~46 captured Google patents (full portfolio estimated at 120-150) by Noam Shazeer. Co-inventor on the foundational Transformer attention architecture (US 10,452,978 with Vaswani, Polosukhin, Uszkoreit, Jones, Gomez, Kaiser, Parmar), the Sparsely-Gated Mixture-of-Experts scaling approach (US 11,769,055 with Dean, Hinton, Le, Mirhoseini), the Switch Transformer (US 12,093,829), and the Navboost implicit-feedback ranking family (US 8,661,029 with Kim/Tong/Diligenti — cross-listed). Also covers LLM-in-assistant response generation, distributed tensor computations (GSPMD/Pathways), and the 2008-2015 pre-Transformer large-scale ML ranking infrastructure. Spans 2008 to 2025.
About the Noam Shazeer, Google Transformer, MoE & Search Patents track
~46 captured Google patents (full portfolio estimated at 120-150) by Noam Shazeer. Co-inventor on the foundational Transformer attention architecture (US 10,452,978 with Vaswani, Polosukhin, Uszkoreit, Jones, Gomez, Kaiser, Parmar), the Sparsely-Gated Mixture-of-Experts scaling approach (US 11,769,055 with Dean, Hinton, Le, Mirhoseini), the Switch Transformer (US 12,093,829), and the Navboost implicit-feedback ranking family (US 8,661,029 with Kim/Tong/Diligenti — cross-listed). Also covers LLM-in-assistant response generation, distributed tensor computations (GSPMD/Pathways), and the 2008-2015 pre-Transformer large-scale ML ranking infrastructure. Spans 2008 to 2025.
Transformer & Attention
- Attention-Based Sequence Transduction Neural Networks (Transformer) (US 10,452,978 · October 22, 2019)
- Attention-Based Sequence Transduction (2020 continuation) (US 10,719,764 · July 21, 2020)
- Attention-Based Sequence Transduction (2021) (US 11,113,602 · September 7, 2021)
- Attention-Based Sequence Transduction (2025) (US 12,217,173 · February 4, 2025)
- Decoder-Only Transformer Architecture (US 12,354,005 · July 8, 2025)
- Attention-Based Image Generation (US 12,142,034 · November 12, 2024)
- Speech-Recognition Attention RNN (US 12,100,391 · September 24, 2024)
Mixture of Experts & Sparse Scaling
- Mixture of Experts Neural Networks (US 11,769,055 · September 26, 2023)
- Mixture of Experts (2024) (US 12,067,476 · August 20, 2024)
- Neural Networks with Switch Layers (Switch Transformer) (US 12,093,829 · September 17, 2024)
LLM-Driven Search & Assistant
- Using Large Language Models in Generating Automated Assistant Responses (US 12,148,421 · November 19, 2024)
- Evaluating Output Sequences Using an Auto-Regressive Language Model (US 12,086,713 · September 10, 2024)
- Distributing Tensor Computations (Pathways / GSPMD) (US 12,265,903 · April 1, 2025)
Pre-Transformer Ranking & Navboost (cross-list)
- Modifying Search Result Ranking Based on Implicit User Feedback (Navboost) (US 8,661,029 · February 25, 2014)
- Ranking Documents Based on Large Data Sets (US 9,116,976 · August 25, 2015)
- Model Generation for Ranking Documents (US 7,743,050 · June 22, 2010)
- Equivalent Descriptions for an Information Need (US 7,392,244 · June 24, 2008)
- Determining Geographical Relevance of Web Documents (US 8,086,690 · December 27, 2011)
Why this inventor matters
Each inventor track inside the Nizam SEO War Room patents archive isolates one engineer's research arc — typically a decade or more of continuations, divisionals, and follow-up patents on a coherent research thread. Reading by inventor (rather than by topic) recovers the narrative: how the original disclosure evolved, what the continuations added, which claims got carved out into divisional applications, and how the thread eventually intersected with other research lines at Google or Microsoft. This is how working SEOs build durable intuition about search-engine internals — not by memorizing claim language, but by following the research bibliography that shipped the algorithms we now optimize against.
How to read this track
Start with the earliest filing — it sets the foundational disclosure. Continuations refine the claims; divisional applications split out separable inventions; the follow-up patents tend to introduce performance optimizations, edge-case handling, or downstream integration with other systems. Each patent on this site is annotated with the ranking surface it touches — query understanding, document retrieval, ranking, behavioral signals, knowledge graph, or AI search — so the practitioner can map the research back to the algorithm output observed on live SERPs.