Abstract: A computing system determines that a third party system has been exposed to a digital security violation. The computing system identifies a first user account of a user registered with the computing system that has a corresponding account associated with the third party system. The computing system determines that the first user account has stored a first set of user credentials for the corresponding account associated with the third party system at a storage location accessible by the computing system. The computing system launches a series of web browsers configured to access a first website associated with the third party system. The computing system executes, via a first web browser of the series of web browsers, a first automated script specific to the first website associated with the third party system. The computing system performs at least one of a plurality of remedial operations with respect to the corresponding account.

Abstract: A device receives, from a user device, a request to generate a verification code for a transaction associated with a user of the user device, and receives user profile information associated with a transaction card to be used for the transaction. The device generates a random verification code for the transaction based on the request and the user profile information, and authenticates the random verification code, based on the user profile information, to generate an authenticated random verification code. The device provides the authenticated random verification code to the user device, and receives transaction information, including the authenticated random verification code, from a merchant device associated with the transaction. The device validates the transaction based on the transaction information, and provides, to the merchant device associated with the transaction, information indicating that the transaction is validated.

Abstract: The innovation disclosed, in one aspect thereof, comprises systems and methods of a browser extension for product quality. Online shopping by a user can be monitored on a browser by a browser extension or integrated application with the browser. A potential purchase of a product is detected by the integrated application. The product is identified via analysis of a web page and cross reference to known products. The integrated application retrieves warranty claims data from an accessible warranty database. The warranty claims data is analyzed to determine a recommendation of the product. The integrated application presents the recommendation via the browser as an alert to the user.

Abstract: Systems and methods of the present disclosure enable database search. The systems and/or methods may include receiving a search query that includes an input document having text. Word embeddings are generated within the input document, where the word embeddings include vector representations of words in the text of the input document. An average input document word embedding vector is determined for the word embeddings of the input document. A set of stored documents is accessed, where each stored document includes a stored text has a particular average stored document word embedding vector. A similarity model is used to determine a similarity metric measuring the similarity between the input document and each stored document based on the average input document word embedding vector and the particular average stored document word embedding vector of each stored document.

Abstract: Disclosed embodiments may include systems and methods for providing modified order recommendations. Line item data from a set of transactions associated with a vendor can be collected. The line item includes transactional data such as price, quantity, and frequency. The set of transactions are associated with a plurality of customers of the vendor. Correlations between line item data can be determined via one or more machine learning techniques to train a recommendation model. An order recommendation to a customer can be determine based on the correlations of the recommendation model. An order of the customer can be modified according to the order recommendation.

Abstract: Methods, systems, and apparatuses are described herein for improving computer authentication processes through the exclusion of certain merchants that may cause confusion. Indications of a plurality of different merchants, including merchant logos may be received. The indications may be processed to identify at least one similarity between a first merchant and a second merchant. A request for access to an account associated with a user and transaction data corresponding to the account may be received. Based on the similarity between the first merchant and the second merchant, at least one transaction corresponding to the first merchant may be removed to generate processed transaction record. An authentication question may be generated and a candidate response to the authentication question may be received. Based on the candidate response, access to the account may be provided.

Abstract: Aspects described herein may allow for authenticating a user by generating a customized set of authentication questions based on patterns that are automatically detected and extracted from user data. The user data may include transaction data collected over a period of time. By automatically detecting user patterns that correspond to user behavior over a period of time, an authentication system may be able to generate information that is recognizable to an authentic user but difficult to guess or circumvent for any other user.

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; continually monitoring the plurality of activities executed within the computing device for a predetermined period of time; identifying an indication of an incoming interaction session within the predetermined period of time; verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a repeat interaction session; dynamically retrieving at least one relation-specific notation from a plurality of relation-specific notations to display; instructing an input GUI element to display input data associated with the repeat interaction; automatically updating the plurality of relation-specific notations associated with the historical data relationship; and instructing at least one programmable output GUI to display a notif

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: Disclosed are methods and systems for generating a virtual tag for an item in a physical environment. For instance, an image of the physical environment captured by and displayed on a user interface of a computing device may be provided as input to a trained machine learning model configured to identify the item including a first subset of item attributes. The item may be identified and information associated with the item, including a second subset of item attributes, may be received from a data store using the first subset. A spatial location of the item may be identified in the image. An icon may be generated for the item and rendered by the computing device for display on the user interface in association with the spatial location of the item in the image. The icon may be indicative of at least one item attribute from the first or second subset.

