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 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: 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: 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.

Abstract: A docking station for a medical device is described. In some examples, the docking station includes a frame and a base plate coupled to the frame. At least a portion of the base plate is coupled to a lower portion of the frame. In some examples, an electronic connector of the docking station is configured to couple to the medical device and to provide power to the medical device when the medical device is docked to the docking station. In some examples, a docking mechanism is coupled to an upper portion of the frame and configured to retain the medical device.

Abstract: Embodiments disclosed are directed to a computing system that performs steps for providing follow-the-leader tapping control signals to one or more tapping devices. The computing system captures an interaction between a first user device and a user of the first user device. The computing system then generates a tapping control signal configured to instruct a tapping device to interact with a second user device based on the captured interaction. Subsequently, the computing system transmits the tapping control signal to the tapping device.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: An automated teller machine (ATM) may include an input component and one or more processors. The input component may be configured to detect multi-factor input associated with an account. The multi-factor input may include at least two of: a sequence of characters input via the input component, a force with which at least one character, of the sequence of characters, is input via the input component, a length of time over which at least one character, of the sequence of characters, is input via the input component, or a combination of at least two characters, of the sequence of characters, that are input concurrently via the input component. The ATM may receive the multi-factor input, validate the multi-factor input in association with the account, and selectively permit or deny access to one or more actions associated with the account based on validating the multi-factor input.

Abstract: Various embodiments described herein are generally directed to techniques for managing fraud rules and generating authentication directions according to location data from one or more client devices. Embodiments may include using a payment server informed by a client device application to manage and/or apply fraud rules for transactions. In embodiments, fraud rules may be managed based on location data provided by a client device application and/or location data associated with at least one transaction system. In some embodiments, fraud rules may be managed based on location data provided by multiple client device applications and/or location data associated with at least one transaction system. In some embodiments, rules may be managed for an individual authorization request according to information from at least one client device application and location data for a transaction system associated with the authorization request.

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: Methods and systems are described for verifying an identity of a user through contextual knowledge-based authentication. The system described uses contextual knowledge-based authentication. By verifying an identity of a user through contextual knowledge-based authentication, the verification is both more secure and more intuitive to the user. For example, by relying on confidential and/or proprietary information, the system may generate verification questions, the answers to which are known only by the user.

Abstract: A device may receive an authorization request, associated with a transaction made by a user device as part of a checkout procedure of a website, that includes a virtual transaction card identifier of a virtual transaction card that is being used for the transaction, and a first merchant identifier for a merchant associated with the website. The device may perform a first authorization procedure to determine whether the first merchant identifier, included in the authorization request, satisfies a threshold level of similarity with a second merchant identifier associated with a request to generate the virtual transaction card that occurred prior to receiving the authorization request. The device may determine whether to forego a second authorization procedure based on a result of the first authorization procedure, and cause an authorization response, that includes an indication of whether the transaction is approved, to be provided to another device associated with the merchant.

Abstract: Aspects discussed herein may relate to techniques for authenticating a user using transaction-based authentication questions. The transaction-based authentication questions may be provided to the user. The user may provide audible responses to the transaction-based authentication questions that may be captured by a smart device, such as an always on listening device. Authentication of the user may be based on voice analysis of the audible responses to the transaction-based authentication questions and/or based on the answers to the transaction-based authentication questions.

Abstract: In some embodiments, validation of network operations related to use of a token via a token request based on entity characteristics may be facilitated. In some embodiments, a token request indicating use of a token to access an item associated with an entity may be detected, where the token is bound to one or more entity characteristics. Based on the token request, an entity identifier associated with the entity may be obtained. Using the entity identifier, one or more item characteristics of items available via the entity may be determined. Determining whether the entity satisfies the one or more entity characteristic to which the token is bound may be determined based on the one or more item characteristics. Access to the item associated with the entity may be validated based on the determination indicating that the entity satisfies the one or more entity characteristics to which the token is bound.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission to monitor a plurality of activities executed within the second computing device; continuously monitoring the plurality of activities executed within the second computing device for a predetermined period of time; receiving an indication of an incoming interaction session within the predetermined period of time; automatically verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a suspect interaction session; determining when a duration of time associated with the suspect interaction session meets or exceeds a predetermined duration threshold; utilizing a trained detection machine learning model to model a confidence value associated with the suspect interaction session; identifying at least one future activity subsequent to the suspect interaction session; and automatically instructing the firs