IP portfolios and evolution of biomedical additivemanufacturing applications

Amy J. C. Trappey1 • Charles V. Trappey2 •

Curry L. S. Chung1

Received: 4 February 2016 / Published online: 10 February 2017� Akademiai Kiado, Budapest, Hungary 2017

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 appli-

cations (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.

& Amy J. C. Trappeytrappey@ie.nthu.edu.tw

Charles V. Trappeytrappey@faculty.nctu.edu.tw

Curry L. S. Chungs103034561@m103.nthu.edu.tw

1 Department of Industrial Engineering and Engineering Management, National Tsing HuaUniversity, Hsinchu, Taiwan

2 Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan

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Scientometrics (2017) 111:139–157DOI 10.1007/s11192-017-2273-6

Keywords Additive manufacturing � 3D printing � Biomedical � Patent analysis �Ontology � Evolution analysis

Introduction

Additive manufacturing (AM) is a technology used to print three dimensional (3D) objects

via an ongoing process of material additions. By using 3D digital modeling data, the 3D

objects are constructed through layer-by-layer material adherences. The advantage of 3D

printing (or AM) is the ability to fabricate irregular shaped objects with a high degree of

curvature and/or void spaces within the object. AM is currently restricted to the production

of small batches of products. Small batch manufacturing, however, is not necessarily a

hindrance for prosthetic products used in medical applications since many objects are

custom made, one of the kind, for individual patients.

The worldwide market for bio-AM (i.e., 3D printing in medical applications) is

expected to reach US 1 billion dollars by 2019 (Transparency Market Research 2013). The

applications of bio-AM have created unique business opportunities with intense compe-

tition emerging across the industry. Strategically realizing sustainable opportunities in the

bio-AM market requires that companies search for effective strategies to develop and apply

technologies within time, within budget constraints, and without infringing upon the

intellectual property (IP) of others. The common approach used to accomplish these goals

begins with patent analysis. Patent documents contain important research results (the

ability to reconstruct the invention by a person knowledgeable in the art) which is of great

value to industry, legal researchers, and policy advocates in science and technology R&D

(Tseng et al. 2007). Since patents provide legal protection for the IP owners, these

intangible rights may be licensed for creating a continuous revenue stream, sold to others

for substantial profits, or used to enhance the brand values of the patent assignees and

licensees. Hence, planning an appropriate patent strategy is important for a technology

company to leverage its IP rights, to create patent portfolios for better management, to

enhance the value of the firm, and to secure inventions from possible IP infringement

litigation.

This research focuses on developing an innovative methodology to analyze a company’s

IP portfolio, compare the critical strength and position relative to its competitors (Narin

et al. 1987), identify the domain technology evolution, and trace the technology pathways

of the past, current, and future developments (Zhou et al. 2014). In our case study, a unique

patent analytical method is applied to study global and domestic portfolios and the evo-

lution of bio-AM technologies archived in the USPTO patent database and the database of

Taiwan Ministry of Science and Technology (MOST) funded research proposals. The

MOST bio-AM proposals are evaluated against the global bio-AM patent trends. For the

study, R statistical programs are used for text mining and cluster analysis. The normalized

term frequency-inverse document frequency (NTF–IDF) is the measure for evaluating the

importance of extracted key terms. The computer assisted R-program system counts the

normalized term frequencies (NTF) and inverse document frequencies (IDF) across all

documents and constructs a NTF–IDF matrix. The research calculates the correlations

between all pairs of documents, based on the common term frequencies, as the inputs for

clustering algorithms. The K means, K medoids, and Ward’s hierarchical algorithms are

used to cluster the documents. The R package cValid (Brock et al. 2008) is applied to

140 Scientometrics (2017) 111:139–157

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evaluate the performance and select the best clustering results. The graphical clustered

concept evolution is derived using a modified formal concept analysis (MFCA or called

concept lattice) algorithm. The proposed approach provides an objective basis to recom-

mend, within the given knowledge domain, which research development strategies should

be considered as being competitive, potentially profitable, and clear of infringement lia-

bilities. The key sections of the entire research paper, including introduction, literature

reviews, general methodology development, computer-supported prototype implementa-

tion, and bio-AM case study, are outlined in Fig. 1.

