The document summarizes Bill Slawski's presentation on search and social media patents from 2012 and beyond. It discusses various patents Google has acquired related to search, social media, hardware, fiber optic networks, and more. It also outlines patents for phrase-based indexing, concept-based indexing, ranking pages based on user interactions, building a knowledge graph, and developing a planet-scale distributed search index. Slawski suggests Google may expand into hardware, entertainment, internet service provision, and more based on its patent portfolio.
The document discusses the evolution of search engines from basic keyword search to semantic search using knowledge graphs and structured data. It provides examples of how search engines like Google are now able to provide direct answers to queries by searching structured data rather than just documents. It emphasizes the importance of representing web content as structured data using schemas like schema.org to be discoverable in semantic search and knowledge graphs.
For the first time since the emergence of the Web, structured data is playing a key role in search engines and is therefore being collected via a concerted effort. Much of this data is being extracted from the Web, which contains vast quantities of structured data on a variety of domains, such as hobbies, products and reference data. Moreover, the Web provides a platform that encourages publishing more data sets from governments and other public organizations. The Web also supports new data management opportunities, such as effective crisis response, data journalism and crowd-sourcing data sets.
I will describe some of the efforts we are conducting at Google to collect structured data, filter the high-quality content, and serve it to our users. These efforts include providing Google Fusion Tables, a service for easily ingesting, visualizing and integrating data, mining the Web for high-quality HTML tables, and contributing these data assets to Google's other services.
Alon Halevy heads the Structured Data Management Research group at Google. Prior to that, he was a professor of Computer Science at the University of Washington in Seattle, where he founded the database group. In 1999, Dr. Halevy co-founded Nimble Technology, one of the first companies in the Enterprise Information Integration space, and in 2004, Dr. Halevy founded Transformic, a company that created search engines for the deep web, and was acquired by Google. Dr. Halevy is a Fellow of the Association for Computing Machinery, received the the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2000, and was a Sloan Fellow (1999-2000). He received his Ph.D in Computer Science from Stanford University in 1993 and his Bachelors from the Hebrew University in Jerusalem. Halevy is also a coffee culturalist and published the book "The Infinite Emotions of Coffee", published in 2011 and a co-author of the book "Principles of Data Integration", published in 2012.
Here are some options for completing your query:
- Freddie Mercury was the lead singer of Queen
- Brian May was the guitarist for Queen
- Queen was a British rock band formed in 1970
- Freddie Mercury died in 1991 from complications due to AIDS
1. The document discusses Sergey Brin's early work on extracting structured data from unstructured sources like the world wide web through his DIPRE algorithm.
2. It then shows how projects at Google like Google Maps and WebTables have built upon this idea to generate structured data from various online sources.
3. Current initiatives at Google like schema markup, question answering, and crowdsourcing ontologies continue working to understand online information in a more semantic, structured way to improve search.
The document discusses semantic search capabilities at Yahoo. It describes how Yahoo has developed techniques to extract structured data and metadata from webpages to power enhanced search results. This includes information extraction, data fusion, and curating knowledge in a graph. Yahoo uses this knowledge to better understand search queries and present relevant entities and attributes in results. Semantic search remains an active area of research.
Keyword Research and Topic Modeling in a Semantic WebBill Slawski
The document discusses keyword research and topic modeling in the semantic web. It covers identifying named entities, adding schema markup to pages, and verifying listings on Google My Business. It also discusses using context and related phrases to improve search engine optimization, including looking at knowledge bases, disambiguations pages, and clustering related meanings. The document provides examples of using related words and phrases for semantic topic clustering and ranking documents based on included phrases.
What IA, UX and SEO Can Learn from Each OtherIan Lurie
Google has become the arbiter how users experience a website. Their data-driven determinants of what constitute good UX directly influence how a site is found. This is wrong because people, not machines, should determine experience; Google does not tell the SEO or UX community what data is used to measure experience and many elements of experience cannot be measured.This presentation reveals why Google uses UX signals to determine placement in search results and how to create a customer pleasing and highly visible user experience for your website.
