METHOD FOR GENERATING A HUMAN LIKENESS SCORE
First Claim
1. A method for distinguishing a human user from automated software, the method comprising:
- receiving a request from a publisher for a human likeness score of a user accessing a web page through a web browser executing on a computing device;
based on the request, collecting identification data of the user;
collecting a cursor motion entered by the user into the web browser;
extracting a noise component from the cursor motion;
identifying a motion geometry in the cursor motion;
accessing a noise model defining input noise characteristics of cursor inputs previously entered into graphical user interfaces by known humans;
accessing a motion model characterizing cursor motion geometries previously entered into graphical user interfaces by known humans;
calculating a human likeness score of the user based on a comparison of the noise component to the noise model and based on a comparison of the motion geometry to the motion model, the human likeness score lying within a continuum of human likeness scores; and
associating the identification data of the user with a human determination based on the human likeness score falling within a range of human likeness scores corresponding to humans.
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Abstract
One embodiment of the invention is a method utilizing a CAPTCHA to generate a human likeness score including blocks: a) receiving a user solution to the CAPTCHA; b) receiving a user interaction pattern descriptive of an interaction undertaken by the user, through a graphical interface of the CAPTCHA, to achieve the user solution; c) determining the accuracy of the user solution; d) comparing the user interaction pattern against an interaction model generated from interaction patterns of previous users; e) calculating the human likeness score based upon the determination of block c) and the comparison of block d), wherein the human likeness score lies within a continuum of human likeness scores.
20 Citations
21 Claims
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1. A method for distinguishing a human user from automated software, the method comprising:
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receiving a request from a publisher for a human likeness score of a user accessing a web page through a web browser executing on a computing device; based on the request, collecting identification data of the user; collecting a cursor motion entered by the user into the web browser; extracting a noise component from the cursor motion; identifying a motion geometry in the cursor motion; accessing a noise model defining input noise characteristics of cursor inputs previously entered into graphical user interfaces by known humans; accessing a motion model characterizing cursor motion geometries previously entered into graphical user interfaces by known humans; calculating a human likeness score of the user based on a comparison of the noise component to the noise model and based on a comparison of the motion geometry to the motion model, the human likeness score lying within a continuum of human likeness scores; and associating the identification data of the user with a human determination based on the human likeness score falling within a range of human likeness scores corresponding to humans. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method for distinguishing a human user from automated software, the method comprising:
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receiving a request from a publisher for a human likeness score of users accessing a web page affiliated with the publisher; in response access of the web page by a user through a web browser executing on a computing device, collecting a cursor motion entered by the user into the web browser; extracting a noise component from the cursor motion; accessing a noise model defining input noise characteristics of cursor inputs previously entered into graphical user interfaces by known humans; calculating a human likeness score of the user based on a comparison of the noise component to the noise model, the human likeness score lying within a continuum of human likeness scores; and transmitting a human determination of the user to the publisher based on the human likeness score of the user falling within a range of human likeness scores corresponding to humans. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification