Geoffrey Hinton Patents & Innovation Profile — PatSnap Eureka
Geoffrey Hinton: Patent Portfolio & Innovation Analysis
Geoffrey Everest Hinton is a British-Canadian computer scientist and AI researcher who holds 141 patents spanning neural network training, dropout regularisation, convolutional network parallelisation, capsule networks, contrastive learning, and knowledge distillation, with filings from 2000 to 2025. His portfolio is primarily assigned to Google LLC and covers foundational methods that underpin modern deep learning systems globally, making his IP footprint one of the most strategically significant in the AI sector.
Patent Filing Activity
Peak filing year was 2020 with 20 applications; a second major cluster in 2013 anchors the foundational dropout and parallel CNN families.
Geoffrey Hinton’s Patent Filing Patterns
Hinton’s filing activity clusters tightly around two waves: the 2013 deep learning inflection point and a decisive second wave in 2020 covering capsule networks, contrastive learning, and forward-gradient methods.
Annual Patent Filings
Application dates cluster around 2013 (dropout, parallel CNN families) with a decisive peak in 2020 (20 filings) and sustained activity through 2023.
Technology Domain Breakdown
G06N3 (Neural Networks & Deep Learning) dominates with 68% of all IPC-coded patents, followed by G06N7 (ML Methods) at 12% and G06K9 (Pattern Recognition) at 9%.
Geoffrey Hinton’s Core Areas of Innovation
Geoffrey Hinton’s patent portfolio spans five distinct technology clusters, each corresponding to a major contribution to the modern deep learning stack — from foundational training methods to novel neural architectures.
Neural Network Training & Regularisation
~31 patentsThis cluster covers the dropout technique — randomly disabling feature detectors during training to prevent overfitting — along with related regularisation methods. The priority date traces to December 2012, and the family has generated an exceptionally large number of continuation and international filings that remain active.
- System and method for addressing overfitting in a neural network (US9406017B2)
- System and method for addressing overfitting in a neural network (US20140180986A1)
- System and method for addressing overfitting in a neural network (WO2014105866A1)
Parallel Convolutional Neural Networks
~20 patentsPatents in this cluster protect methods for distributing CNN computation across multiple processing nodes, enabling training of networks far larger than single-GPU memory constraints would permit. This family underpinned AlexNet, which won the 2012 ImageNet challenge and catalysed the deep learning era in computer vision.
- System and method for parallelizing convolutional neural networks (US20140180989A1)
- Parallelizing neural networks during training (US9811775B2)
- System and method for parallelizing convolutional neural networks (WO2014105865A1)
Contrastive Learning of Visual Representations
~16 patentsThese patents cover SimCLR-type contrastive learning frameworks using data augmentation and learnable nonlinear transformations to train visual representations without large amounts of labelled data. This family became highly influential in computer vision from 2020 onward, accumulating 138 citations on the lead application within approximately four years.
- Systems and methods for contrastive learning of visual representations (US20210319266A1)
- Systems and methods for contrastive learning of visual representations (US20210327029A1)
- Systems and methods for contrastive learning of visual representations (US11386302B2)
Capsule Neural Networks
~12 patentsThis architecturally novel family protects the capsule neuron concept — a unit that outputs a vector rather than a scalar, enabling neural networks to represent pose and spatial relationships more explicitly. This architecture represents Hinton’s ongoing effort to move beyond standard convolutional networks and address their weaknesses in viewpoint invariance.
- Capsule neural networks (WO2019083553A1)
- Capsule neural networks (US20200285934A1)
- Capsule neural networks (US11494609B2)
Knowledge Distillation & Model Compression
~10 patentsThis family covers knowledge distillation — training a smaller student model to replicate the behaviour of a larger teacher model. Co-invented with Oriol Vinyals and Jeff Dean, this approach is now a standard methodology for deploying large neural networks in resource-constrained environments, with direct relevance to mobile AI and edge computing.
- Training distilled machine learning models (US10289962B2)
- Training distilled machine learning models (EP2953066B1)
- Training distilled machine learning models (US11900232B2)
Geoffrey Hinton’s Highest-Impact IP
The most-cited patent — the parallel CNN architecture — has accumulated 202 citations from AI hardware companies, cloud computing providers, and autonomous systems developers, reflecting its role as a direct predecessor to large-scale deep learning infrastructure.
| Patent Number | Title | Year | Citations | Assignee | Status |
|---|---|---|---|---|---|
| US20140180989A1 | System and method for parallelizing convolutional neural networks | 2012 | 202 ↑ | Google Inc./LLC | Active |
| US20210319266A1 | Systems and methods for contrastive learning of visual representations | 2020 | 138 ↑ | Google LLC | Active |
| US20210327029A1 | Systems and methods for contrastive learning of visual representations | 2020 | 61 ↑ | Google LLC | Active |
| US20140180986A1 | System and method for addressing overfitting in a neural network | 2012 | 53 ↑ | Google Inc./LLC | Active |
| WO2019083553A1 | Capsule neural networks | 2017 | 53 ↑ | Google LLC | Active |
| US20130343641A1 | System and method for labelling aerial images | 2012 | 51 ↑ | Google Inc./LLC | Active |
| US20150339571A1 | System and method for parallelizing convolutional neural networks | 2012 | 39 ↑ | Google Inc./LLC | Active |
| US20140177947A1 | System and method for generating training cases for image classification | 2012 | 38 ↑ | Google Inc./LLC | Active |
| US9811775B2 | Parallelizing neural networks during training | 2012 | 34 ↑ | Google Inc./LLC | Active |
| WO2014105866A1 | System and method for addressing overfitting in a neural network | 2012 | 33 ↑ | Google Inc./LLC | Active |
Geoffrey Hinton’s Research Collaborators
Most Frequent Co-Inventors
Collaboration Highlights
Geoffrey Hinton’s co-inventor network maps directly onto the lineage of his research groups at the University of Toronto and Google Brain. The presence of Alexander Krizhevsky and Ilya Sutskever on the earliest and most commercially cited patents — and their subsequent trajectories founding companies and influencing the broader industry — underscores the direct connection between Hinton’s academic programme and the generation of AI practitioners who now lead the field.
