Method for generating a human likeness score
First Claim
1. A method for generating a human likeness score based on a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), the method comprising:
- selecting a visual advertising asset;
generating the CAPTCHA comprising a graphical interface, the visual advertising asset and a graphical object depicted within the graphical interface;
assigning an instruction for completing the CAPTCHA;
from a user, receiving an input within the graphical interface, the input comprising a solution to the CAPTCHA from the user, the solution comprising a selection of the graphical object within the graphical interface;
extracting a motion pattern from the input;
comparing the motion pattern to a motion model based on input patterns of previous users; and
calculating the human likeness score of the user based on the comparison of the motion pattern to the motion model, the human likeness score lying within a continuum of discrete human likeness scores, wherein calculating the human likeness score comprises;
generating a first confidence group according to an accuracy of the solution in fulfilling the instruction;
generating a second confidence group based on the comparison of the motion pattern to the motion model;
generating a third confidence group based on user data pertaining to at least one of a CAPTCHA previously attempted by the user, an Internet Protocol address of the user, and a cookie associated with the user;
determining a reliability of the first confidence group, the second confidence group, and the third confidence group;
compiling the first confidence group, the second confidence group, and the third confidence group, based on the determined reliability thereof, to generate the human likeness score; and
selecting a number value, from the continuum of human likeness scores comprising a continuum of number values, that correlates with a calculated confidence that the user is human.
15 Assignments
0 Petitions
Accused Products
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.
39 Citations
20 Claims
-
1. A method for generating a human likeness score based on a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), the method comprising:
-
selecting a visual advertising asset; generating the CAPTCHA comprising a graphical interface, the visual advertising asset and a graphical object depicted within the graphical interface; assigning an instruction for completing the CAPTCHA; from a user, receiving an input within the graphical interface, the input comprising a solution to the CAPTCHA from the user, the solution comprising a selection of the graphical object within the graphical interface; extracting a motion pattern from the input; comparing the motion pattern to a motion model based on input patterns of previous users; and calculating the human likeness score of the user based on the comparison of the motion pattern to the motion model, the human likeness score lying within a continuum of discrete human likeness scores, wherein calculating the human likeness score comprises; generating a first confidence group according to an accuracy of the solution in fulfilling the instruction; generating a second confidence group based on the comparison of the motion pattern to the motion model; generating a third confidence group based on user data pertaining to at least one of a CAPTCHA previously attempted by the user, an Internet Protocol address of the user, and a cookie associated with the user; determining a reliability of the first confidence group, the second confidence group, and the third confidence group; compiling the first confidence group, the second confidence group, and the third confidence group, based on the determined reliability thereof, to generate the human likeness score; and selecting a number value, from the continuum of human likeness scores comprising a continuum of number values, that correlates with a calculated confidence that the user is human. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
-
-
16. A method for generating a human likeness score based on a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), the method comprising:
-
based on a target difficulty of the CAPTCHA, setting a number of objects for the CAPTCHA; assigning an instruction for completing the CAPTCHA; generating the CAPTCHA comprising a graphical interface and a set of graphical objects depicted within the graphical interface, the set of graphical objects corresponding to the number of objects for the CAPTCHA; from a user, receiving an input within the graphical interface responsive to a graphical object in the set of graphical objects within the graphical interface, wherein receiving the input comprises receiving a solution to the CAPTCHA from the user; extracting an input pattern from the input; and calculating the human likeness score of the user based on a comparison of the input pattern to an input model, the input model based on an input pattern of a previous user, and the human likeness score lying within a continuum of discrete human likeness scores, wherein calculating the human likeness score comprises; generating a first confidence group according to an accuracy of the solution in fulfilling the instruction; generating a second confidence group based on the comparison of the input pattern to the input model; generating a third confidence group based on user data pertaining to at least one of a CAPTCHA previously attempted by the user, an Internet Protocol address of the user, and a cookie associated with the user; determining a reliability of the first confidence group, the second confidence group, and the third confidence group; compiling the first confidence group, the second confidence group, and the third confidence group, based on the determined reliability thereof; and selecting a number value, from the continuum of human likeness scores comprising a continuum of number values, that correlates with a calculated confidence that the user is human. - View Dependent Claims (17, 18, 19, 20)
-
Specification