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Pre-caching maps based on predicted route of travel and mobile signal strength

IP.com Disclosure Number: IPCOM000249499D
Publication Date: 2017-Mar-01
Document File: 2 page(s) / 54K

Publishing Venue

The IP.com Prior Art Database

Abstract

A solution for pre-caching maps based on predicted route of travel and mobile signal strength. This method does not require the user to have pre-planned a route.

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Pre-caching maps based on predicted route of travel and mobile signal strength

Disclosed is a system for enabling mobile phone owners who have become lost on their travels to determine their location on a map without the need for any mobile signal coverage. Existing navigation applications may allow this if the user has pre-planned a travel route and downloaded or cached map data before setting off; this is not a requirement for this solution. This solution works in the background and does not require user intervention of any description unlike existing applications. The technology problem:

Mobile network coverage can be patchy in certain areas - these may be consistent blackspots or caused due to an outage. The human problem:

Users may not always know they are going to need to use a map. They could have been going sightseeing and got lost. They may be travelling a familiar route but then a road blockage means they need to travel a different route to their destination. How the solution works:

A user is travelling without using a route planner. The system is running in the background on the users mobile phone. It

has not required any interaction from the user. The system is using the GPS component of the mobile device to a) keep

track of the users current location and b) log the users route (it knows the route that has been travelled so far).

An online traffic analytics data service is being queried by the system on the most popular routes used by drivers.

An online map service is being queried as to known attractions and destinations nearby which the user may be travelling to. These destinations have data on peak times for visitors, so the time of day the user is travelling is another key data point for the solution.

The data above is used in analytics in order to determine the most probable future route. For example, 85% of drivers turn left at the next junction at this time of day, having travelled along the same route so far. This is because they are taking the main road towards the aquarium on a Sunday morning with their family.

With a predicted (set) of future routes for the user, the system checks if there are connectivity problems in the next 3 or 4 map tiles along this route. It checks for: outages to networks and known blackspots of poor connectivity.

If a connectivity problem is found for one of the...