32 search/SEO patents by Nitin Gupta (Google Search Quality engineer) covering query suggestions and templates, reranking and personalization, answer-generating neural networks, content-knowledge generation, the latest 2026 generative-search retrieval patent for the AI Overviews era, and a direct SEO patent on domain names and websites. The final 5 patents are co-invented with Steven Baker and are documented in his section to avoid duplicate content.
About the Nitin Gupta — Google Search Patents track
32 search/SEO patents by Nitin Gupta (Google Search Quality engineer) covering query suggestions and templates, reranking and personalization, answer-generating neural networks, content-knowledge generation, the latest 2026 generative-search retrieval patent for the AI Overviews era, and a direct SEO patent on domain names and websites. The final 5 patents are co-invented with Steven Baker and are documented in his section to avoid duplicate content.
Query Suggestions & Autocomplete
- Query suggestion templates (US 9,529,856 · 2016)
- Query suggestion templates (2020 continuation) (US 10,635,717 · 2020)
- Query Suggestion Templates (application) (US App. 20,140,358,940 · 2014)
- Query Suggestion Templates (2017 application) (US App. 20,170,039,209 · 2017)
- Query suggestions based on entity collections of one or more past queries (US 9,342,626 · 2016)
- Query suggestions based on entity collections (2019 continuation) (US 10,360,225 · 2019)
- Query suggestions based on entity collections (2023 continuation) (US 11,853,307 · 2023)
- Query suggestions based on entity collections (2025 continuation) (US 12,216,669 · 2025)
- Query Suggestions Based On Entity Collections (2025 application) (US App. 20,250,258,828 · 2025)
- Query rewrites for generating auto-complete suggestions (US 9,235,654 · 2016)
- Populating query suggestion database using chains of related search queries (US 9,342,600 · 2016)
- Semantically equivalent query templates (US 10,073,882 · 2018)
Reranking & Personalization
- Reranking query completions (US 9,298,852 · 2016)
- Reranking Query Completions (application) (US App. 20,150,169,578 · 2015)
- Personalized suggestions based on past queries (US 10,496,649 · 2019)
- Supplementing search results with historically selected search results of related queries (US 9,298,828 · 2016)
Answer Generation & Knowledge
- Generating elements of answer-seeking queries and elements of answers (US 10,592,540 · 2020)
- Generating Elements of Answer-Seeking Queries (application) (US App. 20,170,011,116 · 2017)
- Answer to question neural networks (US 11,093,813 · 2021)
- Answer to Question Neural Networks (application) (US App. 20,180,114,108 · 2018)
- Candidate answer passages (US 10,180,964 · 2019)
Generative Search & AI-Era Retrieval
- Controlled content diversity in retrieval for generative search (US App. 20,260,072,965 · 2026)
- Content knowledge query generation through computer analysis (US App. 20,230,368,693 · 2023)
- Data facet generation and recommendation (US App. 20,230,401,457 · 2023)
- Card interface for managing domain search results (US App. 20,150,212,710 · 2015)
SEO & Domain Discovery
- Search Engine Optimization of Domain Names and Websites (US App. 20,160,043,989 · 2016)
- System and method for identifying search results satisfying a search query (US 9,405,834 · 2016)
Shared with Steven Baker Section
- Query generation using structural similarity between documents (US 8,346,792 · 2013)
- Query generation using structural similarity between documents (2015 continuation) (US 9,092,479 · 2015)
- Query generation using structural similarity between documents (2016 continuation) (US 9,436,747 · 2016)
- Implicit question query identification (US 9,898,554 · 2018)
- Context scoring adjustments for answer passages (US 9,959,315 · 2018)
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.