A study of Patent Thickets (1.31Mb) - UK Intellectual Property Office

72 A Study of Patent Thickets
Since patent records do not include the registered number of a company even if the applicant
is a registered business, it is not possible to merge data sets using a unique firm identifier;
instead, applicant names in the IP documents and firm names in FAME have to be matched.
Both a firm’s current and previous name(s), were used for matching in order to account for
changes in firm names. Matching on the basis of company names requires names in both data
sets to be `standardized’ prior to the matching process in order to ensure that small (but often
systematic) differences in the way names are recorded in the two data sets do not impede the
correct matching. For more details on the matching see Helmers et al. (2011).
7.2 Methodological analysis of the triples
measure
This appendix contains results of a statistical test of the triples measure similar to that provided
by Milo et al. (2002; 2004a) . As discussed in Section 3.2.2 above counts of triples have been
shown to be statistically significant measure of the structure of networks in a number of contexts
such as the World Wide Web. To date such an analysis has not been performed in the context
of patent data. The aim of the analysis is to determine whether triples or any other network motif
are likely to arise with such frequency randomly that their occurrence in the real data we analyze
cannot be interpreted as a signal of any kind of real structure.
We use the FANMOD software developed by Wernicke and Rasche (2006) to count and test the
significance of various network motifs. The test of the significance of these motifs is undertaken
by comparing the frequency of occurrence of a given motif in the data obtained from the EPO
with the occurrence of the motif in 1000 comparison datasets obtained by perturbing the original
data randomly. We set the parameters of the software such that the randomly created
comparison data preserve the characteristics of the original data as closely as possible.
To perform this test we segmented patent applications at the EPO into 34 technology areas
based on the 2008 version of the ISI-OST-INPI technology classification (Schmoch, 2009). We
then used data on critical references in three of these areas (Telecommunications, Optics and
Medical Technology) for the periods 1997-1999 and 2003-2005 to analyze the significance of
different network motifs as measures that could characterize the structure of the underlying
data. We chose these three technology areas because it is well known that Telecommunications
is affected by a patent thicket, while we expect Optics and Medical Technology to be much less
affected. In the next subsection we show that Optics presents an intermediate case between
Telecommunications and Medical Technology.
Area Years Z-Score
Medical Technology 1997-99 115.6
Optics 1997-99 24.8
Telecommunications 1997-99 9.7
Medical Technology 2003-05 81.6
Optics 2003-05 39.2
Telecommunications 2003-05 9.0
[Table 7.1: Z-Scores for the Triples Measures in Selected Technology Areas]