Imagine walking through a city and seeing an interesting landmark or historic site. You take out your cell phone and point it at that landmark or a restaurant or shop (like a TV remote for the real world). Your cell phone shows you pictures, provides an overview of the site, along with reviews or the menu for the restaurant and a description of today’s special. This is possible with iST’s iPointer “pointing based” mobile search and content delivery platform.
The iPointer mobile search and content delivery platform is network and carrier agnostic and will run on any mobile communication devices with a GPS, Digital Compass and a wireless data connection. Our customers benefit from the dynamic content retrieval and update capabilities of our Web-based geospatial search engine and content delivery architecture.
When a user points their iPointer enabled cell phone at a landmark of interest, such as retailer, restaurant, or historic site, and presses a button, the coordinates from the phone's GPS and directional angles from the Digital Compass are sent over the data network to our Web-based geospatial search engine. Our algorithms turn the latitude and longitude plus compass angle into a directional vector, which is used to decide the single most likely landmark the user pointed to. Once this is decided, which takes only milliseconds, content, such as pictures, menus, and audio overviews about the landmark is streamed back to the user's phone.
The iPointer doesn't need to use RFID tags or beacons in the buildings to work. Instead all we need is to use the map data that already exists and is used by car navigation systems and Internet maps like Google Maps. In fact one of the largest map providers on the planet wants to partner with us because our franchise model will help to maintain and validate their data-set.
Theck out this video showcasing our system being used in NYC.
The core of our intellectual property and product is focused on dealing with the accuracy issue, since metropolitan cities have an impact on GPS accuracy, with urban canyons creating multi-path errors. However, more geographic data is available for these areas and the data are more robust and accurate, including 3D data. Our system works quite well in metropolitan cites and is highly accurate. I have used it while standing very close to buildings as well as being several blocks away.
Our product uses a probabilistic approach based on the perspective view of the user to select the most likely object pointed at. Using an approach based on the 3D perspective of the user significantly improves the performance and level of accuracy when dealing with high rise buildings in metropolitan cities. The need for 3D building structures is only necessary in metropolitan areas. In suburban and rural areas we can use standard 2D and even point geographic data. Our product is like a multi-player game engine that uses many types of geographic data including 3D data.