This document discusses semantic search and how it can improve traditional information retrieval systems. It provides examples of how semantic search uses structured data and schemas to better understand user intent and content meaning. This allows semantic search to enhance various stages of the information retrieval process from query interpretation to result presentation. The document also outlines the growing adoption of semantic web standards like RDFa and schema.org to expose structured data on webpages.
Semantic Search tutorial at SemTech 2012Peter Mika
This document provides an introduction to a semantic search tutorial given by Peter Mika and Tran Duc Thanh. The agenda covers semantic web data, including the RDF data model and publishing RDF data. It also covers query processing, ranking, result presentation, evaluation, and a question period. The document discusses why semantic search is needed to address poorly solved queries and enable novel search tasks using structured data and background knowledge.
Slawski New Approaches for Structured Data:Evolution of Question Answering Bill Slawski
Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.
Semantic search uses language processing to analyze the meaning of content and search queries to return more relevant results. It involves classifying content using taxonomies, identifying named entities, extracting relationships between entities, and matching these based on meaning. Implementing semantic search requires preparing content through classification, metadata, and information architecture, as well as technologies for semantic tagging, entity extraction, triple stores, and integrating these capabilities with existing search and content management systems.
This is a high-level summary of three important ways to help people find information. The slides were presented at Vera Rhoades' information architecture class at the University of Maryland.
William slawski-google-patents- how-do-they-influence-searchBill Slawski
Bill Slawski presented a webinar on analyzing patents related to search engines and SEO. He discussed 12 Google patents covering topics like PageRank, Google's news ranking algorithm, analyzing images to detect brand penetration, and building user location history. The patents described Google's work in building knowledge graphs from web pages, ranking entities in search results, question answering, and determining quality visits to local businesses.
Is search always the right solution? There are many things you can do with a hammer, but it’s not so great if you need to turn a screw.
Text Classification is an alternative to search that may be more appropriate for social media data analysis. Text classification is the task of assigning predefined categories to free-text documents. It can provide conceptual views of document collections and has important applications in the real world. Using text classification as the foundation for analysis – i.e., teaching a machine to categorize posts the way humans do – can dramatically improve your ability to gather the right data and, ultimately, increase the chances that you’ll uncover what you need to know.
Bearish SEO: Defining the User Experience for Google’s Panda Search LandscapeMarianne Sweeny
The search sun shifted in March 2011 when Google started rolling out the beginning of the Panda update. Instead of using the famous PageRank, a link-based relevance calculation, Panda rests on a machine interpretation of user experience to decide which sites are most relevant to a searchers quest for knowledge. This means that IA and UX practitioners need to start thinking about the machine implications of the way they structure information on the web, and think ahead about the human implications for how search engines present their sites in response to searcher queries. Bearish SEO will present real, actionable methods for content providers, information architects and user experience designers to directly influence search engine discoverability. Need is an experience. It is a state of being. The goal for this presentation is to ensure that user experience professionals become an integral part of designing search experience.
The document discusses issues with how computer science has directed the development of search systems, focusing on efficiency over user experience. It argues search systems have paid minimal attention to the user experience beyond results relevance and ad-matching. The goal of the plenary is to inspire designing search experiences that do more than just sell products well.
Acting local in a global world (local SEO)Similarweb
Earlier today Gerald Murphy represented 7thingsmedia at the Internet Advertising Bureau (IAB) as part of the Connected Consumer -- Devices and Data session.
What are the quick wins for your local SEO campaign? How can you increase reviews, quickly?
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results.
In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results.
Semantic SEO: Going Beyond Keywords
Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content.
Topical Authority: Establishing Expertise and Trustworthiness
Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility.
PageRank: Measuring the Importance of Webpages
PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results.
The Interrelation of Semantic SEO, Topical Authority, and PageRank
Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility.
The Benefits of Semantic SEO for Search Engines
Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience.
In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.