Literature review

This research relies on text mining, data mining, and clustering methods to construct the

ontology of bio-AM technologies and applications. The computer-assisted system, incor-

porating the algorithmic subroutines, is built using R programs. Text mining extracts

information from documents and the extracted term frequencies are used for accurately

clustering these documents. Without accurate clustering, a suitable ontology is difficult, if

not impossible, to derive. In the following sub-sections, the background and literature of

the fundamental algorithms and previous research methodologies are reviewed.

Text mining of patent documents

Most of the information in the patent document is text although illustrations are used to

clarify the uniqueness of claims and evaluate the advancement of technology beyond the

prior art. Text mining is used to extract knowledge from the unstructured language of

patent claims and descriptions (Sanchez et al. 2008). The outcome of the process provides

unique information about the invention and helps to correlate similar information found in

Fig. 1 The research framework and the paper outline

Scientometrics (2017) 111:139–157 141

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other patents (Sullivan 2001). Text mining technology is often used in combination with

term frequency analysis and the approach is divided into text refining and knowledge

distillation. Text refining transforms free form text documents into an intermediate form,

and knowledge distillation derives patterns or knowledge from the intermediate form (Tan

1999). Text mining requires a substantial number of text documents to create statistically

valid relationships and provide a sufficient pool of term frequencies to analyze and match

the results of patent analysis. Common patent analysis methods include categorical anal-

ysis, cluster analysis, and relational analysis.

Most text mining methods are based on words, but the existence of synonyms and

polysemy affects the results unless controlled. Zhong et al. (2012) present an effective

pattern discovery technique to improve the effectiveness of the text mining technique. The

first step of text mining begins with data pre-processing (Mierswa et al. 2006) and the basic

text processing steps (Te Liew et al. 2014) include importing and classifying text files,

transforming upper and lower cases, creating tokens (e.g., the terms ‘greenhouse gases’ and

‘greenhouse gas’ are replaced with the token ‘GHG’), splitting text into a sequence of

tokens, filtering English stop-words, filtering user defined stop-words, and generating

n grams (n grams are a contiguous sequence of n objects used to capture phrases from the

text).

After data pre-processing, methods to count term frequencies, variance, and term fre-

quency—inverse density frequency (TF-IDF) are used to extracted keywords from patent

documents. Lee et al. (2009) conducted variance and frequency comparison analyses to

extract keywords. A high variance means that a keyword has a high frequency in specific

documents and a low frequency in other documents, and better identifies the technical

features of patent documents (Bermudez-Edo et al. 2015).

Clustering

Clustering is used as an unsupervised learning method with the goal to discover a new set

of categories (Maimon and Rokach2005). For practical clustering, the goal is to minimize

the distances (similarity maximization) among entities in the same cluster and maximize

the distance (similarity minimization) between entities in different clusters. The clustering

method is divided into two categories (Jain et al. 1999):

1. Partitioning algorithms that produce non-hierarchical clusters. This method assigns a

number of clustering centers and an iteration algorithm determines the cluster group

centers. Frequently used partitioning algorithms include Self-Organizing Maps (SOM)

and K means clustering (Trappey et al. 2013). Among these clustering algorithms,

K means clustering is most widely used and studied (Kanungo et al. 2002). K means

clustering is very efficient in terms of computational time, but it is sensitive to outliers.

For this reason, K medoids clustering is used to reduce outlier bias.

2. Hierarchical algorithms produce hierarchical clusters using divisive and agglomerative

approaches (Rokach and Maimon 2005). The divisive approach is rarely used. The

agglomerative approach views each sample as a small cluster, iteratively combined

into a larger cluster using techniques such as Ward’s method.

Ontology

Ontology is a branch of philosophy often called a synonym of metaphysics (Floridi 2008).

The underlying concept of ontology is to discuss the existence or features of real objects.

142 Scientometrics (2017) 111:139–157

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Gruninger and Fox (1995) defined ontology as a formal description of a domain set of

entities, their properties, behaviors, and relations.

With the development of Information Technology (IT), ontologies are applied in the

area of Knowledge Sharing (KS) for innovative product or service development (Lee et al.

2015; Yan et al. 2005, 2009). An ontology defines the basic terms and relations comprising

the vocabulary of the domain knowledge as well as the rules for combining terms and

relations to define extensions to the vocabulary (Neches et al. 1991). Gruber (1995) defines

an ontology as an explicit specification of a conceptualization, which describes a knowl-

edge domain consisting of a set of relations, objects concepts, and functions.

