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Location Based Services and Predictive Analytics for Departure and Appointment Determination Disclosure Number: IPCOM000236972D
Publication Date: 2014-May-23
Document File: 4 page(s) / 62K

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The folowing description provides ways to determine in real-time the likelihood that consumers will be able to keep appointments, or make a planned departure time on a transit system. Having this information, in turn, allows new efficiencies for businesses running such services, and thus, mitigates lost revenues for such businesses.

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Location Based Services and Predictive Analytics for Departure and Appointment Determination

This article is generally applicable to consumers keeping appointments for services, and could broadly be applied accordingly, including those with doctors, various forms of advisors, or any other timed meetings. In this article, for illustration purposes, the embodiments described will focus on passengers arriving for a timed departure, such as might be found for reserved seats on aircraft, trains, and other forms of transit. This embodiment is highlighted because of the wealth of options available once a connection is determined to be missed. However, again, these principles broadly hold for any such appointment plans.


Today, appointments for services, such as passengers departing at a planned transit time, are filled with natural inefficiencies. Considering the departure of an aircraft provides one such example. Although web check-in is now available up to 24 hours in advance, the airline has no way of determining the actual fill rate of an aircraft until a last call for boarding is made. Whether a flight is over-booked or under-booked, the operating company has no feedback mechanism until passengers check-in and/or board the aircraft.

Airlines counter this problem by creating stand-by lists, wherein potential passengers wait near the boarding area to learn whether or not they will secure a seat on the flight? Only after missing passengers are called by name, and do not respond, do most airlines begin allowing stand-by passengers onto a flight. This last-minute scramble risks departure delays, customer frustrations, and other such intangibles which can be translated to financial losses. Additionally, passengers who do not make their prescribed time subsequently must deal with reschedules and finding alternative routes to their destination, or more generally, to obtain the desired service.

This solution addresses this inefficiency by proposing a location based services (LBS) and predictive analytics. Ideally, this would be an opt-in service, with customers receiving a financial benefit for participating. However, the business model (opt-in, mandatory, etc) surrounding this is beyond the point of the article, other than to describe options to make this as attractive as possible to both businesses and consumers.

This article has several components:

(1) Means of ascertaining user location.

(2) Means of ascertaining likelihood of on-time arrival.

(3) Means of taking alternative or mitigating actions.

The process associated with the use of each is now described below:

(1) User location is most likely determined by use of a mobile device. e.g., cellular telephone, with such LBS capabilities installed. If a user has opted in to the system, the device would be activated at some point (e.g., one hour or more) before the appointment or departure. In some embodiments, the user would receive a prompt or reminder that location would be r...