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 a graphical user interface (GUI) of the web browser;
extracting a noise component from the cursor motion, the cursor motion comprising a cursor input path viewable within the GUI having visible smooth input portions and visible oscillation input portions or visible deviation input portions defined by a visible cursor within the GUI that moves along the GUI, the noise component being defined by some or all the visible oscillation input portions or the visible deviation input portions of the cursor input path;
identifying a motion geometry in the cursor motion, the motion geometry defined by one or more visible angles of direction change or one or more visible linearities of portions of the cursor input path viewable within the GUI;
accessing a noise model defining input noise characteristics of cursor inputs previously entered into graphical user interfaces by known humans different from the user;
accessing a motion model characterizing cursor motion geometries previously entered into graphical user interfaces by known humans different from the user;
calculating the 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.
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Citations
20 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 a graphical user interface (GUI) of the web browser; extracting a noise component from the cursor motion, the cursor motion comprising a cursor input path viewable within the GUI having visible smooth input portions and visible oscillation input portions or visible deviation input portions defined by a visible cursor within the GUI that moves along the GUI, the noise component being defined by some or all the visible oscillation input portions or the visible deviation input portions of the cursor input path; identifying a motion geometry in the cursor motion, the motion geometry defined by one or more visible angles of direction change or one or more visible linearities of portions of the cursor input path viewable within the GUI; accessing a noise model defining input noise characteristics of cursor inputs previously entered into graphical user interfaces by known humans different from the user; accessing a motion model characterizing cursor motion geometries previously entered into graphical user interfaces by known humans different from the user; calculating the 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, 19)
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12. 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 to accessing 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 a graphical user interface (GUI) of the web browser; extracting a noise component from the cursor motion, the cursor motion comprising a cursor input path viewable within the GUI having visible smooth input portions and visible oscillation input portions or visible deviation input portions defined by a visible cursor within the GUI that moves along the GUI, the noise component being defined by some or all the visible oscillation input portions or the visible deviation input portions of the cursor input path; identifying a motion geometry in the cursor motion, the motion geometry defined by one or more visible angles of direction change or one or more visible linearities of portions of the cursor input path viewable within the GUI; accessing a noise model defining input noise characteristics of cursor inputs previously entered into graphical user interfaces by known humans different from the user; accessing a motion model characterizing cursor motion geometries previously entered into graphical user interfaces by known humans different from the user; calculating the 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 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 (13, 14, 15, 16, 17, 18, 20)
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Specification