A formal methodology to develop ontologies was proposed by Noy and McGuinness

(2001). The systematic steps include determining the domain and scope of the ontology,

considering the reuse of existing ontologies, enumerating important terms in the ontology,

defining the classes, their hierarchy, and their properties as class facets, and finally creating

instances of ontology classes.

Using R subroutines for text and data analytics

R language is a standard set of computer code and subroutines for statistical analysis and

graphical visualization originally developed in 1996 (Ihaka and Gentleman 1996). R

language has a similar architecture to the earlier statistical programming language S. After

years of development and enhancement, R has become a widely used open access statis-

tical software language.

Methodology

This section describes the approaches and algorithms used for the IP portfolio and evo-

lution analytics. The proposed methodology includes steps for extracting key terms per-

forming cosine similarity analysis, creating and validating technical clusters, and tracing

the technology evolution (Fig. 2).

Key term extraction

This research uses the normalized term frequency-inversed document frequency (NTF–

IDF) ranking method to identify key terms. Term Frequency (TF) prioritizes the words

appearing in a text document. Due to variations in length of patents or project documents,

we normalize the term frequencies for the entire set of text documents. The normalized

term frequency (NTF) is used to provide a precise representation of term frequency

occurrences in a document. The mathematical expression of NTF is shown in Eq. (1)

ntfij ¼ tfij �PN

s¼1 dns

N � dnjð1Þ

tfij: The number of times that term i appears in document j; dnj: The total number of words

in document j; N: The total number of documents.

Inverse document frequency (IDF) is a measure used to calculate a terms’ importance

and its ability to distinguish a document from others. The IDF value is calculated in two

steps. The first step represents the total number of documents (N) divided by the number of

documents where the term i appears (dfi). The second step provides the quotient of the first

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step and represents it in the form of a logarithm. The mathematical formula is shown by

Eq. (2)

idfi ¼ logN

dfi

� �

ð2Þ

dfi: The number of documents where the term i appears.

The NTF–IDF value is the product of Eqs. (1) and (2). The reason for using NTF–IDF is

to reduce bias if a term appears frequently in one document but rarely appears in other

documents. This allows NTF–IDF to be used as a method to distinguish between different

categories and is very suitable for clustering. Lower NTF–IDF values represent the lower

degrees of discrimination and higher values represent higher degrees of discrimination.

Thus, NTF–IDF values are a means to distinguish important terms. R statistical software

with text mining functions is adopted to calculate all NFT–IDF values of a given set of

documents as shown in Table 1, where NTF - IDFij is the product of ntfij and idfi. The

NTF–IDF data are used as inputs for cosine similarity analysis.

Cosine similarity analysis

The cosine similarity theorem (Salton et al. 1975) is used to determine the similarity

between documents. The angle between the inner product of two vectors determines the

cosine similarity. Since the NTF–IDF value of terms are unlikely to be negative, the cosine

similarity of the two documents range from zero to one. The mathematical formula is

shown in Eq. (3). Referring to Eq. (3), the numerator represents the inner product of the

Fig. 2 The step-by-step procedure of the IP evolution method and algorithm sequence

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two key terms vectors x and y and the denominator represents the product of the vector

lengths.

Similarity x; yð Þ ¼ cos hð Þ ¼ x � yxk k yk k ð3Þ

x�y inner product of vectors x and y; kxk: length of vector x; kyk: length of vector y.

Clustering algorithms and performance validation

Brock et al. (2008) presented three types of cluster validation approaches: internal, sta-

bility, and biological. This research uses internal and stability validation to measure the

quality of clustering and to determine which algorithm is most suitable for analyzing the

bio-AM case.

For internal validation, there are three measures including connectivity, silhouette

width, and Dunn index. Connectivity measures the connectedness of observations whereas

silhouette width and the Dunn index measure the non-linear combinations of the com-

pactness and separation of the cluster partitions.

For stability validation, there are four measures for the average proportion of non-

overlap (APN), the average distance (AD), the average distance between means (ADM),

and the figure of merit (FOM). These measures compare the clustering results based on the

full set of data.

