Context is King: Google Parameterless Searches

In the very near future, you may be able to perform searches at Google without bothering to type or speak a query. Instead, you might be able to just shake your phone, or hold down a button for a certain amount of time, and tell your phone something like “search now”. Known as parameterless searches, this type of search can depend upon the context within which the search is performed.

Screenshot from the patent showing someone shaking a phone, context information for that search, and a search result

For instance, imagine being driven to work at 50mph, and you shake your phone. It tells you that there’s congestion ahead, and offers an alternative route.Or it shows you a map with color-coded traffic information for different streets nearby according to traffic conditions. Or, you may have an appointment with a client made by email and included on your calendar, and you want to find and check the email to make sure that you have the right phone number. It could show the number and offer to make the call on your behalf. If you regularly take a train at around 8:00 am on weekday mornings, shaking your phone at 7:50 am might trigger a realtime schedule for the rails.

Context information for a parameterless search could include things such as:

  • Time/date information,
  • Geographic location information,
  • Calendar information,
  • Rate of speed information, and
  • Device activity information (like the sending of emails for different purposes).

The patent is:

Providing results to parameterless search queries
Invented by Sumit Agarwal, Vic Gundotra, Alex Nicolaou
Assigned to Google
US Patent 8,478,519
Granted July 2, 2013
Filed: August 30, 2010


In one implementation, a computer-implemented method includes receiving a parameterless search request, which was provided to a mobile computing device, for information that is relevant to a user of the mobile computing device. The method also includes, in response to the received parameterless search request, identifying with a digital computer system one or more results that are determined to be relevant to the user of the mobile computing device based upon a current context of the mobile computing device. The method further includes providing the results for display to a user of the mobile computing device.

Context information could more specifically include things such as:

  • Geographic location,
  • Weather conditions,
  • Nearby businesses,
  • Volume of ambient noise,
  • Level of ambient light,
  • An image captured by the mobile device’s camera,
  • Rate of traveling speed,
  • Time and date information,
  • Calendar appointments,
  • Recent user activity,
  • Habitual user activity

The current context can be determined by data and sensors that are local, or even remote to the mobile computing device. For example, traffic speed and congestion might be determined by sensors in the phones on the road ahead.

If someone has been checking a developing news story via their browser or a news application, and checked a few times, a parameterless search may indicate a desire to see an update to the story.

If someone is driving down the road, and following a news story, a parameterless query could trigger responses to both.

This system is self training, and can learn about different categories of information that you might be interested in, based on contexts. For example, if the results of a parameterless query ends up showing a list of local restaurants, and you end up choosing one of them and driving to it, this system learns that in contexts like that, a set of restaurant results is an appropriate response (and it may learn about your taste in food as well). It can take learn about other user actions as well:

For example, if the user is provided with a recent update to a blog that the user frequently reads and the user sends a link to the updated blog posting to his/her friends, the user sending the link can be recorded as user behavior data in the user behavior data repository of the mobile computing device and used to provide results to future parameterless search requests.

The patent tells us that this parameterless search query approach isn’t limited to mobile phones, but can work with tablets, laptops, car navigation systems, personal digital assistants, or desktop computers.

I’m wondering how Google Analytics might indicate that a search leading to a site might indicate referral information – no query?


Author: Bill Slawski

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