Google And Samsung Top The List Of Applicants For AI-related Patents ...
The term “artificial intelligence” is sometimes used in European patent applications and patents without further explanation or elaboration, AI apparently being taken to be no more than a known, off-the-shelf option. It is very unlikely that such applications and patents are concerned with developments in AI.
The International Patent Classification (IPC) helps us here. It has an extensive dictionary in which catchwords are linked to classifications. The catchword “Artificial Intelligence” is linked to only one class: G06N. That at least covers machine learning and neural networks, technologies at the core of developments related to artificial intelligence.
The details of the technologies covered by G06N are given further below. Applications and patents given the classification G06N provide a useful indication of EPO-related trends.
AI applications at the EPO growing
There is huge growth in AI filings at the EPO. The chart below shows the numbers of European patent applications having the classification G06N which were published year by year from 2010 to 2020.
Growth in the numbers of published G06N applications took off from 2014. Taking that year as the base, the number of published G06N applications in 2020 was over 12 times the level of 2014. This means that the percentage growth in published G06N applications over that period has been far greater than the percentage growth in total European patent applications published (shown in a comparison graph below). Also, Covid-19 does not appear to have affected this growth.
The applicants
Between 2016 and 2020, just over 4,000 AI (G06N) applications were published. Around 1,000 different applicants were responsible for them. The top 25 applicants are listed in the table below. They accounted for just about half of all applications.
Rank | Applicant | No. of AI (G06N) Applications 2016-2020 |
---|---|---|
1 | 266 | |
2 | Samsung Group | 187 |
3 | Microsoft Technology Licensing LLC | 170 |
4 | Intel Corporation | 140 |
5 | Siemens Group | 140 |
6 | DeepMind Technologies Limited | 91 |
7 | Cambricon Technologies Group | 90 |
8 | Fujitsu Limited | 81 |
9 | Qualcomm Incorporated | 81 |
10 | Huawei Technologies Co. Ltd. | 68 |
11 | StradVision, Inc. | 67 |
12 | Sony Group | 61 |
13 | Robert Bosch GmbH | 58 |
14 | Accenture Global Group | 46 |
15 | Nokia Group | 44 |
16 | Tata Consultancy Services Limited | 41 |
17 | Koninklijke Philips N.V. | 39 |
18 | Baidu Group | 37 |
19 | HRL Laboratories LLC | 32 |
20 | Commissariat à l'Energie Atomique et aux Energies Alternatives | 31 |
21 | Panasonic Group | 31 |
22 | Advanced New Technologies Co., Ltd. | 27 |
23 | General Electric Group | 25 |
24 | Hitachi Group | 24 |
25 | Northrop Grumman Systems Corporation | 24 |
Number of grants increasing but lower than in other fields
Apart from grant as a patent, processing of a European patent application may be concluded by the application being refused, withdrawn or deemed withdrawn. The charts below show year by year numbers of AI (G06N) applications processed to conclusions.
For AI (G06N) applications processed to conclusions, the proportion granted as EP patents has been increasing. For AI (G06N) applications concluded in 2019 and 2020 grant as patents was the most likely outcome but was still the outcome for less than half of the applications. The 40% grant rate for AI (G06N) applications in 2020 well behind the EPO’s overall 69% grant rate in the same year, while the 16% refusal rate in 2020 was far higher than the overall 4% EPO refusal rate.
These statistics are likely to be caused by the classification of AI at the EPO as a mathematical method which is not technical and cannot support an inventive step unless linked to implementation or specific technical application. The percentage outcomes for AI and all European applications are shown clearly below.
Although the statistics indicate that, in general, grant is less likely and refusal more likely for AI applications than for EPO applications overall, outcomes do vary greatly for different AI applicants.
The outcomes of applications processed to conclusions over the five-year period 2016 to 2020 for different AI applicants/patentees are indicated in the chart below. The proportion of patents granted varies from 15% to 74% and the proportion of applications refused ranges from 5% to 35%.
Different applicants/patentees also appear to have quite different policies regarding withdrawal of applications – two of the 10 applicants did not positively withdraw any applications as opposed to allowing them to be deemed withdrawn. Other applicants have positively withdrawn good proportions of their applications (which may be as a result of a general policy, or a reaction to poor prospects for success).
