SPURRING
INNOVATION
While innovation is booming in machine learning
companies need to be aware of the importance of
effective patent protection, as Karl Barnfather and
Harry Strange explain
New research data has
confirmed that innovation
activity linked to machine
learning (ML) is gathering pace
in Europe, potentially allowing
businesses to realise value from their
investment in artificial intelligence (AI)
technologies sooner rather than later.
Research conducted by European
intellectual property firm, Withers &
Rogers, has revealed that a record
number of patent applications linked
to machine learning were published
by the European Patent Office (EPO)
in 2019. The actual number of patent
publications related to this field
has increased from around 1,000
publications in 2016, to around 3,000
publications in 2019 – an increase of
more than 200 percent in the space of
three years.
This activity is being fuelled by
growing cross-sector interest in the
potential of AI technologies to drive
value by improving business’ process
and workflow efficiency.
For electronics manufacturers
and other OEMs, the main area of
potential lies in automation, which is
already possible to a limited degree
using traditional computers. Now,
with the rise of AI and the application
of machine learning, it has become
possible to automate much more;
generating far greater enterprise
value. While much progress has been
made to date in the development
of algorithms and their application
to solve real-world problems, many
experts believe we are only beginning
to see the impact of this field of
science.
To help strengthen the industry’s
understanding of the direction of
ML-related innovation, Withers &
Rogers has developed a means
of categorising individual patent
applications according to their chief
area of focus. There are essentially
five categories of ML-related
innovation – data processing methods;
underlying algorithms; training
methods; computing platforms; and
the field of application.
Close analysis of the data by
category indicates that the largest
number of patent applications filed
at the EPO, are directly linked to
real-world applications, where a ML
invention has been developed for use
in solving a specific problem. This is
unsurprising as a critical test of patent
eligibility is an invention’s technicality
and its ability to solve a technical
problem.
One of the biggest areas of
growth for innovation activity linked
to machine learning in recent years is
ML-specific hardware. Over 13 percent
of all ML-related patents published
by the EPO over the last three years
have been directed to the ‘computing
platform’ category. Whilst the majority
of these patent applications have
been made by big names such as
Intel, Nvidia, and Qualcomm, a
significant proportion are from newer
players such as Graphcore, Kalray,
and Cerebras.
This high level of interest and
investment in hardware, which is
purpose built for machine learning
approaches such as deep learning, is
expected to continue.
Patent applications
In the case of the underlying
algorithms, many of these are
inventive in their own right and
it is good to see that over 16
percent of all ML-related patent
applications published by the EPO
over the last three years fall into the
‘algorithm’ category. Importantly,
these applications are not just being
filed, the data shows that many of
these applications are also being
granted. This means it is possible for
companies to obtain patent protection
for innovative ML algorithms, provided
the applications have been well
drafted, address technical problems
and provide technical details of the
algorithm’s implementation.
Greater knowledge of the success
of such patent applications should
help to spur on innovation activity.
New guidelines published by the
European Patent Office (EPO) on
1st November 2019 will also help
to smooth the way for innovators to
secure patent protection for AI and ML
inventions.
Karl Barnfather The new ‘Guidelines for
30 14 January 2020 www.newelectronics.co.uk
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