Abstract: A method for facilitating automated application management based on activity is disclosed. The method includes monitoring metrics that correspond to an application, the metrics including a network traffic metric; determining, for the application, whether the metrics satisfy a predetermined threshold, the predetermined threshold including an idle time threshold; deactivating the application when the corresponding metrics satisfy the predetermined threshold; receiving, from a client application, a request for the deactivated application; initiating a health check for the application in response to the request; and activating the application based on a result of the health check.

Abstract: A system for expeditiously de-archiving archived objects. The system may include operations that comprise: configuring a first application programing interface (API) gateway between an auxiliary memory system and a repository of archived objects; receiving, via an application, a first user request for a first archived object that is stored within the repository of archived objects; processing the first user request to retrieve the first archived object; pushing, via the first API gateway, the first archived object into the auxiliary memory system that provides high-speed access to objects within the auxiliary memory system; and updating first metadata for the first archived object to store a first log of the retrieving and the pushing.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Various methods, apparatuses/systems, and media for automatically detecting discrepancy between preset data and input data are provided. A processor implements a platform and language agnostic smart data discrepancy detection module that automatically detects discrepancy between preset data and input data (i.e., larger than the preset data) during data processing of a commercial transaction involving a gratuity, particularly when a credit card payment is being made. The processor also implements an optimized processes of conducting a commercial transaction that includes a gratuity data based on user preferences that are obtainable from an application during execution thereof, and automatically alerts the user when data discrepancy is detected, i.e., when the gratuity data is more than preset gratuity data set by the user on the application.

Abstract: A method and a system for detecting suspicious transactions and providing warnings in advance of executing the transactions in order to prevent fraud are provided. The method includes: receiving a request to conduct a transaction with an entity by using a digital payment application, the request including first profile information that relates to the entity; retrieving second profile information that relates to the requestor; comparing the first profile information with the second profile information; and determining, based on a result of the comparison, whether to suspend the requested transaction. When a determination is made that the proposed transaction may be fraudulent, the transaction may be suspended and a warning message may be sent to the requestor in order to afford an opportunity to avoid the fraud.

Abstract: Systems and methods for automated remediation of check data errors are disclosed. In one embodiment, a method for automated remediation of check data errors may include: (1) receiving, by a routing computer program, a check image from an acquisition channel; (2) extracting, by an extraction computer program, transaction information from the check image; (3) determining, by the routing computer program, that there is an error in the transaction information; (4) identifying, by a remediation computer program, a correction for the error a confidence score in the correction using a trained machine learning correction engine; and (5) correcting, by the remediation computer program, the error with the correction in response to the confidence score being above a confidence score threshold.

Abstract: A method and a system for detecting suspicious transactions and providing warnings in advance of executing the transactions in order to prevent fraud are provided. The method includes: receiving a request to conduct a transaction with an entity by using a digital payment application, the request including first profile information that relates to the entity; retrieving second profile information that relates to the requestor; comparing the first profile information with the second profile information; and determining, based on a result of the comparison, whether to suspend the requested transaction. When a determination is made that the proposed transaction may be fraudulent, the transaction may be suspended and a warning message may be sent to the requestor in order to afford an opportunity to avoid the fraud.

Abstract: A method for facilitating intelligent itinerary management via a progressive reservation system is disclosed. The method includes aggregating itinerary information for a user from user transactions, the itinerary information corresponding to a transit reservation; continuously monitoring, in real-time via an application programming interface, the transit reservation by using the itinerary information; determining, based on a result of the continuous monitoring, whether parameters that correspond to the transit reservation satisfy a predetermined threshold, the parameters including an operational status; automatically retrieving, from a repository, historical information that corresponds to the user when the parameters satisfy the predetermined threshold; automatically identifying, in real-time using a model, a future transit reservation based on the itinerary information and the historical information; and providing, via a graphical user interface, the identified future transit reservation to the user.

Abstract: Various methods, apparatuses/systems, and media for automatically detecting discrepancy between preset data and input data are provided. A processor implements a platform and language agnostic smart data discrepancy detection module that automatically detects discrepancy between preset data and input data (i.e., larger than the preset data) during data processing of a commercial transaction involving a gratuity, particularly when a credit card payment is being made. The processor also implements an optimized processes of conducting a commercial transaction that includes a gratuity data based on user preferences that are obtainable from an application during execution thereof, and automatically alerts the user when data discrepancy is detected, i.e., when the gratuity data is more than preset gratuity data set by the user on the application.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: A method for facilitating automated application management based on activity is disclosed. The method includes monitoring metrics that correspond to an application, the metrics including a network traffic metric; determining, for the application, whether the metrics satisfy a predetermined threshold, the predetermined threshold including an idle time threshold; deactivating the application when the corresponding metrics satisfy the predetermined threshold; receiving, from a client application, a request for the deactivated application; initiating a health check for the application in response to the request; and activating the application based on a result of the health check.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Various methods, apparatuses/systems, and media for automatically upgrading an application are disclosed. A processor creates a dynamic machine learning (ML) model; trains the dynamic ML model and scans for SDK upgrade for the application against the dynamic ML model by implementing ML algorithm for predictions. The processor executes the SDK upgrade in response to detecting that the training of the dynamic ML model is completed to trigger the processor to perform the following automated processes: implement the ML algorithm against the trained dynamic ML model to generate predictive results data for deprecated reference corresponding to the application; evaluate the predictive results data to determine whether there is a match for the deprecated reference; and when it is determined that there is a match for the deprecated reference, automatically replace code and upgrade the application to newer version of the programming language specification.

Abstract: Examples of systems and methods are described for selecting firmware versions of multiple components for a distributed computing system (e.g., a cluster). Multiple selection interfaces may be presented to a user who may select at least one firmware version of at least one component. Based on the user selection(s), a service manager may evaluate firmware component dependencies and identify compatible firmware versions of other components for installation. In this manner, a set of firmware versions may be installed and/or upgraded which may be different than a current set of versions and/or a set of versions recommended in a wholly automated manner.

Abstract: Examples of systems and methods are described for selecting firmware versions of multiple components for a distributed computing system (e.g., a cluster). Multiple selection interfaces may be presented to a user who may select at least one firmware version of at least one component. Based on the user selection(s), a service manager may evaluate firmware component dependencies and identify compatible firmware versions of other components for installation. In this manner, a set of firmware versions may be installed and/or upgraded which may be different than a current set of versions and/or a set of versions recommended in a wholly automated manner.

Abstract: A platform bed, when collapsed from its fully assembled position, appears to be and functions as a storage chest as follows: The slats are removed from between the hinged side panels and off of the rails. The rails are then lifted out of the rail supports. Manually exerting pressure on the footboard/chest front toward the headboard/chest back forces the hinged side panels to fold allowing the collapsible platform bed with integral storage chest to fold in an accordion-like fashion. Once fully collapsed, the rails and slats are stacked and stored on the folded hinged side panels, headboard beam, middle beam, and footboard beam within the open chest. Then the hinged headboard panel/chest lid is rotated downward to rest on the footboard/chest front to conceal the contents in what appears to be a closed storage chest.