Travel-related prediction system

• US 8,694,346 B2
• Filed: 05/10/2012
• Issued: 04/08/2014
• Est. Priority Date: 02/17/2006
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Crean, Hugh, Bartot, Jay, Fridgen, Michael, Hsu, David
• Primary Examiner(s): EPSTEIN, BRIAN M
• Application Number:
  US13/469,034
• Publication Number:
  US 20120239455A1
• Time in Patent Office: 698 Days
• Field of Search: 705/36, 705/37, 705/5, 705/6
• US Class Current: 705/5
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/025 Coordination of plural reservations, e.g. plural trip segments, transportation combined with accommodation
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0629 for generating comparisons

Abstract

A method and system is provided for making price-related predictions of items based on training a classifier using historical information including pricing information for the items and labels relating to the price-related information that are generated automatically from the historical information. A prediction system provides price-related predictions for airline tickets. The prediction system collects flight information for flights on a daily basis. The prediction system generates training data to train a classifier to make the price-related predictions using the flight information. After training the classifier, the prediction system then can make the price-related predictions for a trip.

Claims

1. A computer-readable storage device storing computer-executable instructions for controlling a computing system to generate a classifier for predicting travel item prices, the computer-executable instructions implementing a method comprising:

providing travel information for travel items on various observation dates;

generating a feature vector for each travel item for each observation date based on the provided travel information;

assigning a label to each travel item for each observation date with enough subsequent observation dates to generate a label indicating what the price direction prediction should have been for the travel item on the observation date, the label for each travel item for each observation date being derived from the provided travel information for that travel from subsequent observation dates; and

training a classifier to make price predictions for travel items using the feature vectors and their labels as training data.

2. The computer-readable storage device of claim 1 wherein the travel items are airline flights.

3. The computer-readable storage device of claim 1 wherein the travel items are hotel rooms.

4. The computer-readable storage device of claim 1 including generating a prediction for a target travel item by applying the trained classifier to a feature vector representing travel information of the target travel item.

5. The computer-readable storage device of claim 1 wherein the price predictions include direction of prices.

6. A computer-readable storage device storing computer-executable instructions for controlling a computing system to make price-related predictions for airline trips, the computer-executable instructions comprising:

a component that provides flight information for airline flights for trips on various observation dates, a trip having one or more available flights;

a component that generates trip information for a trip by aggregating the flight information for the airline flights associated with the trip, the trip information being represented as feature vectors;

a component that assigns a label to each trip for each observation date with enough subsequent observation dates to generate a label indicating what the price-related prediction should have been for the trip on the observation date, the label for each trip for each observation date being derived from the provided flight information for that trip from subsequent observation dates;

a component that trains a classifier to make price-related predictions for trips using the feature vectors and their labels as training data; and

a component that generates a price-related prediction for a trip using a feature vector for the trip and the trained classifier.

7. The computer-readable storage device of claim 6 wherein a separate classifier is trained for each market, a market being a combination of a departure location and a return location.

8. The computer-readable storage device of claim 6 wherein a single classifier is trained for multiple markets, a market being a combination of a departure location and a return location.

9. The computer-readable storage device of claim 6 wherein a classifier is trained for a market using feature vectors and labels of a different market, a market being a combination of a departure location and a return location.

10. The computer-readable storage device of claim 6 wherein the price-relate prediction is direction of prices.

11. The computer-readable storage device of claim 10 wherein the direction includes up, flat or up, flat, flat or down, and down.

12. A computer-readable storage device storing computer-executable instructions for controlling a computing system to make price-related predictions for airline tickets for trips, by a method comprising:

providing airline flight information for trips on various observation dates;

generating trip information for a trip by aggregating the flight information for airline flights associated with the trip; and

generating a price-related prediction for a trip based on a classifier trained to make price predictions for trips using labels and trip information as training data, the labels assigned to airline flights with enough subsequent observation dates to indicate what the price-related prediction should have been on that observation date, the label for each airline flight for each observation date being derived from the provided trip information for the trip.

13. The computer-readable storage device of claim 12 wherein the trip information is represented by a feature vector.

14. The computer-readable storage device of claim 12 wherein a separate classifier is trained for each market, a market being a combination of a departure location and a return location.

15. The computer-readable storage device of claim 12 wherein a single classifier is trained for multiple markets, a market being a combination of a departure location and a return location.

16. The computer-readable storage device of claim 12 wherein a classifier is trained for a market using feature vectors and labels of a different market, a market being a combination of locations and flight class.

17. The computer-readable storage device of claim 12 wherein a trip is a combination of departure location, return location, departure date, and return date.

18. The computer-readable storage device of claim 12 wherein the price-relate prediction is direction of prices.