In this research, three clustering algorithms (K means algorithm, K medoids algorithm,

and Ward’s algorithm) are applied to cluster the same set of documents. The K means

approach (MacQueen 1967) requires the specification of the number of clusters (k), then

through iteration, reduces the differences between data within a cluster and the cluster

center until cluster members are fixed. K means clustering is divided into the following five

steps (Velmurugan and Santhanam 2010):

1. Randomly select k numbers of data in the data set as the initial cluster centers and

estimate the number of clusters (K clusters).

2. Calculate the distance of each random cluster center to each data, and assign each data

to the nearest cluster center. This will form a cluster boundary resulting in an initial

cluster of members.

3. According to the cluster boundary, calculate the mean point of each cluster to the new

cluster center.

Table 1 Document versus key term NTF–IDF matrix

NTF � IDFij Terms (Ti; i ¼ 1; 2; . . .;m)

T1 T2 … Tm

DocumentsðDj; j ¼ 1; 2; . . .; n)

D1 NTFIDF1,1 NTFIDF2,1 … NTFIDFm,1

D2 NTFIDF1,2 NTFIDF2,2 … NTFIDFm,2

D3 NTFIDF1,3 NTFIDF2,3 … NTFIDFm,3

… … … … …Dn NTFIDF1,n NTFIDF2,n … NTFIDFm,n

Sumvalue

Pnj¼1 NTF � IDF1;j

Pnj¼1 NTF � IDF2;j …

Pnj¼1 NTF � IDFm;j

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4. After specifying the new cluster center, calculate the distances again and redistribute

the data to the nearest cluster centers.

5. Steps 3 and 4 are repeated until there are no more changes in the cluster members.

The number K will affect the clustering result, so the R Squared (RS) and root-mean-

square standard deviation (RMSSTD) statistics are used to estimate the number of clusters.

RS indicates the degree of difference among clusters. ANOVA analysis of variance is used

to calculate the RS values which are between zero and one. RMSSTD is a measure of

homogeneity within clusters. Large values of RS mean that there is a greater difference

between clusters. Small values of RMSSTD indicates that the clusters are homogenous.

The optimal number of clusters are selected using larger RS values and smaller RMSSTD

values.

The K medoids clustering algorithm is an algorithm derived from the K means algo-

rithm. Kaufman and Rousseeuw (1990) demonstrated that Partitioning Around Medoids

(PAM) is an improved version of the K medoids algorithm. The PAM algorithm is

described as follows (Velmurugan and Santhanam 2010):

1. Arbitrarily choose k objects in the data set as the initial clustering medoids.

2. Assign each remaining object to the cluster with the nearest medoid.

3. Randomly select a non medoid object Orandom in each cluster and compute the distance

change S. If S\0, replace the original medoid with Orandom.

4. Repeat steps 2 and 3 until there is no change.

Hierarchical clustering is a method where the data are repeatedly divided or combined

to produce the final tree structure. The most commonly used method is agglomerative

hierarchical clustering which aggregates layers from the bottom of the tree to form clusters

hierarchically. The algorithm follows four steps:

1. Each data set will be treated as a cluster Ci, i = 1 to n.

2. Find the closest two clusters among all the clusters, Ci and Cj.

3. Merge the two clusters Ci and Cj into a new cluster.

4. If the current number of clusters exceeds the expected number, then repeat steps 2–4

until the number of clusters satisfy the requirements.

In step 2, two close clusters are found. The common algorithms used are a single-

linkage agglomerative algorithm, complete-linkage agglomerative algorithm, average-

linkage agglomerative algorithm and Ward’s method (Jain et al. 1999). In this research, we

use Ward’s method to find the two closest clusters. Ward’s (1963) method is a common

algorithm for hierarchical clustering and its mathematical representation is shown in

Eq. (4):

dðCi;CjÞ ¼X

a2Ci[Cj

a� lk k ð4Þ

l The mean vector of Ci [ Cj.

Tracing technology evolution

Based on the NTF–IDF matrix and the clustering results, a dynamic concept lattice graph

for a given set of technical documents (patents and proposals) is constructed. First, the

NTF–IDF matrix of entire document set provides the basic data for clustering and each

cluster also has its own NTF–IDF matrix. Second, all NTF–IDF values are ranked in the

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NTF–IDF matrices by ascending order. The top quartiles of documents are included for

evolution analysis. Thus, if the NTF–IDF value exceeds the first quartile threshold value,

then NTF–IDF index value is 1, otherwise the index is 0. Finally, define the correlation

threshold value (e.g., 0.3). If the correlation value between two documents is greater than

the threshold, there is a strong relationship (link) between these two nodes. If the docu-

ments have common terms in branch, a solid line is used to connect the documents. If not, a

dotted line is used to link the documents. The evolution algorithm’s pseudo code and

explanatory comments are provided in Appendix 1. The portfolio and evolution analysis of

bio-AM patents and project proposals are described in ‘‘Case analysis of bio-AM patents

and projects’’ section.

