Google Cross Device Tracking and Audio Watermarks

Historical recording Aboriginal Corroboree, ,a href=Australian Broadcasting Corporation, Some rights reserved” width=”800″ height=”621″ class=”size-full wp-image-16020″ srcset=”×233.jpg 300w,×596.jpg 768w, 800w” sizes=”(max-width: 800px) 100vw, 800px” />
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.


In November, the Federal Trade Commission (FTC) held a workshop on Cross Device Tracking, where they investigated practices that different companies were getting involved with, and they did some diving into the topic in a very informative way. There are a couple of videos linked to from that page that are worth watching if you want to become better informed on the topic.

I noticed a couple of recent patent filings at Google that were relevant to the workshop and to that article that fit into these changes, and are worth thinking about:

The first of these considers some of the different types of devices that are quickly growing in usage across the Web, such as smart phones and tablets, that don’t use browsers that save cookie files, and aren’t tracked from that type of approach as these Web users travel across the Web. Instead, these devices often use an Advertising ID, or other means of tracking behavior from one device and sharing that tracking with other devices.

Cross Device Notifications


Pub. No.: WO/2015/200051


Publication Date: 30.12.2015


International Filing Date: 16.06.2015


Inventors: Michael Koss Campbell, Justin Dewitt, Katie Jane Misserly, Dmitry Titov




Techniques for cross-device notifications are provided. An example method includes receiving a first indication of an event detected at a first device associated with a user account, determining one or more characteristics of the event based on the first indication of the event, detecting whether the determined characteristics match at least one selection criterion, automatically identifying a second device from one or more devices associated with the user account, and providing, if the determined characteristics match the at least one selection criterion, the first indication of the event to the second device associated with the user account, where the provided first indication of the event is displayed at the second device to allow management of the event at the first device from the second device.

This second approach, involving high-frequency sounds that you wouldn’t even hear, surprised me. The patent itself doesn’t talk about that kind of tracking itself. But, it does share information about what you’ve been subjected to (a particular advertisement) so that your future activities involving that advertisement might then be tracked.

Communicating Information Between Devices using Ultra High Frequency Audio


Pub. No.: WO/2015/195808


Publication Date: 23.12.2015


International Filing Date: 17.06.2015


Inventors: Shyam Narayan, Naveen Aerrabotu, Sreenivasulu Rayanki, Yun-Ming Wang



A client device encodes data into an audio signal and communicates the audio data to an additional client device, which decodes the data from the audio signal. The data is partitioned into characters, which are subsequently partitioned into a plurality of sub-characters. Each sub- character is encoded into a frequency, and multiple frequencies that encode sub-characters are combined by the client device to generate an audio signal. Frequencies encoding sub -characters may be above 16 kilohertz, so the sub-characters are transmitted using frequencies that are inaudible to humans. The audio signal is communicated to an additional client device, which decodes frequencies from the audio signal to sub-characters, which are then combined into characters by the additional client device to generate the data.

The future of advertising and tracking advertisements on the Web will involve multiple types of devices, and may also involve the use of high pitched frequencies that are outside of the range of normal hearing by human beings. Google now has patent filings that describe their possible use of this kind of technology.

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How Google Might Make Better Synonym Substitutions Using Knowledge Base Categories

Leigh Miller - Yankee Stadium, francis_leigh, Some rights reserved
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:


For example, learning that “restaurants” is a good synonym for “food” in the query [food in San Francisco] is relatively straightforward, because the volume of query traffic including the query term “San Francisco” is very large. For much smaller cities, such as Grey Bull, Wyo., the query stream may have never seen any supporting evidence for this synonym substitution.

That both cities are entities that fit into the same category, that of “Cities” means that they could potentially be good synonyms for each other. That’s what the inventors of this patent tell us specifically, using the San Francisco and Grey Bull example:

For example, if “San Francisco” and “Grey Bull” are both cities, and “restaurants” is a good synonym for “food” in queries about San Francisco, the synonym relationship may apply to queries related to “Grey Bull” as well. Thus, the category “city” may be considered a useful category when identifying synonyms for query expansion in circumstances such as this.

So, we are told that the process involved in this patent is to identify categories from a knowledge base involving a number of entities where other entities within that same category could potentially be synonyms for each other in similar contexts. The process from the patent involves identifying those entities from a query stream, and identifying the category as one that they call a “coherent” category.

The patent tells us that a coherent category is one in which a certain threshold of terms tend to co-occur in a query stream involving those entities. The patent tells us, for instance that a category that might include entities that are cities, villages, and towns might see a lot of co-occurring terms involving hotels and roads. If the number of co-occurring terms appearing in that query stream meet a certain threshold, it would be considered a coherent category, and the entities from the same categories could possibly then be used as synonyms for each other.