The technologies covered by AI patent applications at the EPO
The IPC can help us again here. The 4,000 European patent applications having G06N IPC classifications published between 2016 and 2020 have lead classifications and, usually, further classifications along with these. The 4,000 lead classifications are spread over more than 700 different individual classifications and the applications have a total of around 15,000 classifications spread over about 2,000 different individual classifications.
The detailed hierarchy of IPC classifications of the technologies covered by G06N is given in the table below, along with the number of occurrences of the classifications across the 4,000 European patent applications. By a clear margin the most frequent classification, G06N 3/04, concerns architecture of neural network models, followed by learning methods for neural network models
International Patent Classification G06N Computer systems based on specific computational models (Catchword: “Artificial Intelligence”) | Occurrences of this classification as lead IPC | Total occurrences of this classification |
---|---|---|
G06N 3/00 Computer systems based on biological models | 84 | 253 |
• G06N 3/02 using neural network models | 77 | 289 |
• • G06N 3/04 Architecture, eg interconnection topology | 643 | 1502 |
• • G06N 3/06 Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons | 12 | 31 |
• • • G06N 3/063 using electronic means | 220 | 546 |
• • • G06N 3/067 using optical means | 7 | 8 |
• • G06N 3/08 Learning methods | 247 | 1258 |
• • G06N 3/10 Simulation on general purpose computers | 15 | 50 |
• G06N 3/12 using genetic models | 15 | 83 |
G06N 5/00 Computer systems using knowledge-based models | 47 | 226 |
• G06N 5/02 Knowledge representation | 85 | 248 |
• G06N 5/04 Inference methods or devices | 53 | 255 |
G06N 7/00 Computer systems based on specific mathematical models | 52 | 314 |
• G06N 7/02 using fuzzy logic | 0 | 8 |
• • G06N 7/04 Physical realisation | 2 | 5 |
• • G06N 7/06 Simulation on general purpose computers | 3 | 5 |
• G06N 7/08 using chaos models or non-linear system models | 3 | 5 |
G06N 10/00 Quantum computers, i.e. computer systems based on quantum-mechanical phenomena | 97 | 149 |
G06N 20/00 Machine learning | 82 | 522 |
• G06N 20/10 using kernel methods, eg support vector machines [SVM] | 6 | 48 |
• G06N 20/20 Ensemble learning | 26 | 110 |
G06N 99/00 Subject matter not provided for in other groups of this subclass | 214 | 555 |
G06F 15/18 Digital computers/data processing equipment in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines Most of subclass G06N was added to the IPC in the year 2000 but groups and subgroups G06N 10/** and G06N 20/** were not added until 2019. Parts of the subject-matters of what are now G06N 10/** and G06N 20/** were formerly included in G06N 99/00 but machine learning was formerly included in G06F 15/18 (now subsumed into G06N 20/**). | 17 | 40 |
Where AI is being used, according to EPO applications
AI has found use across many fields, from internet search engines through self-driving cars, to medical diagnostics, finance and even agriculture. If potential fields of use of a development in AI are only mentioned in the description in a European patent application, they may not be reflected in an IPC classification applied to the application. Nonetheless, and particularly if a potential field of use appears in a claim of the application, this may be reflected in an IPC classification applied to the application, either as lead classification or as a further classification, which is not a G06N classification.
The most common non-G06N classifications applied to the 4,000 European patent applications published between 2016 and 2020 are indicated in the table below. The most frequent classifications relate to pattern recognition and image analysis. Of course, these techniques can, in turn, be used in many different contexts.