Case analysis of bio-AM patents and projects

The global bio-AM patents, excluding sole dental applications, are retrieved and archived

using a strategic combination of keywords. The search keywords are divided into parts

including 3D printing (additive manufacture) and biomedical scaffolding. First, the search

keywords are defined as additive manufacture, 3d, three dimension, print, fabricate, and

manufacture. Then these keywords are extended and combined with the keywords, such as

biomedical, bionic scaffold, and implant, to improve the search accuracy within biomed-

ical applications. The research uses the word stemming function commonly available in the

patent search engine (e.g., FreePatentsOnline, FPO). Thus, we can also identify patents

which use inflected and not exact words.

Among 220 granted US patents identified from the preliminary search, 58 patents are

eventually selected by the domain expert as having a high technological impact. A set of

nine Taiwan Ministry of Science and Technology (MOST) bio-AM research proposals and

58 published patents (P10–P67) are archived for the lattice construction. A total of 67

documents are selected to demonstrate the methodology used in the case study.

Key term extraction

The stemDocument function of R statistical software filters out the English stop words.

This step reduces the number of generic English function words in the text. The text

mining subroutines (e.g., tm, NLP, textir, lsa) are used to generate the NTF–IDF matrix. A

partial example of the NTF–IDF matrix is shown in Table 2, where NTF–IDF values are

summed by column to calculate the total NTF–IDF values of key terms. The top ranked 56

terms for the case document set are listed in Appendix 2.

Table 2 A partial NTF–IDFmatrix for the case analysis

Implant Scaffold Bone Guide Model

P1 0.00 48.09 0.00 0.00 4.06

P2 0.00 0.00 0.00 0.00 4.90

P3 1.39 0.00 14.46 0.00 0.00

P4 0.00 8.49 0.00 0.00 0.00

… … … … … …Total 369.80 348.14 302.22 282.30 277.29

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In order to reduce the bias of choosing similar key phrases, key term selection rules are

defined. The first rule is that if the terms describe an identical or near identical concept, the

most generalized term is selected as the representing the token. For example, we select

‘‘scaffold’’ instead of ‘‘cellular scaffold.’’ The second rule is that if the phrase and its

divided words are all in the top 100 rankings, the words in the phrases are selected. For

example, we select both ‘‘tissue’’ and ‘‘construct’’ as key terms, instead of ‘‘tissue con-

struct.’’ The third rule is to select the terms which have higher NTF–IDF values. Before

clustering the documents, the NTF–IDF matrix is organized using the cosine similarity

algorithm. The resulting partial matrix is shown in Table 3.

Bio-AM technology clustering and the ontology schema

Before clustering, the R package (clValid) is used to evaluate the most suitable numbers of

clusters. Three popular algorithms, K means, Partitioning Around Medoids (PAM or

K medoids), and Wards hierarchical are selected for clustering. The comparative analysis

of the clustering algorithms, based on their internal and stability validation values

(Table 4), suggests PAM with six clusters and hierarchy with two clusters yield good

results.

For the case analysis, the six cluster K medoids clustering analysis is performed and the

result is listed in Appendix 3. This algorithm generates a six cluster result that is a better

representation with more distinguished cluster features than the other clustering algo-

rithms. As observed, cluster one identifies the tissue engineering applications. Cluster two

is similar to cluster one but is focused on methods for bio-printing. Cluster three identifies

the methods of manufacturing bone implants. Cluster four identifies the material compo-

sition for implants and prosthesis. Cluster five is related to surgical guides and models.

Finally, cluster six focuses on the joint implant applications. Shown in Appendix 4, this

research adjusts the K medoids six clusters into three meaningful sub-domain categories

for the technology evolution analysis (in the ‘‘Bio-AM technology evolution analysis’’

section). These three sub-domains for AM-bio applications are (1) tissue engineering and

bioprinting, (2) surgical guides and modeling, and (3) implants and prosthesis as identified

in the literature (Rengier et al. 2010; Klein et al. 2013; Gross et al. 2014).

