Microsoft Technology Licensing, LLC

Fabrice Canel

Kumar Hemachandra Chellapilla

Junaid Ahmed

Alessandro Catorcini

Abhishek Kumar Singh

Walter Sun

Balaji Shyamkumar

Thomas Francis McElroy

Dean M Wierman

Vishnu Challam

Mark P Hahn

Amy J Michaels

Charles (Xi) Zhang

Charles Zhang

A client application installed on end user computers generates metadata from the content of web pages visited by end users and provides the metadata to a search engine. When an end user visits a web page, the end user's computer downloads and displays the web page to the end user. The client application may simultaneously access the web page content and generate this metadata in the form of a content signature of the web page from the web page content. The client application then provides the content signature to a search engine. The search engine may employ content signatures to identify new web pages to crawl and index. Additionally, the search engine may employ content signatures to identify changes to web pages and determine the crawl frequency of web pages.

Content signature notification

Embodiments of our technology provide a method, system, and media for presenting relevant information incident to attempting to present information that is unavailable by way of a website. One embodiment of the method includes receiving a request to present a desired web page, determining that the desired web page is unavailable for presentation, determining search criteria associated with the request, dynamically generating a second web page that includes search results that were obtained based on the search criteria, and presenting the second web page on a display device.

Smart, search-enabled web error pages

A politeness manager estimates traffic to the sites based on historical log data generated and sent by plug-ins or toolbars on client web browsers. The historical log data details dates and times the web browsers visit different web sites that is used to understand what timeframes specific web sites are busy and what timeframes the web sites are not busy. Crawl rates for different timeframes for a web site are determined based on the historical log data, and web crawlers are scheduled to crawl the web site according to the crawl rates to minimize the chances that web crawler requests are responsible for the site crashing.

Optimizing web crawling with user history

Growing community of inventors

Redmond, WA, United States of America

Fabrice Canel