IPC Classification | Occurrences of this classification as Lead IPC | Total occurrences of this Classification | |
---|---|---|---|
G06K 9/00 | 99 | 320 | Methods or arrangements for reading or recognising printed or written characters or for recognising patterns; eg, fingerprints |
G06K 9/46 | 28 | 147 | Extraction of features or characteristics of the image |
G06K 9/62 | 56 | 337 | Methods or arrangements for recognition using electronic means |
G06F 17/30 | 49 | 113 | Database structures for information retrieval for digital computing or data processing equipment or methods |
H04L 29/06 | 49 | 164 | Communication control/Communication processing characterised by a protocol |
G06F 9/50 | 46 | 91 | Allocation of resources; eg, of the central processing unit [CPU] in arrangements for program control; eg, control units |
G06F 3/01 | 37 | 84 | Input arrangements or combined input and output arrangements for interaction between user and computer |
G06Q 10/06 | 29 | 70 | Resources, workflows, human or project management; eg, organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models |
B25J 9/16 | 27 | 46 | Program controls for program-controlled manipulators |
G06F 17/27 | 26 | 60 | Automatic analysis; eg, parsing, orthograph correction, when handling natural language data |
H04L 29/08 | 17 | 91 | Transmission control procedure; eg, data link level control procedure, for communication control/communication processing |
G06Q 30/02 | 25 | 86 | Marketing; eg, market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination |
G06T 7/00 | 19 | 77 | Image analysis |
A61B 5/00 | 15 | 64 | Measuring for diagnostic purposes in medical or veterinary science |
Some of the recent AI applications granted at the EPO
Below are the 32 AI (G06N) patents granted in November 2021, with links to the European Patents Register.
That use for AI has been found across many fields could be illustrated by the patents granted in November, which have concerns from cell (biological) analysis and motor vehicle loss assessment (for insurance purposes), through optimisation of mobile phone networks, to detecting whether a self-driving vehicle is travelling in a one-way street.
AI (G06N) Patents Granted in November 2021
Patent No. | Patentee | Title |
---|---|---|
3262417 | Cellanyx Diagnostics, LLC | Cell Imaging And Analysis To Differentiate Clinically Relevant Sub-Populations Of Cells |
3444758 | Cambricon Technologies Corporation Limited | Discrete Data Representation-Supporting Apparatus And Method For Back-Training Of Artificial Neural Network |
3471005 | Nokia Technologies Oy | Artificial Neural Network |
3520045 | Advanced New Technologies Co., Ltd. | Image-Based Vehicle Loss Assessment Method, Apparatus, And System, And Electronic Device |
3292471 | Hasan, Syed Kamran | Method And Device For Managing Security In A Computer Network |
3407265 | Cambricon Technologies Corporation Limited | Device And Method For Executing Forward Calculation Of Artificial Neural Network |
3619652 | Midea Group Co., Ltd. | Adaptive Bit-Width Reduction For Neural Networks |
3392809 | Accenture Global Solutions Limited | Quantum Computing Machine Learning Module |
3469496 | Neura, Inc. | Situation Forecast Mechanisms For Internet Of Things Integration Platform |
3557484 | Shanghai Cambricon Information Technology Co., Ltd | Neural Network Convolution Operation Device And Method |
3557487 | ZF Friedrichshafen AG | Generation Of Validation Data With Generative Contradictory Networks |
3636001 | Huawei Technologies Co., Ltd. | Optimizing Cellular Networks Using Deep Learning |
3686837 | StradVision, Inc. | Learning Method And Learning Device For Reducing Distortion Occurred In Warped Image Generated In Process Of Stabilizing Jittered Image By Using GAN To Enhance Fault Tolerance And Fluctuation Robustness In Extreme Situations |
3703332 | Advanced New Technologies Co., Ltd. | Graph Structure Model Training And Junk Account Identification |
3706267 | ABB Schweiz AG | Artificial Intelligence Monitoring System Using Infrared Images To Identify Hotspots In A Switchgear |
3719447 | Honeywell International Inc. | Deep Neural Network-Based Inertial Measurement Unit (IMU) Sensor Compensation Method |