- Alexander Krizhevsky Dropout, parallel CNN, training data families
- Ilya Sutskever Foundational CNN & dropout families
- Ting Chen Contrastive learning, Pix2Seq, segmentation
- Sara Sabour (Aghdam) Capsule neural network patents
- Nicholas Frosst Capsule networks & nearest-neighbour loss
- Oriol Vinyals & Jeff Dean Knowledge distillation family
Research Literature by Geoffrey Hinton
50 papers indexed in the dataset (194 total hits) · Research spans generative learning, supervised deep learning at scale, and architectural innovation including capsule networks and knowledge distillation.
| Title | Year | Citations | Notes |
|---|---|---|---|
| Deep learning | 2015 | 59,089 ↑ | Nature; with LeCun & Bengio |
| ImageNet classification with deep convolutional neural networks | 2017 | 26,275 ↑ | AlexNet paper; NeurIPS |
| Deep Neural Networks for Acoustic Modeling in Speech Recognition | 2012 | 7,711 ↑ | IEEE Signal Processing Magazine |
| Speech recognition with deep recurrent neural networks | 2013 | 4,450 ↑ | ICASSP |
| Distilling the Knowledge in a Neural Network | 2015 | 1,143 ↑ | NeurIPS Workshop; precedes US patent grant |
| Improving neural networks by preventing co-adaptation of feature detectors | 2012 | 380 ↑ | arXiv; dropout paper; precedes patent filing |
Generative & Unsupervised Learning
Work on deep belief networks, restricted Boltzmann machines, and deep generative models that dominated Hinton’s Toronto-era output from the mid-2000s through 2012, establishing the theoretical basis for unsupervised pre-training in deep networks.
Supervised Deep Learning at Scale
Centred on the AlexNet work and its applications to speech and vision, producing the most highly cited papers in the dataset — including the 59,089-cited Nature deep learning review and the 26,275-cited ImageNet classification paper.
Architectural Innovation & Training Methodology
Includes capsule networks, knowledge distillation, transforming auto-encoders, the Forward-Forward algorithm, and contrastive learning — reflecting a sustained effort to develop alternatives to standard backpropagation and convolutional architectures.
Patent Jurisdictions
Geoffrey Hinton’s portfolio has been filed across six jurisdictions, reflecting Google LLC’s deliberate international IP strategy to protect commercially significant AI methods in key global markets.
Filing Markets
The concentration of active grants in the US (62 patents), with WO/PCT coverage on 13 major families and EP grants on 6, is consistent with a portfolio managed to protect AI training infrastructure, computer vision APIs, and on-device deployment methods in the world’s three largest technology markets. PCT applications on all major families enable flexible national phase entry. China and India filings (3 each) reflect expansion into object detection and panoptic segmentation families. Australia (2) covers early foundational families.
Why Geoffrey Hinton’s Portfolio Matters
Strategic implications for patent attorneys, in-house IP teams, and R&D strategists working in AI, machine learning, and computer vision.
FTO Considerations
The dropout and CNN parallelisation families, assigned to Google LLC with priority from 2012, cover methods embedded in virtually every deep learning training framework — including PyTorch, TensorFlow, and JAX. Patent practitioners advising clients who build on these stacks should examine the granted claims in the dropout family (e.g., US9406017B2) and its numerous continuations. The contrastive learning family (US20210319266A1 and related grants) is directly relevant to any organisation building self-supervised vision systems, foundation models, or multimodal representations.
Prior Art Relevance
The combination of high-citation academic publications and corresponding patent filings means Geoffrey Hinton’s papers frequently serve as prior art against third-party applications in neural network training and architecture. The 2012 arXiv dropout preprint, the AlexNet NeurIPS paper, and the knowledge distillation paper are routinely cited by patent examiners in AI-related prosecution. Anyone filing in regularisation, model compression, or contrastive learning domains should conduct a thorough prior art search anchored on these publications and their patent counterparts.
Geoffrey Hinton Patents: Common Questions
Analyse Geoffrey Hinton’s Full Patent Portfolio
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References & Patent Sources
- US20140180989A1 — System and method for parallelizing convolutional neural networks · Google Inc./LLC · PatSnap Eureka
- US20210319266A1 — Systems and methods for contrastive learning of visual representations · Google LLC · PatSnap Eureka
- WO2019083553A1 — Capsule neural networks · Google LLC · PatSnap Eureka
- US10289962B2 — Training distilled machine learning models · Google LLC · PatSnap Eureka
- USPTO Patent Full-Text Database — patents.google.com
- European Patent Office — Espacenet Patent Search
- WIPO PatentScope — patentscope.wipo.int
- PatSnap Eureka Literature: Deep learning (LeCun, Bengio, Hinton, 2015)