Fig 1.
Competitive technology intelligence (CTI) and expert perspective hybrid data model.
This flow chart outlines the 10 main steps of the methodology implemented in the present study. The steps are indicated in boxes and sub-steps are indicated in ovals. The methodology begins with step one (the planning process) and continues through step 10 (decision making). The steps are repeated iteratively until the desired result has been obtained.
Fig 2.
Main query terminology for the database searches.
Words marked with an asterisk (*) are root words, indicating that all possible suffixes are covered under the query.
Fig 3.
Global scientific trends in 3D bioprinting.
A summary of the publications on 3D bioprinting that are indexed in Scopus and the Web of Science according to (A) publication output by year, from 2000 to 2015; (B) the 10 most frequent affiliation countries of the authors; (C) the 10 most frequent organizational affiliations of the authors (11 institutions are reported due to a tie for tenth place); and (D) the 10 journals with the most occurrences of the search terms.
Table 1.
Journals with more than 100 articles or conference proceedings on 3D bioprinting.
Fig 4.
Publication rates and geographical distribution of 3D bioprinting patents.
(A) The number of 3D bioprinting patent families (PFs) by year from 2000 to 2015. (B) The number of 3D bioprinting patents applied for in each of the top 10 most prolific priority countries (i.e. countries in which the first patent of a PF was applied for). Four countries (Belgium, Canada, India and Japan) were tied for the tenth position. (C) The number of PFs ranked according to assignee institutions by year, from January 1, 2000 until July 1, 2016. Three institutions (Shandong University, Tongji University and Wuhan University) were tied for the tenth position. In all three graphs, yellow indicates grants that were applied for, green indicates PFs that were granted, and red indicates inactive PFs.
Fig 5.
Global technology trends in 3D bioprinting.
(A) The top 10 International Patent Classification (IPC) classes in which 3D bioprinting patent families (PFs) identified in this study are found. (B) The top 10 “knowledge clusters” for IPC 3D bioprinting patents since 2000. The knowledge clusters were developed using a clustering algorithm based on unique terms occurring in the title, abstract and independent claims of patents. (C) The IPC classes of patents by each of the top assignees identified in Fig 4. Yellow indicates PFs that have been applied for, green indicates granted PFs and red indicates inactive PFs. It is important to note that each patent may be listed under several IPC classes, which is why the total number of PFs in (A) and (B) are greater than the number of PFs presented in (C).
Table 2.
Knowledge cluster breakdown.
Table 3.
Linkage between 3D bioprinting drivers identified by experts and knowledge clusters generated through text mining software.