Using the frequently appearring key terms identified through text mining of the case

documents, the biomedical-AM ontology schema is constructed as shown in Fig. 3. The

scaffold node is subdivided into mechanical aids including in vitro and in vivo implants.

Mechanical aids in vitro are linked to selective laser sintering (SLS) and selective laser

melting (SLM) techniques to produce prosthetics and use polyamide (PA) and Ti–6Al–4V

as construction materials. In vivo implants are used to construct scaffolds for growing

organs and tissues. The materials commonly used include polycaprolactone (PCL), poly-

lactic acid (PLA), sodium carboxymethyl cellulose, biosynthetic cellulose, nano-cellulose,

Table 3 Similarity matrix(partial)

P1 P2 P3 P4 P5

P1 1.000 0.035 0.001 0.157 0.003

P2 0.035 1.000 0.001 0.009 0.346

P3 0.001 0.001 1.000 0.012 0.429

P4 0.157 0.009 0.012 1.000 0.024

P5 0.003 0.346 0.429 0.024 1.000

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Connective Growth Factor (CTGF), and Transforming Growth Factor b3 (TGFb3). The

bone node in the ontology schema is in the implant sub-domain, which uses biomedical

grade metal powders (e.g., Ti–6Al–4V and Co–Cr–Mo) to build prostheses to replace the

original bone structure. Commonly used AM techniques are selective laser sintering (SLS)

and selective laser melting (SLM). The soft organ category has a key link to stem cells.

Stem cells are obtained from umbilical cord blood, bone marrow mesenchymal and adipose

tissue. The 3D printing bio ink is created from biological stem cells and sprayed onto the

Table 4 The validation result of hierarchical, k means and PAM clustering

Internal validationValidation Cluster sizesMeasures: 2 3 4 5 6

hierarchical Connectivity 3.8972 8.6143 21.5067 28.7341 32.0742Dunn 0.4880 0.5451 0.4734 0.4919 0.4919Silhouette 0.2756 0.2713 0.2461 0.2531 0.2611

k-means Connectivity 3.8972 8.6143 22.2504 28.7722 37.2702Dunn 0.4880 0.5451 0.3529 0.3666 0.3785Silhouette 0.2756 0.2713 0.2518 0.2673 0.2654

pam Connectivity 3.8972 8.6143 27.6004 34.0972 40.2456Dunn 0.4880 0.5451 0.3459 0.3648 0.4060Silhouette 0.2756 0.2713 0.2347 0.2554 0.2766

Optimal Scores:Score Method Clusters

Connectivity 3.8972 hierarchical 2Dunn 0.5451 hierarchical 3Silhouette 0.2766 pam 6Stability validationClustering Validation Cluster sizemethod measure 2 3 4 5 6hierarchical APN 0.0000 0.0170 0.0033 0.0261 0.0856

AD 1.8397 1.6874 1.5195 1.4357 1.4002ADM 0.0000 0.0274 0.0059 0.0822 0.2009FOM 0.1643 0.1603 0.1417 0.1414 0.1416

k-means APN 0.0000 0.0157 0.0004 0.0285 0.0458AD 1.8397 1.6874 1.5114 1.4150 1.3852ADM 0.0000 0.0274 0.0008 0.0781 0.2126FOM 0.1643 0.1603 0.1409 0.1390 0.1394

pam APN 0.0253 0.1682 0.1194 0.1503 0.0705AD 1.8569 1.7598 1.5824 1.4950 1.3373ADM 0.0519 0.3013 0.2162 0.3015 0.1332FOM 0.1636 0.1605 0.1468 0.1397 0.1347

Optimal Scores:Score Method Clusters

APN 0.0000 hierarchical 2AD 1.3373 pam 6ADM 0.0000 hierarchical 2FOM 0.1347 pam 6

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scaffold with a hydrogel used to attach the cells. These cells are grown in a bioreactor

breeding vessel which enables the cells to grow and generate tissues and organs. After-

ward, the scaffold is biodegraded. The tissues and cells collection branch is similar to the

soft organ category. This category is unique innovation and is included as an AM

application.

