JP2019079568A - Method and server for providing set of price estimates, such as airfare price estimates - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method and a server for assembling an airfare price estimate. A computer server, along with a request for a price for an article or service (eg, airfare), along with parameters that define these article or service, such as action type, airfare, hotel reservation, railfare, date range, Receive destinations, origins, desired weather conditions, star ratings, keywords, and any other user-defined preferences. Analyzing patterns in a data set that one or more processors programmed with software then infer, estimate, or predict an estimated price from an incomplete historical price data set. And send the price quote to an end-user computing device, such as a personal computer, smartphone, or tablet. [Selection diagram] FIG. 16

Description

  The technical field is an estimated price, for example an airline fare, a train fare, a hotel price, an article (in fact any type of article or service, the price is not fixed but instead is variable, Accordingly, the present invention relates to a method, server and computer program product for providing a set of quotes for a price, for which it is useful to be able to provide a quote for those prices.

  In the context of airline fares, the price of an airline ticket for an itinerary is complicated to calculate. It is the result of the calculation of possible routings, approved fares, and the availability of these fares. These calculations are typically performed in the Global Delivery System (GDS), and performing queries on such systems can be slow, expensive, and use energy to perform calculations. You need to send the result.

  Storing ticket prices and flight availability for all possible airports between all possible routes, for a sufficiently long time range (e.g. 1 year), requires very considerable data storage capacity It will be.

  FR 2841018 (B1) is a method for loading a flight schedule change, receiving at least one set of flight changes, and taking out the individual changes in the set, in a register for future scheduling Storing, and simulating reassignment of reservations related to flight schedule changes, by accessing both the recording and flight schedule databases via the reservation distribution server, and reservation margins And finally updating the flight schedule database in minutes.

  As an example, in the context of airfare flight pricing, flight pricing is conventionally obtained from the GDS (how the GDS system works, and more generally, how the flight pricing works) For a more detailed description of (see section C). Flight comparison services (e.g. Skyscanner) and some airlines pay a fee and get a live reservable price from GDS. These prices are the actual reservable prices that can be booked by prospective passengers. However, many prospective passengers are often only browsing, do not need a clearly accurate bookable price, and will instead be satisfied with the quote. As a result, it would be very useful if it was possible to devise a method and system for estimating the exact price, and in particular that it not only costs money for third party resources (eg GDS) (access This is true if reliance on access to (which may not be available to obtain the required information) is removed.

  Thus, it is possible to obtain a price and flight availability estimate between any two airports over a period of time that is not too late to access one or more remote servers multiple times to get a quote. It would be desirable to provide a method that does not require the huge amount of data storage needed to pre-store results for all possible queries.

  According to a first aspect of the invention, there is provided a method of providing a price quote comprising: (i) receiving by a computer server a price for an item or service, eg a request for parameters defining the item or service together with an airfare. And (ii) determining the estimated price from the incomplete past price data set at any time for step (i) above, as by analyzing the patterns in that data set, 1 Or (iii) configuring one or more processors to calculate an estimate for the required price for an article or service that meets the parameters; (iv) a price quote for the end user Computing Vice, a method is provided comprising the steps of: sending example, personal computers, smartphones or tablet.

  One of the advantages is that with this method, one or more remote servers are accessed many times to require the time or energy needed to actually set the freight availability and calculate the freight price. And not. A further advantage is that the method does not require the huge data storage capacity needed to pre-store the results for all possible queries. The computer performing the method operates in a new manner and overcomes recognized problems with the method. Note that steps (ii) and (iii) in the method are not necessarily separate steps, but may instead be part of the same step.

  The method defines parameters for those goods or services, such as activity type, such as airfare, hotel reservation, railway fare, date range, destination, origin, desired weather conditions, star rating, keywords, any other One or more of the user-defined preferences may be included.

  The method may be performed by determining, estimating or deriving the estimated price.

  The method comprises, in step (ii), (a) obtaining past price estimates from the computer data store, (b) grouping the past price estimates by category, and (c) for each group (D) storing, for each group, a classifier containing the derived statistical data on the computer, and (e) using the stored classifier, the requested price corresponds The identification of a group may be included.

  The method comprises, in step (iii), calculating a set of estimates for the required price over a specified date range using statistical data obtained from the stored classifiers corresponding to the identified groups. May be included.

  The method may also include the step (ii) analyzing the patterns in the data set using rules.

  In the method, step (ii) includes a simple Bayesian classifier machine learning approach that generates a probabilistic model of prices, which may be used to predict unobserved prices.

  The method may be to train the classifiers using one observed price and a corresponding set of features.

  The method is characterized in terms of requirements, and does it involve the day of departure of the week, the length of stay, and the trip to spend Saturday nights at the destination? It may include one or more of an airline, time to travel, a route, and a month.

  The method then causes the classifier to predict the price of the unobserved price by being given a set of features and giving the price that is most likely to have those features. It is good.

  The method may derive the features by training the plurality of models with different features and comparing the prediction accuracy of the different models.

  The method includes, in step (ii), constructing a statistical model from past prices, identifying missing candidate estimates, and pricing candidate estimates based on the statistical model. It may be

  The method comprises: estimating a price for each candidate quote; extracting category feature values from the estimates; retrieving a trained classifier for the extracted categories from a data base; The step of extracting all feature values from categorizing the candidate estimates, computing the Bayesian posterior probability for each price range stored in the classifier, and the price range with the highest Bayesian posterior probability It may be performed in the steps of selecting a class, adding a price class to a candidate estimate, and performing the step.

  The method may be that the input to the statistical model includes a list of paths, a classifier categorization scheme, past estimates, and a set of supported features with weights.

  The method may filter past estimates by age.

  The method may be such that the inputs to the statistical model include reverse path equivalents.

  The method may include the step of including the fare price cached in the set of price quotes.

  The method may be such that the set of price estimates is configured without querying the distribution system after step (i).

  The method may be such that the price is for a trip that is a one-way trip.

  The method may be such that the price is for a trip that is a round trip.

  In the method, the price may include the airfare price.

  In the method, the price may include the railway fare price.

  In the method, the price may include the rental car price.

  The method may include the hotel price in the price.

  In the method, the request may include a flexible search request.

  The method may be such that the final result of the method is at most one estimate per input date or input date pair.

  The method determines (A) at any point in time for step (i), by determining the confidence range of the estimated price from the incomplete past price data set, by analyzing the patterns in that data set Configuring one or more processors, and (B) configuring one or more processors to calculate a confidence range of an estimated price for a required price for an article or service that meets the parameters. It may be.

  The method may further include the step of: (C) providing the fare price quote with the confidence range to an end user computing device, such as a personal computer, a smartphone or a tablet.

  The method may further include the step of using the confidence range to determine whether to display the price to the user or to provide the user with a likely price range.

  The method may also display the likely price range as an error bar.

  The method may be such that the date range includes only one departure date.

  The method may be such that the date range includes only one return date.

  The method may include a method of providing a set of best round trip fare price estimates for the specified departure date range and the specified return date range. Step (i) includes receiving a request for a specified departure date range for the trip from the origin to the destination and a return fare price for the specified return date range, and step (iv) includes the specified departure It includes providing a set of best round trip fare price estimates for the day range and the designated return date range.

  The method may be that the set of best round trip price estimates is provided in the form of a bar chart.

  The method is such that the estimation process is random variation added to the Bayesian posterior probability to avoid ties, the minimum Bayesian posterior probability necessary to allow classification results, the maximum number of path operators involved in generating a candidate, or It may be parameterized by one or more of

  The method may be performed on a server provided with a quote service for fare availability and price.

  According to a second aspect of the invention, a server configured to provide a price quote, wherein: (i) a request for a price for an item or service, for example an airfare, along with a parameter defining the item or service is received The parameters include: activity type, eg airline fare, hotel reservation, railway fare; date range; destination; origin; desired weather conditions; star rating; keyword; any other user-defined preferences Analyze the patterns in that dataset, including receiving one or more of: (ii) determining the estimated price from the incomplete past price dataset at any point in time for (i) above And (iii) the required price for the goods or services that meet the parameters And calculating an estimate against, (iv) providing a price quote, is arranged server to perform is provided.

