Mining Changes in Patent Trends for Competitive Intelligence

Shih, Meng-Jung; Liu, Duen‐Ren; Hsu, Ming-Li

doi:10.1007/978-3-540-68125-0_105

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…Significant-rare keywords refer to the keywords which are actually rare in the documents. Shin et al used patent indicators to measure the value of patents [26]. The patent indicators include patent age, originality (reflected by the diversity of cited patents) and generality (reflected by the diversity of citing patents).…”

Section: Patent Processingmentioning

confidence: 99%

A design rationale representation model using patent documents

Liang

Liu

Kwong

et al. 2009

Proceedings of the 2nd International Workshop on Patent Information Retrieval

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Design rationale (DR) refers to the explanation of why an artifact is designed the way it is. The management of DR in engineering design is an important task since DR is often regarded as crucial information in design decision support, design analysis and design knowledge management. The existing DR systems largely target at manually capturing DRs along design activities. These systems are not only weak in revealing the internal relationships of design components, but also time consuming and they can not extract DR from archival design documents which are usually unstructured or semi-structured texts, such as design changes and test reports. Furthermore, the lack of open accessible DR data has limited the research and spreading of DR systems. In view of such challenges, it has motivated us to propose a new DR model to intelligently discover DR from patent documents since patents are deemed as quality data sources open for public access and they contain rich information about DR, invention details, technology development and so on. Our approach of DR discovery from patents consists of two stages. In the first stage, we propose a novel triple layer DR representation model, i.e. issue layer, design solution layer and artifact layer, to mine DR in a single patent document. The second stage aims to form a DR network based on the salient patent information that is discovered and explicitly represented in the first stage. We then exemplify our representation model using a case study of patents of inkjet printer. Lastly, we briefly discuss some potential applications by taking advantage of our representation model, such as DR reasoning and technology development trend mining.

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Section: Patent Processingmentioning

confidence: 99%

A design rationale representation model using patent documents

Liang

Liu

Kwong

et al. 2009

Proceedings of the 2nd International Workshop on Patent Information Retrieval

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“…It is important for enterprises to obtain an overview of patent trends. There have been a number of research publications and applications that delve into the problem of analyzing patent evolution [6,10,12]. For example, Shih et al assume that a patent trend can be represented by the frequent patents in a specific period, and propose to explore patent trend using association rule mining [12].…”

Section: Introductionmentioning

confidence: 99%

PatentLine

Zhang

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grab the evolution of technologies in order to better understand the technology trend. The availability of huge volumes of granted patent documents provides a reasonable basis for analyzing technology evolution. In this paper, we propose a unified framework, named PatentLine, to generate a technology evolution tree for a given topic or a classification code related to granted patents. The framework integrates different types of patent information, including patent content, citations of patents, temporal relations, etc., and provides a concise yet comprehensive evolution summary. The generated summary enables a variety of patent-related analyses such as identifying relevant prior art and detecting technology gap. A case study on a collection of US patents demonstrates the efficacy of our proposed framework.

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“…' The above sentiment has been expressed many times by many people in recent times [1][2][3][4]. Signals extracted from the scientific literature concerning emerging technologies may be useful in predicting when and where technological breakthroughs might be expected [2,3,[5][6][7][8][9][10][11][12][13][14]. These two ideas together suggest our main premise: that emerging relationships between disparate fields may presage potential scientific breakthrough, and that prediction of such breakthroughs based on analysis of the scientific literature may be possible.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the Potential for Collaboration at the Interface

Priebe

Solka

Marchette

et al. 2012

Statistical Analysis

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'The identification of potential breakthroughs before they happen' is a vague data analysis problem and 'the scientific literature' is a massive, complex dataset. Hence QHS for MTS might seem to be prototypical of the data miner's lament: 'Here's some data we have. . . can you find something interesting?' Nonetheless, the problem is real and important, and we develop an innovative statistical approach thereto-not a final etched-in-stone approach, but perhaps the first complete quantitative methodology explicitly addressing QHS for MTS.

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Mining Changes in Patent Trends for Competitive Intelligence

Cited by 14 publications

References 7 publications

A design rationale representation model using patent documents

A design rationale representation model using patent documents

PatentLine

Quantitative Horizon Scanning for Mitigating Technological Surprise: Detecting the Potential for Collaboration at the Interface

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