Abstract: A device may provide a verification indicator to a device associated with a website. The verification indicator may be associated with verifying access to the website. The device may detect that the verification indicator has been associated with code associated with the website based on processing the code. The device may provide a script to the device. The script may be included in the code. The script may be associated with monitoring operations of the website. The device may receive data related to the operations. The device may analyze the data using a model. The model may be associated with making a prediction related to at least one of: a value to be received via the website, or traffic associated with the website. The device may perform one or more actions related to the website based on a result of the analyzing.

Abstract: Systems and methods for improved security authentication are disclosed. In some embodiments, an improved system for security authentication may include a plurality of computing devices, and a server system communicatively coupled to the plurality of computing devices. The server system may be configured to receive a request for security authentication, determine an authorization providing computing device from among the plurality of computer devices based on authentication preferences stored in a database communicatively coupled to the server system, generate and transmit authentication information to the determined authorization providing computing device, receive, from an initiating computing device an authentication input, determine whether the received authentication input matches the transmitted authentication information, and complete the request for security authentication when the received authentication input matches the generated and transmitted authentication information.

Abstract: A computer-implemented method comprising: instructing a computing device to obtain a permission from the user to monitor a plurality of activities executed within the computing device; receiving a call session request from a computing device associated with a second user; utilizing a trained machine learning algorithm to confirm an identity of the user based on the call session interaction parameter to confirm the call interaction session as a verified call interaction session; generating a fictitious call session interaction parameter that differs from the at least one call session interaction parameter; modifying the call session request into a verified call session request by replacing the call session interaction parameter with the fictitious call session interaction parameter; utilizing a telecommunication network to transmit the verified call session request with the at least one fictitious session interaction parameter to the first calling-enabled computing device; automatically instructing the first c

Abstract: Methods, systems, and apparatuses are described herein for improving computer authentication processes through computer-based authentication in a manner that uses knowledge of former devices. A computing device may train a machine learning model to output an indication of device reliability data associated with a particular device. The computing device may receive a request for access to an account from a user. The computing device may receive account data and provide the account data to the trained machine learning model. The computing device may receive data indicating device reliability for a set of devices from the machine learning model. The computing device may generate a modified set of device choices for the user by excluding devices having reliability levels below a threshold value. An authentication question may be generated, and access to the account may be provided based on a response to the authentication question.

Abstract: A server device receives, from a user device, a session initiation request and information identifying a location of the user device, and receives, from a monitoring device that is separate from the user device, an authentication request and information identifying a location of the monitoring device. The server device processes the session initiation request and the authentication request to authenticate a user of the user device, and determines, based on the location of the user device and the location of the monitoring device, that the user device and the monitoring device are collocated. The server device creates, after authenticating the user of the user device and determining that user device and the monitoring device are collocated, a session token, and sends the session token to the user device to enable the user device to access at least one resource of the server device.

Abstract: The present disclosure provides an exemplary method, system, and computing device that may include the steps of receiving a first indication that information of a user has been detected at one or more dark web resources; classifying the item of the compromised information into an information type category; receiving a permission indicator to detect communications by the computing device; receiving a second indication of a communication; receiving a third indication that the user engages an interaction with the communication; instructing the computing device to execute a technique to obtain data for the communication; receiving the data for the communication; determining the communication is a spam communication; determining a current information type category being discussed during the spam communication; making a determination that the current information type category corresponds to the information type category; and instructing a graphical user interface to display an alert to the user.

Abstract: Systems as described herein may include predicting item level data based on merchant advertisement information. A transaction pattern may be detected. The merchant advertisement information may be retrieved and parsed to generate a price list. A number of transactions that each shares a common payment amount may be determined and the number may reach a threshold value. Items from the price list may be matched with the common payment amount. The transaction records may be updated to indicate likely item level transaction information. In a variety of embodiments, the likely transaction information may be presented to a user.

Abstract: Systems and methods of the present disclosure enable automated identification of restrictions on reversals of data entries by receiving location data from a computing device associated with a user, and utilizing a data profile classification machine learning model to classify a particular data profile according to a data profile classification type based at least in part on a history of data entries associated with the particular data profile when the physical location is within a predetermined proximity of another physical location associated with the particular data profile. A reversal rate of data entries in the history of data entries is determined for the particular data profile. An electronic activity reversal restriction is determined where the reversal rate is below a predetermined value, and a pop-up notification is presented on the computing device notifying the user of the electronic activity reversal restriction of the particular data profile.

Abstract: Disclosed herein are system, method, and computer program product embodiments for protecting a device from skill squatting by verifying the legitimacy of a skill to a user before operating the skill based on user instructions. Upon receiving an audio signal from a user to operate a skill on a device, the skill can retrieve unique and non-confidential information related to the user or the device, and further present the unique and non-confidential information to the user such that the information is used to verify the legitimacy of the skill. Upon a verification of the legitimacy of the skill, the user can operate the skill by voice or audio instructions.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: A proppant delivery assembly receives and supports a plurality of containers having proppant stored therein. A cradle has a top surface which receives and supports the plurality of containers when positioned thereon. The cradle enables the plurality of containers to dispense the proppant stored therein. A proppant mover is positioned to underlie and extend along the top surface of the cradle aligned with the plurality of containers to receive proppant from the plurality of containers. The proppant mover carries proppant away from the plurality of containers. A chute is coupled to the cradle for receiving proppant from the proppant mover and directing the proppant to a blender hopper. A hood assembly is disposed at an end of the chute opposite the proppant mover for directing a vacuum air flow that removes a volume of air containing proppant dust particles directed from the chute.

