Google on Aggregating Ad Data, Yahoo Messes with Map Reduce, Microsoft Explores Hierarchical Tagging

I don’t always have time to dig as deeply as I like into some of the patent filings that I uncover each week, and end up not writing about some interesting things that the search engines come out with.

Instead of not mentioning those at all, I think I might try to do a weekly post pointing out some of the most interesting of those each week, like the following. If you find something interesting about any of them that you would like to share in the comments, please be my guest…


Google explores providing better data for advertisers, especially those with multiple web sites focusing upon broad geographical areas, in the following patent application:

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How Search Engines Can Learn From Looking at Sequences of Search Queries

Whenever someone searches at a search engine, they not only get information in response to their search, but they also provide information to the search engine about the things they are searching for – information which the search engine might find useful in helping other searchers.

If that searcher performs another search related to their first search, then the search engine might create an association between the two search phrases that the searcher used, if the two phrases appear to be related. If they perform a series, or sequence, of searches on a concept, then the search engine might take advantage of that information.

If a lot of people perform that first search, and then the same second search, or that same search within a search session, then the search engine might decide that the phrases are semantically related to each other. Knowing that relationship exists between search queries might help the search engine help people find things on the web, and it might help provide better advertisements from the search engine.

A patent application from Yahoo explores how the search engine might find semantically related terms by looking at queries searched for by people in search sessions, and describes some of the processes behind how the search engine might determine that phrases may be related to each other. It also describes how a search engine might identify whether a query comes from a person, or from a program.

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Phone Keyboards and Seachers Using Predictive Query Suggestions

A few years back, finding myself stranded on the side of the road with a broken down pickup truck and being over an hour’s drive from home, I convinced myself to finally get a mobile phone.

broken down pickup truck

I didn’t necessarily want to have a phone hanging at my side all of the time, and I didn’t need it for work at that time. But it would have been useful in that emergency, and I wanted to start seeing what web sites looked like on a phone.

Search engines are also paying more attention to the smaller screens, and the more limited keyboards available to people who access the Web by phone. What influence do these constraints have upon the future of mobile search?

Studying Query Suggestions on Phones

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How Search Engines May Substitute Search Terms for Yours

When you search for something at a search engine, the search engine might not just try to find pages on the web which match the keywords that you searched with, but may first try to expand upon those keywords by finding similar or related terms, and may substitute search terms for the ones you have chosen.

A Yahoo search box with a search for refurbished laptops and search query suggestions shown above the search results

This kind of substitute search terms are most visible when one of the query terms that you use is a misspelling, and a search engine might display results with the correctly spelled words if it is pretty confident that one of the terms is misspelled.

How does a search engine know that a term is misspelled, or that there might be related phrases that might provide better and more helpful results to a searcher?

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Community Tagging and Ranking in Images of Landmarks

In addition to collecting a lot of information about the Web by using crawling programs to index content across the internet, search engines can learn a lot about pages and images and videos and other objects on the web by watching what we choose when we search, by seeing how we browse web pages through their toolbars, and by noting what words we might choose when we annotate and tag images and pages.

As publishers of text and links and pictures, as users of web pages, and as interactive participants on pages when leaving comments and tags and annotations, we provide search engines with information about our interests and what we might be interested in seeing on the Web.

As those search engines learn about us and our interests from the pages that we like to visit and the images and text that we might publish, they can compare what we see and what we do online with other travelers and publishers on the Web, and they might view us as communities who may share some common interests, and whom they can learn from.

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