Abstract: Systems and methods may involve processing of entity data by nested machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. Entity data for each entity may be separately processed by entity-specific machine learning models to generate intermediate entity risk scores. The intermediate entity risk scores may be input to an aggregate machine learning model, which may output an aggregate risk score. A resource management server may cause the predefined actions to be taken after comparing the aggregate risk score to the one or more thresholds.

Abstract: Systems and methods may involve processing of entity data by machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. The exam registration and delivery process may include multiple states—each being associated with a different set of entities. Aggregate risk scores for a given state may be calculated using only entity data for the set of entities associated with that state. The predetermined actions taken may also be dependent on the current state.

Abstract: Techniques described herein relate to executing and monitoring physical simulations within a digital credential platform. Physical simulation evaluation systems may be implemented including sensor-based monitoring systems, simulation output systems, and/or simulation environment control systems. Particular types of digital credential simulations may be determined and executed within a physical environment, during which a plurality of sensors may be used to monitor the physical actions of a credential receiver during physical simulation. The physical action data may be analyzed and compared to digital credential requirements to determine one or more digital credentials to be generated and issued to the credential receiver.

Abstract: Systems and methods may involve processing of entity data by machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. The exam registration and delivery process may include multiple states—each being associated with a different set of entities. Aggregate risk scores for a given state may be calculated using only entity data for the set of entities associated with that state. The predetermined actions taken may also be dependent on the current state.

Abstract: Systems and methods may involve processing of entity data by nested machine learning models to produce one or more aggregate risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. Entity data for each entity may be separately processed by entity-specific machine learning models to generate intermediate entity risk scores. The intermediate entity risk scores may be input to an aggregate machine learning model, which may output an aggregate risk score. A resource management server may cause the predefined actions to be taken after comparing the aggregate risk score to the one or more thresholds.

Abstract: Systems and methods may involve processing of entity data by machine learning models to produce one or more entity and/aggregate risk scores and/or aggregate anticipated risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. The exam registration and delivery process may include multiple states—each being associated with a different set of entities. Aggregate risk scores for a given state may be calculated using only entity data for the set of entities associated with that state. The predetermined actions taken may also be dependent on the current state.

Abstract: Systems and methods may involve processing of entity data by machine learning models to produce one or more entity and/aggregate risk scores and/or aggregate anticipated risk scores, which may be compared to one or more thresholds to determine when one or more predefined actions should be taken. The entity data may be collected for various entities related to an exam registration and delivery process, which may include a candidate, an exam, a test center, an exam registration event, a proctor, and an exam delivery event. The exam registration and delivery process may include multiple states—each being associated with a different set of entities. Aggregate risk scores for a given state may be calculated using only entity data for the set of entities associated with that state. The predetermined actions taken may also be dependent on the current state.

Abstract: Techniques described herein relate to generation and time-based degradation of digital credentials within digital credentialing environments. A digital credential generation system may include a digital credential issuer and a platform server configured to determine and implement time-based degradation of the digital credentials issued to credential receivers. Different digital credentials may have different associated degradation functions, based on the type of the digital credential and/or additional factors such as the additional credentials issued to the receiver and the monitoring data of the receiver. The magnitude values computed for issued digital credentials, using the degradation functions, may be used to determine credential expiration times, re-credentialing times and/or requirements, and the like.

Abstract: Techniques relate to managing digital credentials. An employee records database is accessed to identify a plurality of employees. Within the plurality of employees, a first set of employees is identified. For each employee in the first set of employees, a server computer hosting a digital credential repository is accessed to identify a plurality of digital credentials associated with the employee. The plurality of digital credentials of each employee in the first set of employees are analyzed to generate a statistical analysis of the plurality of digital credentials. With the statistical analysis complete, data identifying the statistical analysis is transmitted to a user device.

Abstract: A system includes one or more server hardware computing devices includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the system to receive a request to verify an identity of a user attempting to complete an examination provided by an online proctored examination platform to a candidate user authorized to complete the examination and repeatedly, at a predefined interval during the examination, determine, based at least in part on user data collected by the online proctored examination platform during the examination, the user data identifying the user at a time that the interval elapses, whether the user is the candidate user, and, responsive to a determination that the user is not the candidate user, perform one or more of a plurality of actions associated with a failed identity verification, the plurality of actions including stopping the examination.

Abstract: A system includes a candidate device including a processor configured to implement a candidate evaluation software application and a server computer configured to communicate with the candidate device using a network. The server computer is configured to receive, from the candidate device, a request to access an evaluation service. The request includes a candidate identifier. The server computer is configured to access historical candidate data in a memory accessible by the server computer to determine a trust score for a candidate associated with the candidate identifier, and when the trust score exceeds a threshold, transmit a credential to the candidate device enabling an evaluation of the candidate to be performed on the candidate device via a secured local area network.

