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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Google Assigns 148 Medical Patents to Verily Life Sciences

verily logo

Back in September of 2009, I wrote a blog post that I titled Google’s 10 Oddest Patents. The first of those that I included in that list was one named Instrument for medical purposes, I included it mostly because Google was a search company, and it felt odd that Google would have a patent on a medical process. That one used “ultrasonic sound to investigate the structural makeup of biological tissue in organs and vessels.”

Times have changed, and since that time, Google has restructured and put itself under a holding company structure with the name Alphabet running all elements of the company. A branch of the Company had evolved that was being referred to as “Google Life Sciences,” and it changed names recently as well, to Verily Life Sciences.

What role and what kind of impact might these new subsidiary have? I was wondering if Google would make changes to the patent assignments it had made along with the name changes, and I was surprised to see them do so, where they assigned 148 patents to Verily Life Sciences on two different days. It’s an interesting list, and I’ve provided it here. They may technically have ownership under other patents as well, but this list points to a number that could possibly become products that the company offers to the public, after any government approval that they may need to pursue.

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Image Search and Trends in Google Search Using FreeBase Entity Numbers

Google is organizing more and more things in its index based upon entity numbers. I have a couple of examples for you that show how they are being used.

It’s possible that you may have missed a reference to Freebase Entities in a Google Research Blog post from 2013. I missed it myself. The post is
Improving Photo Search: A Step Across the Semantic Gap.

In the post, the author (Chuck Rosenberg) tells us how they improve image searching at Google by labeling images with entities, rather than text strings. The entities they used are entities that you would find at a source such as Freebase. He tells us that they use Freebase Machine ID numbers for those labels:

As in ImageNet, the classes were not text strings, but are entities, in our case we use Freebase entities which form the basis of the Knowledge Graph used in Google search. An entity is a way to uniquely identify something in a language-independent way. In English when we encounter the word “jaguar”, it is hard to determine if it represents the animal or the car manufacturer. Entities assign a unique ID to each, removing that ambiguity, in this case “/m/0449p” for the former and “/m/012×34” for the latter.

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Google Cross Device Tracking and Audio Watermarks

Historical recording Aboriginal Corroboree,  ,a href="">Australian Broadcasting Corporation, Some rights reserved
Historical recording Aboriginal Corroboree, Australian Broadcasting Corporation, Some rights reserved

Advertising on the Web is going through some changes because of how smart phones and tablets track visitors on a site, and how advertisements may broadcast high-frequency sounds that may act as audio watermarks that other devices can pick up upon. Imagine watching TV, and your TV broadcasts a high-frequency sound from an advertisement that your phone hears, and shares with the advertiser, who may then track whether you search for or purchase the product offered on a web site?

These are well described in the following Irish Examiner article, Future of Mobile: Advertisers and the quest for your data. If you read that and have some familiarity with how Google works, you may ask yourself if Google has followed such practices, or shown any sign of doing so.

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How Google Might Make Better Synonym Substitutions Using Knowledge Base Categories

Shea Stadium
Leigh Miller – Yankee Stadium, francis_leigh, Some rights reserved

A couple of months ago, I wrote about a Google patent that involved rewriting queries, titled Investigating Google RankBrain and Query Term Substitutions. There’s likely a lot more to how Google’s RankBrain approach works, but I came across a patent that seems to be related to the patent I wrote about in that post, and thought it was worth sharing and starting a discussion about. The patent I wrote about in that post was Using concepts as contexts for query term substitutions. The title for this new patent was very similar to that one (Synonym identification based on categorical contexts), and the more recent patent was granted on December 1st of this year.

The new patent starts off describing a scenario that is a good example of how it works. The inventors tell us:

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Do Search Click-Throughs Help Determine Whether a Page Appears in Google Search Results?

In a recent article at Search Engine Land we were told that Google Posts That Local Results Are Influenced By Clicks, Then Deletes That. It caught my attention, and had me investigating further.

Patents Involving Clicks Influencing Search Results

It made me recall three patents which described when clicks might influence whether or not pages appeared for certain queries.

The first patent I wrote about in a post titled Google Patents Click-Through Feedback on Search Results to Improve Rankings. The patent that post was about was Modifying search result ranking based on a temporal element of user feedback

Google User Feedback Relevance

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Will Google Start Reading Text in Images on the Web Soon?

Googlebot Doesn’t Read Pictures of Text During Web Crawls

When I was an Administrator at Cre8asiteforums (2002-2007), one of my favorite forums on the site was one called the Website Hospital. People would come with their sites and questions about how they could improve them. One problem that often appeared was people having problems being found in search results for their sites for geographically related queries. One symptom for many sites experiencing that problem was that the only time the address of their business appeared on the site was in pictures of text, rather than actual text. This can be a problem when it comes to Google indexing that information. Google tells us they like text, and can have troubles indexing content found within images:

Most search engines are text-based. If you use JavaScript, DHTML, images, or rich media such as Silverlight to create navigation and links, Googlebot and other spiders may have trouble crawling your site.

Google’s web crawler couldn’t read pictures of text, and Google wasn’t indexing that location information for their sites’ because of that. Site owners were often happy to find out that they just needed to include the address of their business in text, so that Google could crawl and index that information, and make it more likely that they could be found for their location.

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