Abstract: Systems and methods of location-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: utilizing a trained call annotation machine learning model to determine one or both of an annotating condition and annotating location granularity, and associate one location of a user with one phone number of the user based at least on one or both of the annotating condition and the annotating location granularity; receiving second transactional information of one transaction associated with a first user; extracting second location information from the second transactional information of the one transaction; utilizing the trained call annotation machine learning model to automatically annotate one phone number record of one phone number of the first user, with the second location information at the annotating location granularity to form at least one user-specific location-specific annotated phone number record.

Abstract: Techniques for verifying employment of a user are provided. A financial product or service may be requested by a user. Work schedule information for the user may be determined that may identify an expected work location of the user and expected work times for the user. Location data associated with the user may be obtained and compared to the expected work location and times for the user. A confidence score of the user may be generated based on the comparison. The requested financial product or service may be provided to the user if the confidence score meets or exceeds a predetermined threshold. In this manner, employment and/or reliability may be determined for a user having no credit history.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; receiving monitoring data of the activities executed within the plurality of computing devices for a predetermined period of time; identifying incoming interaction sessions across the plurality of computing devices; verifying one common session parameter associated with the incoming interaction sessions to identify the incoming interaction sessions as suspect interaction sessions; determining a frequency metric for the suspect interaction sessions; determining a threshold value for the frequency metric; receiving new monitoring data; determining that the new incoming interaction session has at least one common session interaction parameter with the suspect interaction sessions; automatically generating an interaction notification for transmission to the computing device; receiving a response to the int

Abstract: In some implementations, a system may receive, at a front-end device and from an augmented reality (AR) device associated with a user, a request to use the front-end device. The system may transmit, to the AR device, a request to access an optical sensor of the AR device. The system may receive, from the AR device, an output, from the optical sensor, that indicates a hand gesture performed by the user of the AR device. Accordingly, the system may determine, based on the hand gesture, a corresponding input key of the front-end device. The system may perform an action based on the corresponding input key.

Abstract: A card for storing both emergency information and payment information is disclosed. The card can be used by an emergency responder to retrieve emergency information for the user. The emergency information can enable the emergency responder to better render aid to the user. The card can have one or more processors, a sensor, and a memory in communication with the one or more processors and storing an application thereon. The application can cause the card to receive a request from a user device (e.g., from an emergency medical technician, or EMT) to access emergency information stored in the memory, transmit a security challenge to the user device, authenticate a received response to the security challenge from the user device, and transmit the emergency information to the user device.

Abstract: Systems and methods of the present disclosure include computer systems for improving data security. To do so, an authorization request associated with a user profile is received, including a time-stamp for a profile activity, a profile identifier, and a value. Application analytics data for a software application on a user device associated with the user profile is accessed, including an open event indicator indicating a time of a loading, and a close event indicator indicating a time of a termination. An open period of the software application at the time-stamp is determined based on the application open event indicator and the application close event indicator. A reduced set of fraud checks is selected and executed when the time-stamp associated with the profile activity is within the open period of the software application. A fraud determination is generated, and an authorization notification is generated based on the fraud determination.

Abstract: In some embodiments, a queue may be monitored to perform an automated adjustment related to a data item in the queue. In some embodiments, the data item may be associated with a product or service and include a code related to an access to the product or service. Based on the code, configuration information related to the product or service may be obtained. Based on a detected change related to the product or service, an indication of a set of self-support actions may be sent to a user device. One or more self-support actions (performed via the user device) may be determined, and an adjustment related to the access to the product or service may be performed based on the determined self-support actions. In some embodiments, the adjustment may include modifying a configuration of the access to the product or service based on the determined self-support actions.

Abstract: Platform behavior may be adapted via machine-learning-based entity lifecycle monitoring. A web crawler agent collects data comprising an entity identifier token. A machine learning model is trained to determine, based at least in part on the entity identifier token, whether a corresponding entity is associated with the computing platform (e.g., whether the corresponding entity is a platform subscriber entity for the computing platform). Based on the output of the machine learning model applied to the entity identifier token (in some embodiments, in combination with other relevant data parsed from the collected data), an indication of an entity lifecycle status and a confidence value therefor are determined. Based on the entity lifecycle status and the confidence value, a listener is bound to the platform subscriber entity. The listener monitors activity of the platform subscriber entity with respect to the platform and identifies platform action to take in response.

Abstract: Embodiments of systems, methods, and devices for protecting service personnel by deploying a physical barrier are described. Further embodiments describe additional security features such as lights, sirens, cameras, electronic locks, timers, and/or notifying security personnel or local law enforcement when the system is activated.