  The server, with respect to (ii), (a) obtaining the past price from the computer data store, (b) grouping the past price by category, (c) each group (D) storing, for each group, a classifier containing the derived statistical data; and (e) identifying the group using the stored classifier to which the requested price corresponds. It may be arranged to do and do.

  The server uses, for (iii), statistical data obtained from the stored classifiers corresponding to the group which specified that the server calculate the set of estimates for the requested price over the specified date range It may be arranged to be performed.

  The server may be further arranged to perform the method of any method according to the first aspect of the invention.

  According to a third aspect of the invention, a computer program product embodied on a non-transitory storage medium, the computer program product providing a price quote when executed on a computer Arranged and the computer program product, when executed on a computer, receives (i) a price for an item or service, eg a request for parameters defining the item or service together with Date type; destination; origin; desired weather conditions; star rating; keyword; includes one or more of any other user-defined preferences Receiving (ii) incomplete past at any time with respect to (i) above Determining the estimated price from the case data set by analyzing the patterns in the data set, (iii) calculating an estimate for the required price for an article or service that meets the parameters, (iv) the price There is provided a computer program product arranged to provide a quote.

  The computer program product may be arranged to perform a method belonging to any of the methods according to the first aspect of the invention.

  According to a fourth aspect of the present invention there is provided a computer terminal connected to a server of any server according to the second aspect of the present invention, the computer terminal being designated for a trip from a departure point to a destination point The computer terminal is arranged to send a request for a price quote for the date range, and the computer terminal is further provided with a computer terminal arranged to receive the price quote.

  According to a fifth aspect of the present invention, an article or service, such as an airline ticket, for which the freight estimate for the article or service is carried out using the method according to the first aspect of the present invention, or An article or service provided using a server according to an aspect of the present invention is provided.

  According to a sixth aspect of the present invention, there is provided a web based price comparison web site, wherein an end user is required to interact with the web based price comparison web site for a price for an article or service. A server based on the method of any of the methods according to the first aspect of the present invention, and the web based price comparison web site providing A web based price comparison web site is provided.

  The foregoing and other aspects of the present invention will now be described, by way of example only, with reference to the following figures.

FIG. 2 is an example of a system for estimating prices from other prices, with the purpose of using the prices to increase the observed fare cash. FIG. 6 illustrates an example of how a “difference” engine may be used to derive fare class availability from live updates. FIG. 6 shows an example of an equation suitable for use in simple Bayesian classification. FIG. 6 shows an example in which the feature values are represented by numbered areas and the frequency information is represented by price bands (buckets). FIG. 5 illustrates an example of the entire training process. FIG. 7 illustrates an embodiment where each candidate quote placeholder can be priced using a simple Bayesian classifier. FIG. 6 shows an example of the whole process of inferring missing estimates. FIG. 5 illustrates an example of a connection between a server providing a quote service for fare availability and price, an input data source, and outputs to partners and users. FIG. 5 illustrates an example of a user interface. FIG. 5 illustrates an example of a user interface. FIG. 1 illustrates an embodiment of a system for performing a search process. FIG. 5 illustrates an example of a user interface. FIG. 6 illustrates an example of user interface output. FIG. 6 illustrates an example of user interface output. FIG. 5 illustrates an example of an architecture for browsing a search. FIG. 1 illustrates an example of the overall system architecture for a system that includes the provision of a quote service for fare availability and price. FIG. 7 is a diagram showing a GDS configuration example. It is a figure which shows the example of the fare regarding a fare class record, a fare category, and a category data table. FIG. 6 illustrates an example of a process for providing a fare pricing option. FIG. 5 illustrates an example of the relationship between pricing engine, AVS / NAVS, seamless (polling), cache DB, and airline host. FIG. 1 illustrates an example system that includes an availability server. FIG. 7 is a graphical illustration of an example of a confidence range for an estimated price using a bar, where the bar extends from the lowest price to the highest price in the range in which the price should be found. Bars are provided for a range of outbound dates and a range of inbound dates.

  One implementation of the present invention provides "model based pricing". It is described in section A below.

A. Model Based Pricing Flight pricing estimates are provided using model based data interpolation.

Background / Motivation Ticket prices for itinerary are complex to calculate. It is the result of the calculation of possible routings, approved fares, and the availability of these fares. These calculations are typically performed in the Global Delivery System (GDS), and performing queries on such systems can be slow and expensive.

Airlines that file fares with GDS typically use a pricing model called "scheduled". The sum of such important things should be noted about the model, if the round-trip travel the day O ₁ to return to the starting day R _1, the price of round-trip ticket P (O _{1, R 1),} the two one-way ticket It is not equal to P (O ₁ ) + P (R ₁ ). An alternative pricing model (referred to as a "low-budget" pricing) _is a model, which is a _{P (O 1, R 1)} = P (O 1) + P (R 1).

  Two important consequences of the scheduled pricing model are the limitations imposed on observed price reuse and the space needed to store them.

In the low-budget model, two “round-trip” queries are made, one for date O ₁ R ₁ and the other for date O ₂ R ₂ , the resulting price P (O ₁ ), P (R ₁ ), P (O ₂ ), and P (R ₂ ), up to four round trip fares (ie, P (O ₁ , R ₁ ), P (O ₁ , R ₂₎ ), P (O ₂ , R ₁ ), P (O ₂ , R ₂ )) can be calculated, and the four fares P (O ₁ ), P (R ₁ ), P (P O ₂ ) and P (R ₂ )). If the query were run on a "scheduled" model, then only two fares would be P (O ₁ , R ₁ ) and P (O ₂ , R ₂ ). Thus, in a low budget model, eight queries can be answered by two queries, while in a scheduled model, two queries can be answered by two queries.

Assuming that flight prices are only available on the day and the next 364 days, finding all possible one-way and round-trip fares on the route is possible by making 365 round trip queries Become. For a scheduled model, 365 ^* (365 + 1) / 2 = 66,795 round trip queries, as well as 365 one-way queries (in each direction) would be required. Thus, to build a complete list of prices for a route, a scheduled model requires about 185 times more queries than a low budget model.

  With the results of all these queries, the storage required to save the fare will necessarily be about 185 times larger.

The foregoing is merely an example for a route between two defined airports. In fact, there are about 5,000 private airports in the world. Thus the number of possible trips between airports is about (5,000) ² . Considering the number 365 ^* (365 + 1) / 2 = 66, 795 possible round trip inquiries mentioned above, this gives (5,000) ^{2 *} 365 ^* as the total number of possible inquiries between all airports per year (365 + 1) / 2 is obtained, which is about 10 ¹² . The implied amount of data storage is actually very large.

  Combining the scheduled model path with the cost of the GDS but increasing the number of queries required to form the "big picture" makes it impractical to build such a cache Means that). And the result is incomplete cash, and there are "holes" for which no fare has been observed.

  However, the price (returned by GDS) is the result of applying fare rules and availability. As a result, there is a pattern and consistency in the observed fares. In this example, based on using these patterns, it is possible to interpolate the missing fare from the cache using a model that reflects the fare rules that generated the price at the first place.

A system for estimating prices from other prices A system for estimating prices from other prices, using the prices to be observed (e.g. requesting a price for a live reservable fare Provided with the purpose of increasing the freight cash observed by A model embodiment is shown in FIG.

  The estimator predicts other estimates using statistical data derived from the estimates. A very simple estimator could calculate that all missing fares are equal to the average value of all observed fares. The prediction level can be enhanced by making the model more sophisticated to take into account the type of rules used to generate the fare in the first place.

  For example, a common "rule" defining whether fares are valid for an itinerary is whether the itinerary involves staying on a Saturday night. Typically, the meaning of such a rule is that a date involving not spending Saturday nights at the destination is much more expensive. The rules are usually designed to penalize business travelers who wish to return during the working week. Thus, a better model can be calculated that includes two mean values. That is, the average of all observed fares with a Saturday night stay and the average of all observed fares without a Saturday night stay. The algorithm then predicts the price against date pairs that have not been observed so far, first by calculating whether there was a Saturday night between the date pairs and then by returning the corresponding average Do. This results in an increase in the accuracy of the estimate.

