# How Websites Get Ranked by Importance > This patent describes a computer method for scoring documents in a linked database, like the internet, by considering the importance of other documents that link to them, helping search engines find better results. - **Patent:** US 6285999 - **Original title:** Method for node ranking in a linked database - **Owner:** Leland Stanford Junior University - **Granted:** 2001 - **Status:** Public domain (expired) - **Times cited:** 818 - **Field:** software, telecommunications, ai_ml, ecommerce, consumer_electronics ## What it does This patent outlines a computer method for assigning an "importance score" to documents within a network, such as web pages (Claim 1). It works by first obtaining a collection of documents where some link to others. Then, it calculates a score for each linked document based on the scores of all the other documents that point to it (Claim 1). For example, if a highly-scored website links to your blog post, your blog post's score would increase. The patent also describes various ways to adjust these scores, such as considering how many links a linking document has (Claim 2) or even how likely a user is to access that linking document (Claim 3). This process helps in organizing and presenting documents based on their calculated importance. ## What it does NOT cover - Does not cover ranking documents solely based on the words they contain, without considering incoming links. - Does not cover ranking documents where the score is not influenced by the scores of other documents linking to it. - Does not cover ranking systems that only count the number of links to a document, without adjusting for the importance of the linking document itself. - Does not cover ranking based purely on user behavior metrics like clicks, unless those metrics are used to adjust the linking document's weighting factor (Claim 7). - Does not cover ranking where a "random traversal" (Claim 10) does not assign rank based on how many times a document has been traversed. ## The clever bit The truly novel idea was to treat links as "votes" of importance, where a link from a more important page counts more than a link from a less important page. This recursive definition of importance, where a page's score depends on the scores of the pages linking to it, was a breakthrough for ranking information in vast, interconnected networks. ## Real-world examples 1. Google Search (PageRank algorithm) 2. Academic citation indexing (e.g., impact factors) 3. Social network influence scoring 4. Recommendation systems for linked content ## Why it matters This patent describes the core principles behind PageRank, the algorithm that powered Google's initial success and revolutionized web search. Before this, search engines often ranked pages primarily by keyword matching, leading to easily manipulated and low-quality results. By using the link structure of the web to determine importance, this method provided a far more relevant and robust way to find information, fundamentally changing how people navigate the internet. ## Frequently asked questions ### What does How Websites Get Ranked by Importance cover? This patent describes a computer method for scoring documents in a linked database, like the internet, by considering the importance of other documents that link to them, helping search engines find better results. ### Who owns patent US 6285999? Leland Stanford Junior University owns this patent, granted in 2001. ### When does this patent expire? This patent has expired and is now in the public domain — anyone can use the invention freely. ### What is patent US 6285999 cited by? This patent has been cited by 818 later patents that build on its ideas. ### What problem does this patent solve? This patent describes the core principles behind PageRank, the algorithm that powered Google's initial success and revolutionized web search. Before this, search engines often ranked pages primarily by keyword matching, leading to easily manipulated and low-quality results. By using the link structure of the web to determine importance, this method provided a far more relevant and robust way to find information, fundamentally changing how people navigate the internet. ### What does this patent NOT cover? Does not cover ranking documents solely based on the words they contain, without considering incoming links. **Full plain-English explainer:** https://patentbrief.org/patent/us/6285999/google-pagerank **Original patent:** https://patents.google.com/patent/US6285999 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How Google Uses User Feedback to Rank Websites by Quality](https://patentbrief.org/patent/us/8615514/evaluating-website-properties-by-partitioning-user-feedback) — A method for Google to judge a website's quality by looking at how users interact with its search results across different categories of search queries. - [How Facebook Ranks Search Results Based on Your Friends' Activity](https://patentbrief.org/patent/us/8914392/facebook-events) — A method for ranking search results by prioritizing links that your social network friends have clicked on previously. - [How Google's Patent Scores Authors and Posts on Messaging Systems](https://patentbrief.org/patent/us/8606792/facebook-timeline) — Google's 2013 patent describes a system for ranking authors and their posts on a messaging platform based on user interactions and subscriptions, influencing content visibility. - [How Search Engines Personalize Results Based on Your Browsing History](https://patentbrief.org/patent/us/7296016/bing-msn-search) — Google's patent for narrowing down search results by prioritizing websites that match your personal interests and past browsing habits. - [How Search Engines Rank Images Using User Ratings](https://patentbrief.org/patent/us/7836050/bing-search-ranking) — A system that improves image search results by combining how well an image matches a search term with how highly users have rated that image elsewhere.