Sign registration analysis
Publication Date: 2015-Feb-17
The IP.com Prior Art Database
This article describes a system that links multiple physical points of vision together, using a device that can register those points of vision to produce a user's "vision flow" for a particular journey or time period. This "vision flow" may subsequently be analysed to assess the effectiveness of sign placement. Such implementations are directly applicable to commercial situations, though extendable to generic situations that require the analysis of effective sign placement through tracking signs acknowledged and acted upon by a user.
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Sign registration analysis
Analysis of how effective adverts, billboards and other media are is a huge business, with much research dedicated to data gathering and metrics associated with views, click-throughs, click-purchase and overall returns on investment (ROI) for specified advertising locations and types. Much of this data is easily accessible online and in general, it is now quite easy to use this data in order to measure the effectiveness of an advertising campaign. However, this proves to still be a problem in the physical world.
There is considerable prior art which describes finding ways to link physical adverts with some kind of unique URL in order to keep count of how well physical signs are being read by e.g. some kind of QR code, whereupon usual internet analytics can be used to calculate ROI, click-throughs etc. This can be seen in links such as: http://www.business2community.com/marketing/how-to-analyze-the-effectiveness-o f-your-print-advertising-campaign-0354694
There is also prior art around augmented reality shopping that provides an efficient routing algorithm to get items needed (https://www.google.com/patents/US20110246064), as well as examples for providing further targeted advertising based on eye tracking of adverts that appear. None of this prior art
provides information about how a user may be influenced by one specific sign or notice.
There is a problem when an assessment of both the sign itself and how it affects the end result is required, both of which are in the physical world and not necessarily related solely to the realms of commerce. Examples of this are physical store purchases or driving results based on road signs (e.g. has a user seen the new lane merge sign or not? And what actions did a user take as a result of seeing the sign?)
This article proposes to link multiple physical points of vision together with a device that can register those points of vision to produce a user's "vision flow" for a particular
journey or time period. A device the user is wearing (such as a pair of augmented reality glasses) can register whenever a user has spotted a sign (by registering a unique code embedded on the sign when a user looks in that direction) and linking that information with further physical signs or activity spotted later on. For instance, a user is driving and the AR glasses or similar register that he/she has spotted a sign telling them of a new speed limit. The user's actions afterwards can be assessed for timely responses to this sign via speed or other metrics and can be also linked with GPS or other car positioning system to determine the success of the user for this road section. Similarly, if a user has been speeding at a certain geographical location, an assessment can be made as to whether the user has spotted the sign and chose to ignore it or simply missed the notification and hence the sign placement is inappropriate.
In the advertising world...