  Similar improvements can be made to the model. For example, travel prices vary depending on the day of the week (Monday is typically more expensive than Wednesday). Therefore, calculating and using an average that also takes into account the day of the week will also potentially improve model accuracy.

  Thus, providing a statistical model to predict freight may be performed, for example, using statistical data and rules that may have been derived by humans. Alternative methods of forming a statistical model are also possible. In this approach, a machine learning approach called a "simple Bayesian classifier" is used to form a probabilistic model of fares, which is used to predict unobserved fares. Training of the classifier is performed using the observed price and the corresponding set of "features". Features could include the departure date of the week, length of stay, stay on saturday, airline etc for the queries that generated the fare. The classifier then predicts the price of unobserved fares by being given a set of features and giving the price that is most likely to have those features.

  The statistical models for predicting prices can then be consulted to predict fares not previously observed, or to predict observed fares (if one wishes to measure the accuracy of the model).

Key Features and their Calculations Features that may be used to train the classifier include:
・ Saturday stay ・ Day of stay ・ Weekly day of week ・ Time to travel ・ Airline ・ Web site ・ route ・ Date-Date and year-Month level ・ Month

  These features determine the analysis of all fares listed in the FROP (from fare rule output product, ATPCO (Airline Tariff Publishing Company)) and which rule to apply to the maximum number of fares It is derived by doing. For example, 54% of fares have a minimum stay days rule, and 31% of all fares require a minimum stay days rule to spend Saturday nights at a destination. Thus, the features may be useful features to classify based on price. Deriving a more accurate estimate of the utility of a feature may be done by training multiple models with different features and comparing the prediction accuracy of the different models.

Estimate good and missing flight price data, and use data generation features to form a good forecasting model

Potential market and / or application domain models may be used in the flight industry to estimate prices that have not been observed before, to display the prices to the user, or to inquire to return prices. It may be determined whether there is (for example, it may be a good value).

  This model may be used in other fields where there is the disadvantage of obtaining the price (e.g. inquiry cost) but the price generation is done by structured means (e.g. pricing rules). This includes travel products such as hotels, trains, and rental cars, as well as other products with structured pricing, such as insurance.

Other Applications Consumer Price Comparison Web site companies attempt to price competitively by comparing prices to competitor prices.

Model-Based Pricing-Guess Missing Estimates Guessing missing quotes may be done in two stages (training and quotes).
・ Training ○ Build a statistical model from past prices ・ Estimate ○ Identify missing estimate candidates ○ Price estimate candidates based on the statistical model

  In an example, the statistical model is a set of simple Bayesian classifiers trained regularly (e.g., daily) using past prices. Missing estimates are inferred based on the route operator's data and priced using a classifier.

Example of a Statistical Model Simple Bayesian classification is a dependency between the probability that an object described by a set of features belongs to a class and the probability that an object from this class features a set of features Based on above. See equations (1) and (2) in FIG.

In FIG. 3, equations (1) and (2) can be simplified into equation (3). In formulas (1) to (3),
C is a class into which the input objects can be classified (e.g., in this case the price range, for example, 75-125 EUR).
F _i is the feature value (eg, Friday, the Yale France airline website, etc. in the present case).

  The classification can be expressed using equation (4) in FIG.

  For certain feature value pairs where it is desired to find a class (price range), the feature values occur most frequently, so the probability that the class characterizes the input set is maximized.

Training The intent of the classifier training is to calculate how often the feature value occurs for the observed price range. For example, in the process shown in FIG. 4, the feature value is represented by a numbered area, and the price range (bucket) represents frequency information (for example, the feature value quantity is in the range of 75-125 EUR, 126-175 EUR, etc.) .

Enter the following elements for the training process example:
List of paths classifier classification scheme past estimates set of supported features with weights

  The list of paths sets the boundaries for training. The categories define a range of classifiers, and the categories are represented by a set of feature values. The experimentally selected category consists of city level routes and airlines (e.g. London-Paris, Air France), but other options are possible (e.g. city level routes without airlines). There is always one classifier per category. Classifier training is performed using only past estimates matching the category (e.g., only estimates for the London-Paris route (operated by Yale France)).

  In the first step of the training process, a number of recent historical estimates with the highest age are retrieved from the data base. Evaluate categories against any estimates. Group estimates by category. Each group is a training set for one classifier.

  The training set can be extended with estimates for reverse path equivalents. Example: For the past estimates on the route London Stansted to Rome Ciampino (Eale France), the reverse route equivalent is Rome Ciampino to London Stansted (airline and other details eg prices are Would be the same). Include the estimate for the reverse path in the scaled training with weights that adjust its importance.

Then, for each group, we build a classifier with the following statistical information extracted from the estimates.
Weighted number of estimates including each different feature value Weighted number of estimates including each different feature value per price range of a certain granularity Many observed price ranges, estimated price ranges, and all estimates

  The weights applied to many estimates can be varied between features to emphasize that some of them are more influential (eg, it may be more important for the travel to include weekdays than stay length) is there).

  The process ends with one classifier for each training set that contains statistical information (e.g., one for the London-Paris route (operated by Yale France)). Memorize the model for future use. Retraining the classifier can be done, for example, periodically or when many past estimates change.

  The formation of a classifier may only be performed if a certain number of estimates matching the category are available.

  An example of the entire training process is shown in FIG.

In addition to the parameters listed above, the training process can be parameterized by:
・ Size of price range per route (for example, 1 EUR, 5 EUR, 10 EUR)
Weighting applied to reverse route Weighting function applied based on estimated age

  Estimate the missing estimate

  In an embodiment, the user searches for a one way or round trip flight for a certain route and date range. The system responds to the query with the set of lowest estimates obtained from the data base. At most one for each date (or date vs. for round-trip flights). The response may not include a quote for a certain date (a date pair). Inferring missing estimates can be done using a trained simple Bayesian classifier.

The following elements may be input to the guessing process:
A list of dates (or date pairs) with the route and missing lowest estimate information

  Since the classifier can be used only to price existing estimates, candidate estimates must be evaluated first. Candidates are a combination of all available airport pairs available, date (date pairs), airlines, and agents for the query path. The generation of the list is based on the process input and the data base of the route operator.

  Next, each candidate quote placeholder is priced using a simple Bayesian classifier. See, for example, FIG.

Price estimation may be performed for each candidate estimate in the following steps.
Extract category feature values from estimates.
Extract classifiers trained on extracted categories from the data base Extract all feature values from estimate candidates classify candidate estimates according to: ○ For each price range stored in the classifier Calculate the Bayesian posterior probability versus selecting the price range class with the highest Bayesian posterior probability and assigning the price class to candidate estimates

  If there is no classifier for a given candidate quote, no price guess is made, and the date (date pair) slot remains empty.

  The process can result in multiple priced quote candidates per date (date to date). For this reason, the next step is to select a single quote for each date (date pair). The choice can be made based on the price. For example, select the lowest estimate per date (date pair).

  The end result of the process is at most one estimate per input date (date pair). Inferred estimates can be included in the response returned to the user. An example of the whole process of inferring missing estimates is shown in FIG.

Besides the parameters already listed, the inference process can be parameterized by:
· Minimum Bayesian posterior probability necessary to allow classification results · Maximum number of path operators involved in candidate generation · Random variation added to Bayesian posterior probability to avoid ties

Method of Estimating Price Confidence As a method of estimating, you may provide something that gives confidence in price. For example, you are 99% confident that it is within $ 5 and $ 1000. Therefore, the estimation method may provide the following.
Calculation and return of these beliefs.
• Use these beliefs to determine whether to display the price to the user or indicate to the user the likely price range (eg, an error bar).

  Many methods of price estimation can provide an indication of confidence in the price. For example, in the case of a simple Bayesian classifier, the selected class is the class with the highest probability, but that probability can be used as an indicator of confidence in the estimate. The closer the probability is to one, the higher the confidence that the predicted price is accurate.

The resulting beliefs may be used in many ways. If the confidence in the estimate is very low, you may decide to:
Do not show a quote because it is not useful to the user Trigger a live fetch of the price to be able to show the user the exact value. Confidence can be shown to the user.

