Method and server for providing fare availabilities, such as airfare availabilities

Abstract

The field of the invention relates to methods, servers and computer program products for inferring which fare classes are available. There is provided a method of inferring fare class availability for a specific flight or flights by: (a) receiving observable, live bookable prices for that flight(s), in which that price information is not accompanied by complete fare class information for those flight(s); (b) comparing the observable, live bookable prices with a set of prices calculated from stored data, such as valid itinerary data, fare data such as FROP data, and tax/surcharge data; and (c) determining which fare class was used in the observable live bookable price by determining which fare class was used in the calculated price that matches most closely to the observable live bookable price.

1 . A computer-implemented method of reducing data storage requirements, and of inferring which fare classes are available, the method including the steps of:
 (i) a computer server receiving a request for a price for hotel services, together with parameters defining those hotel services,   (ii) configuring one or more processors to determine estimated prices from an incomplete historical price dataset in a computer data store by analysing patterns in that dataset, at any time with respect to step (i) above, the incomplete historical travel-related price dataset using a smaller data storage capacity than a complete historical travel-related price dataset, wherein this step comprises   (a) obtaining historical price quotes from the computer data store;   (b) grouping the historical price quotes by category;   (c) deriving statistics for each group;   (d) storing on a computer for each group a classifier including the derived statistics, and   (e) identifying groups with stored classifiers to which the requested prices correspond;   (iii) configuring one or more processors to calculate estimates for the requested price for the hotel services that satisfy the parameters, and calculating estimates for the requested price that satisfy the parameters, including calculating estimates for the requested price using statistics from the stored classifiers corresponding to the identified groups;   (iv) sending the request to a Distribution System for fare prices;   (v) receiving from the Distribution System the Distribution System's fare prices;   (vi) comparing the calculated estimates for the requested price from step (iii) with the Distribution System's fare prices received in step (v) so as to infer fare class availability, and   (vii) providing the inferred fare class availability to a computing device.   
     
     
         2 . The method of  claim 1 , wherein the parameters defining those hotel services include one or more of the following: date range; destination; origin; desired weather conditions; star ratings; keywords. 
     
     
         3 . The method of  claim 1 , wherein determination of estimated prices is performed by inferring, deriving or predicting estimated prices. 
     
     
         4 . The method of  claim 1 , wherein step (iii) comprises: calculating a set of estimates for the requested price over a specified date range using statistics from the stored classifiers corresponding to the identified groups. 
     
     
         5 . The method of  claim 1 , wherein the Distribution System is a Global Distribution System. 
     
     
         6 . The method of  claim 1 , wherein an inferred fare class price is included with each inferred fare class availability. 
     
     
         7 . The method of  claim 1 , wherein step (vii) includes sending the inferred fare class availability to a server. 
     
     
         8 . The method of  claim 1 , wherein step (ii) includes using rules in order to analyse patterns in the dataset. 
     
     
         9 . The method of  claim 1 , wherein step (ii) includes a naïve Bayes classifier machine learning approach which produces a probabilistic model of prices, and that model is used to predict unobserved prices. 
     
     
         10 . The method of  claim 1 , wherein step (ii) includes building a statistical model from historical prices, identifying missing quote candidates, and pricing quote candidates based on the statistical model. 
     
     
         11 . The method of  claim 1 , the method including the step of: including cached fare prices in the set of price estimates. 
     
     
         12 . The method of  claim 1 , wherein classifiers are trained using observed prices and sets of features that correspond to them. 
     
     
         13 . The method of  claim 12 , wherein the features relate to the request, and include one or more of: departure day of week, length of stay, stay Saturday, month. 
     
     
         14 . The method of  claim 13 , wherein a classifier then predicts the price of an unobserved price by being given a set of features and providing a most likely price to have those features. 
     
     
         15 . The method of  claim 12 , wherein features are derived by training multiple models with different features and comparing the predictive accuracy of the different models. 
     
     
         16 . The method of  claim 1 , wherein estimating prices for each candidate quote occurs in the following steps: extracting category feature values from the quote; retrieving from a database a classifier trained for an extracted category; extracting all feature values from the quote candidate; classifying the candidate quote by calculating Bayes posterior probabilities for each price range stored in a classifier and choosing a price range class with a highest Bayes posterior probability, and attaching a price class to a candidate quote. 
     
     
         17 . The method of  claim 1 , wherein inputs for the statistical model include: classifier categorization scheme, historical quotes, and a set of supported features with weights. 
     
     
         18 . The method of  claim 1 , wherein the request comprises a flexible search request. 
     
     
         19 . A system including a server, a storage computer and a computer data store, the server configured to infer which fare classes are available, the server configured to:
 (i) receive a request for a requested price for hotel services, together with parameters defining those hotel services,   (ii) configure one or more processors to determine estimated prices from an incomplete historical price dataset stored in the computer data store by analysing statistical patterns in that dataset, at any time with respect to (i) above, the incomplete historical travel-related price dataset using a smaller data storage capacity than a complete historical travel-related price dataset, wherein the configuring comprises:   (a) obtaining historical price quotes from the computer data store;   (b) grouping the historical price quotes by category;   (c) deriving statistics for each group;   (d) storing on the storage computer for each group a classifier including the derived statistics, and   (e) identifying groups with stored classifiers to which the requested prices correspond;   (iii) configure one or more processors to calculate estimates for the requested price for the hotel services that satisfy the parameters, and calculate estimates for the requested price that satisfy the parameters, including to calculate estimates for the requested price using statistics from the stored classifiers corresponding to the identified groups;   (iv) send the request to a Distribution System for fare prices;   (v) receive from the Distribution System the Distribution System's fare prices;   (vi) compare the calculated estimates for the requested price from (iii) with the Distribution System's fare prices received in (v) so as to infer fare class availability, and   (vii) provide the inferred fare class availability to a computing device.   
     
     
         20 . A computer program product embodied on a non-transitory storage medium, the computer program product when running on a computer arranged to infer which fare classes are available, the computer program product when running on a computer arranged to:
 (i) receive a request for a price for hotel services, together with parameters defining those hotel services,   (ii) configure one or more processors to determine estimated prices from an incomplete historical price dataset by analysing patterns in that dataset, at any time with respect to (i) above;   (iii) configure one or more processors to calculate estimates for the requested price for the hotel services that satisfy the parameters;   (iv) send the request to a Distribution System for fare prices;   (v) receive from the Distribution System the Distribution System's fare prices;   (vi) compare the calculated estimates for the requested price from (iii) with the Distribution System's fare prices received in (v) so as to infer fare class availability, and   (vii) provide the inferred fare class availability to a computing device.