Patent №
US 10,657,494
Granted
2020-05-19
Filed 2014
Owner
DUQUESNE UNIVERSITY OF THE HOLY SPIRIT
Lab
—
AI components
3
ml · nlp · hardware
Assignment
Recorded
Dataset
AIPD
2023_r1 edition
Application
14114956
Novel distractorless authorship verification technology optionally combines with novel algorithms to solve authorship attribution as to an open set of candidates—such as without limitation by analyzing the voting of “mixture of experts” and outputting the result to a user using the following: if z (z=pi−pj√pi+pj−(pi−pj)2/n) is larger than a first predetermined threshold then author j cannot be the correct author; or if z (z=pi−pj√pi+pj−(pi−pj)2/n) is smaller than a second predetermined threshold then author i cannot be the correct author; or if no author garners significantly more votes than all other contenders then none of the named authors is the author of a document in question—in a number of novel applications. Personality profiling and authorship attribution may also be used to verify user identity to a computer.
AI classification
Ownership
DUQUESNE UNIVERSITY OF THE HOLY SPIRIT
assignment · 286350479
Assignors
JUOLA, PATRICK, OVERLY, JAMES O, NOECKER, JOHN ISAAC, JR, RYAN, MICHAEL, GRAY, CHRISTINE, JUOLA, PATRICK, OVERLY, JAMES O, NOECKER, JOHN ISAAC, JR., RYAN, MICHAEL, GRAY, CHRISTINE
On an employer assignment, the assignors are typically the inventors.