Traffic estimation

• US 7,668,946 B1
• Filed: 08/30/2002
• Issued: 02/23/2010
• Est. Priority Date: 08/31/2001
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: AOL Advertising Incorporated (Apollo Global Management, Inc.)
• Inventors: Raines, Taylor, Garcia-Franco, Jose Carlos, Kamprath, Michael F., Luenberger, Rob
• Primary Examiner(s): Walsh; John B.
• Application Number:
  US10/231,025
• Time in Patent Office: 2,734 Days
• Field of Search: None
• US Class Current: 709/223
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/0876 Network utilisation, e.g. volume of load or congestion level

Abstract

Methods, systems, and articles of manufacture of the present invention may assist in planning, execution, and evaluation of advertising campaigns on the Internet. Particularly, methods, systems, and articles of manufacture of the present invention may help evaluate and/or predict traffic volume on the Internet. An exemplary method for predicting traffic may comprise receiving historical traffic data for a location, and computing a prediction of traffic volume for a particular time at the location using the historical traffic data and at least one prediction algorithm.

Claims

1. A method comprising:

receiving historical traffic data for a location;

computing, by a computer system comprising one or more processors, a prediction of traffic volume for a particular time at the location using the historical traffic data and at least one prediction algorithm;

receiving observed traffic volume data from the location;

comparing the computed prediction against the observed traffic volume data;

computing, by the computer system, a predictability score for the location from the comparison;

comparing the predictability score for the location with a predictability score of another location; and

generating an advertising campaign based on at least one of the predictability scores.

2. The method of claim 1, further comprising:

generating a predictability map of the locations based on at least the comparison of the predictability scores for the locations.

3. The method of claim 1, further comprising:

selecting at least one of the locations based on at least the comparison between the predictability scores; and

conducting the advertising campaign at one or more of the selected locations.

4. A system, comprising:

one or more processors configured to receive historical traffic data for a location, compute a prediction of traffic volume for a particular time at the location using the historical traffic data and at least one prediction algorithm, receive observed traffic volume data from the location, compare the computed prediction against the observed traffic volume data, compute a predictability score for the location from the comparison, compare the predictability score for the location with a predictability score of another location, and generate an advertising campaign based on at least one of the predictability scores.

5. A recording medium readable by a processor, wherein the recording medium comprises:

instructions for one or more processors to receive historical traffic data for a location, compute a prediction of traffic volume for a particular time at the location using the historical traffic data and at least one prediction algorithm, receive observed traffic volume data from the location, compare the computed prediction against the observed traffic volume data, compute a predictability score for the location from the comparison, compare the predictability score for the location with a predictability score of another location, and generate an advertising campaign based on at least one of the predictability scores.