UK: EPO Review 2018 - Machine Understanding: Neural Networks, Machine Learning And Artificial Intelligence

A recent EPO report1 talks about "A new era of technological development characterised by digital transformation", based on "information and communication technologies" ("ICT") and amounting to a "fourth industrial revolution". The present review looks at three specific aspects of ICT – neural networks, machine learning and artificial intelligence – which the EPO report groups together as "enabling machine understanding".

Developments of these aspects may relate to their implementing hardware and software or to any of the extensive range of their possible applications, for example from assisting medical diagnosis to image recognition to natural language understanding to operating wind turbines to playing the game of go. This means that capture of relevant patents and applications using the International Patent Classification (IPC) is challenging, as incidentally illustrated by the EPO report.

This review takes a simple and direct approach: using full texts2 and keywords "neural network", "machine learning" and "artificial intelligence"3, searches for European patents having patent (B1) publication dates over the 10-year period 2008 to 2017 were carried out.

5052 patents published (B1) in that 10-year period reference at least one keyword, 750 with references in claims.

The chart below gives counts by year of patents referencing any of the keywords anywhere in titles, abstracts, descriptions or claims, and counts of those with references in claims. The counts increased significantly in 2016/2017.

The increase should be seen in the context of the overall increase in total grants (patents published) since 2015.

The top ten IPC subclasses of the keyword patents include A61B (Diagnosis; Surgery; Identification), C12Q (Measuring or Testing Processes involving Enzymes, Nucleic Acids or Microorganisms), A61K (Preparations for Medical, Dental, or Toilet Purposes) and C12N (Microorganisms or Enzymes; Compositions thereof), showing the growing importance of bioinformatics.

Rank IPC Subclass No. of Patents Rank IPC Subclass No. of Patents
1 G06F 688 6 G06T 321
2 G01N 589 7 C12Q 317
3 A61B 473 8 A61K 287
4 G06K 375 9 G05B 279
5 H04L 354 10 C12N 264

Top patentees for patents referencing the keywords are listed in the table below.

Patentee Keyword Patents Publd (B1) 2008-2017 Keyword(s) in Patents claims
Qualcomm 121 3
Siemens 119 47
Philips 111 12
Microsoft 102 23
Honda 66 14
Samsung 64 7
Sony 54 8
Roche 51 2
GE 50 6
Ericsson 46 0
Rockwell Automation 43 13
Robert Bosch 40 4
ABB 39 12
Celera Corp 34 0
Fraunhofer Ges 33 3
Alcatel Lucent 32 6
BASF Enzymes 32 0
Nokia 32 7
Canon 30 5
Xerox 29 3
Google 27 1
Deutsche Telekom 26 0
Novozymes 25 0
LG 22 2
Panasonic 22 7
Huawei 21 2
BP 20 1
Honeywell 20 6
Astute Medical 19 0
Fisher Rosemount 19 1
Verenium Corp 19 0
British Telecomms 18 1
Fujifilm 18 3
Nestec 18 3
IBM 17 5
Telecom Italia 17 3
Volkswagen 17 3
Avaya 16 0
Fujitsu 16 1
Harman Becker Auto 16 4
Agensys 15 0
Blackberry 15 1
Boeing 15 3
Commissariat A L Energie Atomique ETC 15 3
Medimmune 14 0
United Technologies 14 1

The charts on the right give counts by year of patents referencing each of the keywords anywhere in titles, abstracts, descriptions or claims, and counts of those with references in claims.

As also seen above, the levels of patents referencing the keywords in claims are a fraction (ca. 15%) of the levels of patents referencing the keywords anywhere in titles, abstracts, descriptions or claims. Overall, of patents referencing the keywords in claims only around 30% reference the keywords in claim 1.

This might suggest that inventions involved are less often focused on developments to "neural network", "machine learning" or "artificial intelligence" devices or processes as such, but rather more often concerned with developments which may incidentally or optionally involve such devices or processes as component parts or steps in something aimed at achieving some further "technical" goal (e.g. operating wind turbines or image recognition). This may reflect an expectation that claiming developments to "neural network", "machine learning" or "artificial intelligence" devices or processes per se could challenge the limits of what the EPO regards as "technical" (as opposed to unpatentably "non-technical", e.g. "as such" programs for computers or mathematical methods). However, an improved AI system per se, if functioning as operational layer software which improves the way a computer itself operates, may well be seen as technical by the EPO. Otherwise, directing claims to a further "technical" goal employing the "neural network", "machine learning" or "artificial intelligence" might be expected to mitigate problems at the EPO.

In any event, the trend for patents linked to machine understanding through the keywords "neural network", "machine learning" and "artificial intelligence" has recently been upwards, though the levels of patents concerned are as yet very small fractions still of total European patents.


1. "Patents and the Fourth Industrial Revolution: the inventions behind digital transformation" December 2017

2. As available on the EPO's online EP full text search database ( )

3. The exact terms or near, in English, French and German

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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