective and efficient systems for managing such tremendous amounts of data becomes inevitably important. Patent Retrieval (PR) is considered the pillar of almost all patent analysis tasks. PR is a subfield of Information Retrieval (IR) which is concerned with developing techniques and methods that effectively and efficiently retrieve relevant patent documents in response to a given search request. In this paper we present a comprehensive review on PR methods and approaches. It is clear that, recent successes and maturity in IR applications such as Web search cannot be transferred directly to PR without deliberate domain adaptation and customization. Furthermore, state-of-the-art performance in automatic PR is still around average in terms of recall. These observations motivate the need for interactive search tools which provide cognitive assistance to patent professionals with minimal effort. These tools must also be developed in hand with patent professionals considering their practices and expectations. We additionally touch on related tasks to PR such as patent valuation, litigation, licensing, and highlight potential opportunities and open directions for computational scientists in these domains.

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“…For instance, Rai [20] introduced a predictive modeling approach by text-mining and several machine learning techniques based on the various features extracted from patents to predict the patent legal validity and patent quality. Furthermore, Hido et al [22] proposed a model computing the patentability score based on a set of feature variables including text contents of patent documents. Following this line of research, we adopt the TF-IDF and machine learning techniques on patent documents, especially on patent claims to build a binary classification model.…”

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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Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining

Cited by 13 publications

References 11 publications

PatentDom

PatentDom

Patent retrieval: a literature review

On the Patent Claim Eligibility Prediction Using Text Mining Techniques

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