Bio-AM technology evolution analysis

The clusters and evolution pathways for bio-AM patents and research projects using the

concept lattice algorithm are shown in Fig. 4. The derived IP portfolio’s (including patents

and MOST projects) evolution for the adjusted sub-domain clusters are depicted in this

figure. The evolution of the bio-AM sub-domain for surgical guide production and mod-

eling are described in Fig. 5.

As shown in the example case in Fig. 5, the bio-AM IPs for the surgical guide sub-

domain include two US patents and two MOST projects (detailed in Table 5) and th

evolutionary relationships are drawn using the concept lattice algorithm. The key terms of

each patent/project are shown in Fig. 4 and are highlighted in the boxes.

Patent 8352056 illustrates a method of manufacturing a surgical implant guide to

increase the precision, safety and reliability of the surgery. Patent 8758357 discloses a

system and method for developing customized apparatus for use in one or more surgical

procedures. The common key terms of these two patents are data, image, surgical, implant,

and guide. With these key terms as concept lattices, it indicates that these two patents are

using image data to manufacture surgical implant guides. PJ17 describes the clinical

applications of manufacturing bone structures. The common key terms for patent 8352056

and PJ17 are medical, image, tissue, surgical, and guide, which identify their strong

evolutionary link algorithmically. PJ21 develops software for 3D modeling to create

surgical guide fixtures and other devices for high-precision clinical applications. The

Fig. 3 The ontology schema of bio-AM technologies and key terms

150 Scientometrics (2017) 111:139–157

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common key terms with patent 8352056 are medical, image, surgical, and guide. These key

terms identify PJ21 as an application for surgical guides and models.

Patent 8352056 describes planning procedures for implanting screws into affected tis-

sues that can be used to construct replacement teeth. Patent 8758357 describes a patient

specific pedicle screw guide that anatomically mates with the spinous processes of a

particular vertebral body (key terms: anatomic, pedicle screw). PJ17 is the clinic appli-

cation of additive manufacturing bone (key terms: bone, screw, orthopedic). PJ21 describes

not only a surgical guide but also the clinic application of the pedicel screw. These four

Fig. 4 The evolution map of patents and projects in three sub-domain clusters

Fig. 5 The key terms indicate the evolving links between patents and projects

Scientometrics (2017) 111:139–157 151

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patents and projects are linked by the evolutionary relationship. The content describes

applications for surgical guides and models. An important discovery for this research is

that patent 8352056 is from a Taiwan assignee (Chang-Gung University). This patent was

developed earlier than the other patents and indicates that we have developed significant

bio-AM information technology that provides a strategic advantage for manufacturing and

commercializing related surgical applications.

Conclusions

This research uses additive manufacturing (AM or called 3D printing) applied in the

biomedical field as a case example to construct the domain ontology and perform IP

portfolio evolution analysis. The algorithms have advantages and limitations. The

advantages are that the approach is a computer-supported system, which can objectively

mine knowledge text and data from patent and project documents. The system can conduct

key terms extraction and perform document similarity analysis. The documents are clus-

tered and the evolutionary trends of technologies, based on the text mining, can be

interpreted and extrapolated. The evolution analysis is to calculate the similarity between

documents over time with solid evidence that they are correlated beyond a threshold value.

The key terms of each document can be interpreted for their context and meaning. The

limits of this method are the sample of documents and the selection of key terms may not

be a complete representation of the body of knowledge under investigation. Although this

research defines the rules of selecting key terms, the results require a domain expert to

check and validate the results. For future research, additional validation and verification

methods of the analytical results will be derived and tested to ensure valid and reliable

decision support for defining IP trends and R&D strategies.

Acknowledgements This research is partially supported by the Ministry of Science and Technologyresearch Grants (MOST 104-2218-E-007-015-MY2).

Table 5 Patent/project number and titles related to bio-AM surgical guide and modeling sub-domain

Patent/projectnumber

Title

8352056 Surgical implant guide and method of manufacturing the same

8758357 Patient matching surgical guide and method for using the same

PJ17 Clinical applications of geometry-based techniques on musculoskeletal disorders—aninvestigation on forearm brace manufacture and fracture reduction

PJ21 Software development in 3d modeling and manipulation for precisely clinicalapplications

152 Scientometrics (2017) 111:139–157

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Appendices

Appendix 1

Patent evolution algorithm.

