Exploratory analytics on patent data sets using the SIMPLE platform

Spangler, Scott; Chen, Ying; Kreulen, Jeffrey; Boyer, Stephen; Griffin, Thomas D.; Alba, Alfredo; Kato, Linda; Lelescu, Ana; Su, Yan

doi:10.1016/j.wpi.2011.07.001

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…Data mining (DM) is defined as computer automated exploratory data analysis of large complex data sets that can be used to discover patterns and relationships in data with an emphasis on large observational databases (Spangler et al, 2011). The tasks of DM can be modeled as either Predictive or Descriptive in nature (Luan, 2002).…”

Section: Methodsmentioning

confidence: 99%

Identifying Patterns of Students Academic Performance from Tracer Evaluation using Descriptive Data Mining

2019

ijrte

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The Ministry of Higher Education Malaysia has collected data through tracer study since 2007. The aim is to gather feedbacks from graduates as a basis improve to basis in improving. The availability of tracer study data in digital format offers various advantages to decision makers as many tools are available to extract and discover the hidden knowledge within the large databases. This paper presents the applicability of descriptive data mining and logistic regression to discover the hidden knowledge within the tracer study data with respect to measuring academic performance of Arts and Sciences graduates of Malaysia public universities. The impact of independent variables, i.e. Bahasa Melayu, English Language and Malaysian University English Test on the academic performance is investigated. The empirical results suggest that the academic performance between male and female graduates from Arts and Science fields is significantly different. Variables such as Bahasa Melayu, English Language and Malaysian University English Test showed a significant correlation with academic performance. The results also exhibit that the impact on academic performance of Arts graduates is different from the Science graduates. Guided by these empirical findings, this study suggests an academic performance model for Arts and Science graduates of Malaysia public universities.

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Section: Methodsmentioning

confidence: 99%

Identifying Patterns of Students Academic Performance from Tracer Evaluation using Descriptive Data Mining

2019

ijrte

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“…Typically, computational grouping is carried out by the relative distance between text mined data from compared patents, so outcomes do not provide clear categorical clustering (Trappey et al 2010), which is required to navigate effectively among patents. Most of the clustered outcomes do not clearly establish cluster meaning or provide valuable labels for each cluster (Yoon & Park 2004;Spangler et al 2011;Xu et al 2013). Additionally, the loss of semantic information through the translation of high-level data in patent documents into specific design functions or requirements can lead to inaccurate clustering (Fan et al 2011;Tekic et al 2012;Abbas et al 2014) and uncertainty in establishing the distance between patents (Yoon et al 2012).…”

Section: Text Mining Authorsmentioning

confidence: 99%

“…Most of the clustered outcomes do not clearly establish cluster meaning or provide valuable labels for each cluster (Yoon & Park 2004; Spangler et al. 2011; Xu et al. 2013).…”

Section: Patent Clusteringmentioning

confidence: 99%

The generation of problem-focussed patent clusters: a comparative analysis of crowd intelligence with algorithmic and expert approaches

Wodehouse¹,

Vasantha²,

Corney³

et al. 2017

Des. Sci.

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This paper presents a new crowdsourcing approach to the construction of patent clusters, and systematically benchmarks it against previous expert and algorithmic approaches. Patent databases should be rich sources of inspiration which could lead engineering designers to novel solutions for creative problems. However, the sheer volume and complexity of patent information means that this potential is rarely realised. Rather than the keyword driven searches common in commercial systems, designers need tools that help them to understand patents in the context of the problem they are considering. This paper presents an approach to address this problem by using crowd intelligence for effective generation of patent clusters at lower cost and with greater rationale. A systematic study was carried out to compare the crowd's efficiency with both expert and algorithmic patent clusters, with the results indicating that the crowd was able to create 80% more patent pairs with appropriate rationale.

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“…This kind of software facilitates search activities and fosters the use of patent information for all user types [5]. Spangler et al [6] have developed a holistic webbased IP mining tool called SIMPLE that facilitates patent data processing, warehousing, and analysis. Others have investigated data visualization tools which enhance the interpretation and analysis of collected patent information [7].…”

Section: It Tools For Ip Management E State Of the Artmentioning

confidence: 99%

The role of IT for managing intellectual property – An empirical analysis

Gassmann

Ziegler

Ruether

et al. 2012

World Patent Information

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a b s t r a c tThe effective management of intellectual property (IP) is an increasingly complex challenge in today's global knowledge economy, especially for firms with large IP portfolios. Although information technology (IT) tools are a means to support the management of these portfolios, there is little insight in how firms actually make use of IT tools in this regard. Hence, this article analyzes how and for which processes firms use IT tools to support their IP management. Based on a data set of 106 IP intensive firms worldwide, we find that firms use at least one of three major IT tools for IP management: search tools, administrative tools, and evaluation tools. We also find that the use of IT for IP processes is decreasing along the IP value chain: firms use IT mainly in the early IP generation phase, e.g., for absorbing technological developments. The article concludes by outlining where and how IT tools can improve the management of IP.

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Exploratory analytics on patent data sets using the SIMPLE platform

Cited by 15 publications

References 7 publications

Identifying Patterns of Students Academic Performance from Tracer Evaluation using Descriptive Data Mining

Identifying Patterns of Students Academic Performance from Tracer Evaluation using Descriptive Data Mining

The generation of problem-focussed patent clusters: a comparative analysis of crowd intelligence with algorithmic and expert approaches

The role of IT for managing intellectual property – An empirical analysis

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