Method and server for providing a set of price estimates, such as air fare price estimates

Abstract

The field of the invention relates to methods, servers and computer program products for providing a set of prices. A computer server receives a request for a price for goods or services, such as airfares, together with parameters defining those goods or services, for example: activity type, such as airfare, hotel booking, train fare; date range; destination; origin; desired weather conditions; star ratings; keywords; any other user defined preference. One or more processors programmed with software then infer, estimate or predict estimated prices from an incomplete historical price dataset by analysing patterns in that dataset and provide the price estimates to an end-user computing device, such as a personal computer, smartphone or tablet.

1 . A method of reducing data storage requirements, of providing travel-related price estimates, and of inferring which fare classes are available for a journey from a starting location to a destination location on a particular date, the method including the steps of:
 (i) a computer server receiving a request for prices for travel-related goods or services, together with parameters defining those travel-related goods or services, the parameters defining a request for fare prices for a journey from a starting location to a destination location on a particular date;   (ii) configuring one or more processors to determine estimated prices from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium by analysing patterns in that travel-related price 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, the configured one or more processors:   (a) obtaining historical price quotes from the incomplete historical travel-related price dataset embodied on the non-transitory storage medium;   (b) grouping the historical price quotes by category;   (c) deriving statistics for each group;   (d) storing on a computer for each group a plurality of classifiers 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 prices for the travel-related goods or services that satisfy the parameters, the configured one or more processors calculating a set of estimates, for the requested prices on the particular date using statistics from the stored classifiers corresponding to the identified groups for the travel-related goods or services that satisfy the parameters;   (iv) the computer server providing the calculated price estimates;   (v) the computer server sending the request to a Distribution System for fare prices for the journey from the starting location to the destination location on the particular date;   (vi) receiving from the Distribution System the Distribution System's fare prices for the journey from the starting location to the destination location on the particular date;   (vii) comparing the calculated estimates for the requested fare prices from step (iii) with the Distribution System's fare prices received in step (vi) so as to infer which fare classes are available for the journey from the starting location to the destination location on the particular date, and   (viii) outputting the inferred fare class availability for the journey from the starting location to the destination location on the particular date.   
     
     
         2 . The method of  claim 1 , wherein the parameters defining those travel-related goods or services further include one or more of the following: activity type, airfare, train fare; 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 (ii) includes using rules in order to analyse patterns in the dataset. 
     
     
         5 . 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. 
     
     
         6 . The method of  claim 5 , wherein classifiers are trained using observed prices and sets of features that correspond to them. 
     
     
         7 . The method of  claim 6 , wherein the features relate to the request, and include one or more of: departure day of week, length of stay, stay Saturday, airline, time to travel, route, month. 
     
     
         8 . The method of  claim 6 , 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. 
     
     
         9 . The method of  claim 6 , wherein features may be derived by training multiple models with different features and comparing the predictive accuracy of the different models. 
     
     
         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 10 , 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. 
     
     
         12 . The method of  claim 10 , wherein inputs for the statistical model include: list of routes, classifier categorization scheme, historical quotes, and a set of supported features with weights. 
     
     
         13 . The method of  claim 12 , wherein historical quotes are filtered by age. 
     
     
         14 . The method of  claim 10 , wherein inputs for the statistical model include reversed route equivalents. 
     
     
         15 . The method of  claim 1 , the method including the step of: including cached fare prices in the set of price estimates. 
     
     
         16 . The method of  claim 1 , wherein the prices are for a journey that is a one-way journey. 
     
     
         17 . The method of  claim 1 , wherein the prices are for a journey that is a return journey. 
     
     
         18 . The method of  claim 1 , wherein the prices include air fare prices. 
     
     
         19 . The method of  claim 1 , wherein the prices includes train fare prices. 
     
     
         20 . The method of  claim 1 , wherein the prices include car hire prices. 
     
     
         21 . The method of  claim 1 , wherein the prices include hotel prices. 
     
     
         22 . The method of  claim 1 , wherein the request comprises a flexible search request. 
     
     
         23 . The method of  claim 1 , including the steps of
 (A) configuring the one or more processors to determine confidence ranges of the estimated prices from an incomplete historical price dataset by analysing patterns in that dataset, at any time with respect to step (i), and   (B) configuring the one or more processors to calculate confidence ranges of the estimated prices for the requested prices for the goods or services that satisfy the parameters.   
     