Abstract: Systems and methods of caller identification differentiation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: receiving a permission indicator identifying a permission by the user to detect calls being received by a computing device; receiving an indication of a current call being received; utilizing a trained call differentiation machine learning model to determine a likelihood that the current call is of a first call type or a second call type, where the first call type is associated with a first type of activity and the second call type is associated with a second type of activity.

Abstract: Methods and systems for generating electronic signatures are disclosed. In some embodiments, the method includes: storing pixel features of a signing device; capturing, via an imaging device, a plurality of image frames including the pixel features of the signing device; identifying in the plurality of image frames, by a processor, first pixels matching the stored pixel features of the signing device; generating, by the processor, a first image including the identified pixels; and connecting, by the processor, the identified pixels to form at least one line drawing representing a signature.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a trained spam upsurge detection machine learning model that determines when a current frequency associated with spam communications received by a current user exceeds a baseline frequency associated with the current user; receiving a permission indicator identifying a permission by the user to detect communications being received by the computing device; receiving an indication of at least one communication being received; determining the at least one communication as a particular spam communication; updating a frequency at which spam communications have been received by the user based at least in part on the particular spam communication; utilizing the trained spam upsurge detection machine learning model to determine that the frequency exceeds a baseline frequency associated with the user; and initiating a scan of one or more dark web resources.

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: The disclosed systems and methods may request a merchant identifier associated with a merchant from a third party, receive the merchant identifier, retrieve or receive a first uniform resource locator (URL) associated with the merchant, and store the merchant identifier and the first URL in a database. In response to a web browser extension detecting that a user is preparing to make an online purchase associated with the merchant, the system may receive an age request for an age of the merchant from a user device and identify the merchant identifier in the database based on a second URL included in the age request. The system may then request the age of the merchant from the database based on the merchant identifier, receive the age of the merchant from the third party, and transmit the age of the merchant to the user device for display.

Abstract: Disclosed embodiments may include a system for curating online vehicle reservations. The system may determine, from received vehicle data including first timing data and indicating a selected vehicle, that the selected vehicle is electric. The system may receive accommodation data including second timing data and indicating a selected accommodation. The system may determine, from comparing the first timing data to the second timing data, that the selected vehicle will likely require charging at the selected accommodation. The system may determine whether the accommodation data includes an indication of electric vehicle charging facilities. Responsive to determining that the selected accommodation does not provide the indication of electric vehicle charging facilities, the system may generate a first graphical user interface warning that no charging station is available for the selected vehicle at the selected accommodation and transmit the first graphical user interface to a user device for display.

Abstract: Methods, systems, and apparatuses are described herein for improving the accuracy of synthetic authentication questions by analyzing third party account data. A request for access to a first account associated with a user may be received. The first account may be managed by a first organization. A transactions database might be queried for first account data. Second account data corresponding to a second account associated with the user might be received. That second account may be managed by a second organization different from the first organization. One or more second transactions, unique to the second account, may be identified. A synthetic transaction, configured to be different from transactions in the first account and the one or more second transactions, may be generated. An authentication question may be generated based on the synthetic transaction. Access to the first account might be provided based on a response to the authentication question.

Abstract: Systems and methods for fraud detection and prevention is disclosed. The system may receive a transaction request for a first customer including a transaction location, transaction time stamp, and merchant type code. The system may determine whether the transaction location is expected for the first customer. When the transaction location is unexpected, the system may identify a last-known video detection having a last-known time stamp and last-known location. The system may determine a travel time estimate between the last-known location and the transaction location and determine a buffer based on the merchant type code. The system may compare the travel time estimate to an allotted time that includes a difference between the transaction time stamp and last-known time stamp less the buffer. When the travel time estimate exceeds the allotted time, the system may execute one or more fraud prevention steps.