The Beginner's Guide to Blog Optimization & Content PromotionRyan Stewart
This document discusses Ryan Stewart's SEO services and methods for growing website traffic. It provides tips for choosing keywords, creating content in different formats, and promoting content across owned, earned, and paid channels. The document contains strategies for keyword research, developing audience profiles, optimizing content for search engines, repurposing content, outreach to influencers and publications, and paid social media advertising. The goal is to generate an online buzz and traffic through content marketing.
The New Content SEO - Sydney SEO Conference 2023Amanda King
This document summarizes Amanda King's presentation on the new content SEO at the Sydney SEO Conference. It discusses how Google has moved beyond keywords and now understands content semantically through natural language processing and systems like BERT. It also explains how Google analyzes content through parsing, entity detection, and understanding relationships to score and rank pages. The presentation recommends doing a full content inventory to identify entities, related terms, and differences from top ranking pages to update content accordingly.
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
This document discusses search engine optimization and the development of search systems. It notes that computer science has directed search system development with a focus on results relevance, while neglecting user experience. The intent is to inspire deeper engagement in designing search experiences that do more than just sell products. It also discusses challenges like the volume of online information, differences in language and perception, and the limitations of current search systems.
Using the Wisdom of the Crowd for Content ExcellenceKeith Goode
Presented at Pubcon SFIMA on February 25, 2016 in the session "Mining the Keyword Goldmine," alongside data guru Bill Hunt, this slide deck covers the process of rethinking your approach to SEO, content creation, keyword research and tapping into the conversations going on around your site's core keywords. This deck was presented by seoClarity's Chief SEO Evangelist, Keith Goode.
Pubcon Las Vegas 2012 - Social Signals on Search, presented by Rob GarnerRob Garner
The document summarizes how social signals influence search engine rankings. It discusses how search engines now factor social signals like shares, likes, comments and +1s into their algorithms when determining relevance. Signals from a user's social connections and circles within social networks like Google+ can boost the search performance of content. The document also explains how search insights can help social marketers find and understand their target audiences across different online communities.
Solving Real World Challenges with Enterprise SearchSPC Adriatics
Agnes Molnar is an international SharePoint consultant and Microsoft MVP who has over 10 years of experience with SharePoint. In her presentation, she discusses some of the real world challenges organizations face with enterprise search, including information overload, the complexity of content and metadata, security, scaling, and relevance ranking. She emphasizes that search is an application that requires understanding user needs and behaviors as well as content sources in order to be successful.
The inbounder London 2. May 2017 Gianluca Fiorelli We Are Marketing
The document discusses Google's various products, services, and areas of focus based on the company's patents, research papers, and acquisitions. It notes Google's focus on areas like natural language processing, information retrieval, learning from user interactions, and personalized search. It also lists some of Google's acquisitions and products in areas like video/images, AI/chatbots, VR, and machine learning datasets. The document suggests Google's alphabet includes entities, context, parsing, semantics, and understanding as key areas.
SEOs fail because they tend thinking only tactically and forget strategy. In this deck, Gianluca presents the most interesting trends in Google Search, which we can discover simply by looking with attention the same Google sources: patents, papers, acquisitions, people hired and research blog post.
Video and images, Parsing and Semantics, Local Search and Personalization, Natural Language and Machine Learning.
These are the things we should create an SEO strategy around, and not fixate ourselves on Unnamed (or Fred) updates.
The document discusses search engines and how they have evolved over time. It explains that early search engines ranked results based mainly on content, while modern engines also consider factors like page structure, popularity, and reputation. The document provides definitions of key search-related terms and outlines some of the main components and processes involved in how search engines work, such as crawling websites, indexing pages, and ranking results. It also discusses different types of search tools and how to choose the best one depending on your information needs.
How to SEO a Terrific - and Profitable - User ExperienceBrightEdge
Tune in for Portent SEO Marianne Sweeny’s January webinar: “How to SEO a Terrific – and Profitable – User Experience.” Learn how search engine algorithms are now incorporating IA, UX and content strategy, as well as methods for directing Google, Bing & Co. to perform better for your users.
