Computer-implemented systems and methods for behavioral identification of non-human web sessions

• US 8,311,876 B2
• Filed: 04/09/2009
• Issued: 11/13/2012
• Est. Priority Date: 04/09/2009
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: House, William Jeffrey
• Primary Examiner(s): Jeanty, Romain
• Assistant Examiner(s): Koneru, Sujay
• Application Number:
  US12/420,992
• Publication Number:
  US 20100262457A1
• Time in Patent Office: 1,314 Days
• Field of Search: None
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

Computer-implemented systems and methods are provided for analyzing session data generated by a website in response to a visitor requesting data from the website. A system and method can be configured for receiving the session data and analyzing the session data to determine a plurality of website interaction behavioral metrics of the session data. The website interaction behavioral metrics of the session data are compared to detection settings. A determination is made as to whether the visitor to the website was human or non-human based upon the comparing step.

Claims

1. A computer-implemented method for analyzing website interactions, comprising:

receiving, using one or more data processors, one or more behavioral detection settings corresponding to threshold values that are used to detect whether a website visitor is human or non-human, wherein the detection settings include a minimum session requests threshold, a maximum time spacing threshold, and a certain session requests threshold;

receiving, using the one or more data processors, session data, wherein the session data includes one or more website visitor requests associated with a session;

analyzing, using the one or more data processors, the session data to determine interaction metrics for the session, wherein the interaction metrics include a total session requests metric and an average time spacing metric, and wherein the total session requests metric identifies a number of web page requests made by the website visitor during the session;

classifying, using the one or more data processors, the visitor as a human visitor when the total session requests metric is less than the minimum session requests threshold;

classifying, using the one or more data processors, the visitor as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold and when the average time spacing metric is less than the maximum time spacing threshold

classifying, using the one or more data processors, the visitor as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is greater than the certain session requests threshold; and

classifying, using the one or more data processors, the visitor as a human visitor when the total sessions request metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is less than the certain session requests threshold.

2. The method of claim 1, wherein the detection settings are adjustable.

3. The method of claim 1, wherein the session data is clickstream data.

4. The method of claim 1, further comprising:

storing the session data when the visitor is classified as a human visitor, wherein the stored session data is usable for website activity analysis.

5. The method of claim 1, wherein the visitor is classified as a non-human visitor, and wherein the non-human visitor is a bot, a smart bot, or a pinger.

6. The method of claim 1, wherein the maximum time spacing threshold is less than eight seconds, and wherein the certain session requests threshold is less than 10,000.

7. The method of claim 1, further comprising:

storing the session data in a first data store when the visitor is classified as a human visitor; and

storing the session data in a second data store when the visitor is classified as a non-human visitor.

8. The method of claim 1, wherein the method is performed in a batch mode, and wherein batch mode includes analyzing multiple sessions in sequence.

9. The method of claim 1, wherein the session data includes a request for a webpage associated with a website.

10. The method of claim 1, further comprising:

analyzing performance of the website based upon the visitor classification.

11. The method of claim 1, further comprising:

compensating a referrer based upon the visitor classification.

12. The method of claim 1, further comprising:

generating a website usage assessment based upon the visitor classification.

13. The method of claim 12, wherein the website usage assessment includes statistics regarding activity.

14. The method of claim 13, wherein content on a website is modified based upon the website usage assessment.

15. The method of claim 1, wherein the visitor is classified as a non-human visitor when the total session requests metric is greater than or equal to the minimum session requests threshold and when the average time spacing metric is less than the maximum time spacing threshold.

16. The method of claim 1, wherein the visitor is classified as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is greater than or equal to the certain session requests threshold.

17. The method of claim 1, wherein the total session requests metric measures a total number of website visitor requests during a session, and wherein the average time spacing metric measures an average time between website visitor requests during a session.

18. The method of claim 1, wherein the minimum sessions request threshold requires a minimum number of website visitor requests during a session to classify a visitor as a non-human.

19. The method of claim 1, wherein when the visitor is classified as a non-human visitor and wherein when the average time spacing metric is less than the maximum time spacing threshold, the visitor is classified as a bot.

20. The method of claim 1, wherein when the visitor is classified as a non-human visitor, wherein when the average time spacing metric is greater than the maximum time spacing threshold, and wherein the total session requests metric is greater than the certain session requests threshold, the visitor is classified as a smart bot or pinger.

21. A computer-implemented system for analyzing website interactions, comprising:

one or more data processors;

one or more computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations including;

receiving one or more behavioral detection settings corresponding to threshold values that are used to detect whether a website visitor is human or non-human, wherein the detection settings include a minimum session requests threshold, a maximum time spacing threshold, and a certain session requests threshold;

receiving session data, wherein the session data includes one or more website visitor requests associated with a session;

analyzing the session data to determine interaction metrics for the session, wherein the interaction metrics include a total session requests metric and an average time spacing metric, and wherein the total session requests metric identifies a number of web page requests made by the website visitor during the session;

classifying the visitor as a human visitor when the total session requests metric is less than the minimum session requests threshold;

classifying the visitor as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold and when the average time spacing metric is less than the maximum time spacing threshold

classifying the visitor as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is greater than the certain session requests threshold; and

classifying the visitor as a human visitor when the total sessions request metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is less than the certain session requests threshold.

22. A computer-program product for analyzing website interactions, tangibly embodied in a machine-readable non-transitory storage medium, including instructions configured to cause a data processing apparatus to:

receive one or more behavioral detection settings corresponding to threshold values that are used to detect whether a website visitor is human or non-human, wherein the detection settings include a minimum session requests threshold, a maximum time spacing threshold, and a certain session requests threshold;

receive session data, wherein the session data includes one or more website visitor requests associated with a session;

analyze the session data to determine interaction metrics for the session, wherein the interaction metrics include a total session requests metric and an average time spacing metric, and wherein the total session requests metric identifies a number of web page requests made by the website visitor during the session;

classify the visitor as a human visitor when the total session requests metric is less than the minimum session requests threshold;

classify the visitor as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold and when the average time spacing metric is less than the maximum time spacing threshold

classify the visitor as a non-human visitor when the total session requests metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is greater than the certain session requests threshold; and

classify the visitor as a human visitor when the total sessions request metric is greater than the minimum session requests threshold, when the average time spacing metric is greater than the maximum time spacing threshold, and when the total session requests metric is less than the certain session requests threshold.