METHOD AND SYSTEM FOR GENERATING A CONTEXTUAL SEGMENTATION CHALLENGE FOR AN AUTOMATED AGENT

• US 20100302255A1
• Filed: 05/25/2010
• Published: 12/02/2010
• Est. Priority Date: 05/26/2009
• Status: Abandoned Application

• Current Assignee: Dynamic Representation Systems Llc-Part VII
• Original Assignee: Dynamic Representation Systems Llc-Part VII
• Inventors: Koziol, Jason D., Koziol, Anthony R., Brown, Timothy J.
• Application Number:
  US12/786,711
• Publication Number:
  US 20100302255A1
• Time in Patent Office: Days
• US Class Current: 345/473
• CPC Class Codes: G06F 21/31 User authentication
• CPC Class Codes: G06F 2221/2133 Verifying human interaction, e.g., Captcha
• CPC Class Codes: G06T 1/0021 Image watermarking
• CPC Class Codes: G06T 13/80 2D [Two Dimensional] animation, e.g. using sprites

Abstract

Provided is a system and method for generating a contextual segmentation challenge that poses an identification challenge. The method including obtaining at least one ad element and obtaining a test element. The ad element and the test element then combined to provide a composite image. At least one noise characteristic is then applied to the composite image. The composite image is then animated as a plurality of views as a contextual segmentation challenge. A system for performing the method is also provided.

Claims

1. A method of generating a contextual segmentation challenge for an automated agent, the method comprising:

obtaining at least one ad element;

obtaining a test element;

combining the ad element and the test element to provide a composite image;

adding at least one noise characteristic to the composite image; and

animating the composite image as a plurality of views as a contextual segmentation challenge.

2. The method of claim 1, wherein adding at least one noise characteristic and animating the composite image comprises:

applying a first visual property and a second visual to the composite image; and

generating the plurality of views by transitioning between the first visual property and the second visual property.

3. The method of claim 2, wherein the transitioning between the first visual property and the second visual property of the ad element and the test element occurs simultaneously.

4. The method of claim 2, wherein the transitioning between the first visual property and the second visual property of the ad element and the test element occurs independently.

5. The method of claim 4, wherein an additional noise characteristic is applied to the test element.

6. The method of claim 1, wherein the context of the test element is discrete from the context of the ad element.

7. The method of claim 1, wherein the composite image presents the ad element and the test element adjacent to one another.

8. The method of claim 1, wherein the composite image presents the ad element and the test element at least partially imposed upon each other, the animation transitioning between the ad element and the test element.

9. The method of claim 1, further including receiving at least one data point prior to obtaining the ad element, the ad element selected at least in part based upon the at least one data point.

10. The method of claim 9, wherein the at least one data point is selected from the group consisting of server data, client data, user data and or combinations thereof.

11. The method of claim 9, the test element selected at least in part based upon the at least one data point.

12. The method of claim 1, further including tracking at least one user behavior during presentation of the animated composite image to a user.

13. The method of claim 1, wherein the test element is rendered with at least one characteristic of the ad element.

14. The method of claim 13, wherein the at least one characteristic is selected from the group consisting of font style, font size, character spacing, and or combinations thereof.

15. The method of claim 1, wherein at least a first portion of the ad element remains continuously visible as part of the animated composite image, at least a second portion of the ad element being about entirely obscured by the noise characteristic as part of the animated composite image.

16. The method of claim 1, wherein the method is stored on a non-transitory computer-readable medium as a computer program which, when executed by a computer will perform the steps of generating a contextual segmentation challenge.

28. A system for performing the method of claim 11, the system comprising:

a receiver structured and arranged with an input device for permitting at least one ad element to be obtained and at least one test element to be received;

an initializer structured and arranged to initialize each ad element and each test element with a first visual property and a second visual property, the initializer further structured and arranged to integrate the ad element and the test element to provide a composite image;

a transitioner structured and arranged to transition between the first visual property and the second visual property of the ad element and the test element; and

a view generator structured and arranged to generate a plurality of views of the composite image as the ad element and test element are transitioned between their respective first and second visual properties.

17. A method of generating a contextual segmentation challenge for an automated agent, the method comprising:

obtaining at least one ad element;

obtaining a test element;

integrating the ad element and the test element to provide a composite image;

applying one or more one noise characteristics, at least one noise characteristic including at least a first visual property and a second visual to the ad element and the test element of the composite image; and

generating a plurality of views by transitioning between the first visual property and the second visual property, the views presenting an animated contextual segmentation challenge.

18. The method of claim 17, wherein the context of the test element is discrete from the context of the ad element.

19. The method of claim 17, wherein transitioning between the first visual property and the second visual property of the ad element and the test element occurs simultaneously.

20. The method of claim 17, wherein transitioning between the first visual property and the second visual property of the ad element and the test element occurs independently.

21. The method of claim 17, wherein the first visual property and the second visual property are established by parameters of the ad element.

22. The method of claim 17, wherein in a first instance the composite image presents the ad element and the test element adjacent to one another, and in a second instance the composite image presents the ad element and the test element at least partially imposed upon each other, the animation transitioning between the ad element and the test element.

23. The method of claim 17, wherein the first visual property of the ad element is about equal to the second visual property of the test element and the second visual property of the ad element is about equal to the first visual property of the test element.

24. The method of claim 17, further including receiving at least one data point prior to obtaining the ad element, the ad element selected at least in part based upon the at least one data point.

25. The method of claim 24, wherein the at least one data point is selected from the group consisting of server data, client data, user data and or combinations thereof.

26. The method of claim 17, further including tracking at least one user behavior during presentation of the animated composite image to a user.

27. The method of claim 17, further including imposing a grid upon composite image, the grid defining pixel locations for the ad element and the test element.

30. The method of claim 17, wherein the method is stored on a non-transitory computer-readable medium as a computer program which, when executed by a computer will perform the steps of generating a contextual segmentation challenge.

29. The system of claim 29, further including a data collector routine structured and arranged to collect at least one data point prior to the selection of the ad element, the data point used at least in part by the receiver to selectively obtain the ad element.

31. A method of generating a contextual segmentation challenge for an automated agent, the method comprising:

receiving at least one data point regarding an apparent user;

obtaining at least one ad element based at least in part upon at least one data point;

obtaining a test element;

integrating the ad element and the test element to provide a composite image;

applying one or more one noise characteristics, at least one noise characteristic including a first visual property and a second visual to the ad element and the test element of the composite image;

generating a plurality of views by transitioning between the first visual property and the second visual property, the views presenting an animated contextual segmentation challenge; and

recording at least one behavior of the apparent user proximate to the presentation of the animated contextual segmentation challenge.

32. The method of claim 31, wherein the at least one data point is selected from the group consisting of server data, client data, user data and or combinations thereof.

33. The method of claim 31, wherein the context of the test element is discrete from the context of the ad element.

34. The method of claim 31, wherein in a first instance the transitioning between the first visual property and the second visual property of the ad element and the test element occur simultaneously, and in a second instance the transitioning between the first visual property and the second visual property of the ad element and the test element occurs independently.

35. The method of claim 31, the test element selected at least in part based upon the at least one data point.

36. The method of claim 31, wherein the method is stored on a non-transitory computer-readable medium as a computer program which, when executed by a computer will perform the steps of generating a contextual segmentation challenge.