The document provides an overview of the interplay between patents and search engine technology, particularly focusing on Google's innovations. It details various patents related to knowledge graphs, entity extraction, and improved search results, highlighting how they enhance search efficiency and accuracy. Additionally, it stresses the importance of patents in spurring innovation and protecting intellectual property in the tech industry.

1. SEO & Patents
Bill Slawski @bill_slawski

2. Bill Slawski
● Author at https://www.seobythesea.com
● Director of SEO Research at
https://gofishdigital.com
● Twitter: https://twitter.com/bill_slawski
● LinkedIn: https://www.linkedin.com/in/slawski/
● Jurisdoctor Degree: Widener University School of
Law
● SEO since 1996

3. Why Patents?
1. Protect a Search Engine’s Intellectual Property
2. Exclude other search engines from using technology
3. Not for marketing
4. Related Patents and white papers exist
5. Continuation Patents have updated claims sections.
6. The Purpose Behind Patents are to Spur Innovation.

4. In Patents Live Algorithms
● Algorithms solve problems
● A patent shows the problems it solves
● Prior history is in the patent
● Patent must be novel, non-obvious, and useful
● A patent must be understandable to someone “learned in
the art.”
● Patents show what might be used.

5. Knowledge in Search Results!
Augmented Search Queries Using Knowledge Graph
Information
Providing search results using augmented search
queries
Inventors: Emily Moxley and Sean Liu
Assignee: Google LLC
US Patent: 10,055,462
Granted: August 21, 2018
Filed: March 15, 2013

6. A Query w/an Entity Shows Knowledge SERPs
Rich Snippets
Featured Snippets
Structured Snippets
Carousels
Local Pack
People Also Ask Questions
People Also Search For
Related Entities
Knowledge Panels

7. 4th Updated Universal Search has Knowledge
Google’s New Universal Search Results
Interface for a universal search
Inventors: Bret S. Taylor, Marissa Ann
Mayer, Orkut Buyukkokten
Assignee: Google LLC (Mountain View, CA)
US Patent: 10,409,865
Granted: September 10, 2019
Filed: April 15, 2016
From the Version Granted in 2009:
9. The method of claim 1, where the
Document categories include at
least one of a news category,
an image category,
or a product category.

8. Knowledge Graphs (Plural!)

9. Entity Extraction
Entity Extractions for Knowledge Graphs at Google
Computerized systems and methods for extracting
and storing information regarding entities
Inventors: Christopher Semturs, Lode Vandevenne,
Danila Sinopalnikov, Alexander Lyashuk, Sebastian
Steiger, Henrik Grimm, Nathanael Martin Scharli
and David Lecomte
Assignee: GOOGLE LLC
US Patent: 10,198,491
Granted: February 5, 2019
Filed: July 6, 2015
In web crawling, a node is
a page, and an edge is a
link between pages; in data
crawling, a node is an
entity, and an edge is a
relationship between
entities.
It's an evolution
in thinking about the web.

11. A Move Away From Wikipedia
Extracting Entities from News
and Authoritative sites means less
reliance on manually edited
Knowledge bases such as
Wikipedia or IMDB

12. Answering Questions Using Knowledge Graphs
Answering Questions Using Knowledge
Graphs
Natural Language Processing With An N-Gram
Machine
Pub. No.: WO2019083519A1
Publication Date: May 2, 2019
International Filing Date: October 25, 2017
Inventors: Ni Lao, Jiazhong Nie, Fan Yang
Business books 2020

13. Confidence Scores

14. Entity Rankings
from Query
Knowledge Graph

15. Ranked Entities in Search Results
Ranked Entities in Search Results
at Google
Generating ranked lists of entities
Inventors: Toshiaki Fujiki, Slaven Bilac,
Kavi J. Goel, Shuhei Takahashi,
Tomohiko Kimura
Assignee: Google LLC
US Patent: 10,691,702
Granted: June 23, 2020
Filed: August 31, 2017
Best Science Fiction Books of 2020
Best Houseplants for air quality

16. More Analysis: Less
Knowledge Bases

17. Quote Search: Knowledge Bases to Videos
● Google Has Updated Quote Searching
to Focus on Videos
Systems and methods for searching
quotes of entities using a database
● Inventors: Eyal Segalis, Gal Chechik,
Yossi Matias, Yaniv Leviathan, and
Yoav Tzur
● Assignee: Google LLC
● US Patent: 10,198,508
● Granted: February 5, 2019
● Filed: June 26, 2017

18. Claims Updated in Continuation Patent
2017 Version:
match the one or more keywords to knowledge graph items associated with candidate
subject entities in a knowledge graph stored in one or more databases,
2019 Version
performing audio analysis on the audio content to identify a quote in the audio content;
determining the user as an author of the audio content based on recognizing the user as the
speaker of the audio content; identifying, based on words or phrases extracted from the
quote, one or more subject entities associated with the quote;

19. Google Learning from Images on the Web
● How Google May Annotate Images to
Improve Search Results
● Computerized systems and methods
for enriching a knowledge base for
search queries
Inventors: Ran El Manor and Yaniv
Leviathan
Assignee: Google LLC
US Patent: 10,534,810
Granted: January 14, 2020
Filed: February 29, 2016

20. Bears Hunt Fish in Rivers
Main Objects: Bears
Secondary Objects: Fish

21. Semantic Image
Categories

22. Image Search Categories: Entities/Ontologies
● Google Image Search Labels Becoming More
Semantic?
● System and method for associating images with
semantic entities
Inventors: Maks Ovsjanikov, Yuan Li, Hartwig
Adam and Charles Joseph Rosenberg;
Assignee: Google LLC
US Patent: 10,268,703
Granted: April 23, 2019
Filed: December 8, 2016

23. George Washington Image Categories
Catgegories contain Semantically Related Entities which are an ontology,
Useful if you want to visually learn about an entity

24. Website Categories

25. Classification of Websites
● Google Using Website Representation Vectors to
Classify with Expertise and Authority
● Website Representation Vector to Generate Search
Results and Classify Website
Publication number: WO2020033805
Applicants: GOOGLE LLC
Inventors: Yevgen Tsykynovskyy
Publication Number WO/2020/033805
Filed: August 10, 2018
Publication Date February 13, 2020

26. Categories Based on Industry/Expertise Levels
● For instance, the website classifications may include the first category of
websites authored by experts in the knowledge domain, e.g., doctors, the
second category of websites authored by apprentices in the knowledge
domain, e.g., medical students, and a third category of websites authored by
laypersons in the knowledge domain.

27. Queries are Categorized using Query logs, and return results only from
the appropriate Category of Website…
A Query such as “What are the Symptoms of Diabetes Type 2,” would
be answered with An expert medical site run by Doctors.
Sites need to meet Thresholds of Quality to Rank for a query.
Because the Categories for Queries and for Websites need to match, this
Means Google has less pages to sort and rank to respond to a query with,
Making it more efficient

28. Thank you very much!
Any Questions? You can always ask later on Twitter: https://twitter.com/bill_slawski
I will be sharing these Slides on Slideshare: https://www.slideshare.net/billslawski