A couple of weeks ago, a federal bankruptcy judge approved the sale of Kodak’s patent portfolio to a group of companies that joined together to buy them at a discounted price. The group included Apple, Google, Facebook, and others. There were more than 1,000 patents involved, related to photography, storing photos, and sharing photos.
It makes sense for Google to have been interested in those patents, considering their involvement in smart phones with cameras, and their work on Google Glass, where taking pictures and recording video will likely be one of its strengths.
Google’s patents have provided a great number of hints over the past 10 years about local search and how Google treats businesses and landmarks in Maps and Web results and elsewhere. I’ve been fortunate enough to have uncovered some of these patents and written about many of the algorithms and approaches that Google has used, including concepts like location prominence, location sensitivity, Maps in Universal Search, Google’s Crowdsensus Algorithm, and more.
I am going to be the keynote speaker at Local U Advanced, Baltimore, starting Friday night, March 8 at 7:00 pm through Saturday at 5:00 pm on March 9 (There’s an early bird discount of $100 if you sign up before Feb. 8th). This Local University presentation will be taking place in Hunt Valley, MD. There’s an amazing group of speakers lined up for the event, covering local, mobile, and social aspects of local search.
Will Google Plus show advertisements one day? If they do, how will they decide upon the ads to show different users of the social network? A 2010 paper, AdHeat: An Influence-based Diffusion Model for Propagating Hints to Match Ads (PDF), described one method of advertising on a social network that was actually tested on Google’s world wide (except for the US) set of Q&A type sites with the code name of Confucius. It also incorporated the Confucius User Rank into displaying those ads. The user rank approach to reputation scoring for Confucius, and for choosing advertisements for users of the system appears to be an method that would work well in deciding upon reputation scores for users at Google Plus.
A Google patent granted in early December, 2012, provides a different approach for showing advertisements and other content items to users of a social network like Google Plus. The patent makes it clear that while the approach described within it might be used for advertisements, it might also be used to show other content as well.
On Friday afternoon, I took a walk to the auto repair shop working on my car, about a mile and a half down the road. A phone alert made me aware of a Google Now card springing up to give me directions to the shop, and telling me that it would take me less than a minute to get there. I guess Google Now wasn’t looking at the accelerometer on my phone, or it would have realized that I was moving too slowly to be driving. I couldn’t help but think though how Google Now could be a feature that would work well in the heads up display that Google’s working on under the name Google Glass.
As we wait to see what kinds of features might be incorporated into Google Glass, it appears that Google acquired a patent first filed a dozen years ago, granted in 2006, and recorded at the USPTO on Thursday. The patent was originally filed by Agilent Technologies, transferred to a company in Singapore in 2006, and then to Intellectual Discovery Co., located in South Korean. Google was assigned the patent on November 16, 2012, and the transaction was recorded at the USTPO on January 8, 2013.
When we talk about indexing and crawling content on the Web, it’s usually within the context of pages being ranked on the basis of a number of signals found on Web pages that might be ranked in response to queries. Google has told us that the future of search involves Knowledge Bases, and the indexing of Things, Not Strings. Gianluca Fiorelli explored Google’s ideas of Search in the Knowledge Graph Era earlier this week.
A few years back, I wrote some posts about some Google Patents that explored how Google might be extracting and visualizing facts, and using Data Janitors to process that information and clean it up and sort it. Google was granted another patent this week that’s very much related, looking at how Google might understand locations for places collected from Web pages. One of the inventors, Andrew Hogue, gave this Google Tech Talk presentation last year:
When someone searches at Google, their query might not express the informational or situational need that they have. It might be too broad, too ambiguous, or vague in some other manner. A well-formulated query instead might contain terms returing resources addressing the searcher’s intent, which might be measured by performance metrics. For vague queries, search results that satisfy a searcher’s need for information might not be highly ranked, and may not be presented on a first page of search results.
Well Performing Queries as Search Suggestions
A search engine may identify well-performing queries from queries entered by users. This is especially true in cases where many searchers have selected results from those queries. These well-performing queries may be suggested to searchers when similar queries are presented to a search engine.
The most important step in doing keyword research is entering a keyword phrase into a search engine like Google, and seeing what results show up, and trying to understand why the pages that appear within results are there. If you can’t do that, then it’s time to dig down and start learning.
Whether you’re a searcher looking for information on the Web, or someone doing keyword research for a website, it’s important to have an idea of the many different ways that a search engine might treat a search you perform. For instance, if your search is one that might trigger Google to show results from a specific web page associated with a named entity (a particular person, place, or thing) at the top of those results, you shouldn’t necessarily be surprised to see that site listed first in search results. This is something that is done algorithmically by Google. Just stating that Google has a “magical” brand preference is a mistake in that instance. It’s better to try to understand how that algorithm might be triggered instead.
Likewise, when you perform a search for a term such as [hospice], Google might decide to show a map result from Google Maps in Web search results because their universal search algorithm suggests that the query has a local intent, and the searcher is likely looking for a nearby hospice. Again, it would be a mistake to make the assumption that Google is favoring their own “property” in Google Maps when the reality is that the vertical search result of Google Maps is what searchers are actually looking for.
Imagine the Earth broken down into a series of cells, and each of those cells broken down into a series of even smaller cells, and then into smaller cells again, and so on, in a spatial index. Each of the levels become increasingly narrow, and increasingly more precise areas or zoom levels of the surface of the Earth.
As these cells decrease in size, they increase in numbers, which has the impact of increasing the zoom level and the accuracy of areas represented in such an index. Might work good in a place like China, where latitude and longitude are banned for export as munitions. Such a set of cells might be part of a geospatial analyzing module that links specific businesses and points of interest (parks, public regions, landmarks, etc.) to specific places on this model or index of the earth. That might be one index of the businesses and one index for the points of interest, or a combined database that includes both.
Sometimes that index might include a business and a landmark within the same cell. While that could be correct in some instances, such as a shop appearing within the Empire State Building, Often its an error, and sometimes even an intentional error. People will sometimes enter incorrect information into a geographic system like this to try to gain some kind of advantage.
If people search for something like a motel “near” a particular park for instance, the motel that appears to be next to, or even within the boundaries of that part might seem to have something of an an advantage in terms of distance from that part when it comes to ranking the motel. And, sometimes Google doesn’t seem to do the best job in the world at putting businesses in the right locations at Google Maps.