33 search-engine patents by Ramanathan V. Guha, co-founder of Schema.org and lead inventor on Google Custom Search Engine. Covers the Programmable Search Engine family, trust-based search ranking, spam detection for programmable contexts, social-signal propagation, classifying search results, customized web summaries, filtering by user annotations, contextual searching by query intersections, query identification, and the generating-tasks UI family. Spans 2003 to 2019.
About the Ramanathan V. Guha, Google Search Patents track
33 search-engine patents by Ramanathan V. Guha, co-founder of Schema.org and lead inventor on Google Custom Search Engine. Covers the Programmable Search Engine family, trust-based search ranking, spam detection for programmable contexts, social-signal propagation, classifying search results, customized web summaries, filtering by user annotations, contextual searching by query intersections, query identification, and the generating-tasks UI family. Spans 2003 to 2019.
Programmable & Custom Search
- Programmable Search Engine (US 7,693,830 · April 6, 2010)
- Programmable Search Engine (continuation 2012) (US 8,316,040 · November 20, 2012)
- Programmable Search Engine (continuation 2015) (US 9,031,937 · May 12, 2015)
- Programmable Search Engine (app 2010) (US App 2010/0217756 · August 26, 2010)
- Programmable Search Engine (app 2013) (US App 2013/0124510 · May 16, 2013)
- Programmable Search Engines (app 2016) (US App 2016/0299983 · October 13, 2016)
- Aggregating Context Data for Programmable Search Engines (US 7,716,199 · May 11, 2010)
- Aggregating Context Data (continuation 2011) (US 8,051,063 · November 1, 2011)
- Aggregating Context Data (app 2010) (US App 2010/0250513 · September 30, 2010)
- Aggregating Context Data (continuation 2014) (US 8,756,210 · June 17, 2014)
- Generating Specialized Search Results in Response to Patterned Queries (US 7,593,939 · September 22, 2009)
- Dynamic Specification of Custom Search Engines at Query-Time (US 8,892,552 · November 18, 2014)
- Generating and Presenting Advertisements Based on Context Data for Programmable Search Engines (US App 2013/0110627 · May 2, 2013)
Trust & Quality Ranking
- Search Result Ranking Based on Trust (US 8,352,467 · January 8, 2013)
- Search Result Ranking Based on Trust (continuation 2014) (US 8,818,995 · August 26, 2014)
- Search Result Ranking Based on Trust (continuation 2019) (US 10,268,641 · April 23, 2019)
- Detecting Spam Related and Biased Contexts for Programmable Search Engines (US 7,743,045 · June 22, 2010)
- Detecting Spam Related and Biased Contexts (app 2010) (US App 2010/0223250 · September 2, 2010)
- Detecting Spam Search Results for Context Processed Search Queries (US 8,452,746 · May 28, 2013)
- Classifying Search Results (US 9,043,322 · May 26, 2015)
- Classifying Search Results to Determine Page Elements (US 8,600,987 · December 3, 2013)
- Classifying Search Results to Determine Page Elements (app) (US App 2014/0089305 · March 27, 2014)
Social Signals & Personalization
- Propagating Promotional Information on a Social Network (US 9,466,077 · October 11, 2016)
- Propagating Promotional Information (continuation 2018) (US 10,074,109 · September 11, 2018)
- Propagating Promotional Information (app 2010) (US App 2010/0332330 · December 30, 2010)
- Propagating Promotional Information (app 2016) (US App 2016/0379273 · December 29, 2016)
- Customized Web Summaries and Alerts Based on Custom Search Engines (US 8,725,716 · May 13, 2014)
- Customized Web Summaries and Alerts (continuation) (US 9,323,853 · April 26, 2016)
Specialized Surfaces
- Filtering Search Results Using Annotations (US 8,341,150 · December 25, 2012)
- Generating and Displaying Tasks (US App 2014/0172853 · June 19, 2014)
- Generating and Displaying Tasks (companion app) (US App 2014/0156623 · June 5, 2014)
- Contextual Searching by Determining Intersections of Search Results (US 6,539,373 · March 25, 2003)
- Query Identification and Association (US 8,631,003 · January 14, 2014)
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.