Confidence can be shown to the user. This can be shown in many ways.
O A bar indicating the range of possible values for the price (see, eg, FIG. 22).
○ Decrease the visibility / intensity of the displayed price (for example, using coloring, shading or dotted lines).
The confidence range for the quote price ranges from the lowest price to the highest price range that leads to the confidence that the result of the quote method will find the price. A range of confidence for the estimated price may be shown graphically using a bar. A bar may be displayed for each date in the date range. The display of the bar for each date in the date range may be performed for the forward and reverse passes. The bars are color-coded, for example, the bars correspond to recent prices, for the booking function, for a price range derived from multiple tickets sharing the departure date, and similar itinerary It may indicate that the price may be stable for the price range estimated from. One bar may be provided for a certain date. Multiple bars may be provided for a given date. An example is shown in FIG.

B. Derived Availability Engine Background / Motivation Motivation is very similar to that in the model-based pricing example.

  The meaning of the "scheduled" price model is that two single estimates can not be used to form a round-trip estimate, so the query must be performed for every possible date pair, date It is forbidden to do this because of the large number of pairs, the reason being the cost of obtaining this data and the space needed to store the data. However, the round trip price is the result of combining the available fares available for the outbound and inbound legs, along with some rules. Thus, if the availability and rules can be known, then it is possible to break the round trip queries into "reusable" legs and use them to construct new "round trip" prices, a low budget price model. This is possible in a similar (but different) way to that used for airlines with.

Example of derived availability engine The Global Distribution System uses schedules from OAG (official airline guide: see, for example, www.oag.com), fares from ATPCO, and fare classes from airlines. Gather the possibilities and store this information in its own system. This stored information is regularly updated from those sources. When the GDS is queried for a quote for a route and date, the GDS performs the following steps:
1. Determine a valid itinerary from the schedule.
2. Calculate valid fares for your itinerary (some GDSs do not have a pricing engine and can be provided by third party engines, such as those provided by SITA (see, for example, http://www.sita.aero/ Using) to ensure that the correct price is applied for each travel itinerary).
3. Find the availability of these fares.
4. Add accurate taxes and surcharges.

  Step 1 may be implemented in a route service. Specifically, the implementation of step 1 may be performed using a timetable and rules about which can be sold. A user inquiry can identify a valid itinerary from which a ticket can be purchased.

  The airline sends fares to ATPCO, which provides an offer for these data. However, converting these raw fares and rules into a system for determining the correct fare for a query may be a great effort. Fortunately, ATPCO supplies combined freight and rule data in the data supply called FROP (freight rule output product). Send the FROP data in a fixed length record file containing rule conditions and fare information summarized for the major categories. Thus, step 2 can be performed using FROP data. It can also be realized by combining fare rules and prices from the airline with a pricing engine.

  ATPCO also provides for the provision of itinerary and service fees. Take the data in these applications as tax and surcharge engines and use them to calculate the overall price that the customer will pay for a certain fare, thus building an engine that emulates step 4 be able to.

  The only remaining item for low cost calculation of GDS fares is Availability-Step 3. It is possible to derive the availability by comparing the result of the live update (final result of steps 1 to 4) with the calculations of steps 1, 2 and 4 outlined above, ie route service, FROP It is possible to calculate all the possible fares by combining the fares obtained from the and the tax and surcharge engine, and using the observed fares obtained from the live update, which of the possible fares It is possible to infer what was used and hence which of the calculated fares were available. Shown in the diagram in FIG. 2 is the way in which the availability of fare classes can be derived from live updates using such a "difference" engine.

  If the available fare is known for both the forward and return leg of the round trip flight, the round trip price calculation can be performed even if a specific date pair in the round trip is not observed. This method of modeling the estimate is known as a combinable single leg (SLC). SLC is similar to the "low budget" price model. However, in the case of low budget, it is excluded that the forward and return legs of the round trip are also effective as a one-way ticket. For low budgets, configuring the return is simply a matter of adding the return and outbound leg prices (as the SLC structure needs to apply availability after finding a valid fare for a date pair, Slightly more complicated).

  If the price of the freight has increased but the availability remains the same, it is possible to calculate a new price using the derived availability engine, even if it has not been observed.

  There are additional benefits to doing so providing the derived availability compared to using GDS. Although doing pure calculations in the GDS does not guarantee that the fare can be "found" on the airline or OTA web site, the observed fare is, by definition, found. Also, pure calculations do not take into account additional charges, margin, discounts, and personal fares (which may be incorporated into the observed fares).

  It should be noted that the fare prices do not necessarily change, and there may be many different fares for a particular flight, and the availability of each fare may change. Because revenue management software may change continuously which fares are available, the disappearance of cheaper tickets can happen because revenue management cancels the ticket and increases revenue .

It is not necessary to inquire the GDS or airline reservation system to find the profit and availability, and derive the availability.
Calculate a new price based on the estimated availability rather than the true availability.
Estimating new prices based on availability and fare rules, without the observed case of new prices.

Potential Other Applications Providing airlines with their own product pricing. This is because airlines still do this pricing from GDS at high cost at this time.

  Companies are interested in finding flight data prices at a fairly low cost.

Architecture Overview Example A server (which may be real or virtual) is provided on which a quote service for fare availability and price is provided. Related websites, smartphone apps, inter-company services, connections with traditional airlines (eg UK Airlines, Qantas, KLM etc), low budget airlines (eg Ryanair, Easyjet, German Wings etc) And travel booking websites (eg, lastminute.com, Opodo, Thomson, etc.).

  The travel booking web site and the airline may provide input data to a server providing a quote service for fare availability and price. The output may be provided by a server to a public application programming interface (API), such as a partner company. It may be for the user to provide the output to the search website, to the search mobile website and to the application on the smartphone. An example of the connection configuration between the server and the input data source, the outputs for the partner and the user are shown in FIG.

  In a search at the server, the user may specify a departure location (e.g., may be an individual airport, a city including multiple airports, or a country including multiple airports). In the search at the server, the user may specify a destination (for example, it may be an individual airport, a city including multiple airports, or a country including multiple airports). In a search at the server, the user specifies a departure date or a range of departure dates (for example, a date range, or a calendar month, or an unspecified date (which may include a date range such as any date in the next year)) You may. In a search on the server, the user specifies a return date or range of return dates (for example, a date range or calendar month, or an unspecified date (which may include a date range such as any date in the next year)) You may. In the search on the server, the user may specify a one-way ticket or may specify a round trip ticket. The user may specify the number of passengers in a particular age range. The user may specify a class of tickets (e.g., economy, premium economy, business class or first class, etc.). You may do a parallel search for the hotel. It is also possible to conduct a parallel search for rental cars. You may indicate your preference for direct flights. An example user interface is shown in FIG. A further user interface embodiment is shown in FIG. This is an example of a flexible search. The departure indicates that the airport used in London (UK) may be anything, and the destination indicates that any airport in Australia is permitted.

  The output search results may include a list of flights that meet the search criteria. Graphical indicators (e.g., slider bars) may be provided to limit the departure time range of foreign-bound flights. Graphical indicators (e.g., slider bars) may be provided to limit the departure time range of the round trip flight. Selectable tabs may be provided to arrange the flights in order of increasing cost. Selectable tabs may be provided to arrange the flights in order of increasing travel time. Selectable tabs may be provided to arrange flights in alphabetical order by airline name. An example user interface is shown in FIG.

  In the user search embodiment, if the flight between the departure point and the destination (which may be a one-way flight or a round-trip flight) applies to the day of the foreign flight, Required for round trip flight dates. In the search process embodiment, in a first step, a partner search web site is selected that exhibits output. The second step is to identify the relevant price already cached. The third step is to identify and update unavailable related prices. In the fourth step, new data is cleaned up and stored (eg, taxes and fees may be removed, the actual flight operator may be identified). In the fifth step, the price was returned to the user. An example of a system for performing the search process is shown in FIG.

  For flexible search, the output data may also provide a list of destination airports that meet the flexible search criteria (for example, the destination is any airport in Australia and departs from any airport in London, UK) good. The list may be listed in ascending order of price. An example of user interface output is shown in FIG. If a specific airport is selected, the lowest price for the outbound leg of the trip may be indicated as a function of the departure date for the journey, for example, for selectable calendar months; As a function of day, for example, for selectable calendar months, it may be shown for selected departure and destination cities or airports. Example of User Interface Output Shown in FIG.

