News Column

Patent Application Titled "System and Method for Data Anomaly Detection Process in Assessments" Published Online

May 15, 2014



By a News Reporter-Staff News Editor at Computer Weekly News -- According to news reporting originating from Washington, D.C., by VerticalNews journalists, a patent application by the inventors Shepherd, Eric (Miami Beach, FL); Kleeman, John (London, GB), filed on December 27, 2013, was made available online on May 1, 2014.

The assignee for this patent application is Questionmark Computing Limited.

Reporters obtained the following quote from the background information supplied by the inventors: "Assessments (e.g., exams) are used in many parts of society to measure knowledge, skills, abilities and behaviors, e.g., in order to certify people for job roles and qualifications or grant licenses to work or perform tasks. For instance, educational institutions may use exams to validate work, knowledge, and skills to give educational qualifications. As another example, during the recruitment or promotion processes, an organization may test how a candidate behaves under certain circumstances to see if the candidate will fit in with the culture. As yet another example, companies including Information Technology (IT) and other high-tech companies may issue certifications to people who can use or maintain products, or who are skilled with products. Organizations may use internal exams to confirm that people are competent to do jobs where failure has a high risk (e.g., financial services and trading, operators of power stations and transport operators, etc.). Government agencies may provide licenses-to-work based on exam results for many professional trades such as doctors, nurses, crane operators, etc., and for licenses to drive.

"Some of these exams may be delivered by paper and/or remotely by computer, with a candidate using, e.g., a workstation or other device to answer questions. Part of the process of conducting an exam may be to minimize cheating. Common forms of cheating may include, for example, identity fraud (e.g., where someone other than the candidate claims to be the candidate), use of cheating materials (e.g., having access to books, the internet, or other resources in a closed-book exam), prompting another person giving the right answer (e.g., someone sitting by the candidate or via telephone), and copying answers (e.g., looking at how others taking the exams at the same time are answering questions and using the same answers).

"There are many variants of cheating and with exams where little, inadequate, or no supervision is provided to the candidate, cheating may be a problem where society may not fully trust the integrity of the certifications, qualifications and licenses that the exams provide.

"Lower stakes assessments may also be used to check understanding after e-learning or after other on-screen learning for instance during regulatory compliance competency checking, where employees are required to undergo training to teach regulations, processes and procedures and need to pay attention both during the learning and during the assessment."

In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "In one implementation, a method, performed by one or more computing devices, comprises 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.

"One or more of the following features may be included. Analyzing the attention level of the user may include comparing the attention level of the user with a second attention level, wherein the second attention level may be from at least one of the user and a second user. Analyzing the attention level of the user may include comparing the attention level of the user with a difficulty level of the action of the user. Analyzing the attention level of the user may further include comparing an amount of time spent by the user to perform the action with the difficulty level of the action.

"Analyzing the attention level of the user may include 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. The action of the user may include answering one or more questions. An alert of the attention deficiency event may be provided to at least one of the user and a second user. The at least one attribute may include blood flow velocity. The at least one attribute may include bodily movement detection. The at least one attribute may include eye blink detection. The at least one attribute may include gaze detection. The at least one attribute may include heartbeat rate detection. The at least one attribute may include breathing detection. The at least one attribute may include brain electrical activity detection. The at least one attribute may include body posture detection. The at least one attribute may include sweat detection. The attention level of the user may be determined with a combination of at least two attributes of the user. The attention deficiency event may include a lack of learning by the user during a learning process. The attention deficiency event may include cheating by the user during an assessment.

"In another implementation, a computer program product resides on a computer readable medium that has a plurality of instructions stored on it. When executed by a processor, the instructions cause the processor to perform operations comprising 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.

"One or more of the following features may be included. Analyzing the attention level of the user may include comparing the attention level of the user with a second attention level, wherein the second attention level may be from at least one of the user and a second user. Analyzing the attention level of the user may include comparing the attention level of the user with a difficulty level of the action of the user. Analyzing the attention level of the user may further include comparing an amount of time spent by the user to perform the action with the difficulty level of the action.

"Analyzing the attention level of the user may include 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. The action of the user may include answering one or more questions. An alert of the attention deficiency event may be provided to at least one of the user and a second user. The at least one attribute may include gaze detection. The at least one attribute may include bodily movement detection. The at least one attribute may include eye blink detection. The at least one attribute may include gaze detection. The at least one attribute may include heartbeat rate detection. The at least one attribute may include breathing detection. The at least one attribute may include brain electrical activity detection. The at least one attribute may include body posture detection. The at least one attribute may include sweat detection. The attention level of the user may be determined with a combination of at least two attributes of the user. The attention deficiency event may include a lack of learning by the user during a learning process. The attention deficiency event may include cheating by the user during an assessment.

"In another implementation, a computing system includes a processor and memory configured to perform operations comprising 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.

"One or more of the following features may be included. Analyzing the attention level of the user may include comparing the attention level of the user with a second attention level, wherein the second attention level may be from at least one of the user and a second user. Analyzing the attention level of the user may include comparing the attention level of the user with a difficulty level of the action of the user. Analyzing the attention level of the user may further include comparing an amount of time spent by the user to perform the action with the difficulty level of the action.

"Analyzing the attention level of the user may include 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. The action of the user may include answering one or more questions. An alert of the attention deficiency event may be provided to at least one of the user and a second user. The at least one attribute may include gaze detection. The at least one attribute may include bodily movement detection. The at least one attribute may include eye blink detection. The at least one attribute may include gaze detection. The at least one attribute may include heartbeat rate detection. The at least one attribute may include breathing detection. The at least one attribute may include brain electrical activity detection. The at least one attribute may include body posture detection. The at least one attribute may include sweat detection. The attention level of the user may be determined with a combination of at least two attributes of the user. The attention deficiency event may include a lack of learning by the user during a learning process. The attention deficiency event may include cheating by the user during an assessment.

"The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features and advantages will become apparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

"FIG. 1 is an illustrative diagrammatic view of a data anomaly detection process coupled to a distributed computing network;

"FIG. 2 is an illustrative flowchart of the data anomaly detection process of FIG. 1; and

"FIG. 3 is an illustrative table containing information that may be used by the data anomaly detection process of FIG. 1.

"Like reference symbols in the various drawings indicate like elements."

For more information, see this patent application: Shepherd, Eric; Kleeman, John. System and Method for Data Anomaly Detection Process in Assessments. Filed December 27, 2013 and posted May 1, 2014. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=1596&p=32&f=G&l=50&d=PG01&S1=20140424.PD.&OS=PD/20140424&RS=PD/20140424

Keywords for this news article include: Legal Issues, Questionmark Computing Limited.

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Source: Computer Weekly News


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