61 search and IR patents by Susan Dumais, Microsoft Technical Fellow and Gerard Salton Award winner. Co-inventor on the foundational Latent Semantic Indexing patent (US 4,839,853, Bellcore 1989) — the conceptual ancestor of every dense-embedding retrieval system. At Microsoft Research her work covers automated SERP satisfaction measurement, preference-judgment click models, activity-based-context ranking, time-aware ranking with temporal dynamics, personalized navigation and search results, per-user domain-expertise determination, web-page-change × revisitation freshness, implicit device-related query reformulation, authority ranking. Spans 1989 to 2026. Co-authors include Jaime Teevan, Eric Horvitz, Ryen White, Adam Fourney, Joshua Goodman, Eric Brill.
About the Susan Dumais, Microsoft IR & Search Patents track
61 search and IR patents by Susan Dumais, Microsoft Technical Fellow and Gerard Salton Award winner. Co-inventor on the foundational Latent Semantic Indexing patent (US 4,839,853, Bellcore 1989) — the conceptual ancestor of every dense-embedding retrieval system. At Microsoft Research her work covers automated SERP satisfaction measurement, preference-judgment click models, activity-based-context ranking, time-aware ranking with temporal dynamics, personalized navigation and search results, per-user domain-expertise determination, web-page-change × revisitation freshness, implicit device-related query reformulation, authority ranking. Spans 1989 to 2026. Co-authors include Jaime Teevan, Eric Horvitz, Ryen White, Adam Fourney, Joshua Goodman, Eric Brill.
Latent Semantic Indexing & Foundational Retrieval
- Computer Information Retrieval Using Latent Semantic Structure (LSI) (US 4,839,853 · June 13, 1989)
Click Models & Implicit Feedback
- Automated Satisfaction Measurement for Web Search (US 7,937,340 · May 3, 2011)
- Preference Judgements for Relevance (US 8,069,179 · November 29, 2011)
- Cursor Activity Evaluation for Search Result Enhancement (US App 2013/0246383 · September 19, 2013)
- Relating Web Page Change with Revisitation Patterns (US 8,078,974 · December 13, 2011)
- Web Page Change × Revisitation (2015) (US 9,069,872 · June 30, 2015)
Personalization, Context & User Authority
- Modeling Intent and Ranking Search Results Using Activity-Based Context (US App 2012/0158685 · June 21, 2012)
- Personalized Navigation Using a Search Engine (US 8,799,280 · August 5, 2014)
- Functionality for Personalizing Search Results (US 8,700,544 · April 15, 2014)
- Domain Expertise Determination (US 8,122,021 · February 21, 2012)
- Domain Expertise (2013) (US 8,402,024 · March 19, 2013)
- Domain Expertise (2015) (US 8,930,357 · January 6, 2015)
- Authority Ranking (US 8,260,789 · September 4, 2012)
Temporal Ranking & Query Reformulation
- Time-Aware Ranking Adapted to a Search Engine Application (US 9,244,931 · January 26, 2016)
- Time-Aware Ranking (2019) (US 10,346,413 · July 9, 2019)
- Assigning Relevance Weights Based on Temporal Dynamics (US 10,353,967 · July 16, 2019)
- Automatic Identification and Contextual Reformulation of Implicit Device-Related Queries (US 11,386,105 · July 12, 2022)
- Using Categorical Metadata to Rank Search Results (US 9,020,936 · April 28, 2015)
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.