  A browsing search may be provided. A browsing service may be provided to smartphone app users or search web site users. The browsing service may access the browsed price data. The viewed price data may include data scraped from the website content. The browsing service may be connected to a public application programming interface (API). The browsing service may not be connected to the airline server, or may not be connected to other travel information service provider servers. An example of the architecture for browsing search is shown in FIG.

  An example of the overall system architecture for a system that includes the provision of a quote service for fare availability and price is shown in FIG.

  The following sections provide background information on how to calculate airfare and the role of the GDS.

C. Global Delivery System Primer The Global Delivery System (GDS) may be a system that includes availability, prices, and related services for airlines, and may be defined as a system through which reservations can be made and tickets can be issued. . GDS makes these functions available to subscription travel agencies, booking engines, airlines, and other subscribers. A GDS configuration example is shown in FIG. A more general term is "delivery system". This may include being able to make reservations for modes of transport other than air transport (e.g. trains etc).

Route Search Historically, as airline networks grew, route searches were added by GDS. When the number of potential possible connections increases exponentially (SOF-NYC has many connection points. For example, SOF-IST-NYC, SOF-FRA-NYC, SOF-MOW-CPH- GDS has added layers of this "connected" structure to reduce the number of different priceable paths, such as NYC). Therefore, there is no need to process many fares and rules. They did it in a very dull way-static path and connection table. This method conveys to them essentially a route-searching city between SOF and NYC in order to fly between zones (defined by ATPCO-US-EUR, EUR-APAC to simplify the discussion) In addition, it includes connections in the wrong direction (eg IST), but rejects a large number of cities as possible connections. For example, the flight SOF-PRG-CPH-NYC may be considered too complex and potentially costly, so it may be dropped in the path search phase.

Connection Construction After setting up the routing, connection construction begins to look at the airline, the number of flights, and the connection limits (included in the minimum connect time (MCT) table). MCT (delivered by OAG) is a complex document that specifies general rules about how a flight / airline can connect in time. For example, if a long distance US-EU connects to the EU-EU in LHR, 120 minutes MCT will apply. General rules are easy to interpret but important. If someone tries to reserve outside the range of MCT parameters, a manual override is required.

  Of interest is the MCT exception (about 200,000). This may take 55 minutes if DL flight # 003 connects to AF flight # 004. However, when the DL flight # 003 arrives at the gate 19 of the CDG and the connection flight AF 009 departs from the gate 49, 65 minutes is applied as the MCT, and so on. Of all the exceptions, about 70,000 are for Ale France (AF) and Paris Charles de Gaulle Airport (CDG). That's why many passengers think it is the worst to fly.

Freight An example of a freight is a pricing contract. This specifies the price (base fare) for the service provided by the airline. The price is defined by the service class, class type (Y, Q, J, M, H etc.), and type of travel (one way, round trip, multi-city, round the world (RTW)) .

  In the example, each fare is accompanied by the number of rules and restrictions applied to it, and the fare is not a simple Excel line (spreadsheet or CSV file) but a contract with a length of 20 to 30 pages Think of it. Rather than thinking of the fare as a row in a large Excel spreadsheet, and the column showing which features the fare has, instead the fare itself involves many parameters and restrictions. It should be considered a complex matter, and thus rather close to a contract. The fare is who, when and how the fare-passenger type can be used, when the fare is applicable (NVB, not valid before NVA, not valid after), and many others Stipulates restrictions. The fare may be a public fare (accessible to any reseller) or a personal fare (accessible to a designated reseller / seller).

  The fare is given by the airline. They are defined at the general level for the market and are limited by travel and ticketing dates. Naturally, fares represent price points.

Freight Upload and Delivery ATPCO is an industry association that handles freight and freight delivery in the industry. There are a few airlines that deliver only to SITA (Bulgaria is an example).

Scheduling To date, ATPCO has released fares on a specific schedule, but at the request of the airline, the organization has begun to release fares on an hourly basis. This allows the airline to ensure a quick response to market changes that are not only suitable for availability control.

  SITA releases the fare four times a day on weekdays and three times on weekends. In addition, there are existing fares within GDS (GDS-specific fares or negotiations). This fare is completely under GDS control and does not follow the general delivery schedule.

Freight rules and restrictions and categories Provide freight data, freight class data, and freight restriction data.

How does pricing work (very high level)?
The rules are calculated by the pricing engine in a very strict order of rule records, tables and sub-tables. If no specific rule records are found, there are general rules that define them all.

Limit data-The fare class tells the engine that there are rules for a specific fare.
Category Control tells the engine what type of restriction exists.
-The category data table is last as the engine reads to get relevant information.

  For example, the fare class tells the engine that the rule is attached. A rule is a category 15 (sales restriction), which, after examining the category data table, derives additional information about it (what type it is, what is the rule and restriction).

  An example of fares for fare class records, fare categories, and category data tables is shown in FIG.

Freight Labeling vs. Freight Quotes and Process Many industry experts use the terms freight label and freight quote interchangeably, which is unacceptable for the skilled person. Explain that.

Fare Indication Comprehensive integrated concept of market fare and related rules. This allows the agent to:
-Compare fare rules (not NB! Prices)
-Informing the passenger of the contract period (fare rules)

Freight Estimate Final price after applying the contract term. This captures most of what you want to give to the passengers.
-All taxes-All additional charges-All associated rules-Final price

  An example of a process for providing fare pricing options is shown in FIG.

Availability Knowledge of availability is very important in the industry. Availability allows the airline to control its margins (sheets) and also adds another way of managing how profitable the route is by opening and closing the availability bucket Is done. This is managed by the Revenue Management System (RM).

  Some RMs are highly capable of managing complex rules, while the complexity is high because most of the availability rules are formed by the internals of statisticians and mathematicians.

  There are three main ways to receive availability when you want to check if a sheet / class service is available or not.

i) NAVS / AVS
NAVS = Availability by simple numbers per sheet class. Example: Y9H3M0L8. . .
AVS = Availability Status-This is flight open / closed, open for wait list, etc.

ii) Polling CXR (Airline) can use NAVS / AVS as an indication if better connectivity is reduced. These CXRs use revenue management rules and need to be polled to obtain actual availability and all availability information.

  Some airlines and / or flights require polling, and the order to do so is MR (Market Limited). If the tag is an MR when the GDS requests an AVA, the GDS needs to be polled to determine the correct availability. Unfortunately, because NAVS / AVS does not indicate these tags, GDS using the NAVS / AVS for tagged flights may display incorrect availability.

iii) Cache DB
The pricing engine has evolved a complex cache DB to reduce polling and NAVS / AVS. Although they are not accurate, they do achieve some of the goal of reducing calls when storing information.

  An example of the relationship between pricing engine, AVS / NAVS, seamless (polling), cash DB, and airline host is shown in FIG.

Availability Server Implementation The caching mechanism may be supplied by off-line traffic, but is used by the on-line channel. The availability server may be able to update cached data based on rules (bi-directional polling) and also warn of minor conflicts with reservations. A system embodiment including an availability server is shown in FIG.

NOTE Implementing the steps described above can be performed using standard well known programming techniques. The novelty of the embodiments described above involves achieving the described results using the described steps, rather than being of a particular programming technique. Software programming code that embodies or forms part of the present invention is typically stored on permanent non-transitory storage. In a client / server environment, such software programming code may be stored using storage associated with the server. The software programming code may be embodied on any of a variety of known media (eg, a diskette, or a hard drive, or a CD-ROM) for use with a data processing system. The code may be distributed on such media, or distributed to the user, from the memory or storage of one computer system to another computer system via some type of network. It may be made available to users of such other systems. Techniques and methods for embodying software program code on physical media and / or distributing software code via a network are well known and will not be further described herein.

  It will be appreciated that a general purpose and / or special purpose hardware based system or a general purpose and / or special purpose hardware performing the specified functions or steps, to carry out the elements of the description and combinations of elements in the description. It can be done by a combination of hardware and computer instructions.

