Ian Goodfellow Patents & Innovation Profile — PatSnap Eureka
Ian Goodfellow: Patent Portfolio & Innovation Analysis
Ian Goodfellow is a deep learning researcher and inventor of Generative Adversarial Networks (GANs), holding 67 patents across neural network training, adversarial machine learning, and AI content provenance, with filings spanning 2013 to 2025. His portfolio is primarily assigned to Google LLC and covers foundational methods in adversarial training, neural network security, and generative image synthesis that underpin modern AI systems.
Patent Filing Activity
Peak year was 2016 with 8 filings; renewed surge to 7 filings in 2024 under DeepMind.
Ian Goodfellow's Patent Filing Patterns
Filing activity peaked in 2016 with 8 patents and surged again in 2024 with 7 new DeepMind-assigned filings, reflecting a shift toward AI content provenance and watermarking.
Annual Patent Filings
Peak year was 2016 with 8 filings; a renewed surge of 7 filings occurred in 2024 under DeepMind Technologies Limited.
Technology Domain Breakdown
Neural Networks & Deep Learning (G06N3) dominates with 29 of 42 classified patents — 69% of the portfolio.
Ian Goodfellow's Core Areas of Innovation
Ian Goodfellow's portfolio spans four distinct but interconnected domains — from foundational neural network training to cutting-edge AI content authentication.
Neural Network Architectures & Training
29 patentsThe most heavily represented domain covers the design, training, and scaling of neural networks, including adversarial training techniques that improve model robustness and discretisation methods that harden networks against malicious input manipulation. Patents in this family address how smaller networks can be scaled into larger ones while preserving learned behaviour.
- Adversarial training of neural networks
- Generating larger neural networks
- Increasing security of neural networks by discretizing neural network inputs
Neural Network Security & Adversarial Robustness
8 patentsEight patents under G06F21 address protecting neural networks from adversarial inputs that can cause misclassification or system compromise — one of the most commercially significant challenges in deployed AI. The discretisation-layer security family is filed across US, EP, WO, CN, and AT jurisdictions, signalling broad geographic coverage intent.
- Increasing security of neural networks by discretizing neural network inputs (EP)
- Verifying the provenance of a digital object using watermarking and embeddings (US)
- Verifying the provenance of a digital object using watermarking and embeddings (WO)
Generative Models & Image Synthesis
2 patentsA focused cluster of patents covers neural-network-based image generation and super-resolution — direct extensions of Goodfellow's foundational GAN research into applied computer vision. These inventions tackle how discriminator and generator networks can be co-trained to produce high-fidelity outputs for media production, satellite imagery, and medical imaging.
- Generating super-resolution images using neural networks (US)
- Generating super-resolution images using neural networks (WO)
Computer Vision & Sequence Recognition
2 patentsTwo patents under G06V10 relate to visual recognition through deep neural networks, anchored by the sequence transcription family filed from 2013 — one of the earliest entries in the portfolio. These patents address how neural networks can be trained to read character sequences from images, with direct application in OCR, document processing, and autonomous navigation.
- Sequence transcription with deep neural networks (2013)
- Sequence transcription with deep neural networks (2014)
Image Transformation & Processing
1 patentA single patent under G06T3 extends Goodfellow's generative model work into image transformation and geometric processing, complementing the super-resolution synthesis family. This domain reflects the applied computer vision applications of GAN-based architectures in structured image manipulation tasks.
- Generating super-resolution images using neural networks
Ian Goodfellow's Highest-Impact IP
The sequence transcription patent from 2013 leads with 56 forward citations, while the adversarial training patent has accumulated 37 citations from AI security filers worldwide.
| Patent Number | Title | Year | Citations | Assignee | Status |
|---|---|---|---|---|---|
| US8965112B1 | Sequence transcription with deep neural networks | 2013 | 56 ↑ | Google LLC | Active |
| US10521718B1 | Adversarial training of neural networks | 2016 | 37 ↑ | Google LLC | Active |
| US20210407042A1 | Generating super-resolution images using neural networks | 2019 | 17 ↑ | Google LLC | Active |
| WO2020102812A1 | Generating super-resolution images using neural networks | 2019 | 12 ↑ | Google LLC | — |
| US11173599B2 | Machine learning methods for predicting motion(s) of object(s) in a robot's environment | 2017 | 11 ↑ | Google LLC | Active |
| US12094474B1 | Verifying the provenance of a digital object using watermarking and embeddings | 2023 | 11 ↑ | DeepMind Technologies Limited | Active |
| US9454714B1 | Sequence transcription with deep neural networks | 2014 | 8 ↑ | Google LLC | Active |
| US10699191B2 | Generating larger neural networks | 2016 | 7 ↑ | Google LLC | Active |
Ian Goodfellow's Research Collaborators
Most Frequent Co-Inventors
Collaboration Highlights
Ian Goodfellow's most frequent co-inventor, Chen Tianqi, appears on 27 joint patent records — predominantly in the neural network architecture and scaling family. His DeepMind-era filings introduce a distinct new cohort including David Stutz, Christopher Gamble, and Olivia Anne Wiles, all working on watermarking and provenance, reflecting his consistent ability to build focused, project-specific invention teams around distinct research programmes.
