System and method for data anomaly detection process in assessments

• US 9,763,613 B2
• Filed: 06/23/2016
• Issued: 09/19/2017
• Est. Priority Date: 11/04/2011
• Status: Active Grant

• Current Assignee: Questionmark Computing Limited (Learnosity Ltd.)
• Original Assignee: Questionmark Computing Limited (Learnosity Ltd.)
• Inventors: Shepherd, Eric, Kleeman, John
• Primary Examiner(s): Tejani, Ankit D
• Application Number:
  US15/190,393
• Publication Number:
  US 20160296152A1
• Time in Patent Office: 453 Days
• Field of Search: None
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/164 Lie detection
• CPC Class Codes: A61B 5/168 Evaluating attention deficit, hyperactivity
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4266 sweat secretion
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method, computer program product, and computer system for identifying at least one attribute of a user. An attention level of the user is determined with the identified at least one attribute. The attention level of the user is analyzed. An action of the user is classified as an attention deficiency event using the analyzed attention level of the user.

Claims

1. A computer-implemented method comprising:

identifying, by at least one computing device of one or more computing devices, at least one attribute of a user;

determining, by at least one computing device of the one or more computing devices, an attention level of the user with the identified at least one attribute during a learning process using learning material other than an assessment;

analyzing, by at least one computing device of the one or more computing devices, the attention level of the user; and

classifying, by at least one computing device of the one or more computing devices, an action of the user as an attention deficiency event using the analyzed attention level of the user, wherein the attention deficiency event includes an indication of possible lack of learning by the user during the learning process using the learning material.

2. The computer-implemented method of claim 1 wherein analyzing the attention level of the user includes at least one of:

comparing the attention level of the user with a second attention level, wherein the second attention level is from at least one of the user and a second user;

comparing the attention level of the user with a difficulty level of the action of the user; and

comparing an amount of time spent by the user to perform the action with the difficulty level of the action.

3. The computer-implemented method of claim 1 wherein the learning process includes compliance training.

4. The computer-implemented method of claim 3 further comprising documenting whether the compliance training has taken place based upon, at least in part, the attention level of the user.

5. The computer-implemented method of claim 1 wherein analyzing the attention level of the user includes:

identifying the action of the user as requiring the attention level of the user to reach a threshold attention level; and

determining that the attention level of the user is less than the threshold attention level for the action of the user.

6. The computer-implemented method of claim 1 wherein the learning process includes viewing the learning material via on screen learning.

7. The computer-implemented method of claim 1 further comprising providing an alert of the attention deficiency event to at least one of the user and a second user.

8. The computer-implemented method of claim 1 wherein the at least one attribute includes at least one of a gaze detection, a bodily movement detection, an eye blink detection, a blood flow velocity, a heartbeat rate detection, a breathing detection, a brain electrical activity detection, a body posture detection, and a sweat detection.

9. The computer-implemented method of claim 8 wherein the attention level of the user is determined with a combination of at least two attributes of the user.

10. A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:

identifying at least one attribute of a user;

determining an attention level of the user with the identified at least one attribute during a learning process using learning material other than an assessment;

analyzing the attention level of the user; and

classifying an action of the user as an attention deficiency event using the analyzed attention level of the user, wherein the attention deficiency event includes an indication of possible lack of learning by the user during the learning process using the learning material.

11. The computer program product of claim 10 wherein analyzing the attention level of the user includes at least one of:

comparing the attention level of the user with a second attention level, wherein the second attention level is from at least one of the user and a second user;

comparing the attention level of the user with a difficulty level of the action of the user; and

comparing an amount of time spent by the user to perform the action with the difficulty level of the action.

12. The computer program product of claim 10 wherein the learning process includes compliance training.

13. The computer program product of claim 12 wherein the operations further comprise documenting whether the compliance training has taken place based upon, at least in part, the attention level of the user.

14. The computer program product of claim 10 wherein analyzing the attention level of the user includes:

identifying the action of the user as requiring the attention level of the user to reach a threshold attention level; and

determining that the attention level of the user is less than the threshold attention level for the action of the user.

15. The computer program product of claim 10 wherein the learning process includes viewing the learning material via on screen learning.

16. The computer program product of claim 10 wherein the operations further comprise providing an alert of the attention deficiency event to at least one of the user and a second user.

17. The computer program product of claim 10 wherein the at least one attribute includes at least one of a gaze detection, a bodily movement detection, an eye blink detection, a blood flow velocity, a heartbeat rate detection, a breathing detection, a brain electrical activity detection, a body posture detection, and a sweat detection.

18. The computer program product of claim 17 wherein the attention level of the user is determined with a combination of at least two attributes of the user.

19. A computing system including a processor and memory configured to perform operations comprising:

identifying at least one attribute of a user;

determining an attention level of the user with the identified at least one attribute during a learning process using learning material other than an assessment;

analyzing the attention level of the user; and

classifying an action of the user as an attention deficiency event using the analyzed attention level of the user, wherein the attention deficiency event includes an indication of possible lack of learning by the user during the learning process using the learning material.

20. The computing system of claim 19 wherein analyzing the attention level of the user includes at least one of:

comparing the attention level of the user with a second attention level, wherein the second attention level is from at least one of the user and a second user;

comparing the attention level of the user with a difficulty level of the action of the user; and

comparing an amount of time spent by the user to perform the action with the difficulty level of the action.

21. The computing system of claim 19 wherein the learning process includes compliance training.

22. The computing system of claim 21 wherein the operations further comprise documenting whether the compliance training has taken place based upon, at least in part, the attention level of the user.

23. The computing system of claim 19 wherein analyzing the attention level of the user includes:

identifying the action of the user as requiring the attention level of the user to reach a threshold attention level; and

determining that the attention level of the user is less than the threshold attention level for the action of the user.

24. The computing system of claim 19 wherein the learning process includes viewing the learning material via on screen learning.

25. The computing system of claim 19 wherein the operations further comprise providing an alert of the attention deficiency event to at least one of the user and a second user.

26. The computing system of claim 19 wherein the at least one attribute includes at least one of a gaze detection, a bodily movement detection, an eye blink detection, a blood flow velocity, a heartbeat rate detection, a breathing detection, a brain electrical activity detection, a body posture detection, and a sweat detection.

27. The computing system of claim 26 wherein the attention level of the user is determined with a combination of at least two attributes of the user.