Patterns of technology life cycles: stochastic analysis based on patent citations. Lee, C., Kim, J., Noh, M., Woo, H., & Gang, K. Technology Analysis & Strategic Management, 29(1):53-67, 1, 2017.
Patterns of technology life cycles: stochastic analysis based on patent citations [link]Website  abstract   bibtex   
Patent analysis has been considered as an effective means of estimating phases of a technology life cycle. However, previous studies have not considered the dynamic and idiosyncratic aspects of a technology’s progression since they were based on deterministic methods, mainly fitting s- or double s-shaped curves to patent application counts. Moreover, previous methods cannot be executed at the individual patent level. We propose a stochastic technology life cycle analysis to trace the phases of a technology’s progression based on patent citations and identify the patterns of technology life cycles at the individual patent level. At the heart of the proposed approach are a hidden Markov model to estimate the probability of a system being at a certain hidden state from observation and cluster analysis to group a set of objects according to their similarities. A case study of patents about laser technology in lithography is presented.
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 title = {Patterns of technology life cycles: stochastic analysis based on patent citations},
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 year = {2017},
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 keywords = {Patterns of technology life cycles,cluster analysis,hidden Markov model,patent citations},
 pages = {53-67},
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 abstract = {Patent analysis has been considered as an effective means of estimating phases of a technology life cycle. However, previous studies have not considered the dynamic and idiosyncratic aspects of a technology’s progression since they were based on deterministic methods, mainly fitting s- or double s-shaped curves to patent application counts. Moreover, previous methods cannot be executed at the individual patent level. We propose a stochastic technology life cycle analysis to trace the phases of a technology’s progression based on patent citations and identify the patterns of technology life cycles at the individual patent level. At the heart of the proposed approach are a hidden Markov model to estimate the probability of a system being at a certain hidden state from observation and cluster analysis to group a set of objects according to their similarities. A case study of patents about laser technology in lithography is presented.},
 bibtype = {article},
 author = {Lee, Changyong and Kim, Juram and Noh, Meansun and Woo, Han-Gyun and Gang, Kwangwook},
 journal = {Technology Analysis & Strategic Management},
 number = {1}
}

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