Tips and tools for effective SEO and brand recognition - eCommerce Expo Melbo...Bespoke Agency
My presentation from the 2014 eCommerce conference and expo in Melbourne. In it i highlight a low cost scale able data driven content marketing technique for eCommerce sites. I call out tools you can use and how this fits into your wider SEO and content efforts. enjoy.
1) The document discusses the evolution of search engines and algorithms over time from early concepts like Hilltop and PageRank to more modern techniques like RankBrain that use neural networks.
2) It also examines how search engines have incorporated personalization and contextualization by using implicit and explicit user data and feedback to better understand search intent and tailor results.
3) Several studies summarized found that most users expect to find information within the first 2 minutes of searching, spend little time viewing individual results, and refine queries through an iterative process as understanding develops.
Semantic mark-up with schema.org: helping search engines understand the WebPeter Mika
This document discusses semantic markup with schema.org to help search engines understand web pages better. It describes how schema.org was created as a collaborative effort by major search engines to define a shared set of schemas. This allows publishers to markup their content in a consistent way so it can be understood by different search engines and applications. The document outlines how schema.org has grown significantly in adoption and detail over time. It also discusses how schema.org builds on semantic web standards and can describe actions websites can take to help with task completion.
The document provides an overview of search engines and search algorithms. It discusses (1) the key concepts of search including user intent, queries, documents and results; (2) the technical aspects such as indexing, ranking, and learning algorithms; and (3) current and future challenges for search. Learning algorithms covered include pointwise, pairwise, and listwise approaches. The goal of search engines is to accurately match user intent with relevant documents from a large corpus.
Similar to Search and social patents for 2012 and beyond (20)
Semantic search Bill Slawski DEEP SEA ConBill Slawski
1) Google uses various techniques to extract structured information like entities, relationships, and properties from unstructured text on the web and databases. This extracted information is then used to generate knowledge graphs and provide augmented responses to user queries.
2) One key technique is to identify patterns in which tuples of information are stored in databases, and then extract additional tuples by repeating the process and utilizing the identified patterns.
3) Google also extracts entities from user queries and may generate a knowledge graph to answer questions by providing information about the entities from sources like its own knowledge graph and information extracted from the web.
y Keynote Presentation from today at SMXL Milan 2019 - Loving Italy about entities and augmentation queries and question answer through building knowledge graphs.
Keyword Research requires knowing your audiences and tasks for each of them. It can include taxonomies, Ontologies, context terms and disambiguation and optimizing for a knowledge graph and finding related entities.
Changes in Structured Data at Google (SEO Camp 'us in Paris)Bill Slawski
This document discusses how Google uses structured data and annotations to power its search results. It describes Andrew Hogue's work using annotation frameworks to link unstructured data to a fact repository known as a knowledge graph. The document outlines several patents related to augmenting queries, generating related questions, and identifying structured data and candidate answer passages to provide contextual search results.
Guidelines and best practices for successful seo william slawski smxl milan...Bill Slawski
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This document discusses changes in search engine optimization (SEO) and how to cut through noise. It summarizes patents related to ranking news articles over time and how they show changes in what signals are used to evaluate news sources. It recommends optimizing content for things and voice search by adding structured data for entities and speakable schema to help digital assistants answer questions about the content. Additional reading on entity-oriented search, voice search, and leaving no valuable data behind is also provided.
The document discusses the history and development of Google's search technology. It describes how Google founders Larry Page and Sergey Brin met at Stanford University and collaborated on early search projects. It then outlines key milestones in Google's search capabilities, including the development of PageRank, knowledge graphs, and using contextual information to better understand user queries.
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First question I asked everyone is, "How many of you have never moved to a new home? Moving a courthouse is a whole lot more work." No one raised their hand. They can related to the challege.
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My presentation slides from SMX East on future search interfaces on a conceptual level, and how spoken, visual, and even parameterless searches may impact seo and online marketing.
Everything you wanted to know about crawling, but didn't know where to askBill Slawski
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Promoting the blog with search engine optimizationBill Slawski
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7 Most Powerful Solar Storms in the History of Earth.pdfEnterprise Wired
Solar Storms (Geo Magnetic Storms) are the motion of accelerated charged particles in the solar environment with high velocities due to the coronal mass ejection (CME).