- 陈天奇 (Chen Tianqi) 27 joint patents
- David Stutz 8 joint patents
- Christopher Gamble 8 joint patents
- Jacob Buckman 7 joint patents
- Aurko Roy 7 joint patents
- Colin Abraham Raffel 7 joint patents
- Jonathon Shlens 7 joint patents
- Olivia Anne Wiles 7 joint patents
Research Literature by Ian Goodfellow
345 papers indexed · Research spans generative modelling, adversarial example characterisation, and ML robustness benchmarking.
| Title | Year | Citations | Institution / Source |
|---|---|---|---|
| Generative adversarial networks | 2020 | 11,616 ↑ | Université de Montréal / Google Brain |
| Explaining and harnessing adversarial examples | 2015 | 5,448 ↑ | Google LLC |
| Practical black-box attacks against machine learning | 2017 | 2,254 ↑ | OpenAI / Pennsylvania State University |
| Adversarial examples in the physical world | 2018 | 1,739 ↑ | Google Brain |
| Adversarial examples in the physical world | 2016 | 1,361 ↑ | Google Brain |
Generative Modelling via Adversarial Frameworks
The GAN paper, accumulating over 11,616 citations, is the foundational contribution of Ian Goodfellow's academic career. Subsequent NIPS tutorials and improved GAN training techniques papers extended this work into practical training methodology, running directly parallel to his patent activity in generative image synthesis.
Adversarial Examples & Model Robustness
A substantial proportion of the literature addresses how machine learning models can be attacked through adversarial inputs (5,448 and 1,739 citations) and how to defend against them. The research-to-patent pipeline here is unusually clean — academic publications preceded patent filings by one to two years in most cases.
Benchmarking & Community Infrastructure
Goodfellow contributed to adversarial robustness evaluation frameworks including the CleverHans library and the NIPS 2017 competition on adversarial attacks and defences. This work shaped how the community measures and compares results, explaining his outsized influence beyond raw citation counts.
Patent Jurisdictions
Ian Goodfellow's portfolio spans 9 jurisdictions with a tiered strategy — highest-value inventions receive the broadest geographic coverage while specialised applications receive targeted national filings.
Filing Markets
The United States leads with 20 patents as the primary prosecution jurisdiction with the broadest claim scope. The European Patent Office (6 patents), China (4 patents), and PCT route (4 patents) reflect a structured international protection strategy consistent with large-technology-company IP practice. The inclusion of India in the 2025 provenance verification family signals a deliberate broadening of coverage in that cluster, consistent with increasing regulatory attention to AI-generated content authentication globally.
Why Ian Goodfellow's Portfolio Matters
Strategic implications for patent attorneys, in-house IP teams, and R&D strategists working in deep learning, generative AI, and machine learning security.
FTO Considerations
The adversarial training family — spanning US10521718B1, US11416745B1, US11651218B1, and their international counterparts — is actively maintained and broadly claimed. Any product or service that trains neural networks using adversarial perturbations during the training loop risks falling within the scope of these claims. Similarly, the neural network discretisation security cluster (US11354574B2, EP3701431B1) covers a widely adopted robustness technique. Teams building AI security products should conduct thorough FTO analysis against these assets before commercialisation.
Prior Art Relevance
The literature record — particularly "Explaining and Harnessing Adversarial Examples" (5,448 citations), the original GAN paper (11,616 citations), and "Adversarial Examples in the Physical World" (1,739 citations) — constitutes some of the most widely invoked prior art in the deep learning field. Any applicant seeking to claim novelty in adversarial robustness, generative image synthesis, or neural network training methodology must account for this body of work in claim drafting and prosecution strategy. The sequence transcription patent US8965112B1 leads the portfolio with 56 forward citations.
Ian Goodfellow Patent Portfolio: Common Questions
Analyse Ian Goodfellow's Full Portfolio in PatSnap Eureka IP
Access all 67 patents, citation mapping, claim-level FTO analysis, and real-time monitoring of the watermarking and provenance cluster — the fastest-growing area in this portfolio.
References & External Resources
- USPTO — US8965112B1: Sequence transcription with deep neural networks
- USPTO — US10521718B1: Adversarial training of neural networks
- EPO Espacenet — European patent search database
- WIPO Patentscope — International PCT applications database
- PatSnap Eureka — Generative adversarial networks (11,616 citations)
- PatSnap Eureka — Explaining and harnessing adversarial examples (5,448 citations)
PatSnap Eureka searches 208M+ patents and papers to answer instantly.