2973248 | PPG Industries Ohio, Inc. | Systems And Methods For Determining A Coating Formulation |
3192017 | Northrop Grumman Systems Corporation | Tunable Transmon Circuit Assembly |
3291090 | Deutsche Telekom AG | Method And System For Forming A Digital Interface Between Terminal And Application Logic Via Deep Learning And Cloud |
3301611 | STMicroelectronics S.r.l. | Artificial Neural Networks For Human Activity Recognition |
3343392 | INTEL Corporation | Hardware Accelerator Architecture And Template For Web-Scale K-Means Clustering |
3398295 | Dish Technologies L.L.C. | Systems And Methods For Bandwidth Estimation In Oscillating Networks |
3399431 | ServiceNow, Inc. | Shared Machine Learning |
3399716 | ServiceNow, Inc. | Network Security Threat Intelligence Sharing |
3490449 | Tata Consultancy Services Limited | System And Method For Aiding Communication |
3542322 | Google LLC | Management And Evaluation Of Machine-Learned Models Based On Locally Logged Data |
3611472 | Mobileye Vision Technologies Ltd. | Controlling Host Vehicle Based On Detected Parked Vehicle Characteristics |
3627213 | Eagle Technology, LLC | Multi-Channel Laser System Including An Acousto-Optic Modulator (AOM) With Beam Polarization Switching And Related Methods |
3631623 | Microsoft Technology Licensing, LLC | Tensor Processor Instruction Set Architecture |
3662515 | International Business Machines Corporation | Josephson Junctions For Improved Qubits |
3671526 | Accenture Global Solutions Limited | Dependency Graph Based Natural Language Processing |
3783477 | Cambricon Technologies Corporation Limited | Integrated Circuit Chip Device |
AI patents being opposed at the EPO
Since 2010, only nine AI (G06N) European patents have been opposed, as listed in the table below.
Two oppositions have been finally decided by first instance decisions (1825424 – Opposition rejected, no appeal; 2748686 – Opposition rejected, appeal withdrawn). No oppositions have yet been finally decided after appeal.
The last four oppositions listed in the table appear to be “straw man” oppositions.
Patent No. | Proprietor | Title (en) | IPC | Opponent |
---|---|---|---|---|
1825424 | Becton, Dickinson and Company | Graphical User Interface For Use With Open Expert System | G06N 5/02 | Beckman Coulter, Inc. |
2449510 | Dow AgroSciences LLC | Application Of Machine Learning Methods For Mining Association Rules In Plant And Animal Data Sets Containing Molecular Genetic Markers, Followed By Classification Or Prediction Utilizing Features Created From These Association Rules | G06N 5/02 | KWS SAAT SE & Co. KGaA |
2458178 | General Electric Company | Turbine Performance Diagnostic System And Methods | F02C 9/00G06N 7/00 | Siemens Aktiengesellschaft |
2582341 | Fred Bergman Healthcare Pty Ltd | Method For Analysing Events From Sensor Data By Optimization | A61F 13/42G08B 19/00G08B 23/00G06N 5/04G06N 99/00 | Ontex BVBA |
2748686 | Robert Bosch GmbH | Method For The Creation Of A Function For A Control Device | G05B 17/00G06N 7/00G06F 17/50G05B 13/04 | FEV GmbH |
2807526 | Omron Corporation | Autonomous Mobile Robot For Handling Job Assignments In A Physical Environment Inhabited By Stationary And Non-Stationary Obstacles | G05B 19/18G05D 1/02G06N 3/00 | Verschelde, Claire |
3111380 | Rigetti & Co., Inc. | Processing Signals In A Quantum Computing System | G06N 10/00G06F 13/36G06F 13/40G06F 15/80 | Ueberfluss, Eva U. |
3111381 | Rigetti & Co., Inc. | Operating A Multi-Dimensional Array Of Qubit Devices | G06N 10/00G06F 13/36G06F 13/40 | Schorr, Frank |
3217336 | Rigetti & Co., Inc. | Impedance-Matched Microwave Quantum Circuit Systems | G06N 99/00H01L 39/02H01L 39/22H03H 7/38G06F 15/80G06N 10/00H01L 27/18H01P 1/201H01P 5/02 | Ueberfluss, Eva U. |
Frances Wilding is a partner of Haseltine Lake Kempner, based in the firm’s London office.
Previous articles by Haseltine Lake Kempner authors in this series can be accessed here:
How to secure AI patents in Europe
Drafting AI patent applications for success at the EPO – eligibility and claim formulation
Drafting AI patent applications for success at the EPO – drafting the full specification
Technology trends – why patent your hidden AI?
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