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IP portfolios and evolution of biomedical additive manufacturing applications

Author

Listed:
  • Amy J. C. Trappey

    (National Tsing Hua University)

  • Charles V. Trappey

    (National Chiao Tung University)

  • Curry L. S. Chung

    (National Tsing Hua University)

Abstract

Additive manufacturing (AM) or 3D printing includes techniques capable of manufacturing regular and irregular shapes for small batches of customized products. The ability to customize unusual shapes makes the process particularly suitable for prosthetic products used in biomedical applications. AM adoption in the field of biomedical applications (called bio-AM in this research) has seen significant growth over the last few years. This research develops an Intellectual Property (IP) analytical methodology to explore the portfolios and evolution of patents, as well as their relevance to Taiwan’s Ministry of Science and Technology (MOST) research projects in bio-AM domain. Specifically, global and domestic IP portfolios for bio-AM innovations are studied using the proposed method. First, the domain documents (of US patents and MOST projects) are collected from a global patent database and MOST project database. The key term frequency counts and technical clustering analysis of the collected documents are derived. The key terms and appearance frequencies in documents form the basis for document clustering and similarity analysis. The ontology of bio-AM is constructed based on the clustering results. Finally, the patents and projects in the adjusted clusters are subject to evolution analysis using concept lattice analysis. This research provides a computer supported IP evolution analysis system, based on the developed algorithms, for the decision support of IP and R&D strategic planning.

Suggested Citation

  • Amy J. C. Trappey & Charles V. Trappey & Curry L. S. Chung, 2017. "IP portfolios and evolution of biomedical additive manufacturing applications," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 139-157, April.
  • Handle: RePEc:spr:scient:v:111:y:2017:i:1:d:10.1007_s11192-017-2273-6
    DOI: 10.1007/s11192-017-2273-6
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    References listed on IDEAS

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    1. Xiao Zhou & Yi Zhang & Alan L. Porter & Ying Guo & Donghua Zhu, 2014. "A patent analysis method to trace technology evolutionary pathways," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 705-721, September.
    2. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    3. Narin, Francis & Noma, Elliot & Perry, Ross, 1987. "Patents as indicators of corporate technological strength," Research Policy, Elsevier, vol. 16(2-4), pages 143-155, August.
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