Machine Learning Inside Google

By OSX - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=12890983
By OSX – Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=12890983

Understanding Systems

When I was in high school, one of the required classes I had to take was a shop class. I had been taking mostly what the school called “enriched” courses, or what were mostly academic classes that featured primarily reading, writing, and arithmetic. A shop class had more of a trade focus. I was surprised when the first lesson on the first day of my shop class was a richer academic experience than any of the enriched classes I had taken.

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How Google May Map a Query to an Entity for Suggestions

Search predictions come from:

– The terms you’re typing.

– What other people are searching for, including trending searches. Trending searches are popular stories in your area that change throughout the day. Trending searches aren’t related to your search history.

– Relevant searches you’ve done in the past (if you’re signed in to your Google Account and have Web & App Activity turned on).

Note: Search predictions aren’t the answer to your search, and they’re not statements by other people or Google about your search terms.

~ Search on Google using autocomplete

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Yahoo Assigns 2648 Patents to Mystery Excalibur IP, LLC Group

Google is possibly most well known for the patenting of an algorithm that sorted and ordered search results based upon a metric known as PageRank, named after Google Co-Founder Lawrence Page, while he was a student at Stanford University. Yahoo started off as a Web Directory, which became a Search Engine, and the patent it might be most well known for is one that it purchased from Overture (Originally Goto.com), and successfully sued Google with (winning a settlement out of the litigation) which describes paid search. That patent appears to have been assigned by Yahoo, along with a number of other patents last month.

A couple of weeks ago, an article on Yahoo’s patent portfolio ran, and provided some insights into what value those patents might hold. The article, Yahoo Has a Strong Patent Portfolio, But Reported Valuation is Too High gives us some ideas regarding how much the Search Engine’s patents might be worth (4 Billion?), and what they’ve been doing with them. Has Yahoo sold a good amount of those patents not much less than a week after that article? We don’t know for certain. It’s possible that they may have to one of the remaining bidders for the company: Exclusive: Yahoo’s bidder shortlist points to cash deal -sources.

On April 18th, 2016 an assignment was recorded at the United States Patent and Trademark Office (USPTO) on a transaction that appears to have been executed on April 18th, 2016 involving the assignment of 2648 patents from Yahoo! Inc. to Excalibur IP, LLC. It’s possible that name is made up to hold the patents temporarily. The address that the assignment indicates is Excalibur’s is “701 FIRST AVENUE SUNNYVALE, CALIFORNIA UNITED STATES OF AMERICA 94089”. A search for that address points to the headquarters of Yahoo! as we see in the knowledge panel below, so the actual purchaser appears unknown.

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Google’s Reasonable Surfer Patent Updated

surfer

Systems and methods consistent with the principles of the invention may provide a reasonable surfer model that indicates that when a surfer accesses a document with a set of links, the surfer will follow some of the links with higher probability than others. This reasonable surfer model reflects the fact that not all of the links associated with a document are equally likely to be followed. Examples of unlikely followed links may include “Terms of Service” links, banner advertisements, and links unrelated to the document.

Google’s original PageRank algorithm is based upon what its inventor referred to as the Random Surfer model, where it ranked pages on the Web based upon a probability that a person following links at random on the Web might end up upon a particular page:

The rank of a page can be interpreted as the probability that a surfer will be at the page after following a large number of forward links. The constant α in the formula is interpreted as the probability that the web surfer will jump randomly to any web page instead of following a forward link.

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Satisfaction a Future Ranking Signal in Google Search Results?

Do you search through Google on your phone? How do you know whether or not Google is watching you as you do and keeps on eye on whether or not you like the results you receive during your searches? Could Satisfaction with search results be a ranking signal that Google may use now, or in the future?

A newly published Google patent application describes technology that would modify scoring and ranking of query results using biometric indicators of user satisfaction or negative engagement with a search result. In other words; Google would track how satisfied or unsatisfied someone might be with search results, and using machine learning, build a model based upon that satisfaction, raising or lowering search results for a query. This kind of reaction might be captured using a camera on a searcher’s phone to see their reaction to a search result, as depicted in the following screenshot from the patent:

Google biometric responses

This satisfaction would be based upon Google tracking and measuring biometric parameters of a user obtained after thst search result is presented to the user, to determine whether those may indicate negative engagement by the user with a search result.

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Selecting Entities on Sites and Performing Tasks On Them Through Google

Visitors to a website may want to perform certain actions related to Entities (specific places or people or things) that are displayed to them on the Web.

For example, at a page for a restaurant (an entity), a person viewing the site may want to create a reservation or get driving directions to the restaurant from their current location. Doing those things may require a person to take a number of steps, such as selecting the name of the restaurant and copying it, pasting that information into a search box, and submitting it as a search query, selecting the site from search results, determining if making a reservation is possible on the site, and then providing information necessary to make a reservation; getting driving directions may also require multiple steps.

Using a touch screen device may potentially be even more difficult because the site would possibly then be limited to touch input.

A patent granted to Google this week describes a way to easily identify an entity such as a restaurant on a touch device, and select it online and take some action associated with that entity based upon the context of a site the entity is found upon. Actions such as booking a reservation at a restaurant found on a website, or procuring driving directions to that site, or other actions could be easily selected by the user of a site.

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New Visual Search Photo Features from Google

Visual Search Image outlining

These details come from an anonymous source who also gave us a bit more details on the project. The report states there will be a new feature integrated, allowing users to outline specific areas of the image in order to directly target their searches.

In Google Goggles, one can only search the whole image, which has proven to bring plenty of discrepancies. Images often display plenty of distractions, background items and other objects that may throw off a search result. According to the sketch provided, the system will also be able to recommend retailers for purchasing products, as well as other details.

Furthermore, it is said this technology has also been tested in “wearable computing devices”. This could suggest this technology may come to products like Google Glass and possibly even VR (or AR) headsets.

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Google Granted Patent on How Self-Driving Cars Might Handle Tailgaters

Self-Driving Car

Google’s self driving cars have covered over a million miles of roadway, and recently, one of them crashed into a slow moving bus. Details about the accident can be found in Google’s bus crash is changing the conversation around self-driving cars. Oddly timed, but appropriate, Google seems to have been working on the issue that caused that problem, as we are told in this article: A Month After Google’s Car Hit a Bus, Google Got a Patent for Robot Cars to Detect Buses.

Because of the timing of that patent, I’m not surprised by another one being granted today involving self-driving cars, on how it might respond to tailgaters. That patent is:

Detecting and responding to tailgaters
Inventors: Dmitri A. Dolgov, Philip Nemec, Anne Kristiina Aula
Assigned to: Google
United States Patent 9,290,181
Granted March 22, 2016
Filed: May 4, 2015

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