Comparison Table of DiskWarrior Alternatives.pdfAndrey Yasko
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The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era.
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You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
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Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
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Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
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Search and social patents for 2012 and beyond
1. Search and Social Patents for 2012
and Beyond
Thursday, Mar 15 2012, 5:30p.m. - 7:00p.m.
SEER Interactive "Search Church",
Philadelphia, PA, United States
Bill Slawski, SEO by the Sea
http://www.seobythesea.com @bill_slawski
2. Disclaimer
Some of the stuff described in patents might
happen, and some of the stuff described in
patents might not.
That’s half the fun…
3. A Different Perspective…
Image courtesy of Silveira Neto at http://www.flickr.com/photos/silveiraneto/
10 Most Important SEO Patents: Part 8 - Assigning Geographic Relevance to Web Pages
I had an epiphany in second grade…
4. While Google has been developing their own
patents and acquiring small and large
companies, they’ve also been acquiring some
unusual patents…
Terminator Vision?
Google Acquires Swimming Goggle Patent
Google Picks Up Hardware and Media Patents from Outland Research
5. Granted Google Patents
• October 24, 2008 – 187 granted patents
• February 6, 2011 – 809 granted patents
• March 14, 2012 – 4,163 granted patents
• Approximately 17,000 granted Motorola patents
to come.
Official Google Blog - Patents and innovation
6. Will Google Become a Hardware
Manufacturer?
• Motorola
Phones/Set Top
Boxes
• Self aware driving
cars
• Google Store
Rumors
• Net Appliances
• Google TVs
7. Hires Wii hacker/Kinect developer Johnny Chung Lee from Microsoft
Acquires patent for Harry Potter like voice activated game controller
Launches Google Play
High Definition Streaming Video patents from Swarmcast
Will Google Become an Entertainment
Center?
9. Will Google Become an ISP/internet
television provider?
• Google Fiber Community Project
• Kansas City Application to provide TV
• Pirelli fiber optics patent acquisition
• Periodic Micro Transmission Line Patents
• FCC application for Earth Station Receivers
Google Acquires Fiber Optic Networking Patents (Kansas City and then the World?)
Is Google Aiming at Building Faster Networks and Data Transmissions?
10. Will Google build flying motorcycles?*
*Not Google’s patent
Combination powered parachute and motorcycle
11. Will Google Stay a Search Engine?
• Xerox patents on scoring document quality
• IBM patents on databases, computer architecture,
network devices, search
• Updated Stanford PageRank patents
• 500 + Google updates/year on core ranking algorithms,
some with patents.
12. Expanding Relevance
• Keyword Matching
• Categorization
• Informational Needs
• Situational Needs
Nature and Manifestations of Relevance - Tefko Saracevic (pdf)
Relevance: The ability (as of an information retrieval system) to retrieve material that
satisfies the needs of the user. —Merriam-Webster Dictionary (2005)
13. Concurrent Search Approaches
• Phrase-Based Indexing
• Concept-Based Indexing
• Triples of Data (User, Query, Site)
• Building a Knowledge Base
• Planet Scale Distributed Data
17. Triples of Data
An instance is a “triple” of data: (u, q, d) where:
• u is user information,
• q is query data from the user,
• d is document information relating to pages
returned from the query data.
Imagine a search engine building a huge statistical model about
searchers, searches and documents, where it creates profiles for
all three, and uses “instances” of data to recommend pages for
searchers similar to how Amazon might recommend books or
other products…
Google and Large Scale Data Models Like Panda
18. A system ranks documents based, at least in
part, on a ranking model. The ranking model
may be generated to predict the likelihood
that a document will be selected. The system
may receive a search query and identify
documents relating to the search query. The
system may then rank the documents based,
at least in part, on the ranking model and
form search results for the search query from
the ranked documents.
