Data processing: artificial intelligence – Knowledge processing system – Knowledge representation and reasoning technique
Reexamination Certificate
1999-02-22
2002-11-05
Starks, Jr., Wilbert L. (Department: 2122)
Data processing: artificial intelligence
Knowledge processing system
Knowledge representation and reasoning technique
C704S008000, C706S025000, C706S058000
Reexamination Certificate
active
06477520
ABSTRACT:
FIELD OF THE INVENTION
The present invention is generally related to travel purchasing, and more particularly to an adaptive, dynamic travel purchasing optimization system for calculating and providing a process for reducing the total cost of business travel at the point of purchase.
BACKGROUND OF THE INVENTION
Corporate travel management is a complicated task and the corporate travel industry currently uses a variety of travel software packages to implement travel purchasing. Current software packages aimed at the corporate travel industry, such as direct booking (under the trade names Sabre BTS, Internet Travel Network, etc.) and expense report processing systems, focus on increasing the efficiency of overhead costs and administrative processes. However, they have not addressed the major cost associated with the travel process—the cost of the actual travel. The cost of travel represents over 95% of total travel costs.
Unfortunately, corporations are unable to control travel costs because they do not have an effective way to analyze the options available to them at the point of purchase. In the corporate travel environment, agents and travelers have only static pre-set policies to guide them in a very dynamic marketplace. Currently, the major factor used by corporations to differentiate travel choices is price. However, corporations are unable to calculate the true price associated with each travel event due to the inaccuracy of fare cost calculation and the incomplete analyses of costs associated with an individual trip.
Inaccuracy of fare cost calculation stems from current systems that fail to calculate and display all of the dynamic options associated with each trip event. Corporations are unable to control travel costs, because they do not have an effective way to analyze the multitude of options available to them at the point of purchase. In the corporate travel environment, agents and travelers have only static pre-set policies to guide them in a very dynamic marketplace.
Currently, the major criterion used by corporations to differentiate travel choices is price or the “cheapest fare.” Unfortunately, corporations are unable to calculate the true price associated with each travel event, due to static viewing of dynamic options that can equal as much as 15 percent of the total fare cost. Dynamic options include fare discounts and negotiated pricing, among others. Thus, statically viewing the travel market leads to the selection of trips which may have one desirable element, such as, cheapest fare, but are actually significantly costlier than necessary due to other dynamic factors.
For example, hypothetical corporate traveler Smith needs a flight between Cleveland and Atlanta. Smith's corporate travel agent, using an existing travel purchasing system, looks within a two hour departure window for the flight and finds the cheapest fare, according to company policy. However, there is a problem with the static policy-dedicated decision based on the cheapest fare. The existing travel purchasing system failed to reflect the overrides and back-end discounts negotiated by the company. Another flight, that is fifty dollars more according to the existing travel purchasing system, actually includes seventy-five dollars in discounts, making it the optimal flight from a dynamic cost perspective.
Incompleteness of cost calculation stems from current systems that have no way of calculating variables associated with each trip event that are even more dynamic and less tangible than price. These more dynamic and less tangible variables include costs created by differences in travel time, the productivity impact of inconvenience, the probability that lower priced choices will become available in the near future, and the impact of each choice on the ability of the airline to negotiate price discounts or bulk purchases with suppliers. In the current travel environment, no known solution addresses all of these variables, resulting in needlessly increased expenditures and reduced productivity.
Pointedly, travel policies, the most popular current cost-control method, are ineffective without an understanding of the true costs involved for each trip choice. Business travelers and their agents do not have the time, training, incentives or tools to analyze the variables. Without direct corporate intervention at the point of purchase, companies essentially rely on pure luck to ensure that agents and travelers are selecting the best trip options.
Referring back to the previous hypothetical example, corporate traveler Smith again needs a flight between Cleveland and Atlanta. Smith's corporate travel agent, using an existing travel purchasing system, looks within a two hour departure window for the flight and finds the cheapest fare, according to company policy. However, the existing travel purchasing system did not account for Smith's salary of seventy-five dollars per hour and Smith's preference for short trips due to his asthma. Another flight, that is fifty dollars more according to the existing travel purchasing system, has both a seventy-five dollar back-end discount negotiated by the company and no layover, reducing the fare by twenty-five dollars and saving Smith two hours in travel time. As a result of using a static policy-based travel purchasing system, the company lost twenty-five dollars in overall fare, lost 150 dollars in travel time for Smith's salary, and lost employee morale by putting Smith on a longer flight.
Known automated travel planners depend upon standard crisp logic decisions, basically following a series of decisions based upon crisp “Yes or No” decisions without appropriately taking into account qualitative considerations such as productivity impact of travel, negotiated contract compliance, inconvenience of traveler, airline affinity or loyalty, employee morale, frequent flyer miles, and airline policies. Such qualitative considerations may easily vary in priority from trip to trip, business to business, traveler to traveler and even day to day. Capturing varying degrees of significance of many such qualitative considerations in a conventional crisp algorithm based computer program would be a daunting programming task.
Further, such conventional systems are not easily adaptable or tunable to a particular user. Tuning or adapting such a system to changing conditions typically may be carried out by changing the algorithm. Changing an algorithm requires editing program steps and recompiling the necessary code. Essentially the program must be rewritten in order to modify preferences or change conditions. This is a time consuming and non-productive use of programming personnel.
By way of background, one example of a known system may be found in U.S. Pat. No. 5,331,546, to Webber et al., entitled “TRIP PLANNER OPTIMIZING TRAVEL ITINERARY SELECTION CONFORMING TO INDIVIDUALIZED TRAVEL POLICIES,” issued Jul. 19, 1994. The entire contents of U.S. Pat. No. 5,331,546 are incorporated by reference into this patent application. Another example is U.S. Pat. No. 5,832,453 to O'Brien issued Nov. 3, 1998 and entitled “COMPUTER SYSTEM AND METHOD FOR DETERMINING A TRAVEL SCHEME MINIMIZING TRAVEL COSTS FOR AN ORGANIZATION.” The entire contents of U.S. Pat. No. 5,832,453 are incorporated by reference into this patent application.
SUMMARY OF THE INVENTION
In contrast to the prior art, one embodiment of the travel purchasing optimization system (TPOS) of the invention comprises the first solution focused specifically on optimizing corporate travel decisions at the point of purchase using the elegance and power of fuzzy membership functions. The invention is based on the concept that corporations already possess enough information to efficiently calculate the true cost of travel. The invented travel purchasing optimization system collects available information, brings it to the point of purchase, and analyzes it for the various choices available in the marketplace using a process referred to as dynamic optimization. Dynamic optimization synthesizes qualitative and qu
Malaviya Ashutosh
Malaviya Paritosh
Saklani Mukul
Saklani Praful
Leone George A.
Starks, Jr. Wilbert L.
Yatra Corporation
LandOfFree
Adaptive travel purchasing optimization system does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Adaptive travel purchasing optimization system, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive travel purchasing optimization system will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2926043