The patent in question is:

Synonym identification based on categorical contexts


Invented by: Zachary A. Garrett, Takahiro Nakajima, Tasuku Oonishi


Assignee: Google


US Patent 9,201,945


Granted December 1, 2015


Filed: March 8, 2013



Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training recognition canonical representations corresponding to named-entity phrases in a second natural language based on translating a set of allowable expressions with canonical representations from a first natural language, which may be generated by expanding a context-free grammar for the allowable expressions for the first natural language.

Take Aways

When I wrote about the query term substitution patent I refer to at the start of this post, I included a number of examples of queries that were re-written based upon some substitutions of query terms that might seem reasonable to a search engine looking at words that tended to show up, or co-occur, in a query stream involving those search terms.

For instance, someone searching for [New York Yankees stadium] was likely searching for results that involved “baseball” since queries that included “New York Yankees” and “stadium” also often included the term “baseball.”

That patent didn’t use the term “co-occur” nor did it explain how a knowledge base might be used to substitute entities that might be in the same categories like this one does, but the idea that a shared context like entity categories can be used to trigger entity substitutions in a query is interesting.

It’s worth spending time with both patents and reading through each of them multiple times and thinking about how they are being used.

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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


The second patent was one I wrote about in the post How Google May Identify Implicitly Local Queries, which told us that Google might start showing additional web search results in response to queries, when they had some kind of local significance. I noticed Google doing this for a client in Florida, and on searches in the two counties where the client operated, a link to the site was showing up at number 4 in the search results for a very relevant and broad query. The patent that this post was about was Identification of implicitly local queries.

Google local relevance

In February of 2012, Google reported upon an upgrade of this nature in the post Search quality highlights: 40 changes for February, where they told us about this:

Improved local results. We launched a new system to find results from a user’s city more reliably. Now we’re better able to detect when both queries and documents are local to the user.

But that first version of the patent had nothing to do with click-throughs. Those appear to have possibly been an added feature. I noticed that this patent had been updated a couple of years later in a continuation patent, where an author had been added to the patent (Navneet Panda – the search quality engineer that the Google Panda update was named after), and the claims section was rewritten to include both web search results and local search results, and a kind of a click requirement appeared to be added, so that the elevation of results that were showing that might have been added to results because of some local significance seemed to depend upon whether or not they were getting clicked upon sufficient numbers of times. As the claims section of the newer version of the patent tells us:

9. The method of claim 1, wherein selecting a final set of search results responsive to the search query comprises selecting, for inclusion in the final search results, local search results that have at least a minimum threshold click-through-rate when presented in response to the local search query.

The newer version of the patent is:

Locally Significant Search Queries

locally significanct search queries

Check to See if Your Pages are Considered Locally Significant

It can help you to perform searches that may appear to be local to a place to see if it is showing up in search results as a locally significant page. One of my co-workers, Dan Hinkley, wrote a post today on the Go Fish Digital blog about how you can use the Emulator built into Google Chrome to do so, in the post How to Change Your Location for Local Search Results – The Always Up-To-Date Guide. Try it out, but be careful. I was being blocked by a Chrome Extension from using the Emulator built into Chrome correctly.

In 2007, I wrote about another Google patent in the post. Google’s OneBox Patent, where I wrote about the Google patent Determination of a desired repository. This patent told us about when Google would choose to show off an Answerbox result in response to a query, which often would be a local search Maps result at the top of search results (and sometimes it would be an image result, or a news result, and possibly now Question Answer results). The patent seemed to tell us that it would decide upon what to show as an One Box result based upon people clicking that result, and showing an interest in it, and the decision of what to show us might be based upon history of searches associated with a query:

21. The system of claim 18, further comprising: a model generation system to generate a model that determines a score associated with a likelihood that a particular user desires information from a repository when the user provides a particular search query.

22. The system of claim 21, wherein the model is a lookup table and the score corresponds to a click-through rate associated with a repository when the user provides the particular search query.

23. The system of claim 21, wherein when determining a score for each of a plurality of repositories, the search engine system is configured to: determine a score for each of the repositories based on the model.

24. The system of claim 21, wherein when generating a model, the model generation system is configured to: store log data associated with a plurality of prior searches, and use the log data to train the model.

Google Model based upon Instances


Google does have some patents that illustrate how they might use clicks upon specific searches to determine what results they might display in search results, and where those might be ranked.

Whether or not Google uses information about click throughs seems to be something that they have been denying, and we can’t be certain about. Google has told us that just because they have a patent on something doesn’t necessarily mean they are using what is described in that patent:

Do click-throughs determine whether certain pages appear in search results? We can’t be certain, but it appears like a possibility.

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