Abstract: Techniques relate to managing digital credentials. An operation evaluation system monitors, using sensors configured to detect physical attributes of a user in a physical environment, physical attributes of the user while the user performs a user action in a testing environment. Data corresponding to the physical attributes of the user is transmitted to one or more digital credential generators. A digital credential template associated with at least one of the physical attributes of the user is determined. For the digital credential template, a criteria associated with the digital credential template is retrieved and compared to the physical attributes. Based on the comparison, whether or not the user is eligible to receive a digital credential is determined based on the digital credential template, and, in response to determining that the user is eligible to receive a digital credential based on the digital credential template, a digital credential is generated.

Abstract: A system includes one or more server hardware computing devices communicatively coupled to a network. The one or more server hardware computing devices include one or more processors and memory storing specific computer-executable instructions that, when executed by the one or more processors, cause the system to receive a first request to calculate a trust score for a user associated with a user account of an online proctored examination platform. The trust score represents results of a risk analysis of the user. The instructions cause the system to obtain internal data of the online proctored examination platform, the internal data describing usage of the online proctored examination platform by the user, perform the risk analysis using at least the internal data to produce the trust score, associate the trust score with the user account, and perform an action based on the trust score.

Abstract: A system includes a candidate client device including a processor, a camera, and a microphone. The system includes one or more server hardware computing devices communicatively coupled to a network and in communication with the candidate client device. Each of the one or more server hardware computing devices includes at least one processor executing specific computer-executable instructions within a memory that, when executed, cause the one or more server hardware computing devices to receive the sequence of images and the audio data from the candidate client device, extract a first video attribute from the sequence of images, extract a first audio attribute from the audio data, determine that at least one of the first video attribute violates a first video condition and the first audio attribute violates a first audio condition, and prevent the candidate client device from performing a candidate evaluation function using candidate evaluation software application.

Abstract: A system includes a client device and a server computer coupled to a network and in communication, via the network, with the client device. The server computer is configured to receive, from the client device, a queue request. The queue request includes a candidate identifier.

Abstract: A system includes a client device including a processor configured to implement a candidate evaluation software application and a server computer in communication, via a network, with the client device. The server computer is configured to receive, from the client device, a queue request. The queue request includes a candidate identifier. The server computer includes determine, using the candidate identifier, a candidate target attribute, identify a first candidate queue from a plurality of candidate queues using the candidate target attribute, and encode into the first candidate queue a reference to the candidate identifier. The server computer is configured to identify, in a first administrative queue, an administrator identifier, the first administrative queue being associated with the candidate target attribute, and establish at least one of a video and an audio data connection between the client device and an administrator device associated with the administrator identifier.

Abstract: A system includes a candidate client device including a processor, a camera, and a microphone. The system includes one or more server hardware computing devices communicatively coupled to a network and in communication with the candidate client device. Each of the one or more server hardware computing devices includes at least one processor executing specific computer-executable instructions within a memory that, when executed, cause the one or more server hardware computing devices to receive the sequence of images and the audio data from the candidate client device, extract a first video attribute from the sequence of images, extract a first audio attribute from the audio data, determine that at least one of the first video attribute violates a first video condition and the first audio attribute violates a first audio condition, and prevent the candidate client device from performing a candidate evaluation function using candidate evaluation software application.

Abstract: A system includes a client device and a server computer coupled to a network and in communication, via the network, with the client device. The server computer is configured to receive, from the client device, a queue request. The queue request includes a candidate identifier.

Abstract: A system includes a client device including a processor configured to implement a candidate evaluation software application and a server computer in communication, via a network, with the client device. The server computer is configured to receive, from the client device, a queue request. The queue request includes a candidate identifier. The server computer includes determine, using the candidate identifier, a candidate target attribute, identify a first candidate queue from a plurality of candidate queues using the candidate target attribute, and encode into the first candidate queue a reference to the candidate identifier. The server computer is configured to identify, in a first administrative queue, an administrator identifier, the first administrative queue being associated with the candidate target attribute, and establish at least one of a video and an audio data connection between the client device and an administrator device associated with the administrator identifier.

Abstract: A system includes one or more server hardware computing devices communicatively coupled to a network. The one or more server hardware computing devices includes one or more processors and memory storing specific computer-executable instructions that, when executed by the one or more processors, cause the system to receive decision data describing usage conditions of an online proctored examination platform, determine, based at least on the decision data, a need for additional proctoring resources on the platform to meet a demand for proctoring resources, and send a request for proctoring services to each of one or more on-demand proctor users agreeing to receive requests to become available on the platform when the additional proctoring resources are needed.