     
         24 . The method of  claim 23 , further comprising the step of:
 (C) providing the confidence ranges together with the prices estimates to an end-user computing device.   
     
     
         25 . The method of  claim 23 , further comprising the step of:
 using the confidence ranges to decide whether to display a price to a user, or to provide a probable range of prices to a user.   
     
     
         26 . The method of  claim 25 , wherein the probable range of prices are displayed as error bars. 
     
     
         27 . The method of  claim 1 , wherein an estimation process is parameterized by one or more of: a minimum Bayes posterior probability required to accept a classification result; a maximum number of route operators involved in candidates' generation, or a random variation added to Bayes posterior probability to avoid ties. 
     
     
         28 . A server configured to provide travel-related price estimates, and to infer which fare classes are available for a journey from a starting location to a destination location on a particular date, the server arranged to:
 (i) receive a request for prices for travel-related goods or services, together with parameters defining those travel-related goods or services, the parameters defining a request for fare prices for a journey from a starting location to a destination location on a particular date;   (ii) determine estimated prices from an incomplete historical travel-related price dataset embodied on a non-transitory storage medium by analysing patterns in that travel-related price 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 server is arranged to:   (a) obtain historical price quotes from the incomplete historical travel-related price dataset embodied on the non-transitory storage medium;   (b) group the historical price quotes by category;   (c) derive statistics for each group;   (d) store for each group a plurality of classifiers including the derived statistics, and   (e) identify groups with stored classifiers to which the requested prices correspond;   (iii) calculate estimates for the requested prices for the travel-related goods or services that satisfy the parameters, in which the server is arranged to: calculate a set of estimates, for the requested prices on the particular date using statistics from the stored classifiers corresponding to the identified groups;   (iv) provide the calculated price estimates;   (v) send the request to a Distribution System for fare prices for the journey from the starting location to the destination location on the particular date;   (vi) receive from the Distribution System the Distribution System's fare prices for the journey from the starting location to the destination location on the particular date;   (vii) compare the calculated estimates for the requested fare prices from (iii) with the Distribution System's fare prices received at (vi) so as to infer which fare classes are available for the journey from the starting location to the destination location on the particular date, and   (viii) output the inferred fare class availability for the journey from the starting location to the destination location on the particular date.   
     
     
         29 . The server of  claim 28 , further arranged to use rules in order to analyse patterns in the dataset. 
     
     
         30 . The server of  claim 28 , wherein the parameters defining those travel-related goods or services further include one or more of the following: activity type, airfare, hotel booking, train fare; desired weather conditions; star ratings; keywords. 
     
     
         31 . A computer program product embodied on a first non-transitory storage medium, the computer program product executable on a computer to provide travel-related price estimates, and to infer which fare classes are available for a journey from a starting location to a destination location on a particular date, the computer program product executable on a computer to:
 (i) receive a request for prices for travel-related goods or services, together with parameters defining those travel-related goods or services, the parameters defining a request for fare prices for a journey from a starting location to a destination location on a particular date;   (ii) determine estimated prices from an incomplete historical travel-related price dataset embodied on a second non-transitory storage medium by analysing patterns in that travel-related price 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; in particular to:   (a) obtain historical price quotes from the incomplete historical travel-related price dataset embodied on the second non-transitory storage medium;   (b) group the historical price quotes by category;   (c) derive statistics for each group;   (d) store on a computer for each group a plurality of classifiers including the derived statistics, and   (e) identify groups with stored classifiers to which the requested price corresponds;   (iii) calculate estimates for the requested prices for the travel-related goods or services that satisfy the parameters, by calculating a set of estimates, including one estimate per day, for the requested prices on the particular date using statistics from the stored classifiers corresponding to the identified groups;   (iv) provide the calculated price estimates;   (v) send the request to a Distribution System for fare prices for the journey from the starting location to the destination location on the particular date;   (vi) receive from the Distribution System the Distribution System's fare prices for the journey from the starting location to the destination location on the particular date;   (vii) compare the calculated estimates for the requested fare prices from (iii) with the Distribution System's fare prices received at (vi) so as to infer which fare classes are available for the journey from the starting location to the destination location on the particular date, and   (viii) output the inferred fare class availability for the journey from the starting location to the destination location on the particular date.   
     
     
         32 . The computer program product of  claim 31 , wherein the parameters defining those travel-related goods or services further include one or more of the following: activity type, airfare, hotel booking, train fare; desired weather conditions; star ratings; keywords.