48 search-engine patents by Krishna Bharat, founding inventor of Google News and creator of the Hilltop algorithm. Covers the Hilltop link-analysis algorithm, query rewriting with entity detection, semantic units in queries, query and search augmentation, document ranking by usage statistics, image selection for news search, editorial-opinion ranking, search engine coverage estimation, query-specific bookmarking, and entity-aware ad rendering. Spans 2001 to 2024.
About the Krishna Bharat, Google Search Patents track
48 search-engine patents by Krishna Bharat, founding inventor of Google News and creator of the Hilltop algorithm. Covers the Hilltop link-analysis algorithm, query rewriting with entity detection, semantic units in queries, query and search augmentation, document ranking by usage statistics, image selection for news search, editorial-opinion ranking, search engine coverage estimation, query-specific bookmarking, and entity-aware ad rendering. Spans 2001 to 2024.
Query & Ranking
- Ranking Search Results by Reranking Based on Local Inter-Connectivity (Hilltop Algorithm) (US 6,526,440 · February 25, 2003)
- Ranking Search Results by Reranking Based on Local Inter-Connectivity (continuation) (US 6,725,259 · April 20, 2004)
- System and Method for Supporting Editorial Opinion in the Ranking of Search Results (US 7,096,214 · August 22, 2006)
- System and Method for Supporting Editorial Opinion in the Ranking of Search Results (continuation) (US 7,386,543 · June 10, 2008)
- Identification of Semantic Units from Within a Search Query (US 7,249,121 · July 24, 2007)
- Identification of Semantic Units from Within a Search Query (continuation) (US 8,321,410 · November 27, 2012)
- Identification of Semantic Units from Within a Search Query (later continuation) (US 8,719,262 · May 6, 2014)
- Query Rewriting with Entity Detection (US 7,536,382 · May 19, 2009)
- Query Rewriting with Entity Detection (continuation 2012) (US 8,112,432 · February 7, 2012)
- Query Rewriting with Entity Detection (continuation 2013-1) (US 8,452,799 · May 28, 2013)
- Query Rewriting with Entity Detection (continuation 2013-2) (US 8,805,867 · August 12, 2014)
- Query Rewriting with Entity Detection (app 2005) (US App 2005/0222977 · October 6, 2005)
- Query Rewriting with Entity Detection (app 2009) (US App 2009/0204592 · August 13, 2009)
- Query Rewriting with Entity Detection (app 2012) (US App 2012/0136885 · May 31, 2012)
- Query Rewriting with Entity Detection (app 2013) (US App 2013/0262499 · October 3, 2013)
- Systems and Methods for Improving the Ranking of News Articles (US 7,577,655 · August 18, 2009)
- Systems and Methods for Improving the Ranking of News Articles (continuation 2012-1) (US 8,126,876 · February 28, 2012)
- Systems and Methods for Improving the Ranking of News Articles (continuation 2012-2) (US 8,332,382 · December 11, 2012)
- Systems and Methods for Improving the Ranking of News Articles (continuation 2014) (US 8,645,368 · February 4, 2014)
- Systems and Methods for Improving the Ranking of News Articles (app 2005) (US App 2005/0060312 · March 17, 2005)
- Systems and Methods for Improving the Ranking of News Articles (app 2009) (US App 2009/0276429 · November 5, 2009)
- Systems and Methods for Improving the Ranking of News Articles (app 2012) (US App 2012/0158711 · June 21, 2012)
- Systems and Methods for Improving the Ranking of News Articles (app 2013) (US App 2013/0159294 · June 20, 2013)
- Methods and Apparatus for Employing Usage Statistics in Document Retrieval (US 8,001,118 · August 16, 2011)
- Methods and Apparatus for Employing Usage Statistics in Document Retrieval (continuation 2012-1) (US 8,156,100 · April 10, 2012)
- Methods and Apparatus for Employing Usage Statistics in Document Retrieval (continuation 2013) (US 8,352,452 · January 8, 2013)
- Methods and Apparatus for Employing Usage Statistics in Document Retrieval (app 2002) (US App 2002/0123988 · September 5, 2002)
- Methods and Apparatus for Employing Usage Statistics in Document Retrieval (app 2011) (US App 2011/0179023 · July 21, 2011)
- Methods and Apparatus for Employing Usage Statistics in Document Retrieval (app 2012) (US App 2012/0226705 · September 6, 2012)
- Methods and Apparatus for Ranking Documents (US 8,090,717 · January 3, 2012)
- Methods and Apparatus for Ranking Documents (continuation 2014) (US 8,843,479 · September 23, 2014)
- Methods and Apparatus for Ranking Documents (continuation 2016) (US 9,477,714 · October 25, 2016)
- Methods and Apparatus for Ranking Documents (continuation 2019) (US 10,496,652 · December 3, 2019)
- Query Augmentation (US 9,128,945 · September 8, 2015)
- Query Augmentation (continuation 2018) (US 9,916,366 · March 13, 2018)
- Search Augmentation (US 8,346,791 · January 1, 2013)
- Image Selection for News Search (US 8,775,436 · July 8, 2014)
- Image Selection for News Search (continuation) (US 9,613,061 · April 4, 2017)
- Method and Apparatus for Query-Specific Bookmarking and Data Collection (US App 2005/0114299 · May 26, 2005)
- Method for Estimating Coverage of Web Search Engines (US App 2005/0055342 · March 10, 2005)
Entity & Knowledge Graph
- Using an Expanded View to Display Links Related to a Topic (US 9,678,618 · June 13, 2017)
- Entity Identification Model Training (US 9,251,141 · February 2, 2016)
- Rendering Advertisements with Documents Having One or More Topics Using User Topic Interest (US 7,346,606 · March 18, 2008)
- Rendering Advertisements with Documents (continuation 2012-1) (US 8,090,706 · January 3, 2012)
- Rendering Advertisements with Documents (continuation 2012-2) (US 8,296,285 · October 23, 2012)
- Rendering Advertisements with Documents (app 2004) (US App 2004/0267723 · December 30, 2004)
- Rendering Advertisements with Documents (app 2008) (US App 2008/0097833 · April 24, 2008)
- Rendering Advertisements with Documents (app 2012) (US App 2012/0072291 · March 22, 2012)
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.