  Providing these program instructions to the processor to form a device may be performed such that the instructions executed on the processor form means for performing the functions specified in the description. Computer program instructions are executed by the processor to cause the processor to perform a series of operational steps to form a computer implemented process that, when executed on the processor, performs the functions specified in the description. It is also possible to give steps for this. Accordingly, FIGS. 1-17 and 22 support a combination of means for performing the specified function, a combination of steps for performing the specified function, and program instruction means for performing the specified function. .

  It will be appreciated that the above described arrangement merely illustrates the application to the principles of the present invention. Many modifications and alternative arrangements can be devised without departing from the spirit and scope of the present invention. While the present invention has been shown in the drawings and with full detail described above with individuality and detail, it has been carried out with respect to what are presently considered to be the most realistic and preferred embodiments of the present invention, to the person skilled in the art As is apparent, numerous modifications can be made without departing from the principles and concepts of the present invention as described herein.

Concepts Several concepts (referred to below as concepts "A-H") are included in the present disclosure. The following may be useful in defining these concepts. You may combine the aspect of a concept.

A. A method of providing a price quote comprising: (i) receiving by a computer server a price for an item or service, eg a request for parameters defining the item or service together with an air fare (ii 2.) At any point in time for step (i) above, determining one or more estimated prices from the incomplete past price data set by analyzing the patterns in that data set, The steps of configuring, (iii) configuring one or more processors to calculate an estimate for the required price for an article or service that meets the parameters, (iv) price estimates for the end-user computing Sending to a vice, eg a personal computer, a smartphone or a tablet.

The foregoing may further include any of the following alone or in combination.
• In this method, the parameters defining those goods or services include: activity type, eg airline fare, hotel reservation, railway fare, date range; destination; starting point; desired weather conditions; star rating; One or more of the user-defined preferences of.
-The determination of the estimated price is performed by estimating, deriving or predicting the estimated price.
Step (ii): (a) obtaining past price estimates from the computer data store, (b) grouping the past price estimates by category, and (c) statistical data for each group (D) storing, for each group, a classifier containing the derived statistical data on the computer (d) using the stored classifier (e), the group to which the requested price corresponds Identifying and are included.
Step (iii) includes calculating the set of estimates for the requested price over the specified date range using statistical data obtained from the stored classifier corresponding to the identified group Be
Step (ii) involves analyzing the patterns in the data set using rules.
Step (ii) involves a simple Bayesian classifier machine learning approach to generate a probabilistic model of prices, which is used to predict unobserved prices.
Train the classifiers using the observed prices and their corresponding feature sets.
Features are in terms of requirements and include one or more of departure day of week, length of stay, stay on Saturday, airline, time to travel, route, month.
The classifier then predicts the price of the unobserved price by being given a set of features and giving the price that is most likely to have those features.
Feature derivation may be performed by training multiple models with different features and comparing the prediction accuracy of different models.
Step (ii) includes constructing a statistical model from past prices, identifying missing candidate estimates, and pricing candidate estimates based on the statistical model.
Estimating the price for each candidate quote, extracting category feature values from the estimates, retrieving the trained classifier from the database for the extracted categories, all from the candidate estimates Extracting feature values, classifying candidate estimates, calculating Bayesian posterior probability for each price band stored in the classifier, and selecting a price band class with the highest Bayesian posterior probability And the step of attaching a price class to the candidate quote.
Inputs to the statistical model include a list of paths, a classifier category classification scheme, past estimates, and a set of supported features with weights.
Filter past estimates by age.
The inputs to the statistical model include reverse path equivalents.
The method includes the steps of including the fare price cached in the set of price quotes.
The set of price quotes are configured without querying the distribution system after step (i).
Prices are for trips that are one-way trips.
The price is for trips that are round trips.
The price includes the airfare price.
The price includes the railway fare price.
The price includes the rental car price.
・ Price includes hotel price.
The request includes a flexible search request.
The final result of the method is at most one input date or one estimate per input date pair.
The method comprises (A) determining the confidence range of the estimated price from the incomplete past price data set at any point in time for step (i) by analyzing the patterns in that data set Configuring one or more processors to configure one or more processors, and (B) calculating a confidence range of an estimated price for a required price for an article or service that meets the parameters. It is done.
The method further comprises the step of: (C) providing the fare price quote with the confidence range to an end user computing device, such as a personal computer, a smartphone or a tablet.
The method further includes the step of determining with the confidence range whether to display the price to the user or to provide the user with a likely price range.
Display the likely price range as an error bar.
The date range includes only one departure date.
• The date range includes only one return date.
The method includes providing a set of best round trip fare price estimates for a specified departure date range and a specified return date range, wherein step (i) comprises: Receiving the request for the round-trip fare price for the specified departure date range and the specified return date range, step (iv) includes the best round trip fare price estimate for the specified departure date range and the specified return date range. Including providing a set.
The best round-trip fare price quote set is provided in the form of a bar chart.
Of the minimum Bayesian posterior probability necessary for the estimation process to allow classification results, the maximum number of path operators involved in generating a candidate, or of the random variation added to the Bayesian posterior probability to avoid ties. Parameterized by one or more.
The method is performed on a server provided with a quote service for fare availability and price.

  A server configured to provide a price quote, and (i) receiving a price for an item or service, eg, a request for a parameter defining the item or service along with the airfare, the parameter including an activity type Destination, starting point, desired weather conditions, star rating, keywords, including one or more of any other user-defined preferences, receiving and receiving (Ii) determining the estimated price from the incomplete past price data set at any point in time for (i) above by analyzing the patterns in that data set; (iii) parameters Calculate an estimate for the required price for the goods or services that meet If, (iv) providing a price quote, it is arranged server to perform is provided.

The server may further include any of the following alone or in combination.
(Ii) The server obtains past prices from a computer data store, groups past prices by category, deriving statistical data for each group, statistics derived for each group It is arranged to store a classifier containing the data, and to specify a group using the stored classifier to which the requested price corresponds.
The server is arranged to calculate the set of estimates for the requested price over the specified date range using statistical data obtained from the stored classifiers corresponding to the identified groups.
The server is further arranged to carry out a method belonging to any of the aspects of concept A.

  A computer program product embodied on a non-transitory storage medium, the computer program product being arranged to provide a price quote when executed on a computer, the computer program product comprising When executed on a computer, (i) receiving a price for an item or service, eg a request for a parameter defining the item or service together with the airfare, the parameters include an activity type, for example an airfare, a hotel reservation Date range; destination; origin; desired weather conditions; star rating; keywords; including one or more of any other user-defined preferences, receiving (ii) as described above Estimate from an incomplete past price data set at any time for i) Determining the case by analyzing the patterns in the data set, (iii) calculating an estimate for the required price for an article or service that meets the parameters, and (iv) providing a price estimate. A server is provided which is arranged to:

  A computer program product may be arranged to perform the method of any aspect of Concept A.

  A computer terminal connected to a server of any server of Concept A, the computer terminal being arranged to send a request for a price quote for a specified date range for a trip from a departure point to a destination point, the computer The terminal is further provided with a computer terminal arranged to receive a price quote.

  An article or service, for example an airline ticket, a price quote for the article or service is provided using a server defined in any aspect of the method or concept A defined in any aspect of the concept A An article or service is provided.

  A web based price comparison web site where end users can interact with the web based price comparison web site by providing a request for the price for the goods or services, Web Based Price Comparison Web Site A web based price comparison web site is provided which provides the request to a server that estimates the price using a method belonging to any aspect of Concept A.

B. How to estimate which fare classes are available for a trip How to estimate which fare classes are available for a trip from a departure point to a destination on a particular date. The method comprises the steps of (i) defining the requirements for the fare price for the trip from the departure place to the destination on a specific date, and (ii) the estimated fare prices from the incomplete historical price data set (E) estimating, estimating, or predicting by analyzing the patterns in the data set; (iii) calculating the estimate for the requested freight price using the results of step (ii) iv) sending the request to the delivery system for the fare price for the trip from the departure point to the destination on a specific date, and (v) from the delivery system, the delivery system for the trip from the departure point to the destination on a specific date Step of receiving the fare price of (v) and the requested fare price obtained from step (iii) Comparing the calculated estimates for the delivery system fare price received in step (v) to estimate which fare classes are available for the trip from the departure point to the destination on a particular date And (vii) outputting the estimated fare class availability for the trip from the departure point to the destination on a specific date.

