Inter-cluster connectivity analysis for technology opportunity discovery

Kim, Byung–Hoon; Gazzola, Gianluca; Lee, Jae-Min; Kim, Dohyun; Kim, Kang-hoe; Jeong, Myong K.

doi:10.1007/s11192-013-1097-2

Cited by 32 publications

(7 citation statements)

References 23 publications

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“…Keyword co-occurrence and network analysis methods have proven effective in: analyzing research topics (Wang et al 2016a); exploring evolutionary processes (Ye et al 2015); and tracing innovation activities (Liu et al 2015). Similar methods have been employed in patent network analysis to identify appropriate technology opportunities (Kim et al 2014;Lee et al 2014) or technology trends, significant patents, and novel technologies that might help to improve R&D efficiency (Choi and Hwang 2014;Park et al 2013). Patent network analysis has also been used for technology forecasting (Choi et al 2011) and to reveal potentially disruptive technological trends and visualize advances in emerging technologies (Zhang et al 2017a).…”

Section: Patent Network Analysismentioning

confidence: 99%

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

et al. 2019

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Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur's theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn's theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a fieldits communities, its technologies, and their interactions-is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.

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Section: Patent Network Analysismentioning

confidence: 99%

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

et al. 2019

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“…In the paper, keywords are extracted from collected patent documents by text mining, and patents are visualized in a two-dimensional space, and vacant cells, potential candidates for developing new technologies, are identified with their estimated keyword vectors by generative topographic mapping (GTM). Kim et al [27] also introduced TOD via technology convergence, using both patent textual data and patent citation networks. The authors carried out TOD by extracting representative keywords from patent pairs selected based on their technological relatedness and organizing them into the basic description of a new invention, which the potential convergence of the patent pair might produce.…”

Section: Bibliometricsmentioning

confidence: 99%

Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Choi

Chang

2020

Sustainability

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In this paper, we suggest a new methodology to identify promising technology areas by analyzing merger and acquisition (M&A) information. First, we present decision models for estimating the velocity and acceleration of M&A transactions to identify promising areas based on M&A information. Second, we identify the promising technology areas with longitudinal analyses of M&As over the entire period. Third, cross-sectional analysis is proposed to determine which technology areas are more promising through a relative comparison among technology areas within the IT sector for a specific period. The main significance of our research is that it is a prior data-based analytic method based on M&A transaction information to identify the growth of industry and technology. We hope this study will provide insights for R&D (Research&Development) policymakers and investment firms as a new approach that complements previous methods in exploring promising industry or technology areas.

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“…Recently, technology opportunity discovery (TOD) has been emphasized as a core competitive factor for companies and governments, and dominating technology in advance can provide considerable competitive advantages [1,2]. Technology opportunity contains the potential for technological progress in general or within a specific field.…”

Section: Introductionmentioning

confidence: 99%

“…Scholars have studied TOD from three aspects: framework, methodology, and extension of fields. Specifically, the TOD framework has been developed from the perspective of function [7], knowledge flow [8], inner-cluster connectivity [2], key technological performance [4], etc. A large number of techniques for TOD have also been proposed by designing computer programs [9], utilizing morphology analysis (MA) [10], theory of inventive problem solving (TRIZ) [11], text mining [1], patent mapping [12,13], novelty detection [14], and others.…”

Section: Introductionmentioning

confidence: 99%

Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

et al. 2019

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As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity from patent data. In line with it, this paper aims to propose a hybrid approach based on morphological analysis (MA) and unified structured inventive thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to explore the intrinsic links of innovation elements. A basic morphology matrix is constructed according to patent information and then is extended using the innovation algorithms that are reorganized from USIT. Technology opportunities are analyzed at two layers to generate new technical ideas. To illustrate the research process and validate its utility, this paper selects the technology of coalbed methane (CBM) extraction as a use case. This hybrid approach contributes by suggesting a semi-autonomous and systematic procedure to perform MA for TOD. By integrating the innovation algorithms, this approach improves the procedure of value extension in MA.

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Inter-cluster connectivity analysis for technology opportunity discovery

Cited by 32 publications

References 23 publications

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

A hybrid approach to detecting technological recombination based on text mining and patent network analysis

Development of a New Methodology to Identity Promising Technology Areas Using M&A Information

Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

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