US 12,462,702 B2
System for online automated exam proctoring
Matthew Jaeh, Pelham, AL (US); Jarrod Morgan, Hoover, AL (US); and Andrew Millin, Kalamazoo, MI (US)
Assigned to PROCTORU, INC., Livermore, CA (US)
Filed by PROCTORU, INC., Livermore, CA (US)
Filed on Jun. 5, 2024, as Appl. No. 18/735,106.
Application 17/683,970 is a division of application No. 17/342,364, filed on Jun. 8, 2021, granted, now 11,295,626.
Application 16/258,140 is a division of application No. 15/891,734, filed on Feb. 8, 2018, abandoned.
Application 18/735,106 is a continuation of application No. 17/683,970, filed on Mar. 1, 2022, granted, now 12,039,887.
Application 17/342,364 is a continuation of application No. 16/258,140, filed on Jan. 25, 2019, granted, now 11,205,349, issued on Dec. 21, 2021.
Application 15/891,734 is a continuation in part of application No. 15/462,676, filed on Mar. 17, 2017, granted, now 10,083,619, issued on Sep. 25, 2018.
Application 15/462,676 is a continuation of application No. 14/067,796, filed on Oct. 30, 2013, granted, now 9,601,024, issued on Mar. 21, 2017.
Application 14/067,796 is a continuation of application No. 13/007,341, filed on Jan. 14, 2011, abandoned.
Claims priority of provisional application 61/295,508, filed on Jan. 15, 2010.
Prior Publication US 2024/0386806 A1, Nov. 21, 2024
Int. Cl. G09B 5/00 (2006.01); G09B 7/00 (2006.01); G06Q 10/10 (2023.01); G06Q 10/109 (2023.01); G06Q 10/1093 (2023.01); G09B 3/00 (2006.01); G09B 7/02 (2006.01); G09B 7/06 (2006.01)
CPC G09B 5/00 (2013.01) [G09B 7/00 (2013.01); G06Q 10/10 (2013.01); G06Q 10/109 (2013.01); G06Q 10/1095 (2013.01); G09B 3/00 (2013.01); G09B 7/02 (2013.01); G09B 7/06 (2013.01)] 5 Claims
OG exemplary drawing
 
1. A method, comprising:
controlling, via a processing server, a remote computer to administer an automated exam to a test-taker, the remote computer having a plurality of input devices;
receiving by the Processing server from the remote computer one or more streams of biometric information about the test-taker, a photo ID of the test-taker, a confirmation of exam rules by the test-taker, and answers to challenge questions by the test-taker collected from the input devices prior to the exam and/or during the exam;
establishing, by the processing server via implementation of supervised machine learning a threshold for normal by defining a baseline of an environment of the test-taker prior to the exam based on the one or more streams of biometric information, lighting of the environment, sound of the environment, movement of the test-taker, input from an institution, and input from an instructor, wherein establishing the threshold for normal comprises calibrating the processing server to operate within an acceptable predefined limit of the baseline;
recording, by the Processing server via the remote computer, biometric information from the one or more streams of biometric information, audio data, and video data of the test-taker during the exam;
comparing, by the processing server via implementation of supervised machine learning, the recorded biometric information, the recorded audio data, and the recorded video data to the baseline, and recognizing patterns of questionable behavior outside the threshold for normal;
determining, by the processing server by implementation of the supervised machine learning, that the test-taker is exhibiting questionable behavior based on the recognized patterns of questionable behavior outside the threshold for normal;
stopping, by the Processing server, the exam when it is determined that the test-taker is exhibiting questionable behavior;
in response to stopping the exam, flagging, by the processing server, the exam for further review by a live human proctor;
in response to the flagging, enabling the live human proctor to communicate remotely with the test-taker via a computer associated with the live human proctor and the remote computer by a chat feature or voice communication, review why the exam was stopped and either confirm or deny the determined questionable behavior, and then allow the test-taker to continue the exam or end the exam based on their review; and
automatically refining and updating, by the processing server, using the supervised machine learning implemented on the processing server, the initial pre-programmed behavioral patterns based on the accuracy of the recognized pattern of questionable behavior according to the subsequent confirmation or denial by the live human proctor.