1) The author is building a harmonized international trademark database by linking equivalent trademarks across national trademark office databases from different countries. 2) So far the database includes over 8 million trademarks from the US, Australia and New Zealand trademark offices spanning over 100 years. Additional country offices will be added. 3) The author developed an algorithm to identify equivalent trademarks across jurisdictions that uses trademark application data like text, filing date and classification to group likely equivalent trademarks. This algorithm is being improved further with machine learning techniques.

1. Building a harmonised
international trademark
database
Stephen M. Petrie – Centre for Transformative Innovation, Swinburne University of Technology
Background
Trademark application data can provide an evidence base to
inform government policy regarding international issues such
astrademarksquatting,cluttering,anddilution.Trademarkdata
can also produce quantitative insights into economic trends
andbranddynamics1
.Currently,nationaltrademarkdatabases
can provide insight into economic and brand dynamics at the
national level, but gaining such insight at an international
level is more difficult due to a lack of internationally linked
trademark data.
We are in the process of building a harmonised international
trademark database (the “Patstat of trademarks”), in which
equivalent trademarks have been identified across national
offices. This will provide a quantitative evidence base for
economics research, brand research, and government policy.
Office Filing date TM text
Nice
Class
USPTO 2011-03-09 NATURAL INSTINCT 31
IPA 2008-01-09 NI NATURAL INSTINCT 3
IPA 2010-05-11 NI NATURAL INSTINCT 3, 5
IPA 2010-05-11 NATURAL INSTINCT 3, 5
IPA 2012-01-18 NI NATURAL INSTINCT 3, 5
IPA 2012-10-18 NATURAL INSTINCT 3
IPONZ 2006-08-18 Natural Instinct 5
IPONZ 2009-07-23 Natural Instinct 31
IPONZ 2013-02-14 NATURAL INSTINCT 3
IPONZ 2015-08-24 NATURAL INSTINCT 3
Pilot database
Linking algorithm
Impact
Improving linking with machine learning
We have developed a pilot database that incorporates
6.4 million U.S., 1.3 million Australian, and 0.5 million New
Zealand trademark applications, spanning over 100 years.
The database will be extended to incorporate trademark data
from other participating intellectual property (IP) offices as
they join the project. Confirmed partners currently include
the United Kingdom, Canada, WIPO, and EUIPO.
In addition to building the pilot database, we have developed
a linking algorithm that identifies equivalent trademarks
(TMs) across jurisdictions. The algorithm uses TM application
data — such as TM text, filing date, and Nice classification
— to categorise applications into groups that are likely to
include equivalent TMs. A blocking (or “binning”) procedure is
used in conjunction with hash tables to efficiently group TM
applications by similar TM text (e.g. see Table 1). This reduces
thenumberofpair-wiseTM-TMlinksthatneedtobeprocessed
and thereby improves the efficiency of the algorithm, while
reducing false-positives.
When complete, the internationally linked trademark
database will enable researchers and policy-makers to easily
investigate trademark application dynamics — and hence
economic and brand dynamics — at the level of individual
firms, entire sectors, whole countries, or globally.
A major part of improving the TM linking algorithm will involve
combining it with a novel machine learning algorithm we
recently developed, which uses an image classification neural
network2
that we have modified to match inventor names in
patent records. The inventor-matching algorithm involves:
1. converting certain text data (e.g. inventors’ last names)
from a given pair of inventor-name records into an abstract
image representation (see Figure 1),
2. training the neural network to distinguish between
“matched” images (i.e. same inventor; Figure 1a) and “non-
matched” images (different inventors; Figure 1b).
Theinventor-matchingformofthemachinelearningalgorithm
has very low false positive and false negative error rates (~2%),
and we are now adapting it to perform TM matching.
1. Schautschick P & Greenhalgh C (2016), Empirical studies of trade marks – the existing economic literature,
Economics of Innovation and New Technology, 25(4): 358-390
2. Krizhevsky A, Sutskever I & Hinton G (2012), ImageNet Classification with Deep Convolutional Neural
Networks, Advances in Neural Information Processing Systems, 25: 1097-1105
Centre for
Transformative
Innovation
Current algorithm performance
Thealgorithm’sperformancecanbetestedusingequivalentTMs
thatareknowntobelinkedapriori,duetosharedinternational
registration number. The algorithm successfully identifies
~ 97% of these “Known Links”. Additionally, the number of
applications within the candidate positive links identified by
the algorithm (1.04Mn U.S., 534k Australian, and 287k N.Z.
applications) is much larger than the number of applications
within the a priori Known Links (39k U.S., 65k Australian, and
35k N.Z. applications). Current estimates indicate that ~40%
of candidate positive links identified by the algorithm are false
positives. However, we expect the false positive proportion
to become far smaller as we continue to improve the linking
algorithm by, for example, including TM images.
Figure 1: An example of an abstract image generated for (a) two matched records (same
inventor), and (b) two non-matched records (diff. inventors). One name is drawn in red and
the other in green (top row), with blue indicating the start of each word. The two images are
then overlayed to produce the final image (bottom row).
Table 1: An example of a linked bin (for “NATURALINSTINCT”), in which 10 applications
(rows) submitted to the U.S. (USPTO), Australian (IPA), and New Zealand (IPONZ) offices
have been grouped together by the linking algorithm.
L M N H W
D C K/Q B P
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PETRI
L M N H W
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PETRIE
L M N H W
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SMITH
L M N H W
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PETRIE
(a) (b)