19. Building a Knowledge Base
Freebase - Empire State Building entry
Google Gets Smarter with Named Entities: Acquires MetaWeb
Google Knowledge Graph Could Change Search Forever
20. With a named entity in the query, Google assumes that the searcher wanted a
“site:” search
21. Planet Scale Distributed Data
How Google Data Centers may be Split between Regional and Global Data
22. Will Google Stay a Search Social
Engine?
• Grouptivity - Content Shares ranking
• Agent Rank - digital signatures
• New Agent Rank – Not all +1s equal
• Katango – Personal Crowd Control
• Confucius – User Rank
• WOWD – User Click Page Ranking
23. Grouptivity - Content Shares ranking
Patent filings acquired 10 months before Google
Plus
Sometimes sharing with a smaller group but
getting more reshares/retweets is better than
sharing with a larger audience and getting less
Google Plus Roots are Showing in Grouptivity Patent Filings
Page Ranking System Employing User Sharing Data
24. ankesh kumar
02/22/2012 at 6:55 pm
I’m the founder of Grouptivity/Sharetivity and you
are correct in your assertions that we had a
direction we wanted to take the company.
Unfortunately, since we could not get funding nor
traction, I decided to sell the IP and focus on a
new venture. Wish I had Google’s road map,
might have got a few more dollars:)
Inventor's comment
25. Agent Rank - digital signatures
• Digital Signatures associated with content
• Reputation ranking
• Fixes attribution problems with duplicate
content
• Content is associated with profile
Google’s Agent Rank Patent Application
26. New Agent Rank – Not all +1s equal
• Trusted Agents
• Endorsements, not of content, but of author
Are You Trusted by Google?
“Not all references, however, are necessarily of equal significance.
For example, a reference by another agent with a high reputational
score is of greater significance than a reference by another agent
with a low reputational score.
Thus, the reputation of a particular agent, and therefore the
reputational score assigned to the particular agent, should depend
not just on the number of references to the content signed by the
particular agent, but on the importance of the referring documents
and other agents.”
27. Katango – Personal Crowd Control
• Originally created an iPhone App for Facebook
for Clustering Contacts
• Patented an Intelligent User Agent
• Include links to other social networks in your
profile
• Agent monitors social activities outside
Google without buy-in from other social
services
• Those activities could be used in rankings…
Katango Intelligent Social Media Agents
28. Confucius – User Rank
• Onebox Q&A in search results
• Google’s Codename for Q&A social networks
• Confucius Process
• User Rank based upon credential scores
– Contributions
– Meaningful interactions
– Topic specific
Confucius and Its Intelligent Disciples: Integrating Social with Search (pdf)
Ranking User Generated Web Content
How Google Might Rank User Generated Web Content in Google + and Other Social Networks
29. User Rank -Weighing Social
Contributions
• Relevance of content to a query
• Appropriateness of language (e.g., lack of
profanity),
• Originality in relation to previously-posted
content.
30. User Rank - Weighing Quality of
Responses
• Relevance to original post/question,
• Appropriateness of language used (e.g., lack
of profanity)
• Specificity of response (idf),
• Originality in relation to previously-posted
responses, or
• Promptness in relation to the timestamp of
the original posting.
31. User Rank - Credential Scores
• A response to a high quality question/post with a high
quality answer, can positively impact a credential
score.
• A responds to a question/post with a low quality
answer, may negatively impact a credential score.
• Responding to someone with a high credential score,
may more positively impact their credential score than
responding to someone with a low credential score.
• Post something and receive high quality responses
from people with high credential scores, that
interaction can positively impact yours credential
score.
32. WOWD – User Page Ranking
• Crowd sourcing the crawling of websites, by
actually tracking clicks upon links from one
page to another.
• Identifying rankings based upon clicks, time
spent on pages, frequency of visits, keywords
in visited pages, bookmarking of pages…
Wow! Google Acquires Wowd Search Patents
System for User Driven Ranking of Web Pages
System and Method for Recommendation of Interesting Web Pages Based on User Browsing Actions
34. Questions?
• Contact me at bill (at) seobythesea (dot) com
• Or @bill_slawski on Twitter
• At Google Plus
• Or through my contact form at SEO by the Sea