Ohio University

American Chemical Society

Siang Lee Hong

Chang Liu

Matthew Glenn Dunbar

Yelena Lipskerova

Dawn Lorraine George

Jaron Patrick Maxson

Matthew James Toussant

Yingqi Wu

In some embodiments, a computer-implemented method for selecting a polymer for an intended use. The method may include: training a selection model based on a graphical dataset, the graphical dataset comprising a node-edge graph identifying relationships between uses and properties of a polymer; receiving, via a user interface, a request comprising the intended use; based on the selection model, determining a polymer for the intended use; and outputting the determined polymer via the user interface.

Systems and methods for validating and predicting polymer functions using polymer properties

Systems () and methods () securely authenticate an identity of an individual based on a pattern () that is traced by the individual. Embodiments of the present disclosure relate to prompting () an individual with a pattern () to trace when attempting to authenticate the identity of the individual during an identity authentication session. Motion-based behavior data that is generated by motions executed by the individual as the individual traces the pattern () is captured () via a motion-capturing sensor (). The motion-based behavior data is unique to the individual and has a low likelihood of being duplicated by an unauthorized individual attempting to fraudulently pose as the individual. The captured motion-based behavior data is compared () to previously captured motion-based behavior data from previous traces () of the pattern () completed by the individual. The identity of the individual is authenticated () when the motion-based behavior data is within a threshold of the previously captured motion-based behavior data.

Motion-based identity authentication of an individual

Systems and methods securely determine whether a human or a hacking computing device attempts to engage an application. Embodiments of the present disclosure relate to identifying a defined pattern to be displayed by a user interface for an individual to trace. The defined pattern is dynamically displayed so that characteristics associated with the defined pattern change as the defined pattern is displayed. A background is displayed simultaneously with the defined pattern. The background randomly changes as the characteristics associated with the defined pattern change. The likelihood that a hacking computing device can recognize the dynamically changing defined pattern from the dynamically changing background is low while the likelihood that the human brain can do so is high. Thus, user attempting to engage the application is confirmed as a human when a trace of the defined patter is received.

Application engagement identification using a dynamic pattern

Systems, methods and computer storage mediums securely authenticate an identity of an individual based on a pattern that is traced by the individual. Embodiments of the present disclosure relate to prompting an individual with a pattern to trace when attempting to authenticate the identity of the individual during an identity authentication session. Motion-based behavior data that is generated by motions executed by the individual as the individual traces the pattern is captured via a motion-capturing sensor. The motion-based behavior data is unique to the individual and has a low likelihood of being duplicated by an unauthorized individual attempting to fraudulently pose as the individual. The captured motion-based behavior data is compared to previously captured motion-based behavior data from previous traces of the pattern completed by the individual. The identity of the individual is authenticated when the motion-based behavior data is within a threshold of the previously captured motion-based behavior data.

Motion-based identity authentication of an individual with a communications device

Systems and methods securely authenticate an identity of an individual based on a pattern that is traced by the individual. Embodiments relate to prompting an individual with a pattern to trace when attempting to authenticate the identity of the individual during an identity authentication session. Motion-based behavior data that is generated by motions executed by the individual as the individual traces the pattern is captured via a motion-capturing sensor. The motion-based behavior data is unique to the individual and has a low likelihood of being duplicated by an unauthorized individual attempting to fraudulently pose as the individual. The captured motion-based behavior data is compared to previously-captured motion-based behavior data from previous traces of the pattern completed by the individual. The identity of the individual is authenticated when the motion-based behavior data is within a threshold of the previously captured motion-based behavior data.

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Canal Winchester, OH, United States of America

Siang Lee Hong