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: 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: 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: 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: 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: 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: 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

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: 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: In some implementations, a device may obtain event data relating to events involving one or more entities and one or more individuals, where the one or more entities are associated with entity categories. The device may determine, based on origin locations of the one or more individuals and locations of the one or more entities, distances between the origin locations of the one or more individuals and the locations of the one or more entities, the distances indicating travel distances of the one or more individuals to perform exchanges in the entity categories associated with the one or more entities. The device may determine, based on the event data and the distances, that a geographic area is associated with an opportunity for an entity category. The device may transmit a notification indicating the opportunity for the entity category in the geographic area.

Abstract: Disclosed embodiments may include a system for managing assets. The system may establish a connection with a drone comprising Light Detection and Ranging (LiDAR) sensors and an image capture device. The system may receive, via the drone, image data via the LiDAR sensors and the image capture device. The system may identify, from the image data via computer vision, one or more assets, a condition of the one or more assets, and a location associated with the one or more assets. The system may generate a dynamic report comprising the one or more assets, the condition of the one or more assets, and the location associated with the one or more assets.

Abstract: A fraud detection system may identify a suspected fraudulent transaction based on one or more criteria. The transaction may be associated with a user. Based on identifying a suspected fraudulent transaction, the fraud detection system may receive augmented reality data from an augmented reality device of the user. The fraud detection system may determine, based on the augmented reality data, whether the transaction is fraudulent, and may take an action based on the determination.

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: The present disclosure relates to systems and method for securely entering a confidential access code into a user device. A system for allowing secure entry of a confidential access code into a user device may include one or more memories storing instructions and one or more processors configured to execute instruction to perform operations. The operations may include receiving a request for confidential access, prompting the user, via the user interface, to enter a group of inputs into a single-entry field, receiving a dummy sequence of inputs, receiving or providing an indicator signal, receiving an access sequence of inputs, parsing the group of inputs received to identify the access sequence of inputs based on the location of the indicator signal, comparing the access sequence of inputs to the confidential access code associated with the user, and granting or denying access to the confidential information based on the results.

Abstract: A method may include receiving activity data associated with a user, wherein the activity data relates to online activity involving a product type, identifying the product type associated with the activity data, and predicting, based on the activity data, that the user is likely to purchase a product of the product type. The method may include generating, based on predicting that the user is likely interested in purchasing the product of the product type, an annotation to indicate that a potential transaction to purchase the product is forthcoming, and storing the annotation in a profile associated with an account of the user. The method may include detecting a transaction to purchase the product, wherein the transaction involves a payment from the account, and performing an action associated with a fraud analysis of the transaction based on the annotation.

Abstract: An exemplary system, method, and computer-accessible medium for providing a financial account recommendation to person(s) can include receiving a transaction history for the person(s) associated with a first financial account, determining first rewards information based on the transaction history, accessing database(s) containing rewards rates for a plurality of second financial accounts, determining second rewards information for a particular second financial account of the plurality of second financial accounts based on the transaction history, and providing the financial account recommendation to the person(s) to one of (i) keep the first financial account or (ii) switch to the second financial account based on a comparison of the first rewards information to the second rewards information.

Abstract: In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

Abstract: A system includes an application programming interface, a database, and a server. The application programming interface can make recommendations to a user at a point of sale to display a recommended payment method from a set of payment methods associated with the user. The database can store past transactions for each payment method in the set of payment methods. The server can generate payment analytics by analyzing the past transactions in the database for each payment method. The server can determine potential benefits available to the user. The server can determine the recommended payment method by using the payment analytics and maximizing the potential benefits available to the user.

Abstract: A method includes obtaining a first dataset associated with a first set of classes and a second dataset associated with a second set of classes, where the first and second datasets share features. The method also includes creating an aggregated dataset having a quality score for the second set of classes that satisfy a first threshold. Creating the aggregated dataset includes determining a first and second set of clusters based on the first and second datasets. The method includes determining a distance based on a record of the first set of clusters and a second record of the second set of clusters in a feature space. The method further includes determining a set of analysis scores based on the distance and generating an indication for a set of records associated with the set of analysis scores based on the set of analysis scores satisfying a second threshold.