The foregoing may further include any of the following alone or in combination.
In the method, in step (ii), obtaining past fare estimates from the computer data store, grouping the past fare estimates by category, deriving statistical data for each group, Storing for each group a classifier containing derived statistical data on the computer, and using the stored classifier to identify the group to which the requested fare price corresponds .
-In the method, in step (iii) calculating the set of estimates for the requested fare price over the specified date range, the statistical data obtained from the stored classifier corresponding to the identified group It includes what it does.
The delivery system is a global delivery system.
-The estimated fare class price is attached to each estimated fare class availability.
-Includes the step of sending the output to the server.
The server is an airline server.
Step (ii) involves analyzing the patterns in the data set using rules.
Step (ii) involves a simple Bayesian classifier machine learning approach to generate a probabilistic model of freight, which is used to predict unobserved prices.
Train the classifiers using the observed prices and their corresponding feature sets.
The feature relates to the request and includes one or more of the day of departure of the week, the number of days of stay, stay on Saturday, airline, time to travel, route, month.
• The classifier then predicts the price of unobserved freight rates by being given a set of features and giving the price that is most likely to have those features.
Feature derivation may be performed by training multiple models with different features and comparing the prediction accuracy of different models.
Step (ii) includes constructing a statistical model from past prices, identifying missing candidate estimates, and pricing candidate estimates based on the statistical model.
Estimating the price for each candidate quote, extracting category feature values from the estimates, retrieving the trained classifier from the database for the extracted categories, all from the candidate estimates Extracting feature values, classifying candidate estimates, calculating Bayesian posterior probability for each price band stored in the classifier, and selecting a price band class with the highest Bayesian posterior probability And the step of attaching a price class to the candidate quote.
Inputs to the statistical model include a list of paths, a classifier category classification scheme, past estimates, and a set of supported features with weights.
Filter past estimates by age.
The inputs to the statistical model include reverse path equivalents.
The method includes the steps of including the fare price cached in the set of fare price estimates.
・ Travel is a one-way trip.
Travel is a round trip.
・ Fares are air fares.
The request includes a flexible search request.
Of the minimum Bayesian posterior probability necessary for the estimation process to allow classification results, the maximum number of path operators involved in generating a candidate, or of the random variation added to the Bayesian posterior probability to avoid ties. Parameterized by one or more.
The method is performed on a server provided with a quote service for fare availability and price.

Further, a server configured to infer which fare class is available for a trip from a departure point to a destination on a particular date, the server comprising: (i) departure on the particular date Data to specify the requirements for the fare price for the trip from the destination to the destination and (ii) to infer, estimate or predict the estimated fare price from the incomplete past price data set Doing by analyzing the patterns in the set; calculating an estimate for the requested fare price using the estimated fare results of (iii) (ii);
(Iv) sending the request to the delivery system for the fare price for the trip from the departure point to the destination on a specific date;
(V) receiving from the delivery system the fare price of the delivery system for the trip from the departure place to the destination on a specific date;
(Vi) Compare the calculated estimate for the requested fare price obtained from the calculated estimate in (iii) with the fare price of the delivery system received in (v) To arrange which fare classes are available and (vii) output the estimated fare class availability for the trip from the departure point to the destination on a specific date The server being provided is provided.

  At the server, for (ii), the server (a) obtaining past fare estimates from the computer data store, (b) grouping the past fare estimates by category, (c) Using the deriving of statistical data for each group, (d) storing for each group a classifier containing the derived statistical data, and (e) storing of the requested fare price And identifying the corresponding group.

  At the server, for (iii), the server calculates statistical sets for the requested fare price over a specified date range, statistical data obtained from stored classifiers corresponding to the identified groups It is arranged to do using.

  The server is further arranged to perform the method including any of the method limitations described above.

  Further, a computer program product embodied on a non-transitory storage medium, the computer program product, when executed on a computer, for travel from a departure point to a destination on a specific date Are arranged to guess which fare classes are available, and the computer program product, when run on a computer, (i) fare prices for a trip from a departure point to a destination on a particular date Defining the requirements for (i) and (ii) estimating, estimating or predicting the estimated fare price from the incomplete past price data set by analyzing the patterns in that data set And estimates of the fare price requested using the estimated fare results of (iii) (ii) Calculating, (iv) sending the request to the delivery system for the fare price for the trip from the departure point to the destination on a specific date, and (v) from the delivery system to the departure point to the destination on a specific date Receive the delivery system's fare price for the trip, and compare the calculated estimate for the requested fare price obtained from the (vi) (iii) estimate with the delivery system's fare price received in (v) Estimate which fare classes are available for travel from the departure point to the destination on a date of (7) and use of the estimated fare classes for travel from the departure point to the destination on a specific date A computer program product is provided which is arranged to output the possibilities.

  A computer program product is arranged to carry out the method according to any of the above described method limitations.

  A computer connected to a server of any of the server concepts of Concept B above, wherein the computer is arranged to send a request defining a trip from a departure point to a destination on a particular date, the computer being Additionally, a computer is provided that is arranged to receive the output of the estimated fare class availability for the trip from the departure point to the destination on a particular date.

C. A method of estimating a fare price A method of estimating a fare price, the method comprising: (i) obtaining a past fare estimate from a computer data store; and (ii) grouping the past fare estimates by category. (Iii) deriving statistical data for each group, (iv) storing for each group a classifier containing the derived statistical data for each group, (v) at a particular date Receiving a request for the fare price for the trip from the origin to the destination; (vi) using the stored classifier to identify the group to which the requested fare price corresponds; (vii) the requested fare price Estimates from the stored classifiers corresponding to the identified groups And (viii) providing the requested fare price quote.

D. A method of estimating a fare price A method of estimating a fare price, the method comprising: (i) receiving a request for a fare price for a trip from a departure point to a destination on a particular date; Inferring, estimating, or predicting fares from incomplete past price data sets by analyzing the patterns in the data sets, and (iii) using the results of step (ii) Calculating an estimate for the fare price that has been made and (iv) providing the requested fare price estimate.

E. Method of Providing a Set of Round Trip Price Quotes A method of providing a set of round trip price quotes, the method comprising: (i) obtaining a past fare quote from a computer data store; (ii) past Grouping the freight estimates by category into categories, (iii) deriving statistical data for each group, and (iv) storing, on a computer, a classifier including the derived statistical data for each group. Requested using the steps: (v) receiving a request for the specified departure date for the trip from the departure point to the destination and the return fare price for the specified return date; (vi) using the stored classifier Identifying the group to which the round-trip fare price corresponds; (vii) for designated departure and return dates Calculating the set of estimates for the requested round-trip fare price using statistical data obtained from the stored classifiers corresponding to the identified group; and (viii) providing the set of fare price estimates. ,including.

F. Method of Providing the Best Round-trip Price Quote Set A method of providing the best round-trip price quote pair for a specified departure date range and a specified return date range, the method comprising: (i) computer data -Obtaining past fare estimates from the store, (ii) grouping past fare estimates by category, (iii) deriving statistical data for each group, and (iv) each group on a computer Storing the classifier containing the derived statistical data, and (v) receiving a request for a round-trip fare price for the specified departure date range for the trip from the departure point to the destination and for the specified return date range. Step and (vi) using the stored classifier, the requested round trip fare price corresponds to the corresponding glue (Vii) calculating a set of estimates for the requested round trip fare price for the departure and return date pair using statistical data obtained from the stored classifiers corresponding to the identified group; (Viii) selecting the best fare price quote for each pair of departure and return dates, and (ix) providing the set of best round trip fare price estimates for each pair of departure and return dates. .