PatentList = Sort ( PatentCotext, IssuedDate ) for( i = 1 to X of patents) //From 1 to x of patent {if (PatentList (i) is composed of new key terms ) {Make_Node ( Property = PatentList(i), Radius = r )#Make a node for ith patent without linkage, which radius is r} else if (PatentList (i) is composed of new and existing key terms ) {Make_Node ( Property = PatentList(i), Radius = r )#Make a node for ith patent //Using solid or dotted line to link to related patents if ( Related_Node Similarity > = threshold ) {if(Common Keyterms appear in all branch node) {Link ( Related Node, Property is solid line )} else {Link ( Related Node, Property is dotted line )}}} else {if (PatentList (i) is composed of existing key terms in the same year )#Add the patent to existing node {Update_existingNode ( Property add PatentList(i), Radius = r* the number of patents in the node )} else {Make_Node ( Property = PatentList(i), Radius = r )#Make a node for ith patent //Using solid or dotted line to link to related patents if ( Related_Node Similarity > = threshold ) { if(Common Keyterms appear in all branch node) { Link ( Related Node, Property is solid line ) } else { Link ( Related Node, Property is dotted line )}}}}}

Appendix 2

See Table 6.

Scientometrics (2017) 111:139–157 153

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Appendix 3

See Table 7.

Table 6 Top 56 key terms with assigned IDs

ID Keyterms ID Keyterms ID Keyterms ID Keyterms

K1 Implant K16 Polymer K31 Reconstruct K46 Limb

K2 Scaffold K17 Metal K32 Composite K47 Simulate

K3 Bone K18 Leg K33 Liver K48 Knee

K4 Guide K19 Tissue K34 Biocompatible K49 Ligament

K5 Model K20 Anatomic K35 Medical K50 Cartilage

K6 Joint K21 Prosthetic K36 Condylar K51 Lung

K7 Porous K22 Elastomer K37 Splint K52 Polycaprolactone

K8 Bioprint K23 Ink K38 Graft K53 Polyurethane

K9 Data K24 Virtual K39 Jaw K54 Skull

K10 Image K25 Digital K40 Tibial K55 Cellulose

K11 Surgical K26 Prosthesis K41 Pedicle K56 Cardiovascular

K12 Mold K27 Contact K42 Engineering

K13 Bio K28 Acid K43 Biodegradable

K14 Orthopedic K29 Femoral K44 Screw

K15 Articular K30 Cell K45 Osteotomy

Table 7 K medoids clustering result (6 clusters)

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6

PJ02 PJ10 PJ12, PJ22 PJ14, PJ16 PJ17, PJ21 US8036729

US7968026 US8931880 US8532806 PJ23 US8350186 US8545569

US8071007 US9039998 US8551173 US8086336 US8352056 US8617242

US8090540 US9149952 US8679189 US8142886 US8425227 US8682052

US9034378 US9227339 US8843229 US8172907 US8496663 US8715291

US9168328 US8888862 US8236350 US8706285 US8735773

US9255178 US9015922 US8303746 US8758357 US9237950

US9211129 US8366789 US8790408

US9241772 US8369925 US8984731

US8454705

US8457930

US8470231

US8527244

US8582841

US8623397

US8691974

US8775133

US8852192

US8868226

154 Scientometrics (2017) 111:139–157

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Appendix 4

See Table 8.

Table 8 The adjusted clusters with three sub-domain interpretations

Tissue engineering/bioprint Surgical guide/model Implant/prosthesis

PJ02, PJ10 PJ17, PJ21 PJ12, PJ16

PJ14, PJ23 PJ22 US8036729

US7968026 US8352056 US8086336

US8071007 US8369925 US8142886

US8090540 US8425227 US8172907

US8236350 US8496663 US8303746

US8470231 US8527244 US8350186

US8582841 US8706285 US8366789

US8623397 US8758357 US8454705

US8691974 US8790408 US8457930

US8931880 US8843229 US8532806

US9034378 US8984731 US8545569

US9039998 US9241772 US8551173

US9043191 US8617242

US9149952 US8679189

US9168328 US8682052

US9180029 US8715291

US9222932 US8735773

US9227339 US8775133

US9255178 US8852192

US8868226

US8888862

US8920512

US8974535

US8992825

US9015922

Table 7 continued

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6

US8920512

US8974535

US8992825

US9043191

US9060810

US9180029

US9222932

US9226827

Scientometrics (2017) 111:139–157 155

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