G. Method of Providing a Set of Round Trip Price Quotes A method of providing a set of round trip price quotes, the method comprising: (i) a designated departure date and a designated return for a trip from a point of departure to a destination Analyzing the patterns in the data set by receiving a request for round-trip fare price for the day and (ii) estimating, estimating or predicting the estimated fare from the incomplete past price data set Providing a set of estimates for the required return fare price for the specified departure and return dates using the results of step (iii) step (ii), and (iv) providing a set of fare price estimates. And the step of

H. Method of Providing Best Round-trip Price Quote Pairs A method of providing a set of best round-trip fare price estimates for a specified departure date range and a specified return date range, the method comprising the steps of: Receiving the request for the specified departure date range for the trip to the destination and the return fare price for the specified return date range, and (ii) estimating and estimating the estimated fare from the incomplete historical price data set Or forecasting is performed by analyzing the patterns in the data set, and (iii) using the results of step (ii), a set of estimates for the requested round trip fare prices for the departure and return date pairs. Providing a set of best round trip fare price estimates for each pair of departure and return dates. And the following steps:

Claims (19)

A method of running a fare price estimate on a computer,
(I) obtaining past fare estimates from a computer data store;
(Ii) grouping the past fare estimates by category;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a fare price for a trip from a departure point to a destination on a particular date;
(Vi) using the stored classifier to identify a group to which the requested fare price corresponds;
(Vii) calculating an estimate for the requested fare price using statistical data obtained from the stored classifier corresponding to the identified group;
(Viii) providing the requested fare price quote;
Method including.   The past fare estimate is an incomplete past fare estimate data set, or one or more such as to determine a price estimate from the incomplete past fare estimate data set by analyzing the pattern of the data set The method of claim 1, comprising the step of configuring a plurality of processors. (A) configuring the one or more processors to: determine a confidence range of the price quote from the incomplete past fare quote data set by analyzing a pattern of the data set; ,
(B) configuring the one or more processors to calculate a confidence range of the price quote;
(C) providing the fare price quote with the confidence range to an end-user computing device, such as a personal computer, a smartphone, or a tablet,
The method of claim 2.   The method according to claim 2 or 3, wherein step (iii) comprises using a simple Bayesian classifier machine learning approach to generate a probabilistic model of prices, wherein said models are used to predict unobserved prices. .   5. A method according to claim 4, wherein the classifiers are trained using the observed prices and their corresponding set of features.   The feature relates to the request and includes one or more of a day of departure, a number of days of stay, a stay on Saturday, an airline, a time to travel, a route, a month, or a price at which the classifier is not observed next The forecasting of the price of is performed by being given a set of features and giving the highest possible price to have those features, or deriving a feature by multiple models with different features 6. A method according to claim 5, which may be performed by training with using and comparing the prediction accuracy of the different models.   6. A method according to any of the preceding claims, wherein the price quote comprises an airfare price, or the price quote comprises a railway fare price.   The method according to any of the preceding claims, wherein the price quote is configured without querying a distribution system. A server configured to provide a fare price quote,
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a fare price for a trip from a departure point to a destination on a particular date;
(Vi) using the stored classifier to identify a group to which the requested fare price corresponds;
(Vii) calculating an estimate for the requested fare price using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the requested fare price quote;
Server configured to do this. A computer program product embodied on a storage medium, wherein the computer program product is configured to provide a price quote when executed on a computer, the computer program product on a computer When run on
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a fare price for a trip from a departure point to a destination on a particular date;
(Vi) using the stored classifier to identify a group to which the requested fare price corresponds;
(Vii) calculating an estimate for the requested fare price using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the requested fare price quote;
A computer program product that is configured to do A method of running a computer that provides a set of fare price estimates,
(I) obtaining past fare estimates from a computer data store;
(Ii) grouping the past fare estimates by category;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a fare price for a specified date for travel from the place of departure to the destination;
(Vi) using the stored classifier to identify a group to which the requested fare price corresponds;
(Vii) calculating a set of estimates for the requested fare price for the specified date using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the set of fare price estimates;
Method including. A server configured to provide a set of fare price quotes,
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a fare price request for a specified date for travel from the place of departure to the destination;
(Vi) using the stored classifier to identify a group to which the requested fare price corresponds;
(Vii) calculating a set of estimates for the requested fare price for the specified date using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the set of fare price estimates;
Server configured to do this. A computer program product embodied on a storage medium, wherein the computer program product is configured to provide a set of price quotes when executed on a computer, the computer program product comprising: When run on a computer
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a fare price request for a specified date for travel from the place of departure to the destination;
(Vi) using the stored classifier to identify a group to which the requested fare price corresponds;
(Vii) calculating a set of estimates for the requested fare price for the specified date using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the set of fare price estimates;
A computer program product that is configured to do A method of running a computer that provides a set of round trip fare price estimates,
(I) obtaining past fare estimates from a computer data store;
(Ii) grouping the past fare estimates by category;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a round-trip fare price for a designated departure date and a designated return date for travel from the origin to the destination;
(Vi) using the stored classifier to identify a group to which the requested round trip fare price corresponds;
(Vii) calculating a set of estimates for the requested round trip fare price for the designated departure date and return date using statistical data obtained from the stored classifier corresponding to the identified group;
(Viii) providing the set of fare price estimates;
Method including. A server configured to provide a set of round trip fare price estimates,
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a round-trip fare price for a designated departure date and a designated return date for travel from the origin to the destination;
(Vi) identifying the group to which the requested round trip fare price corresponds using the stored classifier;
(Vii) calculating a set of estimates of the requested round trip fare price for the specified departure date and return date using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the set of fare price estimates;
Server configured to do this. A computer program product embodied on a storage medium, wherein the computer program product is configured to provide a set of round trip freight price estimates when executed on a computer, the computer program comprising: , When run on a computer,
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a round-trip fare price for a designated departure date and a designated return date for travel from the origin to the destination;
(Vi) identifying the group to which the requested round trip fare price corresponds using the stored classifier;
(Vii) calculating a set of estimates of the requested round trip fare price for the specified departure date and return date using statistical data obtained from the stored classifier corresponding to the specified group;
(Viii) providing the set of fare price estimates;
A computer program product that is configured to do A computer-implemented method for providing a set of best round trip fare price estimates for a specified departure date range and a specified return date range, comprising:
(I) obtaining past fare estimates from a computer data store;
(Ii) grouping the past fare estimates by category;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a round-trip fare price for a specified departure date range and a specified return date range for a trip from a departure location to a destination location;
(Vi) using the stored classifier to identify a group to which the requested round trip fare price corresponds;
(Vii) calculating the set of requested round trip fare price estimates for the departure date and return date pair using statistical data obtained from the stored classifier corresponding to the identified group;
(Viii) selecting the best fare price quote for each pair of departure and return dates;
(Ix) providing the set of best round trip fare price estimates for each pair of departure and return dates;
Method including. A server configured to provide a set of best round trip fare price estimates for a specified departure date range and a specified return date range,
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a round-trip fare price for a specified departure date range and for a specified return date range for travel from the place of departure to the destination;
(Vi) identifying the group to which the requested round trip fare price corresponds using the stored classifier;
(Vii) calculating the set of requested round trip fare price estimates for the departure date and return date pair using statistical data obtained from the stored classifier corresponding to the identified group;
(Viii) selecting the best fare price quote for each pair of departure and return dates;
(Ix) providing a set of said best round trip fare price estimates for each pair of departure and return dates;
Server configured to do this. A computer program product embodied on a storage medium, said computer program product, when executed on a computer, of a best round trip price estimate for a specified departure date range and a specified return date range. Configured to provide a set, said computer program product being run on a computer,
(I) obtaining past fare estimates from a computer data store,
(Ii) grouping the past fare estimates by categories;
(Iii) deriving statistical data for each group;
(Iv) storing, on a computer, for each group, a classifier including the derived statistical data;
(V) receiving a request for a round-trip fare price for a specified departure date range and for a specified return date range for travel from the place of departure to the destination;
(Vi) identifying the group to which the requested round trip fare price corresponds using the stored classifier;
(Vii) calculating the set of requested round trip fare price estimates for the departure date and return date pair using statistical data obtained from the stored classifier corresponding to the identified group;
(Viii) selecting an optimal fare price quote for each pair of departure and return dates;
(Ix) providing a set of said best round trip fare price estimates for each pair of departure and return dates;
A computer program product that is configured to do